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wolai-frontend/src/types/supabase.ts create mode 100644 wolai-frontend/start-4000.err create mode 100644 wolai-frontend/start-4000.out create mode 100644 wolai-frontend/tsconfig.json create mode 100644 wolai-frontend/vitest.config.ts create mode 100644 wolaiplan copy 2.md create mode 100644 wolaiplan copy.md create mode 100644 wolaiplan.md diff --git a/.agent/rules/mnote.md b/.agent/rules/mnote.md new file mode 100644 index 00000000..a5978c0e --- /dev/null +++ b/.agent/rules/mnote.md @@ -0,0 +1,5 @@ +--- +trigger: always_on +--- + +数据库操作请使用supabase_Local MCP 进行操作。 \ No newline at end of file diff --git a/.codex/.gitignore b/.codex/.gitignore new file mode 100644 index 00000000..3fec32c8 --- /dev/null +++ b/.codex/.gitignore @@ -0,0 +1 @@ +tmp/ diff --git a/.gitignore b/.gitignore new file mode 100644 index 00000000..52cc1391 --- /dev/null +++ b/.gitignore @@ -0,0 +1,25 @@ +node_modules/ +**/node_modules/ +.next/ +**/.next/ +dist/ +**/dist/ +build/ +**/build/ +.turbo/ +pnpm-debug.log* +npm-debug.log* +yarn-debug.log* +yarn-error.log* +*.log +.DS_Store +.env +.env.* +.vscode/ +*.local +tmp/ +temp/ +**/.venv/ +.venv/ +**/__pycache__/ +__pycache__/ diff --git a/CLAUDE.md b/CLAUDE.md new file mode 100644 index 00000000..caabfcdf --- /dev/null +++ b/CLAUDE.md @@ -0,0 +1,383 @@ +When asked to design UI & frontend interface +When asked to design UI & frontend interface +# Role +You are superdesign, a senior frontend designer integrated into VS Code as part of the Super Design extension. +Your goal is to help user generate amazing design using code + +# Instructions +- Use the available tools when needed to help with file operations and code analysis +- When creating design file: + - Build one single html page of just one screen to build a design based on users' feedback/task + - You ALWAYS output design files in '.superdesign/design_iterations' folder as {design_name}_{n}.html (Where n needs to be unique like table_1.html, table_2.html, etc.) or svg file + - If you are iterating design based on existing file, then the naming convention should be {current_file_name}_{n}.html, e.g. if we are iterating ui_1.html, then each version should be ui_1_1.html, ui_1_2.html, etc. +- You should ALWAYS use tools above for write/edit html files, don't just output in a message, always do tool calls + +## Styling +1. superdesign tries to use the flowbite library as a base unless the user specifies otherwise. +2. superdesign avoids using indigo or blue colors unless specified in the user's request. +3. superdesign MUST generate responsive designs. +4. When designing component, poster or any other design that is not full app, you should make sure the background fits well with the actual poster or component UI color; e.g. if component is light then background should be dark, vice versa. +5. Font should always using google font, below is a list of default fonts: 'JetBrains Mono', 'Fira Code', 'Source Code Pro','IBM Plex Mono','Roboto Mono','Space Mono','Geist Mono','Inter','Roboto','Open Sans','Poppins','Montserrat','Outfit','Plus Jakarta Sans','DM Sans','Geist','Oxanium','Architects Daughter','Merriweather','Playfair Display','Lora','Source Serif Pro','Libre Baskerville','Space Grotesk' +6. When creating CSS, make sure you include !important for all properties that might be overwritten by tailwind & flowbite, e.g. h1, body, etc. +7. Unless user asked specifcially, you should NEVER use some bootstrap style blue color, those are terrible color choices, instead looking at reference below. +8. Example theme patterns: +Ney-brutalism style that feels like 90s web design + +:root { + --background: oklch(1.0000 0 0); + --foreground: oklch(0 0 0); + --card: oklch(1.0000 0 0); + --card-foreground: oklch(0 0 0); + --popover: oklch(1.0000 0 0); + --popover-foreground: oklch(0 0 0); + --primary: oklch(0.6489 0.2370 26.9728); + --primary-foreground: oklch(1.0000 0 0); + --secondary: oklch(0.9680 0.2110 109.7692); + --secondary-foreground: oklch(0 0 0); + --muted: oklch(0.9551 0 0); + --muted-foreground: oklch(0.3211 0 0); + --accent: oklch(0.5635 0.2408 260.8178); + --accent-foreground: oklch(1.0000 0 0); + --destructive: oklch(0 0 0); + --destructive-foreground: oklch(1.0000 0 0); + --border: oklch(0 0 0); + --input: oklch(0 0 0); + --ring: oklch(0.6489 0.2370 26.9728); + --chart-1: oklch(0.6489 0.2370 26.9728); + --chart-2: oklch(0.9680 0.2110 109.7692); + --chart-3: oklch(0.5635 0.2408 260.8178); + --chart-4: oklch(0.7323 0.2492 142.4953); + --chart-5: oklch(0.5931 0.2726 328.3634); + --sidebar: oklch(0.9551 0 0); + --sidebar-foreground: oklch(0 0 0); + --sidebar-primary: oklch(0.6489 0.2370 26.9728); + --sidebar-primary-foreground: oklch(1.0000 0 0); + --sidebar-accent: oklch(0.5635 0.2408 260.8178); + --sidebar-accent-foreground: oklch(1.0000 0 0); + --sidebar-border: oklch(0 0 0); + --sidebar-ring: oklch(0.6489 0.2370 26.9728); + --font-sans: DM Sans, sans-serif; + --font-serif: ui-serif, Georgia, Cambria, "Times New Roman", Times, serif; + --font-mono: Space Mono, monospace; + --radius: 0px; + --shadow-2xs: 4px 4px 0px 0px hsl(0 0% 0% / 0.50); + --shadow-xs: 4px 4px 0px 0px hsl(0 0% 0% / 0.50); + --shadow-sm: 4px 4px 0px 0px hsl(0 0% 0% / 1.00), 4px 1px 2px -1px hsl(0 0% 0% / 1.00); + --shadow: 4px 4px 0px 0px hsl(0 0% 0% / 1.00), 4px 1px 2px -1px hsl(0 0% 0% / 1.00); + --shadow-md: 4px 4px 0px 0px hsl(0 0% 0% / 1.00), 4px 2px 4px -1px hsl(0 0% 0% / 1.00); + --shadow-lg: 4px 4px 0px 0px hsl(0 0% 0% / 1.00), 4px 4px 6px -1px hsl(0 0% 0% / 1.00); + --shadow-xl: 4px 4px 0px 0px hsl(0 0% 0% / 1.00), 4px 8px 10px -1px hsl(0 0% 0% / 1.00); + --shadow-2xl: 4px 4px 0px 0px hsl(0 0% 0% / 2.50); + --tracking-normal: 0em; + --spacing: 0.25rem; + + --radius-sm: calc(var(--radius) - 4px); + --radius-md: calc(var(--radius) - 2px); + --radius-lg: var(--radius); + --radius-xl: calc(var(--radius) + 4px); +} + + +Modern dark mode style like vercel, linear + +:root { + --background: oklch(1 0 0); + --foreground: oklch(0.1450 0 0); + --card: oklch(1 0 0); + --card-foreground: oklch(0.1450 0 0); + --popover: oklch(1 0 0); + --popover-foreground: oklch(0.1450 0 0); + --primary: oklch(0.2050 0 0); + --primary-foreground: oklch(0.9850 0 0); + --secondary: oklch(0.9700 0 0); + --secondary-foreground: oklch(0.2050 0 0); + --muted: oklch(0.9700 0 0); + --muted-foreground: oklch(0.5560 0 0); + --accent: oklch(0.9700 0 0); + --accent-foreground: oklch(0.2050 0 0); + --destructive: oklch(0.5770 0.2450 27.3250); + --destructive-foreground: oklch(1 0 0); + --border: oklch(0.9220 0 0); + --input: oklch(0.9220 0 0); + --ring: oklch(0.7080 0 0); + --chart-1: oklch(0.8100 0.1000 252); + --chart-2: oklch(0.6200 0.1900 260); + --chart-3: oklch(0.5500 0.2200 263); + --chart-4: oklch(0.4900 0.2200 264); + --chart-5: oklch(0.4200 0.1800 266); + --sidebar: oklch(0.9850 0 0); + --sidebar-foreground: oklch(0.1450 0 0); + --sidebar-primary: oklch(0.2050 0 0); + --sidebar-primary-foreground: oklch(0.9850 0 0); + --sidebar-accent: oklch(0.9700 0 0); + --sidebar-accent-foreground: oklch(0.2050 0 0); + --sidebar-border: oklch(0.9220 0 0); + --sidebar-ring: oklch(0.7080 0 0); + --font-sans: ui-sans-serif, system-ui, -apple-system, BlinkMacSystemFont, 'Segoe UI', Roboto, 'Helvetica Neue', Arial, 'Noto Sans', sans-serif, 'Apple Color Emoji', 'Segoe UI Emoji', 'Segoe UI Symbol', 'Noto Color Emoji'; + --font-serif: ui-serif, Georgia, Cambria, "Times New Roman", Times, serif; + --font-mono: ui-monospace, SFMono-Regular, Menlo, Monaco, Consolas, "Liberation Mono", "Courier New", monospace; + --radius: 0.625rem; + --shadow-2xs: 0 1px 3px 0px hsl(0 0% 0% / 0.05); + --shadow-xs: 0 1px 3px 0px hsl(0 0% 0% / 0.05); + --shadow-sm: 0 1px 3px 0px hsl(0 0% 0% / 0.10), 0 1px 2px -1px hsl(0 0% 0% / 0.10); + --shadow: 0 1px 3px 0px hsl(0 0% 0% / 0.10), 0 1px 2px -1px hsl(0 0% 0% / 0.10); + --shadow-md: 0 1px 3px 0px hsl(0 0% 0% / 0.10), 0 2px 4px -1px hsl(0 0% 0% / 0.10); + --shadow-lg: 0 1px 3px 0px hsl(0 0% 0% / 0.10), 0 4px 6px -1px hsl(0 0% 0% / 0.10); + --shadow-xl: 0 1px 3px 0px hsl(0 0% 0% / 0.10), 0 8px 10px -1px hsl(0 0% 0% / 0.10); + --shadow-2xl: 0 1px 3px 0px hsl(0 0% 0% / 0.25); + --tracking-normal: 0em; + --spacing: 0.25rem; + + --radius-sm: calc(var(--radius) - 4px); + --radius-md: calc(var(--radius) - 2px); + --radius-lg: var(--radius); + --radius-xl: calc(var(--radius) + 4px); +} + + +## Images & icons +1. For images, just use placeholder image from public source like unsplash, placehold.co or others that you already know exact image url; Don't make up urls +2. For icons, we should use lucid icons or other public icons, import like + +## Script +1. When importing tailwind css, just use , don't load CSS directly as a stylesheet resource like +2. When using flowbite, import like + +## Workflow +You should always follow workflow below unless user explicitly ask you to do something else: +1. Layout design +2. Theme design (Color, font, spacing, shadown), using generateTheme tool, it should save the css to a local file +3. Core Animation design +4. Generate a singlehtml file for the UI +5. You HAVE TO confirm with user step by step, don't do theme design until user sign off the layout design, same for all follownig steps + +### 1. Layout design +Output type: Just text +Think through how should the layout of interface look like, what are different UI components +And present the layout in ASCII wireframe format, here are the guidelines of good ASCII wireframe, you can do ASCII art too for more custom layout or graphic design + +### 2. Theme design +Output type: Tool call +Think through what are the colors, fonts, spacing, etc. +You HAVE TO use generateTheme tool to generate the theme, do NOT just output XML type text for tool-call, that is not allowed + +### 3. Animation design +Output type: Just text +Think through what are the animations, transitions, etc. + +### 4. Generate html file for each UI component and then combine them together to form a single html file +Output type: Tool call +Generate html file for each UI component and then combine them together to form a single html file +Make sure to reference the theme css file you created in step 2, and add custom ones that doesn't exist yet in html file +You HAVE TO use write tool to generate the html file, do NOT just output XML type text for tool-call, that is not allowed + + +design an AI chat UI + + +Let's think through the layout design for an AI chat UI. Here are the key components and layout considerations: + +## Core UI Components + +**Header Area** +- Settings/menu button (Top left) +- Chat title/AI name (Top left) +- Conversation controls (new chat, clear, etc.) (Top right) + +**Main Chat Area** +- Message container (scrollable) (Full width & height) +- User messages (typically right-aligned) +- AI messages (typically left-aligned) +- Message timestamps (Small subtle text at the bottom of each message, aligned to the right/left depending on the message) + +**Input Area** +- Text input field (Full width left) +- Send button (Embed within the input field,Bottom right side of the screen) +- Additional controls (attach files, voice input, etc.) (Embed within the input field, Bottom left) +- Character/token counter (optional) (Embed within the input field, top right corner, small text) + + +## Layout Structure Options + +┌─────────────────────────────────────┐ +│ ☰ HEADER BAR + │ +├─────────────────────────────────────┤ +│ │ +│ ┌─────────────────────────────┐ │ +│ │ AI Message Bubble │ │ +│ └─────────────────────────────┘ │ +│ │ +│ ┌─────────────────────────────┐ │ +│ │ User Message Bubble │ │ +│ └─────────────────────────────┘ │ +│ │ +│ ┌─────────────────────────────┐ │ +│ │ AI Message Bubble │ │ +│ └─────────────────────────────┘ │ +│ │ +│ [CHAT AREA] │ +│ │ +├─────────────────────────────────────┤ +│ [Text Input Field] [Send] │ +└─────────────────────────────────────┘ + +When hamburger (☰) is clicked, sidebar slides out: +┌──────────────┬─────────────────────────────────────┐ +│ SIDEBAR │ ☰ HEADER BAR + │ +│ ┌──────────┐ ├─────────────────────────────────────┤ +│ │ Chat 1 │ │ │ +│ │ Chat 2 │ │ ┌─────────────────────────────┐ │ +│ │ Chat 3 │ │ │ AI Message Bubble │ │ +│ │ + New │ │ └─────────────────────────────┘ │ +│ └──────────┘ │ │ +│ │ ┌─────────────────────────────┐ │ +│ │ │ User Message Bubble │ │ +│ │ └─────────────────────────────┘ │ +│ │ │ +│ │ ┌─────────────────────────────┐ │ +│ │ │ AI Message Bubble │ │ +│ │ └─────────────────────────────┘ │ +│ │ │ +│ │ [CHAT AREA] │ +│ │ │ +│ ├─────────────────────────────────────┤ +│ │ [Text Input Field] [Send] │ +└──────────────┘─────────────────────────────────────┘ + +Would you like to go ahead with this layout & UI interaction or needs any modification? + + + +They looks good + + + +Great, next let's design the color & font theme, + +generateTheme(theme_name='Vercel techy style', reasoning_reference='Reference classic shadcn style that has ...', cssFilePath='.superdesign/design_iterations/theme_1.css', cssSheet=':root { + --background: oklch(1.0000 0 0); + --foreground: oklch(0.1448 0 0); + --card: oklch(1.0000 0 0); + --card-foreground: oklch(0.1448 0 0); + --popover: oklch(1.0000 0 0); + --popover-foreground: oklch(0.1448 0 0); + --primary: oklch(0.5555 0 0); + --primary-foreground: oklch(0.9851 0 0); + --secondary: oklch(0.9702 0 0); + --secondary-foreground: oklch(0.2046 0 0); + --muted: oklch(0.9702 0 0); + --muted-foreground: oklch(0.5486 0 0); + --accent: oklch(0.9702 0 0); + --accent-foreground: oklch(0.2046 0 0); + --destructive: oklch(0.5830 0.2387 28.4765); + --destructive-foreground: oklch(0.9702 0 0); + --border: oklch(0.9219 0 0); + --input: oklch(0.9219 0 0); + --ring: oklch(0.7090 0 0); + --chart-1: oklch(0.5555 0 0); + --chart-2: oklch(0.5555 0 0); + --chart-3: oklch(0.5555 0 0); + --chart-4: oklch(0.5555 0 0); + --chart-5: oklch(0.5555 0 0); + --sidebar: oklch(0.9851 0 0); + --sidebar-foreground: oklch(0.1448 0 0); + --sidebar-primary: oklch(0.2046 0 0); + --sidebar-primary-foreground: oklch(0.9851 0 0); + --sidebar-accent: oklch(0.9702 0 0); + --sidebar-accent-foreground: oklch(0.2046 0 0); + --sidebar-border: oklch(0.9219 0 0); + --sidebar-ring: oklch(0.7090 0 0); + --font-sans: Geist Mono, monospace; + --font-serif: Geist Mono, monospace; + --font-mono: Geist Mono, monospace; + --radius: 0rem; + --shadow-2xs: 0px 1px 0px 0px hsl(0 0% 0% / 0.00); + --shadow-xs: 0px 1px 0px 0px hsl(0 0% 0% / 0.00); + --shadow-sm: 0px 1px 0px 0px hsl(0 0% 0% / 0.00), 0px 1px 2px -1px hsl(0 0% 0% / 0.00); + --shadow: 0px 1px 0px 0px hsl(0 0% 0% / 0.00), 0px 1px 2px -1px hsl(0 0% 0% / 0.00); + --shadow-md: 0px 1px 0px 0px hsl(0 0% 0% / 0.00), 0px 2px 4px -1px hsl(0 0% 0% / 0.00); + --shadow-lg: 0px 1px 0px 0px hsl(0 0% 0% / 0.00), 0px 4px 6px -1px hsl(0 0% 0% / 0.00); + --shadow-xl: 0px 1px 0px 0px hsl(0 0% 0% / 0.00), 0px 8px 10px -1px hsl(0 0% 0% / 0.00); + --shadow-2xl: 0px 1px 0px 0px hsl(0 0% 0% / 0.00); + --tracking-normal: 0em; + --spacing: 0.25rem; +}') + + + +I like the vintage style + + + +Great, next let's design the animation: + +# CHAT UI ANIMATIONS - MICRO-SYNTAX + +## Core Message Flow +userMsg: 400ms ease-out [Y+20→0, X+10→0, S0.9→1] +aiMsg: 600ms bounce [Y+15→0, S0.95→1] +200ms +typing: 1400ms ∞ [Y±8, α0.4→1] stagger+200ms +status: 300ms ease-out [α0.6→1, S1→1.05→1] + +## Interface Transitions +sidebar: 350ms ease-out [X-280→0, α0→1] +overlay: 300ms [α0→1, blur0→4px] +input: 200ms [S1→1.01, shadow+ring] focus +input: 150ms [S1.01→1, shadow-ring] blur + +## Button Interactions +sendBtn: 150ms [S1→0.95→1, R±2°] press +sendBtn: 200ms [S1→1.05, shadow↗] hover +ripple: 400ms [S0→2, α1→0] + +## Loading States +chatLoad: 500ms ease-out [Y+40→0, α0→1] +skeleton: 2000ms ∞ [bg: muted↔accent] +spinner: 1000ms ∞ linear [R360°] + +## Micro Interactions +msgHover: 200ms [Y0→-2, shadow↗] +msgSelect: 200ms [bg→accent, S1→1.02] +error: 400ms [X±5] shake +success: 600ms bounce [S0→1.2→1, R360°] + +## Scroll & Navigation +autoScroll: 400ms smooth +scrollHint: 800ms ∞×3 [Y±5] + +This animation system creates a cohesive, responsive feel throughout the chat interface. Each animation has specific timing and easing to feel natural and purposeful. Would you like me to adjust any of these animations or add additional ones? + + + + +This looks great, lets do it + + + +Great, I will generate the html file for each UI component and then combine them together to form a single html file + +write(file_path='.superdesign/design_iterations/chat_ui.css', content='...') +write(file_path='.superdesign/design_iterations/chat_ui.html', content='...') + +I've created the html design, please reveiw and let me know if you need any changes + + + +IMPORTANT RULES: +1. You MUST use tools call below for any action like generateTheme, write, edit, etc. You are NOT allowed to just output text like 'Called tool: write with arguments: ...' or ...; MUST USE TOOL CALL (This is very important!!) +2. You MUST confirm the layout, and then theme style, and then animation +3. You MUST use .superdesign/design_iterations folder to save the design files, do NOT save to other folders +4. You MUST create follow the workflow above + +# Available Tools +- **read**: Read file contents within the workspace (supports text files, images, with line range options) +- **write**: Write content to files in the workspace (creates parent directories automatically) +- **edit**: Replace text within files using exact string matching (requires precise text matching including whitespace and indentation) +- **multiedit**: Perform multiple find-and-replace operations on a single file in sequence (each edit applied to result of previous edit) +- **glob**: Find files and directories matching glob patterns (e.g., "*.js", "src/**/*.ts") - efficient for locating files by name or path structure +- **grep**: Search for text patterns within file contents using regular expressions (can filter by file types and paths) +- **ls**: List directory contents with optional filtering, sorting, and detailed information (shows files and subdirectories) +- **bash**: Execute shell/bash commands within the workspace (secure execution with timeouts and output capture) +- **generateTheme**: Generate a theme for the design + +When calling tools, you MUST use the actual tool call, do NOT just output text like 'Called tool: write with arguments: ...' or ..., this won't actually call the tool. (This is very important to my life, please follow) \ No newline at end of file diff --git a/LightRAG/.clinerules/01-basic.md b/LightRAG/.clinerules/01-basic.md new file mode 100644 index 00000000..955afa83 --- /dev/null +++ b/LightRAG/.clinerules/01-basic.md @@ -0,0 +1,232 @@ +# LightRAG Project Intelligence (.clinerules) + +## Project Overview +LightRAG is a mature, production-ready Retrieval-Augmented Generation (RAG) system with comprehensive knowledge graph capabilities. The system has evolved from experimental to production-ready status with extensive functionality across all major components. + +## Current System State (August 15, 2025) +- **Status**: Production Ready - Stable and Mature +- **Configuration**: Gemini 2.5 Flash + BAAI/bge-m3 embeddings via custom endpoints +- **Storage**: Default in-memory with file persistence (JsonKVStorage, NetworkXStorage, NanoVectorDBStorage) +- **Language**: Chinese for summaries +- **Workspace**: `space1` for data isolation +- **Authentication**: JWT-based with admin/user accounts + +## Critical Implementation Patterns + +### 1. Embedding Format Compatibility (CRITICAL) +**Pattern**: Always handle both base64 and raw array embedding formats +**Location**: `lightrag/llm/openai.py` - `openai_embed` function +**Issue**: Custom OpenAI-compatible endpoints return embeddings as raw arrays, not base64 strings +**Solution**: +```python +np.array(dp.embedding, dtype=np.float32) if isinstance(dp.embedding, list) +else np.frombuffer(base64.b64decode(dp.embedding), dtype=np.float32) +``` +**Impact**: Document processing fails completely without this dual format support + +### 2. Async Pattern Consistency (CRITICAL) +**Pattern**: Always await coroutines before calling methods on the result +**Common Error**: `coroutine.method()` instead of `(await coroutine).method()` +**Locations**: MongoDB implementations, Neo4j operations +**Example**: `await self._data.list_indexes()` then `await cursor.to_list()` + +### 3. Storage Layer Data Compatibility (CRITICAL) +**Pattern**: Always filter deprecated/incompatible fields during deserialization +**Common Fields to Remove**: `content`, `_id` (MongoDB), database-specific fields +**Implementation**: `data.pop('field_name', None)` before creating dataclass objects +**Locations**: All storage implementations (JSON, Redis, MongoDB, PostgreSQL) + +### 4. Lock Key Generation (CRITICAL) +**Pattern**: Always sort relationship pairs for consistent lock keys +**Implementation**: `sorted_key_parts = sorted([src, tgt])` then `f"{sorted_key_parts[0]}-{sorted_key_parts[1]}"` +**Impact**: Prevents deadlocks in concurrent relationship processing + +### 5. Event Loop Management (CRITICAL) +**Pattern**: Handle event loop mismatches during shutdown gracefully +**Implementation**: Timeout + specific RuntimeError handling for "attached to a different loop" +**Location**: Neo4j storage finalization +**Impact**: Prevents application shutdown failures + +### 6. Async Generator Lock Management (CRITICAL) +**Pattern**: Never hold locks across async generator yields - create snapshots instead +**Issue**: Holding locks while yielding causes deadlock when consumers need the same lock +**Location**: `lightrag/tools/migrate_llm_cache.py` - `stream_default_caches_json` +**Solution**: Create snapshot of data while holding lock, release lock, then iterate over snapshot +```python +# WRONG - Deadlock prone: +async with storage._storage_lock: + for key, value in storage._data.items(): + batch[key] = value + if len(batch) >= batch_size: + yield batch # Lock still held! + +# CORRECT - Snapshot approach: +async with storage._storage_lock: + matching_items = [(k, v) for k, v in storage._data.items() if condition] +# Lock released here +for key, value in matching_items: + batch[key] = value + if len(batch) >= batch_size: + yield batch # No lock held +``` +**Impact**: Prevents deadlocks in Json→Json migrations and similar scenarios where source/target share locks +**Applicable To**: Any async generator that needs to access shared resources while yielding + +## Architecture Patterns + +### 1. Dependency Injection +**Pattern**: Pass configuration through object constructors, not direct imports +**Example**: OllamaAPI receives configuration through LightRAG object +**Benefit**: Better testability and modularity + +### 2. Memory Bank Documentation +**Pattern**: Maintain comprehensive memory bank for development continuity +**Structure**: Core files (projectbrief.md, activeContext.md, progress.md, etc.) +**Purpose**: Essential for context preservation across development sessions + +### 3. Configuration Management +**Pattern**: Centralize defaults in constants.py, use environment variables for runtime config +**Implementation**: Default values in constants, override via .env file +**Benefit**: Consistent configuration across components + +## Development Workflow Patterns + +### 1. Frontend Development (CRITICAL) +**Package Manager**: **ALWAYS USE BUN** - Never use npm or yarn unless Bun is unavailable +**Commands**: +- `bun install` - Install dependencies +- `bun run dev` - Start development server +- `bun run build` - Build for production +- `bun run lint` - Run linting +- `bun test` - Run tests +- `bun run preview` - Preview production build + +**Pattern**: All frontend operations must use Bun commands +**Fallback**: Only use npm/yarn if Bun installation fails +**Testing**: Use `bun test` for all frontend testing + +### 2. Bug Fix Approach +1. **Identify root cause** - Don't just fix symptoms +2. **Implement robust solution** - Handle edge cases and format variations +3. **Maintain backward compatibility** - Preserve existing functionality +4. **Add comprehensive error handling** - Graceful degradation +5. **Document the fix** - Update memory bank with technical details + +### 3. Feature Implementation +1. **Follow existing patterns** - Maintain architectural consistency +2. **Use dependency injection** - Avoid direct imports between modules +3. **Implement comprehensive error handling** - Handle all failure modes +4. **Add proper logging** - Debug and warning messages +5. **Update documentation** - Memory bank and code comments +6. **Comment Language** - Use English for comments and documentation + +### 4. Performance Optimization +1. **Profile before optimizing** - Identify actual bottlenecks +2. **Maintain algorithmic correctness** - Don't sacrifice functionality for speed +3. **Use appropriate data structures** - Match structure to access patterns +4. **Implement caching strategically** - Cache expensive operations +5. **Monitor memory usage** - Prevent memory leaks + +## Technology Stack Intelligence + +### 1. LLM Integration +- **Primary**: Gemini 2.5 Flash via custom endpoint +- **Embedding**: BAAI/bge-m3 via custom endpoint +- **Reranking**: BAAI/bge-reranker-v2-m3 +- **Pattern**: Always handle multiple provider formats + +### 2. Storage Backends +- **Default**: In-memory with file persistence +- **Production Options**: PostgreSQL, MongoDB, Redis, Neo4j +- **Pattern**: Abstract storage interface with multiple implementations + +### 3. API Architecture +- **Framework**: FastAPI with Gunicorn for production +- **Authentication**: JWT-based with role support +- **Compatibility**: Ollama-compatible endpoints for easy integration + +### 4. Frontend +- **Framework**: React with TypeScript +- **Package Manager**: **BUN (REQUIRED)** - Always use Bun for all frontend operations +- **Build Tool**: Vite with Bun runtime +- **Visualization**: Sigma.js for graph rendering +- **State Management**: React hooks with context +- **Internationalization**: i18next for multi-language support + +## Common Pitfalls and Solutions + +### 1. Embedding Format Issues +**Pitfall**: Assuming all endpoints return base64-encoded embeddings +**Solution**: Always check format and handle both base64 and raw arrays + +### 2. Async/Await Patterns +**Pitfall**: Calling methods on coroutines instead of awaited results +**Solution**: Always await coroutines before accessing their methods + +### 3. Data Model Evolution +**Pitfall**: Breaking changes when removing fields from dataclasses +**Solution**: Filter deprecated fields during deserialization, don't break storage + +### 4. Concurrency Issues +**Pitfall**: Inconsistent lock key generation causing deadlocks +**Solution**: Always sort keys for deterministic lock ordering + +### 5. Event Loop Management +**Pitfall**: Event loop mismatches during shutdown +**Solution**: Implement timeout and specific error handling for loop issues + +## Performance Considerations + +### 1. Query Context Building +- **Algorithm**: Linear gradient weighted polling for fair resource allocation +- **Optimization**: Round-robin merging to eliminate mode bias +- **Pattern**: Smart chunk selection based on cross-entity occurrence + +### 2. Graph Operations +- **Optimization**: Batch operations where possible +- **Pattern**: Use appropriate indexing for large datasets +- **Consideration**: Memory usage with large graphs + +### 3. LLM Request Management +- **Pattern**: Priority-based queue for request ordering +- **Optimization**: Connection pooling and retry mechanisms +- **Consideration**: Rate limiting and cost management + +## Security Patterns + +### 1. Authentication +- **Implementation**: JWT tokens with role-based access +- **Pattern**: Stateless authentication with configurable expiration +- **Security**: Proper token validation and refresh mechanisms + +### 2. API Security +- **Pattern**: Input validation and sanitization +- **Implementation**: FastAPI dependency injection for auth +- **Consideration**: Rate limiting and abuse prevention + +## Maintenance Guidelines + +### 1. Memory Bank Updates +- **Trigger**: After significant changes or bug fixes +- **Pattern**: Update activeContext.md and progress.md +- **Purpose**: Maintain development continuity + +### 2. Configuration Management +- **Pattern**: Environment-based configuration with sensible defaults +- **Implementation**: .env files with example templates +- **Consideration**: Security for production deployments + +### 3. Error Handling +- **Pattern**: Comprehensive logging with appropriate levels +- **Implementation**: Graceful degradation where possible +- **Consideration**: User-friendly error messages + +## Project Evolution Notes + +The project has evolved from experimental to production-ready status. Key milestones: +- **Early 2025**: Basic RAG implementation +- **Mid 2025**: Multiple storage backends and LLM providers +- **July 2025**: Major query optimization and algorithm improvements +- **August 2025**: Production-ready stable state + +The system now supports enterprise-level deployments with comprehensive functionality across all components. diff --git a/LightRAG/.dockerignore b/LightRAG/.dockerignore new file mode 100644 index 00000000..f738d586 --- /dev/null +++ b/LightRAG/.dockerignore @@ -0,0 +1,69 @@ +# Python-related files and directories +__pycache__ +.cache + +# Virtual environment directories +*.venv + +# Env +env/ +*.env* +.env_example + +# Distribution / build files +site +dist/ +build/ +.eggs/ +*.egg-info/ +*.tgz +*.tar.gz + +# Exclude siles and folders +*.yml +.dockerignore +Dockerfile +Makefile + +# Exclude other projects +/tests +/scripts +/data +/dickens +/reproduce +/output_complete +/rag_storage +/inputs + +# Python version manager file +.python-version + +# Reports +*.coverage/ +*.log +log/ +*.logfire + +# Cache +.cache/ +.mypy_cache +.pytest_cache +.ruff_cache +.gradio +.logfire +temp/ + +# MacOS-related files +.DS_Store + +# VS Code settings (local configuration files) +.vscode + +# file +TODO.md + +# Exclude Git-related files +.git +.github +.gitignore +.pre-commit-config.yaml diff --git a/LightRAG/.gitattributes b/LightRAG/.gitattributes new file mode 100644 index 00000000..f7f5bc04 --- /dev/null +++ b/LightRAG/.gitattributes @@ -0,0 +1,2 @@ +lightrag/api/webui/** binary +lightrag/api/webui/** linguist-generated diff --git a/LightRAG/.github/ISSUE_TEMPLATE/bug_report.yml b/LightRAG/.github/ISSUE_TEMPLATE/bug_report.yml new file mode 100644 index 00000000..780a2008 --- /dev/null +++ b/LightRAG/.github/ISSUE_TEMPLATE/bug_report.yml @@ -0,0 +1,61 @@ +name: Bug Report +description: File a bug report +title: "[Bug]:" +labels: ["bug", "triage"] + +body: + - type: checkboxes + id: existingcheck + attributes: + label: Do you need to file an issue? + description: Please help us manage our time by avoiding duplicates and common bugs with the steps below. + options: + - label: I have searched the existing issues and this bug is not already filed. + - label: I believe this is a legitimate bug, not just a question or feature request. + - type: textarea + id: description + attributes: + label: Describe the bug + description: A clear and concise description of what the bug is. + placeholder: What went wrong? + - type: textarea + id: reproduce + attributes: + label: Steps to reproduce + description: Steps to reproduce the behavior. + placeholder: How can we replicate the issue? + - type: textarea + id: expected_behavior + attributes: + label: Expected Behavior + description: A clear and concise description of what you expected to happen. + placeholder: What should have happened? + - type: textarea + id: configused + attributes: + label: LightRAG Config Used + description: The LightRAG configuration used for the run. + placeholder: The settings content or LightRAG configuration + value: | + # Paste your config here + - type: textarea + id: screenshotslogs + attributes: + label: Logs and screenshots + description: If applicable, add screenshots and logs to help explain your problem. + placeholder: Add logs and screenshots here + - type: textarea + id: additional_information + attributes: + label: Additional Information + description: | + - LightRAG Version: e.g., v0.1.1 + - Operating System: e.g., Windows 10, Ubuntu 20.04 + - Python Version: e.g., 3.8 + - Related Issues: e.g., #1 + - Any other relevant information. + value: | + - LightRAG Version: + - Operating System: + - Python Version: + - Related Issues: diff --git a/LightRAG/.github/ISSUE_TEMPLATE/config.yml b/LightRAG/.github/ISSUE_TEMPLATE/config.yml new file mode 100644 index 00000000..3ba13e0c --- /dev/null +++ b/LightRAG/.github/ISSUE_TEMPLATE/config.yml @@ -0,0 +1 @@ +blank_issues_enabled: false diff --git a/LightRAG/.github/ISSUE_TEMPLATE/feature_request.yml b/LightRAG/.github/ISSUE_TEMPLATE/feature_request.yml new file mode 100644 index 00000000..3c127d59 --- /dev/null +++ b/LightRAG/.github/ISSUE_TEMPLATE/feature_request.yml @@ -0,0 +1,26 @@ +name: Feature Request +description: File a feature request +labels: ["enhancement"] +title: "[Feature Request]:" + +body: + - type: checkboxes + id: existingcheck + attributes: + label: Do you need to file a feature request? + description: Please help us manage our time by avoiding duplicates and common feature request with the steps below. + options: + - label: I have searched the existing feature request and this feature request is not already filed. + - label: I believe this is a legitimate feature request, not just a question or bug. + - type: textarea + id: feature_request_description + attributes: + label: Feature Request Description + description: A clear and concise description of the feature request you would like. + placeholder: What this feature request add more or improve? + - type: textarea + id: additional_context + attributes: + label: Additional Context + description: Add any other context or screenshots about the feature request here. + placeholder: Any additional information diff --git a/LightRAG/.github/ISSUE_TEMPLATE/question.yml b/LightRAG/.github/ISSUE_TEMPLATE/question.yml new file mode 100644 index 00000000..26fed23d --- /dev/null +++ b/LightRAG/.github/ISSUE_TEMPLATE/question.yml @@ -0,0 +1,26 @@ +name: Question +description: Ask a general question +labels: ["question"] +title: "[Question]:" + +body: + - type: checkboxes + id: existingcheck + attributes: + label: Do you need to ask a question? + description: Please help us manage our time by avoiding duplicates and common questions with the steps below. + options: + - label: I have searched the existing question and discussions and this question is not already answered. + - label: I believe this is a legitimate question, not just a bug or feature request. + - type: textarea + id: question + attributes: + label: Your Question + description: A clear and concise description of your question. + placeholder: What is your question? + - type: textarea + id: context + attributes: + label: Additional Context + description: Provide any additional context or details that might help us understand your question better. + placeholder: Add any relevant information here diff --git a/LightRAG/.github/dependabot.yml b/LightRAG/.github/dependabot.yml new file mode 100644 index 00000000..9d866e39 --- /dev/null +++ b/LightRAG/.github/dependabot.yml @@ -0,0 +1,11 @@ +# To get started with Dependabot version updates, you'll need to specify which +# package ecosystems to update and where the package manifests are located. +# Please see the documentation for all configuration options: +# https://docs.github.com/code-security/dependabot/dependabot-version-updates/configuration-options-for-the-dependabot.yml-file + +version: 2 +updates: + - package-ecosystem: "pip" # See documentation for possible values + directory: "/" # Location of package manifests + schedule: + interval: "weekly" diff --git a/LightRAG/.github/pull_request_template.md b/LightRAG/.github/pull_request_template.md new file mode 100644 index 00000000..6eb2f2ac --- /dev/null +++ b/LightRAG/.github/pull_request_template.md @@ -0,0 +1,32 @@ + + +## Description + +[Briefly describe the changes made in this pull request.] + +## Related Issues + +[Reference any related issues or tasks addressed by this pull request.] + +## Changes Made + +[List the specific changes made in this pull request.] + +## Checklist + +- [ ] Changes tested locally +- [ ] Code reviewed +- [ ] Documentation updated (if necessary) +- [ ] Unit tests added (if applicable) + +## Additional Notes + +[Add any additional notes or context for the reviewer(s).] diff --git a/LightRAG/.github/workflows/docker-build-lite.yml b/LightRAG/.github/workflows/docker-build-lite.yml new file mode 100644 index 00000000..9cbe6289 --- /dev/null +++ b/LightRAG/.github/workflows/docker-build-lite.yml @@ -0,0 +1,84 @@ +name: Build Lite Docker Image + +on: + workflow_dispatch: + inputs: + _notes_: + description: '⚠️ Create lite Docker images only after non-trivial version releases.' + required: false + type: boolean + default: false + +permissions: + contents: read + packages: write + +jobs: + build-and-push-lite: + runs-on: ubuntu-latest + steps: + - name: Checkout code + uses: actions/checkout@v4 + with: + fetch-depth: 0 + + - name: Get latest tag + id: get_tag + run: | + LATEST_TAG=$(git describe --tags --abbrev=0 2>/dev/null || echo "") + if [ -z "$LATEST_TAG" ]; then + LATEST_TAG="sha-$(git rev-parse --short HEAD)" + echo "No tags found, using commit SHA: $LATEST_TAG" + else + echo "Latest tag found: $LATEST_TAG" + fi + echo "tag=$LATEST_TAG" >> $GITHUB_OUTPUT + + - name: Prepare lite tag + id: lite_tag + run: | + LITE_TAG="${{ steps.get_tag.outputs.tag }}-lite" + echo "Lite image tag: $LITE_TAG" + echo "lite_tag=$LITE_TAG" >> $GITHUB_OUTPUT + + - name: Update version in __init__.py + run: | + sed -i "s/__version__ = \".*\"/__version__ = \"${{ steps.get_tag.outputs.tag }}\"/" lightrag/__init__.py + cat lightrag/__init__.py | grep __version__ + + - name: Set up Docker Buildx + uses: docker/setup-buildx-action@v3 + + - name: Login to GitHub Container Registry + uses: docker/login-action@v3 + with: + registry: ghcr.io + username: ${{ github.actor }} + password: ${{ secrets.GITHUB_TOKEN }} + + - name: Extract metadata for Docker + id: meta + uses: docker/metadata-action@v5 + with: + images: ghcr.io/${{ github.repository }} + tags: | + type=raw,value=${{ steps.lite_tag.outputs.lite_tag }} + type=raw,value=lite + + - name: Build and push lite Docker image + uses: docker/build-push-action@v5 + with: + context: . + file: ./Dockerfile.lite + platforms: linux/amd64,linux/arm64 + push: true + tags: ${{ steps.meta.outputs.tags }} + labels: ${{ steps.meta.outputs.labels }} + cache-from: type=gha + cache-to: type=gha,mode=min + + - name: Output image details + run: | + echo "Lite Docker image built and pushed successfully!" + echo "Image tag: ghcr.io/${{ github.repository }}:${{ steps.lite_tag.outputs.lite_tag }}" + echo "Base Git tag used: ${{ steps.get_tag.outputs.tag }}" diff --git a/LightRAG/.github/workflows/docker-build-manual.yml b/LightRAG/.github/workflows/docker-build-manual.yml new file mode 100644 index 00000000..de459d5a --- /dev/null +++ b/LightRAG/.github/workflows/docker-build-manual.yml @@ -0,0 +1,80 @@ +name: Build Test Docker Image manually + +on: + workflow_dispatch: + inputs: + _notes_: + description: '⚠️ Please create a new git tag before building the docker image.' + required: false + type: boolean + default: false + +permissions: + contents: read + packages: write + +jobs: + build-and-push: + runs-on: ubuntu-latest + steps: + - name: Checkout code + uses: actions/checkout@v4 + with: + fetch-depth: 0 # Fetch all history for tags + + - name: Get latest tag + id: get_tag + run: | + # Get the latest tag, fallback to commit SHA if no tags exist + LATEST_TAG=$(git describe --tags --abbrev=0 2>/dev/null || echo "") + if [ -z "$LATEST_TAG" ]; then + LATEST_TAG="sha-$(git rev-parse --short HEAD)" + echo "No tags found, using commit SHA: $LATEST_TAG" + else + echo "Latest tag found: $LATEST_TAG" + fi + echo "tag=$LATEST_TAG" >> $GITHUB_OUTPUT + echo "image_tag=$LATEST_TAG" >> $GITHUB_OUTPUT + + - name: Update version in __init__.py + run: | + sed -i "s/__version__ = \".*\"/__version__ = \"${{ steps.get_tag.outputs.tag }}\"/" lightrag/__init__.py + echo "Updated __init__.py with version ${{ steps.get_tag.outputs.tag }}" + cat lightrag/__init__.py | grep __version__ + + - name: Set up Docker Buildx + uses: docker/setup-buildx-action@v3 + + - name: Login to GitHub Container Registry + uses: docker/login-action@v3 + with: + registry: ghcr.io + username: ${{ github.actor }} + password: ${{ secrets.GITHUB_TOKEN }} + + - name: Extract metadata for Docker + id: meta + uses: docker/metadata-action@v5 + with: + images: ghcr.io/${{ github.repository }} + tags: | + type=raw,value=${{ steps.get_tag.outputs.tag }} + + - name: Build and push Docker image + uses: docker/build-push-action@v5 + with: + context: . + file: ./Dockerfile + platforms: linux/amd64,linux/arm64 + push: true + tags: ${{ steps.meta.outputs.tags }} + labels: ${{ steps.meta.outputs.labels }} + cache-from: type=gha + cache-to: type=gha,mode=max + + - name: Output image details + run: | + echo "Docker image built and pushed successfully!" + echo "Image tags:" + echo " - ghcr.io/${{ github.repository }}:${{ steps.get_tag.outputs.tag }}" + echo "Latest Git tag used: ${{ steps.get_tag.outputs.tag }}" diff --git a/LightRAG/.github/workflows/docker-publish.yml b/LightRAG/.github/workflows/docker-publish.yml new file mode 100644 index 00000000..6c290d59 --- /dev/null +++ b/LightRAG/.github/workflows/docker-publish.yml @@ -0,0 +1,75 @@ +name: Build Latest Docker Image on Release + +on: + release: + types: [published] + workflow_dispatch: + +permissions: + contents: read + packages: write + +jobs: + build-and-push: + runs-on: ubuntu-latest + steps: + - name: Checkout code + uses: actions/checkout@v4 + with: + fetch-depth: 0 # Fetch all history for tags + + - name: Set up Docker Buildx + uses: docker/setup-buildx-action@v3 + + - name: Login to GitHub Container Registry + uses: docker/login-action@v3 + with: + registry: ghcr.io + username: ${{ github.actor }} + password: ${{ secrets.GITHUB_TOKEN }} + + - name: Get latest tag + id: get_tag + run: | + TAG=$(git describe --tags --abbrev=0) + echo "Found tag: $TAG" + echo "tag=$TAG" >> $GITHUB_OUTPUT + + - name: Check if pre-release + id: check_prerelease + run: | + TAG="${{ steps.get_tag.outputs.tag }}" + if [[ "$TAG" == *"rc"* ]] || [[ "$TAG" == *"dev"* ]]; then + echo "is_prerelease=true" >> $GITHUB_OUTPUT + echo "This is a pre-release version: $TAG" + else + echo "is_prerelease=false" >> $GITHUB_OUTPUT + echo "This is a stable release: $TAG" + fi + + - name: Update version in __init__.py + run: | + sed -i "s/__version__ = \".*\"/__version__ = \"${{ steps.get_tag.outputs.tag }}\"/" lightrag/__init__.py + echo "Updated __init__.py with version ${{ steps.get_tag.outputs.tag }}" + cat lightrag/__init__.py | grep __version__ + + - name: Extract metadata for Docker + id: meta + uses: docker/metadata-action@v5 + with: + images: ghcr.io/${{ github.repository }} + tags: | + type=raw,value=${{ steps.get_tag.outputs.tag }} + type=raw,value=latest,enable=${{ steps.check_prerelease.outputs.is_prerelease == 'false' }} + + - name: Build and push Docker image + uses: docker/build-push-action@v5 + with: + context: . + file: ./Dockerfile + platforms: linux/amd64,linux/arm64 + push: true + tags: ${{ steps.meta.outputs.tags }} + labels: ${{ steps.meta.outputs.labels }} + cache-from: type=gha + cache-to: type=gha,mode=max diff --git a/LightRAG/.github/workflows/linting.yaml b/LightRAG/.github/workflows/linting.yaml new file mode 100644 index 00000000..aa054369 --- /dev/null +++ b/LightRAG/.github/workflows/linting.yaml @@ -0,0 +1,30 @@ +name: Linting and Formatting + +on: + push: + branches: + - main + pull_request: + branches: + - main + +jobs: + lint-and-format: + runs-on: ubuntu-latest + + steps: + - name: Checkout code + uses: actions/checkout@v2 + + - name: Set up Python + uses: actions/setup-python@v2 + with: + python-version: '3.x' + + - name: Install dependencies + run: | + python -m pip install --upgrade pip + pip install pre-commit + + - name: Run pre-commit + run: pre-commit run --all-files --show-diff-on-failure diff --git a/LightRAG/.github/workflows/pypi-publish.yml b/LightRAG/.github/workflows/pypi-publish.yml new file mode 100644 index 00000000..14c2bcc5 --- /dev/null +++ b/LightRAG/.github/workflows/pypi-publish.yml @@ -0,0 +1,92 @@ +name: Upload LightRAG-hku Package + +on: + release: + types: [published] + workflow_dispatch: + +permissions: + contents: read + +jobs: + release-build: + runs-on: ubuntu-latest + + steps: + - uses: actions/checkout@v4 + with: + fetch-depth: 0 # Fetch all history for tags + + # Build frontend WebUI + - name: Setup Bun + uses: oven-sh/setup-bun@v1 + with: + bun-version: latest + + - name: Build Frontend WebUI + run: | + cd lightrag_webui + bun install --frozen-lockfile + bun run build + cd .. + + - name: Verify Frontend Build + run: | + if [ ! -f "lightrag/api/webui/index.html" ]; then + echo "❌ Error: Frontend build failed - index.html not found" + exit 1 + fi + echo "✅ Frontend build verified" + echo "Frontend files:" + ls -lh lightrag/api/webui/ | head -10 + + - uses: actions/setup-python@v5 + with: + python-version: "3.x" + + - name: Get version from tag + id: get_version + run: | + TAG=$(git describe --tags --abbrev=0) + echo "Found tag: $TAG" + echo "Extracted version: $TAG" + echo "version=$TAG" >> $GITHUB_OUTPUT + + - name: Update version in __init__.py + run: | + sed -i "s/__version__ = \".*\"/__version__ = \"${{ steps.get_version.outputs.version }}\"/" lightrag/__init__.py + echo "Updated __init__.py with version ${{ steps.get_version.outputs.version }}" + cat lightrag/__init__.py | grep __version__ + + - name: Build release distributions + run: | + python -m pip install build + python -m build + + - name: Upload distributions + uses: actions/upload-artifact@v4 + with: + name: release-dists + path: dist/ + + pypi-publish: + runs-on: ubuntu-latest + needs: + - release-build + permissions: + id-token: write + + environment: + name: pypi + + steps: + - name: Retrieve release distributions + uses: actions/download-artifact@v4 + with: + name: release-dists + path: dist/ + + - name: Publish release distributions to PyPI + uses: pypa/gh-action-pypi-publish@release/v1 + with: + packages-dir: dist/ diff --git a/LightRAG/.github/workflows/stale.yaml b/LightRAG/.github/workflows/stale.yaml new file mode 100644 index 00000000..30643dda --- /dev/null +++ b/LightRAG/.github/workflows/stale.yaml @@ -0,0 +1,27 @@ +# .github/workflows/stale.yml +name: Mark stale issues and pull requests + +on: + schedule: + - cron: '30 22 * * *' # run at 22:30+08 every day + +permissions: + issues: write + pull-requests: write + +jobs: + stale: + runs-on: ubuntu-latest + steps: + - uses: actions/stale@v9 + with: + days-before-stale: 90 # 90 days + days-before-close: 7 # 7 days after marked as stale + stale-issue-message: 'This issue has been automatically marked as stale because it has not had recent activity. It will be closed if no further activity occurs. Thank you for your contributions.' + close-issue-message: 'This issue has been automatically closed because it has not had recent activity. Please open a new issue if you still have this problem.' + stale-pr-message: 'This pull request has been automatically marked as stale because it has not had recent activity. It will be closed if no further activity occurs.' + close-pr-message: 'This pull request has been automatically closed because it has not had recent activity.' + # If there are specific labels, exempt them from being marked as stale, for example: + exempt-issue-labels: 'enhancement,tracked' + # exempt-pr-labels: 'bug,enhancement,help wanted' + repo-token: ${{ secrets.GITHUB_TOKEN }} # token provided by GitHub diff --git a/LightRAG/.gitignore b/LightRAG/.gitignore new file mode 100644 index 00000000..8a5059c8 --- /dev/null +++ b/LightRAG/.gitignore @@ -0,0 +1,74 @@ +# Python-related files +__pycache__/ +*.py[cod] +*.egg-info/ +.eggs/ +*.tgz +*.tar.gz +*.ini + +# Virtual Environment +.venv/ +venv/ + +# Enviroment Variable Files +.env + +# Build / Distribution +dist/ +build/ +site/ + +# Logs / Reports +*.log +*.log.* +*.logfire +*.coverage/ +log/ + +# Caches +.cache/ +.mypy_cache/ +.pytest_cache/ +.ruff_cache/ +.gradio/ +.history/ +temp/ + +# IDE / Editor Files +.idea/ +.vscode/ +.vscode/settings.json + +# Framework-specific files +local_neo4jWorkDir/ +neo4jWorkDir/ + +# Data & Storage +inputs/ +output/ +rag_storage/ +data/ + +# Evaluation results +lightrag/evaluation/results/ + +# Miscellaneous +.DS_Store +TODO.md +ignore_this.txt +*.ignore.* + +# Project-specific files +/dickens*/ +/book.txt +download_models_hf.py + +# Frontend build output (built during PyPI release) +/lightrag/api/webui/ + +# temporary test files in project root +/test_* + +# Cline files +memory-bank diff --git a/LightRAG/.pre-commit-config.yaml b/LightRAG/.pre-commit-config.yaml new file mode 100644 index 00000000..0629b743 --- /dev/null +++ b/LightRAG/.pre-commit-config.yaml @@ -0,0 +1,28 @@ +repos: + - repo: https://github.com/pre-commit/pre-commit-hooks + rev: v5.0.0 + hooks: + - id: trailing-whitespace + exclude: ^lightrag/api/webui/ + - id: end-of-file-fixer + exclude: ^lightrag/api/webui/ + - id: requirements-txt-fixer + exclude: ^lightrag/api/webui/ + + + - repo: https://github.com/astral-sh/ruff-pre-commit + rev: v0.6.4 + hooks: + - id: ruff-format + exclude: ^lightrag/api/webui/ + - id: ruff + args: [--fix, --ignore=E402] + exclude: ^lightrag/api/webui/ + + + - repo: https://github.com/mgedmin/check-manifest + rev: "0.49" + hooks: + - id: check-manifest + stages: [manual] + exclude: ^lightrag/api/webui/ diff --git a/LightRAG/AGENTS.md b/LightRAG/AGENTS.md new file mode 100644 index 00000000..e1e4da11 --- /dev/null +++ b/LightRAG/AGENTS.md @@ -0,0 +1,39 @@ +# Repository Guidelines + +LightRAG is an advanced Retrieval-Augmented Generation (RAG) framework designed to enhance information retrieval and generation through graph-based knowledge representation. + +## Project Structure & Module Organization +- `lightrag/`: Core Python package with orchestrators (`lightrag/lightrag.py`), storage adapters in `kg/`, LLM bindings in `llm/`, and helpers such as `operate.py` and `utils_*.py`. +- `lightrag-api/`: FastAPI service (`lightrag_server.py`) with routers under `routers/` and Gunicorn launcher `run_with_gunicorn.py`. +- `lightrag_webui/`: React 19 + TypeScript client driven by Bun + Vite; UI components live in `src/`. +- Tests live in `tests/` and root-level `test_*.py`. Working datasets stay in `inputs/`, `rag_storage/`, `temp/`; deployment collateral lives in `docs/`, `k8s-deploy/`, and `docker-compose.yml`. + +## Build, Test, and Development Commands +- `python -m venv .venv && source .venv/bin/activate`: set up the Python runtime. +- `pip install -e .` / `pip install -e .[api]`: install the package and API extras in editable mode. +- `lightrag-server` or `uvicorn lightrag.api.lightrag_server:app --reload`: start the API locally; ensure `.env` is present. +- `python -m pytest tests` or `python test_graph_storage.py`: run the full suite or a targeted script. +- `ruff check .`: lint Python sources before committing. +- `bun install`, `bun run dev`, `bun run build`, `bun test`: manage the web UI workflow (Bun is mandatory). + +## Coding Style & Naming Conventions +- Backend code follow PEP 8 with four-space indentation, annotate functions, and reach for dataclasses when modelling state. +- Use `lightrag.utils.logger` instead of `print`; respect logger configuration flags. +- Extend storage or pipeline abstractions via `lightrag.base` and keep reusable helpers in the existing `utils_*.py`. +- Python modules remain lowercase with underscores; React components use `PascalCase.tsx` and hooks-first patterns. +- Front-end code should remain in TypeScript with two-space indentation, rely on functional React components with hooks, and follow Tailwind utility style. + +## Testing Guidelines +- Add pytest cases beside the affected module or the relevant `test_*.py`; functions should start with `test_`. +- Export required `LIGHTRAG_*` environment variables before running integration or storage tests. +- For UI updates, pair code with Vitest specs and run `bun test`. + +## Commit & Pull Request Guidelines +- Use concise, imperative commit subjects (e.g., `Fix lock key normalization`) and add body context only when necessary. +- PRs should include a summary, operational impact, linked issues, and screenshots or API samples for user-facing work. +- Verify `ruff check .`, `python -m pytest`, and affected Bun commands succeed before requesting review; note the runs in the PR text. + +## Security & Configuration Tips +- Copy `.env.example` and `config.ini.example`; never commit secrets or real connection strings. +- Configure storage backends through `LIGHTRAG_*` variables and validate them with `docker-compose` services when needed. +- Treat `lightrag.log*` as local artefacts; purge sensitive information before sharing logs or outputs. diff --git a/LightRAG/Dockerfile b/LightRAG/Dockerfile new file mode 100644 index 00000000..aaa3c84b --- /dev/null +++ b/LightRAG/Dockerfile @@ -0,0 +1,107 @@ +# syntax=docker/dockerfile:1 + +# Frontend build stage +FROM oven/bun:1 AS frontend-builder + +WORKDIR /app + +# Copy frontend source code +COPY lightrag_webui/ ./lightrag_webui/ + +# Build frontend assets for inclusion in the API package +RUN --mount=type=cache,target=/root/.bun/install/cache \ + cd lightrag_webui \ + && bun install --frozen-lockfile \ + && bun run build + +# Python build stage - using uv for faster package installation +FROM ghcr.io/astral-sh/uv:python3.12-bookworm-slim AS builder + +ENV DEBIAN_FRONTEND=noninteractive +ENV UV_SYSTEM_PYTHON=1 +ENV UV_COMPILE_BYTECODE=1 + +WORKDIR /app + +# Install system deps (Rust is required by some wheels) +RUN apt-get update \ + && apt-get install -y --no-install-recommends \ + curl \ + build-essential \ + pkg-config \ + && rm -rf /var/lib/apt/lists/* \ + && curl --proto '=https' --tlsv1.2 -sSf https://sh.rustup.rs | sh -s -- -y + +ENV PATH="/root/.cargo/bin:/root/.local/bin:${PATH}" + +# Ensure shared data directory exists for uv caches +RUN mkdir -p /root/.local/share/uv + +# Copy project metadata and sources +COPY pyproject.toml . +COPY setup.py . +COPY uv.lock . + +# Install base, API, and offline extras without the project to improve caching +RUN --mount=type=cache,target=/root/.local/share/uv \ + uv sync --frozen --no-dev --extra api --extra offline --no-install-project --no-editable + +# Copy project sources after dependency layer +COPY lightrag/ ./lightrag/ + +# Include pre-built frontend assets from the previous stage +COPY --from=frontend-builder /app/lightrag/api/webui ./lightrag/api/webui + +# Sync project in non-editable mode and ensure pip is available for runtime installs +RUN --mount=type=cache,target=/root/.local/share/uv \ + uv sync --frozen --no-dev --extra api --extra offline --no-editable \ + && /app/.venv/bin/python -m ensurepip --upgrade + +# Prepare offline cache directory and pre-populate tiktoken data +# Use uv run to execute commands from the virtual environment +RUN mkdir -p /app/data/tiktoken \ + && uv run lightrag-download-cache --cache-dir /app/data/tiktoken || status=$?; \ + if [ -n "${status:-}" ] && [ "$status" -ne 0 ] && [ "$status" -ne 2 ]; then exit "$status"; fi + +# Final stage +FROM python:3.12-slim + +WORKDIR /app + +# Install uv for package management +COPY --from=ghcr.io/astral-sh/uv:latest /uv /usr/local/bin/uv + +ENV UV_SYSTEM_PYTHON=1 + +# Copy installed packages and application code +COPY --from=builder /root/.local /root/.local +COPY --from=builder /app/.venv /app/.venv +COPY --from=builder /app/lightrag ./lightrag +COPY pyproject.toml . +COPY setup.py . +COPY uv.lock . + +# Ensure the installed scripts are on PATH +ENV PATH=/app/.venv/bin:/root/.local/bin:$PATH + +# Install dependencies with uv sync (uses locked versions from uv.lock) +# And ensure pip is available for runtime installs +RUN --mount=type=cache,target=/root/.local/share/uv \ + uv sync --frozen --no-dev --extra api --extra offline --no-editable \ + && /app/.venv/bin/python -m ensurepip --upgrade + +# Create persistent data directories AFTER package installation +RUN mkdir -p /app/data/rag_storage /app/data/inputs /app/data/tiktoken + +# Copy offline cache into the newly created directory +COPY --from=builder /app/data/tiktoken /app/data/tiktoken + +# Point to the prepared cache +ENV TIKTOKEN_CACHE_DIR=/app/data/tiktoken +ENV WORKING_DIR=/app/data/rag_storage +ENV INPUT_DIR=/app/data/inputs + +# Expose API port +EXPOSE 9621 + +ENTRYPOINT ["python", "-m", "lightrag.api.lightrag_server"] diff --git a/LightRAG/Dockerfile.lite b/LightRAG/Dockerfile.lite new file mode 100644 index 00000000..3f488886 --- /dev/null +++ b/LightRAG/Dockerfile.lite @@ -0,0 +1,108 @@ +# syntax=docker/dockerfile:1 + +# Frontend build stage +FROM oven/bun:1 AS frontend-builder + +WORKDIR /app + +# Copy frontend source code +COPY lightrag_webui/ ./lightrag_webui/ + +# Build frontend assets for inclusion in the API package +RUN --mount=type=cache,target=/root/.bun/install/cache \ + cd lightrag_webui \ + && bun install --frozen-lockfile \ + && bun run build + +# Python build stage - using uv for package installation +FROM ghcr.io/astral-sh/uv:python3.12-bookworm-slim AS builder + +ENV DEBIAN_FRONTEND=noninteractive +ENV UV_SYSTEM_PYTHON=1 +ENV UV_COMPILE_BYTECODE=1 + +WORKDIR /app + +# Install system dependencies required by some wheels +RUN apt-get update \ + && apt-get install -y --no-install-recommends \ + curl \ + build-essential \ + pkg-config \ + && rm -rf /var/lib/apt/lists/* \ + && curl --proto '=https' --tlsv1.2 -sSf https://sh.rustup.rs | sh -s -- -y + +ENV PATH="/root/.cargo/bin:/root/.local/bin:${PATH}" + +# Ensure shared data directory exists for uv caches +RUN mkdir -p /root/.local/share/uv + +# Copy project metadata and sources +COPY pyproject.toml . +COPY setup.py . +COPY uv.lock . + +# Install project dependencies (base + API extras) without the project to improve caching +RUN --mount=type=cache,target=/root/.local/share/uv \ + uv sync --frozen --no-dev --extra api --no-install-project --no-editable + +# Copy project sources after dependency layer +COPY lightrag/ ./lightrag/ + +# Include pre-built frontend assets from the previous stage +COPY --from=frontend-builder /app/lightrag/api/webui ./lightrag/api/webui + +# Sync project in non-editable mode and ensure pip is available for runtime installs +RUN --mount=type=cache,target=/root/.local/share/uv \ + uv sync --frozen --no-dev --extra api --no-editable \ + && /app/.venv/bin/python -m ensurepip --upgrade + +# Prepare tiktoken cache directory and pre-populate tokenizer data +# Ignore exit code 2 which indicates assets already cached +RUN mkdir -p /app/data/tiktoken \ + && uv run lightrag-download-cache --cache-dir /app/data/tiktoken || status=$?; \ + if [ -n "${status:-}" ] && [ "$status" -ne 0 ] && [ "$status" -ne 2 ]; then exit "$status"; fi + +# Final stage +FROM python:3.12-slim + +WORKDIR /app + +# Install uv for package management +COPY --from=ghcr.io/astral-sh/uv:latest /uv /usr/local/bin/uv + +ENV UV_SYSTEM_PYTHON=1 + +# Copy installed packages and application code +COPY --from=builder /root/.local /root/.local +COPY --from=builder /app/.venv /app/.venv +COPY --from=builder /app/lightrag ./lightrag +COPY pyproject.toml . +COPY setup.py . +COPY uv.lock . + +# Ensure the installed scripts are on PATH +ENV PATH=/app/.venv/bin:/root/.local/bin:$PATH + +# Sync dependencies inside the final image using uv +# And ensure pip is available for runtime installs +RUN --mount=type=cache,target=/root/.local/share/uv \ + uv sync --frozen --no-dev --extra api --no-editable \ + && /app/.venv/bin/python -m ensurepip --upgrade + +# Create persistent data directories +RUN mkdir -p /app/data/rag_storage /app/data/inputs /app/data/tiktoken + +# Copy cached tokenizer assets prepared in the builder stage +COPY --from=builder /app/data/tiktoken /app/data/tiktoken + +# Docker data directories +ENV TIKTOKEN_CACHE_DIR=/app/data/tiktoken +ENV WORKING_DIR=/app/data/rag_storage +ENV INPUT_DIR=/app/data/inputs + +# Expose API port +EXPOSE 9621 + +# Set entrypoint +ENTRYPOINT ["python", "-m", "lightrag.api.lightrag_server"] diff --git a/LightRAG/LICENSE b/LightRAG/LICENSE new file mode 100644 index 00000000..ff0c29b4 --- /dev/null +++ b/LightRAG/LICENSE @@ -0,0 +1,21 @@ +MIT License + +Copyright (c) 2025 LightRAG Team + +Permission is hereby granted, free of charge, to any person obtaining a copy +of this software and associated documentation files (the "Software"), to deal +in the Software without restriction, including without limitation the rights +to use, copy, modify, merge, publish, distribute, sublicense, and/or sell +copies of the Software, and to permit persons to whom the Software is +furnished to do so, subject to the following conditions: + +The above copyright notice and this permission notice shall be included in all +copies or substantial portions of the Software. + +THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR +IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, +FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE +AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER +LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, +OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE +SOFTWARE. diff --git a/LightRAG/MANIFEST.in b/LightRAG/MANIFEST.in new file mode 100644 index 00000000..cf12c1e9 --- /dev/null +++ b/LightRAG/MANIFEST.in @@ -0,0 +1,4 @@ +include requirements.txt +include lightrag/api/requirements.txt +recursive-include lightrag/api/webui * +recursive-include lightrag/api/static * diff --git a/LightRAG/README-zh.md b/LightRAG/README-zh.md new file mode 100644 index 00000000..8dcbc0e5 --- /dev/null +++ b/LightRAG/README-zh.md @@ -0,0 +1,1824 @@ +
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+ LightRAG Logo +
+ +# 🚀 LightRAG: Simple and Fast Retrieval-Augmented Generation + +
+ HKUDS%2FLightRAG | Trendshift +
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+ LightRAG Diagram +
+ +--- + +## 🎉 新闻 + +- [x] [2025.11.05]🎯📢添加**基于RAGAS的**评估框架和**Langfuse**可观测性支持。 +- [x] [2025.10.22]🎯📢消除处理**大规模数据集**的瓶颈。 +- [x] [2025.09.15]🎯📢显著提升**小型LLM**（如Qwen3-30B-A3B）的知识图谱提取准确性。 +- [x] [2025.08.29]🎯📢现已支持**Reranker**，显著提升混合查询性能。 +- [x] [2025.08.04]🎯📢支持**文档删除**并重新生成知识图谱以确保查询性能。 +- [x] [2025.06.16]🎯📢我们的团队发布了[RAG-Anything](https://github.com/HKUDS/RAG-Anything)，一个用于无缝处理文本、图像、表格和方程式的全功能多模态 RAG 系统。 +- [X] [2025.06.05]🎯📢LightRAG现已集成[RAG-Anything](https://github.com/HKUDS/RAG-Anything)，支持全面的多模态文档解析与RAG能力（PDF、图片、Office文档、表格、公式等）。详见下方[多模态处理模块](https://github.com/HKUDS/LightRAG?tab=readme-ov-file#多模态文档处理rag-anything集成)。 +- [X] [2025.03.18]🎯📢LightRAG现已支持引文功能。 +- [X] [2025.02.05]🎯📢我们团队发布了[VideoRAG](https://github.com/HKUDS/VideoRAG)，用于理解超长上下文视频。 +- [X] [2025.01.13]🎯📢我们团队发布了[MiniRAG](https://github.com/HKUDS/MiniRAG)，使用小型模型简化RAG。 +- [X] [2025.01.06]🎯📢现在您可以[使用PostgreSQL进行存储](#using-postgresql-for-storage)。 +- [X] [2024.11.25]🎯📢LightRAG现在支持无缝集成[自定义知识图谱](https://github.com/HKUDS/LightRAG?tab=readme-ov-file#insert-custom-kg)，使用户能够用自己的领域专业知识增强系统。 +- [X] [2024.11.19]🎯📢LightRAG的综合指南现已在[LearnOpenCV](https://learnopencv.com/lightrag)上发布。非常感谢博客作者。 +- [X] [2024.11.11]🎯📢LightRAG现在支持[通过实体名称删除实体](https://github.com/HKUDS/LightRAG?tab=readme-ov-file#delete)。 +- [X] [2024.11.09]🎯📢推出[LightRAG Gui](https://lightrag-gui.streamlit.app)，允许您插入、查询、可视化和下载LightRAG知识。 +- [X] [2024.11.04]🎯📢现在您可以[使用Neo4J进行存储](https://github.com/HKUDS/LightRAG?tab=readme-ov-file#using-neo4j-for-storage)。 +- [X] [2024.10.29]🎯📢LightRAG现在通过`textract`支持多种文件类型，包括PDF、DOC、PPT和CSV。 +- [X] [2024.10.20]🎯📢我们为LightRAG添加了一个新功能：图形可视化。 +- [X] [2024.10.18]🎯📢我们添加了[LightRAG介绍视频](https://youtu.be/oageL-1I0GE)的链接。感谢作者！ +- [X] [2024.10.17]🎯📢我们创建了一个[Discord频道](https://discord.gg/yF2MmDJyGJ)！欢迎加入分享和讨论！🎉🎉 +- [X] [2024.10.16]🎯📢LightRAG现在支持[Ollama模型](https://github.com/HKUDS/LightRAG?tab=readme-ov-file#quick-start)！ +- [X] [2024.10.15]🎯📢LightRAG现在支持[Hugging Face模型](https://github.com/HKUDS/LightRAG?tab=readme-ov-file#quick-start)！ + +
+ + 算法流程图 + + +![LightRAG索引流程图](https://learnopencv.com/wp-content/uploads/2024/11/LightRAG-VectorDB-Json-KV-Store-Indexing-Flowchart-scaled.jpg) +*图1：LightRAG索引流程图 - 图片来源：[Source](https://learnopencv.com/lightrag/)* +![LightRAG检索和查询流程图](https://learnopencv.com/wp-content/uploads/2024/11/LightRAG-Querying-Flowchart-Dual-Level-Retrieval-Generation-Knowledge-Graphs-scaled.jpg) +*图2：LightRAG检索和查询流程图 - 图片来源：[Source](https://learnopencv.com/lightrag/)* + +
+ +## 安装 + +> **💡 使用 uv 进行包管理**: 本项目使用 [uv](https://docs.astral.sh/uv/) 进行快速可靠的 Python 包管理。 +> 首先安装 uv: `curl -LsSf https://astral.sh/uv/install.sh | sh` (Unix/macOS) 或 `powershell -c "irm https://astral.sh/uv/install.ps1 | iex"` (Windows) +> +> **注意**: 如果您更喜欢使用 pip 也可以，但我们推荐使用 uv 以获得更好的性能和更可靠的依赖管理。 + +### 安装LightRAG服务器 + +LightRAG服务器旨在提供Web UI和API支持。Web UI便于文档索引、知识图谱探索和简单的RAG查询界面。LightRAG服务器还提供兼容Ollama的接口，旨在将LightRAG模拟为Ollama聊天模型。这使得AI聊天机器人（如Open WebUI）可以轻松访问LightRAG。 + +* 从PyPI安装 + +```bash +# 使用 uv (推荐) +uv pip install "lightrag-hku[api]" +# 或使用 pip +# pip install "lightrag-hku[api]" + +cp env.example .env # 使用你的LLM和Embedding模型访问参数更新.env文件 + +lightrag-server +``` + +* 从源代码安装 + +```bash +git clone https://github.com/HKUDS/LightRAG.git +cd LightRAG + +# 使用 uv (推荐) +# 注意: uv sync 会自动在 .venv/ 目录创建虚拟环境 +uv sync --extra api +source .venv/bin/activate # 激活虚拟环境 (Linux/macOS) +# Windows 系统: .venv\Scripts\activate + +# 或使用 pip 和虚拟环境 +# python -m venv .venv +# source .venv/bin/activate # Windows: .venv\Scripts\activate +# pip install -e ".[api]" + +cp env.example .env # 使用你的LLM和Embedding模型访问参数更新.env文件 + +# 构建前端代码 +cd lightrag_webui +bun install --frozen-lockfile +bun run build +cd .. + +lightrag-server +``` + +* 使用 Docker Compose 启动 LightRAG 服务器 + +```bash +git clone https://github.com/HKUDS/LightRAG.git +cd LightRAG +cp env.example .env # 使用你的LLM和Embedding模型访问参数更新.env文件 +# modify LLM and Embedding settings in .env +docker compose up +``` + +> 在此获取LightRAG docker镜像历史版本: [LightRAG Docker Images]( https://github.com/HKUDS/LightRAG/pkgs/container/lightrag) + +### 安装LightRAG Core + +* 从源代码安装（推荐） + +```bash +cd LightRAG +# 注意: uv sync 会自动在 .venv/ 目录创建虚拟环境 +uv sync +source .venv/bin/activate # 激活虚拟环境 (Linux/macOS) +# Windows 系统: .venv\Scripts\activate + +# 或: pip install -e . +``` + +* 从PyPI安装 + +```bash +uv pip install lightrag-hku +# 或: pip install lightrag-hku +``` + +## 快速开始 + +### LightRAG的LLM及配套技术栈要求 + +LightRAG对大型语言模型（LLM）的能力要求远高于传统RAG，因为它需要LLM执行文档中的实体关系抽取任务。配置合适的Embedding和Reranker模型对提高查询表现也至关重要。 + +- **LLM选型**： + - 推荐选用参数量至少为32B的LLM。 + - 上下文长度至少为32KB，推荐达到64KB。 + - 在文档索引阶段不建议选择推理模型。 + - 在查询阶段建议选择比索引阶段能力更强的模型，以达到更高的查询效果。 +- **Embedding模型**： + - 高性能的Embedding模型对RAG至关重要。 + - 推荐使用主流的多语言Embedding模型，例如：BAAI/bge-m3 和 text-embedding-3-large。 + - **重要提示**：在文档索引前必须确定使用的Embedding模型，且在文档查询阶段必须沿用与索引阶段相同的模型。有些存储（例如PostgreSQL）在首次建立数表的时候需要确定向量维度，因此更换Embedding模型后需要删除向量相关库表，以便让LightRAG重建新的库表。 +- **Reranker模型配置**： + - 配置Reranker模型能够显著提升LightRAG的检索效果。 + - 启用Reranker模型后，推荐将“mix模式”设为默认查询模式。 + - 推荐选用主流的Reranker模型，例如：BAAI/bge-reranker-v2-m3 或 Jina 等服务商提供的模型。 + +### 使用LightRAG服务器 + +**有关LightRAG服务器的更多信息，请参阅[LightRAG服务器](./lightrag/api/README.md)。** + +### 使用LightRAG Core + +LightRAG核心功能的示例代码请参见`examples`目录。您还可参照[视频](https://www.youtube.com/watch?v=g21royNJ4fw)视频完成环境配置。若已持有OpenAI API密钥，可以通过以下命令运行演示代码： + +```bash +### you should run the demo code with project folder +cd LightRAG +### provide your API-KEY for OpenAI +export OPENAI_API_KEY="sk-...your_opeai_key..." +### download the demo document of "A Christmas Carol" by Charles Dickens +curl https://raw.githubusercontent.com/gusye1234/nano-graphrag/main/tests/mock_data.txt > ./book.txt +### run the demo code +python examples/lightrag_openai_demo.py +``` + +如需流式响应示例的实现代码，请参阅 `examples/lightrag_openai_compatible_demo.py`。运行前，请确保根据需求修改示例代码中的LLM及嵌入模型配置。 + +**注意1**：在运行demo程序的时候需要注意，不同的测试程序可能使用的是不同的embedding模型，更换不同的embeding模型的时候需要把清空数据目录（`./dickens`），否则层序执行会出错。如果你想保留LLM缓存，可以在清除数据目录时保留`kv_store_llm_response_cache.json`文件。 + +**注意2**：官方支持的示例代码仅为 `lightrag_openai_demo.py` 和 `lightrag_openai_compatible_demo.py` 两个文件。其他示例文件均为社区贡献内容，尚未经过完整测试与优化。 + +## 使用LightRAG Core进行编程 + +> ⚠️ **如果您希望将LightRAG集成到您的项目中，建议您使用LightRAG Server提供的REST API**。LightRAG Core通常用于嵌入式应用，或供希望进行研究与评估的学者使用。 + +### 一个简单程序 + +以下Python代码片段演示了如何初始化LightRAG、插入文本并进行查询： + +```python +import os +import asyncio +from lightrag import LightRAG, QueryParam +from lightrag.llm.openai import gpt_4o_mini_complete, gpt_4o_complete, openai_embed +from lightrag.kg.shared_storage import initialize_pipeline_status +from lightrag.utils import setup_logger + +setup_logger("lightrag", level="INFO") + +WORKING_DIR = "./rag_storage" +if not os.path.exists(WORKING_DIR): + os.mkdir(WORKING_DIR) + +async def initialize_rag(): + rag = LightRAG( + working_dir=WORKING_DIR, + embedding_func=openai_embed, + llm_model_func=gpt_4o_mini_complete, + ) + await rag.initialize_storages() + await initialize_pipeline_status() + return rag + +async def main(): + try: + # 初始化RAG实例 + rag = await initialize_rag() + # 插入文本 + await rag.insert("Your text") + + # 执行混合检索 + mode = "hybrid" + print( + await rag.query( + "这个故事的主要主题是什么？", + param=QueryParam(mode=mode) + ) + ) + + except Exception as e: + print(f"发生错误: {e}") + finally: + if rag: + await rag.finalize_storages() + +if __name__ == "__main__": + asyncio.run(main()) +``` + +重要说明： +- 运行脚本前请先导出你的OPENAI_API_KEY环境变量。 +- 该程序使用LightRAG的默认存储设置，所有数据将持久化在WORKING_DIR/rag_storage目录下。 +- 该示例仅展示了初始化LightRAG对象的最简单方式：注入embedding和LLM函数，并在创建LightRAG对象后初始化存储和管道状态。 + +### LightRAG初始化参数 + +以下是完整的LightRAG对象初始化参数清单： + +
+ 参数 + +| **参数** | **类型** | **说明** | **默认值** | +|--------------|----------|-----------------|-------------| +| **working_dir** | `str` | 存储缓存的目录 | `lightrag_cache+timestamp` | +| **kv_storage** | `str` | Storage type for documents and text chunks. Supported types: `JsonKVStorage`,`PGKVStorage`,`RedisKVStorage`,`MongoKVStorage` | `JsonKVStorage` | +| **vector_storage** | `str` | Storage type for embedding vectors. Supported types: `NanoVectorDBStorage`,`PGVectorStorage`,`MilvusVectorDBStorage`,`ChromaVectorDBStorage`,`FaissVectorDBStorage`,`MongoVectorDBStorage`,`QdrantVectorDBStorage` | `NanoVectorDBStorage` | +| **graph_storage** | `str` | Storage type for graph edges and nodes. Supported types: `NetworkXStorage`,`Neo4JStorage`,`PGGraphStorage`,`AGEStorage` | `NetworkXStorage` | +| **doc_status_storage** | `str` | Storage type for documents process status. Supported types: `JsonDocStatusStorage`,`PGDocStatusStorage`,`MongoDocStatusStorage` | `JsonDocStatusStorage` | +| **chunk_token_size** | `int` | 拆分文档时每个块的最大令牌大小 | `1200` | +| **chunk_overlap_token_size** | `int` | 拆分文档时两个块之间的重叠令牌大小 | `100` | +| **tokenizer** | `Tokenizer` | 用于将文本转换为 tokens（数字）以及使用遵循 TokenizerInterface 协议的 .encode() 和 .decode() 函数将 tokens 转换回文本的函数。 如果您不指定，它将使用默认的 Tiktoken tokenizer。 | `TiktokenTokenizer` | +| **tiktoken_model_name** | `str` | 如果您使用的是默认的 Tiktoken tokenizer，那么这是要使用的特定 Tiktoken 模型的名称。如果您提供自己的 tokenizer，则忽略此设置。 | `gpt-4o-mini` | +| **entity_extract_max_gleaning** | `int` | 实体提取过程中的循环次数，附加历史消息 | `1` | +| **node_embedding_algorithm** | `str` | 节点嵌入算法（当前未使用） | `node2vec` | +| **node2vec_params** | `dict` | 节点嵌入的参数 | `{"dimensions": 1536,"num_walks": 10,"walk_length": 40,"window_size": 2,"iterations": 3,"random_seed": 3,}` | +| **embedding_func** | `EmbeddingFunc` | 从文本生成嵌入向量的函数 | `openai_embed` | +| **embedding_batch_num** | `int` | 嵌入过程的最大批量大小（每批发送多个文本） | `32` | +| **embedding_func_max_async** | `int` | 最大并发异步嵌入进程数 | `16` | +| **llm_model_func** | `callable` | LLM生成的函数 | `gpt_4o_mini_complete` | +| **llm_model_name** | `str` | 用于生成的LLM模型名称 | `meta-llama/Llama-3.2-1B-Instruct` | +| **summary_context_size** | `int` | 合并实体关系摘要时送给LLM的最大令牌数 | `10000`（由环境变量 SUMMARY_MAX_CONTEXT 设置） | +| **summary_max_tokens** | `int` | 合并实体关系描述的最大令牌数长度 | `500`（由环境变量 SUMMARY_MAX_TOKENS 设置） | +| **llm_model_max_async** | `int` | 最大并发异步LLM进程数 | `4`（默认值由环境变量MAX_ASYNC更改） | +| **llm_model_kwargs** | `dict` | LLM生成的附加参数 | | +| **vector_db_storage_cls_kwargs** | `dict` | 向量数据库的附加参数，如设置节点和关系检索的阈值 | cosine_better_than_threshold: 0.2（默认值由环境变量COSINE_THRESHOLD更改） | +| **enable_llm_cache** | `bool` | 如果为`TRUE`，将LLM结果存储在缓存中；重复的提示返回缓存的响应 | `TRUE` | +| **enable_llm_cache_for_entity_extract** | `bool` | 如果为`TRUE`，将实体提取的LLM结果存储在缓存中；适合初学者调试应用程序 | `TRUE` | +| **addon_params** | `dict` | 附加参数，例如`{"language": "Simplified Chinese", "entity_types": ["organization", "person", "location", "event"]}`：设置示例限制、输出语言和文档处理的批量大小 | language: English` | +| **embedding_cache_config** | `dict` | 问答缓存的配置。包含三个参数：`enabled`：布尔值，启用/禁用缓存查找功能。启用时，系统将在生成新答案之前检查缓存的响应。`similarity_threshold`：浮点值（0-1），相似度阈值。当新问题与缓存问题的相似度超过此阈值时，将直接返回缓存的答案而不调用LLM。`use_llm_check`：布尔值，启用/禁用LLM相似度验证。启用时，在返回缓存答案之前，将使用LLM作为二次检查来验证问题之间的相似度。 | 默认：`{"enabled": False, "similarity_threshold": 0.95, "use_llm_check": False}` | + +
+ +### 查询参数 + +使用QueryParam控制你的查询行为： + +```python +class QueryParam: + """Configuration parameters for query execution in LightRAG.""" + + mode: Literal["local", "global", "hybrid", "naive", "mix", "bypass"] = "global" + """Specifies the retrieval mode: + - "local": Focuses on context-dependent information. + - "global": Utilizes global knowledge. + - "hybrid": Combines local and global retrieval methods. + - "naive": Performs a basic search without advanced techniques. + - "mix": Integrates knowledge graph and vector retrieval. + """ + + only_need_context: bool = False + """If True, only returns the retrieved context without generating a response.""" + + only_need_prompt: bool = False + """If True, only returns the generated prompt without producing a response.""" + + response_type: str = "Multiple Paragraphs" + """Defines the response format. Examples: 'Multiple Paragraphs', 'Single Paragraph', 'Bullet Points'.""" + + stream: bool = False + """If True, enables streaming output for real-time responses.""" + + top_k: int = int(os.getenv("TOP_K", "60")) + """Number of top items to retrieve. Represents entities in 'local' mode and relationships in 'global' mode.""" + + chunk_top_k: int = int(os.getenv("CHUNK_TOP_K", "20")) + """Number of text chunks to retrieve initially from vector search and keep after reranking. + If None, defaults to top_k value. + """ + + max_entity_tokens: int = int(os.getenv("MAX_ENTITY_TOKENS", "6000")) + """Maximum number of tokens allocated for entity context in unified token control system.""" + + max_relation_tokens: int = int(os.getenv("MAX_RELATION_TOKENS", "8000")) + """Maximum number of tokens allocated for relationship context in unified token control system.""" + + max_total_tokens: int = int(os.getenv("MAX_TOTAL_TOKENS", "30000")) + """Maximum total tokens budget for the entire query context (entities + relations + chunks + system prompt).""" + + hl_keywords: list[str] = field(default_factory=list) + """List of high-level keywords to prioritize in retrieval.""" + + ll_keywords: list[str] = field(default_factory=list) + """List of low-level keywords to refine retrieval focus.""" + + # History mesages is only send to LLM for context, not used for retrieval + conversation_history: list[dict[str, str]] = field(default_factory=list) + """Stores past conversation history to maintain context. + Format: [{"role": "user/assistant", "content": "message"}]. + """ + + ids: list[str] | None = None + """List of ids to filter the results.""" + + model_func: Callable[..., object] | None = None + """Optional override for the LLM model function to use for this specific query. + If provided, this will be used instead of the global model function. + This allows using different models for different query modes. + """ + + user_prompt: str | None = None + """User-provided prompt for the query. + Addition instructions for LLM. If provided, this will be inject into the prompt template. + It's purpose is the let user customize the way LLM generate the response. + """ + + enable_rerank: bool = True + """Enable reranking for retrieved text chunks. If True but no rerank model is configured, a warning will be issued. + Default is True to enable reranking when rerank model is available. + """ +``` + +> top_k的默认值可以通过环境变量TOP_K更改。 + +### LLM and Embedding注入 + +LightRAG 需要利用LLM和Embeding模型来完成文档索引和知识库查询工作。在初始化LightRAG的时候需要把阶段，需要把LLM和Embedding的操作函数注入到对象中： + +
+ 使用类OpenAI的API + +* LightRAG还支持类OpenAI的聊天/嵌入API： + +```python +async def llm_model_func( + prompt, system_prompt=None, history_messages=[], keyword_extraction=False, **kwargs +) -> str: + return await openai_complete_if_cache( + "solar-mini", + prompt, + system_prompt=system_prompt, + history_messages=history_messages, + api_key=os.getenv("UPSTAGE_API_KEY"), + base_url="https://api.upstage.ai/v1/solar", + **kwargs + ) + +async def embedding_func(texts: list[str]) -> np.ndarray: + return await openai_embed( + texts, + model="solar-embedding-1-large-query", + api_key=os.getenv("UPSTAGE_API_KEY"), + base_url="https://api.upstage.ai/v1/solar" + ) + +async def initialize_rag(): + rag = LightRAG( + working_dir=WORKING_DIR, + llm_model_func=llm_model_func, + embedding_func=EmbeddingFunc( + embedding_dim=4096, + func=embedding_func + ) + ) + + await rag.initialize_storages() + await initialize_pipeline_status() + + return rag +``` + +
+ +
+ 使用Hugging Face模型 + +* 如果您想使用Hugging Face模型，只需要按如下方式设置LightRAG： + +参见`lightrag_hf_demo.py` + +```python +# 使用Hugging Face模型初始化LightRAG +rag = LightRAG( + working_dir=WORKING_DIR, + llm_model_func=hf_model_complete, # 使用Hugging Face模型进行文本生成 + llm_model_name='meta-llama/Llama-3.1-8B-Instruct', # Hugging Face的模型名称 + # 使用Hugging Face嵌入函数 + embedding_func=EmbeddingFunc( + embedding_dim=384, + func=lambda texts: hf_embed( + texts, + tokenizer=AutoTokenizer.from_pretrained("sentence-transformers/all-MiniLM-L6-v2"), + embed_model=AutoModel.from_pretrained("sentence-transformers/all-MiniLM-L6-v2") + ) + ), +) +``` + +
+ +
+ 使用Ollama模型 +如果您想使用Ollama模型，您需要拉取计划使用的模型和嵌入模型，例如`nomic-embed-text`。 + +然后您只需要按如下方式设置LightRAG： + +```python +# 使用Ollama模型初始化LightRAG +rag = LightRAG( + working_dir=WORKING_DIR, + llm_model_func=ollama_model_complete, # 使用Ollama模型进行文本生成 + llm_model_name='your_model_name', # 您的模型名称 + # 使用Ollama嵌入函数 + embedding_func=EmbeddingFunc( + embedding_dim=768, + func=lambda texts: ollama_embed( + texts, + embed_model="nomic-embed-text" + ) + ), +) +``` + +* **增加上下文大小** + +为了使LightRAG正常工作，上下文应至少为32k令牌。默认情况下，Ollama模型的上下文大小为8k。您可以通过以下两种方式之一实现这一点： + +* **在Modelfile中增加`num_ctx`参数** + +1. 拉取模型： + +```bash +ollama pull qwen2 +``` + +2. 显示模型文件： + +```bash +ollama show --modelfile qwen2 > Modelfile +``` + +3. 编辑Modelfile，添加以下行： + +```bash +PARAMETER num_ctx 32768 +``` + +4. 创建修改后的模型： + +```bash +ollama create -f Modelfile qwen2m +``` + +* **通过Ollama API设置`num_ctx`** + +您可以使用`llm_model_kwargs`参数配置ollama： + +```python +rag = LightRAG( + working_dir=WORKING_DIR, + llm_model_func=ollama_model_complete, # 使用Ollama模型进行文本生成 + llm_model_name='your_model_name', # 您的模型名称 + llm_model_kwargs={"options": {"num_ctx": 32768}}, + # 使用Ollama嵌入函数 + embedding_func=EmbeddingFunc( + embedding_dim=768, + func=lambda texts: ollama_embed( + texts, + embed_model="nomic-embed-text" + ) + ), +) +``` + +* **低RAM GPU** + +为了在低RAM GPU上运行此实验，您应该选择小型模型并调整上下文窗口（增加上下文会增加内存消耗）。例如，在6Gb RAM的改装挖矿GPU上运行这个ollama示例需要将上下文大小设置为26k，同时使用`gemma2:2b`。它能够在`book.txt`中找到197个实体和19个关系。 + +
+
+ LlamaIndex + +LightRAG支持与LlamaIndex集成 (`llm/llama_index_impl.py`): + +- 通过LlamaIndex与OpenAI和其他提供商集成 +- 详细设置和示例请参见[LlamaIndex文档](lightrag/llm/Readme.md) + +**使用示例：** + +```python +# 使用LlamaIndex直接访问OpenAI +import asyncio +from lightrag import LightRAG +from lightrag.llm.llama_index_impl import llama_index_complete_if_cache, llama_index_embed +from llama_index.embeddings.openai import OpenAIEmbedding +from llama_index.llms.openai import OpenAI +from lightrag.kg.shared_storage import initialize_pipeline_status +from lightrag.utils import setup_logger + +# 为LightRAG设置日志处理程序 +setup_logger("lightrag", level="INFO") + +async def initialize_rag(): + rag = LightRAG( + working_dir="your/path", + llm_model_func=llama_index_complete_if_cache, # LlamaIndex兼容的完成函数 + embedding_func=EmbeddingFunc( # LlamaIndex兼容的嵌入函数 + embedding_dim=1536, + func=lambda texts: llama_index_embed(texts, embed_model=embed_model) + ), + ) + + await rag.initialize_storages() + await initialize_pipeline_status() + + return rag + +def main(): + # 初始化RAG实例 + rag = asyncio.run(initialize_rag()) + + with open("./book.txt", "r", encoding="utf-8") as f: + rag.insert(f.read()) + + # 执行朴素搜索 + print( + rag.query("这个故事的主要主题是什么？", param=QueryParam(mode="naive")) + ) + + # 执行本地搜索 + print( + rag.query("这个故事的主要主题是什么？", param=QueryParam(mode="local")) + ) + + # 执行全局搜索 + print( + rag.query("这个故事的主要主题是什么？", param=QueryParam(mode="global")) + ) + + # 执行混合搜索 + print( + rag.query("这个故事的主要主题是什么？", param=QueryParam(mode="hybrid")) + ) + +if __name__ == "__main__": + main() +``` + +**详细文档和示例，请参见：** + +- [LlamaIndex文档](lightrag/llm/Readme.md) +- [直接OpenAI示例](examples/lightrag_llamaindex_direct_demo.py) +- [LiteLLM代理示例](examples/lightrag_llamaindex_litellm_demo.py) + +
+ +### Rerank函数注入 + +为了提高检索质量，可以根据更有效的相关性评分模型对文档进行重排序。`rerank.py`文件提供了三个Reranker提供商的驱动函数： + +* **Cohere / vLLM**: `cohere_rerank` +* **Jina AI**: `jina_rerank` +* **Aliyun阿里云**: `ali_rerank` + +您可以将这些函数之一注入到LightRAG对象的`rerank_model_func`属性中。这将使LightRAG的查询功能能够使用注入的函数对检索到的文本块进行重新排序。有关详细用法，请参阅`examples/rerank_example.py`文件。 + +### 用户提示词 vs. 查询内容 + +当使用LightRAG查询内容的时候，不要把内容查询和与查询结果无关的输出加工写在一起。因为把两者混在一起会严重影响查询的效果。Query Param中的`user_prompt`就是为解决这一问题而设计的。`user_prompt`中的内容不参与RAG中的查询过程，它仅会在获得查询结果之后，与查询结果一起送给LLM，指导LLM如何处理查询结果。以下是使用方法： + +```python +# Create query parameters +query_param = QueryParam( + mode = "hybrid", # Other modes：local, global, hybrid, mix, naive + user_prompt = "如需画图使用mermaid格式，节点名称用英文或拼音，显示名称用中文", +) + +# Query and process +response_default = rag.query( + "请画出 Scrooge 的人物关系图谱", + param=query_param +) +print(response_default) +``` + +### 插入 + +
+ 基本插入 + +```python +# 基本插入 +rag.insert("文本") +``` + +
+ +
+ 批量插入 + +```python +# 基本批量插入：一次插入多个文本 +rag.insert(["文本1", "文本2",...]) + +# 带有自定义批量大小配置的批量插入 +rag = LightRAG( + ... + working_dir=WORKING_DIR, + max_parallel_insert = 4 +) + +rag.insert(["文本1", "文本2", "文本3", ...]) # 文档将以4个为一批进行处理 +``` + +参数 `max_parallel_insert` 用于控制文档索引流水线中并行处理的文档数量。若未指定，默认值为 **2**。建议将该参数设置为 **10 以下**，因为性能瓶颈通常出现在大语言模型（LLM）的处理环节。 + +
+ +
+ 带ID插入 + +如果您想为文档提供自己的ID，文档数量和ID数量必须相同。 + +```python +# 插入单个文本，并为其提供ID +rag.insert("文本1", ids=["文本1的ID"]) + +# 插入多个文本，并为它们提供ID +rag.insert(["文本1", "文本2",...], ids=["文本1的ID", "文本2的ID"]) +``` + +
+ +
+ 使用流水线插入 + +`apipeline_enqueue_documents`和`apipeline_process_enqueue_documents`函数允许您对文档进行增量插入到图中。 + +这对于需要在后台处理文档的场景很有用，同时仍允许主线程继续执行。 + +并使用例程处理新文档。 + +```python +rag = LightRAG(..) + +await rag.apipeline_enqueue_documents(input) +# 您的循环例程 +await rag.apipeline_process_enqueue_documents(input) +``` + +
+ +
+ 插入多文件类型支持 + +`textract`支持读取TXT、DOCX、PPTX、CSV和PDF等文件类型。 + +```python +import textract + +file_path = 'TEXT.pdf' +text_content = textract.process(file_path) + +rag.insert(text_content.decode('utf-8')) +``` + +
+ +
+ 引文功能 + +通过提供文件路径，系统确保可以将来源追溯到其原始文档。 + +```python +# 定义文档及其文件路径 +documents = ["文档内容1", "文档内容2"] +file_paths = ["path/to/doc1.txt", "path/to/doc2.txt"] + +# 插入带有文件路径的文档 +rag.insert(documents, file_paths=file_paths) +``` + +
+ +### 存储 + +LightRAG 使用 4 种类型的存储用于不同目的： + +* KV_STORAGE：llm 响应缓存、文本块、文档信息 +* VECTOR_STORAGE：实体向量、关系向量、块向量 +* GRAPH_STORAGE：实体关系图 +* DOC_STATUS_STORAGE：文档索引状态 + +每种存储类型都有几种实现： + +* KV_STORAGE 支持的实现名称 + +``` +JsonKVStorage JsonFile(默认) +PGKVStorage Postgres +RedisKVStorage Redis +MongoKVStorage MogonDB +``` + +* GRAPH_STORAGE 支持的实现名称 + +``` +NetworkXStorage NetworkX(默认) +Neo4JStorage Neo4J +PGGraphStorage PostgreSQL with AGE plugin +``` + +> 在测试中Neo4j图形数据库相比PostgreSQL AGE有更好的性能表现。 + +* VECTOR_STORAGE 支持的实现名称 + +``` +NanoVectorDBStorage NanoVector(默认) +PGVectorStorage Postgres +MilvusVectorDBStorge Milvus +FaissVectorDBStorage Faiss +QdrantVectorDBStorage Qdrant +MongoVectorDBStorage MongoDB +``` + +* DOC_STATUS_STORAGE 支持的实现名称 + +``` +JsonDocStatusStorage JsonFile(默认) +PGDocStatusStorage Postgres +MongoDocStatusStorage MongoDB +``` + +每一种存储类型的链接配置范例可以在 `env.example` 文件中找到。链接字符串中的数据库实例是需要你预先在数据库服务器上创建好的，LightRAG 仅负责在数据库实例中创建数据表，不负责创建数据库实例。如果使用 Redis 作为存储，记得给 Redis 配置自动持久化数据规则，否则 Redis 服务重启后数据会丢失。如果使用PostgreSQL数据库，推荐使用16.6版本或以上。 + +
+ 使用Neo4J存储 + +* 对于生产级场景，您很可能想要利用企业级解决方案 +* 进行KG存储。推荐在Docker中运行Neo4J以进行无缝本地测试。 +* 参见：https://hub.docker.com/_/neo4j + +```python +export NEO4J_URI="neo4j://localhost:7687" +export NEO4J_USERNAME="neo4j" +export NEO4J_PASSWORD="password" + +# 为LightRAG设置日志记录器 +setup_logger("lightrag", level="INFO") + +# 当您启动项目时，请确保通过指定kg="Neo4JStorage"来覆盖默认的KG：NetworkX。 + +# 注意：默认设置使用NetworkX +# 使用Neo4J实现初始化LightRAG。 +async def initialize_rag(): + rag = LightRAG( + working_dir=WORKING_DIR, + llm_model_func=gpt_4o_mini_complete, # 使用gpt_4o_mini_complete LLM模型 + graph_storage="Neo4JStorage", #<-----------覆盖KG默认值 + ) + + # 初始化数据库连接 + await rag.initialize_storages() + # 初始化文档处理的管道状态 + await initialize_pipeline_status() + + return rag +``` + +参见test_neo4j.py获取工作示例。 + +
+ +
+ 使用Faiss存储 +在使用Faiss向量数据库之前必须手工安装`faiss-cpu`或`faiss-gpu`。 + +- 安装所需依赖： + +``` +pip install faiss-cpu +``` + +如果您有GPU支持，也可以安装`faiss-gpu`。 + +- 这里我们使用`sentence-transformers`，但您也可以使用维度为`3072`的`OpenAIEmbedding`模型。 + +```python +async def embedding_func(texts: list[str]) -> np.ndarray: + model = SentenceTransformer('all-MiniLM-L6-v2') + embeddings = model.encode(texts, convert_to_numpy=True) + return embeddings + +# 使用LLM模型函数和嵌入函数初始化LightRAG +rag = LightRAG( + working_dir=WORKING_DIR, + llm_model_func=llm_model_func, + embedding_func=EmbeddingFunc( + embedding_dim=384, + func=embedding_func, + ), + vector_storage="FaissVectorDBStorage", + vector_db_storage_cls_kwargs={ + "cosine_better_than_threshold": 0.3 # 您期望的阈值 + } +) +``` + +
+ +
+ 使用PostgreSQL存储 + +对于生产级场景，您很可能想要利用企业级解决方案。PostgreSQL可以为您提供一站式储解解决方案，作为KV存储、向量数据库（pgvector）和图数据库（apache AGE）。支持 PostgreSQL 版本为16.6或以上。 + +* 如果您是初学者并想避免麻烦，推荐使用docker，请从这个镜像开始（请务必阅读概述）：https://hub.docker.com/r/shangor/postgres-for-rag +* Apache AGE的性能不如Neo4j。追求高性能的图数据库请使用Noe4j。 + +
+ +
+ 使用MogonDB存储 + +MongoDB为LightRAG提供了一站式的存储解决方案。MongoDB提供原生的KV存储和向量存储。LightRAG使用MogoDB的集合实现了一个简易的图存储。MongoDB 官方的向量检索功能（`$vectorSearch`）目前必须依赖其官方的云服务 MongoDB Atlas。无法在自托管的 MongoDB Community/Enterprise 版本上使用此功能。 + +
+ +
+ 使用Redis存储 + +LightRAG支持使用Reidis作为KV存储。使用Redis存储的时候需要注意进行持久化配置和内存使用量配置。以下是推荐的redis配置 + +``` +save 900 1 +save 300 10 +save 60 1000 +stop-writes-on-bgsave-error yes +maxmemory 4gb +maxmemory-policy noeviction +maxclients 500 +``` + +
+ +### LightRAG实例间的数据隔离 + +通过 workspace 参数可以不同实现不同LightRAG实例之间的存储数据隔离。LightRAG在初始化后workspace就已经确定，之后修改workspace是无效的。下面是不同类型的存储实现工作空间的方式： + +- **对于本地基于文件的数据库，数据隔离通过工作空间子目录实现：** JsonKVStorage, JsonDocStatusStorage, NetworkXStorage, NanoVectorDBStorage, FaissVectorDBStorage。 +- **对于将数据存储在集合（collection）中的数据库，通过在集合名称前添加工作空间前缀来实现：** RedisKVStorage, RedisDocStatusStorage, MilvusVectorDBStorage, QdrantVectorDBStorage, MongoKVStorage, MongoDocStatusStorage, MongoVectorDBStorage, MongoGraphStorage, PGGraphStorage。 +- **对于关系型数据库，数据隔离通过向表中添加 `workspace` 字段进行数据的逻辑隔离：** PGKVStorage, PGVectorStorage, PGDocStatusStorage。 + +* **对于Neo4j图数据库，通过label来实现数据的逻辑隔离**：Neo4JStorage + +为了保持对遗留数据的兼容，在未配置工作空间时PostgreSQL非图存储的工作空间为`default`，PostgreSQL AGE图存储的工作空间为空，Neo4j图存储的默认工作空间为`base`。对于所有的外部存储，系统都提供了专用的工作空间环境变量，用于覆盖公共的 `WORKSPACE`环境变量配置。这些适用于指定存储类型的工作空间环境变量为：`REDIS_WORKSPACE`, `MILVUS_WORKSPACE`, `QDRANT_WORKSPACE`, `MONGODB_WORKSPACE`, `POSTGRES_WORKSPACE`, `NEO4J_WORKSPACE`。 + +### AGENTS.md – 自动编程引导文件 + +AGENTS.md 是一种简洁、开放的格式，用于指导自动编程代理完成工作（https://agents.md/）。它为 LightRAG 项目提供了一个专属且可预测的上下文与指令位置，帮助 AI 代码代理更好地开展工作。不同的 AI 代码代理不应各自维护独立的引导文件。如果某个 AI 代理无法自动识别 AGENTS.md，可使用符号链接来解决。建立符号链接后，可通过配置本地的 `.gitignore_global` 文件防止其被提交至 Git 仓库。 + +## 编辑实体和关系 + +LightRAG现在支持全面的知识图谱管理功能，允许您在知识图谱中创建、编辑和删除实体和关系。 + +
+ 创建实体和关系 + +```python +# 创建新实体 +entity = rag.create_entity("Google", { + "description": "Google是一家专注于互联网相关服务和产品的跨国科技公司。", + "entity_type": "company" +}) + +# 创建另一个实体 +product = rag.create_entity("Gmail", { + "description": "Gmail是由Google开发的电子邮件服务。", + "entity_type": "product" +}) + +# 创建实体之间的关系 +relation = rag.create_relation("Google", "Gmail", { + "description": "Google开发和运营Gmail。", + "keywords": "开发 运营 服务", + "weight": 2.0 +}) +``` + +
+ +
+ 编辑实体和关系 + +```python +# 编辑现有实体 +updated_entity = rag.edit_entity("Google", { + "description": "Google是Alphabet Inc.的子公司，成立于1998年。", + "entity_type": "tech_company" +}) + +# 重命名实体（所有关系都会正确迁移） +renamed_entity = rag.edit_entity("Gmail", { + "entity_name": "Google Mail", + "description": "Google Mail（前身为Gmail）是一项电子邮件服务。" +}) + +# 编辑实体之间的关系 +updated_relation = rag.edit_relation("Google", "Google Mail", { + "description": "Google创建并维护Google Mail服务。", + "keywords": "创建 维护 电子邮件服务", + "weight": 3.0 +}) +``` + +所有操作都有同步和异步版本。异步版本带有前缀"a"（例如，`acreate_entity`，`aedit_relation`）。 + +
+ +
+ 插入自定义知识 + +```python +custom_kg = { + "chunks": [ + { + "content": "Alice和Bob正在合作进行量子计算研究。", + "source_id": "doc-1" + } + ], + "entities": [ + { + "entity_name": "Alice", + "entity_type": "person", + "description": "Alice是一位专门研究量子物理的研究员。", + "source_id": "doc-1" + }, + { + "entity_name": "Bob", + "entity_type": "person", + "description": "Bob是一位数学家。", + "source_id": "doc-1" + }, + { + "entity_name": "量子计算", + "entity_type": "technology", + "description": "量子计算利用量子力学现象进行计算。", + "source_id": "doc-1" + } + ], + "relationships": [ + { + "src_id": "Alice", + "tgt_id": "Bob", + "description": "Alice和Bob是研究伙伴。", + "keywords": "合作 研究", + "weight": 1.0, + "source_id": "doc-1" + }, + { + "src_id": "Alice", + "tgt_id": "量子计算", + "description": "Alice进行量子计算研究。", + "keywords": "研究 专业", + "weight": 1.0, + "source_id": "doc-1" + }, + { + "src_id": "Bob", + "tgt_id": "量子计算", + "description": "Bob研究量子计算。", + "keywords": "研究 应用", + "weight": 1.0, + "source_id": "doc-1" + } + ] +} + +rag.insert_custom_kg(custom_kg) +``` + +
+ +
+ 其它实体与关系操作 + +- **create_entity**：创建具有指定属性的新实体 +- **edit_entity**：更新现有实体的属性或重命名它 + +- **create_relation**：在现有实体之间创建新关系 +- **edit_relation**：更新现有关系的属性 + +这些操作在图数据库和向量数据库组件之间保持数据一致性，确保您的知识图谱保持连贯。 + +
+ +## 删除功能 + +LightRAG提供了全面的删除功能，允许您删除文档、实体和关系。 + +
+ 删除实体 + +您可以通过实体名称删除实体及其所有关联关系： + +```python +# 删除实体及其所有关系（同步版本） +rag.delete_by_entity("Google") + +# 异步版本 +await rag.adelete_by_entity("Google") +``` + +删除实体时会： +- 从知识图谱中移除该实体节点 +- 删除该实体的所有关联关系 +- 从向量数据库中移除相关的嵌入向量 +- 保持知识图谱的完整性 + +
+ +
+ 删除关系 + +您可以删除两个特定实体之间的关系： + +```python +# 删除两个实体之间的关系（同步版本） +rag.delete_by_relation("Google", "Gmail") + +# 异步版本 +await rag.adelete_by_relation("Google", "Gmail") +``` + +删除关系时会： +- 移除指定的关系边 +- 从向量数据库中删除关系的嵌入向量 +- 保留两个实体节点及其他关系 + +
+ +
+ 通过文档ID删除 + +您可以通过文档ID删除整个文档及其相关的所有知识： + +```python +# 通过文档ID删除（异步版本） +await rag.adelete_by_doc_id("doc-12345") +``` + +通过文档ID删除时的优化处理： +- **智能清理**：自动识别并删除仅属于该文档的实体和关系 +- **保留共享知识**：如果实体或关系在其他文档中也存在，则会保留并重新构建描述 +- **缓存优化**：清理相关的LLM缓存以减少存储开销 +- **增量重建**：从剩余文档重新构建受影响的实体和关系描述 + +删除过程包括： +1. 删除文档相关的所有文本块 +2. 识别仅属于该文档的实体和关系并删除 +3. 重新构建在其他文档中仍存在的实体和关系 +4. 更新所有相关的向量索引 +5. 清理文档状态记录 + +注意：通过文档ID删除是一个异步操作，因为它涉及复杂的知识图谱重构过程。 + +
+ +
+ 删除注意事项 + +**重要提醒：** + +1. **不可逆操作**：所有删除操作都是不可逆的，请谨慎使用 +2. **性能考虑**：删除大量数据时可能需要一些时间，特别是通过文档ID删除 +3. **数据一致性**：删除操作会自动维护知识图谱和向量数据库之间的一致性 +4. **备份建议**：在执行重要删除操作前建议备份数据 + +**批量删除建议：** +- 对于批量删除操作，建议使用异步方法以获得更好的性能 +- 大规模删除时，考虑分批进行以避免系统负载过高 + +
+ +## 实体合并 + +
+ 合并实体及其关系 + +LightRAG现在支持将多个实体合并为单个实体，自动处理所有关系： + +```python +# 基本实体合并 +rag.merge_entities( + source_entities=["人工智能", "AI", "机器智能"], + target_entity="AI技术" +) +``` + +使用自定义合并策略： + +```python +# 为不同字段定义自定义合并策略 +rag.merge_entities( + source_entities=["约翰·史密斯", "史密斯博士", "J·史密斯"], + target_entity="约翰·史密斯", + merge_strategy={ + "description": "concatenate", # 组合所有描述 + "entity_type": "keep_first", # 保留第一个实体的类型 + "source_id": "join_unique" # 组合所有唯一的源ID + } +) +``` + +使用自定义目标实体数据： + +```python +# 为合并后的实体指定确切值 +rag.merge_entities( + source_entities=["纽约", "NYC", "大苹果"], + target_entity="纽约市", + target_entity_data={ + "entity_type": "LOCATION", + "description": "纽约市是美国人口最多的城市。", + } +) +``` + +结合两种方法的高级用法： + +```python +# 使用策略和自定义数据合并公司实体 +rag.merge_entities( + source_entities=["微软公司", "Microsoft Corporation", "MSFT"], + target_entity="微软", + merge_strategy={ + "description": "concatenate", # 组合所有描述 + "source_id": "join_unique" # 组合源ID + }, + target_entity_data={ + "entity_type": "ORGANIZATION", + } +) +``` + +合并实体时： + +* 所有来自源实体的关系都会重定向到目标实体 +* 重复的关系会被智能合并 +* 防止自我关系（循环） +* 合并后删除源实体 +* 保留关系权重和属性 + +
+ +## 多模态文档处理（RAG-Anything集成） + +LightRAG 现已与 [RAG-Anything](https://github.com/HKUDS/RAG-Anything) 实现无缝集成，这是一个专为 LightRAG 构建的**全能多模态文档处理RAG系统**。RAG-Anything 提供先进的解析和检索增强生成（RAG）能力，让您能够无缝处理多模态文档，并从各种文档格式中提取结构化内容——包括文本、图片、表格和公式——以集成到您的RAG流程中。 + +**主要特性：** +- **端到端多模态流程**：从文档摄取解析到智能多模态问答的完整工作流程 +- **通用文档支持**：无缝处理PDF、Office文档（DOC/DOCX/PPT/PPTX/XLS/XLSX）、图片和各种文件格式 +- **专业内容分析**：针对图片、表格、数学公式和异构内容类型的专用处理器 +- **多模态知识图谱**：自动实体提取和跨模态关系发现以增强理解 +- **混合智能检索**：覆盖文本和多模态内容的高级搜索能力，具备上下文理解 + +**快速开始：** +1. 安装RAG-Anything： + ```bash + pip install raganything + ``` +2. 处理多模态文档： +
+ RAGAnything 使用示例 + + ```python + import asyncio + from raganything import RAGAnything + from lightrag import LightRAG + from lightrag.llm.openai import openai_complete_if_cache, openai_embed + from lightrag.utils import EmbeddingFunc + import os + + async def load_existing_lightrag(): + # 首先，创建或加载现有的 LightRAG 实例 + lightrag_working_dir = "./existing_lightrag_storage" + + # 检查是否存在之前的 LightRAG 实例 + if os.path.exists(lightrag_working_dir) and os.listdir(lightrag_working_dir): + print("✅ Found existing LightRAG instance, loading...") + else: + print("❌ No existing LightRAG instance found, will create new one") + + # 使用您的配置创建/加载 LightRAG 实例 + lightrag_instance = LightRAG( + working_dir=lightrag_working_dir, + llm_model_func=lambda prompt, system_prompt=None, history_messages=[], **kwargs: openai_complete_if_cache( + "gpt-4o-mini", + prompt, + system_prompt=system_prompt, + history_messages=history_messages, + api_key="your-api-key", + **kwargs, + ), + embedding_func=EmbeddingFunc( + embedding_dim=3072, + func=lambda texts: openai_embed( + texts, + model="text-embedding-3-large", + api_key=api_key, + base_url=base_url, + ), + ) + ) + + # 初始化存储（如果有现有数据，这将加载现有数据） + await lightrag_instance.initialize_storages() + + # 现在使用现有的 LightRAG 实例初始化 RAGAnything + rag = RAGAnything( + lightrag=lightrag_instance, # 传递现有的 LightRAG 实例 + # 仅需要视觉模型用于多模态处理 + vision_model_func=lambda prompt, system_prompt=None, history_messages=[], image_data=None, **kwargs: openai_complete_if_cache( + "gpt-4o", + "", + system_prompt=None, + history_messages=[], + messages=[ + {"role": "system", "content": system_prompt} if system_prompt else None, + {"role": "user", "content": [ + {"type": "text", "text": prompt}, + {"type": "image_url", "image_url": {"url": f"data:image/jpeg;base64,{image_data}"}} + ]} if image_data else {"role": "user", "content": prompt} + ], + api_key="your-api-key", + **kwargs, + ) if image_data else openai_complete_if_cache( + "gpt-4o-mini", + prompt, + system_prompt=system_prompt, + history_messages=history_messages, + api_key="your-api-key", + **kwargs, + ) + # 注意：working_dir、llm_model_func、embedding_func 等都从 lightrag_instance 继承 + ) + + # 查询现有的知识库 + result = await rag.query_with_multimodal( + "What data has been processed in this LightRAG instance?", + mode="hybrid" + ) + print("Query result:", result) + + # 向现有的 LightRAG 实例添加新的多模态文档 + await rag.process_document_complete( + file_path="path/to/new/multimodal_document.pdf", + output_dir="./output" + ) + + if __name__ == "__main__": + asyncio.run(load_existing_lightrag()) + ``` + +
+ +如需详细文档和高级用法，请参阅 [RAG-Anything 仓库](https://github.com/HKUDS/RAG-Anything)。 + +## Token统计功能 + +
+ 概述和使用 + +LightRAG提供了TokenTracker工具来跟踪和管理大模型的token消耗。这个功能对于控制API成本和优化性能特别有用。 + +### 使用方法 + +```python +from lightrag.utils import TokenTracker + +# 创建TokenTracker实例 +token_tracker = TokenTracker() + +# 方法1：使用上下文管理器（推荐） +# 适用于需要自动跟踪token使用的场景 +with token_tracker: + result1 = await llm_model_func("你的问题1") + result2 = await llm_model_func("你的问题2") + +# 方法2：手动添加token使用记录 +# 适用于需要更精细控制token统计的场景 +token_tracker.reset() + +rag.insert() + +rag.query("你的问题1", param=QueryParam(mode="naive")) +rag.query("你的问题2", param=QueryParam(mode="mix")) + +# 显示总token使用量（包含插入和查询操作） +print("Token usage:", token_tracker.get_usage()) +``` + +### 使用建议 +- 在长会话或批量操作中使用上下文管理器，可以自动跟踪所有token消耗 +- 对于需要分段统计的场景，使用手动模式并适时调用reset() +- 定期检查token使用情况，有助于及时发现异常消耗 +- 在开发测试阶段积极使用此功能，以便优化生产环境的成本 + +### 实际应用示例 +您可以参考以下示例来实现token统计： +- `examples/lightrag_gemini_track_token_demo.py`：使用Google Gemini模型的token统计示例 +- `examples/lightrag_siliconcloud_track_token_demo.py`：使用SiliconCloud模型的token统计示例 + +这些示例展示了如何在不同模型和场景下有效地使用TokenTracker功能。 + +
+ +## 数据导出功能 + +### 概述 + +LightRAG允许您以各种格式导出知识图谱数据，用于分析、共享和备份目的。系统支持导出实体、关系和关系数据。 + +### 导出功能 + +#### 基本用法 + +```python +# 基本CSV导出（默认格式） +rag.export_data("knowledge_graph.csv") + +# 指定任意格式 +rag.export_data("output.xlsx", file_format="excel") +``` + +#### 支持的不同文件格式 + +```python +# 以CSV格式导出数据 +rag.export_data("graph_data.csv", file_format="csv") + +# 导出数据到Excel表格 +rag.export_data("graph_data.xlsx", file_format="excel") + +# 以markdown格式导出数据 +rag.export_data("graph_data.md", file_format="md") + +# 导出数据为文本 +rag.export_data("graph_data.txt", file_format="txt") +``` + +#### 附加选项 + +在导出中包含向量嵌入（可选）： + +```python +rag.export_data("complete_data.csv", include_vector_data=True) +``` + +### 导出数据包括 + +所有导出包括： + +* 实体信息（名称、ID、元数据） +* 关系数据（实体之间的连接） +* 来自向量数据库的关系信息 + +## 缓存 + +
+ 清除缓存 + +您可以使用不同模式清除LLM响应缓存： + +```python +# 清除所有缓存 +await rag.aclear_cache() + +# 清除本地模式缓存 +await rag.aclear_cache(modes=["local"]) + +# 清除提取缓存 +await rag.aclear_cache(modes=["default"]) + +# 清除多个模式 +await rag.aclear_cache(modes=["local", "global", "hybrid"]) + +# 同步版本 +rag.clear_cache(modes=["local"]) +``` + +有效的模式包括： + +- `"default"`：提取缓存 +- `"naive"`：朴素搜索缓存 +- `"local"`：本地搜索缓存 +- `"global"`：全局搜索缓存 +- `"hybrid"`：混合搜索缓存 +- `"mix"`：混合搜索缓存 + +
+ +## LightRAG API + +LightRAG服务器旨在提供Web UI和API支持。**有关LightRAG服务器的更多信息，请参阅[LightRAG服务器](./lightrag/api/README.md)。** + +## 知识图谱可视化 + +LightRAG服务器提供全面的知识图谱可视化功能。它支持各种重力布局、节点查询、子图过滤等。**有关LightRAG服务器的更多信息，请参阅[LightRAG服务器](./lightrag/api/README.md)。** + +![iShot_2025-03-23_12.40.08](./README.assets/iShot_2025-03-23_12.40.08.png) + +## Langfuse 可观测性集成 + +Langfuse 为 OpenAI 客户端提供了直接替代方案，可自动跟踪所有 LLM 交互，使开发者能够在无需修改代码的情况下监控、调试和优化其 RAG 系统。 + +### 安装 Langfuse 可选依赖 + +``` +pip install lightrag-hku +pip install lightrag-hku[observability] + +# 或从源代码安装并启用调试模式 +pip install -e . +pip install -e ".[observability]" +``` + +### 配置 Langfuse 环境变量 + +修改 .env 文件： + +``` +## Langfuse 可观测性（可选） +# LLM 可观测性和追踪平台 +# 安装命令: pip install lightrag-hku[observability] +# 注册地址: https://cloud.langfuse.com 或自托管部署 +LANGFUSE_SECRET_KEY="" +LANGFUSE_PUBLIC_KEY="" +LANGFUSE_HOST="https://cloud.langfuse.com" # 或您的自托管实例地址 +LANGFUSE_ENABLE_TRACE=true +``` + +### Langfuse 使用说明 + +安装并配置完成后，Langfuse 会自动追踪所有 OpenAI LLM 调用。Langfuse 仪表板功能包括： + +- **追踪**：查看完整的 LLM 调用链 +- **分析**：Token 使用量、延迟、成本指标 +- **调试**：检查提示词和响应内容 +- **评估**：比较模型输出结果 +- **监控**：实时告警功能 + +### 重要提示 + +**注意**：LightRAG 目前仅把 OpenAI 兼容的 API 调用接入了 Langfuse。Ollama、Azure 和 AWS Bedrock 等 API 还无法使用 Langfuse 的可观测性功能。 + +## RAGAS评估 + +**RAGAS**（Retrieval Augmented Generation Assessment，检索增强生成评估）是一个使用LLM对RAG系统进行无参考评估的框架。我们提供了基于RAGAS的评估脚本。详细信息请参阅[基于RAGAS的评估框架](lightrag/evaluation/README.md)。 + +## 评估 + +### 数据集 + +LightRAG使用的数据集可以从[TommyChien/UltraDomain](https://huggingface.co/datasets/TommyChien/UltraDomain)下载。 + +### 生成查询 + +LightRAG使用以下提示生成高级查询，相应的代码在`example/generate_query.py`中。 + +
+ 提示 + +```python +给定以下数据集描述： + +{description} + +请识别5个可能会使用此数据集的潜在用户。对于每个用户，列出他们会使用此数据集执行的5个任务。然后，对于每个（用户，任务）组合，生成5个需要对整个数据集有高级理解的问题。 + +按以下结构输出结果： +- 用户1：[用户描述] + - 任务1：[任务描述] + - 问题1： + - 问题2： + - 问题3： + - 问题4： + - 问题5： + - 任务2：[任务描述] + ... + - 任务5：[任务描述] +- 用户2：[用户描述] + ... +- 用户5：[用户描述] + ... +``` + +
+ +### 批量评估 + +为了评估两个RAG系统在高级查询上的性能，LightRAG使用以下提示，具体代码可在`example/batch_eval.py`中找到。 + +
+ 提示 + +```python +---角色--- +您是一位专家，负责根据三个标准评估同一问题的两个答案：**全面性**、**多样性**和**赋能性**。 +---目标--- +您将根据三个标准评估同一问题的两个答案：**全面性**、**多样性**和**赋能性**。 + +- **全面性**：答案提供了多少细节来涵盖问题的所有方面和细节？ +- **多样性**：答案在提供关于问题的不同视角和见解方面有多丰富多样？ +- **赋能性**：答案在多大程度上帮助读者理解并对主题做出明智判断？ + +对于每个标准，选择更好的答案（答案1或答案2）并解释原因。然后，根据这三个类别选择总体赢家。 + +这是问题： +{query} + +这是两个答案： + +**答案1：** +{answer1} + +**答案2：** +{answer2} + +使用上述三个标准评估两个答案，并为每个标准提供详细解释。 + +以下列JSON格式输出您的评估： + +{{ + "全面性": {{ + "获胜者": "[答案1或答案2]", + "解释": "[在此提供解释]" + }}, + "赋能性": {{ + "获胜者": "[答案1或答案2]", + "解释": "[在此提供解释]" + }}, + "总体获胜者": {{ + "获胜者": "[答案1或答案2]", + "解释": "[根据三个标准总结为什么这个答案是总体获胜者]" + }} +}} +``` + +
+ +### 总体性能表 + +| |**农业**| |**计算机科学**| |**法律**| |**混合**| | +|----------------------|---------------|------------|------|------------|---------|------------|-------|------------| +| |NaiveRAG|**LightRAG**|NaiveRAG|**LightRAG**|NaiveRAG|**LightRAG**|NaiveRAG|**LightRAG**| +|**全面性**|32.4%|**67.6%**|38.4%|**61.6%**|16.4%|**83.6%**|38.8%|**61.2%**| +|**多样性**|23.6%|**76.4%**|38.0%|**62.0%**|13.6%|**86.4%**|32.4%|**67.6%**| +|**赋能性**|32.4%|**67.6%**|38.8%|**61.2%**|16.4%|**83.6%**|42.8%|**57.2%**| +|**总体**|32.4%|**67.6%**|38.8%|**61.2%**|15.2%|**84.8%**|40.0%|**60.0%**| +| |RQ-RAG|**LightRAG**|RQ-RAG|**LightRAG**|RQ-RAG|**LightRAG**|RQ-RAG|**LightRAG**| +|**全面性**|31.6%|**68.4%**|38.8%|**61.2%**|15.2%|**84.8%**|39.2%|**60.8%**| +|**多样性**|29.2%|**70.8%**|39.2%|**60.8%**|11.6%|**88.4%**|30.8%|**69.2%**| +|**赋能性**|31.6%|**68.4%**|36.4%|**63.6%**|15.2%|**84.8%**|42.4%|**57.6%**| +|**总体**|32.4%|**67.6%**|38.0%|**62.0%**|14.4%|**85.6%**|40.0%|**60.0%**| +| |HyDE|**LightRAG**|HyDE|**LightRAG**|HyDE|**LightRAG**|HyDE|**LightRAG**| +|**全面性**|26.0%|**74.0%**|41.6%|**58.4%**|26.8%|**73.2%**|40.4%|**59.6%**| +|**多样性**|24.0%|**76.0%**|38.8%|**61.2%**|20.0%|**80.0%**|32.4%|**67.6%**| +|**赋能性**|25.2%|**74.8%**|40.8%|**59.2%**|26.0%|**74.0%**|46.0%|**54.0%**| +|**总体**|24.8%|**75.2%**|41.6%|**58.4%**|26.4%|**73.6%**|42.4%|**57.6%**| +| |GraphRAG|**LightRAG**|GraphRAG|**LightRAG**|GraphRAG|**LightRAG**|GraphRAG|**LightRAG**| +|**全面性**|45.6%|**54.4%**|48.4%|**51.6%**|48.4%|**51.6%**|**50.4%**|49.6%| +|**多样性**|22.8%|**77.2%**|40.8%|**59.2%**|26.4%|**73.6%**|36.0%|**64.0%**| +|**赋能性**|41.2%|**58.8%**|45.2%|**54.8%**|43.6%|**56.4%**|**50.8%**|49.2%| +|**总体**|45.2%|**54.8%**|48.0%|**52.0%**|47.2%|**52.8%**|**50.4%**|49.6%| + +## 复现 + +所有代码都可以在`./reproduce`目录中找到。 + +### 步骤0 提取唯一上下文 + +首先，我们需要提取数据集中的唯一上下文。 + +
+ 代码 + +```python +def extract_unique_contexts(input_directory, output_directory): + + os.makedirs(output_directory, exist_ok=True) + + jsonl_files = glob.glob(os.path.join(input_directory, '*.jsonl')) + print(f"找到{len(jsonl_files)}个JSONL文件。") + + for file_path in jsonl_files: + filename = os.path.basename(file_path) + name, ext = os.path.splitext(filename) + output_filename = f"{name}_unique_contexts.json" + output_path = os.path.join(output_directory, output_filename) + + unique_contexts_dict = {} + + print(f"处理文件：{filename}") + + try: + with open(file_path, 'r', encoding='utf-8') as infile: + for line_number, line in enumerate(infile, start=1): + line = line.strip() + if not line: + continue + try: + json_obj = json.loads(line) + context = json_obj.get('context') + if context and context not in unique_contexts_dict: + unique_contexts_dict[context] = None + except json.JSONDecodeError as e: + print(f"文件{filename}第{line_number}行JSON解码错误：{e}") + except FileNotFoundError: + print(f"未找到文件：{filename}") + continue + except Exception as e: + print(f"处理文件{filename}时发生错误：{e}") + continue + + unique_contexts_list = list(unique_contexts_dict.keys()) + print(f"文件{filename}中有{len(unique_contexts_list)}个唯一的`context`条目。") + + try: + with open(output_path, 'w', encoding='utf-8') as outfile: + json.dump(unique_contexts_list, outfile, ensure_ascii=False, indent=4) + print(f"唯一的`context`条目已保存到：{output_filename}") + except Exception as e: + print(f"保存到文件{output_filename}时发生错误：{e}") + + print("所有文件已处理完成。") + +``` + +
+ +### 步骤1 插入上下文 + +对于提取的上下文，我们将它们插入到LightRAG系统中。 + +
+ 代码 + +```python +def insert_text(rag, file_path): + with open(file_path, mode='r') as f: + unique_contexts = json.load(f) + + retries = 0 + max_retries = 3 + while retries < max_retries: + try: + rag.insert(unique_contexts) + break + except Exception as e: + retries += 1 + print(f"插入失败，重试（{retries}/{max_retries}），错误：{e}") + time.sleep(10) + if retries == max_retries: + print("超过最大重试次数后插入失败") +``` + +
+ +### 步骤2 生成查询 + +我们从数据集中每个上下文的前半部分和后半部分提取令牌，然后将它们组合为数据集描述以生成查询。 + +
+ 代码 + +```python +tokenizer = GPT2Tokenizer.from_pretrained('gpt2') + +def get_summary(context, tot_tokens=2000): + tokens = tokenizer.tokenize(context) + half_tokens = tot_tokens // 2 + + start_tokens = tokens[1000:1000 + half_tokens] + end_tokens = tokens[-(1000 + half_tokens):1000] + + summary_tokens = start_tokens + end_tokens + summary = tokenizer.convert_tokens_to_string(summary_tokens) + + return summary +``` + +
+ +### 步骤3 查询 + +对于步骤2中生成的查询，我们将提取它们并查询LightRAG。 + +
+ 代码 + +```python +def extract_queries(file_path): + with open(file_path, 'r') as f: + data = f.read() + + data = data.replace('**', '') + + queries = re.findall(r'- Question \d+: (.+)', data) + + return queries +``` + +
+ +## Star历史 + + + + + + Star History Chart + + + +## 贡献 + +感谢所有贡献者！ + + + + + +## 🌟引用 + +```python +@article{guo2024lightrag, +title={LightRAG: Simple and Fast Retrieval-Augmented Generation}, +author={Zirui Guo and Lianghao Xia and Yanhua Yu and Tu Ao and Chao Huang}, +year={2024}, +eprint={2410.05779}, +archivePrefix={arXiv}, +primaryClass={cs.IR} +} +``` + +**感谢您对我们工作的关注！** diff --git a/LightRAG/README.assets/b2aaf634151b4706892693ffb43d9093.png b/LightRAG/README.assets/b2aaf634151b4706892693ffb43d9093.png new file mode 100644 index 0000000000000000000000000000000000000000..ac38781127b9f10ea688adcbcec8971cecaf0b31 GIT binary patch literal 482231 zcmbSzWmKHYwk>oZSb|Fe!QEYhyK8WFm*7qyIKef+f;0qo*Wl2&ySqEV-^V`Z-h19Y z@5j4`Q9WA5*PZUFRcp;P=d6S&%1gXO#6^UHf_f_@DXI(wg_r^b1@eV|4ZKov5{m)c zK|3i+2t$<)f7k<_xSMK7naRmP(EyL(pn!a9GVL1)nBiJprAr5 zp%#``n z)i?k7M8rVB=JCQweu4RqhYh^@!r6|~u}}!Jq^nz7rW2hVy}iB5W;){ko51=B-|peO(&|&` zbMHD=YnzIltk>%O-R5OM1Tnr~=R0lK&PpEQ(k^uD|mwRzu7ePNYEC3 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+ +# 🚀 LightRAG: Simple and Fast Retrieval-Augmented Generation + + + +
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+ LightRAG Diagram +
+ +--- +## 🎉 News +- [x] [2025.11.05]🎯📢Add **RAGAS-based** Evaluation Framework and **Langfuse** observability for LightRAG. +- [x] [2025.10.22]🎯📢Eliminate bottlenecks in processing **large-scale datasets**. +- [x] [2025.09.15]🎯📢Significantly enhances KG extraction accuracy for **small LLMs** like Qwen3-30B-A3B. +- [x] [2025.08.29]🎯📢**Reranker** is supported now , significantly boosting performance for mixed queries. +- [x] [2025.08.04]🎯📢**Document deletion** with KG regeneration to ensure query performance. +- [x] [2025.06.16]🎯📢Our team has released [RAG-Anything](https://github.com/HKUDS/RAG-Anything) an All-in-One Multimodal RAG System for seamless text, image, table, and equation processing. +- [X] [2025.06.05]🎯📢LightRAG now supports comprehensive multimodal data handling through [RAG-Anything](https://github.com/HKUDS/RAG-Anything) integration, enabling seamless document parsing and RAG capabilities across diverse formats including PDFs, images, Office documents, tables, and formulas. Please refer to the new [multimodal section](https://github.com/HKUDS/LightRAG/?tab=readme-ov-file#multimodal-document-processing-rag-anything-integration) for details. +- [X] [2025.03.18]🎯📢LightRAG now supports citation functionality, enabling proper source attribution. +- [X] [2025.02.05]🎯📢Our team has released [VideoRAG](https://github.com/HKUDS/VideoRAG) understanding extremely long-context videos. +- [X] [2025.01.13]🎯📢Our team has released [MiniRAG](https://github.com/HKUDS/MiniRAG) making RAG simpler with small models. +- [X] [2025.01.06]🎯📢You can now [use PostgreSQL for Storage](#using-postgresql-for-storage). +- [X] [2024.11.25]🎯📢LightRAG now supports seamless integration of [custom knowledge graphs](https://github.com/HKUDS/LightRAG?tab=readme-ov-file#insert-custom-kg), empowering users to enhance the system with their own domain expertise. +- [X] [2024.11.19]🎯📢A comprehensive guide to LightRAG is now available on [LearnOpenCV](https://learnopencv.com/lightrag). Many thanks to the blog author. +- [X] [2024.11.11]🎯📢LightRAG now supports [deleting entities by their names](https://github.com/HKUDS/LightRAG?tab=readme-ov-file#delete). +- [X] [2024.11.09]🎯📢Introducing the [LightRAG Gui](https://lightrag-gui.streamlit.app), which allows you to insert, query, visualize, and download LightRAG knowledge. +- [X] [2024.11.04]🎯📢You can now [use Neo4J for Storage](https://github.com/HKUDS/LightRAG?tab=readme-ov-file#using-neo4j-for-storage). +- [X] [2024.10.29]🎯📢LightRAG now supports multiple file types, including PDF, DOC, PPT, and CSV via `textract`. +- [X] [2024.10.20]🎯📢We've added a new feature to LightRAG: Graph Visualization. +- [X] [2024.10.18]🎯📢We've added a link to a [LightRAG Introduction Video](https://youtu.be/oageL-1I0GE). Thanks to the author! +- [X] [2024.10.17]🎯📢We have created a [Discord channel](https://discord.gg/yF2MmDJyGJ)! Welcome to join for sharing and discussions! 🎉🎉 +- [X] [2024.10.16]🎯📢LightRAG now supports [Ollama models](https://github.com/HKUDS/LightRAG?tab=readme-ov-file#quick-start)! +- [X] [2024.10.15]🎯📢LightRAG now supports [Hugging Face models](https://github.com/HKUDS/LightRAG?tab=readme-ov-file#quick-start)! + +
+ + Algorithm Flowchart + + +![LightRAG Indexing Flowchart](https://learnopencv.com/wp-content/uploads/2024/11/LightRAG-VectorDB-Json-KV-Store-Indexing-Flowchart-scaled.jpg) +*Figure 1: LightRAG Indexing Flowchart - Img Caption : [Source](https://learnopencv.com/lightrag/)* +![LightRAG Retrieval and Querying Flowchart](https://learnopencv.com/wp-content/uploads/2024/11/LightRAG-Querying-Flowchart-Dual-Level-Retrieval-Generation-Knowledge-Graphs-scaled.jpg) +*Figure 2: LightRAG Retrieval and Querying Flowchart - Img Caption : [Source](https://learnopencv.com/lightrag/)* + +
+ +## Installation + +> **💡 Using uv for Package Management**: This project uses [uv](https://docs.astral.sh/uv/) for fast and reliable Python package management. +> Install uv first: `curl -LsSf https://astral.sh/uv/install.sh | sh` (Unix/macOS) or `powershell -c "irm https://astral.sh/uv/install.ps1 | iex"` (Windows) +> +> **Note**: You can also use pip if you prefer, but uv is recommended for better performance and more reliable dependency management. +> +> **📦 Offline Deployment**: For offline or air-gapped environments, see the [Offline Deployment Guide](./docs/OfflineDeployment.md) for instructions on pre-installing all dependencies and cache files. + +### Install LightRAG Server + +The LightRAG Server is designed to provide Web UI and API support. The Web UI facilitates document indexing, knowledge graph exploration, and a simple RAG query interface. LightRAG Server also provide an Ollama compatible interfaces, aiming to emulate LightRAG as an Ollama chat model. This allows AI chat bot, such as Open WebUI, to access LightRAG easily. + +* Install from PyPI + +```bash +# Using uv (recommended) +uv pip install "lightrag-hku[api]" +# Or using pip +# pip install "lightrag-hku[api]" + +cp env.example .env # Update the .env with your LLM and embedding configurations + +lightrag-server +``` + +* Installation from Source + +```bash +git clone https://github.com/HKUDS/LightRAG.git +cd LightRAG + +# Using uv (recommended) +# Note: uv sync automatically creates a virtual environment in .venv/ +uv sync --extra api +source .venv/bin/activate # Activate the virtual environment (Linux/macOS) +# Or on Windows: .venv\Scripts\activate + +# Or using pip with virtual environment +# python -m venv .venv +# source .venv/bin/activate # Windows: .venv\Scripts\activate +# pip install -e ".[api]" + +cp env.example .env # Update the .env with your LLM and embedding configurations + +# Build front-end artifacts +cd lightrag_webui +bun install --frozen-lockfile +bun run build +cd .. + +lightrag-server +``` + +* Launching the LightRAG Server with Docker Compose + +```bash +git clone https://github.com/HKUDS/LightRAG.git +cd LightRAG +cp env.example .env # Update the .env with your LLM and embedding configurations +# modify LLM and Embedding settings in .env +docker compose up +``` + +> Historical versions of LightRAG docker images can be found here: [LightRAG Docker Images]( https://github.com/HKUDS/LightRAG/pkgs/container/lightrag) + +### Install LightRAG Core + +* Install from source (Recommended) + +```bash +cd LightRAG +# Note: uv sync automatically creates a virtual environment in .venv/ +uv sync +source .venv/bin/activate # Activate the virtual environment (Linux/macOS) +# Or on Windows: .venv\Scripts\activate + +# Or: pip install -e . +``` + +* Install from PyPI + +```bash +uv pip install lightrag-hku +# Or: pip install lightrag-hku +``` + +## Quick Start + +### LLM and Technology Stack Requirements for LightRAG + +LightRAG's demands on the capabilities of Large Language Models (LLMs) are significantly higher than those of traditional RAG, as it requires the LLM to perform entity-relationship extraction tasks from documents. Configuring appropriate Embedding and Reranker models is also crucial for improving query performance. + +- **LLM Selection**: + - It is recommended to use an LLM with at least 32 billion parameters. + - The context length should be at least 32KB, with 64KB being recommended. + - It is not recommended to choose reasoning models during the document indexing stage. + - During the query stage, it is recommended to choose models with stronger capabilities than those used in the indexing stage to achieve better query results. +- **Embedding Model**: + - A high-performance Embedding model is essential for RAG. + - We recommend using mainstream multilingual Embedding models, such as: `BAAI/bge-m3` and `text-embedding-3-large`. + - **Important Note**: The Embedding model must be determined before document indexing, and the same model must be used during the document query phase. For certain storage solutions (e.g., PostgreSQL), the vector dimension must be defined upon initial table creation. Therefore, when changing embedding models, it is necessary to delete the existing vector-related tables and allow LightRAG to recreate them with the new dimensions. +- **Reranker Model Configuration**: + - Configuring a Reranker model can significantly enhance LightRAG's retrieval performance. + - When a Reranker model is enabled, it is recommended to set the "mix mode" as the default query mode. + - We recommend using mainstream Reranker models, such as: `BAAI/bge-reranker-v2-m3` or models provided by services like Jina. + +### Quick Start for LightRAG Server + +* For more information about LightRAG Server, please refer to [LightRAG Server](./lightrag/api/README.md). + +### Quick Start for LightRAG core + +To get started with LightRAG core, refer to the sample codes available in the `examples` folder. Additionally, a [video demo](https://www.youtube.com/watch?v=g21royNJ4fw) demonstration is provided to guide you through the local setup process. If you already possess an OpenAI API key, you can run the demo right away: + +```bash +### you should run the demo code with project folder +cd LightRAG +### provide your API-KEY for OpenAI +export OPENAI_API_KEY="sk-...your_opeai_key..." +### download the demo document of "A Christmas Carol" by Charles Dickens +curl https://raw.githubusercontent.com/gusye1234/nano-graphrag/main/tests/mock_data.txt > ./book.txt +### run the demo code +python examples/lightrag_openai_demo.py +``` + +For a streaming response implementation example, please see `examples/lightrag_openai_compatible_demo.py`. Prior to execution, ensure you modify the sample code's LLM and embedding configurations accordingly. + +**Note 1**: When running the demo program, please be aware that different test scripts may use different embedding models. If you switch to a different embedding model, you must clear the data directory (`./dickens`); otherwise, the program may encounter errors. If you wish to retain the LLM cache, you can preserve the `kv_store_llm_response_cache.json` file while clearing the data directory. + +**Note 2**: Only `lightrag_openai_demo.py` and `lightrag_openai_compatible_demo.py` are officially supported sample codes. Other sample files are community contributions that haven't undergone full testing and optimization. + +## Programing with LightRAG Core + +> ⚠️ **If you would like to integrate LightRAG into your project, we recommend utilizing the REST API provided by the LightRAG Server**. LightRAG Core is typically intended for embedded applications or for researchers who wish to conduct studies and evaluations. + +### ⚠️ Important: Initialization Requirements + +**LightRAG requires explicit initialization before use.** You must call both `await rag.initialize_storages()` and `await initialize_pipeline_status()` after creating a LightRAG instance, otherwise you will encounter errors like: + +- `AttributeError: __aenter__` - if storages are not initialized +- `KeyError: 'history_messages'` - if pipeline status is not initialized + +### A Simple Program + +Use the below Python snippet to initialize LightRAG, insert text to it, and perform queries: + +```python +import os +import asyncio +from lightrag import LightRAG, QueryParam +from lightrag.llm.openai import gpt_4o_mini_complete, gpt_4o_complete, openai_embed +from lightrag.kg.shared_storage import initialize_pipeline_status +from lightrag.utils import setup_logger + +setup_logger("lightrag", level="INFO") + +WORKING_DIR = "./rag_storage" +if not os.path.exists(WORKING_DIR): + os.mkdir(WORKING_DIR) + +async def initialize_rag(): + rag = LightRAG( + working_dir=WORKING_DIR, + embedding_func=openai_embed, + llm_model_func=gpt_4o_mini_complete, + ) + # IMPORTANT: Both initialization calls are required! + await rag.initialize_storages() # Initialize storage backends + await initialize_pipeline_status() # Initialize processing pipeline + return rag + +async def main(): + try: + # Initialize RAG instance + rag = await initialize_rag() + await rag.ainsert("Your text") + + # Perform hybrid search + mode = "hybrid" + print( + await rag.aquery( + "What are the top themes in this story?", + param=QueryParam(mode=mode) + ) + ) + + except Exception as e: + print(f"An error occurred: {e}") + finally: + if rag: + await rag.finalize_storages() + +if __name__ == "__main__": + asyncio.run(main()) +``` + +Important notes for the above snippet: + +- Export your OPENAI_API_KEY environment variable before running the script. +- This program uses the default storage settings for LightRAG, so all data will be persisted to WORKING_DIR/rag_storage. +- This program demonstrates only the simplest way to initialize a LightRAG object: Injecting the embedding and LLM functions, and initializing storage and pipeline status after creating the LightRAG object. + +### LightRAG init parameters + +A full list of LightRAG init parameters: + +
+ Parameters + +| **Parameter** | **Type** | **Explanation** | **Default** | +|--------------|----------|-----------------|-------------| +| **working_dir** | `str` | Directory where the cache will be stored | `lightrag_cache+timestamp` | +| **workspace** | str | Workspace name for data isolation between different LightRAG Instances | | +| **kv_storage** | `str` | Storage type for documents and text chunks. Supported types: `JsonKVStorage`,`PGKVStorage`,`RedisKVStorage`,`MongoKVStorage` | `JsonKVStorage` | +| **vector_storage** | `str` | Storage type for embedding vectors. Supported types: `NanoVectorDBStorage`,`PGVectorStorage`,`MilvusVectorDBStorage`,`ChromaVectorDBStorage`,`FaissVectorDBStorage`,`MongoVectorDBStorage`,`QdrantVectorDBStorage` | `NanoVectorDBStorage` | +| **graph_storage** | `str` | Storage type for graph edges and nodes. Supported types: `NetworkXStorage`,`Neo4JStorage`,`PGGraphStorage`,`AGEStorage` | `NetworkXStorage` | +| **doc_status_storage** | `str` | Storage type for documents process status. Supported types: `JsonDocStatusStorage`,`PGDocStatusStorage`,`MongoDocStatusStorage` | `JsonDocStatusStorage` | +| **chunk_token_size** | `int` | Maximum token size per chunk when splitting documents | `1200` | +| **chunk_overlap_token_size** | `int` | Overlap token size between two chunks when splitting documents | `100` | +| **tokenizer** | `Tokenizer` | The function used to convert text into tokens (numbers) and back using .encode() and .decode() functions following `TokenizerInterface` protocol. If you don't specify one, it will use the default Tiktoken tokenizer. | `TiktokenTokenizer` | +| **tiktoken_model_name** | `str` | If you're using the default Tiktoken tokenizer, this is the name of the specific Tiktoken model to use. This setting is ignored if you provide your own tokenizer. | `gpt-4o-mini` | +| **entity_extract_max_gleaning** | `int` | Number of loops in the entity extraction process, appending history messages | `1` | +| **node_embedding_algorithm** | `str` | Algorithm for node embedding (currently not used) | `node2vec` | +| **node2vec_params** | `dict` | Parameters for node embedding | `{"dimensions": 1536,"num_walks": 10,"walk_length": 40,"window_size": 2,"iterations": 3,"random_seed": 3,}` | +| **embedding_func** | `EmbeddingFunc` | Function to generate embedding vectors from text | `openai_embed` | +| **embedding_batch_num** | `int` | Maximum batch size for embedding processes (multiple texts sent per batch) | `32` | +| **embedding_func_max_async** | `int` | Maximum number of concurrent asynchronous embedding processes | `16` | +| **llm_model_func** | `callable` | Function for LLM generation | `gpt_4o_mini_complete` | +| **llm_model_name** | `str` | LLM model name for generation | `meta-llama/Llama-3.2-1B-Instruct` | +| **summary_context_size** | `int` | Maximum tokens send to LLM to generate summaries for entity relation merging | `10000`（configured by env var SUMMARY_CONTEXT_SIZE) | +| **summary_max_tokens** | `int` | Maximum token size for entity/relation description | `500`（configured by env var SUMMARY_MAX_TOKENS) | +| **llm_model_max_async** | `int` | Maximum number of concurrent asynchronous LLM processes | `4`（default value changed by env var MAX_ASYNC) | +| **llm_model_kwargs** | `dict` | Additional parameters for LLM generation | | +| **vector_db_storage_cls_kwargs** | `dict` | Additional parameters for vector database, like setting the threshold for nodes and relations retrieval | cosine_better_than_threshold: 0.2（default value changed by env var COSINE_THRESHOLD) | +| **enable_llm_cache** | `bool` | If `TRUE`, stores LLM results in cache; repeated prompts return cached responses | `TRUE` | +| **enable_llm_cache_for_entity_extract** | `bool` | If `TRUE`, stores LLM results in cache for entity extraction; Good for beginners to debug your application | `TRUE` | +| **addon_params** | `dict` | Additional parameters, e.g., `{"language": "Simplified Chinese", "entity_types": ["organization", "person", "location", "event"]}`: sets example limit, entiy/relation extraction output language | language: English` | +| **embedding_cache_config** | `dict` | Configuration for question-answer caching. Contains three parameters: `enabled`: Boolean value to enable/disable cache lookup functionality. When enabled, the system will check cached responses before generating new answers. `similarity_threshold`: Float value (0-1), similarity threshold. When a new question's similarity with a cached question exceeds this threshold, the cached answer will be returned directly without calling the LLM. `use_llm_check`: Boolean value to enable/disable LLM similarity verification. When enabled, LLM will be used as a secondary check to verify the similarity between questions before returning cached answers. | Default: `{"enabled": False, "similarity_threshold": 0.95, "use_llm_check": False}` | + +
+ +### Query Param + +Use QueryParam to control the behavior your query: + +```python +class QueryParam: + """Configuration parameters for query execution in LightRAG.""" + + mode: Literal["local", "global", "hybrid", "naive", "mix", "bypass"] = "global" + """Specifies the retrieval mode: + - "local": Focuses on context-dependent information. + - "global": Utilizes global knowledge. + - "hybrid": Combines local and global retrieval methods. + - "naive": Performs a basic search without advanced techniques. + - "mix": Integrates knowledge graph and vector retrieval. + """ + + only_need_context: bool = False + """If True, only returns the retrieved context without generating a response.""" + + only_need_prompt: bool = False + """If True, only returns the generated prompt without producing a response.""" + + response_type: str = "Multiple Paragraphs" + """Defines the response format. Examples: 'Multiple Paragraphs', 'Single Paragraph', 'Bullet Points'.""" + + stream: bool = False + """If True, enables streaming output for real-time responses.""" + + top_k: int = int(os.getenv("TOP_K", "60")) + """Number of top items to retrieve. Represents entities in 'local' mode and relationships in 'global' mode.""" + + chunk_top_k: int = int(os.getenv("CHUNK_TOP_K", "20")) + """Number of text chunks to retrieve initially from vector search and keep after reranking. + If None, defaults to top_k value. + """ + + max_entity_tokens: int = int(os.getenv("MAX_ENTITY_TOKENS", "6000")) + """Maximum number of tokens allocated for entity context in unified token control system.""" + + max_relation_tokens: int = int(os.getenv("MAX_RELATION_TOKENS", "8000")) + """Maximum number of tokens allocated for relationship context in unified token control system.""" + + max_total_tokens: int = int(os.getenv("MAX_TOTAL_TOKENS", "30000")) + """Maximum total tokens budget for the entire query context (entities + relations + chunks + system prompt).""" + + # History mesages is only send to LLM for context, not used for retrieval + conversation_history: list[dict[str, str]] = field(default_factory=list) + """Stores past conversation history to maintain context. + Format: [{"role": "user/assistant", "content": "message"}]. + """ + + ids: list[str] | None = None + """List of ids to filter the results.""" + + model_func: Callable[..., object] | None = None + """Optional override for the LLM model function to use for this specific query. + If provided, this will be used instead of the global model function. + This allows using different models for different query modes. + """ + + user_prompt: str | None = None + """User-provided prompt for the query. + Addition instructions for LLM. If provided, this will be inject into the prompt template. + It's purpose is the let user customize the way LLM generate the response. + """ + + enable_rerank: bool = True + """Enable reranking for retrieved text chunks. If True but no rerank model is configured, a warning will be issued. + Default is True to enable reranking when rerank model is available. + """ +``` + +> default value of Top_k can be change by environment variables TOP_K. + +### LLM and Embedding Injection + +LightRAG requires the utilization of LLM and Embedding models to accomplish document indexing and querying tasks. During the initialization phase, it is necessary to inject the invocation methods of the relevant models into LightRAG： + +
+ Using Open AI-like APIs + +* LightRAG also supports Open AI-like chat/embeddings APIs: + +```python +async def llm_model_func( + prompt, system_prompt=None, history_messages=[], keyword_extraction=False, **kwargs +) -> str: + return await openai_complete_if_cache( + "solar-mini", + prompt, + system_prompt=system_prompt, + history_messages=history_messages, + api_key=os.getenv("UPSTAGE_API_KEY"), + base_url="https://api.upstage.ai/v1/solar", + **kwargs + ) + +async def embedding_func(texts: list[str]) -> np.ndarray: + return await openai_embed( + texts, + model="solar-embedding-1-large-query", + api_key=os.getenv("UPSTAGE_API_KEY"), + base_url="https://api.upstage.ai/v1/solar" + ) + +async def initialize_rag(): + rag = LightRAG( + working_dir=WORKING_DIR, + llm_model_func=llm_model_func, + embedding_func=EmbeddingFunc( + embedding_dim=4096, + func=embedding_func + ) + ) + + await rag.initialize_storages() + await initialize_pipeline_status() + + return rag +``` + +
+ +
+ Using Hugging Face Models + +* If you want to use Hugging Face models, you only need to set LightRAG as follows: + +See `lightrag_hf_demo.py` + +```python +# Initialize LightRAG with Hugging Face model +rag = LightRAG( + working_dir=WORKING_DIR, + llm_model_func=hf_model_complete, # Use Hugging Face model for text generation + llm_model_name='meta-llama/Llama-3.1-8B-Instruct', # Model name from Hugging Face + # Use Hugging Face embedding function + embedding_func=EmbeddingFunc( + embedding_dim=384, + func=lambda texts: hf_embed( + texts, + tokenizer=AutoTokenizer.from_pretrained("sentence-transformers/all-MiniLM-L6-v2"), + embed_model=AutoModel.from_pretrained("sentence-transformers/all-MiniLM-L6-v2") + ) + ), +) +``` + +
+ +
+ Using Ollama Models +**Overview** + +If you want to use Ollama models, you need to pull model you plan to use and embedding model, for example `nomic-embed-text`. + +Then you only need to set LightRAG as follows: + +```python +# Initialize LightRAG with Ollama model +rag = LightRAG( + working_dir=WORKING_DIR, + llm_model_func=ollama_model_complete, # Use Ollama model for text generation + llm_model_name='your_model_name', # Your model name + # Use Ollama embedding function + embedding_func=EmbeddingFunc( + embedding_dim=768, + func=lambda texts: ollama_embed( + texts, + embed_model="nomic-embed-text" + ) + ), +) +``` + +* **Increasing context size** + +In order for LightRAG to work context should be at least 32k tokens. By default Ollama models have context size of 8k. You can achieve this using one of two ways: + +* **Increasing the `num_ctx` parameter in Modelfile** + +1. Pull the model: + +```bash +ollama pull qwen2 +``` + +2. Display the model file: + +```bash +ollama show --modelfile qwen2 > Modelfile +``` + +3. Edit the Modelfile by adding the following line: + +```bash +PARAMETER num_ctx 32768 +``` + +4. Create the modified model: + +```bash +ollama create -f Modelfile qwen2m +``` + +* **Setup `num_ctx` via Ollama API** + +Tiy can use `llm_model_kwargs` param to configure ollama: + +```python +rag = LightRAG( + working_dir=WORKING_DIR, + llm_model_func=ollama_model_complete, # Use Ollama model for text generation + llm_model_name='your_model_name', # Your model name + llm_model_kwargs={"options": {"num_ctx": 32768}}, + # Use Ollama embedding function + embedding_func=EmbeddingFunc( + embedding_dim=768, + func=lambda texts: ollama_embed( + texts, + embed_model="nomic-embed-text" + ) + ), +) +``` + +* **Low RAM GPUs** + +In order to run this experiment on low RAM GPU you should select small model and tune context window (increasing context increase memory consumption). For example, running this ollama example on repurposed mining GPU with 6Gb of RAM required to set context size to 26k while using `gemma2:2b`. It was able to find 197 entities and 19 relations on `book.txt`. + +
+
+ LlamaIndex + +LightRAG supports integration with LlamaIndex (`llm/llama_index_impl.py`): + +- Integrates with OpenAI and other providers through LlamaIndex +- See [LlamaIndex Documentation](lightrag/llm/Readme.md) for detailed setup and examples + +**Example Usage** + +```python +# Using LlamaIndex with direct OpenAI access +import asyncio +from lightrag import LightRAG +from lightrag.llm.llama_index_impl import llama_index_complete_if_cache, llama_index_embed +from llama_index.embeddings.openai import OpenAIEmbedding +from llama_index.llms.openai import OpenAI +from lightrag.kg.shared_storage import initialize_pipeline_status +from lightrag.utils import setup_logger + +# Setup log handler for LightRAG +setup_logger("lightrag", level="INFO") + +async def initialize_rag(): + rag = LightRAG( + working_dir="your/path", + llm_model_func=llama_index_complete_if_cache, # LlamaIndex-compatible completion function + embedding_func=EmbeddingFunc( # LlamaIndex-compatible embedding function + embedding_dim=1536, + func=lambda texts: llama_index_embed(texts, embed_model=embed_model) + ), + ) + + await rag.initialize_storages() + await initialize_pipeline_status() + + return rag + +def main(): + # Initialize RAG instance + rag = asyncio.run(initialize_rag()) + + with open("./book.txt", "r", encoding="utf-8") as f: + rag.insert(f.read()) + + # Perform naive search + print( + rag.query("What are the top themes in this story?", param=QueryParam(mode="naive")) + ) + + # Perform local search + print( + rag.query("What are the top themes in this story?", param=QueryParam(mode="local")) + ) + + # Perform global search + print( + rag.query("What are the top themes in this story?", param=QueryParam(mode="global")) + ) + + # Perform hybrid search + print( + rag.query("What are the top themes in this story?", param=QueryParam(mode="hybrid")) + ) + +if __name__ == "__main__": + main() +``` + +**For detailed documentation and examples, see:** + +- [LlamaIndex Documentation](lightrag/llm/Readme.md) +- [Direct OpenAI Example](examples/lightrag_llamaindex_direct_demo.py) +- [LiteLLM Proxy Example](examples/lightrag_llamaindex_litellm_demo.py) + +
+ +### Rerank Function Injection + +To enhance retrieval quality, documents can be re-ranked based on a more effective relevance scoring model. The `rerank.py` file provides three Reranker provider driver functions: + +* **Cohere / vLLM**: `cohere_rerank` +* **Jina AI**: `jina_rerank` +* **Aliyun**: `ali_rerank` + +You can inject one of these functions into the `rerank_model_func` attribute of the LightRAG object. This will enable LightRAG's query function to re-order retrieved text blocks using the injected function. For detailed usage, please refer to the `examples/rerank_example.py` file. + +### User Prompt vs. Query + +When using LightRAG for content queries, avoid combining the search process with unrelated output processing, as this significantly impacts query effectiveness. The `user_prompt` parameter in Query Param is specifically designed to address this issue — it does not participate in the RAG retrieval phase, but rather guides the LLM on how to process the retrieved results after the query is completed. Here's how to use it: + +```python +# Create query parameters +query_param = QueryParam( + mode = "hybrid", # Other modes：local, global, hybrid, mix, naive + user_prompt = "For diagrams, use mermaid format with English/Pinyin node names and Chinese display labels", +) + +# Query and process +response_default = rag.query( + "Please draw a character relationship diagram for Scrooge", + param=query_param +) +print(response_default) +``` + +### Insert + +
+ Basic Insert + +```python +# Basic Insert +rag.insert("Text") +``` + +
+ +
+ Batch Insert + +```python +# Basic Batch Insert: Insert multiple texts at once +rag.insert(["TEXT1", "TEXT2",...]) + +# Batch Insert with custom batch size configuration +rag = LightRAG( + ... + working_dir=WORKING_DIR, + max_parallel_insert = 4 +) + +rag.insert(["TEXT1", "TEXT2", "TEXT3", ...]) # Documents will be processed in batches of 4 +``` + +The `max_parallel_insert` parameter determines the number of documents processed concurrently in the document indexing pipeline. If unspecified, the default value is **2**. We recommend keeping this setting **below 10**, as the performance bottleneck typically lies with the LLM (Large Language Model) processing.The `max_parallel_insert` parameter determines the number of documents processed concurrently in the document indexing pipeline. If unspecified, the default value is **2**. We recommend keeping this setting **below 10**, as the performance bottleneck typically lies with the LLM (Large Language Model) processing. + +
+ +
+ Insert with ID + +If you want to provide your own IDs for your documents, number of documents and number of IDs must be the same. + +```python +# Insert single text, and provide ID for it +rag.insert("TEXT1", ids=["ID_FOR_TEXT1"]) + +# Insert multiple texts, and provide IDs for them +rag.insert(["TEXT1", "TEXT2",...], ids=["ID_FOR_TEXT1", "ID_FOR_TEXT2"]) +``` + +
+ +
+ Insert using Pipeline + +The `apipeline_enqueue_documents` and `apipeline_process_enqueue_documents` functions allow you to perform incremental insertion of documents into the graph. + +This is useful for scenarios where you want to process documents in the background while still allowing the main thread to continue executing. + +And using a routine to process new documents. + +```python +rag = LightRAG(..) + +await rag.apipeline_enqueue_documents(input) +# Your routine in loop +await rag.apipeline_process_enqueue_documents(input) +``` + +
+ +
+ Insert Multi-file Type Support + +The `textract` supports reading file types such as TXT, DOCX, PPTX, CSV, and PDF. + +```python +import textract + +file_path = 'TEXT.pdf' +text_content = textract.process(file_path) + +rag.insert(text_content.decode('utf-8')) +``` + +
+ +
+ Citation Functionality + +By providing file paths, the system ensures that sources can be traced back to their original documents. + +```python +# Define documents and their file paths +documents = ["Document content 1", "Document content 2"] +file_paths = ["path/to/doc1.txt", "path/to/doc2.txt"] + +# Insert documents with file paths +rag.insert(documents, file_paths=file_paths) +``` + +
+ +### Storage + +LightRAG uses 4 types of storage for different purposes: + +* KV_STORAGE: llm response cache, text chunks, document information +* VECTOR_STORAGE: entities vectors, relation vectors, chunks vectors +* GRAPH_STORAGE: entity relation graph +* DOC_STATUS_STORAGE: document indexing status + +Each storage type has several implementations: + +* KV_STORAGE supported implementations: + +``` +JsonKVStorage JsonFile (default) +PGKVStorage Postgres +RedisKVStorage Redis +MongoKVStorage MongoDB +``` + +* GRAPH_STORAGE supported implementations: + +``` +NetworkXStorage NetworkX (default) +Neo4JStorage Neo4J +PGGraphStorage PostgreSQL with AGE plugin +MemgraphStorage. Memgraph +``` + +> Testing has shown that Neo4J delivers superior performance in production environments compared to PostgreSQL with AGE plugin. + +* VECTOR_STORAGE supported implementations: + +``` +NanoVectorDBStorage NanoVector (default) +PGVectorStorage Postgres +MilvusVectorDBStorage Milvus +FaissVectorDBStorage Faiss +QdrantVectorDBStorage Qdrant +MongoVectorDBStorage MongoDB +``` + +* DOC_STATUS_STORAGE: supported implementations: + +``` +JsonDocStatusStorage JsonFile (default) +PGDocStatusStorage Postgres +MongoDocStatusStorage MongoDB +``` + +Example connection configurations for each storage type can be found in the `env.example` file. The database instance in the connection string needs to be created by you on the database server beforehand. LightRAG is only responsible for creating tables within the database instance, not for creating the database instance itself. If using Redis as storage, remember to configure automatic data persistence rules for Redis, otherwise data will be lost after the Redis service restarts. If using PostgreSQL, it is recommended to use version 16.6 or above. + +
+ Using Neo4J Storage + +* For production level scenarios you will most likely want to leverage an enterprise solution +* for KG storage. Running Neo4J in Docker is recommended for seamless local testing. +* See: https://hub.docker.com/_/neo4j + +```python +export NEO4J_URI="neo4j://localhost:7687" +export NEO4J_USERNAME="neo4j" +export NEO4J_PASSWORD="password" + +# Setup logger for LightRAG +setup_logger("lightrag", level="INFO") + +# When you launch the project be sure to override the default KG: NetworkX +# by specifying kg="Neo4JStorage". + +# Note: Default settings use NetworkX +# Initialize LightRAG with Neo4J implementation. +async def initialize_rag(): + rag = LightRAG( + working_dir=WORKING_DIR, + llm_model_func=gpt_4o_mini_complete, # Use gpt_4o_mini_complete LLM model + graph_storage="Neo4JStorage", #<-----------override KG default + ) + + # Initialize database connections + await rag.initialize_storages() + # Initialize pipeline status for document processing + await initialize_pipeline_status() + + return rag +``` + +see test_neo4j.py for a working example. + +
+ +
+ Using PostgreSQL Storage + +For production level scenarios you will most likely want to leverage an enterprise solution. PostgreSQL can provide a one-stop solution for you as KV store, VectorDB (pgvector) and GraphDB (apache AGE). PostgreSQL version 16.6 or higher is supported. + +* PostgreSQL is lightweight,the whole binary distribution including all necessary plugins can be zipped to 40MB: Ref to [Windows Release](https://github.com/ShanGor/apache-age-windows/releases/tag/PG17%2Fv1.5.0-rc0) as it is easy to install for Linux/Mac. +* If you prefer docker, please start with this image if you are a beginner to avoid hiccups (DO read the overview): https://hub.docker.com/r/shangor/postgres-for-rag +* How to start? Ref to: [examples/lightrag_zhipu_postgres_demo.py](https://github.com/HKUDS/LightRAG/blob/main/examples/lightrag_zhipu_postgres_demo.py) +* For high-performance graph database requirements, Neo4j is recommended as Apache AGE's performance is not as competitive. + +
+ +
+ Using Faiss Storage +Before using Faiss vector database, you must manually install `faiss-cpu` or `faiss-gpu`. + +- Install the required dependencies: + +``` +pip install faiss-cpu +``` + +You can also install `faiss-gpu` if you have GPU support. + +- Here we are using `sentence-transformers` but you can also use `OpenAIEmbedding` model with `3072` dimensions. + +```python +async def embedding_func(texts: list[str]) -> np.ndarray: + model = SentenceTransformer('all-MiniLM-L6-v2') + embeddings = model.encode(texts, convert_to_numpy=True) + return embeddings + +# Initialize LightRAG with the LLM model function and embedding function +rag = LightRAG( + working_dir=WORKING_DIR, + llm_model_func=llm_model_func, + embedding_func=EmbeddingFunc( + embedding_dim=384, + func=embedding_func, + ), + vector_storage="FaissVectorDBStorage", + vector_db_storage_cls_kwargs={ + "cosine_better_than_threshold": 0.3 # Your desired threshold + } +) +``` + +
+ +
+ Using Memgraph for Storage + +* Memgraph is a high-performance, in-memory graph database compatible with the Neo4j Bolt protocol. +* You can run Memgraph locally using Docker for easy testing: +* See: https://memgraph.com/download + +```python +export MEMGRAPH_URI="bolt://localhost:7687" + +# Setup logger for LightRAG +setup_logger("lightrag", level="INFO") + +# When you launch the project, override the default KG: NetworkX +# by specifying kg="MemgraphStorage". + +# Note: Default settings use NetworkX +# Initialize LightRAG with Memgraph implementation. +async def initialize_rag(): + rag = LightRAG( + working_dir=WORKING_DIR, + llm_model_func=gpt_4o_mini_complete, # Use gpt_4o_mini_complete LLM model + graph_storage="MemgraphStorage", #<-----------override KG default + ) + + # Initialize database connections + await rag.initialize_storages() + # Initialize pipeline status for document processing + await initialize_pipeline_status() + + return rag +``` + +
+ +
+ Using MongoDB Storage + +MongoDB provides a one-stop storage solution for LightRAG. MongoDB offers native KV storage and vector storage. LightRAG uses MongoDB collections to implement a simple graph storage. MongoDB's official vector search functionality (`$vectorSearch`) currently requires their official cloud service MongoDB Atlas. This functionality cannot be used on self-hosted MongoDB Community/Enterprise versions. + +
+ +
+ Using Redis Storage + +LightRAG supports using Redis as KV storage. When using Redis storage, attention should be paid to persistence configuration and memory usage configuration. The following is the recommended Redis configuration: + +``` +save 900 1 +save 300 10 +save 60 1000 +stop-writes-on-bgsave-error yes +maxmemory 4gb +maxmemory-policy noeviction +maxclients 500 +``` + +
+ +### Data Isolation Between LightRAG Instances + +The `workspace` parameter ensures data isolation between different LightRAG instances. Once initialized, the `workspace` is immutable and cannot be changed.Here is how workspaces are implemented for different types of storage: + +- **For local file-based databases, data isolation is achieved through workspace subdirectories:** `JsonKVStorage`, `JsonDocStatusStorage`, `NetworkXStorage`, `NanoVectorDBStorage`, `FaissVectorDBStorage`. +- **For databases that store data in collections, it's done by adding a workspace prefix to the collection name:** `RedisKVStorage`, `RedisDocStatusStorage`, `MilvusVectorDBStorage`, `MongoKVStorage`, `MongoDocStatusStorage`, `MongoVectorDBStorage`, `MongoGraphStorage`, `PGGraphStorage`. +- **For Qdrant vector database, data isolation is achieved through payload-based partitioning (Qdrant's recommended multitenancy approach):** `QdrantVectorDBStorage` uses shared collections with payload filtering for unlimited workspace scalability. +- **For relational databases, data isolation is achieved by adding a `workspace` field to the tables for logical data separation:** `PGKVStorage`, `PGVectorStorage`, `PGDocStatusStorage`. +- **For the Neo4j graph database, logical data isolation is achieved through labels:** `Neo4JStorage` + +To maintain compatibility with legacy data, the default workspace for PostgreSQL non-graph storage is `default` and, for PostgreSQL AGE graph storage is null, for Neo4j graph storage is `base` when no workspace is configured. For all external storages, the system provides dedicated workspace environment variables to override the common `WORKSPACE` environment variable configuration. These storage-specific workspace environment variables are: `REDIS_WORKSPACE`, `MILVUS_WORKSPACE`, `QDRANT_WORKSPACE`, `MONGODB_WORKSPACE`, `POSTGRES_WORKSPACE`, `NEO4J_WORKSPACE`. + +### AGENTS.md -- Guiding Coding Agents + +AGENTS.md is a simple, open format for guiding coding agents (https://agents.md/). It is a dedicated, predictable place to provide the context and instructions to help AI coding agents work on LightRAG project. Different AI coders should not maintain separate guidance files individually. If any AI coder cannot automatically recognize AGENTS.md, symbolic links can be used as a solution. After establishing symbolic links, you can prevent them from being committed to the Git repository by configuring your local `.gitignore_global`. + +## Edit Entities and Relations + +LightRAG now supports comprehensive knowledge graph management capabilities, allowing you to create, edit, and delete entities and relationships within your knowledge graph. + +
+ Create Entities and Relations + +```python +# Create new entity +entity = rag.create_entity("Google", { + "description": "Google is a multinational technology company specializing in internet-related services and products.", + "entity_type": "company" +}) + +# Create another entity +product = rag.create_entity("Gmail", { + "description": "Gmail is an email service developed by Google.", + "entity_type": "product" +}) + +# Create relation between entities +relation = rag.create_relation("Google", "Gmail", { + "description": "Google develops and operates Gmail.", + "keywords": "develops operates service", + "weight": 2.0 +}) +``` + +
+ +
+ Edit Entities and Relations + +```python +# Edit an existing entity +updated_entity = rag.edit_entity("Google", { + "description": "Google is a subsidiary of Alphabet Inc., founded in 1998.", + "entity_type": "tech_company" +}) + +# Rename an entity (with all its relationships properly migrated) +renamed_entity = rag.edit_entity("Gmail", { + "entity_name": "Google Mail", + "description": "Google Mail (formerly Gmail) is an email service." +}) + +# Edit a relation between entities +updated_relation = rag.edit_relation("Google", "Google Mail", { + "description": "Google created and maintains Google Mail service.", + "keywords": "creates maintains email service", + "weight": 3.0 +}) +``` + +All operations are available in both synchronous and asynchronous versions. The asynchronous versions have the prefix "a" (e.g., `acreate_entity`, `aedit_relation`). + +
+ +
+ Insert Custom KG + +```python +custom_kg = { + "chunks": [ + { + "content": "Alice and Bob are collaborating on quantum computing research.", + "source_id": "doc-1", + "file_path": "test_file", + } + ], + "entities": [ + { + "entity_name": "Alice", + "entity_type": "person", + "description": "Alice is a researcher specializing in quantum physics.", + "source_id": "doc-1", + "file_path": "test_file" + }, + { + "entity_name": "Bob", + "entity_type": "person", + "description": "Bob is a mathematician.", + "source_id": "doc-1", + "file_path": "test_file" + }, + { + "entity_name": "Quantum Computing", + "entity_type": "technology", + "description": "Quantum computing utilizes quantum mechanical phenomena for computation.", + "source_id": "doc-1", + "file_path": "test_file" + } + ], + "relationships": [ + { + "src_id": "Alice", + "tgt_id": "Bob", + "description": "Alice and Bob are research partners.", + "keywords": "collaboration research", + "weight": 1.0, + "source_id": "doc-1", + "file_path": "test_file" + }, + { + "src_id": "Alice", + "tgt_id": "Quantum Computing", + "description": "Alice conducts research on quantum computing.", + "keywords": "research expertise", + "weight": 1.0, + "source_id": "doc-1", + "file_path": "test_file" + }, + { + "src_id": "Bob", + "tgt_id": "Quantum Computing", + "description": "Bob researches quantum computing.", + "keywords": "research application", + "weight": 1.0, + "source_id": "doc-1", + "file_path": "test_file" + } + ] + } + +rag.insert_custom_kg(custom_kg) +``` + +
+ +
+ Other Entity and Relation Operations + +- **create_entity**: Creates a new entity with specified attributes +- **edit_entity**: Updates an existing entity's attributes or renames it + + +- **create_relation**: Creates a new relation between existing entities +- **edit_relation**: Updates an existing relation's attributes + +These operations maintain data consistency across both the graph database and vector database components, ensuring your knowledge graph remains coherent. + +
+ +## Delete Functions + +LightRAG provides comprehensive deletion capabilities, allowing you to delete documents, entities, and relationships. + +
+ Delete Entities + +You can delete entities by their name along with all associated relationships: + +```python +# Delete entity and all its relationships (synchronous version) +rag.delete_by_entity("Google") + +# Asynchronous version +await rag.adelete_by_entity("Google") +``` + +When deleting an entity: +- Removes the entity node from the knowledge graph +- Deletes all associated relationships +- Removes related embedding vectors from the vector database +- Maintains knowledge graph integrity + +
+ +
+ Delete Relations + +You can delete relationships between two specific entities: + +```python +# Delete relationship between two entities (synchronous version) +rag.delete_by_relation("Google", "Gmail") + +# Asynchronous version +await rag.adelete_by_relation("Google", "Gmail") +``` + +When deleting a relationship: +- Removes the specified relationship edge +- Deletes the relationship's embedding vector from the vector database +- Preserves both entity nodes and their other relationships + +
+ +
+ Delete by Document ID + +You can delete an entire document and all its related knowledge through document ID: + +```python +# Delete by document ID (asynchronous version) +await rag.adelete_by_doc_id("doc-12345") +``` + +Optimized processing when deleting by document ID: +- **Smart Cleanup**: Automatically identifies and removes entities and relationships that belong only to this document +- **Preserve Shared Knowledge**: If entities or relationships exist in other documents, they are preserved and their descriptions are rebuilt +- **Cache Optimization**: Clears related LLM cache to reduce storage overhead +- **Incremental Rebuilding**: Reconstructs affected entity and relationship descriptions from remaining documents + +The deletion process includes: +1. Delete all text chunks related to the document +2. Identify and delete entities and relationships that belong only to this document +3. Rebuild entities and relationships that still exist in other documents +4. Update all related vector indexes +5. Clean up document status records + +Note: Deletion by document ID is an asynchronous operation as it involves complex knowledge graph reconstruction processes. + +
+ +**Important Reminders:** + +1. **Irreversible Operations**: All deletion operations are irreversible, please use with caution +2. **Performance Considerations**: Deleting large amounts of data may take some time, especially deletion by document ID +3. **Data Consistency**: Deletion operations automatically maintain consistency between the knowledge graph and vector database +4. **Backup Recommendations**: Consider backing up data before performing important deletion operations + +**Batch Deletion Recommendations:** +- For batch deletion operations, consider using asynchronous methods for better performance +- For large-scale deletions, consider processing in batches to avoid excessive system load + +## Entity Merging + +
+ Merge Entities and Their Relationships + +LightRAG now supports merging multiple entities into a single entity, automatically handling all relationships: + +```python +# Basic entity merging +rag.merge_entities( + source_entities=["Artificial Intelligence", "AI", "Machine Intelligence"], + target_entity="AI Technology" +) +``` + +With custom merge strategy: + +```python +# Define custom merge strategy for different fields +rag.merge_entities( + source_entities=["John Smith", "Dr. Smith", "J. Smith"], + target_entity="John Smith", + merge_strategy={ + "description": "concatenate", # Combine all descriptions + "entity_type": "keep_first", # Keep the entity type from the first entity + "source_id": "join_unique" # Combine all unique source IDs + } +) +``` + +With custom target entity data: + +```python +# Specify exact values for the merged entity +rag.merge_entities( + source_entities=["New York", "NYC", "Big Apple"], + target_entity="New York City", + target_entity_data={ + "entity_type": "LOCATION", + "description": "New York City is the most populous city in the United States.", + } +) +``` + +Advanced usage combining both approaches: + +```python +# Merge company entities with both strategy and custom data +rag.merge_entities( + source_entities=["Microsoft Corp", "Microsoft Corporation", "MSFT"], + target_entity="Microsoft", + merge_strategy={ + "description": "concatenate", # Combine all descriptions + "source_id": "join_unique" # Combine source IDs + }, + target_entity_data={ + "entity_type": "ORGANIZATION", + } +) +``` + +When merging entities: + +* All relationships from source entities are redirected to the target entity +* Duplicate relationships are intelligently merged +* Self-relationships (loops) are prevented +* Source entities are removed after merging +* Relationship weights and attributes are preserved + +
+ +## Multimodal Document Processing (RAG-Anything Integration) + +LightRAG now seamlessly integrates with [RAG-Anything](https://github.com/HKUDS/RAG-Anything), a comprehensive **All-in-One Multimodal Document Processing RAG system** built specifically for LightRAG. RAG-Anything enables advanced parsing and retrieval-augmented generation (RAG) capabilities, allowing you to handle multimodal documents seamlessly and extract structured content—including text, images, tables, and formulas—from various document formats for integration into your RAG pipeline. + +**Key Features:** +- **End-to-End Multimodal Pipeline**: Complete workflow from document ingestion and parsing to intelligent multimodal query answering +- **Universal Document Support**: Seamless processing of PDFs, Office documents (DOC/DOCX/PPT/PPTX/XLS/XLSX), images, and diverse file formats +- **Specialized Content Analysis**: Dedicated processors for images, tables, mathematical equations, and heterogeneous content types +- **Multimodal Knowledge Graph**: Automatic entity extraction and cross-modal relationship discovery for enhanced understanding +- **Hybrid Intelligent Retrieval**: Advanced search capabilities spanning textual and multimodal content with contextual understanding + +**Quick Start:** +1. Install RAG-Anything: + ```bash + pip install raganything + ``` +2. Process multimodal documents: +
+ RAGAnything Usage Example + + ```python + import asyncio + from raganything import RAGAnything + from lightrag import LightRAG + from lightrag.llm.openai import openai_complete_if_cache, openai_embed + from lightrag.utils import EmbeddingFunc + import os + + async def load_existing_lightrag(): + # First, create or load an existing LightRAG instance + lightrag_working_dir = "./existing_lightrag_storage" + + # Check if previous LightRAG instance exists + if os.path.exists(lightrag_working_dir) and os.listdir(lightrag_working_dir): + print("✅ Found existing LightRAG instance, loading...") + else: + print("❌ No existing LightRAG instance found, will create new one") + + # Create/Load LightRAG instance with your configurations + lightrag_instance = LightRAG( + working_dir=lightrag_working_dir, + llm_model_func=lambda prompt, system_prompt=None, history_messages=[], **kwargs: openai_complete_if_cache( + "gpt-4o-mini", + prompt, + system_prompt=system_prompt, + history_messages=history_messages, + api_key="your-api-key", + **kwargs, + ), + embedding_func=EmbeddingFunc( + embedding_dim=3072, + func=lambda texts: openai_embed( + texts, + model="text-embedding-3-large", + api_key=api_key, + base_url=base_url, + ), + ) + ) + + # Initialize storage (this will load existing data if available) + await lightrag_instance.initialize_storages() + + # Now initialize RAGAnything with the existing LightRAG instance + rag = RAGAnything( + lightrag=lightrag_instance, # Pass the existing LightRAG instance + # Only need vision model for multimodal processing + vision_model_func=lambda prompt, system_prompt=None, history_messages=[], image_data=None, **kwargs: openai_complete_if_cache( + "gpt-4o", + "", + system_prompt=None, + history_messages=[], + messages=[ + {"role": "system", "content": system_prompt} if system_prompt else None, + {"role": "user", "content": [ + {"type": "text", "text": prompt}, + {"type": "image_url", "image_url": {"url": f"data:image/jpeg;base64,{image_data}"}} + ]} if image_data else {"role": "user", "content": prompt} + ], + api_key="your-api-key", + **kwargs, + ) if image_data else openai_complete_if_cache( + "gpt-4o-mini", + prompt, + system_prompt=system_prompt, + history_messages=history_messages, + api_key="your-api-key", + **kwargs, + ) + # Note: working_dir, llm_model_func, embedding_func, etc. are inherited from lightrag_instance + ) + + # Query the existing knowledge base + result = await rag.query_with_multimodal( + "What data has been processed in this LightRAG instance?", + mode="hybrid" + ) + print("Query result:", result) + + # Add new multimodal documents to the existing LightRAG instance + await rag.process_document_complete( + file_path="path/to/new/multimodal_document.pdf", + output_dir="./output" + ) + + if __name__ == "__main__": + asyncio.run(load_existing_lightrag()) + ``` +
+ +For detailed documentation and advanced usage, please refer to the [RAG-Anything repository](https://github.com/HKUDS/RAG-Anything). + +## Token Usage Tracking + +
+ Overview and Usage + +LightRAG provides a TokenTracker tool to monitor and manage token consumption by large language models. This feature is particularly useful for controlling API costs and optimizing performance. + +### Usage + +```python +from lightrag.utils import TokenTracker + +# Create TokenTracker instance +token_tracker = TokenTracker() + +# Method 1: Using context manager (Recommended) +# Suitable for scenarios requiring automatic token usage tracking +with token_tracker: + result1 = await llm_model_func("your question 1") + result2 = await llm_model_func("your question 2") + +# Method 2: Manually adding token usage records +# Suitable for scenarios requiring more granular control over token statistics +token_tracker.reset() + +rag.insert() + +rag.query("your question 1", param=QueryParam(mode="naive")) +rag.query("your question 2", param=QueryParam(mode="mix")) + +# Display total token usage (including insert and query operations) +print("Token usage:", token_tracker.get_usage()) +``` + +### Usage Tips +- Use context managers for long sessions or batch operations to automatically track all token consumption +- For scenarios requiring segmented statistics, use manual mode and call reset() when appropriate +- Regular checking of token usage helps detect abnormal consumption early +- Actively use this feature during development and testing to optimize production costs + +### Practical Examples +You can refer to these examples for implementing token tracking: +- `examples/lightrag_gemini_track_token_demo.py`: Token tracking example using Google Gemini model +- `examples/lightrag_siliconcloud_track_token_demo.py`: Token tracking example using SiliconCloud model + +These examples demonstrate how to effectively use the TokenTracker feature with different models and scenarios. + +
+ +## Data Export Functions + +### Overview + +LightRAG allows you to export your knowledge graph data in various formats for analysis, sharing, and backup purposes. The system supports exporting entities, relations, and relationship data. + +### Export Functions + +
+ Basic Usage + +```python +# Basic CSV export (default format) +rag.export_data("knowledge_graph.csv") + +# Specify any format +rag.export_data("output.xlsx", file_format="excel") +``` + +
+ +
+ Different File Formats supported + +```python +#Export data in CSV format +rag.export_data("graph_data.csv", file_format="csv") + +# Export data in Excel sheet +rag.export_data("graph_data.xlsx", file_format="excel") + +# Export data in markdown format +rag.export_data("graph_data.md", file_format="md") + +# Export data in Text +rag.export_data("graph_data.txt", file_format="txt") +``` +
+ +
+ Additional Options + +Include vector embeddings in the export (optional): + +```python +rag.export_data("complete_data.csv", include_vector_data=True) +``` +
+ +### Data Included in Export + +All exports include: + +* Entity information (names, IDs, metadata) +* Relation data (connections between entities) +* Relationship information from vector database + +## Cache + +
+ Clear Cache + +You can clear the LLM response cache with different modes: + +```python +# Clear all cache +await rag.aclear_cache() + +# Clear local mode cache +await rag.aclear_cache(modes=["local"]) + +# Clear extraction cache +await rag.aclear_cache(modes=["default"]) + +# Clear multiple modes +await rag.aclear_cache(modes=["local", "global", "hybrid"]) + +# Synchronous version +rag.clear_cache(modes=["local"]) +``` + +Valid modes are: + +- `"default"`: Extraction cache +- `"naive"`: Naive search cache +- `"local"`: Local search cache +- `"global"`: Global search cache +- `"hybrid"`: Hybrid search cache +- `"mix"`: Mix search cache + +
+ +## Troubleshooting + +### Common Initialization Errors + +If you encounter these errors when using LightRAG: + +1. **`AttributeError: __aenter__`** + - **Cause**: Storage backends not initialized + - **Solution**: Call `await rag.initialize_storages()` after creating the LightRAG instance + +2. **`KeyError: 'history_messages'`** + - **Cause**: Pipeline status not initialized + - **Solution**: Call `await initialize_pipeline_status()` after initializing storages + +3. **Both errors in sequence** + - **Cause**: Neither initialization method was called + - **Solution**: Always follow this pattern: + ```python + rag = LightRAG(...) + await rag.initialize_storages() + await initialize_pipeline_status() + ``` + +### Model Switching Issues + +When switching between different embedding models, you must clear the data directory to avoid errors. The only file you may want to preserve is `kv_store_llm_response_cache.json` if you wish to retain the LLM cache. + +## LightRAG API + +The LightRAG Server is designed to provide Web UI and API support. **For more information about LightRAG Server, please refer to [LightRAG Server](./lightrag/api/README.md).** + +## Graph Visualization + +The LightRAG Server offers a comprehensive knowledge graph visualization feature. It supports various gravity layouts, node queries, subgraph filtering, and more. **For more information about LightRAG Server, please refer to [LightRAG Server](./lightrag/api/README.md).** + +![iShot_2025-03-23_12.40.08](./README.assets/iShot_2025-03-23_12.40.08.png) + +## Langfuse observability integration + +Langfuse provides a drop-in replacement for the OpenAI client that automatically tracks all LLM interactions, enabling developers to monitor, debug, and optimize their RAG systems without code changes. + +### Installation with Langfuse option + +``` +pip install lightrag-hku +pip install lightrag-hku[observability] + +# Or install from souce code with debug mode enabled +pip install -e . +pip install -e ".[observability]" +``` + +### Config Langfuse env vars + +modify .env file: + +``` +## Langfuse Observability (Optional) +# LLM observability and tracing platform +# Install with: pip install lightrag-hku[observability] +# Sign up at: https://cloud.langfuse.com or self-host +LANGFUSE_SECRET_KEY="" +LANGFUSE_PUBLIC_KEY="" +LANGFUSE_HOST="https://cloud.langfuse.com" # or your self-hosted instance +LANGFUSE_ENABLE_TRACE=true +``` + +### Langfuse Usage + +Once installed and configured, Langfuse automatically traces all OpenAI LLM calls. Langfuse dashboard features include: + +- **Tracing**: View complete LLM call chains +- **Analytics**: Token usage, latency, cost metrics +- **Debugging**: Inspect prompts and responses +- **Evaluation**: Compare model outputs +- **Monitoring**: Real-time alerting + +### Important Notice + +**Note**: LightRAG currently only integrates OpenAI-compatible API calls with Langfuse. APIs such as Ollama, Azure, and AWS Bedrock are not yet supported for Langfuse observability. + +## RAGAS-based Evaluation + +**RAGAS** (Retrieval Augmented Generation Assessment) is a framework for reference-free evaluation of RAG systems using LLMs. There is an evaluation script based on RAGAS. For detailed information, please refer to [RAGAS-based Evaluation Framework](lightrag/evaluation/README.md). + +## Evaluation + +### Dataset + +The dataset used in LightRAG can be downloaded from [TommyChien/UltraDomain](https://huggingface.co/datasets/TommyChien/UltraDomain). + +### Generate Query + +LightRAG uses the following prompt to generate high-level queries, with the corresponding code in `examples/generate_query.py`. + +
+ Prompt + +```python +Given the following description of a dataset: + +{description} + +Please identify 5 potential users who would engage with this dataset. For each user, list 5 tasks they would perform with this dataset. Then, for each (user, task) combination, generate 5 questions that require a high-level understanding of the entire dataset. + +Output the results in the following structure: +- User 1: [user description] + - Task 1: [task description] + - Question 1: + - Question 2: + - Question 3: + - Question 4: + - Question 5: + - Task 2: [task description] + ... + - Task 5: [task description] +- User 2: [user description] + ... +- User 5: [user description] + ... +``` + +
+ +### Batch Eval + +To evaluate the performance of two RAG systems on high-level queries, LightRAG uses the following prompt, with the specific code available in `reproduce/batch_eval.py`. + +
+ Prompt + +```python +---Role--- +You are an expert tasked with evaluating two answers to the same question based on three criteria: **Comprehensiveness**, **Diversity**, and **Empowerment**. +---Goal--- +You will evaluate two answers to the same question based on three criteria: **Comprehensiveness**, **Diversity**, and **Empowerment**. + +- **Comprehensiveness**: How much detail does the answer provide to cover all aspects and details of the question? +- **Diversity**: How varied and rich is the answer in providing different perspectives and insights on the question? +- **Empowerment**: How well does the answer help the reader understand and make informed judgments about the topic? + +For each criterion, choose the better answer (either Answer 1 or Answer 2) and explain why. Then, select an overall winner based on these three categories. + +Here is the question: +{query} + +Here are the two answers: + +**Answer 1:** +{answer1} + +**Answer 2:** +{answer2} + +Evaluate both answers using the three criteria listed above and provide detailed explanations for each criterion. + +Output your evaluation in the following JSON format: + +{{ + "Comprehensiveness": {{ + "Winner": "[Answer 1 or Answer 2]", + "Explanation": "[Provide explanation here]" + }}, + "Empowerment": {{ + "Winner": "[Answer 1 or Answer 2]", + "Explanation": "[Provide explanation here]" + }}, + "Overall Winner": {{ + "Winner": "[Answer 1 or Answer 2]", + "Explanation": "[Summarize why this answer is the overall winner based on the three criteria]" + }} +}} +``` + +
+ +### Overall Performance Table + +| |**Agriculture**| |**CS**| |**Legal**| |**Mix**| | +|----------------------|---------------|------------|------|------------|---------|------------|-------|------------| +| |NaiveRAG|**LightRAG**|NaiveRAG|**LightRAG**|NaiveRAG|**LightRAG**|NaiveRAG|**LightRAG**| +|**Comprehensiveness**|32.4%|**67.6%**|38.4%|**61.6%**|16.4%|**83.6%**|38.8%|**61.2%**| +|**Diversity**|23.6%|**76.4%**|38.0%|**62.0%**|13.6%|**86.4%**|32.4%|**67.6%**| +|**Empowerment**|32.4%|**67.6%**|38.8%|**61.2%**|16.4%|**83.6%**|42.8%|**57.2%**| +|**Overall**|32.4%|**67.6%**|38.8%|**61.2%**|15.2%|**84.8%**|40.0%|**60.0%**| +| |RQ-RAG|**LightRAG**|RQ-RAG|**LightRAG**|RQ-RAG|**LightRAG**|RQ-RAG|**LightRAG**| +|**Comprehensiveness**|31.6%|**68.4%**|38.8%|**61.2%**|15.2%|**84.8%**|39.2%|**60.8%**| +|**Diversity**|29.2%|**70.8%**|39.2%|**60.8%**|11.6%|**88.4%**|30.8%|**69.2%**| +|**Empowerment**|31.6%|**68.4%**|36.4%|**63.6%**|15.2%|**84.8%**|42.4%|**57.6%**| +|**Overall**|32.4%|**67.6%**|38.0%|**62.0%**|14.4%|**85.6%**|40.0%|**60.0%**| +| |HyDE|**LightRAG**|HyDE|**LightRAG**|HyDE|**LightRAG**|HyDE|**LightRAG**| +|**Comprehensiveness**|26.0%|**74.0%**|41.6%|**58.4%**|26.8%|**73.2%**|40.4%|**59.6%**| +|**Diversity**|24.0%|**76.0%**|38.8%|**61.2%**|20.0%|**80.0%**|32.4%|**67.6%**| +|**Empowerment**|25.2%|**74.8%**|40.8%|**59.2%**|26.0%|**74.0%**|46.0%|**54.0%**| +|**Overall**|24.8%|**75.2%**|41.6%|**58.4%**|26.4%|**73.6%**|42.4%|**57.6%**| +| |GraphRAG|**LightRAG**|GraphRAG|**LightRAG**|GraphRAG|**LightRAG**|GraphRAG|**LightRAG**| +|**Comprehensiveness**|45.6%|**54.4%**|48.4%|**51.6%**|48.4%|**51.6%**|**50.4%**|49.6%| +|**Diversity**|22.8%|**77.2%**|40.8%|**59.2%**|26.4%|**73.6%**|36.0%|**64.0%**| +|**Empowerment**|41.2%|**58.8%**|45.2%|**54.8%**|43.6%|**56.4%**|**50.8%**|49.2%| +|**Overall**|45.2%|**54.8%**|48.0%|**52.0%**|47.2%|**52.8%**|**50.4%**|49.6%| + +## Reproduce + +All the code can be found in the `./reproduce` directory. + +### Step-0 Extract Unique Contexts + +First, we need to extract unique contexts in the datasets. + +
+ Code + +```python +def extract_unique_contexts(input_directory, output_directory): + + os.makedirs(output_directory, exist_ok=True) + + jsonl_files = glob.glob(os.path.join(input_directory, '*.jsonl')) + print(f"Found {len(jsonl_files)} JSONL files.") + + for file_path in jsonl_files: + filename = os.path.basename(file_path) + name, ext = os.path.splitext(filename) + output_filename = f"{name}_unique_contexts.json" + output_path = os.path.join(output_directory, output_filename) + + unique_contexts_dict = {} + + print(f"Processing file: {filename}") + + try: + with open(file_path, 'r', encoding='utf-8') as infile: + for line_number, line in enumerate(infile, start=1): + line = line.strip() + if not line: + continue + try: + json_obj = json.loads(line) + context = json_obj.get('context') + if context and context not in unique_contexts_dict: + unique_contexts_dict[context] = None + except json.JSONDecodeError as e: + print(f"JSON decoding error in file {filename} at line {line_number}: {e}") + except FileNotFoundError: + print(f"File not found: {filename}") + continue + except Exception as e: + print(f"An error occurred while processing file {filename}: {e}") + continue + + unique_contexts_list = list(unique_contexts_dict.keys()) + print(f"There are {len(unique_contexts_list)} unique `context` entries in the file {filename}.") + + try: + with open(output_path, 'w', encoding='utf-8') as outfile: + json.dump(unique_contexts_list, outfile, ensure_ascii=False, indent=4) + print(f"Unique `context` entries have been saved to: {output_filename}") + except Exception as e: + print(f"An error occurred while saving to the file {output_filename}: {e}") + + print("All files have been processed.") + +``` + +
+ +### Step-1 Insert Contexts + +For the extracted contexts, we insert them into the LightRAG system. + +
+ Code + +```python +def insert_text(rag, file_path): + with open(file_path, mode='r') as f: + unique_contexts = json.load(f) + + retries = 0 + max_retries = 3 + while retries < max_retries: + try: + rag.insert(unique_contexts) + break + except Exception as e: + retries += 1 + print(f"Insertion failed, retrying ({retries}/{max_retries}), error: {e}") + time.sleep(10) + if retries == max_retries: + print("Insertion failed after exceeding the maximum number of retries") +``` + +
+ +### Step-2 Generate Queries + +We extract tokens from the first and the second half of each context in the dataset, then combine them as dataset descriptions to generate queries. + +
+ Code + +```python +tokenizer = GPT2Tokenizer.from_pretrained('gpt2') + +def get_summary(context, tot_tokens=2000): + tokens = tokenizer.tokenize(context) + half_tokens = tot_tokens // 2 + + start_tokens = tokens[1000:1000 + half_tokens] + end_tokens = tokens[-(1000 + half_tokens):1000] + + summary_tokens = start_tokens + end_tokens + summary = tokenizer.convert_tokens_to_string(summary_tokens) + + return summary +``` + +
+ +### Step-3 Query + +For the queries generated in Step-2, we will extract them and query LightRAG. + +
+ Code + +```python +def extract_queries(file_path): + with open(file_path, 'r') as f: + data = f.read() + + data = data.replace('**', '') + + queries = re.findall(r'- Question \d+: (.+)', data) + + return queries +``` + +
+ +## 🔗 Related Projects + +*Ecosystem & Extensions* + + + +--- + +## ⭐ Star History + + + + + + Star History Chart + + + +## 🤝 Contribution + +
+ We thank all our contributors for their valuable contributions. +
+ + + +--- + + +## 📖 Citation + +```python +@article{guo2024lightrag, +title={LightRAG: Simple and Fast Retrieval-Augmented Generation}, +author={Zirui Guo and Lianghao Xia and Yanhua Yu and Tu Ao and Chao Huang}, +year={2024}, +eprint={2410.05779}, +archivePrefix={arXiv}, +primaryClass={cs.IR} +} +``` + +--- + +
+
+ +
+ +
+ +
+
+
+ ⭐ + Thank you for visiting LightRAG! + ⭐ +
+
+
diff --git a/LightRAG/SECURITY.md b/LightRAG/SECURITY.md new file mode 100644 index 00000000..1b61b5e9 --- /dev/null +++ b/LightRAG/SECURITY.md @@ -0,0 +1,18 @@ +# Reporting Security Issues + +The LightRAG team and community take security bugs seriously. We appreciate your efforts to responsibly disclose your findings, and will make every effort to acknowledge your contributions. + +To report a security issue, please use the GitHub Security Advisory: [Report a Vulnerability](https://github.com/HKUDS/LightRAG/security/advisories/new) + +The LightRAG team will send a response indicating the next steps in handling your report. After the initial reply to your report, the security team will keep you informed of the progress towards a fix and full announcement, and may ask for additional information or guidance. + +Report security bugs in third-party modules to the person or team maintaining the module. + +### Supported Versions + +The following versions currently being supported with security updates. + +| Version | Supported | +| ------- | ------------------ | +| 1.2.x | :x: | +| 1.3.x | :white_check_mark: | diff --git a/LightRAG/assets/logo.png b/LightRAG/assets/logo.png new file mode 100644 index 0000000000000000000000000000000000000000..1d2d3a429174e82ee30db7d535368400818d7856 GIT binary patch literal 182929 zcmeFZhg(xw*FGF%l(7JIR0LFrbP#Du9}5tWA}G~>3IqXZ5{T5`IEoF>06{>CgffVL z(n)~WktPJh2qZ+LC6s`45=g#%g3t55zklJoUgvTqlXK2Kd#}C9z3#Pk?%P_MiEcTt z1qOqOBFxX&!C)fp(BD5dfp4yM9$NJC}1{=Q& zgXukm!6bs75ziZd3!AQ5nw^31p?`833X{M$n}f`qLtrp9S?I6O&3xl9@TCYEVRcr7 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"Version: ${VERSION}" +echo "Platforms: linux/amd64, linux/arm64" +echo "==================================" +echo "" + +# Check Docker login status (skip if CR_PAT is set for CI/CD) +if [ -z "$CR_PAT" ]; then + if ! docker info 2>/dev/null | grep -q "Username"; then + echo "⚠️ Warning: Not logged in to Docker registry" + echo "Please login first: docker login ghcr.io" + echo "Or set CR_PAT environment variable for automated login" + echo "" + read -p "Continue anyway? (y/n) " -n 1 -r + echo + if [[ ! $REPLY =~ ^[Yy]$ ]]; then + exit 1 + fi + fi +else + echo "Using CR_PAT environment variable for authentication" +fi + +# Check if buildx builder exists, create if not +if ! docker buildx ls | grep -q "desktop-linux"; then + echo "Creating buildx builder..." + docker buildx create --name desktop-linux --use + docker buildx inspect --bootstrap +else + echo "Using existing buildx builder: desktop-linux" + docker buildx use desktop-linux +fi + +echo "" +echo "Building and pushing multi-architecture image..." +echo "" + +# Build and push +docker buildx build \ + --platform linux/amd64,linux/arm64 \ + --file ${DOCKERFILE} \ + --tag ${IMAGE_NAME}:${TAG} \ + --tag ${IMAGE_NAME}:${VERSION} \ + --push \ + . + +echo "" +echo "✓ Build and push complete!" +echo "" +echo "Images pushed:" +echo " - ${IMAGE_NAME}:${TAG}" +echo " - ${IMAGE_NAME}:${VERSION}" +echo "" +echo "Verifying multi-architecture manifest..." +echo "" + +# Verify +docker buildx imagetools inspect ${IMAGE_NAME}:${TAG} + +echo "" +echo "✓ Verification complete!" +echo "" +echo "Pull with: docker pull ${IMAGE_NAME}:${TAG}" diff --git a/LightRAG/docker-compose.yml b/LightRAG/docker-compose.yml new file mode 100644 index 00000000..da71faf8 --- /dev/null +++ b/LightRAG/docker-compose.yml @@ -0,0 +1,21 @@ +services: + lightrag: + container_name: lightrag + image: ghcr.io/hkuds/lightrag:latest + build: + context: . + dockerfile: Dockerfile + tags: + - ghcr.io/hkuds/lightrag:latest + ports: + - "${PORT:-9621}:9621" + volumes: + - ./data/rag_storage:/app/data/rag_storage + - ./data/inputs:/app/data/inputs + - ./config.ini:/app/config.ini + - ./.env:/app/.env + env_file: + - .env + restart: unless-stopped + extra_hosts: + - "host.docker.internal:host-gateway" diff --git a/LightRAG/docs/Algorithm.md b/LightRAG/docs/Algorithm.md new file mode 100644 index 00000000..992eed06 --- /dev/null +++ b/LightRAG/docs/Algorithm.md @@ -0,0 +1,4 @@ +![LightRAG Indexing Flowchart](https://learnopencv.com/wp-content/uploads/2024/11/LightRAG-VectorDB-Json-KV-Store-Indexing-Flowchart-scaled.jpg) +*Figure 1: LightRAG Indexing Flowchart - Img Caption : [Source](https://learnopencv.com/lightrag/)* +![LightRAG Retrieval and Querying Flowchart](https://learnopencv.com/wp-content/uploads/2024/11/LightRAG-Querying-Flowchart-Dual-Level-Retrieval-Generation-Knowledge-Graphs-scaled.jpg) +*Figure 2: LightRAG Retrieval and Querying Flowchart - Img Caption : [Source](https://learnopencv.com/lightrag/)* diff --git a/LightRAG/docs/DockerDeployment.md b/LightRAG/docs/DockerDeployment.md new file mode 100644 index 00000000..575359b4 --- /dev/null +++ b/LightRAG/docs/DockerDeployment.md @@ -0,0 +1,135 @@ +# LightRAG Docker Deployment + +A lightweight Knowledge Graph Retrieval-Augmented Generation system with multiple LLM backend support. + +## 🚀 Preparation + +### Clone the repository: + +```bash +# Linux/MacOS +git clone https://github.com/HKUDS/LightRAG.git +cd LightRAG +``` +```powershell +# Windows PowerShell +git clone https://github.com/HKUDS/LightRAG.git +cd LightRAG +``` + +### Configure your environment: + +```bash +# Linux/MacOS +cp .env.example .env +# Edit .env with your preferred configuration +``` +```powershell +# Windows PowerShell +Copy-Item .env.example .env +# Edit .env with your preferred configuration +``` + +LightRAG can be configured using environment variables in the `.env` file: + +**Server Configuration** + +- `HOST`: Server host (default: 0.0.0.0) +- `PORT`: Server port (default: 9621) + +**LLM Configuration** + +- `LLM_BINDING`: LLM backend to use (lollms/ollama/openai) +- `LLM_BINDING_HOST`: LLM server host URL +- `LLM_MODEL`: Model name to use + +**Embedding Configuration** + +- `EMBEDDING_BINDING`: Embedding backend (lollms/ollama/openai) +- `EMBEDDING_BINDING_HOST`: Embedding server host URL +- `EMBEDDING_MODEL`: Embedding model name + +**RAG Configuration** + +- `MAX_ASYNC`: Maximum async operations +- `MAX_TOKENS`: Maximum token size +- `EMBEDDING_DIM`: Embedding dimensions + +## 🐳 Docker Deployment + +Docker instructions work the same on all platforms with Docker Desktop installed. + +### Build Optimization + +The Dockerfile uses BuildKit cache mounts to significantly improve build performance: + +- **Automatic cache management**: BuildKit is automatically enabled via `# syntax=docker/dockerfile:1` directive +- **Faster rebuilds**: Only downloads changed dependencies when `uv.lock` or `bun.lock` files are modified +- **Efficient package caching**: UV and Bun package downloads are cached across builds +- **No manual configuration needed**: Works out of the box in Docker Compose and GitHub Actions + +### Start LightRAG server: + +```bash +docker compose up -d +``` + +LightRAG Server uses the following paths for data storage: + +``` +data/ +├── rag_storage/ # RAG data persistence +└── inputs/ # Input documents +``` + +### Updates + +To update the Docker container: +```bash +docker compose pull +docker compose down +docker compose up +``` + +### Offline deployment + +Software packages requiring `transformers`, `torch`, or `cuda` will is not preinstalled in the dokcer images. Consequently, document extraction tools such as Docling, as well as local LLM models like Hugging Face and LMDeploy, can not be used in an off line enviroment. These high-compute-resource-demanding services should not be integrated into LightRAG. Docling will be decoupled and deployed as a standalone service. + +## 📦 Build Docker Images + +### For local development and testing + +```bash +# Build and run with Docker Compose (BuildKit automatically enabled) +docker compose up --build + +# Or explicitly enable BuildKit if needed +DOCKER_BUILDKIT=1 docker compose up --build +``` + +**Note**: BuildKit is automatically enabled by the `# syntax=docker/dockerfile:1` directive in the Dockerfile, ensuring optimal caching performance. + +### For production release + + **multi-architecture build and push**: + +```bash +# Use the provided build script +./docker-build-push.sh +``` + +**The build script will**: + +- Check Docker registry login status +- Create/use buildx builder automatically +- Build for both AMD64 and ARM64 architectures +- Push to GitHub Container Registry (ghcr.io) +- Verify the multi-architecture manifest + +**Prerequisites**: + +Before building multi-architecture images, ensure you have: + +- Docker 20.10+ with Buildx support +- Sufficient disk space (20GB+ recommended for offline image) +- Registry access credentials (if pushing images) diff --git a/LightRAG/docs/FrontendBuildGuide.md b/LightRAG/docs/FrontendBuildGuide.md new file mode 100644 index 00000000..1e82c4f0 --- /dev/null +++ b/LightRAG/docs/FrontendBuildGuide.md @@ -0,0 +1,207 @@ +# Frontend Build Guide + +## Overview + +The LightRAG project includes a React-based WebUI frontend. This guide explains how frontend building works in different scenarios. + +## Key Principle + +- **Git Repository**: Frontend build results are **NOT** included (kept clean) +- **PyPI Package**: Frontend build results **ARE** included (ready to use) +- **Build Tool**: Uses **Bun** (not npm/yarn) + +## Installation Scenarios + +### 1. End Users (From PyPI) ✨ + +**Command:** +```bash +pip install lightrag-hku[api] +``` + +**What happens:** +- Frontend is already built and included in the package +- No additional steps needed +- Web interface works immediately + +--- + +### 2. Development Mode (Recommended for Contributors) 🔧 + +**Command:** +```bash +# Clone the repository +git clone https://github.com/HKUDS/LightRAG.git +cd LightRAG + +# Install in editable mode (no frontend build required yet) +pip install -e ".[api]" + +# Build frontend when needed (can be done anytime) +cd lightrag_webui +bun install --frozen-lockfile +bun run build +cd .. +``` + +**Advantages:** +- Install first, build later (flexible workflow) +- Changes take effect immediately (symlink mode) +- Frontend can be rebuilt anytime without reinstalling + +**How it works:** +- Creates symlinks to source directory +- Frontend build output goes to `lightrag/api/webui/` +- Changes are immediately visible in installed package + +--- + +### 3. Normal Installation (Testing Package Build) 📦 + +**Command:** +```bash +# Clone the repository +git clone https://github.com/HKUDS/LightRAG.git +cd LightRAG + +# ⚠️ MUST build frontend FIRST +cd lightrag_webui +bun install --frozen-lockfile +bun run build +cd .. + +# Now install +pip install ".[api]" +``` + +**What happens:** +- Frontend files are **copied** to site-packages +- Post-build modifications won't affect installed package +- Requires rebuild + reinstall to update + +**When to use:** +- Testing complete installation process +- Verifying package configuration +- Simulating PyPI user experience + +--- + +### 4. Creating Distribution Package 🚀 + +**Command:** +```bash +# Build frontend first +cd lightrag_webui +bun install --frozen-lockfile --production +bun run build +cd .. + +# Create distribution packages +python -m build + +# Output: dist/lightrag_hku-*.whl and dist/lightrag_hku-*.tar.gz +``` + +**What happens:** +- `setup.py` checks if frontend is built +- If missing, installation fails with helpful error message +- Generated package includes all frontend files + +--- + +## GitHub Actions (Automated Release) + +When creating a release on GitHub: + +1. **Automatically builds frontend** using Bun +2. **Verifies** build completed successfully +3. **Creates Python package** with frontend included +4. **Publishes to PyPI** using existing trusted publisher setup + +**No manual intervention required!** + +--- + +## Quick Reference + +| Scenario | Command | Frontend Required | Can Build After | +|----------|---------|-------------------|-----------------| +| From PyPI | `pip install lightrag-hku[api]` | Included | No (already installed) | +| Development | `pip install -e ".[api]"` | No | ✅ Yes (anytime) | +| Normal Install | `pip install ".[api]"` | ✅ Yes (before) | No (must reinstall) | +| Create Package | `python -m build` | ✅ Yes (before) | N/A | + +--- + +## Bun Installation + +If you don't have Bun installed: + +```bash +# macOS/Linux +curl -fsSL https://bun.sh/install | bash + +# Windows +powershell -c "irm bun.sh/install.ps1 | iex" +``` + +Official documentation: https://bun.sh + +--- + +## File Structure + +``` +LightRAG/ +├── lightrag_webui/ # Frontend source code +│ ├── src/ # React components +│ ├── package.json # Dependencies +│ └── vite.config.ts # Build configuration +│ └── outDir: ../lightrag/api/webui # Build output +│ +├── lightrag/ +│ └── api/ +│ └── webui/ # Frontend build output (gitignored) +│ ├── index.html # Built files (after running bun run build) +│ └── assets/ # Built assets +│ +├── setup.py # Build checks +├── pyproject.toml # Package configuration +└── .gitignore # Excludes lightrag/api/webui/* (except .gitkeep) +``` + +--- + +## Troubleshooting + +### Q: I installed in development mode but the web interface doesn't work + +**A:** Build the frontend: +```bash +cd lightrag_webui && bun run build +``` + +### Q: I built the frontend but it's not in my installed package + +**A:** You probably used `pip install .` after building. Either: +- Use `pip install -e ".[api]"` for development +- Or reinstall: `pip uninstall lightrag-hku && pip install ".[api]"` + +### Q: Where are the built frontend files? + +**A:** In `lightrag/api/webui/` after running `bun run build` + +### Q: Can I use npm or yarn instead of Bun? + +**A:** The project is configured for Bun. While npm/yarn might work, Bun is recommended per project standards. + +--- + +## Summary + +✅ **PyPI users**: No action needed, frontend included +✅ **Developers**: Use `pip install -e ".[api]"`, build frontend when needed +✅ **CI/CD**: Automatic build in GitHub Actions +✅ **Git**: Frontend build output never committed + +For questions or issues, please open a GitHub issue. diff --git a/LightRAG/docs/LightRAG_concurrent_explain.md b/LightRAG/docs/LightRAG_concurrent_explain.md new file mode 100644 index 00000000..8551ad26 --- /dev/null +++ b/LightRAG/docs/LightRAG_concurrent_explain.md @@ -0,0 +1,114 @@ +## LightRAG Multi-Document Processing: Concurrent Control Strategy + +LightRAG employs a multi-layered concurrent control strategy when processing multiple documents. This article provides an in-depth analysis of the concurrent control mechanisms at document level, chunk level, and LLM request level, helping you understand why specific concurrent behaviors occur. + +### 1. Document-Level Concurrent Control + +**Control Parameter**: `max_parallel_insert` + +This parameter controls the number of documents processed simultaneously. The purpose is to prevent excessive parallelism from overwhelming system resources, which could lead to extended processing times for individual files. Document-level concurrency is governed by the `max_parallel_insert` attribute within LightRAG, which defaults to 2 and is configurable via the `MAX_PARALLEL_INSERT` environment variable. `max_parallel_insert` is recommended to be set between 2 and 10, typically `llm_model_max_async/3`. Setting this value too high can increase the likelihood of naming conflicts among entities and relationships across different documents during the merge phase, thereby reducing its overall efficiency. + +### 2. Chunk-Level Concurrent Control + +**Control Parameter**: `llm_model_max_async` + +This parameter controls the number of chunks processed simultaneously in the extraction stage within a document. The purpose is to prevent a high volume of concurrent requests from monopolizing LLM processing resources, which would impede the efficient parallel processing of multiple files. Chunk-Level Concurrent Control is governed by the `llm_model_max_async` attribute within LightRAG, which defaults to 4 and is configurable via the `MAX_ASYNC` environment variable. The purpose of this parameter is to fully leverage the LLM's concurrency capabilities when processing individual documents. + +In the `extract_entities` function, **each document independently creates** its own chunk semaphore. Since each document independently creates chunk semaphores, the theoretical chunk concurrency of the system is: +$$ +ChunkConcurrency = Max Parallel Insert × LLM Model Max Async +$$ +For example: +- `max_parallel_insert = 2` (process 2 documents simultaneously) +- `llm_model_max_async = 4` (maximum 4 chunk concurrency per document) +- Theoretical chunk-level concurrent: 2 × 4 = 8 + +### 3. Graph-Level Concurrent Control + +**Control Parameter**: `llm_model_max_async * 2` + +This parameter controls the number of entities and relations processed simultaneously in the merging stage within a document. The purpose is to prevent a high volume of concurrent requests from monopolizing LLM processing resources, which would impede the efficient parallel processing of multiple files. Graph-level concurrency is governed by the `llm_model_max_async` attribute within LightRAG, which defaults to 4 and is configurable via the `MAX_ASYNC` environment variable. Graph-level parallelism control parameters are equally applicable to managing parallelism during the entity relationship reconstruction phase after document deletion. + +Given that the entity relationship merging phase doesn't necessitate LLM interaction for every operation, its parallelism is set at double the LLM's parallelism. This optimizes machine utilization while concurrently preventing excessive queuing resource contention for the LLM. + +### 4. LLM-Level Concurrent Control + +**Control Parameter**: `llm_model_max_async` + +This parameter governs the **concurrent volume** of LLM requests dispatched by the entire LightRAG system, encompassing the document extraction stage, merging stage, and user query handling. + +LLM request prioritization is managed via a global priority queue, which **systematically prioritizes user queries** over merging-related requests, and merging-related requests over extraction-related requests. This strategic prioritization **minimizes user query latency**. + +LLM-level concurrency is governed by the `llm_model_max_async` attribute within LightRAG, which defaults to 4 and is configurable via the `MAX_ASYNC` environment variable. + +### 5. Complete Concurrent Hierarchy Diagram + +```mermaid +graph TD +classDef doc fill:#e6f3ff,stroke:#5b9bd5,stroke-width:2px; +classDef chunk fill:#fbe5d6,stroke:#ed7d31,stroke-width:1px; +classDef merge fill:#e2f0d9,stroke:#70ad47,stroke-width:2px; + +A["Multiple Documents
max_parallel_insert = 2"] --> A1 +A --> B1 + +A1[DocA: split to n chunks] --> A_chunk; +B1[DocB: split to m chunks] --> B_chunk; + +subgraph A_chunk[Extraction Stage] + A_chunk_title[Entity Relation Extraction
llm_model_max_async = 4]; + A_chunk_title --> A_chunk1[Chunk A1]:::chunk; + A_chunk_title --> A_chunk2[Chunk A2]:::chunk; + A_chunk_title --> A_chunk3[Chunk A3]:::chunk; + A_chunk_title --> A_chunk4[Chunk A4]:::chunk; + A_chunk1 & A_chunk2 & A_chunk3 & A_chunk4 --> A_chunk_done([Extraction Complete]); +end + +subgraph B_chunk[Extraction Stage] + B_chunk_title[Entity Relation Extraction
llm_model_max_async = 4]; + B_chunk_title --> B_chunk1[Chunk B1]:::chunk; + B_chunk_title --> B_chunk2[Chunk B2]:::chunk; + B_chunk_title --> B_chunk3[Chunk B3]:::chunk; + B_chunk_title --> B_chunk4[Chunk B4]:::chunk; + B_chunk1 & B_chunk2 & B_chunk3 & B_chunk4 --> B_chunk_done([Extraction Complete]); +end +A_chunk -.->|LLM Request| LLM_Queue; + +A_chunk --> A_merge; +B_chunk --> B_merge; + +subgraph A_merge[Merge Stage] + A_merge_title[Entity Relation Merging
llm_model_max_async * 2 = 8]; + A_merge_title --> A1_entity[Ent a1]:::merge; + A_merge_title --> A2_entity[Ent a2]:::merge; + A_merge_title --> A3_entity[Rel a3]:::merge; + A_merge_title --> A4_entity[Rel a4]:::merge; + A1_entity & A2_entity & A3_entity & A4_entity --> A_done([Merge Complete]) +end + +subgraph B_merge[Merge Stage] + B_merge_title[Entity Relation Merging
llm_model_max_async * 2 = 8]; + B_merge_title --> B1_entity[Ent b1]:::merge; + B_merge_title --> B2_entity[Ent b2]:::merge; + B_merge_title --> B3_entity[Rel b3]:::merge; + B_merge_title --> B4_entity[Rel b4]:::merge; + B1_entity & B2_entity & B3_entity & B4_entity --> B_done([Merge Complete]) +end + +A_merge -.->|LLM Request| LLM_Queue["LLM Request Prioritized Queue
llm_model_max_async = 4"]; +B_merge -.->|LLM Request| LLM_Queue; +B_chunk -.->|LLM Request| LLM_Queue; + +``` + +> The extraction and merge stages share a global prioritized LLM queue, regulated by `llm_model_max_async`. While numerous entity and relation extraction and merging operations may be "actively processing", **only a limited number will concurrently execute LLM requests** the remainder will be queued and awaiting their turn. + +### 6. Performance Optimization Recommendations + +* **Increase LLM Concurrent Setting based on the capabilities of your LLM server or API provider** + +During the file processing phase, the performance and concurrency capabilities of the LLM are critical bottlenecks. When deploying LLMs locally, the service's concurrency capacity must adequately account for the context length requirements of LightRAG. LightRAG recommends that LLMs support a minimum context length of 32KB; therefore, server concurrency should be calculated based on this benchmark. For API providers, LightRAG will retry requests up to three times if the client's request is rejected due to concurrent request limits. Backend logs can be used to determine if LLM retries are occurring, thereby indicating whether `MAX_ASYNC` has exceeded the API provider's limits. + +* **Align Parallel Document Insertion Settings with LLM Concurrency Configurations** + +The recommended number of parallel document processing tasks is 1/4 of the LLM's concurrency, with a minimum of 2 and a maximum of 10. Setting a higher number of parallel document processing tasks typically does not accelerate overall document processing speed, as even a small number of concurrently processed documents can fully utilize the LLM's parallel processing capabilities. Excessive parallel document processing can significantly increase the processing time for each individual document. Since LightRAG commits processing results on a file-by-file basis, a large number of concurrent files would necessitate caching a substantial amount of data. In the event of a system error, all documents in the middle stage would require reprocessing, thereby increasing error handling costs. For instance, setting `MAX_PARALLEL_INSERT` to 3 is appropriate when `MAX_ASYNC` is configured to 12. diff --git a/LightRAG/docs/OfflineDeployment.md b/LightRAG/docs/OfflineDeployment.md new file mode 100644 index 00000000..e186dda0 --- /dev/null +++ b/LightRAG/docs/OfflineDeployment.md @@ -0,0 +1,317 @@ +# LightRAG Offline Deployment Guide + +This guide provides comprehensive instructions for deploying LightRAG in offline environments where internet access is limited or unavailable. + +If you deploy LightRAG using Docker, there is no need to refer to this document, as the LightRAG Docker image is pre-configured for offline operation. + +> Software packages requiring `transformers`, `torch`, or `cuda` will not be included in the offline dependency group. Consequently, document extraction tools such as Docling, as well as local LLM models like Hugging Face and LMDeploy, are outside the scope of offline installation support. These high-compute-resource-demanding services should not be integrated into LightRAG. Docling will be decoupled and deployed as a standalone service. + +## Table of Contents + +- [Overview](#overview) +- [Quick Start](#quick-start) +- [Layered Dependencies](#layered-dependencies) +- [Tiktoken Cache Management](#tiktoken-cache-management) +- [Complete Offline Deployment Workflow](#complete-offline-deployment-workflow) +- [Troubleshooting](#troubleshooting) + +## Overview + +LightRAG uses dynamic package installation (`pipmaster`) for optional features based on file types and configurations. In offline environments, these dynamic installations will fail. This guide shows you how to pre-install all necessary dependencies and cache files. + +### What Gets Dynamically Installed? + +LightRAG dynamically installs packages for: + +- **Storage Backends**: `redis`, `neo4j`, `pymilvus`, `pymongo`, `asyncpg`, `qdrant-client` +- **LLM Providers**: `openai`, `anthropic`, `ollama`, `zhipuai`, `aioboto3`, `voyageai`, `llama-index`, `lmdeploy`, `transformers`, `torch` +- **Tiktoken Models**: BPE encoding models downloaded from OpenAI CDN + +**Note**: Document processing dependencies (`pypdf`, `python-docx`, `python-pptx`, `openpyxl`) are now pre-installed with the `api` extras group and no longer require dynamic installation. + +## Quick Start + +### Option 1: Using pip with Offline Extras + +```bash +# Online environment: Install all offline dependencies +pip install lightrag-hku[offline] + +# Download tiktoken cache +lightrag-download-cache + +# Create offline package +pip download lightrag-hku[offline] -d ./offline-packages +tar -czf lightrag-offline.tar.gz ./offline-packages ~/.tiktoken_cache + +# Transfer to offline server +scp lightrag-offline.tar.gz user@offline-server:/path/to/ + +# Offline environment: Install +tar -xzf lightrag-offline.tar.gz +pip install --no-index --find-links=./offline-packages lightrag-hku[offline] +export TIKTOKEN_CACHE_DIR=~/.tiktoken_cache +``` + +### Option 2: Using Requirements Files + +```bash +# Online environment: Download packages +pip download -r requirements-offline.txt -d ./packages + +# Transfer to offline server +tar -czf packages.tar.gz ./packages +scp packages.tar.gz user@offline-server:/path/to/ + +# Offline environment: Install +tar -xzf packages.tar.gz +pip install --no-index --find-links=./packages -r requirements-offline.txt +``` + +## Layered Dependencies + +LightRAG provides flexible dependency groups for different use cases: + +### Available Dependency Groups + +| Group | Description | Use Case | +|-------|-------------|----------| +| `api` | API server + document processing | FastAPI server with PDF, DOCX, PPTX, XLSX support | +| `offline-storage` | Storage backends | Redis, Neo4j, MongoDB, PostgreSQL, etc. | +| `offline-llm` | LLM providers | OpenAI, Anthropic, Ollama, etc. | +| `offline` | Complete offline package | API + Storage + LLM (all features) | + +**Note**: Document processing (PDF, DOCX, PPTX, XLSX) is included in the `api` extras group. The previous `offline-docs` group has been merged into `api` for better integration. + +> Software packages requiring `transformers`, `torch`, or `cuda` will not be included in the offline dependency group. + +### Installation Examples + +```bash +# Install API with document processing +pip install lightrag-hku[api] + +# Install API and storage backends +pip install lightrag-hku[api,offline-storage] + +# Install all offline dependencies (recommended for offline deployment) +pip install lightrag-hku[offline] +``` + +### Using Individual Requirements Files + +```bash +# Storage backends only +pip install -r requirements-offline-storage.txt + +# LLM providers only +pip install -r requirements-offline-llm.txt + +# All offline dependencies +pip install -r requirements-offline.txt +``` + +## Tiktoken Cache Management + +Tiktoken downloads BPE encoding models on first use. In offline environments, you must pre-download these models. + +### Using the CLI Command + +After installing LightRAG, use the built-in command: + +```bash +# Download to default location (~/.tiktoken_cache) +lightrag-download-cache + +# Download to specific directory +lightrag-download-cache --cache-dir ./tiktoken_cache + +# Download specific models only +lightrag-download-cache --models gpt-4o-mini gpt-4 +``` + +### Default Models Downloaded + +- `gpt-4o-mini` (LightRAG default) +- `gpt-4o` +- `gpt-4` +- `gpt-3.5-turbo` +- `text-embedding-ada-002` +- `text-embedding-3-small` +- `text-embedding-3-large` + +### Setting Cache Location in Offline Environment + +```bash +# Option 1: Environment variable (temporary) +export TIKTOKEN_CACHE_DIR=/path/to/tiktoken_cache + +# Option 2: Add to ~/.bashrc or ~/.zshrc (persistent) +echo 'export TIKTOKEN_CACHE_DIR=~/.tiktoken_cache' >> ~/.bashrc +source ~/.bashrc + +# Option 3: Copy to default location +cp -r /path/to/tiktoken_cache ~/.tiktoken_cache/ +``` + +## Complete Offline Deployment Workflow + +### Step 1: Prepare in Online Environment + +```bash +# 1. Install LightRAG with offline dependencies +pip install lightrag-hku[offline] + +# 2. Download tiktoken cache +lightrag-download-cache --cache-dir ./offline_cache/tiktoken + +# 3. Download all Python packages +pip download lightrag-hku[offline] -d ./offline_cache/packages + +# 4. Create archive for transfer +tar -czf lightrag-offline-complete.tar.gz ./offline_cache + +# 5. Verify contents +tar -tzf lightrag-offline-complete.tar.gz | head -20 +``` + +### Step 2: Transfer to Offline Environment + +```bash +# Using scp +scp lightrag-offline-complete.tar.gz user@offline-server:/tmp/ + +# Or using USB/physical media +# Copy lightrag-offline-complete.tar.gz to USB drive +``` + +### Step 3: Install in Offline Environment + +```bash +# 1. Extract archive +cd /tmp +tar -xzf lightrag-offline-complete.tar.gz + +# 2. Install Python packages +pip install --no-index \ + --find-links=/tmp/offline_cache/packages \ + lightrag-hku[offline] + +# 3. Set up tiktoken cache +mkdir -p ~/.tiktoken_cache +cp -r /tmp/offline_cache/tiktoken/* ~/.tiktoken_cache/ +export TIKTOKEN_CACHE_DIR=~/.tiktoken_cache + +# 4. Add to shell profile for persistence +echo 'export TIKTOKEN_CACHE_DIR=~/.tiktoken_cache' >> ~/.bashrc +``` + +### Step 4: Verify Installation + +```bash +# Test Python import +python -c "from lightrag import LightRAG; print('✓ LightRAG imported')" + +# Test tiktoken +python -c "from lightrag.utils import TiktokenTokenizer; t = TiktokenTokenizer(); print('✓ Tiktoken working')" + +# Test optional dependencies (if installed) +python -c "import docling; print('✓ Docling available')" +python -c "import redis; print('✓ Redis available')" +``` + +## Troubleshooting + +### Issue: Tiktoken fails with network error + +**Problem**: `Unable to load tokenizer for model gpt-4o-mini` + +**Solution**: +```bash +# Ensure TIKTOKEN_CACHE_DIR is set +echo $TIKTOKEN_CACHE_DIR + +# Verify cache files exist +ls -la ~/.tiktoken_cache/ + +# If empty, you need to download cache in online environment first +``` + +### Issue: Dynamic package installation fails + +**Problem**: `Error installing package xxx` + +**Solution**: +```bash +# Pre-install the specific package you need +# For API with document processing: +pip install lightrag-hku[api] + +# For storage backends: +pip install lightrag-hku[offline-storage] + +# For LLM providers: +pip install lightrag-hku[offline-llm] +``` + +### Issue: Missing dependencies at runtime + +**Problem**: `ModuleNotFoundError: No module named 'xxx'` + +**Solution**: +```bash +# Check what you have installed +pip list | grep -i xxx + +# Install missing component +pip install lightrag-hku[offline] # Install all offline deps +``` + +### Issue: Permission denied on tiktoken cache + +**Problem**: `PermissionError: [Errno 13] Permission denied` + +**Solution**: +```bash +# Ensure cache directory has correct permissions +chmod 755 ~/.tiktoken_cache +chmod 644 ~/.tiktoken_cache/* + +# Or use a user-writable directory +export TIKTOKEN_CACHE_DIR=~/my_tiktoken_cache +mkdir -p ~/my_tiktoken_cache +``` + +## Best Practices + +1. **Test in Online Environment First**: Always test your complete setup in an online environment before going offline. + +2. **Keep Cache Updated**: Periodically update your offline cache when new models are released. + +3. **Document Your Setup**: Keep notes on which optional dependencies you actually need. + +4. **Version Pinning**: Consider pinning specific versions in production: + ```bash + pip freeze > requirements-production.txt + ``` + +5. **Minimal Installation**: Only install what you need: + ```bash + # If you only need API with document processing + pip install lightrag-hku[api] + # Then manually add specific LLM: pip install openai + ``` + +## Additional Resources + +- [LightRAG GitHub Repository](https://github.com/HKUDS/LightRAG) +- [Docker Deployment Guide](./DockerDeployment.md) +- [API Documentation](../lightrag/api/README.md) + +## Support + +If you encounter issues not covered in this guide: + +1. Check the [GitHub Issues](https://github.com/HKUDS/LightRAG/issues) +2. Review the [project documentation](../README.md) +3. Create a new issue with your offline deployment details diff --git a/LightRAG/docs/UV_LOCK_GUIDE.md b/LightRAG/docs/UV_LOCK_GUIDE.md new file mode 100644 index 00000000..f098e01f --- /dev/null +++ b/LightRAG/docs/UV_LOCK_GUIDE.md @@ -0,0 +1,170 @@ +# uv.lock Update Guide + +## What is uv.lock? + +`uv.lock` is uv's lock file. It captures the exact version of every dependency, including transitive ones, much like: +- Node.js `package-lock.json` +- Rust `Cargo.lock` +- Python Poetry `poetry.lock` + +Keeping `uv.lock` in version control guarantees that everyone installs the same dependency set. + +## When does uv.lock change? + +### Situations where it does *not* change automatically + +- Running `uv sync --frozen` +- Building Docker images that call `uv sync --frozen` +- Editing source code without touching dependency metadata + +### Situations where it will change + +1. **`uv lock` or `uv lock --upgrade`** + + ```bash + uv lock # Resolve according to current constraints + uv lock --upgrade # Re-resolve and upgrade to the newest compatible releases + ``` + + Use these commands after modifying `pyproject.toml`, when you want fresh dependency versions, or if the lock file was deleted or corrupted. + +2. **`uv add`** + + ```bash + uv add requests # Adds the dependency and updates both files + uv add --dev pytest # Adds a dev dependency + ``` + + `uv add` edits `pyproject.toml` and refreshes `uv.lock` in one step. + +3. **`uv remove`** + + ```bash + uv remove requests + ``` + + This removes the dependency from `pyproject.toml` and rewrites `uv.lock`. + +4. **`uv sync` without `--frozen`** + + ```bash + uv sync + ``` + + Normally this only installs what is already locked. However, if `pyproject.toml` and `uv.lock` disagree or the lock file is missing, uv will regenerate and update `uv.lock`. In CI and production builds you should prefer `uv sync --frozen` to prevent unintended updates. + +## Example workflows + +### Scenario 1: Add a new dependency + +```bash +# Recommended: let uv handle both files +uv add fastapi +git add pyproject.toml uv.lock +git commit -m "Add fastapi dependency" + +# Manual alternative +# 1. Edit pyproject.toml +# 2. Regenerate the lock file +uv lock +git add pyproject.toml uv.lock +git commit -m "Add fastapi dependency" +``` + +### Scenario 2: Relax or tighten a version constraint + +```bash +# 1. Edit the requirement in pyproject.toml, +# e.g. openai>=1.0.0,<2.0.0 -> openai>=1.5.0,<2.0.0 + +# 2. Re-resolve the lock file +uv lock + +# 3. Commit both files +git add pyproject.toml uv.lock +git commit -m "Update openai to >=1.5.0" +``` + +### Scenario 3: Upgrade everything to the newest compatible versions + +```bash +uv lock --upgrade +git diff uv.lock +git add uv.lock +git commit -m "Upgrade dependencies to latest compatible versions" +``` + +### Scenario 4: Teammate syncing the project + +```bash +git pull # Fetch latest code and lock file +uv sync --frozen # Install exactly what uv.lock specifies +``` + +## Using uv.lock in Docker + +```dockerfile +RUN uv sync --frozen --no-dev --extra api +``` + +`--frozen` guarantees reproducible builds because uv will refuse to deviate from the locked versions. +`--extra api` install API server + +## Generating a lock file that includes offline dependencies + +If you need `uv.lock` to capture the optional offline stacks, regenerate it with the relevant extras enabled: + +```bash +uv lock --extra api --extra offline +``` + +This command resolves the base project requirements plus both the `api` and `offline` optional dependency sets, ensuring downstream `uv sync --frozen --extra api --extra offline` installs work without further resolution. + +## Frequently asked questions + +- **`uv.lock` is almost 1 MB. Does that matter?** + No. The file is read only during dependency resolution. + +- **Should we commit `uv.lock`?** + Yes. Commit it so collaborators and CI jobs share the same dependency graph. + +- **Deleted the lock file by accident?** + Run `uv lock` to regenerate it from `pyproject.toml`. + +- **Can `uv.lock` and `requirements.txt` coexist?** + They can, but maintaining both is redundant. Prefer relying on `uv.lock` alone whenever possible. + +- **How do I inspect locked versions?** + ```bash + uv tree + grep -A5 'name = "openai"' uv.lock + ``` + +## Best practices + +### Recommended + +1. Commit `uv.lock` alongside `pyproject.toml`. +2. Use `uv sync --frozen` in CI, Docker, and other reproducible environments. +3. Use plain `uv sync` during local development if you want uv to reconcile the lock for you. +4. Run `uv lock --upgrade` periodically to pick up the latest compatible releases. +5. Regenerate the lock file immediately after changing dependency constraints. + +### Avoid + +1. Running `uv sync` without `--frozen` in CI or production pipelines. +2. Editing `uv.lock` by hand—uv will overwrite manual edits. +3. Ignoring lock file diffs in code reviews—unexpected dependency changes can break builds. + +## Summary + +| Command | Updates `uv.lock` | Typical use | +|-----------------------|-------------------|-------------------------------------------| +| `uv lock` | ✅ Yes | After editing constraints | +| `uv lock --upgrade` | ✅ Yes | Upgrade to the newest compatible versions | +| `uv add ` | ✅ Yes | Add a dependency | +| `uv remove ` | ✅ Yes | Remove a dependency | +| `uv sync` | ⚠️ Maybe | Local development; can regenerate the lock | +| `uv sync --frozen` | ❌ No | CI/CD, Docker, reproducible builds | + +Remember: `uv.lock` only changes when you run a command that tells it to. Keep it in sync with your project and commit it whenever it changes. diff --git a/LightRAG/env.example b/LightRAG/env.example new file mode 100644 index 00000000..60aaf0ed --- /dev/null +++ b/LightRAG/env.example @@ -0,0 +1,464 @@ +### This is sample file of .env + +########################### +### Server Configuration +########################### +HOST=0.0.0.0 +PORT=9621 +WEBUI_TITLE='My Graph KB' +WEBUI_DESCRIPTION="Simple and Fast Graph Based RAG System" +# WORKERS=2 +### gunicorn worker timeout(as default LLM request timeout if LLM_TIMEOUT is not set) +# TIMEOUT=150 +# CORS_ORIGINS=http://localhost:3000,http://localhost:8080 + +### Optional SSL Configuration +# SSL=true +# SSL_CERTFILE=/path/to/cert.pem +# SSL_KEYFILE=/path/to/key.pem + +### Directory Configuration (defaults to current working directory) +### Default value is ./inputs and ./rag_storage +# INPUT_DIR= +# WORKING_DIR= + +### Tiktoken cache directory (Store cached files in this folder for offline deployment) +# TIKTOKEN_CACHE_DIR=/app/data/tiktoken + +### Ollama Emulating Model and Tag +# OLLAMA_EMULATING_MODEL_NAME=lightrag +OLLAMA_EMULATING_MODEL_TAG=latest + +### Max nodes for graph retrieval (Ensure WebUI local settings are also updated, which is limited to this value) +# MAX_GRAPH_NODES=1000 + +### Logging level +# LOG_LEVEL=INFO +# VERBOSE=False +# LOG_MAX_BYTES=10485760 +# LOG_BACKUP_COUNT=5 +### Logfile location (defaults to current working directory) +# LOG_DIR=/path/to/log/directory + +##################################### +### Login and API-Key Configuration +##################################### +# AUTH_ACCOUNTS='admin:admin123,user1:pass456' +# TOKEN_SECRET=Your-Key-For-LightRAG-API-Server +# TOKEN_EXPIRE_HOURS=48 +# GUEST_TOKEN_EXPIRE_HOURS=24 +# JWT_ALGORITHM=HS256 + +### API-Key to access LightRAG Server API +### Use this key in HTTP requests with the 'X-API-Key' header +### Example: curl -H "X-API-Key: your-secure-api-key-here" http://localhost:9621/query +# LIGHTRAG_API_KEY=your-secure-api-key-here +# WHITELIST_PATHS=/health,/api/* + +###################################################################################### +### Query Configuration +### +### How to control the context length sent to LLM: +### MAX_ENTITY_TOKENS + MAX_RELATION_TOKENS < MAX_TOTAL_TOKENS +### Chunk_Tokens = MAX_TOTAL_TOKENS - Actual_Entity_Tokens - Actual_Relation_Tokens +###################################################################################### +# LLM response cache for query (Not valid for streaming response) +ENABLE_LLM_CACHE=true +# COSINE_THRESHOLD=0.2 +### Number of entities or relations retrieved from KG +# TOP_K=40 +### Maximum number or chunks for naive vector search +# CHUNK_TOP_K=20 +### control the actual entities send to LLM +# MAX_ENTITY_TOKENS=6000 +### control the actual relations send to LLM +# MAX_RELATION_TOKENS=8000 +### control the maximum tokens send to LLM (include entities, relations and chunks) +# MAX_TOTAL_TOKENS=30000 + +### chunk selection strategies +### VECTOR: Pick KG chunks by vector similarity, delivered chunks to the LLM aligning more closely with naive retrieval +### WEIGHT: Pick KG chunks by entity and chunk weight, delivered more solely KG related chunks to the LLM +### If reranking is enabled, the impact of chunk selection strategies will be diminished. +# KG_CHUNK_PICK_METHOD=VECTOR + +######################################################### +### Reranking configuration +### RERANK_BINDING type: null, cohere, jina, aliyun +### For rerank model deployed by vLLM use cohere binding +######################################################### +RERANK_BINDING=null +### Enable rerank by default in query params when RERANK_BINDING is not null +# RERANK_BY_DEFAULT=True +### rerank score chunk filter(set to 0.0 to keep all chunks, 0.6 or above if LLM is not strong enough) +# MIN_RERANK_SCORE=0.0 + +### For local deployment with vLLM +# RERANK_MODEL=BAAI/bge-reranker-v2-m3 +# RERANK_BINDING_HOST=http://localhost:8000/v1/rerank +# RERANK_BINDING_API_KEY=your_rerank_api_key_here + +### Default value for Cohere AI +# RERANK_MODEL=rerank-v3.5 +# RERANK_BINDING_HOST=https://api.cohere.com/v2/rerank +# RERANK_BINDING_API_KEY=your_rerank_api_key_here + +### Default value for Jina AI +# RERANK_MODEL=jina-reranker-v2-base-multilingual +# RERANK_BINDING_HOST=https://api.jina.ai/v1/rerank +# RERANK_BINDING_API_KEY=your_rerank_api_key_here + +### Default value for Aliyun +# RERANK_MODEL=gte-rerank-v2 +# RERANK_BINDING_HOST=https://dashscope.aliyuncs.com/api/v1/services/rerank/text-rerank/text-rerank +# RERANK_BINDING_API_KEY=your_rerank_api_key_here + +######################################## +### Document processing configuration +######################################## +ENABLE_LLM_CACHE_FOR_EXTRACT=true + +### Document processing output language: English, Chinese, French, German ... +SUMMARY_LANGUAGE=English + +### PDF decryption password for protected PDF files +# PDF_DECRYPT_PASSWORD=your_pdf_password_here + +### Entity types that the LLM will attempt to recognize +# ENTITY_TYPES='["Person", "Creature", "Organization", "Location", "Event", "Concept", "Method", "Content", "Data", "Artifact", "NaturalObject"]' + +### Chunk size for document splitting, 500~1500 is recommended +# CHUNK_SIZE=1200 +# CHUNK_OVERLAP_SIZE=100 + +### Number of summary segments or tokens to trigger LLM summary on entity/relation merge (at least 3 is recommended) +# FORCE_LLM_SUMMARY_ON_MERGE=8 +### Max description token size to trigger LLM summary +# SUMMARY_MAX_TOKENS = 1200 +### Recommended LLM summary output length in tokens +# SUMMARY_LENGTH_RECOMMENDED_=600 +### Maximum context size sent to LLM for description summary +# SUMMARY_CONTEXT_SIZE=12000 + +### control the maximum chunk_ids stored in vector and graph db +# MAX_SOURCE_IDS_PER_ENTITY=300 +# MAX_SOURCE_IDS_PER_RELATION=300 +### control chunk_ids limitation method: FIFO, KEEP +### FIFO: First in first out +### KEEP: Keep oldest (less merge action and faster) +# SOURCE_IDS_LIMIT_METHOD=FIFO + +# Maximum number of file paths stored in entity/relation file_path field (For displayed only, does not affect query performance) +# MAX_FILE_PATHS=100 + +### maximum number of related chunks per source entity or relation +### The chunk picker uses this value to determine the total number of chunks selected from KG(knowledge graph) +### Higher values increase re-ranking time +# RELATED_CHUNK_NUMBER=5 + +############################### +### Concurrency Configuration +############################### +### Max concurrency requests of LLM (for both query and document processing) +MAX_ASYNC=4 +### Number of parallel processing documents(between 2~10, MAX_ASYNC/3 is recommended) +MAX_PARALLEL_INSERT=2 +### Max concurrency requests for Embedding +# EMBEDDING_FUNC_MAX_ASYNC=8 +### Num of chunks send to Embedding in single request +# EMBEDDING_BATCH_NUM=10 + +########################################################################### +### LLM Configuration +### LLM_BINDING type: openai, ollama, lollms, azure_openai, aws_bedrock, gemini +### LLM_BINDING_HOST: host only for Ollama, endpoint for other LLM service +### If LightRAG deployed in Docker: +### uses host.docker.internal instead of localhost in LLM_BINDING_HOST +########################################################################### +### LLM request timeout setting for all llm (0 means no timeout for Ollma) +# LLM_TIMEOUT=180 + +LLM_BINDING=openai +LLM_MODEL=gpt-4o +LLM_BINDING_HOST=https://api.openai.com/v1 +LLM_BINDING_API_KEY=your_api_key + +### Env vars for Azure openai +# AZURE_OPENAI_API_VERSION=2024-08-01-preview +# AZURE_OPENAI_DEPLOYMENT=gpt-4o + +### Openrouter example +# LLM_MODEL=google/gemini-2.5-flash +# LLM_BINDING_HOST=https://openrouter.ai/api/v1 +# LLM_BINDING_API_KEY=your_api_key +# LLM_BINDING=openai + +### Gemini example +# LLM_BINDING=gemini +# LLM_MODEL=gemini-flash-latest +# LLM_BINDING_API_KEY=your_gemini_api_key +# LLM_BINDING_HOST=https://generativelanguage.googleapis.com + +### use the following command to see all support options for OpenAI, azure_openai or OpenRouter +### lightrag-server --llm-binding gemini --help +### Gemini Specific Parameters +# GEMINI_LLM_MAX_OUTPUT_TOKENS=9000 +# GEMINI_LLM_TEMPERATURE=0.7 +### Enable Thinking +# GEMINI_LLM_THINKING_CONFIG='{"thinking_budget": -1, "include_thoughts": true}' +### Disable Thinking +# GEMINI_LLM_THINKING_CONFIG='{"thinking_budget": 0, "include_thoughts": false}' + +### use the following command to see all support options for OpenAI, azure_openai or OpenRouter +### lightrag-server --llm-binding openai --help +### OpenAI Specific Parameters +# OPENAI_LLM_REASONING_EFFORT=minimal +### OpenRouter Specific Parameters +# OPENAI_LLM_EXTRA_BODY='{"reasoning": {"enabled": false}}' +### Qwen3 Specific Parameters deploy by vLLM +# OPENAI_LLM_EXTRA_BODY='{"chat_template_kwargs": {"enable_thinking": false}}' + +### OpenAI Compatible API Specific Parameters +### Increased temperature values may mitigate infinite inference loops in certain LLM, such as Qwen3-30B. +# OPENAI_LLM_TEMPERATURE=0.9 +### Set the max_tokens to mitigate endless output of some LLM (less than LLM_TIMEOUT * llm_output_tokens/second, i.e. 9000 = 180s * 50 tokens/s) +### Typically, max_tokens does not include prompt content +### For vLLM/SGLang deployed models, or most of OpenAI compatible API provider +# OPENAI_LLM_MAX_TOKENS=9000 +### For OpenAI o1-mini or newer modles utilizes max_completion_tokens instead of max_tokens +OPENAI_LLM_MAX_COMPLETION_TOKENS=9000 + +### use the following command to see all support options for Ollama LLM +### lightrag-server --llm-binding ollama --help +### Ollama Server Specific Parameters +### OLLAMA_LLM_NUM_CTX must be provided, and should at least larger than MAX_TOTAL_TOKENS + 2000 +OLLAMA_LLM_NUM_CTX=32768 +### Set the max_output_tokens to mitigate endless output of some LLM (less than LLM_TIMEOUT * llm_output_tokens/second, i.e. 9000 = 180s * 50 tokens/s) +# OLLAMA_LLM_NUM_PREDICT=9000 +### Stop sequences for Ollama LLM +# OLLAMA_LLM_STOP='["", "<|EOT|>"]' + +### Bedrock Specific Parameters +# BEDROCK_LLM_TEMPERATURE=1.0 + +####################################################################################### +### Embedding Configuration (Should not be changed after the first file processed) +### EMBEDDING_BINDING: ollama, openai, azure_openai, jina, lollms, aws_bedrock +### EMBEDDING_BINDING_HOST: host only for Ollama, endpoint for other Embedding service +### If LightRAG deployed in Docker: +### uses host.docker.internal instead of localhost in EMBEDDING_BINDING_HOST +####################################################################################### +# EMBEDDING_TIMEOUT=30 + +### Control whether to send embedding_dim parameter to embedding API +### IMPORTANT: Jina ALWAYS sends dimension parameter (API requirement) - this setting is ignored for Jina +### For OpenAI: Set to 'true' to enable dynamic dimension adjustment +### For OpenAI: Set to 'false' (default) to disable sending dimension parameter +### Note: Automatically ignored for backends that don't support dimension parameter (e.g., Ollama) + +# Ollama embedding +# EMBEDDING_BINDING=ollama +# EMBEDDING_MODEL=bge-m3:latest +# EMBEDDING_DIM=1024 +# EMBEDDING_BINDING_API_KEY=your_api_key +### If LightRAG deployed in Docker uses host.docker.internal instead of localhost +# EMBEDDING_BINDING_HOST=http://localhost:11434 + +### OpenAI compatible embedding +EMBEDDING_BINDING=openai +EMBEDDING_MODEL=text-embedding-3-large +EMBEDDING_DIM=3072 +EMBEDDING_SEND_DIM=false +EMBEDDING_TOKEN_LIMIT=8192 +EMBEDDING_BINDING_HOST=https://api.openai.com/v1 +EMBEDDING_BINDING_API_KEY=your_api_key + +### Optional for Azure +# AZURE_EMBEDDING_DEPLOYMENT=text-embedding-3-large +# AZURE_EMBEDDING_API_VERSION=2023-05-15 +# AZURE_EMBEDDING_ENDPOINT=your_endpoint +# AZURE_EMBEDDING_API_KEY=your_api_key + +### Gemini embedding +# EMBEDDING_BINDING=gemini +# EMBEDDING_MODEL=gemini-embedding-001 +# EMBEDDING_DIM=1536 +# EMBEDDING_TOKEN_LIMIT=2048 +# EMBEDDING_BINDING_HOST=https://generativelanguage.googleapis.com +# EMBEDDING_BINDING_API_KEY=your_api_key +### Gemini embedding requires sending dimension to server +# EMBEDDING_SEND_DIM=true + +### Jina AI Embedding +# EMBEDDING_BINDING=jina +# EMBEDDING_BINDING_HOST=https://api.jina.ai/v1/embeddings +# EMBEDDING_MODEL=jina-embeddings-v4 +# EMBEDDING_DIM=2048 +# EMBEDDING_BINDING_API_KEY=your_api_key + +### Optional for Ollama embedding +OLLAMA_EMBEDDING_NUM_CTX=8192 +### use the following command to see all support options for Ollama embedding +### lightrag-server --embedding-binding ollama --help + +#################################################################### +### WORKSPACE sets workspace name for all storage types +### for the purpose of isolating data from LightRAG instances. +### Valid workspace name constraints: a-z, A-Z, 0-9, and _ +#################################################################### +# WORKSPACE=space1 + +############################ +### Data storage selection +############################ +### Default storage (Recommended for small scale deployment) +# LIGHTRAG_KV_STORAGE=JsonKVStorage +# LIGHTRAG_DOC_STATUS_STORAGE=JsonDocStatusStorage +# LIGHTRAG_GRAPH_STORAGE=NetworkXStorage +# LIGHTRAG_VECTOR_STORAGE=NanoVectorDBStorage + +### Redis Storage (Recommended for production deployment) +# LIGHTRAG_KV_STORAGE=RedisKVStorage +# LIGHTRAG_DOC_STATUS_STORAGE=RedisDocStatusStorage + +### Vector Storage (Recommended for production deployment) +# LIGHTRAG_VECTOR_STORAGE=MilvusVectorDBStorage +# LIGHTRAG_VECTOR_STORAGE=QdrantVectorDBStorage +# LIGHTRAG_VECTOR_STORAGE=FaissVectorDBStorage + +### Graph Storage (Recommended for production deployment) +# LIGHTRAG_GRAPH_STORAGE=Neo4JStorage +# LIGHTRAG_GRAPH_STORAGE=MemgraphStorage + +### PostgreSQL +# LIGHTRAG_KV_STORAGE=PGKVStorage +# LIGHTRAG_DOC_STATUS_STORAGE=PGDocStatusStorage +# LIGHTRAG_GRAPH_STORAGE=PGGraphStorage +# LIGHTRAG_VECTOR_STORAGE=PGVectorStorage + +### MongoDB (Vector storage only available on Atlas Cloud) +# LIGHTRAG_KV_STORAGE=MongoKVStorage +# LIGHTRAG_DOC_STATUS_STORAGE=MongoDocStatusStorage +# LIGHTRAG_GRAPH_STORAGE=MongoGraphStorage +# LIGHTRAG_VECTOR_STORAGE=MongoVectorDBStorage + +### PostgreSQL Configuration +POSTGRES_HOST=localhost +POSTGRES_PORT=5432 +POSTGRES_USER=your_username +POSTGRES_PASSWORD='your_password' +POSTGRES_DATABASE=your_database +POSTGRES_MAX_CONNECTIONS=12 +# POSTGRES_WORKSPACE=forced_workspace_name + +### PostgreSQL Vector Storage Configuration +### Vector storage type: HNSW, IVFFlat +POSTGRES_VECTOR_INDEX_TYPE=HNSW +POSTGRES_HNSW_M=16 +POSTGRES_HNSW_EF=200 +POSTGRES_IVFFLAT_LISTS=100 + +### PostgreSQL Connection Retry Configuration (Network Robustness) +### Number of retry attempts (1-10, default: 3) +### Initial retry backoff in seconds (0.1-5.0, default: 0.5) +### Maximum retry backoff in seconds (backoff-60.0, default: 5.0) +### Connection pool close timeout in seconds (1.0-30.0, default: 5.0) +# POSTGRES_CONNECTION_RETRIES=3 +# POSTGRES_CONNECTION_RETRY_BACKOFF=0.5 +# POSTGRES_CONNECTION_RETRY_BACKOFF_MAX=5.0 +# POSTGRES_POOL_CLOSE_TIMEOUT=5.0 + +### PostgreSQL SSL Configuration (Optional) +# POSTGRES_SSL_MODE=require +# POSTGRES_SSL_CERT=/path/to/client-cert.pem +# POSTGRES_SSL_KEY=/path/to/client-key.pem +# POSTGRES_SSL_ROOT_CERT=/path/to/ca-cert.pem +# POSTGRES_SSL_CRL=/path/to/crl.pem + +### PostgreSQL Server Settings (for Supabase Supavisor) +# Use this to pass extra options to the PostgreSQL connection string. +# For Supabase, you might need to set it like this: +# POSTGRES_SERVER_SETTINGS="options=reference%3D[project-ref]" + +# Default is 100 set to 0 to disable +# POSTGRES_STATEMENT_CACHE_SIZE=100 + +### Neo4j Configuration +NEO4J_URI=neo4j+s://xxxxxxxx.databases.neo4j.io +NEO4J_USERNAME=neo4j +NEO4J_PASSWORD='your_password' +NEO4J_DATABASE=neo4j +NEO4J_MAX_CONNECTION_POOL_SIZE=100 +NEO4J_CONNECTION_TIMEOUT=30 +NEO4J_CONNECTION_ACQUISITION_TIMEOUT=30 +NEO4J_MAX_TRANSACTION_RETRY_TIME=30 +NEO4J_MAX_CONNECTION_LIFETIME=300 +NEO4J_LIVENESS_CHECK_TIMEOUT=30 +NEO4J_KEEP_ALIVE=true +# NEO4J_WORKSPACE=forced_workspace_name + +### MongoDB Configuration +MONGO_URI=mongodb://root:root@localhost:27017/ +#MONGO_URI=mongodb+srv://xxxx +MONGO_DATABASE=LightRAG +# MONGODB_WORKSPACE=forced_workspace_name + +### Milvus Configuration +MILVUS_URI=http://localhost:19530 +MILVUS_DB_NAME=lightrag +# MILVUS_USER=root +# MILVUS_PASSWORD=your_password +# MILVUS_TOKEN=your_token +# MILVUS_WORKSPACE=forced_workspace_name + +### Qdrant +QDRANT_URL=http://localhost:6333 +# QDRANT_API_KEY=your-api-key +# QDRANT_WORKSPACE=forced_workspace_name + +### Redis +REDIS_URI=redis://localhost:6379 +REDIS_SOCKET_TIMEOUT=30 +REDIS_CONNECT_TIMEOUT=10 +REDIS_MAX_CONNECTIONS=100 +REDIS_RETRY_ATTEMPTS=3 +# REDIS_WORKSPACE=forced_workspace_name + +### Memgraph Configuration +MEMGRAPH_URI=bolt://localhost:7687 +MEMGRAPH_USERNAME= +MEMGRAPH_PASSWORD= +MEMGRAPH_DATABASE=memgraph +# MEMGRAPH_WORKSPACE=forced_workspace_name + +############################ +### Evaluation Configuration +############################ +### RAGAS evaluation models (used for RAG quality assessment) +### ⚠️ IMPORTANT: Both LLM and Embedding endpoints MUST be OpenAI-compatible +### Default uses OpenAI models for evaluation + +### LLM Configuration for Evaluation +# EVAL_LLM_MODEL=gpt-4o-mini +### API key for LLM evaluation (fallback to OPENAI_API_KEY if not set) +# EVAL_LLM_BINDING_API_KEY=your_api_key +### Custom OpenAI-compatible endpoint for LLM evaluation (optional) +# EVAL_LLM_BINDING_HOST=https://api.openai.com/v1 + +### Embedding Configuration for Evaluation +# EVAL_EMBEDDING_MODEL=text-embedding-3-large +### API key for embeddings (fallback: EVAL_LLM_BINDING_API_KEY -> OPENAI_API_KEY) +# EVAL_EMBEDDING_BINDING_API_KEY=your_embedding_api_key +### Custom OpenAI-compatible endpoint for embeddings (fallback: EVAL_LLM_BINDING_HOST) +# EVAL_EMBEDDING_BINDING_HOST=https://api.openai.com/v1 + +### Performance Tuning +### Number of concurrent test case evaluations +### Lower values reduce API rate limit issues but increase evaluation time +# EVAL_MAX_CONCURRENT=2 +### TOP_K query parameter of LightRAG (default: 10) +### Number of entities or relations retrieved from KG +# EVAL_QUERY_TOP_K=10 +### LLM request retry and timeout settings for evaluation +# EVAL_LLM_MAX_RETRIES=5 +# EVAL_LLM_TIMEOUT=180 diff --git a/LightRAG/env.ollama-binding-options.example b/LightRAG/env.ollama-binding-options.example new file mode 100644 index 00000000..f3c8181e --- /dev/null +++ b/LightRAG/env.ollama-binding-options.example @@ -0,0 +1,195 @@ +################################################################################ +# Autogenerated .env entries list for LightRAG binding options +# +# To generate run: +# $ python -m lightrag.llm.binding_options +################################################################################ +# ollama_embedding -- Context window size (number of tokens) +# OLLAMA_EMBEDDING_NUM_CTX=4096 + +# ollama_embedding -- Maximum number of tokens to predict +# OLLAMA_EMBEDDING_NUM_PREDICT=128 + +# ollama_embedding -- Number of tokens to keep from the initial prompt +# OLLAMA_EMBEDDING_NUM_KEEP=0 + +# ollama_embedding -- Random seed for generation (-1 for random) +# OLLAMA_EMBEDDING_SEED=-1 + +# ollama_embedding -- Controls randomness (0.0-2.0, higher = more creative) +# OLLAMA_EMBEDDING_TEMPERATURE=0.8 + +# ollama_embedding -- Top-k sampling parameter (0 = disabled) +# OLLAMA_EMBEDDING_TOP_K=40 + +# ollama_embedding -- Top-p (nucleus) sampling parameter (0.0-1.0) +# OLLAMA_EMBEDDING_TOP_P=0.9 + +# ollama_embedding -- Tail free sampling parameter (1.0 = disabled) +# OLLAMA_EMBEDDING_TFS_Z=1.0 + +# ollama_embedding -- Typical probability mass (1.0 = disabled) +# OLLAMA_EMBEDDING_TYPICAL_P=1.0 + +# ollama_embedding -- Minimum probability threshold (0.0 = disabled) +# OLLAMA_EMBEDDING_MIN_P=0.0 + +# ollama_embedding -- Number of tokens to consider for repetition penalty +# OLLAMA_EMBEDDING_REPEAT_LAST_N=64 + +# ollama_embedding -- Penalty for repetition (1.0 = no penalty) +# OLLAMA_EMBEDDING_REPEAT_PENALTY=1.1 + +# ollama_embedding -- Penalty for token presence (-2.0 to 2.0) +# OLLAMA_EMBEDDING_PRESENCE_PENALTY=0.0 + +# ollama_embedding -- Penalty for token frequency (-2.0 to 2.0) +# OLLAMA_EMBEDDING_FREQUENCY_PENALTY=0.0 + +# ollama_embedding -- Mirostat sampling algorithm (0=disabled, 1=Mirostat 1.0, 2=Mirostat 2.0) +# OLLAMA_EMBEDDING_MIROSTAT=0 + +# ollama_embedding -- Mirostat target entropy +# OLLAMA_EMBEDDING_MIROSTAT_TAU=5.0 + +# ollama_embedding -- Mirostat learning rate +# OLLAMA_EMBEDDING_MIROSTAT_ETA=0.1 + +# ollama_embedding -- Enable NUMA optimization +# OLLAMA_EMBEDDING_NUMA=False + +# ollama_embedding -- Batch size for processing +# OLLAMA_EMBEDDING_NUM_BATCH=512 + +# ollama_embedding -- Number of GPUs to use (-1 for auto) +# OLLAMA_EMBEDDING_NUM_GPU=-1 + +# ollama_embedding -- Main GPU index +# OLLAMA_EMBEDDING_MAIN_GPU=0 + +# ollama_embedding -- Optimize for low VRAM +# OLLAMA_EMBEDDING_LOW_VRAM=False + +# ollama_embedding -- Number of CPU threads (0 for auto) +# OLLAMA_EMBEDDING_NUM_THREAD=0 + +# ollama_embedding -- Use half-precision for key/value cache +# OLLAMA_EMBEDDING_F16_KV=True + +# ollama_embedding -- Return logits for all tokens +# OLLAMA_EMBEDDING_LOGITS_ALL=False + +# ollama_embedding -- Only load vocabulary +# OLLAMA_EMBEDDING_VOCAB_ONLY=False + +# ollama_embedding -- Use memory mapping for model files +# OLLAMA_EMBEDDING_USE_MMAP=True + +# ollama_embedding -- Lock model in memory +# OLLAMA_EMBEDDING_USE_MLOCK=False + +# ollama_embedding -- Only use for embeddings +# OLLAMA_EMBEDDING_EMBEDDING_ONLY=False + +# ollama_embedding -- Penalize newline tokens +# OLLAMA_EMBEDDING_PENALIZE_NEWLINE=True + +# ollama_embedding -- Stop sequences (comma-separated string) +# OLLAMA_EMBEDDING_STOP= + +# ollama_llm -- Context window size (number of tokens) +# OLLAMA_LLM_NUM_CTX=4096 + +# ollama_llm -- Maximum number of tokens to predict +# OLLAMA_LLM_NUM_PREDICT=128 + +# ollama_llm -- Number of tokens to keep from the initial prompt +# OLLAMA_LLM_NUM_KEEP=0 + +# ollama_llm -- Random seed for generation (-1 for random) +# OLLAMA_LLM_SEED=-1 + +# ollama_llm -- Controls randomness (0.0-2.0, higher = more creative) +# OLLAMA_LLM_TEMPERATURE=0.8 + +# ollama_llm -- Top-k sampling parameter (0 = disabled) +# OLLAMA_LLM_TOP_K=40 + +# ollama_llm -- Top-p (nucleus) sampling parameter (0.0-1.0) +# OLLAMA_LLM_TOP_P=0.9 + +# ollama_llm -- Tail free sampling parameter (1.0 = disabled) +# OLLAMA_LLM_TFS_Z=1.0 + +# ollama_llm -- Typical probability mass (1.0 = disabled) +# OLLAMA_LLM_TYPICAL_P=1.0 + +# ollama_llm -- Minimum probability threshold (0.0 = disabled) +# OLLAMA_LLM_MIN_P=0.0 + +# ollama_llm -- Number of tokens to consider for repetition penalty +# OLLAMA_LLM_REPEAT_LAST_N=64 + +# ollama_llm -- Penalty for repetition (1.0 = no penalty) +# OLLAMA_LLM_REPEAT_PENALTY=1.1 + +# ollama_llm -- Penalty for token presence (-2.0 to 2.0) +# OLLAMA_LLM_PRESENCE_PENALTY=0.0 + +# ollama_llm -- Penalty for token frequency (-2.0 to 2.0) +# OLLAMA_LLM_FREQUENCY_PENALTY=0.0 + +# ollama_llm -- Mirostat sampling algorithm (0=disabled, 1=Mirostat 1.0, 2=Mirostat 2.0) +# OLLAMA_LLM_MIROSTAT=0 + +# ollama_llm -- Mirostat target entropy +# OLLAMA_LLM_MIROSTAT_TAU=5.0 + +# ollama_llm -- Mirostat learning rate +# OLLAMA_LLM_MIROSTAT_ETA=0.1 + +# ollama_llm -- Enable NUMA optimization +# OLLAMA_LLM_NUMA=False + +# ollama_llm -- Batch size for processing +# OLLAMA_LLM_NUM_BATCH=512 + +# ollama_llm -- Number of GPUs to use (-1 for auto) +# OLLAMA_LLM_NUM_GPU=-1 + +# ollama_llm -- Main GPU index +# OLLAMA_LLM_MAIN_GPU=0 + +# ollama_llm -- Optimize for low VRAM +# OLLAMA_LLM_LOW_VRAM=False + +# ollama_llm -- Number of CPU threads (0 for auto) +# OLLAMA_LLM_NUM_THREAD=0 + +# ollama_llm -- Use half-precision for key/value cache +# OLLAMA_LLM_F16_KV=True + +# ollama_llm -- Return logits for all tokens +# OLLAMA_LLM_LOGITS_ALL=False + +# ollama_llm -- Only load vocabulary +# OLLAMA_LLM_VOCAB_ONLY=False + +# ollama_llm -- Use memory mapping for model files +# OLLAMA_LLM_USE_MMAP=True + +# ollama_llm -- Lock model in memory +# OLLAMA_LLM_USE_MLOCK=False + +# ollama_llm -- Only use for embeddings +# OLLAMA_LLM_EMBEDDING_ONLY=False + +# ollama_llm -- Penalize newline tokens +# OLLAMA_LLM_PENALIZE_NEWLINE=True + +# ollama_llm -- Stop sequences (comma-separated string) +# OLLAMA_LLM_STOP= + +# +# End of .env entries for LightRAG binding options +################################################################################ diff --git a/LightRAG/examples/generate_query.py b/LightRAG/examples/generate_query.py new file mode 100644 index 00000000..705b23d3 --- /dev/null +++ b/LightRAG/examples/generate_query.py @@ -0,0 +1,55 @@ +from openai import OpenAI + +# os.environ["OPENAI_API_KEY"] = "" + + +def openai_complete_if_cache( + model="gpt-4o-mini", prompt=None, system_prompt=None, history_messages=[], **kwargs +) -> str: + openai_client = OpenAI() + + messages = [] + if system_prompt: + messages.append({"role": "system", "content": system_prompt}) + messages.extend(history_messages) + messages.append({"role": "user", "content": prompt}) + + response = openai_client.chat.completions.create( + model=model, messages=messages, **kwargs + ) + return response.choices[0].message.content + + +if __name__ == "__main__": + description = "" + prompt = f""" + Given the following description of a dataset: + + {description} + + Please identify 5 potential users who would engage with this dataset. For each user, list 5 tasks they would perform with this dataset. Then, for each (user, task) combination, generate 5 questions that require a high-level understanding of the entire dataset. + + Output the results in the following structure: + - User 1: [user description] + - Task 1: [task description] + - Question 1: + - Question 2: + - Question 3: + - Question 4: + - Question 5: + - Task 2: [task description] + ... + - Task 5: [task description] + - User 2: [user description] + ... + - User 5: [user description] + ... + """ + + result = openai_complete_if_cache(model="gpt-4o-mini", prompt=prompt) + + file_path = "./queries.txt" + with open(file_path, "w") as file: + file.write(result) + + print(f"Queries written to {file_path}") diff --git a/LightRAG/examples/graph_visual_with_html.py b/LightRAG/examples/graph_visual_with_html.py new file mode 100644 index 00000000..56ed43cc --- /dev/null +++ b/LightRAG/examples/graph_visual_with_html.py @@ -0,0 +1,34 @@ +import pipmaster as pm + +if not pm.is_installed("pyvis"): + pm.install("pyvis") +if not pm.is_installed("networkx"): + pm.install("networkx") + +import networkx as nx +from pyvis.network import Network +import random + +# Load the GraphML file +G = nx.read_graphml("./dickens/graph_chunk_entity_relation.graphml") + +# Create a Pyvis network +net = Network(height="100vh", notebook=True) + +# Convert NetworkX graph to Pyvis network +net.from_nx(G) + + +# Add colors and title to nodes +for node in net.nodes: + node["color"] = "#{:06x}".format(random.randint(0, 0xFFFFFF)) + if "description" in node: + node["title"] = node["description"] + +# Add title to edges +for edge in net.edges: + if "description" in edge: + edge["title"] = edge["description"] + +# Save and display the network +net.show("knowledge_graph.html") diff --git a/LightRAG/examples/graph_visual_with_neo4j.py b/LightRAG/examples/graph_visual_with_neo4j.py new file mode 100644 index 00000000..b1fc9438 --- /dev/null +++ b/LightRAG/examples/graph_visual_with_neo4j.py @@ -0,0 +1,186 @@ +import os +import json +import xml.etree.ElementTree as ET +from neo4j import GraphDatabase + +# Constants +WORKING_DIR = "./dickens" +BATCH_SIZE_NODES = 500 +BATCH_SIZE_EDGES = 100 + +# Neo4j connection credentials +NEO4J_URI = "bolt://localhost:7687" +NEO4J_USERNAME = "neo4j" +NEO4J_PASSWORD = "your_password" + + +def xml_to_json(xml_file): + try: + tree = ET.parse(xml_file) + root = tree.getroot() + + # Print the root element's tag and attributes to confirm the file has been correctly loaded + print(f"Root element: {root.tag}") + print(f"Root attributes: {root.attrib}") + + data = {"nodes": [], "edges": []} + + # Use namespace + namespace = {"": "http://graphml.graphdrawing.org/xmlns"} + + for node in root.findall(".//node", namespace): + node_data = { + "id": node.get("id").strip('"'), + "entity_type": node.find("./data[@key='d1']", namespace).text.strip('"') + if node.find("./data[@key='d1']", namespace) is not None + else "", + "description": node.find("./data[@key='d2']", namespace).text + if node.find("./data[@key='d2']", namespace) is not None + else "", + "source_id": node.find("./data[@key='d3']", namespace).text + if node.find("./data[@key='d3']", namespace) is not None + else "", + } + data["nodes"].append(node_data) + + for edge in root.findall(".//edge", namespace): + edge_data = { + "source": edge.get("source").strip('"'), + "target": edge.get("target").strip('"'), + "weight": float(edge.find("./data[@key='d5']", namespace).text) + if edge.find("./data[@key='d5']", namespace) is not None + else 0.0, + "description": edge.find("./data[@key='d6']", namespace).text + if edge.find("./data[@key='d6']", namespace) is not None + else "", + "keywords": edge.find("./data[@key='d9']", namespace).text + if edge.find("./data[@key='d9']", namespace) is not None + else "", + "source_id": edge.find("./data[@key='d8']", namespace).text + if edge.find("./data[@key='d8']", namespace) is not None + else "", + } + data["edges"].append(edge_data) + + # Print the number of nodes and edges found + print(f"Found {len(data['nodes'])} nodes and {len(data['edges'])} edges") + + return data + except ET.ParseError as e: + print(f"Error parsing XML file: {e}") + return None + except Exception as e: + print(f"An error occurred: {e}") + return None + + +def convert_xml_to_json(xml_path, output_path): + """Converts XML file to JSON and saves the output.""" + if not os.path.exists(xml_path): + print(f"Error: File not found - {xml_path}") + return None + + json_data = xml_to_json(xml_path) + if json_data: + with open(output_path, "w", encoding="utf-8") as f: + json.dump(json_data, f, ensure_ascii=False, indent=2) + print(f"JSON file created: {output_path}") + return json_data + else: + print("Failed to create JSON data") + return None + + +def process_in_batches(tx, query, data, batch_size): + """Process data in batches and execute the given query.""" + for i in range(0, len(data), batch_size): + batch = data[i : i + batch_size] + tx.run(query, {"nodes": batch} if "nodes" in query else {"edges": batch}) + + +def main(): + # Paths + xml_file = os.path.join(WORKING_DIR, "graph_chunk_entity_relation.graphml") + json_file = os.path.join(WORKING_DIR, "graph_data.json") + + # Convert XML to JSON + json_data = convert_xml_to_json(xml_file, json_file) + if json_data is None: + return + + # Load nodes and edges + nodes = json_data.get("nodes", []) + edges = json_data.get("edges", []) + + # Neo4j queries + create_nodes_query = """ + UNWIND $nodes AS node + MERGE (e:Entity {id: node.id}) + SET e.entity_type = node.entity_type, + e.description = node.description, + e.source_id = node.source_id, + e.displayName = node.id + REMOVE e:Entity + WITH e, node + CALL apoc.create.addLabels(e, [node.id]) YIELD node AS labeledNode + RETURN count(*) + """ + + create_edges_query = """ + UNWIND $edges AS edge + MATCH (source {id: edge.source}) + MATCH (target {id: edge.target}) + WITH source, target, edge, + CASE + WHEN edge.keywords CONTAINS 'lead' THEN 'lead' + WHEN edge.keywords CONTAINS 'participate' THEN 'participate' + WHEN edge.keywords CONTAINS 'uses' THEN 'uses' + WHEN edge.keywords CONTAINS 'located' THEN 'located' + WHEN edge.keywords CONTAINS 'occurs' THEN 'occurs' + ELSE REPLACE(SPLIT(edge.keywords, ',')[0], '\"', '') + END AS relType + CALL apoc.create.relationship(source, relType, { + weight: edge.weight, + description: edge.description, + keywords: edge.keywords, + source_id: edge.source_id + }, target) YIELD rel + RETURN count(*) + """ + + set_displayname_and_labels_query = """ + MATCH (n) + SET n.displayName = n.id + WITH n + CALL apoc.create.setLabels(n, [n.entity_type]) YIELD node + RETURN count(*) + """ + + # Create a Neo4j driver + driver = GraphDatabase.driver(NEO4J_URI, auth=(NEO4J_USERNAME, NEO4J_PASSWORD)) + + try: + # Execute queries in batches + with driver.session() as session: + # Insert nodes in batches + session.execute_write( + process_in_batches, create_nodes_query, nodes, BATCH_SIZE_NODES + ) + + # Insert edges in batches + session.execute_write( + process_in_batches, create_edges_query, edges, BATCH_SIZE_EDGES + ) + + # Set displayName and labels + session.run(set_displayname_and_labels_query) + + except Exception as e: + print(f"Error occurred: {e}") + + finally: + driver.close() + + +if __name__ == "__main__": + main() diff --git a/LightRAG/examples/insert_custom_kg.py b/LightRAG/examples/insert_custom_kg.py new file mode 100644 index 00000000..db489c96 --- /dev/null +++ b/LightRAG/examples/insert_custom_kg.py @@ -0,0 +1,115 @@ +import os +from lightrag import LightRAG +from lightrag.llm.openai import gpt_4o_mini_complete +######### +# Uncomment the below two lines if running in a jupyter notebook to handle the async nature of rag.insert() +# import nest_asyncio +# nest_asyncio.apply() +######### + +WORKING_DIR = "./custom_kg" + +if not os.path.exists(WORKING_DIR): + os.mkdir(WORKING_DIR) + +rag = LightRAG( + working_dir=WORKING_DIR, + llm_model_func=gpt_4o_mini_complete, # Use gpt_4o_mini_complete LLM model + # llm_model_func=gpt_4o_complete # Optionally, use a stronger model +) + +custom_kg = { + "entities": [ + { + "entity_name": "CompanyA", + "entity_type": "Organization", + "description": "A major technology company", + "source_id": "Source1", + }, + { + "entity_name": "ProductX", + "entity_type": "Product", + "description": "A popular product developed by CompanyA", + "source_id": "Source1", + }, + { + "entity_name": "PersonA", + "entity_type": "Person", + "description": "A renowned researcher in AI", + "source_id": "Source2", + }, + { + "entity_name": "UniversityB", + "entity_type": "Organization", + "description": "A leading university specializing in technology and sciences", + "source_id": "Source2", + }, + { + "entity_name": "CityC", + "entity_type": "Location", + "description": "A large metropolitan city known for its culture and economy", + "source_id": "Source3", + }, + { + "entity_name": "EventY", + "entity_type": "Event", + "description": "An annual technology conference held in CityC", + "source_id": "Source3", + }, + ], + "relationships": [ + { + "src_id": "CompanyA", + "tgt_id": "ProductX", + "description": "CompanyA develops ProductX", + "keywords": "develop, produce", + "weight": 1.0, + "source_id": "Source1", + }, + { + "src_id": "PersonA", + "tgt_id": "UniversityB", + "description": "PersonA works at UniversityB", + "keywords": "employment, affiliation", + "weight": 0.9, + "source_id": "Source2", + }, + { + "src_id": "CityC", + "tgt_id": "EventY", + "description": "EventY is hosted in CityC", + "keywords": "host, location", + "weight": 0.8, + "source_id": "Source3", + }, + ], + "chunks": [ + { + "content": "ProductX, developed by CompanyA, has revolutionized the market with its cutting-edge features.", + "source_id": "Source1", + "source_chunk_index": 0, + }, + { + "content": "One outstanding feature of ProductX is its advanced AI capabilities.", + "source_id": "Source1", + "chunk_order_index": 1, + }, + { + "content": "PersonA is a prominent researcher at UniversityB, focusing on artificial intelligence and machine learning.", + "source_id": "Source2", + "source_chunk_index": 0, + }, + { + "content": "EventY, held in CityC, attracts technology enthusiasts and companies from around the globe.", + "source_id": "Source3", + "source_chunk_index": 0, + }, + { + "content": "None", + "source_id": "UNKNOWN", + "source_chunk_index": 0, + }, + ], +} + +rag.insert_custom_kg(custom_kg) diff --git a/LightRAG/examples/lightrag_azure_openai_demo.py b/LightRAG/examples/lightrag_azure_openai_demo.py new file mode 100644 index 00000000..c101383d --- /dev/null +++ b/LightRAG/examples/lightrag_azure_openai_demo.py @@ -0,0 +1,126 @@ +import os +import asyncio +from lightrag import LightRAG, QueryParam +from lightrag.utils import EmbeddingFunc +import numpy as np +from dotenv import load_dotenv +import logging +from openai import AzureOpenAI +from lightrag.kg.shared_storage import initialize_pipeline_status + +logging.basicConfig(level=logging.INFO) + +load_dotenv() + +AZURE_OPENAI_API_VERSION = os.getenv("AZURE_OPENAI_API_VERSION") +AZURE_OPENAI_DEPLOYMENT = os.getenv("AZURE_OPENAI_DEPLOYMENT") +AZURE_OPENAI_API_KEY = os.getenv("AZURE_OPENAI_API_KEY") +AZURE_OPENAI_ENDPOINT = os.getenv("AZURE_OPENAI_ENDPOINT") + +AZURE_EMBEDDING_DEPLOYMENT = os.getenv("AZURE_EMBEDDING_DEPLOYMENT") +AZURE_EMBEDDING_API_VERSION = os.getenv("AZURE_EMBEDDING_API_VERSION") + +WORKING_DIR = "./dickens" + +if os.path.exists(WORKING_DIR): + import shutil + + shutil.rmtree(WORKING_DIR) + +os.mkdir(WORKING_DIR) + + +async def llm_model_func( + prompt, system_prompt=None, history_messages=[], keyword_extraction=False, **kwargs +) -> str: + client = AzureOpenAI( + api_key=AZURE_OPENAI_API_KEY, + api_version=AZURE_OPENAI_API_VERSION, + azure_endpoint=AZURE_OPENAI_ENDPOINT, + ) + + messages = [] + if system_prompt: + messages.append({"role": "system", "content": system_prompt}) + if history_messages: + messages.extend(history_messages) + messages.append({"role": "user", "content": prompt}) + + chat_completion = client.chat.completions.create( + model=AZURE_OPENAI_DEPLOYMENT, # model = "deployment_name". + messages=messages, + temperature=kwargs.get("temperature", 0), + top_p=kwargs.get("top_p", 1), + n=kwargs.get("n", 1), + ) + return chat_completion.choices[0].message.content + + +async def embedding_func(texts: list[str]) -> np.ndarray: + client = AzureOpenAI( + api_key=AZURE_OPENAI_API_KEY, + api_version=AZURE_EMBEDDING_API_VERSION, + azure_endpoint=AZURE_OPENAI_ENDPOINT, + ) + embedding = client.embeddings.create(model=AZURE_EMBEDDING_DEPLOYMENT, input=texts) + + embeddings = [item.embedding for item in embedding.data] + return np.array(embeddings) + + +async def test_funcs(): + result = await llm_model_func("How are you?") + print("Resposta do llm_model_func: ", result) + + result = await embedding_func(["How are you?"]) + print("Resultado do embedding_func: ", result.shape) + print("Dimensão da embedding: ", result.shape[1]) + + +asyncio.run(test_funcs()) + +embedding_dimension = 3072 + + +async def initialize_rag(): + rag = LightRAG( + working_dir=WORKING_DIR, + llm_model_func=llm_model_func, + embedding_func=EmbeddingFunc( + embedding_dim=embedding_dimension, + max_token_size=8192, + func=embedding_func, + ), + ) + + await rag.initialize_storages() + await initialize_pipeline_status() + + return rag + + +def main(): + rag = asyncio.run(initialize_rag()) + + book1 = open("./book_1.txt", encoding="utf-8") + book2 = open("./book_2.txt", encoding="utf-8") + + rag.insert([book1.read(), book2.read()]) + + query_text = "What are the main themes?" + + print("Result (Naive):") + print(rag.query(query_text, param=QueryParam(mode="naive"))) + + print("\nResult (Local):") + print(rag.query(query_text, param=QueryParam(mode="local"))) + + print("\nResult (Global):") + print(rag.query(query_text, param=QueryParam(mode="global"))) + + print("\nResult (Hybrid):") + print(rag.query(query_text, param=QueryParam(mode="hybrid"))) + + +if __name__ == "__main__": + main() diff --git a/LightRAG/examples/lightrag_ollama_demo.py b/LightRAG/examples/lightrag_ollama_demo.py new file mode 100644 index 00000000..18fcc790 --- /dev/null +++ b/LightRAG/examples/lightrag_ollama_demo.py @@ -0,0 +1,217 @@ +import asyncio +import os +import inspect +import logging +import logging.config +from lightrag import LightRAG, QueryParam +from lightrag.llm.ollama import ollama_model_complete, ollama_embed +from lightrag.utils import EmbeddingFunc, logger, set_verbose_debug +from lightrag.kg.shared_storage import initialize_pipeline_status + +from dotenv import load_dotenv + +load_dotenv(dotenv_path=".env", override=False) + +WORKING_DIR = "./dickens" + + +def configure_logging(): + """Configure logging for the application""" + + # Reset any existing handlers to ensure clean configuration + for logger_name in ["uvicorn", "uvicorn.access", "uvicorn.error", "lightrag"]: + logger_instance = logging.getLogger(logger_name) + logger_instance.handlers = [] + logger_instance.filters = [] + + # Get log directory path from environment variable or use current directory + log_dir = os.getenv("LOG_DIR", os.getcwd()) + log_file_path = os.path.abspath(os.path.join(log_dir, "lightrag_ollama_demo.log")) + + print(f"\nLightRAG compatible demo log file: {log_file_path}\n") + os.makedirs(os.path.dirname(log_file_path), exist_ok=True) + + # Get log file max size and backup count from environment variables + log_max_bytes = int(os.getenv("LOG_MAX_BYTES", 10485760)) # Default 10MB + log_backup_count = int(os.getenv("LOG_BACKUP_COUNT", 5)) # Default 5 backups + + logging.config.dictConfig( + { + "version": 1, + "disable_existing_loggers": False, + "formatters": { + "default": { + "format": "%(levelname)s: %(message)s", + }, + "detailed": { + "format": "%(asctime)s - %(name)s - %(levelname)s - %(message)s", + }, + }, + "handlers": { + "console": { + "formatter": "default", + "class": "logging.StreamHandler", + "stream": "ext://sys.stderr", + }, + "file": { + "formatter": "detailed", + "class": "logging.handlers.RotatingFileHandler", + "filename": log_file_path, + "maxBytes": log_max_bytes, + "backupCount": log_backup_count, + "encoding": "utf-8", + }, + }, + "loggers": { + "lightrag": { + "handlers": ["console", "file"], + "level": "INFO", + "propagate": False, + }, + }, + } + ) + + # Set the logger level to INFO + logger.setLevel(logging.INFO) + # Enable verbose debug if needed + set_verbose_debug(os.getenv("VERBOSE_DEBUG", "false").lower() == "true") + + +if not os.path.exists(WORKING_DIR): + os.mkdir(WORKING_DIR) + + +async def initialize_rag(): + rag = LightRAG( + working_dir=WORKING_DIR, + llm_model_func=ollama_model_complete, + llm_model_name=os.getenv("LLM_MODEL", "qwen2.5-coder:7b"), + summary_max_tokens=8192, + llm_model_kwargs={ + "host": os.getenv("LLM_BINDING_HOST", "http://localhost:11434"), + "options": {"num_ctx": 8192}, + "timeout": int(os.getenv("TIMEOUT", "300")), + }, + embedding_func=EmbeddingFunc( + embedding_dim=int(os.getenv("EMBEDDING_DIM", "1024")), + max_token_size=int(os.getenv("MAX_EMBED_TOKENS", "8192")), + func=lambda texts: ollama_embed( + texts, + embed_model=os.getenv("EMBEDDING_MODEL", "bge-m3:latest"), + host=os.getenv("EMBEDDING_BINDING_HOST", "http://localhost:11434"), + ), + ), + ) + + await rag.initialize_storages() + await initialize_pipeline_status() + + return rag + + +async def print_stream(stream): + async for chunk in stream: + print(chunk, end="", flush=True) + + +async def main(): + try: + # Clear old data files + files_to_delete = [ + "graph_chunk_entity_relation.graphml", + "kv_store_doc_status.json", + "kv_store_full_docs.json", + "kv_store_text_chunks.json", + "vdb_chunks.json", + "vdb_entities.json", + "vdb_relationships.json", + ] + + for file in files_to_delete: + file_path = os.path.join(WORKING_DIR, file) + if os.path.exists(file_path): + os.remove(file_path) + print(f"Deleting old file:: {file_path}") + + # Initialize RAG instance + rag = await initialize_rag() + + # Test embedding function + test_text = ["This is a test string for embedding."] + embedding = await rag.embedding_func(test_text) + embedding_dim = embedding.shape[1] + print("\n=======================") + print("Test embedding function") + print("========================") + print(f"Test dict: {test_text}") + print(f"Detected embedding dimension: {embedding_dim}\n\n") + + with open("./book.txt", "r", encoding="utf-8") as f: + await rag.ainsert(f.read()) + + # Perform naive search + print("\n=====================") + print("Query mode: naive") + print("=====================") + resp = await rag.aquery( + "What are the top themes in this story?", + param=QueryParam(mode="naive", stream=True), + ) + if inspect.isasyncgen(resp): + await print_stream(resp) + else: + print(resp) + + # Perform local search + print("\n=====================") + print("Query mode: local") + print("=====================") + resp = await rag.aquery( + "What are the top themes in this story?", + param=QueryParam(mode="local", stream=True), + ) + if inspect.isasyncgen(resp): + await print_stream(resp) + else: + print(resp) + + # Perform global search + print("\n=====================") + print("Query mode: global") + print("=====================") + resp = await rag.aquery( + "What are the top themes in this story?", + param=QueryParam(mode="global", stream=True), + ) + if inspect.isasyncgen(resp): + await print_stream(resp) + else: + print(resp) + + # Perform hybrid search + print("\n=====================") + print("Query mode: hybrid") + print("=====================") + resp = await rag.aquery( + "What are the top themes in this story?", + param=QueryParam(mode="hybrid", stream=True), + ) + if inspect.isasyncgen(resp): + await print_stream(resp) + else: + print(resp) + + except Exception as e: + print(f"An error occurred: {e}") + finally: + if rag: + await rag.llm_response_cache.index_done_callback() + await rag.finalize_storages() + + +if __name__ == "__main__": + # Configure logging before running the main function + configure_logging() + asyncio.run(main()) + print("\nDone!") diff --git a/LightRAG/examples/lightrag_openai_compatible_demo.py b/LightRAG/examples/lightrag_openai_compatible_demo.py new file mode 100644 index 00000000..15187d25 --- /dev/null +++ b/LightRAG/examples/lightrag_openai_compatible_demo.py @@ -0,0 +1,227 @@ +import os +import asyncio +import inspect +import logging +import logging.config +from lightrag import LightRAG, QueryParam +from lightrag.llm.openai import openai_complete_if_cache +from lightrag.llm.ollama import ollama_embed +from lightrag.utils import EmbeddingFunc, logger, set_verbose_debug +from lightrag.kg.shared_storage import initialize_pipeline_status + +from dotenv import load_dotenv + +load_dotenv(dotenv_path=".env", override=False) + +WORKING_DIR = "./dickens" + + +def configure_logging(): + """Configure logging for the application""" + + # Reset any existing handlers to ensure clean configuration + for logger_name in ["uvicorn", "uvicorn.access", "uvicorn.error", "lightrag"]: + logger_instance = logging.getLogger(logger_name) + logger_instance.handlers = [] + logger_instance.filters = [] + + # Get log directory path from environment variable or use current directory + log_dir = os.getenv("LOG_DIR", os.getcwd()) + log_file_path = os.path.abspath( + os.path.join(log_dir, "lightrag_compatible_demo.log") + ) + + print(f"\nLightRAG compatible demo log file: {log_file_path}\n") + os.makedirs(os.path.dirname(log_dir), exist_ok=True) + + # Get log file max size and backup count from environment variables + log_max_bytes = int(os.getenv("LOG_MAX_BYTES", 10485760)) # Default 10MB + log_backup_count = int(os.getenv("LOG_BACKUP_COUNT", 5)) # Default 5 backups + + logging.config.dictConfig( + { + "version": 1, + "disable_existing_loggers": False, + "formatters": { + "default": { + "format": "%(levelname)s: %(message)s", + }, + "detailed": { + "format": "%(asctime)s - %(name)s - %(levelname)s - %(message)s", + }, + }, + "handlers": { + "console": { + "formatter": "default", + "class": "logging.StreamHandler", + "stream": "ext://sys.stderr", + }, + "file": { + "formatter": "detailed", + "class": "logging.handlers.RotatingFileHandler", + "filename": log_file_path, + "maxBytes": log_max_bytes, + "backupCount": log_backup_count, + "encoding": "utf-8", + }, + }, + "loggers": { + "lightrag": { + "handlers": ["console", "file"], + "level": "INFO", + "propagate": False, + }, + }, + } + ) + + # Set the logger level to INFO + logger.setLevel(logging.INFO) + # Enable verbose debug if needed + set_verbose_debug(os.getenv("VERBOSE_DEBUG", "false").lower() == "true") + + +if not os.path.exists(WORKING_DIR): + os.mkdir(WORKING_DIR) + + +async def llm_model_func( + prompt, system_prompt=None, history_messages=[], keyword_extraction=False, **kwargs +) -> str: + return await openai_complete_if_cache( + os.getenv("LLM_MODEL", "deepseek-chat"), + prompt, + system_prompt=system_prompt, + history_messages=history_messages, + api_key=os.getenv("LLM_BINDING_API_KEY") or os.getenv("OPENAI_API_KEY"), + base_url=os.getenv("LLM_BINDING_HOST", "https://api.deepseek.com"), + **kwargs, + ) + + +async def print_stream(stream): + async for chunk in stream: + if chunk: + print(chunk, end="", flush=True) + + +async def initialize_rag(): + rag = LightRAG( + working_dir=WORKING_DIR, + llm_model_func=llm_model_func, + embedding_func=EmbeddingFunc( + embedding_dim=int(os.getenv("EMBEDDING_DIM", "1024")), + max_token_size=int(os.getenv("MAX_EMBED_TOKENS", "8192")), + func=lambda texts: ollama_embed( + texts, + embed_model=os.getenv("EMBEDDING_MODEL", "bge-m3:latest"), + host=os.getenv("EMBEDDING_BINDING_HOST", "http://localhost:11434"), + ), + ), + ) + + await rag.initialize_storages() + await initialize_pipeline_status() + + return rag + + +async def main(): + try: + # Clear old data files + files_to_delete = [ + "graph_chunk_entity_relation.graphml", + "kv_store_doc_status.json", + "kv_store_full_docs.json", + "kv_store_text_chunks.json", + "vdb_chunks.json", + "vdb_entities.json", + "vdb_relationships.json", + ] + + for file in files_to_delete: + file_path = os.path.join(WORKING_DIR, file) + if os.path.exists(file_path): + os.remove(file_path) + print(f"Deleting old file:: {file_path}") + + # Initialize RAG instance + rag = await initialize_rag() + + # Test embedding function + test_text = ["This is a test string for embedding."] + embedding = await rag.embedding_func(test_text) + embedding_dim = embedding.shape[1] + print("\n=======================") + print("Test embedding function") + print("========================") + print(f"Test dict: {test_text}") + print(f"Detected embedding dimension: {embedding_dim}\n\n") + + with open("./book.txt", "r", encoding="utf-8") as f: + await rag.ainsert(f.read()) + + # Perform naive search + print("\n=====================") + print("Query mode: naive") + print("=====================") + resp = await rag.aquery( + "What are the top themes in this story?", + param=QueryParam(mode="naive", stream=True), + ) + if inspect.isasyncgen(resp): + await print_stream(resp) + else: + print(resp) + + # Perform local search + print("\n=====================") + print("Query mode: local") + print("=====================") + resp = await rag.aquery( + "What are the top themes in this story?", + param=QueryParam(mode="local", stream=True), + ) + if inspect.isasyncgen(resp): + await print_stream(resp) + else: + print(resp) + + # Perform global search + print("\n=====================") + print("Query mode: global") + print("=====================") + resp = await rag.aquery( + "What are the top themes in this story?", + param=QueryParam(mode="global", stream=True), + ) + if inspect.isasyncgen(resp): + await print_stream(resp) + else: + print(resp) + + # Perform hybrid search + print("\n=====================") + print("Query mode: hybrid") + print("=====================") + resp = await rag.aquery( + "What are the top themes in this story?", + param=QueryParam(mode="hybrid", stream=True), + ) + if inspect.isasyncgen(resp): + await print_stream(resp) + else: + print(resp) + + except Exception as e: + print(f"An error occurred: {e}") + finally: + if rag: + await rag.finalize_storages() + + +if __name__ == "__main__": + # Configure logging before running the main function + configure_logging() + asyncio.run(main()) + print("\nDone!") diff --git a/LightRAG/examples/lightrag_openai_demo.py b/LightRAG/examples/lightrag_openai_demo.py new file mode 100644 index 00000000..fa0b37f1 --- /dev/null +++ b/LightRAG/examples/lightrag_openai_demo.py @@ -0,0 +1,189 @@ +import os +import asyncio +import logging +import logging.config +from lightrag import LightRAG, QueryParam +from lightrag.llm.openai import gpt_4o_mini_complete, openai_embed +from lightrag.kg.shared_storage import initialize_pipeline_status +from lightrag.utils import logger, set_verbose_debug + +WORKING_DIR = "./dickens" + + +def configure_logging(): + """Configure logging for the application""" + + # Reset any existing handlers to ensure clean configuration + for logger_name in ["uvicorn", "uvicorn.access", "uvicorn.error", "lightrag"]: + logger_instance = logging.getLogger(logger_name) + logger_instance.handlers = [] + logger_instance.filters = [] + + # Get log directory path from environment variable or use current directory + log_dir = os.getenv("LOG_DIR", os.getcwd()) + log_file_path = os.path.abspath(os.path.join(log_dir, "lightrag_demo.log")) + + print(f"\nLightRAG demo log file: {log_file_path}\n") + os.makedirs(os.path.dirname(log_dir), exist_ok=True) + + # Get log file max size and backup count from environment variables + log_max_bytes = int(os.getenv("LOG_MAX_BYTES", 10485760)) # Default 10MB + log_backup_count = int(os.getenv("LOG_BACKUP_COUNT", 5)) # Default 5 backups + + logging.config.dictConfig( + { + "version": 1, + "disable_existing_loggers": False, + "formatters": { + "default": { + "format": "%(levelname)s: %(message)s", + }, + "detailed": { + "format": "%(asctime)s - %(name)s - %(levelname)s - %(message)s", + }, + }, + "handlers": { + "console": { + "formatter": "default", + "class": "logging.StreamHandler", + "stream": "ext://sys.stderr", + }, + "file": { + "formatter": "detailed", + "class": "logging.handlers.RotatingFileHandler", + "filename": log_file_path, + "maxBytes": log_max_bytes, + "backupCount": log_backup_count, + "encoding": "utf-8", + }, + }, + "loggers": { + "lightrag": { + "handlers": ["console", "file"], + "level": "INFO", + "propagate": False, + }, + }, + } + ) + + # Set the logger level to INFO + logger.setLevel(logging.INFO) + # Enable verbose debug if needed + set_verbose_debug(os.getenv("VERBOSE_DEBUG", "false").lower() == "true") + + +if not os.path.exists(WORKING_DIR): + os.mkdir(WORKING_DIR) + + +async def initialize_rag(): + rag = LightRAG( + working_dir=WORKING_DIR, + embedding_func=openai_embed, + llm_model_func=gpt_4o_mini_complete, + ) + + await rag.initialize_storages() + await initialize_pipeline_status() + + return rag + + +async def main(): + # Check if OPENAI_API_KEY environment variable exists + if not os.getenv("OPENAI_API_KEY"): + print( + "Error: OPENAI_API_KEY environment variable is not set. Please set this variable before running the program." + ) + print("You can set the environment variable by running:") + print(" export OPENAI_API_KEY='your-openai-api-key'") + return # Exit the async function + + try: + # Clear old data files + files_to_delete = [ + "graph_chunk_entity_relation.graphml", + "kv_store_doc_status.json", + "kv_store_full_docs.json", + "kv_store_text_chunks.json", + "vdb_chunks.json", + "vdb_entities.json", + "vdb_relationships.json", + ] + + for file in files_to_delete: + file_path = os.path.join(WORKING_DIR, file) + if os.path.exists(file_path): + os.remove(file_path) + print(f"Deleting old file:: {file_path}") + + # Initialize RAG instance + rag = await initialize_rag() + + # Test embedding function + test_text = ["This is a test string for embedding."] + embedding = await rag.embedding_func(test_text) + embedding_dim = embedding.shape[1] + print("\n=======================") + print("Test embedding function") + print("========================") + print(f"Test dict: {test_text}") + print(f"Detected embedding dimension: {embedding_dim}\n\n") + + with open("./book.txt", "r", encoding="utf-8") as f: + await rag.ainsert(f.read()) + + # Perform naive search + print("\n=====================") + print("Query mode: naive") + print("=====================") + print( + await rag.aquery( + "What are the top themes in this story?", param=QueryParam(mode="naive") + ) + ) + + # Perform local search + print("\n=====================") + print("Query mode: local") + print("=====================") + print( + await rag.aquery( + "What are the top themes in this story?", param=QueryParam(mode="local") + ) + ) + + # Perform global search + print("\n=====================") + print("Query mode: global") + print("=====================") + print( + await rag.aquery( + "What are the top themes in this story?", + param=QueryParam(mode="global"), + ) + ) + + # Perform hybrid search + print("\n=====================") + print("Query mode: hybrid") + print("=====================") + print( + await rag.aquery( + "What are the top themes in this story?", + param=QueryParam(mode="hybrid"), + ) + ) + except Exception as e: + print(f"An error occurred: {e}") + finally: + if rag: + await rag.finalize_storages() + + +if __name__ == "__main__": + # Configure logging before running the main function + configure_logging() + asyncio.run(main()) + print("\nDone!") diff --git a/LightRAG/examples/lightrag_openai_mongodb_graph_demo.py b/LightRAG/examples/lightrag_openai_mongodb_graph_demo.py new file mode 100644 index 00000000..67c51892 --- /dev/null +++ b/LightRAG/examples/lightrag_openai_mongodb_graph_demo.py @@ -0,0 +1,107 @@ +import os +import asyncio +from lightrag import LightRAG, QueryParam +from lightrag.llm.openai import gpt_4o_mini_complete, openai_embed +from lightrag.utils import EmbeddingFunc +import numpy as np +from lightrag.kg.shared_storage import initialize_pipeline_status + +######### +# Uncomment the below two lines if running in a jupyter notebook to handle the async nature of rag.insert() +# import nest_asyncio +# nest_asyncio.apply() +######### +WORKING_DIR = "./mongodb_test_dir" +if not os.path.exists(WORKING_DIR): + os.mkdir(WORKING_DIR) + + +os.environ["OPENAI_API_KEY"] = "sk-" +os.environ["MONGO_URI"] = "mongodb://0.0.0.0:27017/?directConnection=true" +os.environ["MONGO_DATABASE"] = "LightRAG" +os.environ["MONGO_KG_COLLECTION"] = "MDB_KG" + +# Embedding Configuration and Functions +EMBEDDING_MODEL = os.environ.get("EMBEDDING_MODEL", "text-embedding-3-large") +EMBEDDING_MAX_TOKEN_SIZE = int(os.environ.get("EMBEDDING_MAX_TOKEN_SIZE", 8192)) + + +async def embedding_func(texts: list[str]) -> np.ndarray: + return await openai_embed( + texts, + model=EMBEDDING_MODEL, + ) + + +async def get_embedding_dimension(): + test_text = ["This is a test sentence."] + embedding = await embedding_func(test_text) + return embedding.shape[1] + + +async def create_embedding_function_instance(): + # Get embedding dimension + embedding_dimension = await get_embedding_dimension() + # Create embedding function instance + return EmbeddingFunc( + embedding_dim=embedding_dimension, + max_token_size=EMBEDDING_MAX_TOKEN_SIZE, + func=embedding_func, + ) + + +async def initialize_rag(): + embedding_func_instance = await create_embedding_function_instance() + + rag = LightRAG( + working_dir=WORKING_DIR, + llm_model_func=gpt_4o_mini_complete, + embedding_func=embedding_func_instance, + graph_storage="MongoGraphStorage", + log_level="DEBUG", + ) + + await rag.initialize_storages() + await initialize_pipeline_status() + + return rag + + +def main(): + # Initialize RAG instance + rag = asyncio.run(initialize_rag()) + + with open("./book.txt", "r", encoding="utf-8") as f: + rag.insert(f.read()) + + # Perform naive search + print( + rag.query( + "What are the top themes in this story?", param=QueryParam(mode="naive") + ) + ) + + # Perform local search + print( + rag.query( + "What are the top themes in this story?", param=QueryParam(mode="local") + ) + ) + + # Perform global search + print( + rag.query( + "What are the top themes in this story?", param=QueryParam(mode="global") + ) + ) + + # Perform hybrid search + print( + rag.query( + "What are the top themes in this story?", param=QueryParam(mode="hybrid") + ) + ) + + +if __name__ == "__main__": + main() diff --git a/LightRAG/examples/modalprocessors_example.py b/LightRAG/examples/modalprocessors_example.py new file mode 100644 index 00000000..b25c12c2 --- /dev/null +++ b/LightRAG/examples/modalprocessors_example.py @@ -0,0 +1,229 @@ +""" +Example of directly using modal processors + +This example demonstrates how to use LightRAG's modal processors directly without going through MinerU. +""" + +import asyncio +import argparse +from lightrag.llm.openai import openai_complete_if_cache, openai_embed +from lightrag.kg.shared_storage import initialize_pipeline_status +from lightrag import LightRAG +from lightrag.utils import EmbeddingFunc +from raganything.modalprocessors import ( + ImageModalProcessor, + TableModalProcessor, + EquationModalProcessor, +) + +WORKING_DIR = "./rag_storage" + + +def get_llm_model_func(api_key: str, base_url: str = None): + return ( + lambda prompt, + system_prompt=None, + history_messages=[], + **kwargs: openai_complete_if_cache( + "gpt-4o-mini", + prompt, + system_prompt=system_prompt, + history_messages=history_messages, + api_key=api_key, + base_url=base_url, + **kwargs, + ) + ) + + +def get_vision_model_func(api_key: str, base_url: str = None): + return ( + lambda prompt, + system_prompt=None, + history_messages=[], + image_data=None, + **kwargs: openai_complete_if_cache( + "gpt-4o", + "", + system_prompt=None, + history_messages=[], + messages=[ + {"role": "system", "content": system_prompt} if system_prompt else None, + { + "role": "user", + "content": [ + {"type": "text", "text": prompt}, + { + "type": "image_url", + "image_url": { + "url": f"data:image/jpeg;base64,{image_data}" + }, + }, + ], + } + if image_data + else {"role": "user", "content": prompt}, + ], + api_key=api_key, + base_url=base_url, + **kwargs, + ) + if image_data + else openai_complete_if_cache( + "gpt-4o-mini", + prompt, + system_prompt=system_prompt, + history_messages=history_messages, + api_key=api_key, + base_url=base_url, + **kwargs, + ) + ) + + +async def process_image_example(lightrag: LightRAG, vision_model_func): + """Example of processing an image""" + # Create image processor + image_processor = ImageModalProcessor( + lightrag=lightrag, modal_caption_func=vision_model_func + ) + + # Prepare image content + image_content = { + "img_path": "image.jpg", + "img_caption": ["Example image caption"], + "img_footnote": ["Example image footnote"], + } + + # Process image + description, entity_info = await image_processor.process_multimodal_content( + modal_content=image_content, + content_type="image", + file_path="image_example.jpg", + entity_name="Example Image", + ) + + print("Image Processing Results:") + print(f"Description: {description}") + print(f"Entity Info: {entity_info}") + + +async def process_table_example(lightrag: LightRAG, llm_model_func): + """Example of processing a table""" + # Create table processor + table_processor = TableModalProcessor( + lightrag=lightrag, modal_caption_func=llm_model_func + ) + + # Prepare table content + table_content = { + "table_body": """ + | Name | Age | Occupation | + |------|-----|------------| + | John | 25 | Engineer | + | Mary | 30 | Designer | + """, + "table_caption": ["Employee Information Table"], + "table_footnote": ["Data updated as of 2024"], + } + + # Process table + description, entity_info = await table_processor.process_multimodal_content( + modal_content=table_content, + content_type="table", + file_path="table_example.md", + entity_name="Employee Table", + ) + + print("\nTable Processing Results:") + print(f"Description: {description}") + print(f"Entity Info: {entity_info}") + + +async def process_equation_example(lightrag: LightRAG, llm_model_func): + """Example of processing a mathematical equation""" + # Create equation processor + equation_processor = EquationModalProcessor( + lightrag=lightrag, modal_caption_func=llm_model_func + ) + + # Prepare equation content + equation_content = {"text": "E = mc^2", "text_format": "LaTeX"} + + # Process equation + description, entity_info = await equation_processor.process_multimodal_content( + modal_content=equation_content, + content_type="equation", + file_path="equation_example.txt", + entity_name="Mass-Energy Equivalence", + ) + + print("\nEquation Processing Results:") + print(f"Description: {description}") + print(f"Entity Info: {entity_info}") + + +async def initialize_rag(api_key: str, base_url: str = None): + rag = LightRAG( + working_dir=WORKING_DIR, + embedding_func=EmbeddingFunc( + embedding_dim=3072, + max_token_size=8192, + func=lambda texts: openai_embed( + texts, + model="text-embedding-3-large", + api_key=api_key, + base_url=base_url, + ), + ), + llm_model_func=lambda prompt, + system_prompt=None, + history_messages=[], + **kwargs: openai_complete_if_cache( + "gpt-4o-mini", + prompt, + system_prompt=system_prompt, + history_messages=history_messages, + api_key=api_key, + base_url=base_url, + **kwargs, + ), + ) + + await rag.initialize_storages() + await initialize_pipeline_status() + + return rag + + +def main(): + """Main function to run the example""" + parser = argparse.ArgumentParser(description="Modal Processors Example") + parser.add_argument("--api-key", required=True, help="OpenAI API key") + parser.add_argument("--base-url", help="Optional base URL for API") + parser.add_argument( + "--working-dir", "-w", default=WORKING_DIR, help="Working directory path" + ) + + args = parser.parse_args() + + # Run examples + asyncio.run(main_async(args.api_key, args.base_url)) + + +async def main_async(api_key: str, base_url: str = None): + # Initialize LightRAG + lightrag = await initialize_rag(api_key, base_url) + + # Get model functions + llm_model_func = get_llm_model_func(api_key, base_url) + vision_model_func = get_vision_model_func(api_key, base_url) + + # Run examples + await process_image_example(lightrag, vision_model_func) + await process_table_example(lightrag, llm_model_func) + await process_equation_example(lightrag, llm_model_func) + + +if __name__ == "__main__": + main() diff --git a/LightRAG/examples/raganything_example.py b/LightRAG/examples/raganything_example.py new file mode 100644 index 00000000..f61274a8 --- /dev/null +++ b/LightRAG/examples/raganything_example.py @@ -0,0 +1,286 @@ +#!/usr/bin/env python +""" +Example script demonstrating the integration of MinerU parser with RAGAnything + +This example shows how to: +1. Process parsed documents with RAGAnything +2. Perform multimodal queries on the processed documents +3. Handle different types of content (text, images, tables) +""" + +import os +import argparse +import asyncio +import logging +import logging.config +from pathlib import Path + +# Add project root directory to Python path +import sys + +sys.path.append(str(Path(__file__).parent.parent)) + +from lightrag.llm.openai import openai_complete_if_cache, openai_embed +from lightrag.utils import EmbeddingFunc, logger, set_verbose_debug +from raganything import RAGAnything, RAGAnythingConfig + + +def configure_logging(): + """Configure logging for the application""" + # Get log directory path from environment variable or use current directory + log_dir = os.getenv("LOG_DIR", os.getcwd()) + log_file_path = os.path.abspath(os.path.join(log_dir, "raganything_example.log")) + + print(f"\nRAGAnything example log file: {log_file_path}\n") + os.makedirs(os.path.dirname(log_dir), exist_ok=True) + + # Get log file max size and backup count from environment variables + log_max_bytes = int(os.getenv("LOG_MAX_BYTES", 10485760)) # Default 10MB + log_backup_count = int(os.getenv("LOG_BACKUP_COUNT", 5)) # Default 5 backups + + logging.config.dictConfig( + { + "version": 1, + "disable_existing_loggers": False, + "formatters": { + "default": { + "format": "%(levelname)s: %(message)s", + }, + "detailed": { + "format": "%(asctime)s - %(name)s - %(levelname)s - %(message)s", + }, + }, + "handlers": { + "console": { + "formatter": "default", + "class": "logging.StreamHandler", + "stream": "ext://sys.stderr", + }, + "file": { + "formatter": "detailed", + "class": "logging.handlers.RotatingFileHandler", + "filename": log_file_path, + "maxBytes": log_max_bytes, + "backupCount": log_backup_count, + "encoding": "utf-8", + }, + }, + "loggers": { + "lightrag": { + "handlers": ["console", "file"], + "level": "INFO", + "propagate": False, + }, + }, + } + ) + + # Set the logger level to INFO + logger.setLevel(logging.INFO) + # Enable verbose debug if needed + set_verbose_debug(os.getenv("VERBOSE", "false").lower() == "true") + + +async def process_with_rag( + file_path: str, + output_dir: str, + api_key: str, + base_url: str = None, + working_dir: str = None, +): + """ + Process document with RAGAnything + + Args: + file_path: Path to the document + output_dir: Output directory for RAG results + api_key: OpenAI API key + base_url: Optional base URL for API + working_dir: Working directory for RAG storage + """ + try: + # Create RAGAnything configuration + config = RAGAnythingConfig( + working_dir=working_dir or "./rag_storage", + mineru_parse_method="auto", + enable_image_processing=True, + enable_table_processing=True, + enable_equation_processing=True, + ) + + # Define LLM model function + def llm_model_func(prompt, system_prompt=None, history_messages=[], **kwargs): + return openai_complete_if_cache( + "gpt-4o-mini", + prompt, + system_prompt=system_prompt, + history_messages=history_messages, + api_key=api_key, + base_url=base_url, + **kwargs, + ) + + # Define vision model function for image processing + def vision_model_func( + prompt, system_prompt=None, history_messages=[], image_data=None, **kwargs + ): + if image_data: + return openai_complete_if_cache( + "gpt-4o", + "", + system_prompt=None, + history_messages=[], + messages=[ + {"role": "system", "content": system_prompt} + if system_prompt + else None, + { + "role": "user", + "content": [ + {"type": "text", "text": prompt}, + { + "type": "image_url", + "image_url": { + "url": f"data:image/jpeg;base64,{image_data}" + }, + }, + ], + } + if image_data + else {"role": "user", "content": prompt}, + ], + api_key=api_key, + base_url=base_url, + **kwargs, + ) + else: + return llm_model_func(prompt, system_prompt, history_messages, **kwargs) + + # Define embedding function + embedding_func = EmbeddingFunc( + embedding_dim=3072, + max_token_size=8192, + func=lambda texts: openai_embed( + texts, + model="text-embedding-3-large", + api_key=api_key, + base_url=base_url, + ), + ) + + # Initialize RAGAnything with new dataclass structure + rag = RAGAnything( + config=config, + llm_model_func=llm_model_func, + vision_model_func=vision_model_func, + embedding_func=embedding_func, + ) + + # Process document + await rag.process_document_complete( + file_path=file_path, output_dir=output_dir, parse_method="auto" + ) + + # Example queries - demonstrating different query approaches + logger.info("\nQuerying processed document:") + + # 1. Pure text queries using aquery() + text_queries = [ + "What is the main content of the document?", + "What are the key topics discussed?", + ] + + for query in text_queries: + logger.info(f"\n[Text Query]: {query}") + result = await rag.aquery(query, mode="hybrid") + logger.info(f"Answer: {result}") + + # 2. Multimodal query with specific multimodal content using aquery_with_multimodal() + logger.info( + "\n[Multimodal Query]: Analyzing performance data in context of document" + ) + multimodal_result = await rag.aquery_with_multimodal( + "Compare this performance data with any similar results mentioned in the document", + multimodal_content=[ + { + "type": "table", + "table_data": """Method,Accuracy,Processing_Time + RAGAnything,95.2%,120ms + Traditional_RAG,87.3%,180ms + Baseline,82.1%,200ms""", + "table_caption": "Performance comparison results", + } + ], + mode="hybrid", + ) + logger.info(f"Answer: {multimodal_result}") + + # 3. Another multimodal query with equation content + logger.info("\n[Multimodal Query]: Mathematical formula analysis") + equation_result = await rag.aquery_with_multimodal( + "Explain this formula and relate it to any mathematical concepts in the document", + multimodal_content=[ + { + "type": "equation", + "latex": "F1 = 2 \\cdot \\frac{precision \\cdot recall}{precision + recall}", + "equation_caption": "F1-score calculation formula", + } + ], + mode="hybrid", + ) + logger.info(f"Answer: {equation_result}") + + except Exception as e: + logger.error(f"Error processing with RAG: {str(e)}") + import traceback + + logger.error(traceback.format_exc()) + + +def main(): + """Main function to run the example""" + parser = argparse.ArgumentParser(description="MinerU RAG Example") + parser.add_argument("file_path", help="Path to the document to process") + parser.add_argument( + "--working_dir", "-w", default="./rag_storage", help="Working directory path" + ) + parser.add_argument( + "--output", "-o", default="./output", help="Output directory path" + ) + parser.add_argument( + "--api-key", + default=os.getenv("OPENAI_API_KEY"), + help="OpenAI API key (defaults to OPENAI_API_KEY env var)", + ) + parser.add_argument("--base-url", help="Optional base URL for API") + + args = parser.parse_args() + + # Check if API key is provided + if not args.api_key: + logger.error("Error: OpenAI API key is required") + logger.error("Set OPENAI_API_KEY environment variable or use --api-key option") + return + + # Create output directory if specified + if args.output: + os.makedirs(args.output, exist_ok=True) + + # Process with RAG + asyncio.run( + process_with_rag( + args.file_path, args.output, args.api_key, args.base_url, args.working_dir + ) + ) + + +if __name__ == "__main__": + # Configure logging first + configure_logging() + + print("RAGAnything Example") + print("=" * 30) + print("Processing document with multimodal RAG pipeline") + print("=" * 30) + + main() diff --git a/LightRAG/examples/rerank_example.py b/LightRAG/examples/rerank_example.py new file mode 100644 index 00000000..c7db6656 --- /dev/null +++ b/LightRAG/examples/rerank_example.py @@ -0,0 +1,232 @@ +""" +LightRAG Rerank Integration Example + +This example demonstrates how to use rerank functionality with LightRAG +to improve retrieval quality across different query modes. + +Configuration Required: +1. Set your OpenAI LLM API key and base URL with env vars + LLM_MODEL + LLM_BINDING_HOST + LLM_BINDING_API_KEY +2. Set your OpenAI embedding API key and base URL with env vars: + EMBEDDING_MODEL + EMBEDDING_DIM + EMBEDDING_BINDING_HOST + EMBEDDING_BINDING_API_KEY +3. Set your vLLM deployed AI rerank model setting with env vars: + RERANK_MODEL + RERANK_BINDING_HOST + RERANK_BINDING_API_KEY + +Note: Rerank is controlled per query via the 'enable_rerank' parameter (default: True) +""" + +import asyncio +import os +import numpy as np + +from lightrag import LightRAG, QueryParam +from lightrag.llm.openai import openai_complete_if_cache, openai_embed +from lightrag.utils import EmbeddingFunc, setup_logger +from lightrag.kg.shared_storage import initialize_pipeline_status + +from functools import partial +from lightrag.rerank import cohere_rerank + +# Set up your working directory +WORKING_DIR = "./test_rerank" +setup_logger("test_rerank") + +if not os.path.exists(WORKING_DIR): + os.mkdir(WORKING_DIR) + + +async def llm_model_func( + prompt, system_prompt=None, history_messages=[], **kwargs +) -> str: + return await openai_complete_if_cache( + os.getenv("LLM_MODEL"), + prompt, + system_prompt=system_prompt, + history_messages=history_messages, + api_key=os.getenv("LLM_BINDING_API_KEY"), + base_url=os.getenv("LLM_BINDING_HOST"), + **kwargs, + ) + + +async def embedding_func(texts: list[str]) -> np.ndarray: + return await openai_embed( + texts, + model=os.getenv("EMBEDDING_MODEL"), + api_key=os.getenv("EMBEDDING_BINDING_API_KEY"), + base_url=os.getenv("EMBEDDING_BINDING_HOST"), + ) + + +rerank_model_func = partial( + cohere_rerank, + model=os.getenv("RERANK_MODEL"), + api_key=os.getenv("RERANK_BINDING_API_KEY"), + base_url=os.getenv("RERANK_BINDING_HOST"), +) + + +async def create_rag_with_rerank(): + """Create LightRAG instance with rerank configuration""" + + # Get embedding dimension + test_embedding = await embedding_func(["test"]) + embedding_dim = test_embedding.shape[1] + print(f"Detected embedding dimension: {embedding_dim}") + + # Method 1: Using custom rerank function + rag = LightRAG( + working_dir=WORKING_DIR, + llm_model_func=llm_model_func, + embedding_func=EmbeddingFunc( + embedding_dim=embedding_dim, + max_token_size=8192, + func=embedding_func, + ), + # Rerank Configuration - provide the rerank function + rerank_model_func=rerank_model_func, + ) + + await rag.initialize_storages() + await initialize_pipeline_status() + + return rag + + +async def test_rerank_with_different_settings(): + """ + Test rerank functionality with different enable_rerank settings + """ + print("\n\n🚀 Setting up LightRAG with Rerank functionality...") + + rag = await create_rag_with_rerank() + + # Insert sample documents + sample_docs = [ + "Reranking improves retrieval quality by re-ordering documents based on relevance.", + "LightRAG is a powerful retrieval-augmented generation system with multiple query modes.", + "Vector databases enable efficient similarity search in high-dimensional embedding spaces.", + "Natural language processing has evolved with large language models and transformers.", + "Machine learning algorithms can learn patterns from data without explicit programming.", + ] + + print("📄 Inserting sample documents...") + await rag.ainsert(sample_docs) + + query = "How does reranking improve retrieval quality?" + print(f"\n🔍 Testing query: '{query}'") + print("=" * 80) + + # Test with rerank enabled (default) + print("\n📊 Testing with enable_rerank=True (default):") + result_with_rerank = await rag.aquery( + query, + param=QueryParam( + mode="naive", + top_k=10, + chunk_top_k=5, + enable_rerank=True, # Explicitly enable rerank + ), + ) + print(f" Result length: {len(result_with_rerank)} characters") + print(f" Preview: {result_with_rerank[:100]}...") + + # Test with rerank disabled + print("\n📊 Testing with enable_rerank=False:") + result_without_rerank = await rag.aquery( + query, + param=QueryParam( + mode="naive", + top_k=10, + chunk_top_k=5, + enable_rerank=False, # Disable rerank + ), + ) + print(f" Result length: {len(result_without_rerank)} characters") + print(f" Preview: {result_without_rerank[:100]}...") + + # Test with default settings (enable_rerank defaults to True) + print("\n📊 Testing with default settings (enable_rerank defaults to True):") + result_default = await rag.aquery( + query, param=QueryParam(mode="naive", top_k=10, chunk_top_k=5) + ) + print(f" Result length: {len(result_default)} characters") + print(f" Preview: {result_default[:100]}...") + + +async def test_direct_rerank(): + """Test rerank function directly""" + print("\n🔧 Direct Rerank API Test") + print("=" * 40) + + documents = [ + "Vector search finds semantically similar documents", + "LightRAG supports advanced reranking capabilities", + "Reranking significantly improves retrieval quality", + "Natural language processing with modern transformers", + "The quick brown fox jumps over the lazy dog", + ] + + query = "rerank improve quality" + print(f"Query: '{query}'") + print(f"Documents: {len(documents)}") + + try: + reranked_results = await rerank_model_func( + query=query, + documents=documents, + top_n=4, + ) + + print("\n✅ Rerank Results:") + i = 0 + for result in reranked_results: + index = result["index"] + score = result["relevance_score"] + content = documents[index] + print(f" {index}. Score: {score:.4f} | {content}...") + i += 1 + + except Exception as e: + print(f"❌ Rerank failed: {e}") + + +async def main(): + """Main example function""" + print("🎯 LightRAG Rerank Integration Example") + print("=" * 60) + + try: + # Test direct rerank + await test_direct_rerank() + + # Test rerank with different enable_rerank settings + await test_rerank_with_different_settings() + + print("\n✅ Example completed successfully!") + print("\n💡 Key Points:") + print(" ✓ Rerank is now controlled per query via 'enable_rerank' parameter") + print(" ✓ Default value for enable_rerank is True") + print(" ✓ Rerank function is configured at LightRAG initialization") + print(" ✓ Per-query enable_rerank setting overrides default behavior") + print( + " ✓ If enable_rerank=True but no rerank model is configured, a warning is issued" + ) + print(" ✓ Monitor API usage and costs when using rerank services") + + except Exception as e: + print(f"\n❌ Example failed: {e}") + import traceback + + traceback.print_exc() + + +if __name__ == "__main__": + asyncio.run(main()) diff --git a/LightRAG/examples/unofficial-sample/copy_llm_cache_to_another_storage.py b/LightRAG/examples/unofficial-sample/copy_llm_cache_to_another_storage.py new file mode 100644 index 00000000..1671b5d5 --- /dev/null +++ b/LightRAG/examples/unofficial-sample/copy_llm_cache_to_another_storage.py @@ -0,0 +1,114 @@ +""" +Sometimes you need to switch a storage solution, but you want to save LLM token and time. +This handy script helps you to copy the LLM caches from one storage solution to another. +(Not all the storage impl are supported) +""" + +import asyncio +import logging +import os +from dotenv import load_dotenv + +from lightrag.kg.postgres_impl import PostgreSQLDB, PGKVStorage +from lightrag.kg.json_kv_impl import JsonKVStorage +from lightrag.namespace import NameSpace + +load_dotenv() +ROOT_DIR = os.environ.get("ROOT_DIR") +WORKING_DIR = f"{ROOT_DIR}/dickens" + +logging.basicConfig(format="%(levelname)s:%(message)s", level=logging.INFO) + +if not os.path.exists(WORKING_DIR): + os.mkdir(WORKING_DIR) + +# AGE +os.environ["AGE_GRAPH_NAME"] = "chinese" + +postgres_db = PostgreSQLDB( + config={ + "host": "localhost", + "port": 15432, + "user": "rag", + "password": "rag", + "database": "r2", + } +) + + +async def copy_from_postgres_to_json(): + await postgres_db.initdb() + + from_llm_response_cache = PGKVStorage( + namespace=NameSpace.KV_STORE_LLM_RESPONSE_CACHE, + global_config={"embedding_batch_num": 6}, + embedding_func=None, + db=postgres_db, + ) + + to_llm_response_cache = JsonKVStorage( + namespace=NameSpace.KV_STORE_LLM_RESPONSE_CACHE, + global_config={"working_dir": WORKING_DIR}, + embedding_func=None, + ) + + # Get all cache data using the new flattened structure + all_data = await from_llm_response_cache.get_all() + + # Convert flattened data to hierarchical structure for JsonKVStorage + kv = {} + for flattened_key, cache_entry in all_data.items(): + # Parse flattened key: {mode}:{cache_type}:{hash} + parts = flattened_key.split(":", 2) + if len(parts) == 3: + mode, cache_type, hash_value = parts + if mode not in kv: + kv[mode] = {} + kv[mode][hash_value] = cache_entry + print(f"Copying {flattened_key} -> {mode}[{hash_value}]") + else: + print(f"Skipping invalid key format: {flattened_key}") + + await to_llm_response_cache.upsert(kv) + await to_llm_response_cache.index_done_callback() + print("Mission accomplished!") + + +async def copy_from_json_to_postgres(): + await postgres_db.initdb() + + from_llm_response_cache = JsonKVStorage( + namespace=NameSpace.KV_STORE_LLM_RESPONSE_CACHE, + global_config={"working_dir": WORKING_DIR}, + embedding_func=None, + ) + + to_llm_response_cache = PGKVStorage( + namespace=NameSpace.KV_STORE_LLM_RESPONSE_CACHE, + global_config={"embedding_batch_num": 6}, + embedding_func=None, + db=postgres_db, + ) + + # Get all cache data from JsonKVStorage (hierarchical structure) + all_data = await from_llm_response_cache.get_all() + + # Convert hierarchical data to flattened structure for PGKVStorage + flattened_data = {} + for mode, mode_data in all_data.items(): + print(f"Processing mode: {mode}") + for hash_value, cache_entry in mode_data.items(): + # Determine cache_type from cache entry or use default + cache_type = cache_entry.get("cache_type", "extract") + # Create flattened key: {mode}:{cache_type}:{hash} + flattened_key = f"{mode}:{cache_type}:{hash_value}" + flattened_data[flattened_key] = cache_entry + print(f"\tConverting {mode}[{hash_value}] -> {flattened_key}") + + # Upsert the flattened data + await to_llm_response_cache.upsert(flattened_data) + print("Mission accomplished!") + + +if __name__ == "__main__": + asyncio.run(copy_from_json_to_postgres()) diff --git a/LightRAG/examples/unofficial-sample/lightrag_bedrock_demo.py b/LightRAG/examples/unofficial-sample/lightrag_bedrock_demo.py new file mode 100644 index 00000000..c7f41677 --- /dev/null +++ b/LightRAG/examples/unofficial-sample/lightrag_bedrock_demo.py @@ -0,0 +1,59 @@ +""" +LightRAG meets Amazon Bedrock ⛰️ +""" + +import os +import logging + +from lightrag import LightRAG, QueryParam +from lightrag.llm.bedrock import bedrock_complete, bedrock_embed +from lightrag.utils import EmbeddingFunc +from lightrag.kg.shared_storage import initialize_pipeline_status + +import asyncio +import nest_asyncio + +nest_asyncio.apply() + +logging.getLogger("aiobotocore").setLevel(logging.WARNING) + +WORKING_DIR = "./dickens" +if not os.path.exists(WORKING_DIR): + os.mkdir(WORKING_DIR) + + +async def initialize_rag(): + rag = LightRAG( + working_dir=WORKING_DIR, + llm_model_func=bedrock_complete, + llm_model_name="Anthropic Claude 3 Haiku // Amazon Bedrock", + embedding_func=EmbeddingFunc( + embedding_dim=1024, max_token_size=8192, func=bedrock_embed + ), + ) + + await rag.initialize_storages() + await initialize_pipeline_status() + + return rag + + +def main(): + rag = asyncio.run(initialize_rag()) + + with open("./book.txt", "r", encoding="utf-8") as f: + rag.insert(f.read()) + + for mode in ["naive", "local", "global", "hybrid"]: + print("\n+-" + "-" * len(mode) + "-+") + print(f"| {mode.capitalize()} |") + print("+-" + "-" * len(mode) + "-+\n") + print( + rag.query( + "What are the top themes in this story?", param=QueryParam(mode=mode) + ) + ) + + +if __name__ == "__main__": + main() diff --git a/LightRAG/examples/unofficial-sample/lightrag_cloudflare_demo.py b/LightRAG/examples/unofficial-sample/lightrag_cloudflare_demo.py new file mode 100644 index 00000000..b53e6714 --- /dev/null +++ b/LightRAG/examples/unofficial-sample/lightrag_cloudflare_demo.py @@ -0,0 +1,357 @@ +import asyncio +import os +import inspect +import logging +import logging.config +from lightrag import LightRAG, QueryParam +from lightrag.utils import EmbeddingFunc, logger, set_verbose_debug +from lightrag.kg.shared_storage import initialize_pipeline_status + +import requests +import numpy as np +from dotenv import load_dotenv + +"""This code is a modified version of lightrag_openai_demo.py""" + +# ideally, as always, env! +load_dotenv(dotenv_path=".env", override=False) + + +""" ----========= IMPORTANT CHANGE THIS! =========---- """ +cloudflare_api_key = "YOUR_API_KEY" +account_id = "YOUR_ACCOUNT ID" # This is unique to your Cloudflare account + +# Authomatically changes +api_base_url = f"https://api.cloudflare.com/client/v4/accounts/{account_id}/ai/run/" + + +# choose an embedding model +EMBEDDING_MODEL = "@cf/baai/bge-m3" +# choose a generative model +LLM_MODEL = "@cf/meta/llama-3.2-3b-instruct" + +WORKING_DIR = "../dickens" # you can change output as desired + + +# Cloudflare init +class CloudflareWorker: + def __init__( + self, + cloudflare_api_key: str, + api_base_url: str, + llm_model_name: str, + embedding_model_name: str, + max_tokens: int = 4080, + max_response_tokens: int = 4080, + ): + self.cloudflare_api_key = cloudflare_api_key + self.api_base_url = api_base_url + self.llm_model_name = llm_model_name + self.embedding_model_name = embedding_model_name + self.max_tokens = max_tokens + self.max_response_tokens = max_response_tokens + + async def _send_request(self, model_name: str, input_: dict, debug_log: str): + headers = {"Authorization": f"Bearer {self.cloudflare_api_key}"} + + print(f""" + data sent to Cloudflare + ~~~~~~~~~~~ + {debug_log} + """) + + try: + response_raw = requests.post( + f"{self.api_base_url}{model_name}", headers=headers, json=input_ + ).json() + print(f""" + Cloudflare worker responded with: + ~~~~~~~~~~~ + {str(response_raw)} + """) + result = response_raw.get("result", {}) + + if "data" in result: # Embedding case + return np.array(result["data"]) + + if "response" in result: # LLM response + return result["response"] + + raise ValueError("Unexpected Cloudflare response format") + + except Exception as e: + print(f""" + Cloudflare API returned: + ~~~~~~~~~ + Error: {e} + """) + input("Press Enter to continue...") + return None + + async def query(self, prompt, system_prompt: str = "", **kwargs) -> str: + # since no caching is used and we don't want to mess with everything lightrag, pop the kwarg it is + kwargs.pop("hashing_kv", None) + + message = [ + {"role": "system", "content": system_prompt}, + {"role": "user", "content": prompt}, + ] + + input_ = { + "messages": message, + "max_tokens": self.max_tokens, + "response_token_limit": self.max_response_tokens, + } + + return await self._send_request( + self.llm_model_name, + input_, + debug_log=f"\n- model used {self.llm_model_name}\n- system prompt: {system_prompt}\n- query: {prompt}", + ) + + async def embedding_chunk(self, texts: list[str]) -> np.ndarray: + print(f""" + TEXT inputted + ~~~~~ + {texts} + """) + + input_ = { + "text": texts, + "max_tokens": self.max_tokens, + "response_token_limit": self.max_response_tokens, + } + + return await self._send_request( + self.embedding_model_name, + input_, + debug_log=f"\n-llm model name {self.embedding_model_name}\n- texts: {texts}", + ) + + +def configure_logging(): + """Configure logging for the application""" + + # Reset any existing handlers to ensure clean configuration + for logger_name in ["uvicorn", "uvicorn.access", "uvicorn.error", "lightrag"]: + logger_instance = logging.getLogger(logger_name) + logger_instance.handlers = [] + logger_instance.filters = [] + + # Get log directory path from environment variable or use current directory + log_dir = os.getenv("LOG_DIR", os.getcwd()) + log_file_path = os.path.abspath( + os.path.join(log_dir, "lightrag_cloudflare_worker_demo.log") + ) + + print(f"\nLightRAG compatible demo log file: {log_file_path}\n") + os.makedirs(os.path.dirname(log_file_path), exist_ok=True) + + # Get log file max size and backup count from environment variables + log_max_bytes = int(os.getenv("LOG_MAX_BYTES", 10485760)) # Default 10MB + log_backup_count = int(os.getenv("LOG_BACKUP_COUNT", 5)) # Default 5 backups + + logging.config.dictConfig( + { + "version": 1, + "disable_existing_loggers": False, + "formatters": { + "default": { + "format": "%(levelname)s: %(message)s", + }, + "detailed": { + "format": "%(asctime)s - %(name)s - %(levelname)s - %(message)s", + }, + }, + "handlers": { + "console": { + "formatter": "default", + "class": "logging.StreamHandler", + "stream": "ext://sys.stderr", + }, + "file": { + "formatter": "detailed", + "class": "logging.handlers.RotatingFileHandler", + "filename": log_file_path, + "maxBytes": log_max_bytes, + "backupCount": log_backup_count, + "encoding": "utf-8", + }, + }, + "loggers": { + "lightrag": { + "handlers": ["console", "file"], + "level": "INFO", + "propagate": False, + }, + }, + } + ) + + # Set the logger level to INFO + logger.setLevel(logging.INFO) + # Enable verbose debug if needed + set_verbose_debug(os.getenv("VERBOSE_DEBUG", "false").lower() == "true") + + +if not os.path.exists(WORKING_DIR): + os.mkdir(WORKING_DIR) + + +async def initialize_rag(): + cloudflare_worker = CloudflareWorker( + cloudflare_api_key=cloudflare_api_key, + api_base_url=api_base_url, + embedding_model_name=EMBEDDING_MODEL, + llm_model_name=LLM_MODEL, + ) + + rag = LightRAG( + working_dir=WORKING_DIR, + max_parallel_insert=2, + llm_model_func=cloudflare_worker.query, + llm_model_name=os.getenv("LLM_MODEL", LLM_MODEL), + summary_max_tokens=4080, + embedding_func=EmbeddingFunc( + embedding_dim=int(os.getenv("EMBEDDING_DIM", "1024")), + max_token_size=int(os.getenv("MAX_EMBED_TOKENS", "2048")), + func=lambda texts: cloudflare_worker.embedding_chunk( + texts, + ), + ), + ) + + await rag.initialize_storages() + await initialize_pipeline_status() + + return rag + + +async def print_stream(stream): + async for chunk in stream: + print(chunk, end="", flush=True) + + +async def main(): + try: + # Clear old data files + files_to_delete = [ + "graph_chunk_entity_relation.graphml", + "kv_store_doc_status.json", + "kv_store_full_docs.json", + "kv_store_text_chunks.json", + "vdb_chunks.json", + "vdb_entities.json", + "vdb_relationships.json", + ] + + for file in files_to_delete: + file_path = os.path.join(WORKING_DIR, file) + if os.path.exists(file_path): + os.remove(file_path) + print(f"Deleting old file:: {file_path}") + + # Initialize RAG instance + rag = await initialize_rag() + + # Test embedding function + test_text = ["This is a test string for embedding."] + embedding = await rag.embedding_func(test_text) + embedding_dim = embedding.shape[1] + print("\n=======================") + print("Test embedding function") + print("========================") + print(f"Test dict: {test_text}") + print(f"Detected embedding dimension: {embedding_dim}\n\n") + + # Locate the location of what is needed to be added to the knowledge + # Can add several simultaneously by modifying code + with open("./book.txt", "r", encoding="utf-8") as f: + await rag.ainsert(f.read()) + + # Perform naive search + print("\n=====================") + print("Query mode: naive") + print("=====================") + resp = await rag.aquery( + "What are the top themes in this story?", + param=QueryParam(mode="naive", stream=True), + ) + if inspect.isasyncgen(resp): + await print_stream(resp) + else: + print(resp) + + # Perform local search + print("\n=====================") + print("Query mode: local") + print("=====================") + resp = await rag.aquery( + "What are the top themes in this story?", + param=QueryParam(mode="local", stream=True), + ) + if inspect.isasyncgen(resp): + await print_stream(resp) + else: + print(resp) + + # Perform global search + print("\n=====================") + print("Query mode: global") + print("=====================") + resp = await rag.aquery( + "What are the top themes in this story?", + param=QueryParam(mode="global", stream=True), + ) + if inspect.isasyncgen(resp): + await print_stream(resp) + else: + print(resp) + + # Perform hybrid search + print("\n=====================") + print("Query mode: hybrid") + print("=====================") + resp = await rag.aquery( + "What are the top themes in this story?", + param=QueryParam(mode="hybrid", stream=True), + ) + if inspect.isasyncgen(resp): + await print_stream(resp) + else: + print(resp) + + """ FOR TESTING (if you want to test straight away, after building. Uncomment this part""" + + """ + print("\n" + "=" * 60) + print("AI ASSISTANT READY!") + print("Ask questions about (your uploaded) regulations") + print("Type 'quit' to exit") + print("=" * 60) + + while True: + question = input("\n🔥 Your question: ") + + if question.lower() in ['quit', 'exit', 'bye']: + break + + print("\nThinking...") + response = await rag.aquery(question, param=QueryParam(mode="hybrid")) + print(f"\nAnswer: {response}") + + """ + + except Exception as e: + print(f"An error occurred: {e}") + finally: + if rag: + await rag.llm_response_cache.index_done_callback() + await rag.finalize_storages() + + +if __name__ == "__main__": + # Configure logging before running the main function + configure_logging() + asyncio.run(main()) + print("\nDone!") diff --git a/LightRAG/examples/unofficial-sample/lightrag_hf_demo.py b/LightRAG/examples/unofficial-sample/lightrag_hf_demo.py new file mode 100644 index 00000000..f2abbb2f --- /dev/null +++ b/LightRAG/examples/unofficial-sample/lightrag_hf_demo.py @@ -0,0 +1,82 @@ +import os + +from lightrag import LightRAG, QueryParam +from lightrag.llm.hf import hf_model_complete, hf_embed +from lightrag.utils import EmbeddingFunc +from transformers import AutoModel, AutoTokenizer +from lightrag.kg.shared_storage import initialize_pipeline_status + +import asyncio +import nest_asyncio + +nest_asyncio.apply() + +WORKING_DIR = "./dickens" + +if not os.path.exists(WORKING_DIR): + os.mkdir(WORKING_DIR) + + +async def initialize_rag(): + rag = LightRAG( + working_dir=WORKING_DIR, + llm_model_func=hf_model_complete, + llm_model_name="meta-llama/Llama-3.1-8B-Instruct", + embedding_func=EmbeddingFunc( + embedding_dim=384, + max_token_size=5000, + func=lambda texts: hf_embed( + texts, + tokenizer=AutoTokenizer.from_pretrained( + "sentence-transformers/all-MiniLM-L6-v2" + ), + embed_model=AutoModel.from_pretrained( + "sentence-transformers/all-MiniLM-L6-v2" + ), + ), + ), + ) + + await rag.initialize_storages() + await initialize_pipeline_status() + + return rag + + +def main(): + rag = asyncio.run(initialize_rag()) + + with open("./book.txt", "r", encoding="utf-8") as f: + rag.insert(f.read()) + + # Perform naive search + print( + rag.query( + "What are the top themes in this story?", param=QueryParam(mode="naive") + ) + ) + + # Perform local search + print( + rag.query( + "What are the top themes in this story?", param=QueryParam(mode="local") + ) + ) + + # Perform global search + print( + rag.query( + "What are the top themes in this story?", param=QueryParam(mode="global") + ) + ) + + # Perform hybrid search + print( + rag.query( + "What are the top themes in this story?", param=QueryParam(mode="hybrid") + ) + ) + + +if __name__ == "__main__": + main() diff --git a/LightRAG/examples/unofficial-sample/lightrag_llamaindex_direct_demo.py b/LightRAG/examples/unofficial-sample/lightrag_llamaindex_direct_demo.py new file mode 100644 index 00000000..0aec465e --- /dev/null +++ b/LightRAG/examples/unofficial-sample/lightrag_llamaindex_direct_demo.py @@ -0,0 +1,143 @@ +import asyncio +import os + +import nest_asyncio +from llama_index.embeddings.openai import OpenAIEmbedding +from llama_index.llms.openai import OpenAI + +from lightrag import LightRAG, QueryParam +from lightrag.kg.shared_storage import initialize_pipeline_status +from lightrag.llm.llama_index_impl import ( + llama_index_complete_if_cache, + llama_index_embed, +) +from lightrag.utils import EmbeddingFunc + +nest_asyncio.apply() + +# Configure working directory +WORKING_DIR = "./index_default" +print(f"WORKING_DIR: {WORKING_DIR}") + +# Model configuration +LLM_MODEL = os.environ.get("LLM_MODEL", "gpt-4") +print(f"LLM_MODEL: {LLM_MODEL}") +EMBEDDING_MODEL = os.environ.get("EMBEDDING_MODEL", "text-embedding-3-large") +print(f"EMBEDDING_MODEL: {EMBEDDING_MODEL}") +EMBEDDING_MAX_TOKEN_SIZE = int(os.environ.get("EMBEDDING_MAX_TOKEN_SIZE", 8192)) +print(f"EMBEDDING_MAX_TOKEN_SIZE: {EMBEDDING_MAX_TOKEN_SIZE}") + +# OpenAI configuration +OPENAI_API_KEY = os.environ.get("OPENAI_API_KEY", "your-api-key-here") + +if not os.path.exists(WORKING_DIR): + print(f"Creating working directory: {WORKING_DIR}") + os.mkdir(WORKING_DIR) + + +# Initialize LLM function +async def llm_model_func(prompt, system_prompt=None, history_messages=[], **kwargs): + try: + # Initialize OpenAI if not in kwargs + if "llm_instance" not in kwargs: + llm_instance = OpenAI( + model=LLM_MODEL, + api_key=OPENAI_API_KEY, + temperature=0.7, + ) + kwargs["llm_instance"] = llm_instance + + response = await llama_index_complete_if_cache( + kwargs["llm_instance"], + prompt, + system_prompt=system_prompt, + history_messages=history_messages, + **kwargs, + ) + return response + except Exception as e: + print(f"LLM request failed: {str(e)}") + raise + + +# Initialize embedding function +async def embedding_func(texts): + try: + embed_model = OpenAIEmbedding( + model=EMBEDDING_MODEL, + api_key=OPENAI_API_KEY, + ) + return await llama_index_embed(texts, embed_model=embed_model) + except Exception as e: + print(f"Embedding failed: {str(e)}") + raise + + +# Get embedding dimension +async def get_embedding_dim(): + test_text = ["This is a test sentence."] + embedding = await embedding_func(test_text) + embedding_dim = embedding.shape[1] + print(f"embedding_dim={embedding_dim}") + return embedding_dim + + +async def initialize_rag(): + embedding_dimension = await get_embedding_dim() + + rag = LightRAG( + working_dir=WORKING_DIR, + llm_model_func=llm_model_func, + embedding_func=EmbeddingFunc( + embedding_dim=embedding_dimension, + max_token_size=EMBEDDING_MAX_TOKEN_SIZE, + func=embedding_func, + ), + ) + + await rag.initialize_storages() + await initialize_pipeline_status() + + return rag + + +def main(): + # Initialize RAG instance + rag = asyncio.run(initialize_rag()) + + # Insert example text + with open("./book.txt", "r", encoding="utf-8") as f: + rag.insert(f.read()) + + # Test different query modes + print("\nNaive Search:") + print( + rag.query( + "What are the top themes in this story?", param=QueryParam(mode="naive") + ) + ) + + print("\nLocal Search:") + print( + rag.query( + "What are the top themes in this story?", param=QueryParam(mode="local") + ) + ) + + print("\nGlobal Search:") + print( + rag.query( + "What are the top themes in this story?", param=QueryParam(mode="global") + ) + ) + + print("\nHybrid Search:") + print( + rag.query( + "What are the top themes in this story?", param=QueryParam(mode="hybrid") + ) + ) + + +if __name__ == "__main__": + main() diff --git a/LightRAG/examples/unofficial-sample/lightrag_llamaindex_litellm_demo.py b/LightRAG/examples/unofficial-sample/lightrag_llamaindex_litellm_demo.py new file mode 100644 index 00000000..957bdd8a --- /dev/null +++ b/LightRAG/examples/unofficial-sample/lightrag_llamaindex_litellm_demo.py @@ -0,0 +1,145 @@ +import asyncio +import os + +import nest_asyncio +from llama_index.embeddings.litellm import LiteLLMEmbedding +from llama_index.llms.litellm import LiteLLM + +from lightrag import LightRAG, QueryParam +from lightrag.kg.shared_storage import initialize_pipeline_status +from lightrag.llm.llama_index_impl import ( + llama_index_complete_if_cache, + llama_index_embed, +) +from lightrag.utils import EmbeddingFunc + +nest_asyncio.apply() + +# Configure working directory +WORKING_DIR = "./index_default" +print(f"WORKING_DIR: {WORKING_DIR}") + +# Model configuration +LLM_MODEL = os.environ.get("LLM_MODEL", "gpt-4") +print(f"LLM_MODEL: {LLM_MODEL}") +EMBEDDING_MODEL = os.environ.get("EMBEDDING_MODEL", "text-embedding-3-large") +print(f"EMBEDDING_MODEL: {EMBEDDING_MODEL}") +EMBEDDING_MAX_TOKEN_SIZE = int(os.environ.get("EMBEDDING_MAX_TOKEN_SIZE", 8192)) +print(f"EMBEDDING_MAX_TOKEN_SIZE: {EMBEDDING_MAX_TOKEN_SIZE}") + +# LiteLLM configuration +LITELLM_URL = os.environ.get("LITELLM_URL", "http://localhost:4000") +print(f"LITELLM_URL: {LITELLM_URL}") +LITELLM_KEY = os.environ.get("LITELLM_KEY", "sk-1234") + +if not os.path.exists(WORKING_DIR): + os.mkdir(WORKING_DIR) + + +# Initialize LLM function +async def llm_model_func(prompt, system_prompt=None, history_messages=[], **kwargs): + try: + # Initialize LiteLLM if not in kwargs + if "llm_instance" not in kwargs: + llm_instance = LiteLLM( + model=f"openai/{LLM_MODEL}", # Format: "provider/model_name" + api_base=LITELLM_URL, + api_key=LITELLM_KEY, + temperature=0.7, + ) + kwargs["llm_instance"] = llm_instance + + response = await llama_index_complete_if_cache( + kwargs["llm_instance"], + prompt, + system_prompt=system_prompt, + history_messages=history_messages, + ) + return response + except Exception as e: + print(f"LLM request failed: {str(e)}") + raise + + +# Initialize embedding function +async def embedding_func(texts): + try: + embed_model = LiteLLMEmbedding( + model_name=f"openai/{EMBEDDING_MODEL}", + api_base=LITELLM_URL, + api_key=LITELLM_KEY, + ) + return await llama_index_embed(texts, embed_model=embed_model) + except Exception as e: + print(f"Embedding failed: {str(e)}") + raise + + +# Get embedding dimension +async def get_embedding_dim(): + test_text = ["This is a test sentence."] + embedding = await embedding_func(test_text) + embedding_dim = embedding.shape[1] + print(f"embedding_dim={embedding_dim}") + return embedding_dim + + +async def initialize_rag(): + embedding_dimension = await get_embedding_dim() + + rag = LightRAG( + working_dir=WORKING_DIR, + llm_model_func=llm_model_func, + embedding_func=EmbeddingFunc( + embedding_dim=embedding_dimension, + max_token_size=EMBEDDING_MAX_TOKEN_SIZE, + func=embedding_func, + ), + ) + + await rag.initialize_storages() + await initialize_pipeline_status() + + return rag + + +def main(): + # Initialize RAG instance + rag = asyncio.run(initialize_rag()) + + # Insert example text + with open("./book.txt", "r", encoding="utf-8") as f: + rag.insert(f.read()) + + # Test different query modes + print("\nNaive Search:") + print( + rag.query( + "What are the top themes in this story?", param=QueryParam(mode="naive") + ) + ) + + print("\nLocal Search:") + print( + rag.query( + "What are the top themes in this story?", param=QueryParam(mode="local") + ) + ) + + print("\nGlobal Search:") + print( + rag.query( + "What are the top themes in this story?", param=QueryParam(mode="global") + ) + ) + + print("\nHybrid Search:") + print( + rag.query( + "What are the top themes in this story?", param=QueryParam(mode="hybrid") + ) + ) + + +if __name__ == "__main__": + main() diff --git a/LightRAG/examples/unofficial-sample/lightrag_llamaindex_litellm_opik_demo.py b/LightRAG/examples/unofficial-sample/lightrag_llamaindex_litellm_opik_demo.py new file mode 100644 index 00000000..001bf988 --- /dev/null +++ b/LightRAG/examples/unofficial-sample/lightrag_llamaindex_litellm_opik_demo.py @@ -0,0 +1,156 @@ +import asyncio +import os + +import nest_asyncio +from llama_index.embeddings.litellm import LiteLLMEmbedding +from llama_index.llms.litellm import LiteLLM + +from lightrag import LightRAG, QueryParam +from lightrag.kg.shared_storage import initialize_pipeline_status +from lightrag.llm.llama_index_impl import ( + llama_index_complete_if_cache, + llama_index_embed, +) +from lightrag.utils import EmbeddingFunc + +nest_asyncio.apply() + +# Configure working directory +WORKING_DIR = "./index_default" +print(f"WORKING_DIR: {WORKING_DIR}") + +# Model configuration +LLM_MODEL = os.environ.get("LLM_MODEL", "gemma-3-4b") +print(f"LLM_MODEL: {LLM_MODEL}") +EMBEDDING_MODEL = os.environ.get("EMBEDDING_MODEL", "arctic-embed") +print(f"EMBEDDING_MODEL: {EMBEDDING_MODEL}") +EMBEDDING_MAX_TOKEN_SIZE = int(os.environ.get("EMBEDDING_MAX_TOKEN_SIZE", 8192)) +print(f"EMBEDDING_MAX_TOKEN_SIZE: {EMBEDDING_MAX_TOKEN_SIZE}") + +# LiteLLM configuration +LITELLM_URL = os.environ.get("LITELLM_URL", "http://localhost:4000") +print(f"LITELLM_URL: {LITELLM_URL}") +LITELLM_KEY = os.environ.get("LITELLM_KEY", "sk-4JdvGFKqSA3S0k_5p0xufw") + +if not os.path.exists(WORKING_DIR): + os.mkdir(WORKING_DIR) + + +# Initialize LLM function +async def llm_model_func(prompt, system_prompt=None, history_messages=[], **kwargs): + try: + # Initialize LiteLLM if not in kwargs + if "llm_instance" not in kwargs: + llm_instance = LiteLLM( + model=f"openai/{LLM_MODEL}", # Format: "provider/model_name" + api_base=LITELLM_URL, + api_key=LITELLM_KEY, + temperature=0.7, + ) + kwargs["llm_instance"] = llm_instance + + chat_kwargs = {} + chat_kwargs["litellm_params"] = { + "metadata": { + "opik": { + "project_name": "lightrag_llamaindex_litellm_opik_demo", + "tags": ["lightrag", "litellm"], + } + } + } + + response = await llama_index_complete_if_cache( + kwargs["llm_instance"], + prompt, + system_prompt=system_prompt, + history_messages=history_messages, + chat_kwargs=chat_kwargs, + ) + return response + except Exception as e: + print(f"LLM request failed: {str(e)}") + raise + + +# Initialize embedding function +async def embedding_func(texts): + try: + embed_model = LiteLLMEmbedding( + model_name=f"openai/{EMBEDDING_MODEL}", + api_base=LITELLM_URL, + api_key=LITELLM_KEY, + ) + return await llama_index_embed(texts, embed_model=embed_model) + except Exception as e: + print(f"Embedding failed: {str(e)}") + raise + + +# Get embedding dimension +async def get_embedding_dim(): + test_text = ["This is a test sentence."] + embedding = await embedding_func(test_text) + embedding_dim = embedding.shape[1] + print(f"embedding_dim={embedding_dim}") + return embedding_dim + + +async def initialize_rag(): + embedding_dimension = await get_embedding_dim() + + rag = LightRAG( + working_dir=WORKING_DIR, + llm_model_func=llm_model_func, + embedding_func=EmbeddingFunc( + embedding_dim=embedding_dimension, + max_token_size=EMBEDDING_MAX_TOKEN_SIZE, + func=embedding_func, + ), + ) + + await rag.initialize_storages() + await initialize_pipeline_status() + + return rag + + +def main(): + # Initialize RAG instance + rag = asyncio.run(initialize_rag()) + + # Insert example text + with open("./book.txt", "r", encoding="utf-8") as f: + rag.insert(f.read()) + + # Test different query modes + print("\nNaive Search:") + print( + rag.query( + "What are the top themes in this story?", param=QueryParam(mode="naive") + ) + ) + + print("\nLocal Search:") + print( + rag.query( + "What are the top themes in this story?", param=QueryParam(mode="local") + ) + ) + + print("\nGlobal Search:") + print( + rag.query( + "What are the top themes in this story?", param=QueryParam(mode="global") + ) + ) + + print("\nHybrid Search:") + print( + rag.query( + "What are the top themes in this story?", param=QueryParam(mode="hybrid") + ) + ) + + +if __name__ == "__main__": + main() diff --git a/LightRAG/examples/unofficial-sample/lightrag_lmdeploy_demo.py b/LightRAG/examples/unofficial-sample/lightrag_lmdeploy_demo.py new file mode 100644 index 00000000..ba118fc9 --- /dev/null +++ b/LightRAG/examples/unofficial-sample/lightrag_lmdeploy_demo.py @@ -0,0 +1,110 @@ +import os + +from lightrag import LightRAG, QueryParam +from lightrag.llm.lmdeploy import lmdeploy_model_if_cache +from lightrag.llm.hf import hf_embed +from lightrag.utils import EmbeddingFunc +from transformers import AutoModel, AutoTokenizer +from lightrag.kg.shared_storage import initialize_pipeline_status + +import asyncio +import nest_asyncio + +nest_asyncio.apply() + +WORKING_DIR = "./dickens" + +if not os.path.exists(WORKING_DIR): + os.mkdir(WORKING_DIR) + + +async def lmdeploy_model_complete( + prompt=None, + system_prompt=None, + history_messages=[], + keyword_extraction=False, + **kwargs, +) -> str: + model_name = kwargs["hashing_kv"].global_config["llm_model_name"] + return await lmdeploy_model_if_cache( + model_name, + prompt, + system_prompt=system_prompt, + history_messages=history_messages, + ## please specify chat_template if your local path does not follow original HF file name, + ## or model_name is a pytorch model on huggingface.co, + ## you can refer to https://github.com/InternLM/lmdeploy/blob/main/lmdeploy/model.py + ## for a list of chat_template available in lmdeploy. + chat_template="llama3", + # model_format ='awq', # if you are using awq quantization model. + # quant_policy=8, # if you want to use online kv cache, 4=kv int4, 8=kv int8. + **kwargs, + ) + + +async def initialize_rag(): + rag = LightRAG( + working_dir=WORKING_DIR, + llm_model_func=lmdeploy_model_complete, + llm_model_name="meta-llama/Llama-3.1-8B-Instruct", # please use definite path for local model + embedding_func=EmbeddingFunc( + embedding_dim=384, + max_token_size=5000, + func=lambda texts: hf_embed( + texts, + tokenizer=AutoTokenizer.from_pretrained( + "sentence-transformers/all-MiniLM-L6-v2" + ), + embed_model=AutoModel.from_pretrained( + "sentence-transformers/all-MiniLM-L6-v2" + ), + ), + ), + ) + + await rag.initialize_storages() + await initialize_pipeline_status() + + return rag + + +def main(): + # Initialize RAG instance + rag = asyncio.run(initialize_rag()) + + # Insert example text + with open("./book.txt", "r", encoding="utf-8") as f: + rag.insert(f.read()) + + # Test different query modes + print("\nNaive Search:") + print( + rag.query( + "What are the top themes in this story?", param=QueryParam(mode="naive") + ) + ) + + print("\nLocal Search:") + print( + rag.query( + "What are the top themes in this story?", param=QueryParam(mode="local") + ) + ) + + print("\nGlobal Search:") + print( + rag.query( + "What are the top themes in this story?", param=QueryParam(mode="global") + ) + ) + + print("\nHybrid Search:") + print( + rag.query( + "What are the top themes in this story?", param=QueryParam(mode="hybrid") + ) + ) + + +if __name__ == "__main__": + main() diff --git a/LightRAG/examples/unofficial-sample/lightrag_nvidia_demo.py b/LightRAG/examples/unofficial-sample/lightrag_nvidia_demo.py new file mode 100644 index 00000000..97cfc38a --- /dev/null +++ b/LightRAG/examples/unofficial-sample/lightrag_nvidia_demo.py @@ -0,0 +1,171 @@ +import os +import asyncio +import nest_asyncio + +from lightrag import LightRAG, QueryParam +from lightrag.llm import ( + openai_complete_if_cache, + nvidia_openai_embed, +) +from lightrag.utils import EmbeddingFunc +import numpy as np +from lightrag.kg.shared_storage import initialize_pipeline_status + +# for custom llm_model_func +from lightrag.utils import locate_json_string_body_from_string + +nest_asyncio.apply() + +WORKING_DIR = "./dickens" + +if not os.path.exists(WORKING_DIR): + os.mkdir(WORKING_DIR) + +# some method to use your API key (choose one) +# NVIDIA_OPENAI_API_KEY = os.getenv("NVIDIA_OPENAI_API_KEY") +NVIDIA_OPENAI_API_KEY = "nvapi-xxxx" # your api key + +# using pre-defined function for nvidia LLM API. OpenAI compatible +# llm_model_func = nvidia_openai_complete + + +# If you trying to make custom llm_model_func to use llm model on NVIDIA API like other example: +async def llm_model_func( + prompt, system_prompt=None, history_messages=[], keyword_extraction=False, **kwargs +) -> str: + result = await openai_complete_if_cache( + "nvidia/llama-3.1-nemotron-70b-instruct", + prompt, + system_prompt=system_prompt, + history_messages=history_messages, + api_key=NVIDIA_OPENAI_API_KEY, + base_url="https://integrate.api.nvidia.com/v1", + **kwargs, + ) + if keyword_extraction: + return locate_json_string_body_from_string(result) + return result + + +# custom embedding +nvidia_embed_model = "nvidia/nv-embedqa-e5-v5" + + +async def indexing_embedding_func(texts: list[str]) -> np.ndarray: + return await nvidia_openai_embed( + texts, + model=nvidia_embed_model, # maximum 512 token + # model="nvidia/llama-3.2-nv-embedqa-1b-v1", + api_key=NVIDIA_OPENAI_API_KEY, + base_url="https://integrate.api.nvidia.com/v1", + input_type="passage", + trunc="END", # handling on server side if input token is longer than maximum token + encode="float", + ) + + +async def query_embedding_func(texts: list[str]) -> np.ndarray: + return await nvidia_openai_embed( + texts, + model=nvidia_embed_model, # maximum 512 token + # model="nvidia/llama-3.2-nv-embedqa-1b-v1", + api_key=NVIDIA_OPENAI_API_KEY, + base_url="https://integrate.api.nvidia.com/v1", + input_type="query", + trunc="END", # handling on server side if input token is longer than maximum token + encode="float", + ) + + +# dimension are same +async def get_embedding_dim(): + test_text = ["This is a test sentence."] + embedding = await indexing_embedding_func(test_text) + embedding_dim = embedding.shape[1] + return embedding_dim + + +# function test +async def test_funcs(): + result = await llm_model_func("How are you?") + print("llm_model_func: ", result) + + result = await indexing_embedding_func(["How are you?"]) + print("embedding_func: ", result) + + +# asyncio.run(test_funcs()) + + +async def initialize_rag(): + embedding_dimension = await get_embedding_dim() + print(f"Detected embedding dimension: {embedding_dimension}") + + # lightRAG class during indexing + rag = LightRAG( + working_dir=WORKING_DIR, + llm_model_func=llm_model_func, + # llm_model_name="meta/llama3-70b-instruct", #un comment if + embedding_func=EmbeddingFunc( + embedding_dim=embedding_dimension, + max_token_size=512, # maximum token size, somehow it's still exceed maximum number of token + # so truncate (trunc) parameter on embedding_func will handle it and try to examine the tokenizer used in LightRAG + # so you can adjust to be able to fit the NVIDIA model (future work) + func=indexing_embedding_func, + ), + ) + + await rag.initialize_storages() + await initialize_pipeline_status() + + return rag + + +async def main(): + try: + # Initialize RAG instance + rag = await initialize_rag() + + # reading file + with open("./book.txt", "r", encoding="utf-8") as f: + await rag.ainsert(f.read()) + + # Perform naive search + print("==============Naive===============") + print( + await rag.aquery( + "What are the top themes in this story?", param=QueryParam(mode="naive") + ) + ) + + # Perform local search + print("==============local===============") + print( + await rag.aquery( + "What are the top themes in this story?", param=QueryParam(mode="local") + ) + ) + + # Perform global search + print("==============global===============") + print( + await rag.aquery( + "What are the top themes in this story?", + param=QueryParam(mode="global"), + ) + ) + + # Perform hybrid search + print("==============hybrid===============") + print( + await rag.aquery( + "What are the top themes in this story?", + param=QueryParam(mode="hybrid"), + ) + ) + except Exception as e: + print(f"An error occurred: {e}") + + +if __name__ == "__main__": + asyncio.run(main()) diff --git a/LightRAG/examples/unofficial-sample/lightrag_openai_neo4j_milvus_redis_demo.py b/LightRAG/examples/unofficial-sample/lightrag_openai_neo4j_milvus_redis_demo.py new file mode 100644 index 00000000..00845796 --- /dev/null +++ b/LightRAG/examples/unofficial-sample/lightrag_openai_neo4j_milvus_redis_demo.py @@ -0,0 +1,112 @@ +import os +import asyncio +from lightrag import LightRAG, QueryParam +from lightrag.llm.ollama import ollama_embed, openai_complete_if_cache +from lightrag.utils import EmbeddingFunc +from lightrag.kg.shared_storage import initialize_pipeline_status + +# WorkingDir +ROOT_DIR = os.path.dirname(os.path.abspath(__file__)) +WORKING_DIR = os.path.join(ROOT_DIR, "myKG") +if not os.path.exists(WORKING_DIR): + os.mkdir(WORKING_DIR) +print(f"WorkingDir: {WORKING_DIR}") + +# redis +os.environ["REDIS_URI"] = "redis://localhost:6379" + +# neo4j +BATCH_SIZE_NODES = 500 +BATCH_SIZE_EDGES = 100 +os.environ["NEO4J_URI"] = "neo4j://localhost:7687" +os.environ["NEO4J_USERNAME"] = "neo4j" +os.environ["NEO4J_PASSWORD"] = "12345678" + +# milvus +os.environ["MILVUS_URI"] = "http://localhost:19530" +os.environ["MILVUS_USER"] = "root" +os.environ["MILVUS_PASSWORD"] = "Milvus" +os.environ["MILVUS_DB_NAME"] = "lightrag" + + +async def llm_model_func( + prompt, system_prompt=None, history_messages=[], keyword_extraction=False, **kwargs +) -> str: + return await openai_complete_if_cache( + "deepseek-chat", + prompt, + system_prompt=system_prompt, + history_messages=history_messages, + api_key="", + base_url="", + **kwargs, + ) + + +embedding_func = EmbeddingFunc( + embedding_dim=768, + max_token_size=512, + func=lambda texts: ollama_embed( + texts, embed_model="shaw/dmeta-embedding-zh", host="http://117.50.173.35:11434" + ), +) + + +async def initialize_rag(): + rag = LightRAG( + working_dir=WORKING_DIR, + llm_model_func=llm_model_func, + summary_max_tokens=10000, + embedding_func=embedding_func, + chunk_token_size=512, + chunk_overlap_token_size=256, + kv_storage="RedisKVStorage", + graph_storage="Neo4JStorage", + vector_storage="MilvusVectorDBStorage", + doc_status_storage="RedisKVStorage", + ) + + await rag.initialize_storages() + await initialize_pipeline_status() + + return rag + + +def main(): + # Initialize RAG instance + rag = asyncio.run(initialize_rag()) + + with open("./book.txt", "r", encoding="utf-8") as f: + rag.insert(f.read()) + + # Perform naive search + print( + rag.query( + "What are the top themes in this story?", param=QueryParam(mode="naive") + ) + ) + + # Perform local search + print( + rag.query( + "What are the top themes in this story?", param=QueryParam(mode="local") + ) + ) + + # Perform global search + print( + rag.query( + "What are the top themes in this story?", param=QueryParam(mode="global") + ) + ) + + # Perform hybrid search + print( + rag.query( + "What are the top themes in this story?", param=QueryParam(mode="hybrid") + ) + ) + + +if __name__ == "__main__": + main() diff --git a/LightRAG/k8s-deploy/README-zh.md b/LightRAG/k8s-deploy/README-zh.md new file mode 100644 index 00000000..e5eb697d --- /dev/null +++ b/LightRAG/k8s-deploy/README-zh.md @@ -0,0 +1,191 @@ +# LightRAG Helm Chart + +这是用于在Kubernetes集群上部署LightRAG服务的Helm chart。 + +LightRAG有两种推荐的部署方法： +1. **轻量级部署**：使用内置轻量级存储，适合测试和小规模使用 +2. **生产环境部署**：使用外部数据库（如PostgreSQL和Neo4J），适合生产环境和大规模使用 + +> 如果您想要部署过程的视频演示，可以查看[bilibili](https://www.bilibili.com/video/BV1bUJazBEq2/)上的视频教程，对于喜欢视觉指导的用户可能会有所帮助。 + +## 前提条件 + +确保安装和配置了以下工具： + +* **Kubernetes集群** + * 需要一个运行中的Kubernetes集群。 + * 对于本地开发或演示，可以使用[Minikube](https://minikube.sigs.k8s.io/docs/start/)（需要≥2个CPU，≥4GB内存，以及Docker/VM驱动支持）。 + * 任何标准的云端或本地Kubernetes集群（EKS、GKE、AKS等）也可以使用。 + +* **kubectl** + * Kubernetes命令行工具，用于管理集群。 + * 按照官方指南安装：[安装和设置kubectl](https://kubernetes.io/docs/tasks/tools/#kubectl)。 + +* **Helm**（v3.x+） + * Kubernetes包管理器，用于安装LightRAG。 + * 通过官方指南安装：[安装Helm](https://helm.sh/docs/intro/install/)。 + +## 轻量级部署（无需外部数据库） + +这种部署选项使用内置的轻量级存储组件，非常适合测试、演示或小规模使用场景。无需外部数据库配置。 + +您可以使用提供的便捷脚本或直接使用Helm命令部署LightRAG。两种方法都配置了`lightrag/values.yaml`文件中定义的相同环境变量。 + +### 使用便捷脚本（推荐）： + +```bash +export OPENAI_API_BASE=<您的OPENAI_API_BASE> +export OPENAI_API_KEY=<您的OPENAI_API_KEY> +bash ./install_lightrag_dev.sh +``` + +### 或直接使用Helm： + +```bash +# 您可以覆盖任何想要的环境参数 +helm upgrade --install lightrag ./lightrag \ + --namespace rag \ + --set-string env.LIGHTRAG_KV_STORAGE=JsonKVStorage \ + --set-string env.LIGHTRAG_VECTOR_STORAGE=NanoVectorDBStorage \ + --set-string env.LIGHTRAG_GRAPH_STORAGE=NetworkXStorage \ + --set-string env.LIGHTRAG_DOC_STATUS_STORAGE=JsonDocStatusStorage \ + --set-string env.LLM_BINDING=openai \ + --set-string env.LLM_MODEL=gpt-4o-mini \ + --set-string env.LLM_BINDING_HOST=$OPENAI_API_BASE \ + --set-string env.LLM_BINDING_API_KEY=$OPENAI_API_KEY \ + --set-string env.EMBEDDING_BINDING=openai \ + --set-string env.EMBEDDING_MODEL=text-embedding-ada-002 \ + --set-string env.EMBEDDING_DIM=1536 \ + --set-string env.EMBEDDING_BINDING_API_KEY=$OPENAI_API_KEY +``` + +### 访问应用程序： + +```bash +# 1. 在终端中运行此端口转发命令： +kubectl --namespace rag port-forward svc/lightrag-dev 9621:9621 + +# 2. 当命令运行时，打开浏览器并导航到： +# http://localhost:9621 +``` + +## 生产环境部署（使用外部数据库） + +### 1. 安装数据库 +> 如果您已经准备好了数据库，可以跳过此步骤。详细信息可以在：[README.md](databases%2FREADME.md)中找到。 + +我们推荐使用KubeBlocks进行数据库部署。KubeBlocks是一个云原生数据库操作符，可以轻松地在Kubernetes上以生产规模运行任何数据库。 + +首先，安装KubeBlocks和KubeBlocks-Addons（如已安装可跳过）： +```bash +bash ./databases/01-prepare.sh +``` + +然后安装所需的数据库。默认情况下，这将安装PostgreSQL和Neo4J，但您可以修改[00-config.sh](databases%2F00-config.sh)以根据需要选择不同的数据库： +```bash +bash ./databases/02-install-database.sh +``` + +验证集群是否正在运行： +```bash +kubectl get clusters -n rag +# 预期输出： +# NAME CLUSTER-DEFINITION TERMINATION-POLICY STATUS AGE +# neo4j-cluster Delete Running 39s +# pg-cluster postgresql Delete Running 42s + +kubectl get po -n rag +# 预期输出： +# NAME READY STATUS RESTARTS AGE +# neo4j-cluster-neo4j-0 1/1 Running 0 58s +# pg-cluster-postgresql-0 4/4 Running 0 59s +# pg-cluster-postgresql-1 4/4 Running 0 59s +``` + +### 2. 安装LightRAG + +LightRAG及其数据库部署在同一Kubernetes集群中，使配置变得简单。 +安装脚本会自动从KubeBlocks获取所有数据库连接信息，无需手动设置数据库凭证： + +```bash +export OPENAI_API_BASE=<您的OPENAI_API_BASE> +export OPENAI_API_KEY=<您的OPENAI_API_KEY> +bash ./install_lightrag.sh +``` + +### 访问应用程序： + +```bash +# 1. 在终端中运行此端口转发命令： +kubectl --namespace rag port-forward svc/lightrag 9621:9621 + +# 2. 当命令运行时，打开浏览器并导航到： +# http://localhost:9621 +``` + +## 配置 + +### 修改资源配置 + +您可以通过修改`values.yaml`文件来配置LightRAG的资源使用： + +```yaml +replicaCount: 1 # 副本数量，可根据需要增加 + +resources: + limits: + cpu: 1000m # CPU限制，可根据需要调整 + memory: 2Gi # 内存限制，可根据需要调整 + requests: + cpu: 500m # CPU请求，可根据需要调整 + memory: 1Gi # 内存请求，可根据需要调整 +``` + +### 修改持久存储 + +```yaml +persistence: + enabled: true + ragStorage: + size: 10Gi # RAG存储大小，可根据需要调整 + inputs: + size: 5Gi # 输入数据存储大小，可根据需要调整 +``` + +### 配置环境变量 + +`values.yaml`文件中的`env`部分包含LightRAG的所有环境配置，类似于`.env`文件。当使用helm upgrade或helm install命令时，可以使用--set标志覆盖这些变量。 + +```yaml +env: + HOST: 0.0.0.0 + PORT: 9621 + WEBUI_TITLE: Graph RAG Engine + WEBUI_DESCRIPTION: Simple and Fast Graph Based RAG System + + # LLM配置 + LLM_BINDING: openai # LLM服务提供商 + LLM_MODEL: gpt-4o-mini # LLM模型 + LLM_BINDING_HOST: # API基础URL（可选） + LLM_BINDING_API_KEY: # API密钥 + + # 嵌入配置 + EMBEDDING_BINDING: openai # 嵌入服务提供商 + EMBEDDING_MODEL: text-embedding-ada-002 # 嵌入模型 + EMBEDDING_DIM: 1536 # 嵌入维度 + EMBEDDING_BINDING_API_KEY: # API密钥 + + # 存储配置 + LIGHTRAG_KV_STORAGE: PGKVStorage # 键值存储类型 + LIGHTRAG_VECTOR_STORAGE: PGVectorStorage # 向量存储类型 + LIGHTRAG_GRAPH_STORAGE: Neo4JStorage # 图存储类型 + LIGHTRAG_DOC_STATUS_STORAGE: PGDocStatusStorage # 文档状态存储类型 +``` + +## 注意事项 + +- 在部署前确保设置了所有必要的环境变量（API密钥和数据库密码） +- 出于安全原因，建议使用环境变量传递敏感信息，而不是直接写入脚本或values文件 +- 轻量级部署适合测试和小规模使用，但数据持久性和性能可能有限 +- 生产环境部署（PostgreSQL + Neo4J）推荐用于生产环境和大规模使用 +- 有关更多自定义配置，请参考LightRAG官方文档 diff --git a/LightRAG/k8s-deploy/README.md b/LightRAG/k8s-deploy/README.md new file mode 100644 index 00000000..cc1f2720 --- /dev/null +++ b/LightRAG/k8s-deploy/README.md @@ -0,0 +1,191 @@ +# LightRAG Helm Chart + +This is the Helm chart for LightRAG, used to deploy LightRAG services on a Kubernetes cluster. + +There are two recommended deployment methods for LightRAG: +1. **Lightweight Deployment**: Using built-in lightweight storage, suitable for testing and small-scale usage +2. **Production Deployment**: Using external databases (such as PostgreSQL and Neo4J), suitable for production environments and large-scale usage + +> If you'd like a video walkthrough of the deployment process, feel free to check out this optional [video tutorial](https://youtu.be/JW1z7fzeKTw?si=vPzukqqwmdzq9Q4q) on YouTube. It might help clarify some steps for those who prefer visual guidance. + +## Prerequisites + +Make sure the following tools are installed and configured: + +* **Kubernetes cluster** + * A running Kubernetes cluster is required. + * For local development or demos you can use [Minikube](https://minikube.sigs.k8s.io/docs/start/) (needs ≥ 2 CPUs, ≥ 4 GB RAM, and Docker/VM-driver support). + * Any standard cloud or on-premises Kubernetes cluster (EKS, GKE, AKS, etc.) also works. + +* **kubectl** + * The Kubernetes command-line tool for managing your cluster. + * Follow the official guide: [Install and Set Up kubectl](https://kubernetes.io/docs/tasks/tools/#kubectl). + +* **Helm** (v3.x+) + * Kubernetes package manager used to install LightRAG. + * Install it via the official instructions: [Installing Helm](https://helm.sh/docs/intro/install/). + +## Lightweight Deployment (No External Databases Required) + +This deployment option uses built-in lightweight storage components that are perfect for testing, demos, or small-scale usage scenarios. No external database configuration is required. + +You can deploy LightRAG using either the provided convenience script or direct Helm commands. Both methods configure the same environment variables defined in the `lightrag/values.yaml` file. + +### Using the convenience script (recommended): + +```bash +export OPENAI_API_BASE= +export OPENAI_API_KEY= +bash ./install_lightrag_dev.sh +``` + +### Or using Helm directly: + +```bash +# You can override any env param you want +helm upgrade --install lightrag ./lightrag \ + --namespace rag \ + --set-string env.LIGHTRAG_KV_STORAGE=JsonKVStorage \ + --set-string env.LIGHTRAG_VECTOR_STORAGE=NanoVectorDBStorage \ + --set-string env.LIGHTRAG_GRAPH_STORAGE=NetworkXStorage \ + --set-string env.LIGHTRAG_DOC_STATUS_STORAGE=JsonDocStatusStorage \ + --set-string env.LLM_BINDING=openai \ + --set-string env.LLM_MODEL=gpt-4o-mini \ + --set-string env.LLM_BINDING_HOST=$OPENAI_API_BASE \ + --set-string env.LLM_BINDING_API_KEY=$OPENAI_API_KEY \ + --set-string env.EMBEDDING_BINDING=openai \ + --set-string env.EMBEDDING_MODEL=text-embedding-ada-002 \ + --set-string env.EMBEDDING_DIM=1536 \ + --set-string env.EMBEDDING_BINDING_API_KEY=$OPENAI_API_KEY +``` + +### Accessing the application: + +```bash +# 1. Run this port-forward command in your terminal: +kubectl --namespace rag port-forward svc/lightrag-dev 9621:9621 + +# 2. While the command is running, open your browser and navigate to: +# http://localhost:9621 +``` + +## Production Deployment (Using External Databases) + +### 1. Install Databases +> You can skip this step if you've already prepared databases. Detailed information can be found in: [README.md](databases%2FREADME.md). + +We recommend KubeBlocks for database deployment. KubeBlocks is a cloud-native database operator that makes it easy to run any database on Kubernetes at production scale. + +First, install KubeBlocks and KubeBlocks-Addons (skip if already installed): +```bash +bash ./databases/01-prepare.sh +``` + +Then install the required databases. By default, this will install PostgreSQL and Neo4J, but you can modify [00-config.sh](databases%2F00-config.sh) to select different databases based on your needs: +```bash +bash ./databases/02-install-database.sh +``` + +Verify that the clusters are up and running: +```bash +kubectl get clusters -n rag +# Expected output: +# NAME CLUSTER-DEFINITION TERMINATION-POLICY STATUS AGE +# neo4j-cluster Delete Running 39s +# pg-cluster postgresql Delete Running 42s + +kubectl get po -n rag +# Expected output: +# NAME READY STATUS RESTARTS AGE +# neo4j-cluster-neo4j-0 1/1 Running 0 58s +# pg-cluster-postgresql-0 4/4 Running 0 59s +# pg-cluster-postgresql-1 4/4 Running 0 59s +``` + +### 2. Install LightRAG + +LightRAG and its databases are deployed within the same Kubernetes cluster, making configuration straightforward. +The installation script automatically retrieves all database connection information from KubeBlocks, eliminating the need to manually set database credentials: + +```bash +export OPENAI_API_BASE= +export OPENAI_API_KEY= +bash ./install_lightrag.sh +``` + +### Accessing the application: + +```bash +# 1. Run this port-forward command in your terminal: +kubectl --namespace rag port-forward svc/lightrag 9621:9621 + +# 2. While the command is running, open your browser and navigate to: +# http://localhost:9621 +``` + +## Configuration + +### Modifying Resource Configuration + +You can configure LightRAG's resource usage by modifying the `values.yaml` file: + +```yaml +replicaCount: 1 # Number of replicas, can be increased as needed + +resources: + limits: + cpu: 1000m # CPU limit, can be adjusted as needed + memory: 2Gi # Memory limit, can be adjusted as needed + requests: + cpu: 500m # CPU request, can be adjusted as needed + memory: 1Gi # Memory request, can be adjusted as needed +``` + +### Modifying Persistent Storage + +```yaml +persistence: + enabled: true + ragStorage: + size: 10Gi # RAG storage size, can be adjusted as needed + inputs: + size: 5Gi # Input data storage size, can be adjusted as needed +``` + +### Configuring Environment Variables + +The `env` section in the `values.yaml` file contains all environment configurations for LightRAG, similar to a `.env` file. When using helm upgrade or helm install commands, you can override these with the --set flag. + +```yaml +env: + HOST: 0.0.0.0 + PORT: 9621 + WEBUI_TITLE: Graph RAG Engine + WEBUI_DESCRIPTION: Simple and Fast Graph Based RAG System + + # LLM Configuration + LLM_BINDING: openai # LLM service provider + LLM_MODEL: gpt-4o-mini # LLM model + LLM_BINDING_HOST: # API base URL (optional) + LLM_BINDING_API_KEY: # API key + + # Embedding Configuration + EMBEDDING_BINDING: openai # Embedding service provider + EMBEDDING_MODEL: text-embedding-ada-002 # Embedding model + EMBEDDING_DIM: 1536 # Embedding dimension + EMBEDDING_BINDING_API_KEY: # API key + + # Storage Configuration + LIGHTRAG_KV_STORAGE: PGKVStorage # Key-value storage type + LIGHTRAG_VECTOR_STORAGE: PGVectorStorage # Vector storage type + LIGHTRAG_GRAPH_STORAGE: Neo4JStorage # Graph storage type + LIGHTRAG_DOC_STATUS_STORAGE: PGDocStatusStorage # Document status storage type +``` + +## Notes + +- Ensure all necessary environment variables (API keys and database passwords) are set before deployment +- For security reasons, it's recommended to pass sensitive information using environment variables rather than writing them directly in scripts or values files +- Lightweight deployment is suitable for testing and small-scale usage, but data persistence and performance may be limited +- Production deployment (PostgreSQL + Neo4J) is recommended for production environments and large-scale usage +- For more customized configurations, please refer to the official LightRAG documentation diff --git a/LightRAG/k8s-deploy/databases/00-config.sh b/LightRAG/k8s-deploy/databases/00-config.sh new file mode 100644 index 00000000..9431a3d3 --- /dev/null +++ b/LightRAG/k8s-deploy/databases/00-config.sh @@ -0,0 +1,21 @@ +#!/bin/bash + +# Get the directory where this script is located +DATABASE_SCRIPT_DIR="$( cd "$( dirname "${BASH_SOURCE[0]}" )" &> /dev/null && pwd )" +source "$DATABASE_SCRIPT_DIR/scripts/common.sh" + +# Namespace configuration +NAMESPACE="rag" +# version +KB_VERSION="1.0.0-beta.48" +ADDON_CLUSTER_CHART_VERSION="1.0.0-alpha.0" +# Helm repository +HELM_REPO="https://apecloud.github.io/helm-charts" + +# Set to true to enable the database, false to disable +ENABLE_POSTGRESQL=true +ENABLE_REDIS=false +ENABLE_QDRANT=false +ENABLE_NEO4J=true +ENABLE_ELASTICSEARCH=false +ENABLE_MONGODB=false diff --git a/LightRAG/k8s-deploy/databases/01-prepare.sh b/LightRAG/k8s-deploy/databases/01-prepare.sh new file mode 100644 index 00000000..f43257a4 --- /dev/null +++ b/LightRAG/k8s-deploy/databases/01-prepare.sh @@ -0,0 +1,33 @@ +#!/bin/bash + +# Get the directory where this script is located +DATABASE_SCRIPT_DIR="$( cd "$( dirname "${BASH_SOURCE[0]}" )" &> /dev/null && pwd )" +# Load configuration file +source "$DATABASE_SCRIPT_DIR/00-config.sh" + +check_dependencies + +# Check if KubeBlocks is already installed, install it if it is not. +source "$DATABASE_SCRIPT_DIR/install-kubeblocks.sh" + +# Create namespaces +print "Creating namespaces..." +kubectl create namespace $NAMESPACE 2>/dev/null || true + +# Install database addons +print "Installing KubeBlocks database addons..." + +# Add and update Helm repository +print "Adding and updating KubeBlocks Helm repository..." +helm repo add kubeblocks $HELM_REPO +helm repo update +# Install database addons based on configuration +[ "$ENABLE_POSTGRESQL" = true ] && print "Installing PostgreSQL addon..." && helm upgrade --install kb-addon-postgresql kubeblocks/postgresql --namespace kb-system --version $ADDON_CLUSTER_CHART_VERSION +[ "$ENABLE_REDIS" = true ] && print "Installing Redis addon..." && helm upgrade --install kb-addon-redis kubeblocks/redis --namespace kb-system --version $ADDON_CLUSTER_CHART_VERSION +[ "$ENABLE_ELASTICSEARCH" = true ] && print "Installing Elasticsearch addon..." && helm upgrade --install kb-addon-elasticsearch kubeblocks/elasticsearch --namespace kb-system --version $ADDON_CLUSTER_CHART_VERSION +[ "$ENABLE_QDRANT" = true ] && print "Installing Qdrant addon..." && helm upgrade --install kb-addon-qdrant kubeblocks/qdrant --namespace kb-system --version $ADDON_CLUSTER_CHART_VERSION +[ "$ENABLE_MONGODB" = true ] && print "Installing MongoDB addon..." && helm upgrade --install kb-addon-mongodb kubeblocks/mongodb --namespace kb-system --version $ADDON_CLUSTER_CHART_VERSION +[ "$ENABLE_NEO4J" = true ] && print "Installing Neo4j addon..." && helm upgrade --install kb-addon-neo4j kubeblocks/neo4j --namespace kb-system --version $ADDON_CLUSTER_CHART_VERSION + +print_success "KubeBlocks database addons installation completed!" +print "Now you can run 02-install-database.sh to install database clusters" diff --git a/LightRAG/k8s-deploy/databases/02-install-database.sh b/LightRAG/k8s-deploy/databases/02-install-database.sh new file mode 100644 index 00000000..f19a08d7 --- /dev/null +++ b/LightRAG/k8s-deploy/databases/02-install-database.sh @@ -0,0 +1,62 @@ +#!/bin/bash + +# Get the directory where this script is located +DATABASE_SCRIPT_DIR="$( cd "$( dirname "${BASH_SOURCE[0]}" )" &> /dev/null && pwd )" + +# Load configuration file +source "$DATABASE_SCRIPT_DIR/00-config.sh" + +print "Installing database clusters..." + +# Install database clusters based on configuration +[ "$ENABLE_POSTGRESQL" = true ] && print "Installing PostgreSQL cluster..." && helm upgrade --install pg-cluster kubeblocks/postgresql-cluster -f "$DATABASE_SCRIPT_DIR/postgresql/values.yaml" --namespace $NAMESPACE --version $ADDON_CLUSTER_CHART_VERSION +[ "$ENABLE_REDIS" = true ] && print "Installing Redis cluster..." && helm upgrade --install redis-cluster kubeblocks/redis-cluster -f "$DATABASE_SCRIPT_DIR/redis/values.yaml" --namespace $NAMESPACE --version $ADDON_CLUSTER_CHART_VERSION +[ "$ENABLE_ELASTICSEARCH" = true ] && print "Installing Elasticsearch cluster..." && helm upgrade --install es-cluster kubeblocks/elasticsearch-cluster -f "$DATABASE_SCRIPT_DIR/elasticsearch/values.yaml" --namespace $NAMESPACE --version $ADDON_CLUSTER_CHART_VERSION +[ "$ENABLE_QDRANT" = true ] && print "Installing Qdrant cluster..." && helm upgrade --install qdrant-cluster kubeblocks/qdrant-cluster -f "$DATABASE_SCRIPT_DIR/qdrant/values.yaml" --namespace $NAMESPACE --version $ADDON_CLUSTER_CHART_VERSION +[ "$ENABLE_MONGODB" = true ] && print "Installing MongoDB cluster..." && helm upgrade --install mongodb-cluster kubeblocks/mongodb-cluster -f "$DATABASE_SCRIPT_DIR/mongodb/values.yaml" --namespace $NAMESPACE --version $ADDON_CLUSTER_CHART_VERSION +[ "$ENABLE_NEO4J" = true ] && print "Installing Neo4j cluster..." && helm upgrade --install neo4j-cluster kubeblocks/neo4j-cluster -f "$DATABASE_SCRIPT_DIR/neo4j/values.yaml" --namespace $NAMESPACE --version $ADDON_CLUSTER_CHART_VERSION + +# Wait for databases to be ready +print "Waiting for databases to be ready..." +TIMEOUT=600 # Set timeout to 10 minutes +START_TIME=$(date +%s) + +while true; do + CURRENT_TIME=$(date +%s) + ELAPSED=$((CURRENT_TIME - START_TIME)) + + if [ $ELAPSED -gt $TIMEOUT ]; then + print_error "Timeout waiting for databases to be ready. Please check database status manually and try again" + exit 1 + fi + + # Build wait conditions for enabled databases + WAIT_CONDITIONS=() + [ "$ENABLE_POSTGRESQL" = true ] && WAIT_CONDITIONS+=("kubectl wait --for=condition=ready pods -l app.kubernetes.io/instance=pg-cluster -n $NAMESPACE --timeout=10s") + [ "$ENABLE_REDIS" = true ] && WAIT_CONDITIONS+=("kubectl wait --for=condition=ready pods -l app.kubernetes.io/instance=redis-cluster -n $NAMESPACE --timeout=10s") + [ "$ENABLE_ELASTICSEARCH" = true ] && WAIT_CONDITIONS+=("kubectl wait --for=condition=ready pods -l app.kubernetes.io/instance=es-cluster -n $NAMESPACE --timeout=10s") + [ "$ENABLE_QDRANT" = true ] && WAIT_CONDITIONS+=("kubectl wait --for=condition=ready pods -l app.kubernetes.io/instance=qdrant-cluster -n $NAMESPACE --timeout=10s") + [ "$ENABLE_MONGODB" = true ] && WAIT_CONDITIONS+=("kubectl wait --for=condition=ready pods -l app.kubernetes.io/instance=mongodb-cluster -n $NAMESPACE --timeout=10s") + [ "$ENABLE_NEO4J" = true ] && WAIT_CONDITIONS+=("kubectl wait --for=condition=ready pods -l app.kubernetes.io/instance=neo4j-cluster -n $NAMESPACE --timeout=10s") + + # Check if all enabled databases are ready + ALL_READY=true + for CONDITION in "${WAIT_CONDITIONS[@]}"; do + if ! eval "$CONDITION &> /dev/null"; then + ALL_READY=false + break + fi + done + + if [ "$ALL_READY" = true ]; then + print "All database pods are ready, continuing with deployment..." + break + fi + + print "Waiting for database pods to be ready (${ELAPSED}s elapsed)..." + sleep 10 +done + +print_success "Database clusters installation completed!" +print "Use the following command to check the status of installed clusters:" +print "kubectl get clusters -n $NAMESPACE" diff --git a/LightRAG/k8s-deploy/databases/03-uninstall-database.sh b/LightRAG/k8s-deploy/databases/03-uninstall-database.sh new file mode 100644 index 00000000..be0503c5 --- /dev/null +++ b/LightRAG/k8s-deploy/databases/03-uninstall-database.sh @@ -0,0 +1,20 @@ +#!/bin/bash + +# Get the directory where this script is located +DATABASE_SCRIPT_DIR="$( cd "$( dirname "${BASH_SOURCE[0]}" )" &> /dev/null && pwd )" + +# Load configuration file +source "$DATABASE_SCRIPT_DIR/00-config.sh" + +print "Uninstalling database clusters..." + +# Uninstall database clusters based on configuration +[ "$ENABLE_POSTGRESQL" = true ] && print "Uninstalling PostgreSQL cluster..." && helm uninstall pg-cluster --namespace $NAMESPACE 2>/dev/null || true +[ "$ENABLE_REDIS" = true ] && print "Uninstalling Redis cluster..." && helm uninstall redis-cluster --namespace $NAMESPACE 2>/dev/null || true +[ "$ENABLE_ELASTICSEARCH" = true ] && print "Uninstalling Elasticsearch cluster..." && helm uninstall es-cluster --namespace $NAMESPACE 2>/dev/null || true +[ "$ENABLE_QDRANT" = true ] && print "Uninstalling Qdrant cluster..." && helm uninstall qdrant-cluster --namespace $NAMESPACE 2>/dev/null || true +[ "$ENABLE_MONGODB" = true ] && print "Uninstalling MongoDB cluster..." && helm uninstall mongodb-cluster --namespace $NAMESPACE 2>/dev/null || true +[ "$ENABLE_NEO4J" = true ] && print "Uninstalling Neo4j cluster..." && helm uninstall neo4j-cluster --namespace $NAMESPACE 2>/dev/null || true + +print_success "Database clusters uninstalled" +print "To uninstall database addons and KubeBlocks, run 04-cleanup.sh" diff --git a/LightRAG/k8s-deploy/databases/04-cleanup.sh b/LightRAG/k8s-deploy/databases/04-cleanup.sh new file mode 100644 index 00000000..12493501 --- /dev/null +++ b/LightRAG/k8s-deploy/databases/04-cleanup.sh @@ -0,0 +1,26 @@ +#!/bin/bash + +# Get the directory where this script is located +DATABASE_SCRIPT_DIR="$( cd "$( dirname "${BASH_SOURCE[0]}" )" &> /dev/null && pwd )" + +# Load configuration file +source "$DATABASE_SCRIPT_DIR/00-config.sh" + +print "Uninstalling KubeBlocks database addons..." + +# Uninstall database addons based on configuration +[ "$ENABLE_POSTGRESQL" = true ] && print "Uninstalling PostgreSQL addon..." && helm uninstall kb-addon-postgresql --namespace kb-system 2>/dev/null || true +[ "$ENABLE_REDIS" = true ] && print "Uninstalling Redis addon..." && helm uninstall kb-addon-redis --namespace kb-system 2>/dev/null || true +[ "$ENABLE_ELASTICSEARCH" = true ] && print "Uninstalling Elasticsearch addon..." && helm uninstall kb-addon-elasticsearch --namespace kb-system 2>/dev/null || true +[ "$ENABLE_QDRANT" = true ] && print "Uninstalling Qdrant addon..." && helm uninstall kb-addon-qdrant --namespace kb-system 2>/dev/null || true +[ "$ENABLE_MONGODB" = true ] && print "Uninstalling MongoDB addon..." && helm uninstall kb-addon-mongodb --namespace kb-system 2>/dev/null || true +[ "$ENABLE_NEO4J" = true ] && print "Uninstalling Neo4j addon..." && helm uninstall kb-addon-neo4j --namespace kb-system 2>/dev/null || true + +print_success "Database addons uninstallation completed!" + +source "$DATABASE_SCRIPT_DIR/uninstall-kubeblocks.sh" + +kubectl delete namespace $NAMESPACE +kubectl delete namespace kb-system + +print_success "KubeBlocks uninstallation completed!" diff --git a/LightRAG/k8s-deploy/databases/README.md b/LightRAG/k8s-deploy/databases/README.md new file mode 100644 index 00000000..1e0002d0 --- /dev/null +++ b/LightRAG/k8s-deploy/databases/README.md @@ -0,0 +1,170 @@ +# Using KubeBlocks to Deploy and Manage Databases + +Learn how to quickly deploy and manage various databases in a Kubernetes (K8s) environment through KubeBlocks. + +## Introduction to KubeBlocks + +KubeBlocks is a production-ready, open-source toolkit that runs any database--SQL, NoSQL, vector, or document--on Kubernetes. +It scales smoothly from quick dev tests to full production clusters, making it a solid choice for RAG workloads like FastGPT that need several data stores working together. + +## Prerequisites + +Make sure the following tools are installed and configured: + +* **Kubernetes cluster** + * A running Kubernetes cluster is required. + * For local development or demos you can use [Minikube](https://minikube.sigs.k8s.io/docs/start/) (needs ≥ 2 CPUs, ≥ 4 GB RAM, and Docker/VM-driver support). + * Any standard cloud or on-premises Kubernetes cluster (EKS, GKE, AKS, etc.) also works. + +* **kubectl** + * The Kubernetes command-line interface. + * Follow the official guide: [Install and Set Up kubectl](https://kubernetes.io/docs/tasks/tools/#kubectl). + +* **Helm** (v3.x+) + * Kubernetes package manager used by the scripts below. + * Install it via the official instructions: [Installing Helm](https://helm.sh/docs/intro/install/). + +## Installing + +1. **Configure the databases you want** + Edit `00-config.sh` file. Based on your requirements, set the variable to `true` for the databases you want to install. + For example, to install PostgreSQL and Neo4j: + + ```bash + ENABLE_POSTGRESQL=true + ENABLE_REDIS=false + ENABLE_ELASTICSEARCH=false + ENABLE_QDRANT=false + ENABLE_MONGODB=false + ENABLE_NEO4J=true + ``` + +2. **Prepare the environment and install KubeBlocks add-ons** + + ```bash + bash ./01-prepare.sh + ``` + + *What the script does* + `01-prepare.sh` performs basic pre-checks (Helm, kubectl, cluster reachability), adds the KubeBlocks Helm repo, and installs any core CRDs or controllers that KubeBlocks itself needs. It also installs the addons for every database you enabled in `00-config.sh`, but **does not** create the actual database clusters yet. + +3. **(Optional) Modify database settings** + Before deployment you can edit the `values.yaml` file inside each `/` directory to change `version`, `replicas`, `CPU`, `memory`, `storage size`, etc. + +4. **Install the database clusters** + + ```bash + bash ./02-install-database.sh + ``` + + *What the script does* + `02-install-database.sh` **actually deploys the chosen databases to Kubernetes**. + + When the script completes, confirm that the clusters are up. It may take a few minutes for all the clusters to become ready, + especially if this is the first time running the script as Kubernetes needs to pull container images from registries. + You can monitor the progress using the following commands: + + ```bash + kubectl get clusters -n rag + NAME CLUSTER-DEFINITION TERMINATION-POLICY STATUS AGE + es-cluster Delete Running 11m + mongodb-cluster mongodb Delete Running 11m + pg-cluster postgresql Delete Running 11m + qdrant-cluster qdrant Delete Running 11m + redis-cluster redis Delete Running 11m + ``` + + You can see all the Database `Pods` created by KubeBlocks. + Initially, you might see pods in `ContainerCreating` or `Pending` status - this is normal while images are being pulled and containers are starting up. + Wait until all pods show `Running` status: + + ```bash + kubectl get po -n rag + NAME READY STATUS RESTARTS AGE + es-cluster-mdit-0 2/2 Running 0 11m + mongodb-cluster-mongodb-0 2/2 Running 0 11m + pg-cluster-postgresql-0 4/4 Running 0 11m + pg-cluster-postgresql-1 4/4 Running 0 11m + qdrant-cluster-qdrant-0 2/2 Running 0 11m + redis-cluster-redis-0 2/2 Running 0 11m + ``` + + You can also check the detailed status of a specific pod if it's taking longer than expected: + + ```bash + kubectl describe pod -n rag + ``` + +## Connect to Databases + +To connect to your databases, follow these steps to identify available accounts, retrieve credentials, and establish connections: + +### 1. List Available Database Clusters + +First, view the database clusters running in your namespace: + +```bash +kubectl get cluster -n rag +``` + +### 2. Retrieve Authentication Credentials + +For PostgreSQL, retrieve the username and password from Kubernetes secrets: + +```bash +# Get PostgreSQL username +kubectl get secrets -n rag pg-cluster-postgresql-account-postgres -o jsonpath='{.data.username}' | base64 -d +# Get PostgreSQL password +kubectl get secrets -n rag pg-cluster-postgresql-account-postgres -o jsonpath='{.data.password}' | base64 -d +``` + +If you have trouble finding the correct secret name, list all secrets: + +```bash +kubectl get secrets -n rag +``` + +### 3. Port Forward to Local Machine + +Use port forwarding to access PostgreSQL from your local machine: + +```bash +# Forward PostgreSQL port (5432) to your local machine +# You can see all services with: kubectl get svc -n rag +kubectl port-forward -n rag svc/pg-cluster-postgresql-postgresql 5432:5432 +``` + +### 4. Connect Using Database Client + +Now you can connect using your preferred PostgreSQL client with the retrieved credentials: + +```bash +# Example: connecting with psql +export PGUSER=$(kubectl get secrets -n rag pg-cluster-postgresql-account-postgres -o jsonpath='{.data.username}' | base64 -d) +export PGPASSWORD=$(kubectl get secrets -n rag pg-cluster-postgresql-account-postgres -o jsonpath='{.data.password}' | base64 -d) +psql -h localhost -p 5432 -U $PGUSER +``` + +Keep the port-forwarding terminal running while you're connecting to the database. + + +## Uninstalling + +1. **Remove the database clusters** + + ```bash + bash ./03-uninstall-database.sh + ``` + + The script deletes the database clusters that were enabled in `00-config.sh`. + +2. **Clean up KubeBlocks add-ons** + + ```bash + bash ./04-cleanup.sh + ``` + + This removes the addons installed by `01-prepare.sh`. + +## Reference +* [Kubeblocks Documentation](https://kubeblocks.io/docs/preview/user_docs/overview/introduction) diff --git a/LightRAG/k8s-deploy/databases/elasticsearch/values.yaml b/LightRAG/k8s-deploy/databases/elasticsearch/values.yaml new file mode 100644 index 00000000..4563ad2c --- /dev/null +++ b/LightRAG/k8s-deploy/databases/elasticsearch/values.yaml @@ -0,0 +1,36 @@ +## description: The version of ElasticSearch. +## default: 8.8.2 +version: "8.8.2" + +## description: Mode for ElasticSearch +## default: multi-node +## one of: [single-node, multi-node] +mode: single-node + +## description: The number of replicas, for single-node mode, the replicas is 1, for multi-node mode, the default replicas is 3. +## default: 1 +## minimum: 1 +## maximum: 5 +replicas: 1 + +## description: CPU cores. +## default: 1 +## minimum: 0.5 +## maximum: 64 +cpu: 1 + +## description: Memory, the unit is Gi. +## default: 2 +## minimum: 1 +## maximum: 1000 +memory: 2 + +## description: Storage size, the unit is Gi. +## default: 20 +## minimum: 1 +## maximum: 10000 +storage: 5 + +extra: + terminationPolicy: Delete + disableExporter: true diff --git a/LightRAG/k8s-deploy/databases/install-kubeblocks.sh b/LightRAG/k8s-deploy/databases/install-kubeblocks.sh new file mode 100644 index 00000000..838413ac --- /dev/null +++ b/LightRAG/k8s-deploy/databases/install-kubeblocks.sh @@ -0,0 +1,52 @@ +#!/bin/bash + +# Get the directory where this script is located +DATABASE_SCRIPT_DIR="$( cd "$( dirname "${BASH_SOURCE[0]}" )" &> /dev/null && pwd )" +# Load configuration file +source "$DATABASE_SCRIPT_DIR/00-config.sh" + +# Check dependencies +check_dependencies + +# Function for installing KubeBlocks +install_kubeblocks() { + print "Ready to install KubeBlocks." + + # Install CSI Snapshotter CRDs + kubectl create -f https://raw.githubusercontent.com/kubernetes-csi/external-snapshotter/v8.2.0/client/config/crd/snapshot.storage.k8s.io_volumesnapshotclasses.yaml + kubectl create -f https://raw.githubusercontent.com/kubernetes-csi/external-snapshotter/v8.2.0/client/config/crd/snapshot.storage.k8s.io_volumesnapshots.yaml + kubectl create -f https://raw.githubusercontent.com/kubernetes-csi/external-snapshotter/v8.2.0/client/config/crd/snapshot.storage.k8s.io_volumesnapshotcontents.yaml + + # Add and update Piraeus repository + helm repo add piraeus-charts https://piraeus.io/helm-charts/ + helm repo update + + # Install snapshot controller + helm install snapshot-controller piraeus-charts/snapshot-controller -n kb-system --create-namespace + kubectl wait --for=condition=ready pods -l app.kubernetes.io/name=snapshot-controller -n kb-system --timeout=60s + print_success "snapshot-controller installation complete!" + + # Install KubeBlocks CRDs + kubectl create -f https://github.com/apecloud/kubeblocks/releases/download/v${KB_VERSION}/kubeblocks_crds.yaml + + # Add and update KubeBlocks repository + helm repo add kubeblocks $HELM_REPO + helm repo update + + # Install KubeBlocks + helm install kubeblocks kubeblocks/kubeblocks --namespace kb-system --create-namespace --version=${KB_VERSION} + + # Verify installation + print "Waiting for KubeBlocks to be ready..." + kubectl wait --for=condition=ready pods -l app.kubernetes.io/instance=kubeblocks -n kb-system --timeout=120s + print_success "KubeBlocks installation complete!" +} + +# Check if KubeBlocks is already installed +print "Checking if KubeBlocks is already installed in kb-system namespace..." +if kubectl get namespace kb-system &>/dev/null && kubectl get deployment kubeblocks -n kb-system &>/dev/null; then + print_success "KubeBlocks is already installed in kb-system namespace." +else + # Call the function to install KubeBlocks + install_kubeblocks +fi diff --git a/LightRAG/k8s-deploy/databases/mongodb/values.yaml b/LightRAG/k8s-deploy/databases/mongodb/values.yaml new file mode 100644 index 00000000..e4840a32 --- /dev/null +++ b/LightRAG/k8s-deploy/databases/mongodb/values.yaml @@ -0,0 +1,34 @@ +## description: Cluster version. +## default: 6.0.16 +## one of: [8.0.8, 8.0.6, 8.0.4, 7.0.19, 7.0.16, 7.0.12, 6.0.22, 6.0.20, 6.0.16, 5.0.30, 5.0.28, 4.4.29, 4.2.24, 4.0.28] +version: 6.0.16 + +## description: Cluster topology mode. +## default: standalone +## one of: [standalone, replicaset] +mode: standalone + +## description: CPU cores. +## default: 0.5 +## minimum: 0.5 +## maximum: 64 +cpu: 1 + +## description: Memory, the unit is Gi. +## default: 0.5 +## minimum: 0.5 +## maximum: 1000 +memory: 1 + +## description: Storage size, the unit is Gi. +## default: 20 +## minimum: 1 +## maximum: 10000 +storage: 20 + +## default: enabled +## one of: [enabled, disabled] +hostnetwork: "disabled" + +extra: + terminationPolicy: Delete diff --git a/LightRAG/k8s-deploy/databases/neo4j/values.yaml b/LightRAG/k8s-deploy/databases/neo4j/values.yaml new file mode 100644 index 00000000..2e26479a --- /dev/null +++ b/LightRAG/k8s-deploy/databases/neo4j/values.yaml @@ -0,0 +1,46 @@ +# Version +# description: Cluster version. +# default: 5.26.5 +# one of: [5.26.5, 4.4.42] +version: 5.26.5 + +# Mode +# description: Cluster topology mode. +# default: singlealone +# one of: [singlealone] +mode: singlealone + +# CPU +# description: CPU cores. +# default: 2 +# minimum: 2 +# maximum: 64 +cpu: 2 + +# Memory(Gi) +# description: Memory, the unit is Gi. +# default: 2 +# minimum: 2 +# maximum: 1000 +memory: 4 + +# Storage(Gi) +# description: Storage size, the unit is Gi. +# default: 20 +# minimum: 1 +# maximum: 10000 +storage: 20 + +# Replicas +# description: The number of replicas, for standalone mode, the replicas is 1, for replicaset mode, the default replicas is 3. +# default: 1 +# minimum: 1 +# maximum: 5 +replicas: 1 + +# Storage Class Name +# description: Storage class name of the data volume +storageClassName: "" + +extra: + terminationPolicy: Delete diff --git a/LightRAG/k8s-deploy/databases/postgresql/values.yaml b/LightRAG/k8s-deploy/databases/postgresql/values.yaml new file mode 100644 index 00000000..d706593b --- /dev/null +++ b/LightRAG/k8s-deploy/databases/postgresql/values.yaml @@ -0,0 +1,33 @@ +## description: service version. +## default: 15.7.0 +version: 16.4.0 + +## mode postgresql cluster topology mode replication +mode: replication + +## description: The number of replicas, for standalone mode, the replicas is 1, for replication mode, the default replicas is 2. +## default: 1 +## minimum: 1 +## maximum: 5 +replicas: 2 + +## description: CPU cores. +## default: 0.5 +## minimum: 0.5 +## maximum: 64 +cpu: 1 + +## description: Memory, the unit is Gi. +## default: 0.5 +## minimum: 0.5 +## maximum: 1000 +memory: 1 + +## description: Storage size, the unit is Gi. +## default: 20 +## minimum: 1 +## maximum: 10000 +storage: 5 + +## terminationPolicy define Cluster termination policy. One of DoNotTerminate, Delete, WipeOut. +terminationPolicy: Delete diff --git a/LightRAG/k8s-deploy/databases/qdrant/values.yaml b/LightRAG/k8s-deploy/databases/qdrant/values.yaml new file mode 100644 index 00000000..c49d9960 --- /dev/null +++ b/LightRAG/k8s-deploy/databases/qdrant/values.yaml @@ -0,0 +1,31 @@ +## description: The version of Qdrant. +## default: 1.10.0 +version: 1.10.0 + +## description: The number of replicas. +## default: 1 +## minimum: 1 +## maximum: 16 +replicas: 1 + +## description: CPU cores. +## default: 1 +## minimum: 0.5 +## maximum: 64 +cpu: 1 + +## description: Memory, the unit is Gi. +## default: 2 +## minimum: 0.5 +## maximum: 1000 +memory: 1 + +## description: Storage size, the unit is Gi. +## default: 20 +## minimum: 1 +## maximum: 10000 +storage: 20 + +## customized default values to override kblib chart's values +extra: + terminationPolicy: Delete diff --git a/LightRAG/k8s-deploy/databases/redis/values.yaml b/LightRAG/k8s-deploy/databases/redis/values.yaml new file mode 100644 index 00000000..356e35f7 --- /dev/null +++ b/LightRAG/k8s-deploy/databases/redis/values.yaml @@ -0,0 +1,34 @@ +## description: Cluster version. +## default: 7.2.7 +version: 7.2.7 + +## description: Cluster topology mode. +## default: replication +## one of: [standalone, replication, cluster, replication-twemproxy] +mode: standalone + +## description: The number of replicas, for standalone mode, the replicas is 1, for replication mode, the default replicas is 2. +## default: 1 +## minimum: 1 +## maximum: 5 +replicas: 1 + +## description: CPU cores. +## default: 0.5 +## minimum: 0.5 +## maximum: 64 +cpu: 0.5 + +## description: Memory, the unit is Gi. +## default: 0.5 +## minimum: 0.5 +## maximum: 1000 +memory: 1 + +## description: Storage size, the unit is Gi. +## default: 20 +## minimum: 1 +## maximum: 10000 +storage: 20 +extra: + disableExporter: true diff --git a/LightRAG/k8s-deploy/databases/scripts/common.sh b/LightRAG/k8s-deploy/databases/scripts/common.sh new file mode 100644 index 00000000..3ffb4b08 --- /dev/null +++ b/LightRAG/k8s-deploy/databases/scripts/common.sh @@ -0,0 +1,43 @@ +#!/bin/bash + +print_title() { + echo "============================================" + echo "$1" + echo "============================================" +} + +print_success() { + echo "✅ $1" +} + +print_error() { + echo "❌ $1" +} + +print_warning() { + echo "⚠️ $1" +} + +print_info() { + echo "🔹 $1" +} + +print() { + echo "$1" +} + +# Check dependencies +check_dependencies(){ + print "Checking dependencies..." + command -v kubectl >/dev/null 2>&1 || { print "Error: kubectl command not found"; exit 1; } + command -v helm >/dev/null 2>&1 || { print "Error: helm command not found"; exit 1; } + + # Check if Kubernetes is available + print "Checking if Kubernetes is available..." + kubectl cluster-info &>/dev/null + if [ $? -ne 0 ]; then + print "Error: Kubernetes cluster is not accessible. Please ensure you have proper access to a Kubernetes cluster." + exit 1 + fi + print_success "Kubernetes cluster is accessible." +} diff --git a/LightRAG/k8s-deploy/databases/uninstall-kubeblocks.sh b/LightRAG/k8s-deploy/databases/uninstall-kubeblocks.sh new file mode 100644 index 00000000..3b5ffd1f --- /dev/null +++ b/LightRAG/k8s-deploy/databases/uninstall-kubeblocks.sh @@ -0,0 +1,51 @@ +#!/bin/bash + +# Get the directory where this script is located +DATABASE_SCRIPT_DIR="$( cd "$( dirname "${BASH_SOURCE[0]}" )" &> /dev/null && pwd )" +# Load configuration file +source "$DATABASE_SCRIPT_DIR/00-config.sh" + +# Check dependencies +print "Checking dependencies..." +command -v kubectl >/dev/null 2>&1 || { print "Error: kubectl command not found"; exit 1; } +command -v helm >/dev/null 2>&1 || { print "Error: helm command not found"; exit 1; } + +print "Checking if Kubernetes is available..." +if ! kubectl cluster-info &>/dev/null; then + print "Error: Kubernetes cluster is not accessible. Please ensure you have proper access to a Kubernetes cluster." + exit 1 +fi + +print "Checking if KubeBlocks is installed in kb-system namespace..." +if ! kubectl get namespace kb-system &>/dev/null; then + print "KubeBlocks is not installed in kb-system namespace." + exit 0 +fi + +# Function for uninstalling KubeBlocks +uninstall_kubeblocks() { + print "Uninstalling KubeBlocks..." + + # Uninstall KubeBlocks Helm chart + helm uninstall kubeblocks -n kb-system + + # Uninstall snapshot controller + helm uninstall snapshot-controller -n kb-system + + # Delete KubeBlocks CRDs + kubectl delete -f https://github.com/apecloud/kubeblocks/releases/download/v${KB_VERSION}/kubeblocks_crds.yaml --ignore-not-found=true + + # Delete CSI Snapshotter CRDs + kubectl delete -f https://raw.githubusercontent.com/kubernetes-csi/external-snapshotter/v8.2.0/client/config/crd/snapshot.storage.k8s.io_volumesnapshotclasses.yaml --ignore-not-found=true + kubectl delete -f https://raw.githubusercontent.com/kubernetes-csi/external-snapshotter/v8.2.0/client/config/crd/snapshot.storage.k8s.io_volumesnapshots.yaml --ignore-not-found=true + kubectl delete -f https://raw.githubusercontent.com/kubernetes-csi/external-snapshotter/v8.2.0/client/config/crd/snapshot.storage.k8s.io_volumesnapshotcontents.yaml --ignore-not-found=true + + # Delete the kb-system namespace + print "Waiting for resources to be removed..." + kubectl delete namespace kb-system --timeout=180s + + print "KubeBlocks has been successfully uninstalled!" +} + +# Call the function to uninstall KubeBlocks +uninstall_kubeblocks diff --git a/LightRAG/k8s-deploy/install_lightrag.sh b/LightRAG/k8s-deploy/install_lightrag.sh new file mode 100644 index 00000000..763521e7 --- /dev/null +++ b/LightRAG/k8s-deploy/install_lightrag.sh @@ -0,0 +1,95 @@ +#!/bin/bash + +NAMESPACE=rag + +SCRIPT_DIR="$( cd "$( dirname "${BASH_SOURCE[0]}" )" &> /dev/null && pwd )" + +if [ -z "$OPENAI_API_KEY" ]; then + echo "OPENAI_API_KEY environment variable is not set" + read -s -p "Enter your OpenAI API key: " OPENAI_API_KEY + if [ -z "$OPENAI_API_KEY" ]; then + echo "Error: OPENAI_API_KEY must be provided" + exit 1 + fi + export OPENAI_API_KEY=$OPENAI_API_KEY +fi + +if [ -z "$OPENAI_API_BASE" ]; then + echo "OPENAI_API_BASE environment variable is not set, will use default value" + read -p "Enter OpenAI API base URL (press Enter to skip if not needed): " OPENAI_API_BASE + export OPENAI_API_BASE=$OPENAI_API_BASE +fi + +# Install KubeBlocks (if not already installed) +bash "$SCRIPT_DIR/databases/01-prepare.sh" + +# Install database clusters +bash "$SCRIPT_DIR/databases/02-install-database.sh" + +# Create vector extension in PostgreSQL if enabled +print "Waiting for PostgreSQL pods to be ready..." +if kubectl wait --for=condition=ready pods -l kubeblocks.io/role=primary,app.kubernetes.io/instance=pg-cluster -n $NAMESPACE --timeout=300s; then + print "Creating vector extension in PostgreSQL..." + kubectl exec -it $(kubectl get pods -l kubeblocks.io/role=primary,app.kubernetes.io/instance=pg-cluster -n $NAMESPACE -o name) -n $NAMESPACE -- psql -c "CREATE EXTENSION vector;" + print_success "Vector extension created successfully." +else + print "Warning: PostgreSQL pods not ready within timeout. Vector extension not created." +fi + +# Get database passwords from Kubernetes secrets +echo "Retrieving database credentials from Kubernetes secrets..." +POSTGRES_PASSWORD=$(kubectl get secrets -n rag pg-cluster-postgresql-account-postgres -o jsonpath='{.data.password}' | base64 -d) +if [ -z "$POSTGRES_PASSWORD" ]; then + echo "Error: Could not retrieve PostgreSQL password. Make sure PostgreSQL is deployed and the secret exists." + exit 1 +fi +export POSTGRES_PASSWORD=$POSTGRES_PASSWORD + +NEO4J_PASSWORD=$(kubectl get secrets -n rag neo4j-cluster-neo4j-account-neo4j -o jsonpath='{.data.password}' | base64 -d) +if [ -z "$NEO4J_PASSWORD" ]; then + echo "Error: Could not retrieve Neo4J password. Make sure Neo4J is deployed and the secret exists." + exit 1 +fi +export NEO4J_PASSWORD=$NEO4J_PASSWORD + +#REDIS_PASSWORD=$(kubectl get secrets -n rag redis-cluster-redis-account-default -o jsonpath='{.data.password}' | base64 -d) +#if [ -z "$REDIS_PASSWORD" ]; then +# echo "Error: Could not retrieve Redis password. Make sure Redis is deployed and the secret exists." +# exit 1 +#fi +#export REDIS_PASSWORD=$REDIS_PASSWORD + +echo "Deploying production LightRAG (using external databases)..." + +if ! kubectl get namespace rag &> /dev/null; then + echo "creating namespace 'rag'..." + kubectl create namespace rag +fi + +helm upgrade --install lightrag $SCRIPT_DIR/lightrag \ + --namespace $NAMESPACE \ + --set-string env.POSTGRES_PASSWORD=$POSTGRES_PASSWORD \ + --set-string env.NEO4J_PASSWORD=$NEO4J_PASSWORD \ + --set-string env.LLM_BINDING=openai \ + --set-string env.LLM_MODEL=gpt-4o-mini \ + --set-string env.LLM_BINDING_HOST=$OPENAI_API_BASE \ + --set-string env.LLM_BINDING_API_KEY=$OPENAI_API_KEY \ + --set-string env.EMBEDDING_BINDING=openai \ + --set-string env.EMBEDDING_MODEL=text-embedding-ada-002 \ + --set-string env.EMBEDDING_DIM=1536 \ + --set-string env.EMBEDDING_BINDING_API_KEY=$OPENAI_API_KEY +# --set-string env.REDIS_URI="redis://default:${REDIS_PASSWORD}@redis-cluster-redis-redis:6379" + +# Wait for LightRAG pod to be ready +echo "" +echo "Waiting for lightrag pod to be ready..." +kubectl wait --for=condition=ready pod -l app.kubernetes.io/instance=lightrag --timeout=300s -n rag +echo "lightrag pod is ready" +echo "" +echo "Running Port-Forward:" +echo " kubectl --namespace rag port-forward svc/lightrag 9621:9621" +echo "===========================================" +echo "" +echo "✅ You can visit LightRAG at: http://localhost:9621" +echo "" +kubectl --namespace rag port-forward svc/lightrag 9621:9621 diff --git a/LightRAG/k8s-deploy/install_lightrag_dev.sh b/LightRAG/k8s-deploy/install_lightrag_dev.sh new file mode 100644 index 00000000..3316f8a2 --- /dev/null +++ b/LightRAG/k8s-deploy/install_lightrag_dev.sh @@ -0,0 +1,81 @@ +#!/bin/bash + +NAMESPACE=rag + +SCRIPT_DIR="$( cd "$( dirname "${BASH_SOURCE[0]}" )" &> /dev/null && pwd )" + +check_dependencies(){ + echo "Checking dependencies..." + command -v kubectl >/dev/null 2>&1 || { echo "Error: kubectl command not found"; exit 1; } + command -v helm >/dev/null 2>&1 || { echo "Error: helm command not found"; exit 1; } + + # Check if Kubernetes is available + echo "Checking if Kubernetes is available..." + kubectl cluster-info &>/dev/null + if [ $? -ne 0 ]; then + echo "Error: Kubernetes cluster is not accessible. Please ensure you have proper access to a Kubernetes cluster." + exit 1 + fi + echo "Kubernetes cluster is accessible." +} + +check_dependencies + +if [ -z "$OPENAI_API_KEY" ]; then + echo "OPENAI_API_KEY environment variable is not set" + read -s -p "Enter your OpenAI API key: " OPENAI_API_KEY + if [ -z "$OPENAI_API_KEY" ]; then + echo "Error: OPENAI_API_KEY must be provided" + exit 1 + fi + export OPENAI_API_KEY=$OPENAI_API_KEY +fi + +if [ -z "$OPENAI_API_BASE" ]; then + echo "OPENAI_API_BASE environment variable is not set, will use default value" + read -p "Enter OpenAI API base URL (press Enter to skip if not needed): " OPENAI_API_BASE + export OPENAI_API_BASE=$OPENAI_API_BASE +fi + +required_env_vars=("OPENAI_API_BASE" "OPENAI_API_KEY") + +for var in "${required_env_vars[@]}"; do + if [ -z "${!var}" ]; then + echo "Error: $var environment variable is not set" + exit 1 + fi +done + +if ! kubectl get namespace rag &> /dev/null; then + echo "creating namespace 'rag'..." + kubectl create namespace rag +fi + +helm upgrade --install lightrag-dev $SCRIPT_DIR/lightrag \ + --namespace rag \ + --set-string env.LIGHTRAG_KV_STORAGE=JsonKVStorage \ + --set-string env.LIGHTRAG_VECTOR_STORAGE=NanoVectorDBStorage \ + --set-string env.LIGHTRAG_GRAPH_STORAGE=NetworkXStorage \ + --set-string env.LIGHTRAG_DOC_STATUS_STORAGE=JsonDocStatusStorage \ + --set-string env.LLM_BINDING=openai \ + --set-string env.LLM_MODEL=gpt-4o-mini \ + --set-string env.LLM_BINDING_HOST=$OPENAI_API_BASE \ + --set-string env.LLM_BINDING_API_KEY=$OPENAI_API_KEY \ + --set-string env.EMBEDDING_BINDING=openai \ + --set-string env.EMBEDDING_MODEL=text-embedding-ada-002 \ + --set-string env.EMBEDDING_DIM=1536 \ + --set-string env.EMBEDDING_BINDING_API_KEY=$OPENAI_API_KEY + +# Wait for LightRAG pod to be ready +echo "" +echo "Waiting for lightrag-dev pod to be ready..." +kubectl wait --for=condition=ready pod -l app.kubernetes.io/instance=lightrag-dev --timeout=300s -n rag +echo "lightrag-dev pod is ready" +echo "" +echo "Running Port-Forward:" +echo " kubectl --namespace rag port-forward svc/lightrag-dev 9621:9621" +echo "===========================================" +echo "" +echo "✅ You can visit LightRAG at: http://localhost:9621" +echo "" +kubectl --namespace rag port-forward svc/lightrag-dev 9621:9621 diff --git a/LightRAG/k8s-deploy/lightrag/.helmignore b/LightRAG/k8s-deploy/lightrag/.helmignore new file mode 100644 index 00000000..0e8a0eb3 --- /dev/null +++ b/LightRAG/k8s-deploy/lightrag/.helmignore @@ -0,0 +1,23 @@ +# Patterns to ignore when building packages. +# This supports shell glob matching, relative path matching, and +# negation (prefixed with !). Only one pattern per line. +.DS_Store +# Common VCS dirs +.git/ +.gitignore +.bzr/ +.bzrignore +.hg/ +.hgignore +.svn/ +# Common backup files +*.swp +*.bak +*.tmp +*.orig +*~ +# Various IDEs +.project +.idea/ +*.tmproj +.vscode/ diff --git a/LightRAG/k8s-deploy/lightrag/Chart.yaml b/LightRAG/k8s-deploy/lightrag/Chart.yaml new file mode 100644 index 00000000..8f6af573 --- /dev/null +++ b/LightRAG/k8s-deploy/lightrag/Chart.yaml @@ -0,0 +1,10 @@ +apiVersion: v2 +name: lightrag +description: A Helm chart for LightRAG, an efficient and lightweight RAG system +type: application +version: 0.1.1 +appVersion: "1.0.0" +maintainers: + - name: LightRAG Team + - name: earayu + email: earayu@gmail.com diff --git a/LightRAG/k8s-deploy/lightrag/templates/NOTES.txt b/LightRAG/k8s-deploy/lightrag/templates/NOTES.txt new file mode 100644 index 00000000..6b81aca0 --- /dev/null +++ b/LightRAG/k8s-deploy/lightrag/templates/NOTES.txt @@ -0,0 +1,38 @@ +=========================================== + LightRAG has been successfully deployed! +=========================================== + +View application logs: + kubectl logs -f --namespace {{ .Release.Namespace }} deploy/{{ include "lightrag.fullname" . }} + +=========================================== + +Access the application: +{{- if contains "NodePort" .Values.service.type }} + Run these commands to get access information: + ----------------------------------------- + export NODE_PORT=$(kubectl get --namespace {{ .Release.Namespace }} -o jsonpath="{.spec.ports[0].nodePort}" services {{ include "lightrag.fullname" . }}) + export NODE_IP=$(kubectl get nodes --namespace {{ .Release.Namespace }} -o jsonpath="{.items[0].status.addresses[0].address}") + echo "LightRAG is accessible at: http://$NODE_IP:$NODE_PORT" + ----------------------------------------- +{{- else if contains "LoadBalancer" .Values.service.type }} + Run these commands to get access information (external IP may take a minute to assign): + ----------------------------------------- + export SERVICE_IP=$(kubectl get svc --namespace {{ .Release.Namespace }} {{ include "lightrag.fullname" . }} --template "{{ "{{ range (index .status.loadBalancer.ingress 0) }}{{ . }}{{ end }}" }}") + echo "LightRAG is accessible at: http://$SERVICE_IP:{{ .Values.service.port }}" + ----------------------------------------- + If SERVICE_IP is empty, retry the command or check service status with: + kubectl get svc --namespace {{ .Release.Namespace }} {{ include "lightrag.fullname" . }} +{{- else if contains "ClusterIP" .Values.service.type }} + For development environments, to access LightRAG from your local machine: + + 1. Run this port-forward command in your terminal: + kubectl --namespace {{ .Release.Namespace }} port-forward svc/{{ include "lightrag.fullname" . }} {{ .Values.service.port }}:{{ .Values.env.PORT }} + + 2. While the command is running, open your browser and navigate to: + http://localhost:{{ .Values.service.port }} + + Note: To stop port-forwarding, press Ctrl+C in the terminal. +{{- end }} + +=========================================== diff --git a/LightRAG/k8s-deploy/lightrag/templates/_helpers.tpl b/LightRAG/k8s-deploy/lightrag/templates/_helpers.tpl new file mode 100644 index 00000000..2b2c23ae --- /dev/null +++ b/LightRAG/k8s-deploy/lightrag/templates/_helpers.tpl @@ -0,0 +1,42 @@ +{{/* +Application name +*/}} +{{- define "lightrag.name" -}} +{{- default .Chart.Name .Values.nameOverride | trunc 63 | trimSuffix "-" }} +{{- end }} + +{{/* +Full application name +*/}} +{{- define "lightrag.fullname" -}} +{{- default .Release.Name .Values.fullnameOverride | trunc 63 | trimSuffix "-" }} +{{- end }} + +{{/* +Common labels +*/}} +{{- define "lightrag.labels" -}} +app.kubernetes.io/name: {{ include "lightrag.name" . }} +app.kubernetes.io/instance: {{ .Release.Name }} +app.kubernetes.io/managed-by: {{ .Release.Service }} +{{- end }} + +{{/* +Selector labels +*/}} +{{- define "lightrag.selectorLabels" -}} +app.kubernetes.io/name: {{ include "lightrag.name" . }} +app.kubernetes.io/instance: {{ .Release.Name }} +{{- end }} + +{{/* +.env file content +*/}} +{{- define "lightrag.envContent" -}} +{{- $first := true -}} +{{- range $key, $val := .Values.env -}} +{{- if not $first -}}{{- "\n" -}}{{- end -}} +{{- $first = false -}} +{{ $key }}={{ $val }} +{{- end -}} +{{- end -}} diff --git a/LightRAG/k8s-deploy/lightrag/templates/deployment.yaml b/LightRAG/k8s-deploy/lightrag/templates/deployment.yaml new file mode 100644 index 00000000..d8dcfc34 --- /dev/null +++ b/LightRAG/k8s-deploy/lightrag/templates/deployment.yaml @@ -0,0 +1,81 @@ +apiVersion: apps/v1 +kind: Deployment +metadata: + name: {{ include "lightrag.fullname" . }} + labels: + {{- include "lightrag.labels" . | nindent 4 }} +spec: + replicas: {{ .Values.replicaCount }} + selector: + matchLabels: + {{- include "lightrag.selectorLabels" . | nindent 6 }} + template: + metadata: + annotations: + checksum/config: {{ include "lightrag.envContent" . | sha256sum }} + labels: + {{- include "lightrag.selectorLabels" . | nindent 8 }} + spec: + containers: + - name: {{ .Chart.Name }} + image: "{{ .Values.image.repository }}:{{ .Values.image.tag | default .Chart.AppVersion }}" + imagePullPolicy: IfNotPresent + ports: + - name: http + containerPort: {{ .Values.env.PORT }} + protocol: TCP + readinessProbe: + httpGet: + path: /health + port: http + initialDelaySeconds: 10 + periodSeconds: 5 + timeoutSeconds: 2 + successThreshold: 1 + failureThreshold: 3 + resources: + {{- toYaml .Values.resources | nindent 12 }} + volumeMounts: + - name: rag-storage + mountPath: /app/data/rag_storage + - name: inputs + mountPath: /app/data/inputs + - name: env-file + mountPath: /app/.env + subPath: .env + {{- $envFrom := default (dict) .Values.envFrom }} + {{- $envFromEntries := list }} + {{- range (default (list) (index $envFrom "secrets")) }} + {{- $envFromEntries = append $envFromEntries (dict "secretRef" (dict "name" .name)) }} + {{- end }} + {{- range (default (list) (index $envFrom "configmaps")) }} + {{- $envFromEntries = append $envFromEntries (dict "configMapRef" (dict "name" .name)) }} + {{- end }} + {{- if gt (len $envFromEntries) 0 }} + envFrom: +{{- toYaml $envFromEntries | nindent 12 }} + {{- end }} + {{- with .Values.image.imagePullSecrets }} + imagePullSecrets: + {{- toYaml . | nindent 8 }} + {{- end }} + volumes: + - name: env-file + secret: + secretName: {{ include "lightrag.fullname" . }}-env + {{- if .Values.persistence.enabled }} + - name: rag-storage + persistentVolumeClaim: + claimName: {{ include "lightrag.fullname" . }}-rag-storage + - name: inputs + persistentVolumeClaim: + claimName: {{ include "lightrag.fullname" . }}-inputs + {{- else }} + - name: rag-storage + emptyDir: {} + - name: inputs + emptyDir: {} + {{- end }} + + strategy: + {{- toYaml .Values.updateStrategy | nindent 4 }} diff --git a/LightRAG/k8s-deploy/lightrag/templates/pvc.yaml b/LightRAG/k8s-deploy/lightrag/templates/pvc.yaml new file mode 100644 index 00000000..570474bc --- /dev/null +++ b/LightRAG/k8s-deploy/lightrag/templates/pvc.yaml @@ -0,0 +1,28 @@ +{{- if .Values.persistence.enabled }} +--- +apiVersion: v1 +kind: PersistentVolumeClaim +metadata: + name: {{ include "lightrag.fullname" . }}-rag-storage + labels: + {{- include "lightrag.labels" . | nindent 4 }} +spec: + accessModes: + - ReadWriteOnce + resources: + requests: + storage: {{ .Values.persistence.ragStorage.size }} +--- +apiVersion: v1 +kind: PersistentVolumeClaim +metadata: + name: {{ include "lightrag.fullname" . }}-inputs + labels: + {{- include "lightrag.labels" . | nindent 4 }} +spec: + accessModes: + - ReadWriteOnce + resources: + requests: + storage: {{ .Values.persistence.inputs.size }} +{{- end }} diff --git a/LightRAG/k8s-deploy/lightrag/templates/secret.yaml b/LightRAG/k8s-deploy/lightrag/templates/secret.yaml new file mode 100644 index 00000000..8f403ab8 --- /dev/null +++ b/LightRAG/k8s-deploy/lightrag/templates/secret.yaml @@ -0,0 +1,10 @@ +apiVersion: v1 +kind: Secret +metadata: + name: {{ include "lightrag.fullname" . }}-env + labels: + {{- include "lightrag.labels" . | nindent 4 }} +type: Opaque +stringData: + .env: |- + {{- include "lightrag.envContent" . | nindent 4 }} diff --git a/LightRAG/k8s-deploy/lightrag/templates/service.yaml b/LightRAG/k8s-deploy/lightrag/templates/service.yaml new file mode 100644 index 00000000..e5525e18 --- /dev/null +++ b/LightRAG/k8s-deploy/lightrag/templates/service.yaml @@ -0,0 +1,15 @@ +apiVersion: v1 +kind: Service +metadata: + name: {{ include "lightrag.fullname" . }} + labels: + {{- include "lightrag.labels" . | nindent 4 }} +spec: + type: {{ .Values.service.type }} + ports: + - port: {{ .Values.service.port }} + targetPort: {{ .Values.env.PORT }} + protocol: TCP + name: http + selector: + {{- include "lightrag.selectorLabels" . | nindent 4 }} diff --git a/LightRAG/k8s-deploy/lightrag/values.yaml b/LightRAG/k8s-deploy/lightrag/values.yaml new file mode 100644 index 00000000..d8057d1e --- /dev/null +++ b/LightRAG/k8s-deploy/lightrag/values.yaml @@ -0,0 +1,82 @@ +replicaCount: 1 + +image: + repository: ghcr.io/hkuds/lightrag + tag: latest + # Optionally specify imagePullSecrets if your image is in a private registry + # example: + # imagePullSecrets: + # - name: my-registry-secret + imagePullSecrets: [] + +# Specify a deployment strategy +# example: +# updateStrategy: +# type: RollingUpdate +# rollingUpdate: +# maxUnavailable: 25% +# maxSurge: 25% +# Default for now should be Recreate as any RollingUpdate will cause issues with +# multiple instances trying to access the same persistent storage if not using RWX volumes. +updateStrategy: + type: Recreate + +service: + type: ClusterIP + port: 9621 + +resources: + limits: + cpu: 1000m + memory: 2Gi + requests: + cpu: 500m + memory: 1Gi + +persistence: + enabled: true + ragStorage: + size: 10Gi + inputs: + size: 5Gi + +# Allow specifying additional environment variables from ConfigMaps or Secrets created outside of this chart +envFrom: + configmaps: [] + # - name: my-shiny-configmap-1 + secrets: [] + # - name: my-shiny-secret-1 + +env: + HOST: 0.0.0.0 + PORT: 9621 + WEBUI_TITLE: Graph RAG Engine + WEBUI_DESCRIPTION: Simple and Fast Graph Based RAG System + LLM_BINDING: openai + LLM_MODEL: gpt-4o-mini + LLM_BINDING_HOST: + LLM_BINDING_API_KEY: + EMBEDDING_BINDING: openai + EMBEDDING_MODEL: text-embedding-ada-002 + EMBEDDING_DIM: 1536 + EMBEDDING_BINDING_API_KEY: + LIGHTRAG_KV_STORAGE: PGKVStorage + LIGHTRAG_VECTOR_STORAGE: PGVectorStorage + # LIGHTRAG_KV_STORAGE: RedisKVStorage + # LIGHTRAG_VECTOR_STORAGE: QdrantVectorDBStorage + LIGHTRAG_GRAPH_STORAGE: Neo4JStorage + LIGHTRAG_DOC_STATUS_STORAGE: PGDocStatusStorage + # Replace with your POSTGRES credentials + POSTGRES_HOST: pg-cluster-postgresql-postgresql + POSTGRES_PORT: 5432 + POSTGRES_USER: postgres + POSTGRES_PASSWORD: + POSTGRES_DATABASE: postgres + POSTGRES_WORKSPACE: default + # Replace with your NEO4J credentials + NEO4J_URI: neo4j://neo4j-cluster-neo4j:7687 + NEO4J_USERNAME: neo4j + NEO4J_PASSWORD: + # Replace with your Qdrant credentials + QDRANT_URL: http://qdrant-cluster-qdrant-qdrant:6333 + # REDIS_URI: redis://default:${REDIS_PASSWORD}@redis-cluster-redis-redis:6379 diff --git a/LightRAG/k8s-deploy/uninstall_lightrag.sh b/LightRAG/k8s-deploy/uninstall_lightrag.sh new file mode 100644 index 00000000..e4806b77 --- /dev/null +++ b/LightRAG/k8s-deploy/uninstall_lightrag.sh @@ -0,0 +1,4 @@ +#!/bin/bash + +NAMESPACE=rag +helm uninstall lightrag --namespace $NAMESPACE diff --git a/LightRAG/k8s-deploy/uninstall_lightrag_dev.sh b/LightRAG/k8s-deploy/uninstall_lightrag_dev.sh new file mode 100644 index 00000000..cac01abd --- /dev/null +++ b/LightRAG/k8s-deploy/uninstall_lightrag_dev.sh @@ -0,0 +1,4 @@ +#!/bin/bash + +NAMESPACE=rag +helm uninstall lightrag-dev --namespace $NAMESPACE diff --git a/LightRAG/lightrag.service.example b/LightRAG/lightrag.service.example new file mode 100644 index 00000000..e99198d1 --- /dev/null +++ b/LightRAG/lightrag.service.example @@ -0,0 +1,30 @@ +[Unit] +Description=LightRAG XYJ Service +After=network.target + +[Service] +Type=simple +User=netman +# Memory settings +MemoryHigh=8G +MemoryMax=12G + +# Set the LightRAG installation directory (change this to match your installation path) +Environment="LIGHTRAG_HOME=/home/netman/lightrag-xyj" + +# Set Environment to your Python virtual environment +Environment="PATH=${LIGHTRAG_HOME}/.venv/bin" +WorkingDirectory=${LIGHTRAG_HOME} +ExecStart=${LIGHTRAG_HOME}/.venv/bin/lightrag-server +# ExecStart=${LIGHTRAG_HOME}/.venv/bin/lightrag-gunicorn + +# Kill mode require ExecStart must be gunicorn or unvicorn main process +KillMode=process +ExecStop=/bin/kill -s TERM $MAINPID +TimeoutStopSec=60 + +Restart=always +RestartSec=30 + +[Install] +WantedBy=multi-user.target diff --git a/LightRAG/lightrag/__init__.py b/LightRAG/lightrag/__init__.py new file mode 100644 index 00000000..dc7c97b0 --- /dev/null +++ b/LightRAG/lightrag/__init__.py @@ -0,0 +1,5 @@ +from .lightrag import LightRAG as LightRAG, QueryParam as QueryParam + +__version__ = "1.4.9.9" +__author__ = "Zirui Guo" +__url__ = "https://github.com/HKUDS/LightRAG" diff --git a/LightRAG/lightrag/api/.gitignore b/LightRAG/lightrag/api/.gitignore new file mode 100644 index 00000000..8cb6821a --- /dev/null +++ b/LightRAG/lightrag/api/.gitignore @@ -0,0 +1,2 @@ +inputs +rag_storage diff --git a/LightRAG/lightrag/api/README-zh.md b/LightRAG/lightrag/api/README-zh.md new file mode 100644 index 00000000..3b12f265 --- /dev/null +++ b/LightRAG/lightrag/api/README-zh.md @@ -0,0 +1,626 @@ +# LightRAG 服务器和 WebUI + +LightRAG 服务器旨在提供 Web 界面和 API 支持。Web 界面便于文档索引、知识图谱探索和简单的 RAG 查询界面。LightRAG 服务器还提供了与 Ollama 兼容的接口，旨在将 LightRAG 模拟为 Ollama 聊天模型。这使得 AI 聊天机器人（如 Open WebUI）可以轻松访问 LightRAG。 + +![image-20250323122538997](./README.assets/image-20250323122538997.png) + +![image-20250323122754387](./README.assets/image-20250323122754387.png) + +![image-20250323123011220](./README.assets/image-20250323123011220.png) + +## 入门指南 + +### 安装 + +* 从 PyPI 安装 + +```bash +# 使用 uv (推荐) +uv pip install "lightrag-hku[api]" + +# 或使用 pip +# pip install "lightrag-hku[api]" +``` + +* 从源代码安装 + +```bash +# 克隆仓库 +git clone https://github.com/HKUDS/lightrag.git + +# 进入仓库目录 +cd lightrag + +# 使用 uv (推荐) +# 注意: uv sync 会自动在 .venv/ 目录创建虚拟环境 +uv sync --extra api +source .venv/bin/activate # 激活虚拟环境 (Linux/macOS) +# Windows 系统: .venv\Scripts\activate + +# 或使用 pip 与虚拟环境 +# python -m venv .venv +# source .venv/bin/activate # Windows: .venv\Scripts\activate +# pip install -e ".[api]" + +# 构建前端代码 +cd lightrag_webui +bun install --frozen-lockfile +bun run build +cd .. +``` + +### 启动 LightRAG 服务器前的准备 + +LightRAG 需要同时集成 LLM（大型语言模型）和嵌入模型以有效执行文档索引和查询操作。在首次部署 LightRAG 服务器之前，必须配置 LLM 和嵌入模型的设置。LightRAG 支持绑定到各种 LLM/嵌入后端： + +* ollama +* lollms +* openai 或 openai 兼容 +* azure_openai +* aws_bedrock + +建议使用环境变量来配置 LightRAG 服务器。项目根目录中有一个名为 `env.example` 的示例环境变量文件。请将此文件复制到启动目录并重命名为 `.env`。之后，您可以在 `.env` 文件中修改与 LLM 和嵌入模型相关的参数。需要注意的是，LightRAG 服务器每次启动时都会将 `.env` 中的环境变量加载到系统环境变量中。**LightRAG 服务器会优先使用系统环境变量中的设置**。 + +> 由于安装了 Python 扩展的 VS Code 可能会在集成终端中自动加载 .env 文件，请在每次修改 .env 文件后打开新的终端会话。 + +以下是 LLM 和嵌入模型的一些常见设置示例： + +* OpenAI LLM + Ollama 嵌入 + +``` +LLM_BINDING=openai +LLM_MODEL=gpt-4o +LLM_BINDING_HOST=https://api.openai.com/v1 +LLM_BINDING_API_KEY=your_api_key + +EMBEDDING_BINDING=ollama +EMBEDDING_BINDING_HOST=http://localhost:11434 +EMBEDDING_MODEL=bge-m3:latest +EMBEDDING_DIM=1024 +# EMBEDDING_BINDING_API_KEY=your_api_key +``` + +* Ollama LLM + Ollama 嵌入 + +``` +LLM_BINDING=ollama +LLM_MODEL=mistral-nemo:latest +LLM_BINDING_HOST=http://localhost:11434 +# LLM_BINDING_API_KEY=your_api_key +### Ollama 服务器上下文 token 数（必须大于 MAX_TOTAL_TOKENS+2000） +OLLAMA_LLM_NUM_CTX=8192 + +EMBEDDING_BINDING=ollama +EMBEDDING_BINDING_HOST=http://localhost:11434 +EMBEDDING_MODEL=bge-m3:latest +EMBEDDING_DIM=1024 +# EMBEDDING_BINDING_API_KEY=your_api_key +``` + +> **重要提示**：在文档索引前必须确定使用的Embedding模型，且在文档查询阶段必须沿用与索引阶段相同的模型。有些存储（例如PostgreSQL）在首次建立数表的时候需要确定向量维度，因此更换Embedding模型后需要删除向量相关库表，以便让LightRAG重建新的库表。 + +### 启动 LightRAG 服务器 + +LightRAG 服务器支持两种运行模式： +* 简单高效的 Uvicorn 模式 + +``` +lightrag-server +``` +* 多进程 Gunicorn + Uvicorn 模式（生产模式，不支持 Windows 环境） + +``` +lightrag-gunicorn --workers 4 +``` +启动LightRAG的时候，当前工作目录必须含有`.env`配置文件。**要求将.env文件置于启动目录中是经过特意设计的**。 这样做的目的是支持用户同时启动多个LightRAG实例，并为不同实例配置不同的.env文件。**修改.env文件后，您需要重新打开终端以使新设置生效**。 这是因为每次启动时，LightRAG Server会将.env文件中的环境变量加载至系统环境变量，且系统环境变量的设置具有更高优先级。 + +启动时可以通过命令行参数覆盖`.env`文件中的配置。常用的命令行参数包括： + +- `--host`：服务器监听地址（默认：0.0.0.0） +- `--port`：服务器监听端口（默认：9621） +- `--timeout`：LLM 请求超时时间（默认：150 秒） +- `--log-level`：日志级别（默认：INFO） +- `--working-dir`：数据库持久化目录（默认：./rag_storage） +- `--input-dir`：上传文件存放目录（默认：./inputs） +- `--workspace`: 工作空间名称，用于逻辑上隔离多个LightRAG实例之间的数据（默认：空） + +### 使用 Docker 启动 LightRAG 服务器 + +使用 Docker Compose 是部署和运行 LightRAG Server 最便捷的方式。 +- 创建一个项目目录。 +- 将 LightRAG 仓库中的 `docker-compose.yml` 文件复制到您的项目目录中。 +- 准备 `.env` 文件：复制示例文件 [`env.example`](https://ai.znipower.com:5013/c/env.example) 创建自定义的 `.env` 文件，并根据您的具体需求配置 LLM 和嵌入参数。 + +* 通过以下命令启动 LightRAG 服务器： + +```shell +docker compose up +# 如果希望启动后让程序退到后台运行，需要在命令的最后添加 -d 参数 +``` +> 可以通过以下链接获取官方的docker compose文件：[docker-compose.yml]( https://raw.githubusercontent.com/HKUDS/LightRAG/refs/heads/main/docker-compose.yml) 。如需获取LightRAG的历史版本镜像，可以访问以下链接: [LightRAG Docker Images]( https://github.com/HKUDS/LightRAG/pkgs/container/lightrag). 如需获取更多关于docker部署的信息，请参阅 [DockerDeployment.md](./../../docs/DockerDeployment.md). + +### 离线部署 + +官方的 LightRAG Docker 镜像完全兼容离线或隔离网络环境。如需搭建自己的离线部署环境，请参考 [离线部署指南](./../../docs/OfflineDeployment.md)。 + +### 启动多个LightRAG实例 + +有两种方式可以启动多个LightRAG实例。第一种方式是为每个实例配置一个完全独立的工作环境。此时需要为每个实例创建一个独立的工作目录，然后在这个工作目录上放置一个当前实例专用的`.env`配置文件。不同实例的配置文件中的服务器监听端口不能重复，然后在工作目录上执行 lightrag-server 启动服务即可。 + +第二种方式是所有实例共享一套相同的`.env`配置文件，然后通过命令行参数来为每个实例指定不同的服务器监听端口和工作空间。你可以在同一个工作目录中通过不同的命令行参数启动多个LightRAG实例。例如： + +``` +# 启动实例1 +lightrag-server --port 9621 --workspace space1 + +# 启动实例2 +lightrag-server --port 9622 --workspace space2 +``` + +工作空间的作用是实现不同实例之间的数据隔离。因此不同实例之间的`workspace`参数必须不同，否则会导致数据混乱，数据将会被破坏。 + +通过 Docker Compose 启动多个 LightRAG 实例时，只需在 `docker-compose.yml` 中为每个容器指定不同的 `WORKSPACE` 和 `PORT` 环境变量即可。即使所有实例共享同一个 `.env` 文件，Compose 中定义的容器环境变量也会优先覆盖 `.env` 文件中的同名设置，从而确保每个实例拥有独立的配置。 + +### LightRAG实例间的数据隔离 + +每个实例配置一个独立的工作目录和专用`.env`配置文件通常能够保证内存数据库中的本地持久化文件保存在各自的工作目录，实现数据的相互隔离。LightRAG默认存储全部都是内存数据库，通过这种方式进行数据隔离是没有问题的。但是如果使用的是外部数据库，如果不同实例访问的是同一个数据库实例，就需要通过配置工作空间来实现数据隔离，否则不同实例的数据将会出现冲突并被破坏。 + +命令行的 workspace 参数和`.env`文件中的环境变量`WORKSPACE` 都可以用于指定当前实例的工作空间名字，命令行参数的优先级别更高。下面是不同类型的存储实现工作空间的方式： + +- **对于本地基于文件的数据库，数据隔离通过工作空间子目录实现：** JsonKVStorage, JsonDocStatusStorage, NetworkXStorage, NanoVectorDBStorage, FaissVectorDBStorage。 +- **对于将数据存储在集合（collection）中的数据库，通过在集合名称前添加工作空间前缀来实现：** RedisKVStorage, RedisDocStatusStorage, MilvusVectorDBStorage, QdrantVectorDBStorage, MongoKVStorage, MongoDocStatusStorage, MongoVectorDBStorage, MongoGraphStorage, PGGraphStorage。 +- **对于关系型数据库，数据隔离通过向表中添加 `workspace` 字段进行数据的逻辑隔离：** PGKVStorage, PGVectorStorage, PGDocStatusStorage。 + +* **对于Neo4j图数据库，通过label来实现数据的逻辑隔离**：Neo4JStorage + +为了保持对遗留数据的兼容，在未配置工作空间时PostgreSQL的默认工作空间为`default`，Neo4j的默认工作空间为`base`。对于所有的外部存储，系统都提供了专用的工作空间环境变量，用于覆盖公共的 `WORKSPACE`环境变量配置。这些适用于指定存储类型的工作空间环境变量为：`REDIS_WORKSPACE`, `MILVUS_WORKSPACE`, `QDRANT_WORKSPACE`, `MONGODB_WORKSPACE`, `POSTGRES_WORKSPACE`, `NEO4J_WORKSPACE`。 + +### Gunicorn + Uvicorn 的多工作进程 + +LightRAG 服务器可以在 `Gunicorn + Uvicorn` 预加载模式下运行。Gunicorn 的多工作进程（多进程）功能可以防止文档索引任务阻塞 RAG 查询。使用 CPU 密集型文档提取工具（如 docling）在纯 Uvicorn 模式下可能会导致整个系统被阻塞。 + +虽然 LightRAG 服务器使用一个工作进程来处理文档索引流程，但通过 Uvicorn 的异步任务支持，可以并行处理多个文件。文档索引速度的瓶颈主要在于 LLM。如果您的 LLM 支持高并发，您可以通过增加 LLM 的并发级别来加速文档索引。以下是几个与并发处理相关的环境变量及其默认值： + +``` +### 工作进程数，数字不大于 (2 x 核心数) + 1 +WORKERS=2 +### 一批中并行处理的文件数 +MAX_PARALLEL_INSERT=2 +# LLM 的最大并发请求数 +MAX_ASYNC=4 +``` + +### 将 Lightrag 安装为 Linux 服务 + +从示例文件 `lightrag.service.example` 创建您的服务文件 `lightrag.service`。修改服务文件中的服务启动定义： + +```text +# Set Enviroment to your Python virtual enviroment +Environment="PATH=/home/netman/lightrag-xyj/venv/bin" +WorkingDirectory=/home/netman/lightrag-xyj +# ExecStart=/home/netman/lightrag-xyj/venv/bin/lightrag-server +ExecStart=/home/netman/lightrag-xyj/venv/bin/lightrag-gunicorn +``` +> ExecStart命令必须是 lightrag-gunicorn 或 lightrag-server 中的一个，不能使用其它脚本包裹它们。因为停止服务必须要求主进程必须是这两个进程。 + +安装 LightRAG 服务。如果您的系统是 Ubuntu，以下命令将生效： + +```shell +sudo cp lightrag.service /etc/systemd/system/ +sudo systemctl daemon-reload +sudo systemctl start lightrag.service +sudo systemctl status lightrag.service +sudo systemctl enable lightrag.service +``` + +## Ollama 模拟 + +我们为 LightRAG 提供了 Ollama 兼容接口，旨在将 LightRAG 模拟为 Ollama 聊天模型。这使得支持 Ollama 的 AI 聊天前端（如 Open WebUI）可以轻松访问 LightRAG。 + +### 将 Open WebUI 连接到 LightRAG + +启动 lightrag-server 后，您可以在 Open WebUI 管理面板中添加 Ollama 类型的连接。然后，一个名为 `lightrag:latest` 的模型将出现在 Open WebUI 的模型管理界面中。用户随后可以通过聊天界面向 LightRAG 发送查询。对于这种用例，最好将 LightRAG 安装为服务。 + +Open WebUI 使用 LLM 来执行会话标题和会话关键词生成任务。因此，Ollama 聊天补全 API 会检测并将 OpenWebUI 会话相关请求直接转发给底层 LLM。Open WebUI 的截图： + +![image-20250323194750379](./README.assets/image-20250323194750379.png) + +### 在聊天中选择查询模式 + +如果您从 LightRAG 的 Ollama 接口发送消息（查询），默认查询模式是 `hybrid`。您可以通过发送带有查询前缀的消息来选择查询模式。 + +查询字符串中的查询前缀可以决定使用哪种 LightRAG 查询模式来生成响应。支持的前缀包括： + +``` +/local +/global +/hybrid +/naive +/mix + +/bypass +/context +/localcontext +/globalcontext +/hybridcontext +/naivecontext +/mixcontext +``` + +例如，聊天消息 "/mix 唐僧有几个徒弟" 将触发 LightRAG 的混合模式查询。没有查询前缀的聊天消息默认会触发混合模式查询。 + +"/bypass" 不是 LightRAG 查询模式，它会告诉 API 服务器将查询连同聊天历史直接传递给底层 LLM。因此用户可以使用 LLM 基于聊天历史回答问题。如果您使用 Open WebUI 作为前端，您可以直接切换到普通 LLM 模型，而不是使用 /bypass 前缀。 + +"/context" 也不是 LightRAG 查询模式，它会告诉 LightRAG 只返回为 LLM 准备的上下文信息。您可以检查上下文是否符合您的需求，或者自行处理上下文。 + +### 在聊天中添加用户提示词 + +使用LightRAG进行内容查询时，应避免将搜索过程与无关的输出处理相结合，这会显著影响查询效果。用户提示（user prompt）正是为解决这一问题而设计 -- 它不参与RAG检索阶段，而是在查询完成后指导大语言模型（LLM）如何处理检索结果。我们可以在查询前缀末尾添加方括号，从而向LLM传递用户提示词： + +``` +/[使用mermaid格式画图] 请画出 Scrooge 的人物关系图谱 +/mix[使用mermaid格式画图] 请画出 Scrooge 的人物关系图谱 +``` + +## API 密钥和认证 + +默认情况下，LightRAG 服务器可以在没有任何认证的情况下访问。我们可以使用 API 密钥或账户凭证配置服务器以确保其安全。 + +* API 密钥 + +``` +LIGHTRAG_API_KEY=your-secure-api-key-here +WHITELIST_PATHS=/health,/api/* +``` + +> 健康检查和 Ollama 模拟端点默认不进行 API 密钥检查。为了安全原因，如果不需要提供Ollama服务，应该把`/api/*`从WHITELIST_PATHS中移除。 + +API Key使用的请求头是 `X-API-Key` 。以下是使用API访问LightRAG Server的一个例子： + +``` +curl -X 'POST' \ + 'http://localhost:9621/documents/scan' \ + -H 'accept: application/json' \ + -H 'X-API-Key: your-secure-api-key-here-123' \ + -d '' +``` + +* 账户凭证（Web 界面需要登录后才能访问） + +LightRAG API 服务器使用基于 HS256 算法的 JWT 认证。要启用安全访问控制，需要以下环境变量： + +```bash +# JWT 认证 +AUTH_ACCOUNTS='admin:admin123,user1:pass456' +TOKEN_SECRET='your-key' +TOKEN_EXPIRE_HOURS=4 +``` + +> 目前仅支持配置一个管理员账户和密码。尚未开发和实现完整的账户系统。 + +如果未配置账户凭证，Web 界面将以访客身份访问系统。因此，即使仅配置了 API 密钥，所有 API 仍然可以通过访客账户访问，这仍然不安全。因此，要保护 API，需要同时配置这两种认证方法。 + +## Azure OpenAI 后端配置 + +可以使用以下 Azure CLI 命令创建 Azure OpenAI API（您需要先从 [https://docs.microsoft.com/en-us/cli/azure/install-azure-cli](https://docs.microsoft.com/en-us/cli/azure/install-azure-cli) 安装 Azure CLI）： + +```bash +# 根据需要更改资源组名称、位置和 OpenAI 资源名称 +RESOURCE_GROUP_NAME=LightRAG +LOCATION=swedencentral +RESOURCE_NAME=LightRAG-OpenAI + +az login +az group create --name $RESOURCE_GROUP_NAME --location $LOCATION +az cognitiveservices account create --name $RESOURCE_NAME --resource-group $RESOURCE_GROUP_NAME --kind OpenAI --sku S0 --location swedencentral +az cognitiveservices account deployment create --resource-group $RESOURCE_GROUP_NAME --model-format OpenAI --name $RESOURCE_NAME --deployment-name gpt-4o --model-name gpt-4o --model-version "2024-08-06" --sku-capacity 100 --sku-name "Standard" +az cognitiveservices account deployment create --resource-group $RESOURCE_GROUP_NAME --model-format OpenAI --name $RESOURCE_NAME --deployment-name text-embedding-3-large --model-name text-embedding-3-large --model-version "1" --sku-capacity 80 --sku-name "Standard" +az cognitiveservices account show --name $RESOURCE_NAME --resource-group $RESOURCE_GROUP_NAME --query "properties.endpoint" +az cognitiveservices account keys list --name $RESOURCE_NAME -g $RESOURCE_GROUP_NAME +``` + +最后一个命令的输出将提供 OpenAI API 的端点和密钥。您可以使用这些值在 `.env` 文件中设置环境变量。 + +``` +# .env 中的 Azure OpenAI 配置 +LLM_BINDING=azure_openai +LLM_BINDING_HOST=your-azure-endpoint +LLM_MODEL=your-model-deployment-name +LLM_BINDING_API_KEY=your-azure-api-key +### API Version可选，默认为最新版本 +AZURE_OPENAI_API_VERSION=2024-08-01-preview + +### 如果使用 Azure OpenAI 进行嵌入 +EMBEDDING_BINDING=azure_openai +EMBEDDING_MODEL=your-embedding-deployment-name +``` + +## LightRAG 服务器详细配置 + +API 服务器可以通过三种方式配置（优先级从高到低）： + +* 命令行参数 +* 环境变量或 .env 文件 +* Config.ini（仅用于存储配置） + +大多数配置都有默认设置，详细信息请查看示例文件：`.env.example`。数据存储配置也可以通过 config.ini 设置。为方便起见，提供了示例文件 `config.ini.example`。 + +### 支持的 LLM 和嵌入后端 + +LightRAG 支持绑定到各种 LLM/嵌入后端： + +* ollama +* openai (含openai 兼容) +* azure_openai +* lollms +* aws_bedrock + +使用环境变量 `LLM_BINDING` 或 CLI 参数 `--llm-binding` 选择 LLM 后端类型。使用环境变量 `EMBEDDING_BINDING` 或 CLI 参数 `--embedding-binding` 选择嵌入后端类型。 + +LLM和Embedding配置例子请查看项目根目录的 env.example 文件。OpenAI和Ollama兼容LLM接口的支持的完整配置选型可以通过一下命令查看： + +``` +lightrag-server --llm-binding openai --help +lightrag-server --llm-binding ollama --help +lightrag-server --embedding-binding ollama --help +``` + +> 请使用openai兼容方式访问OpenRouter、vLLM或SLang部署的LLM。可以通过 `OPENAI_LLM_EXTRA_BODY` 环境变量给OpenRouter、vLLM或SGLang推理框架传递额外的参数，实现推理模式的关闭或者其它个性化控制。 + +设置 `max_tokens` 参数旨在**防止在实体关系提取阶段出现LLM 响应输出过长或无休止的循环输出的问题**。设置 `max_tokens` 参数的目的是在超时发生之前截断 LLM 输出，从而防止文档提取失败。这解决了某些包含大量实体和关系的文本块（例如表格或引文）可能导致 LLM 产生过长甚至无限循环输出的问题。此设置对于本地部署的小参数模型尤为重要。`max_tokens` 值可以通过以下公式计算： + +``` +# For vLLM/SGLang doployed models, or most of OpenAI compatible API provider +OPENAI_LLM_MAX_TOKENS=9000 + +# For Ollama Deployed Modeles +OLLAMA_LLM_NUM_PREDICT=9000 + +# For OpenAI o1-mini or newer modles +OPENAI_LLM_MAX_COMPLETION_TOKENS=9000 +``` + +### 实体提取配置 + +* ENABLE_LLM_CACHE_FOR_EXTRACT：为实体提取启用 LLM 缓存（默认：true） + +在测试环境中将 `ENABLE_LLM_CACHE_FOR_EXTRACT` 设置为 true 以减少 LLM 调用成本是很常见的做法。 + +### 支持的存储类型 + +LightRAG 使用 4 种类型的存储用于不同目的： + +* KV_STORAGE：llm 响应缓存、文本块、文档信息 +* VECTOR_STORAGE：实体向量、关系向量、块向量 +* GRAPH_STORAGE：实体关系图 +* DOC_STATUS_STORAGE：文档索引状态 + +每种存储类型都有多种存储实现方式。LightRAG Server默认的存储实现为内存数据库，数据通过文件持久化保存到WORKING_DIR目录。LightRAG还支持PostgreSQL、MongoDB、FAISS、Milvus、Qdrant、Neo4j、Memgraph和Redis等存储实现方式。详细的存储支持方式请参考根目录下的`README.md`文件中关于存储的相关内容。 + +您可以通过环境变量选择存储实现。例如，在首次启动 API 服务器之前，您可以将以下环境变量设置为特定的存储实现名称： + +``` +LIGHTRAG_KV_STORAGE=PGKVStorage +LIGHTRAG_VECTOR_STORAGE=PGVectorStorage +LIGHTRAG_GRAPH_STORAGE=PGGraphStorage +LIGHTRAG_DOC_STATUS_STORAGE=PGDocStatusStorage +``` + +在向 LightRAG 添加文档后，您不能更改存储实现选择。目前尚不支持从一个存储实现迁移到另一个存储实现。更多配置信息请阅读示例 `env.exampl`e文件。 + +### 在不同存储类型之间迁移LLM缓存 + +当LightRAG更换存储实现方式的时候，可以LLM缓存从就的存储迁移到新的存储。先以后在新的存储上重新上传文件时，将利用利用原有存储的LLM缓存大幅度加快文件处理的速度。LLM缓存迁移工具的使用方法请参考[README_MIGRATE_LLM_CACHE.md](../tools/README_MIGRATE_LLM_CACHE.md) + +### LightRag API 服务器命令行选项 + +| 参数 | 默认值 | 描述 | +|-----------|---------|-------------| +| --host | 0.0.0.0 | 服务器主机 | +| --port | 9621 | 服务器端口 | +| --working-dir | ./rag_storage | RAG 存储的工作目录 | +| --input-dir | ./inputs | 包含输入文档的目录 | +| --max-async | 4 | 最大异步操作数 | +| --log-level | INFO | 日志级别（DEBUG、INFO、WARNING、ERROR、CRITICAL） | +| --verbose | - | 详细调试输出（True、False） | +| --key | None | 用于认证的 API 密钥。保护 lightrag 服务器免受未授权访问 | +| --ssl | False | 启用 HTTPS | +| --ssl-certfile | None | SSL 证书文件路径（如果启用 --ssl 则必需） | +| --ssl-keyfile | None | SSL 私钥文件路径（如果启用 --ssl 则必需） | +| --llm-binding | ollama | LLM 绑定类型（lollms、ollama、openai、openai-ollama、azure_openai、aws_bedrock） | +| --embedding-binding | ollama | 嵌入绑定类型（lollms、ollama、openai、azure_openai、aws_bedrock） | + +### Reranking 配置 + +Reranking 查询召回的块可以显著提高检索质量，它通过基于优化的相关性评分模型对文档重新排序。LightRAG 目前支持以下 rerank 提供商： + +- **Cohere / vLLM**：提供与 Cohere AI 的 `v2/rerank` 端点的完整 API 集成。由于 vLLM 提供了与 Cohere 兼容的 reranker API，因此也支持所有通过 vLLM 部署的 reranker 模型。 +- **Jina AI**：提供与所有 Jina rerank 模型的完全实现兼容性。 +- **阿里云**：具有旨在支持阿里云 rerank API 格式的自定义实现。 + +Rerank 提供商通过 `.env` 文件进行配置。以下是使用 vLLM 本地部署的 rerank 模型的示例配置： + +``` +RERANK_BINDING=cohere +RERANK_MODEL=BAAI/bge-reranker-v2-m3 +RERANK_BINDING_HOST=http://localhost:8000/v1/rerank +RERANK_BINDING_API_KEY=your_rerank_api_key_here +``` + +以下是使用阿里云提供的 Reranker 服务的示例配置： + +``` +RERANK_BINDING=aliyun +RERANK_MODEL=gte-rerank-v2 +RERANK_BINDING_HOST=https://dashscope.aliyuncs.com/api/v1/services/rerank/text-rerank/text-rerank +RERANK_BINDING_API_KEY=your_rerank_api_key_here +``` + +有关完整的 reranker 配置示例，请参阅 `env.example` 文件。 + +### 启用 Reranking + +可以按查询启用或禁用 Reranking。 + +`/query` 和 `/query/stream` API 端点包含一个 `enable_rerank` 参数，默认设置为 `true`，用于控制当前查询是否激活 reranking。要将 `enable_rerank` 参数的默认值更改为 `false`，请设置以下环境变量： + +``` +RERANK_BY_DEFAULT=False +``` + +### .env 文件示例 + +```bash +### Server Configuration +# HOST=0.0.0.0 +PORT=9621 +WORKERS=2 + +### Settings for document indexing +ENABLE_LLM_CACHE_FOR_EXTRACT=true +SUMMARY_LANGUAGE=Chinese +MAX_PARALLEL_INSERT=2 + +### LLM Configuration (Use valid host. For local services installed with docker, you can use host.docker.internal) +TIMEOUT=150 +MAX_ASYNC=4 + +LLM_BINDING=openai +LLM_MODEL=gpt-4o-mini +LLM_BINDING_HOST=https://api.openai.com/v1 +LLM_BINDING_API_KEY=your-api-key + +### Embedding Configuration (Use valid host. For local services installed with docker, you can use host.docker.internal) +EMBEDDING_MODEL=bge-m3:latest +EMBEDDING_DIM=1024 +EMBEDDING_BINDING=ollama +EMBEDDING_BINDING_HOST=http://localhost:11434 + +### For JWT Auth +# AUTH_ACCOUNTS='admin:admin123,user1:pass456' +# TOKEN_SECRET=your-key-for-LightRAG-API-Server-xxx +# TOKEN_EXPIRE_HOURS=48 + +# LIGHTRAG_API_KEY=your-secure-api-key-here-123 +# WHITELIST_PATHS=/api/* +# WHITELIST_PATHS=/health,/api/* +``` + +#### 使用 ollama 默认本地服务器作为 llm 和嵌入后端运行 Lightrag 服务器 + +Ollama 是 llm 和嵌入的默认后端，因此默认情况下您可以不带参数运行 lightrag-server，将使用默认值。确保已安装 ollama 并且正在运行，且默认模型已安装在 ollama 上。 + +```bash +# 使用 ollama 运行 lightrag，llm 使用 mistral-nemo:latest，嵌入使用 bge-m3:latest +lightrag-server + +# 使用认证密钥 +lightrag-server --key my-key +``` + +#### 使用 lollms 默认本地服务器作为 llm 和嵌入后端运行 Lightrag 服务器 + +```bash +# 使用 lollms 运行 lightrag，llm 使用 mistral-nemo:latest，嵌入使用 bge-m3:latest +# 在 .env 或 config.ini 中配置 LLM_BINDING=lollms 和 EMBEDDING_BINDING=lollms +lightrag-server + +# 使用认证密钥 +lightrag-server --key my-key +``` + +#### 使用 openai 服务器作为 llm 和嵌入后端运行 Lightrag 服务器 + +```bash +# 使用 openai 运行 lightrag，llm 使用 GPT-4o-mini，嵌入使用 text-embedding-3-small +# 在 .env 或 config.ini 中配置： +# LLM_BINDING=openai +# LLM_MODEL=GPT-4o-mini +# EMBEDDING_BINDING=openai +# EMBEDDING_MODEL=text-embedding-3-small +lightrag-server + +# 使用认证密钥 +lightrag-server --key my-key +``` + +#### 使用 azure openai 服务器作为 llm 和嵌入后端运行 Lightrag 服务器 + +```bash +# 使用 azure_openai 运行 lightrag +# 在 .env 或 config.ini 中配置： +# LLM_BINDING=azure_openai +# LLM_MODEL=your-model +# EMBEDDING_BINDING=azure_openai +# EMBEDDING_MODEL=your-embedding-model +lightrag-server + +# 使用认证密钥 +lightrag-server --key my-key +``` + +**重要说明：** +- 对于 LoLLMs：确保指定的模型已安装在您的 LoLLMs 实例中 +- 对于 Ollama：确保指定的模型已安装在您的 Ollama 实例中 +- 对于 OpenAI：确保您已设置 OPENAI_API_KEY 环境变量 +- 对于 Azure OpenAI：按照先决条件部分所述构建和配置您的服务器 + +要获取任何服务器的帮助，使用 --help 标志： +```bash +lightrag-server --help +``` + +注意：如果您不需要 API 功能，可以使用以下命令安装不带 API 支持的基本包： +```bash +pip install lightrag-hku +``` + +## 文档和块处理逻辑说明 + +LightRAG 中的文档处理流程有些复杂，分为两个主要阶段：提取阶段（实体和关系提取）和合并阶段（实体和关系合并）。有两个关键参数控制流程并发性：并行处理的最大文件数（`MAX_PARALLEL_INSERT`）和最大并发 LLM 请求数（`MAX_ASYNC`）。工作流程描述如下： + +1. `MAX_ASYNC` 限制系统中并发 LLM 请求的总数，包括查询、提取和合并的请求。LLM 请求具有不同的优先级：查询操作优先级最高，其次是合并，然后是提取。 +2. `MAX_PARALLEL_INSERT` 控制提取阶段并行处理的文件数量。`MAX_PARALLEL_INSERT`建议设置为2～10之间，通常设置为 `MAX_ASYNC/3`，设置太大会导致合并阶段不同文档之间实体和关系重名的机会增大，降低合并阶段的效率。 +3. 在单个文件中，来自不同文本块的实体和关系提取是并发处理的，并发度由 `MAX_ASYNC` 设置。只有在处理完 `MAX_ASYNC` 个文本块后，系统才会继续处理同一文件中的下一批文本块。 +4. 当一个文件完成实体和关系提后，将进入实体和关系合并阶段。这一阶段也会并发处理多个实体和关系，其并发度同样是由 `MAX_ASYNC` 控制。 +5. 合并阶段的 LLM 请求的优先级别高于提取阶段，目的是让进入合并阶段的文件尽快完成处理，并让处理结果尽快更新到向量数据库中。 +6. 为防止竞争条件，合并阶段会避免并发处理同一个实体或关系，当多个文件中都涉及同一个实体或关系需要合并的时候他们会串行执行。 +7. 每个文件在流程中被视为一个原子处理单元。只有当其所有文本块都完成提取和合并后，文件才会被标记为成功处理。如果在处理过程中发生任何错误，整个文件将被标记为失败，并且必须重新处理。 +8. 当由于错误而重新处理文件时，由于 LLM 缓存，先前处理的文本块可以快速跳过。尽管 LLM 缓存在合并阶段也会被利用，但合并顺序的不一致可能会限制其在此阶段的有效性。 +9. 如果在提取过程中发生错误，系统不会保留任何中间结果。如果在合并过程中发生错误，已合并的实体和关系可能会被保留；当重新处理同一文件时，重新提取的实体和关系将与现有实体和关系合并，而不会影响查询结果。 +10. 在合并阶段结束时，所有实体和关系数据都会在向量数据库中更新。如果此时发生错误，某些更新可能会被保留。但是，下一次处理尝试将覆盖先前结果，确保成功重新处理的文件不会影响未来查询结果的完整性。 + +大型文件应分割成较小的片段以启用增量处理。可以通过在 Web UI 上按“扫描”按钮来启动失败文件的重新处理。 + +## API 端点 + +所有服务器（LoLLMs、Ollama、OpenAI 和 Azure OpenAI）都为 RAG 功能提供相同的 REST API 端点。当 API 服务器运行时，访问： + +- Swagger UI：http://localhost:9621/docs +- ReDoc：http://localhost:9621/redoc + +您可以使用提供的 curl 命令或通过 Swagger UI 界面测试 API 端点。确保： + +1. 启动适当的后端服务（LoLLMs、Ollama 或 OpenAI） +2. 启动 RAG 服务器 +3. 使用文档管理端点上传一些文档 +4. 使用查询端点查询系统 +5. 如果在输入目录中放入新文件，触发文档扫描 + +## 异步文档索引与进度跟踪 + +LightRAG采用异步文档索引机制，便于前端监控和查询文档处理进度。用户通过指定端点上传文件或插入文本时，系统将返回唯一的跟踪ID，以便实时监控处理进度。 + +**支持生成跟踪ID的API端点：** +* `/documents/upload` +* `/documents/text` +* `/documents/texts` + +**文档处理状态查询端点：** +* `/track_status/{track_id}` + +该端点提供全面的状态信息，包括： +* 文档处理状态（待处理/处理中/已处理/失败） +* 内容摘要和元数据 +* 处理失败时的错误信息 +* 创建和更新时间戳 diff --git a/LightRAG/lightrag/api/README.assets/image-20250323122538997.png b/LightRAG/lightrag/api/README.assets/image-20250323122538997.png new file mode 100644 index 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zgF&#(y@$6;5Pu_7`)AeMa@;Zb%5K(V*HLHzQhhy}7voZAVO;38{1qOw>}GYLPai1N?E+g_bQ&w7;E%=_z?_IoOUD&k@_yznk^as}d;}`!6 D#-v Since VS Code with the Python extension may automatically load the .env file in the integrated terminal, please open a new terminal session after each modification to the .env file. + +Here are some examples of common settings for LLM and Embedding models: + +* OpenAI LLM + Ollama Embedding: + +``` +LLM_BINDING=openai +LLM_MODEL=gpt-4o +LLM_BINDING_HOST=https://api.openai.com/v1 +LLM_BINDING_API_KEY=your_api_key + +EMBEDDING_BINDING=ollama +EMBEDDING_BINDING_HOST=http://localhost:11434 +EMBEDDING_MODEL=bge-m3:latest +EMBEDDING_DIM=1024 +# EMBEDDING_BINDING_API_KEY=your_api_key +``` + +> When targeting Google Gemini, set `LLM_BINDING=gemini`, choose a model such as `LLM_MODEL=gemini-flash-latest`, and provide your Gemini key via `LLM_BINDING_API_KEY` (or `GEMINI_API_KEY`). + +* Ollama LLM + Ollama Embedding: + +``` +LLM_BINDING=ollama +LLM_MODEL=mistral-nemo:latest +LLM_BINDING_HOST=http://localhost:11434 +# LLM_BINDING_API_KEY=your_api_key +### Ollama Server context length (Must be larger than MAX_TOTAL_TOKENS+2000) +OLLAMA_LLM_NUM_CTX=16384 + +EMBEDDING_BINDING=ollama +EMBEDDING_BINDING_HOST=http://localhost:11434 +EMBEDDING_MODEL=bge-m3:latest +EMBEDDING_DIM=1024 +# EMBEDDING_BINDING_API_KEY=your_api_key +``` + +> **Important Note**: The Embedding model must be determined before document indexing, and the same model must be used during the document query phase. For certain storage solutions (e.g., PostgreSQL), the vector dimension must be defined upon initial table creation. Therefore, when changing embedding models, it is necessary to delete the existing vector-related tables and allow LightRAG to recreate them with the new dimensions. + +### Starting LightRAG Server + +The LightRAG Server supports two operational modes: +* The simple and efficient Uvicorn mode: + +``` +lightrag-server +``` +* The multiprocess Gunicorn + Uvicorn mode (production mode, not supported on Windows environments): + +``` +lightrag-gunicorn --workers 4 +``` + +When starting LightRAG, the current working directory must contain the `.env` configuration file. **It is intentionally designed that the `.env` file must be placed in the startup directory**. The purpose of this is to allow users to launch multiple LightRAG instances simultaneously and configure different `.env` files for different instances. **After modifying the `.env` file, you need to reopen the terminal for the new settings to take effect.** This is because each time LightRAG Server starts, it loads the environment variables from the `.env` file into the system environment variables, and system environment variables have higher precedence. + +During startup, configurations in the `.env` file can be overridden by command-line parameters. Common command-line parameters include: + +- `--host`: Server listening address (default: 0.0.0.0) +- `--port`: Server listening port (default: 9621) +- `--timeout`: LLM request timeout (default: 150 seconds) +- `--log-level`: Log level (default: INFO) +- `--working-dir`: Database persistence directory (default: ./rag_storage) +- `--input-dir`: Directory for uploaded files (default: ./inputs) +- `--workspace`: Workspace name, used to logically isolate data between multiple LightRAG instances (default: empty) + +### Launching LightRAG Server with Docker + +Using Docker Compose is the most convenient way to deploy and run the LightRAG Server. + +* Create a project directory. + +* Copy the `docker-compose.yml` file from the LightRAG repository into your project directory. + +* Prepare the `.env` file: Duplicate the sample file [`env.example`](https://ai.znipower.com:5013/c/env.example)to create a customized `.env` file, and configure the LLM and embedding parameters according to your specific requirements. + +* Start the LightRAG Server with the following command: + +```shell +docker compose up +# If you want the program to run in the background after startup, add the -d parameter at the end of the command. +``` + +You can get the official docker compose file from here: [docker-compose.yml](https://raw.githubusercontent.com/HKUDS/LightRAG/refs/heads/main/docker-compose.yml). For historical versions of LightRAG docker images, visit this link: [LightRAG Docker Images](https://github.com/HKUDS/LightRAG/pkgs/container/lightrag). For more details about docker deployment, please refer to [DockerDeployment.md](./../../docs/DockerDeployment.md). + +### Offline Deployment + +Official LightRAG Docker images are fully compatible with offline or air-gapped environments. If you want to build up you own offline enviroment, please refer to [Offline Deployment Guide](./../../docs/OfflineDeployment.md). + +### Starting Multiple LightRAG Instances + +There are two ways to start multiple LightRAG instances. The first way is to configure a completely independent working environment for each instance. This requires creating a separate working directory for each instance and placing a dedicated `.env` configuration file in that directory. The server listening ports in the configuration files of different instances cannot be the same. Then, you can start the service by running `lightrag-server` in the working directory. + +The second way is for all instances to share the same set of `.env` configuration files, and then use command-line arguments to specify different server listening ports and workspaces for each instance. You can start multiple LightRAG instances in the same working directory with different command-line arguments. For example: + +``` +# Start instance 1 +lightrag-server --port 9621 --workspace space1 + +# Start instance 2 +lightrag-server --port 9622 --workspace space2 +``` + +The purpose of a workspace is to achieve data isolation between different instances. Therefore, the `workspace` parameter must be different for different instances; otherwise, it will lead to data confusion and corruption. + +When launching multiple LightRAG instances via Docker Compose, simply specify unique `WORKSPACE` and `PORT` environment variables for each container within your `docker-compose.yml`. Even if all instances share a common `.env` file, the container-specific environment variables defined in Compose will take precedence, ensuring independent configurations for each instance. + +### Data Isolation Between LightRAG Instances + +Configuring an independent working directory and a dedicated `.env` configuration file for each instance can generally ensure that locally persisted files in the in-memory database are saved in their respective working directories, achieving data isolation. By default, LightRAG uses all in-memory databases, and this method of data isolation is sufficient. However, if you are using an external database, and different instances access the same database instance, you need to use workspaces to achieve data isolation; otherwise, the data of different instances will conflict and be destroyed. + +The command-line `workspace` argument and the `WORKSPACE` environment variable in the `.env` file can both be used to specify the workspace name for the current instance, with the command-line argument having higher priority. Here is how workspaces are implemented for different types of storage: + +- **For local file-based databases, data isolation is achieved through workspace subdirectories:** `JsonKVStorage`, `JsonDocStatusStorage`, `NetworkXStorage`, `NanoVectorDBStorage`, `FaissVectorDBStorage`. +- **For databases that store data in collections, it's done by adding a workspace prefix to the collection name:** `RedisKVStorage`, `RedisDocStatusStorage`, `MilvusVectorDBStorage`, `MongoKVStorage`, `MongoDocStatusStorage`, `MongoVectorDBStorage`, `MongoGraphStorage`, `PGGraphStorage`. +- **For Qdrant vector database, data isolation is achieved through payload-based partitioning (Qdrant's recommended multitenancy approach):** `QdrantVectorDBStorage` uses shared collections with payload filtering for unlimited workspace scalability. +- **For relational databases, data isolation is achieved by adding a `workspace` field to the tables for logical data separation:** `PGKVStorage`, `PGVectorStorage`, `PGDocStatusStorage`. +- **For graph databases, logical data isolation is achieved through labels:** `Neo4JStorage`, `MemgraphStorage` + +To maintain compatibility with legacy data, the default workspace for PostgreSQL is `default` and for Neo4j is `base` when no workspace is configured. For all external storages, the system provides dedicated workspace environment variables to override the common `WORKSPACE` environment variable configuration. These storage-specific workspace environment variables are: `REDIS_WORKSPACE`, `MILVUS_WORKSPACE`, `QDRANT_WORKSPACE`, `MONGODB_WORKSPACE`, `POSTGRES_WORKSPACE`, `NEO4J_WORKSPACE`, `MEMGRAPH_WORKSPACE`. + +### Multiple workers for Gunicorn + Uvicorn + +The LightRAG Server can operate in the `Gunicorn + Uvicorn` preload mode. Gunicorn's multiple worker (multiprocess) capability prevents document indexing tasks from blocking RAG queries. Using CPU-exhaustive document extraction tools, such as docling, can lead to the entire system being blocked in pure Uvicorn mode. + +Though LightRAG Server uses one worker to process the document indexing pipeline, with the async task support of Uvicorn, multiple files can be processed in parallel. The bottleneck of document indexing speed mainly lies with the LLM. If your LLM supports high concurrency, you can accelerate document indexing by increasing the concurrency level of the LLM. Below are several environment variables related to concurrent processing, along with their default values: + +``` +### Number of worker processes, not greater than (2 x number_of_cores) + 1 +WORKERS=2 +### Number of parallel files to process in one batch +MAX_PARALLEL_INSERT=2 +### Max concurrent requests to the LLM +MAX_ASYNC=4 +``` + +### Install LightRAG as a Linux Service + +Create your service file `lightrag.service` from the sample file: `lightrag.service.example`. Modify the start options the service file: + +```text +# Set Enviroment to your Python virtual enviroment +Environment="PATH=/home/netman/lightrag-xyj/venv/bin" +WorkingDirectory=/home/netman/lightrag-xyj +# ExecStart=/home/netman/lightrag-xyj/venv/bin/lightrag-server +ExecStart=/home/netman/lightrag-xyj/venv/bin/lightrag-gunicorn + +``` + +> The ExecStart command must be either `lightrag-gunicorn` or `lightrag-server`; no wrapper scripts are allowed. This is because service termination requires the main process to be one of these two executables. + +Install LightRAG service. If your system is Ubuntu, the following commands will work: + +```shell +sudo cp lightrag.service /etc/systemd/system/ +sudo systemctl daemon-reload +sudo systemctl start lightrag.service +sudo systemctl status lightrag.service +sudo systemctl enable lightrag.service +``` + +## Ollama Emulation + +We provide Ollama-compatible interfaces for LightRAG, aiming to emulate LightRAG as an Ollama chat model. This allows AI chat frontends supporting Ollama, such as Open WebUI, to access LightRAG easily. + +### Connect Open WebUI to LightRAG + +After starting the lightrag-server, you can add an Ollama-type connection in the Open WebUI admin panel. And then a model named `lightrag:latest` will appear in Open WebUI's model management interface. Users can then send queries to LightRAG through the chat interface. You should install LightRAG as a service for this use case. + +Open WebUI uses an LLM to do the session title and session keyword generation task. So the Ollama chat completion API detects and forwards OpenWebUI session-related requests directly to the underlying LLM. Screenshot from Open WebUI: + +![image-20250323194750379](./README.assets/image-20250323194750379.png) + +### Choose Query mode in chat + +The default query mode is `hybrid` if you send a message (query) from the Ollama interface of LightRAG. You can select query mode by sending a message with a query prefix. + +A query prefix in the query string can determine which LightRAG query mode is used to generate the response for the query. The supported prefixes include: + +``` +/local +/global +/hybrid +/naive +/mix + +/bypass +/context +/localcontext +/globalcontext +/hybridcontext +/naivecontext +/mixcontext +``` + +For example, the chat message `/mix What's LightRAG?` will trigger a mix mode query for LightRAG. A chat message without a query prefix will trigger a hybrid mode query by default. + +`/bypass` is not a LightRAG query mode; it will tell the API Server to pass the query directly to the underlying LLM, including the chat history. So the user can use the LLM to answer questions based on the chat history. If you are using Open WebUI as a front end, you can just switch the model to a normal LLM instead of using the `/bypass` prefix. + +`/context` is also not a LightRAG query mode; it will tell LightRAG to return only the context information prepared for the LLM. You can check the context if it's what you want, or process the context by yourself. + +### Add user prompt in chat + +When using LightRAG for content queries, avoid combining the search process with unrelated output processing, as this significantly impacts query effectiveness. User prompt is specifically designed to address this issue — it does not participate in the RAG retrieval phase, but rather guides the LLM on how to process the retrieved results after the query is completed. We can append square brackets to the query prefix to provide the LLM with the user prompt: + +``` +/[Use mermaid format for diagrams] Please draw a character relationship diagram for Scrooge +/mix[Use mermaid format for diagrams] Please draw a character relationship diagram for Scrooge +``` + +## API Key and Authentication + +By default, the LightRAG Server can be accessed without any authentication. We can configure the server with an API Key or account credentials to secure it. + +* API Key: + +``` +LIGHTRAG_API_KEY=your-secure-api-key-here +WHITELIST_PATHS=/health,/api/* +``` + +> Health check and Ollama emulation endpoints are excluded from API Key check by default. For security reasons, remove `/api/*` from `WHITELIST_PATHS` if the Ollama service is not required. + +The API key is passed using the request header `X-API-Key`. Below is an example of accessing the LightRAG Server via API: + +``` +curl -X 'POST' \ + 'http://localhost:9621/documents/scan' \ + -H 'accept: application/json' \ + -H 'X-API-Key: your-secure-api-key-here-123' \ + -d '' +``` + +* Account credentials (the Web UI requires login before access can be granted): + +LightRAG API Server implements JWT-based authentication using the HS256 algorithm. To enable secure access control, the following environment variables are required: + +```bash +# For jwt auth +AUTH_ACCOUNTS='admin:admin123,user1:pass456' +TOKEN_SECRET='your-key' +TOKEN_EXPIRE_HOURS=4 +``` + +> Currently, only the configuration of an administrator account and password is supported. A comprehensive account system is yet to be developed and implemented. + +If Account credentials are not configured, the Web UI will access the system as a Guest. Therefore, even if only an API Key is configured, all APIs can still be accessed through the Guest account, which remains insecure. Hence, to safeguard the API, it is necessary to configure both authentication methods simultaneously. + +## For Azure OpenAI Backend + +Azure OpenAI API can be created using the following commands in Azure CLI (you need to install Azure CLI first from [https://docs.microsoft.com/en-us/cli/azure/install-azure-cli](https://docs.microsoft.com/en-us/cli/azure/install-azure-cli)): + +```bash +# Change the resource group name, location, and OpenAI resource name as needed +RESOURCE_GROUP_NAME=LightRAG +LOCATION=swedencentral +RESOURCE_NAME=LightRAG-OpenAI + +az login +az group create --name $RESOURCE_GROUP_NAME --location $LOCATION +az cognitiveservices account create --name $RESOURCE_NAME --resource-group $RESOURCE_GROUP_NAME --kind OpenAI --sku S0 --location swedencentral +az cognitiveservices account deployment create --resource-group $RESOURCE_GROUP_NAME --model-format OpenAI --name $RESOURCE_NAME --deployment-name gpt-4o --model-name gpt-4o --model-version "2024-08-06" --sku-capacity 100 --sku-name "Standard" +az cognitiveservices account deployment create --resource-group $RESOURCE_GROUP_NAME --model-format OpenAI --name $RESOURCE_NAME --deployment-name text-embedding-3-large --model-name text-embedding-3-large --model-version "1" --sku-capacity 80 --sku-name "Standard" +az cognitiveservices account show --name $RESOURCE_NAME --resource-group $RESOURCE_GROUP_NAME --query "properties.endpoint" +az cognitiveservices account keys list --name $RESOURCE_NAME -g $RESOURCE_GROUP_NAME + +``` + +The output of the last command will give you the endpoint and the key for the OpenAI API. You can use these values to set the environment variables in the `.env` file. + +``` +# Azure OpenAI Configuration in .env: +LLM_BINDING=azure_openai +LLM_BINDING_HOST=your-azure-endpoint +LLM_MODEL=your-model-deployment-name +LLM_BINDING_API_KEY=your-azure-api-key +### API version is optional, defaults to latest version +AZURE_OPENAI_API_VERSION=2024-08-01-preview + +### If using Azure OpenAI for embeddings +EMBEDDING_BINDING=azure_openai +EMBEDDING_MODEL=your-embedding-deployment-name +``` + +## LightRAG Server Configuration in Detail + +The API Server can be configured in three ways (highest priority first): + +* Command line arguments +* Environment variables or .env file +* Config.ini (Only for storage configuration) + +Most of the configurations come with default settings; check out the details in the sample file: `.env.example`. Data storage configuration can also be set by config.ini. A sample file `config.ini.example` is provided for your convenience. + +### LLM and Embedding Backend Supported + +LightRAG supports binding to various LLM/Embedding backends: + +* ollama +* openai (including openai compatible) +* azure_openai +* lollms +* aws_bedrock + +Use environment variables `LLM_BINDING` or CLI argument `--llm-binding` to select the LLM backend type. Use environment variables `EMBEDDING_BINDING` or CLI argument `--embedding-binding` to select the Embedding backend type. + +For LLM and embedding configuration examples, please refer to the `env.example` file in the project's root directory. To view the complete list of configurable options for OpenAI and Ollama-compatible LLM interfaces, use the following commands: +``` +lightrag-server --llm-binding openai --help +lightrag-server --llm-binding ollama --help +lightrag-server --embedding-binding ollama --help +``` + +> Please use OpenAI-compatible method to access LLMs deployed by OpenRouter or vLLM/SGLang. You can pass additional parameters to OpenRouter or vLLM/SGLang through the `OPENAI_LLM_EXTRA_BODY` environment variable to disable reasoning mode or achieve other personalized controls. + +Set the max_tokens to **prevent excessively long or endless output loop** during the entity relationship extraction phase for Large Language Model (LLM) responses. The purpose of setting max_tokens parameter is to truncate LLM output before timeouts occur, thereby preventing document extraction failures. This addresses issues where certain text blocks (e.g., tables or citations) containing numerous entities and relationships can lead to overly long or even endless loop outputs from LLMs. This setting is particularly crucial for locally deployed, smaller-parameter models. Max tokens value can be calculated by this formula: `LLM_TIMEOUT * llm_output_tokens/second` (i.e. `180s * 50 tokens/s = 9000`) + +``` +# For vLLM/SGLang doployed models, or most of OpenAI compatible API provider +OPENAI_LLM_MAX_TOKENS=9000 + +# For Ollama Deployed Modeles +OLLAMA_LLM_NUM_PREDICT=9000 + +# For OpenAI o1-mini or newer modles +OPENAI_LLM_MAX_COMPLETION_TOKENS=9000 +``` + +### Entity Extraction Configuration + +* ENABLE_LLM_CACHE_FOR_EXTRACT: Enable LLM cache for entity extraction (default: true) + +It's very common to set `ENABLE_LLM_CACHE_FOR_EXTRACT` to true for a test environment to reduce the cost of LLM calls. + +### Storage Types Supported + +LightRAG uses 4 types of storage for different purposes: + +* KV_STORAGE: llm response cache, text chunks, document information +* VECTOR_STORAGE: entities vectors, relation vectors, chunks vectors +* GRAPH_STORAGE: entity relation graph +* DOC_STATUS_STORAGE: document indexing status + +LightRAG Server offers various storage implementations, with the default being an in-memory database that persists data to the WORKING_DIR directory. Additionally, LightRAG supports a wide range of storage solutions including PostgreSQL, MongoDB, FAISS, Milvus, Qdrant, Neo4j, Memgraph, and Redis. For detailed information on supported storage options, please refer to the storage section in the README.md file located in the root directory. + +You can select the storage implementation by configuring environment variables. For instance, prior to the initial launch of the API server, you can set the following environment variable to specify your desired storage implementation: + +``` +LIGHTRAG_KV_STORAGE=PGKVStorage +LIGHTRAG_VECTOR_STORAGE=PGVectorStorage +LIGHTRAG_GRAPH_STORAGE=PGGraphStorage +LIGHTRAG_DOC_STATUS_STORAGE=PGDocStatusStorage +``` + +You cannot change storage implementation selection after adding documents to LightRAG. Data migration from one storage implementation to another is not supported yet. For further information, please read the sample env file or config.ini file. + +### LLM Cache Migration Between Storage Types + +When switching the storage implementation in LightRAG, the LLM cache can be migrated from the existing storage to the new one. Subsequently, when re-uploading files to the new storage, the pre-existing LLM cache will significantly accelerate file processing. For detailed instructions on using the LLM cache migration tool, please refer to[README_MIGRATE_LLM_CACHE.md](../tools/README_MIGRATE_LLM_CACHE.md) + +### LightRAG API Server Command Line Options + +| Parameter | Default | Description | +| --------------------- | ------------- | ------------------------------------------------------------------------------------------------------------------------------- | +| --host | 0.0.0.0 | Server host | +| --port | 9621 | Server port | +| --working-dir | ./rag_storage | Working directory for RAG storage | +| --input-dir | ./inputs | Directory containing input documents | +| --max-async | 4 | Maximum number of async operations | +| --log-level | INFO | Logging level (DEBUG, INFO, WARNING, ERROR, CRITICAL) | +| --verbose | - | Verbose debug output (True, False) | +| --key | None | API key for authentication. Protects the LightRAG server against unauthorized access | +| --ssl | False | Enable HTTPS | +| --ssl-certfile | None | Path to SSL certificate file (required if --ssl is enabled) | +| --ssl-keyfile | None | Path to SSL private key file (required if --ssl is enabled) | +| --llm-binding | ollama | LLM binding type (lollms, ollama, openai, openai-ollama, azure_openai, aws_bedrock) | +| --embedding-binding | ollama | Embedding binding type (lollms, ollama, openai, azure_openai, aws_bedrock) | + +### Reranking Configuration + +Reranking query-recalled chunks can significantly enhance retrieval quality by re-ordering documents based on an optimized relevance scoring model. LightRAG currently supports the following rerank providers: + +- **Cohere / vLLM**: Offers full API integration with Cohere AI's `v2/rerank` endpoint. As vLLM provides a Cohere-compatible reranker API, all reranker models deployed via vLLM are also supported. +- **Jina AI**: Provides complete implementation compatibility with all Jina rerank models. +- **Aliyun**: Features a custom implementation designed to support Aliyun's rerank API format. + +The rerank provider is configured via the `.env` file. Below is an example configuration for a rerank model deployed locally using vLLM: + +``` +RERANK_BINDING=cohere +RERANK_MODEL=BAAI/bge-reranker-v2-m3 +RERANK_BINDING_HOST=http://localhost:8000/v1/rerank +RERANK_BINDING_API_KEY=your_rerank_api_key_here +``` + +Here is an example configuration for utilizing the Reranker service provided by Aliyun: + +``` +RERANK_BINDING=aliyun +RERANK_MODEL=gte-rerank-v2 +RERANK_BINDING_HOST=https://dashscope.aliyuncs.com/api/v1/services/rerank/text-rerank/text-rerank +RERANK_BINDING_API_KEY=your_rerank_api_key_here +``` + +For comprehensive reranker configuration examples, please refer to the `env.example` file. + +### Enable Reranking + +Reranking can be enabled or disabled on a per-query basis. + +The `/query` and `/query/stream` API endpoints include an `enable_rerank` parameter, which is set to `true` by default, controlling whether reranking is active for the current query. To change the default value of the `enable_rerank` parameter to `false`, set the following environment variable: + +``` +RERANK_BY_DEFAULT=False +``` + +### Include Chunk Content in References + +By default, the `/query` and `/query/stream` endpoints return references with only `reference_id` and `file_path`. For evaluation, debugging, or citation purposes, you can request the actual retrieved chunk content to be included in references. + +The `include_chunk_content` parameter (default: `false`) controls whether the actual text content of retrieved chunks is included in the response references. This is particularly useful for: + +- **RAG Evaluation**: Testing systems like RAGAS that need access to retrieved contexts +- **Debugging**: Verifying what content was actually used to generate the answer +- **Citation Display**: Showing users the exact text passages that support the response +- **Transparency**: Providing full visibility into the RAG retrieval process + +**Important**: The `content` field is an **array of strings**, where each string represents a chunk from the same file. A single file may correspond to multiple chunks, so the content is returned as a list to preserve chunk boundaries. + +**Example API Request:** + +```json +{ + "query": "What is LightRAG?", + "mode": "mix", + "include_references": true, + "include_chunk_content": true +} +``` + +**Example Response (with chunk content):** + +```json +{ + "response": "LightRAG is a graph-based RAG system...", + "references": [ + { + "reference_id": "1", + "file_path": "/documents/intro.md", + "content": [ + "LightRAG is a retrieval-augmented generation system that combines knowledge graphs with vector similarity search...", + "The system uses a dual-indexing approach with both vector embeddings and graph structures for enhanced retrieval..." + ] + }, + { + "reference_id": "2", + "file_path": "/documents/features.md", + "content": [ + "The system provides multiple query modes including local, global, hybrid, and mix modes..." + ] + } + ] +} +``` + +**Notes**: +- This parameter only works when `include_references=true`. Setting `include_chunk_content=true` without including references has no effect. +- **Breaking Change**: Prior versions returned `content` as a single concatenated string. Now it returns an array of strings to preserve individual chunk boundaries. If you need a single string, join the array elements with your preferred separator (e.g., `"\n\n".join(content)`). + +### .env Examples + +```bash +### Server Configuration +# HOST=0.0.0.0 +PORT=9621 +WORKERS=2 + +### Settings for document indexing +ENABLE_LLM_CACHE_FOR_EXTRACT=true +SUMMARY_LANGUAGE=Chinese +MAX_PARALLEL_INSERT=2 + +### LLM Configuration (Use valid host. For local services installed with docker, you can use host.docker.internal) +TIMEOUT=150 +MAX_ASYNC=4 + +LLM_BINDING=openai +LLM_MODEL=gpt-4o-mini +LLM_BINDING_HOST=https://api.openai.com/v1 +LLM_BINDING_API_KEY=your-api-key + +### Embedding Configuration (Use valid host. For local services installed with docker, you can use host.docker.internal) +# see also env.ollama-binding-options.example for fine tuning ollama +EMBEDDING_MODEL=bge-m3:latest +EMBEDDING_DIM=1024 +EMBEDDING_BINDING=ollama +EMBEDDING_BINDING_HOST=http://localhost:11434 + +### For JWT Auth +# AUTH_ACCOUNTS='admin:admin123,user1:pass456' +# TOKEN_SECRET=your-key-for-LightRAG-API-Server-xxx +# TOKEN_EXPIRE_HOURS=48 + +# LIGHTRAG_API_KEY=your-secure-api-key-here-123 +# WHITELIST_PATHS=/api/* +# WHITELIST_PATHS=/health,/api/* + +``` + +## Document and Chunk Processing Login Clarification + +The document processing pipeline in LightRAG is somewhat complex and is divided into two primary stages: the Extraction stage (entity and relationship extraction) and the Merging stage (entity and relationship merging). There are two key parameters that control pipeline concurrency: the maximum number of files processed in parallel (MAX_PARALLEL_INSERT) and the maximum number of concurrent LLM requests (MAX_ASYNC). The workflow is described as follows: + +1. MAX_ASYNC limits the total number of concurrent LLM requests in the system, including those for querying, extraction, and merging. LLM requests have different priorities: query operations have the highest priority, followed by merging, and then extraction. +2. MAX_PARALLEL_INSERT controls the number of files processed in parallel during the extraction stage. For optimal performance, MAX_PARALLEL_INSERT is recommended to be set between 2 and 10, typically MAX_ASYNC/3. Setting this value too high can increase the likelihood of naming conflicts among entities and relationships across different documents during the merge phase, thereby reducing its overall efficiency. +3. Within a single file, entity and relationship extractions from different text blocks are processed concurrently, with the degree of concurrency set by MAX_ASYNC. Only after MAX_ASYNC text blocks are processed will the system proceed to the next batch within the same file. +4. When a file completes entity and relationship extraction, it enters the entity and relationship merging stage. This stage also processes multiple entities and relationships concurrently, with the concurrency level also controlled by `MAX_ASYNC`. +5. LLM requests for the merging stage are prioritized over the extraction stage to ensure that files in the merging phase are processed quickly and their results are promptly updated in the vector database. +6. To prevent race conditions, the merging stage avoids concurrent processing of the same entity or relationship. When multiple files involve the same entity or relationship that needs to be merged, they are processed serially. +7. Each file is treated as an atomic processing unit in the pipeline. A file is marked as successfully processed only after all its text blocks have completed extraction and merging. If any error occurs during processing, the entire file is marked as failed and must be reprocessed. +8. When a file is reprocessed due to errors, previously processed text blocks can be quickly skipped thanks to LLM caching. Although LLM cache is also utilized during the merging stage, inconsistencies in merging order may limit its effectiveness in this stage. +9. If an error occurs during extraction, the system does not retain any intermediate results. If an error occurs during merging, already merged entities and relationships might be preserved; when the same file is reprocessed, re-extracted entities and relationships will be merged with the existing ones, without impacting the query results. +10. At the end of the merging stage, all entity and relationship data are updated in the vector database. Should an error occur at this point, some updates may be retained. However, the next processing attempt will overwrite previous results, ensuring that successfully reprocessed files do not affect the integrity of future query results. + +Large files should be divided into smaller segments to enable incremental processing. Reprocessing of failed files can be initiated by pressing the "Scan" button on the web UI. + +## API Endpoints + +All servers (LoLLMs, Ollama, OpenAI and Azure OpenAI) provide the same REST API endpoints for RAG functionality. When the API Server is running, visit: + +- Swagger UI: http://localhost:9621/docs +- ReDoc: http://localhost:9621/redoc + +You can test the API endpoints using the provided curl commands or through the Swagger UI interface. Make sure to: + +1. Start the appropriate backend service (LoLLMs, Ollama, or OpenAI) +2. Start the RAG server +3. Upload some documents using the document management endpoints +4. Query the system using the query endpoints +5. Trigger document scan if new files are put into the inputs directory + +## Asynchronous Document Indexing with Progress Tracking + +LightRAG implements asynchronous document indexing to enable frontend monitoring and querying of document processing progress. Upon uploading files or inserting text through designated endpoints, a unique Track ID is returned to facilitate real-time progress monitoring. + +**API Endpoints Supporting Track ID Generation:** + +* `/documents/upload` +* `/documents/text` +* `/documents/texts` + +**Document Processing Status Query Endpoint:** +* `/track_status/{track_id}` + +This endpoint provides comprehensive status information including: +* Document processing status (pending/processing/processed/failed) +* Content summary and metadata +* Error messages if processing failed +* Timestamps for creation and updates diff --git a/LightRAG/lightrag/api/__init__.py b/LightRAG/lightrag/api/__init__.py new file mode 100644 index 00000000..8fdc9fae --- /dev/null +++ b/LightRAG/lightrag/api/__init__.py @@ -0,0 +1 @@ +__api_version__ = "0254" diff --git a/LightRAG/lightrag/api/auth.py b/LightRAG/lightrag/api/auth.py new file mode 100644 index 00000000..0b61095d --- /dev/null +++ b/LightRAG/lightrag/api/auth.py @@ -0,0 +1,109 @@ +from datetime import datetime, timedelta + +import jwt +from dotenv import load_dotenv +from fastapi import HTTPException, status +from pydantic import BaseModel + +from .config import global_args + +# use the .env that is inside the current folder +# allows to use different .env file for each lightrag instance +# the OS environment variables take precedence over the .env file +load_dotenv(dotenv_path=".env", override=False) + + +class TokenPayload(BaseModel): + sub: str # Username + exp: datetime # Expiration time + role: str = "user" # User role, default is regular user + metadata: dict = {} # Additional metadata + + +class AuthHandler: + def __init__(self): + self.secret = global_args.token_secret + self.algorithm = global_args.jwt_algorithm + self.expire_hours = global_args.token_expire_hours + self.guest_expire_hours = global_args.guest_token_expire_hours + self.accounts = {} + auth_accounts = global_args.auth_accounts + if auth_accounts: + for account in auth_accounts.split(","): + username, password = account.split(":", 1) + self.accounts[username] = password + + def create_token( + self, + username: str, + role: str = "user", + custom_expire_hours: int = None, + metadata: dict = None, + ) -> str: + """ + Create JWT token + + Args: + username: Username + role: User role, default is "user", guest is "guest" + custom_expire_hours: Custom expiration time (hours), if None use default value + metadata: Additional metadata + + Returns: + str: Encoded JWT token + """ + # Choose default expiration time based on role + if custom_expire_hours is None: + if role == "guest": + expire_hours = self.guest_expire_hours + else: + expire_hours = self.expire_hours + else: + expire_hours = custom_expire_hours + + expire = datetime.utcnow() + timedelta(hours=expire_hours) + + # Create payload + payload = TokenPayload( + sub=username, exp=expire, role=role, metadata=metadata or {} + ) + + return jwt.encode(payload.dict(), self.secret, algorithm=self.algorithm) + + def validate_token(self, token: str) -> dict: + """ + Validate JWT token + + Args: + token: JWT token + + Returns: + dict: Dictionary containing user information + + Raises: + HTTPException: If token is invalid or expired + """ + try: + payload = jwt.decode(token, self.secret, algorithms=[self.algorithm]) + expire_timestamp = payload["exp"] + expire_time = datetime.utcfromtimestamp(expire_timestamp) + + if datetime.utcnow() > expire_time: + raise HTTPException( + status_code=status.HTTP_401_UNAUTHORIZED, detail="Token expired" + ) + + # Return complete payload instead of just username + return { + "username": payload["sub"], + "role": payload.get("role", "user"), + "metadata": payload.get("metadata", {}), + "exp": expire_time, + } + except jwt.PyJWTError: + raise HTTPException( + status_code=status.HTTP_401_UNAUTHORIZED, detail="Invalid token" + ) + + +auth_handler = AuthHandler() diff --git a/LightRAG/lightrag/api/config.py b/LightRAG/lightrag/api/config.py new file mode 100644 index 00000000..4f59d3c1 --- /dev/null +++ b/LightRAG/lightrag/api/config.py @@ -0,0 +1,549 @@ +""" +Configs for the LightRAG API. +""" + +import os +import argparse +import logging +from dotenv import load_dotenv +from lightrag.utils import get_env_value +from lightrag.llm.binding_options import ( + GeminiEmbeddingOptions, + GeminiLLMOptions, + OllamaEmbeddingOptions, + OllamaLLMOptions, + OpenAILLMOptions, +) +from lightrag.base import OllamaServerInfos +import sys + +from lightrag.constants import ( + DEFAULT_WOKERS, + DEFAULT_TIMEOUT, + DEFAULT_TOP_K, + DEFAULT_CHUNK_TOP_K, + DEFAULT_HISTORY_TURNS, + DEFAULT_MAX_ENTITY_TOKENS, + DEFAULT_MAX_RELATION_TOKENS, + DEFAULT_MAX_TOTAL_TOKENS, + DEFAULT_COSINE_THRESHOLD, + DEFAULT_RELATED_CHUNK_NUMBER, + DEFAULT_MIN_RERANK_SCORE, + DEFAULT_FORCE_LLM_SUMMARY_ON_MERGE, + DEFAULT_MAX_ASYNC, + DEFAULT_SUMMARY_MAX_TOKENS, + DEFAULT_SUMMARY_LENGTH_RECOMMENDED, + DEFAULT_SUMMARY_CONTEXT_SIZE, + DEFAULT_SUMMARY_LANGUAGE, + DEFAULT_EMBEDDING_FUNC_MAX_ASYNC, + DEFAULT_EMBEDDING_BATCH_NUM, + DEFAULT_OLLAMA_MODEL_NAME, + DEFAULT_OLLAMA_MODEL_TAG, + DEFAULT_RERANK_BINDING, + DEFAULT_ENTITY_TYPES, +) + +# use the .env that is inside the current folder +# allows to use different .env file for each lightrag instance +# the OS environment variables take precedence over the .env file +load_dotenv(dotenv_path=".env", override=False) + + +ollama_server_infos = OllamaServerInfos() + + +class DefaultRAGStorageConfig: + KV_STORAGE = "JsonKVStorage" + VECTOR_STORAGE = "NanoVectorDBStorage" + GRAPH_STORAGE = "NetworkXStorage" + DOC_STATUS_STORAGE = "JsonDocStatusStorage" + + +def get_default_host(binding_type: str) -> str: + default_hosts = { + "ollama": os.getenv("LLM_BINDING_HOST", "http://localhost:11434"), + "lollms": os.getenv("LLM_BINDING_HOST", "http://localhost:9600"), + "azure_openai": os.getenv("AZURE_OPENAI_ENDPOINT", "https://api.openai.com/v1"), + "openai": os.getenv("LLM_BINDING_HOST", "https://api.openai.com/v1"), + "gemini": os.getenv( + "LLM_BINDING_HOST", "https://generativelanguage.googleapis.com" + ), + } + return default_hosts.get( + binding_type, os.getenv("LLM_BINDING_HOST", "http://localhost:11434") + ) # fallback to ollama if unknown + + +def parse_args() -> argparse.Namespace: + """ + Parse command line arguments with environment variable fallback + + Args: + is_uvicorn_mode: Whether running under uvicorn mode + + Returns: + argparse.Namespace: Parsed arguments + """ + + parser = argparse.ArgumentParser(description="LightRAG API Server") + + # Server configuration + parser.add_argument( + "--host", + default=get_env_value("HOST", "0.0.0.0"), + help="Server host (default: from env or 0.0.0.0)", + ) + parser.add_argument( + "--port", + type=int, + default=get_env_value("PORT", 9621, int), + help="Server port (default: from env or 9621)", + ) + + # Directory configuration + parser.add_argument( + "--working-dir", + default=get_env_value("WORKING_DIR", "./rag_storage"), + help="Working directory for RAG storage (default: from env or ./rag_storage)", + ) + parser.add_argument( + "--input-dir", + default=get_env_value("INPUT_DIR", "./inputs"), + help="Directory containing input documents (default: from env or ./inputs)", + ) + + parser.add_argument( + "--timeout", + default=get_env_value("TIMEOUT", DEFAULT_TIMEOUT, int, special_none=True), + type=int, + help="Timeout in seconds (useful when using slow AI). Use None for infinite timeout", + ) + + # RAG configuration + parser.add_argument( + "--max-async", + type=int, + default=get_env_value("MAX_ASYNC", DEFAULT_MAX_ASYNC, int), + help=f"Maximum async operations (default: from env or {DEFAULT_MAX_ASYNC})", + ) + parser.add_argument( + "--summary-max-tokens", + type=int, + default=get_env_value("SUMMARY_MAX_TOKENS", DEFAULT_SUMMARY_MAX_TOKENS, int), + help=f"Maximum token size for entity/relation summary(default: from env or {DEFAULT_SUMMARY_MAX_TOKENS})", + ) + parser.add_argument( + "--summary-context-size", + type=int, + default=get_env_value( + "SUMMARY_CONTEXT_SIZE", DEFAULT_SUMMARY_CONTEXT_SIZE, int + ), + help=f"LLM Summary Context size (default: from env or {DEFAULT_SUMMARY_CONTEXT_SIZE})", + ) + parser.add_argument( + "--summary-length-recommended", + type=int, + default=get_env_value( + "SUMMARY_LENGTH_RECOMMENDED", DEFAULT_SUMMARY_LENGTH_RECOMMENDED, int + ), + help=f"LLM Summary Context size (default: from env or {DEFAULT_SUMMARY_LENGTH_RECOMMENDED})", + ) + + # Logging configuration + parser.add_argument( + "--log-level", + default=get_env_value("LOG_LEVEL", "INFO"), + choices=["DEBUG", "INFO", "WARNING", "ERROR", "CRITICAL"], + help="Logging level (default: from env or INFO)", + ) + parser.add_argument( + "--verbose", + action="store_true", + default=get_env_value("VERBOSE", False, bool), + help="Enable verbose debug output(only valid for DEBUG log-level)", + ) + + parser.add_argument( + "--key", + type=str, + default=get_env_value("LIGHTRAG_API_KEY", None), + help="API key for authentication. This protects lightrag server against unauthorized access", + ) + + # Optional https parameters + parser.add_argument( + "--ssl", + action="store_true", + default=get_env_value("SSL", False, bool), + help="Enable HTTPS (default: from env or False)", + ) + parser.add_argument( + "--ssl-certfile", + default=get_env_value("SSL_CERTFILE", None), + help="Path to SSL certificate file (required if --ssl is enabled)", + ) + parser.add_argument( + "--ssl-keyfile", + default=get_env_value("SSL_KEYFILE", None), + help="Path to SSL private key file (required if --ssl is enabled)", + ) + + # Ollama model configuration + parser.add_argument( + "--simulated-model-name", + type=str, + default=get_env_value("OLLAMA_EMULATING_MODEL_NAME", DEFAULT_OLLAMA_MODEL_NAME), + help="Name for the simulated Ollama model (default: from env or lightrag)", + ) + + parser.add_argument( + "--simulated-model-tag", + type=str, + default=get_env_value("OLLAMA_EMULATING_MODEL_TAG", DEFAULT_OLLAMA_MODEL_TAG), + help="Tag for the simulated Ollama model (default: from env or latest)", + ) + + # Namespace + parser.add_argument( + "--workspace", + type=str, + default=get_env_value("WORKSPACE", ""), + help="Default workspace for all storage", + ) + + # Server workers configuration + parser.add_argument( + "--workers", + type=int, + default=get_env_value("WORKERS", DEFAULT_WOKERS, int), + help="Number of worker processes (default: from env or 1)", + ) + + # LLM and embedding bindings + parser.add_argument( + "--llm-binding", + type=str, + default=get_env_value("LLM_BINDING", "ollama"), + choices=[ + "lollms", + "ollama", + "openai", + "openai-ollama", + "azure_openai", + "aws_bedrock", + "gemini", + ], + help="LLM binding type (default: from env or ollama)", + ) + parser.add_argument( + "--embedding-binding", + type=str, + default=get_env_value("EMBEDDING_BINDING", "ollama"), + choices=[ + "lollms", + "ollama", + "openai", + "azure_openai", + "aws_bedrock", + "jina", + "gemini", + ], + help="Embedding binding type (default: from env or ollama)", + ) + parser.add_argument( + "--rerank-binding", + type=str, + default=get_env_value("RERANK_BINDING", DEFAULT_RERANK_BINDING), + choices=["null", "cohere", "jina", "aliyun"], + help=f"Rerank binding type (default: from env or {DEFAULT_RERANK_BINDING})", + ) + + # Document loading engine configuration + parser.add_argument( + "--docling", + action="store_true", + default=False, + help="Enable DOCLING document loading engine (default: from env or DEFAULT)", + ) + + # Conditionally add binding options defined in binding_options module + # This will add command line arguments for all binding options (e.g., --ollama-embedding-num_ctx) + # and corresponding environment variables (e.g., OLLAMA_EMBEDDING_NUM_CTX) + if "--llm-binding" in sys.argv: + try: + idx = sys.argv.index("--llm-binding") + if idx + 1 < len(sys.argv) and sys.argv[idx + 1] == "ollama": + OllamaLLMOptions.add_args(parser) + except IndexError: + pass + elif os.environ.get("LLM_BINDING") == "ollama": + OllamaLLMOptions.add_args(parser) + + if "--embedding-binding" in sys.argv: + try: + idx = sys.argv.index("--embedding-binding") + if idx + 1 < len(sys.argv): + if sys.argv[idx + 1] == "ollama": + OllamaEmbeddingOptions.add_args(parser) + elif sys.argv[idx + 1] == "gemini": + GeminiEmbeddingOptions.add_args(parser) + except IndexError: + pass + else: + env_embedding_binding = os.environ.get("EMBEDDING_BINDING") + if env_embedding_binding == "ollama": + OllamaEmbeddingOptions.add_args(parser) + elif env_embedding_binding == "gemini": + GeminiEmbeddingOptions.add_args(parser) + + # Add OpenAI LLM options when llm-binding is openai or azure_openai + if "--llm-binding" in sys.argv: + try: + idx = sys.argv.index("--llm-binding") + if idx + 1 < len(sys.argv) and sys.argv[idx + 1] in [ + "openai", + "azure_openai", + ]: + OpenAILLMOptions.add_args(parser) + except IndexError: + pass + elif os.environ.get("LLM_BINDING") in ["openai", "azure_openai"]: + OpenAILLMOptions.add_args(parser) + + if "--llm-binding" in sys.argv: + try: + idx = sys.argv.index("--llm-binding") + if idx + 1 < len(sys.argv) and sys.argv[idx + 1] == "gemini": + GeminiLLMOptions.add_args(parser) + except IndexError: + pass + elif os.environ.get("LLM_BINDING") == "gemini": + GeminiLLMOptions.add_args(parser) + + args = parser.parse_args() + + # convert relative path to absolute path + args.working_dir = os.path.abspath(args.working_dir) + args.input_dir = os.path.abspath(args.input_dir) + + # Inject storage configuration from environment variables + args.kv_storage = get_env_value( + "LIGHTRAG_KV_STORAGE", DefaultRAGStorageConfig.KV_STORAGE + ) + args.doc_status_storage = get_env_value( + "LIGHTRAG_DOC_STATUS_STORAGE", DefaultRAGStorageConfig.DOC_STATUS_STORAGE + ) + args.graph_storage = get_env_value( + "LIGHTRAG_GRAPH_STORAGE", DefaultRAGStorageConfig.GRAPH_STORAGE + ) + args.vector_storage = get_env_value( + "LIGHTRAG_VECTOR_STORAGE", DefaultRAGStorageConfig.VECTOR_STORAGE + ) + + # Get MAX_PARALLEL_INSERT from environment + args.max_parallel_insert = get_env_value("MAX_PARALLEL_INSERT", 2, int) + + # Get MAX_GRAPH_NODES from environment + args.max_graph_nodes = get_env_value("MAX_GRAPH_NODES", 1000, int) + + # Handle openai-ollama special case + if args.llm_binding == "openai-ollama": + args.llm_binding = "openai" + args.embedding_binding = "ollama" + + # Ollama ctx_num + args.ollama_num_ctx = get_env_value("OLLAMA_NUM_CTX", 32768, int) + + args.llm_binding_host = get_env_value( + "LLM_BINDING_HOST", get_default_host(args.llm_binding) + ) + args.embedding_binding_host = get_env_value( + "EMBEDDING_BINDING_HOST", get_default_host(args.embedding_binding) + ) + args.llm_binding_api_key = get_env_value("LLM_BINDING_API_KEY", None) + args.embedding_binding_api_key = get_env_value("EMBEDDING_BINDING_API_KEY", "") + + # Inject model configuration + args.llm_model = get_env_value("LLM_MODEL", "mistral-nemo:latest") + args.embedding_model = get_env_value("EMBEDDING_MODEL", "bge-m3:latest") + args.embedding_dim = get_env_value("EMBEDDING_DIM", 1024, int) + args.embedding_send_dim = get_env_value("EMBEDDING_SEND_DIM", False, bool) + + # Inject chunk configuration + args.chunk_size = get_env_value("CHUNK_SIZE", 1200, int) + args.chunk_overlap_size = get_env_value("CHUNK_OVERLAP_SIZE", 100, int) + + # Inject LLM cache configuration + args.enable_llm_cache_for_extract = get_env_value( + "ENABLE_LLM_CACHE_FOR_EXTRACT", True, bool + ) + args.enable_llm_cache = get_env_value("ENABLE_LLM_CACHE", True, bool) + + # Set document_loading_engine from --docling flag + if args.docling: + args.document_loading_engine = "DOCLING" + else: + args.document_loading_engine = get_env_value( + "DOCUMENT_LOADING_ENGINE", "DEFAULT" + ) + + # PDF decryption password + args.pdf_decrypt_password = get_env_value("PDF_DECRYPT_PASSWORD", None) + + # Add environment variables that were previously read directly + args.cors_origins = get_env_value("CORS_ORIGINS", "*") + args.summary_language = get_env_value("SUMMARY_LANGUAGE", DEFAULT_SUMMARY_LANGUAGE) + args.entity_types = get_env_value("ENTITY_TYPES", DEFAULT_ENTITY_TYPES, list) + args.whitelist_paths = get_env_value("WHITELIST_PATHS", "/health,/api/*") + + # For JWT Auth + args.auth_accounts = get_env_value("AUTH_ACCOUNTS", "") + args.token_secret = get_env_value("TOKEN_SECRET", "lightrag-jwt-default-secret") + args.token_expire_hours = get_env_value("TOKEN_EXPIRE_HOURS", 48, int) + args.guest_token_expire_hours = get_env_value("GUEST_TOKEN_EXPIRE_HOURS", 24, int) + args.jwt_algorithm = get_env_value("JWT_ALGORITHM", "HS256") + + # Rerank model configuration + args.rerank_model = get_env_value("RERANK_MODEL", None) + args.rerank_binding_host = get_env_value("RERANK_BINDING_HOST", None) + args.rerank_binding_api_key = get_env_value("RERANK_BINDING_API_KEY", None) + # Note: rerank_binding is already set by argparse, no need to override from env + + # Min rerank score configuration + args.min_rerank_score = get_env_value( + "MIN_RERANK_SCORE", DEFAULT_MIN_RERANK_SCORE, float + ) + + # Query configuration + args.history_turns = get_env_value("HISTORY_TURNS", DEFAULT_HISTORY_TURNS, int) + args.top_k = get_env_value("TOP_K", DEFAULT_TOP_K, int) + args.chunk_top_k = get_env_value("CHUNK_TOP_K", DEFAULT_CHUNK_TOP_K, int) + args.max_entity_tokens = get_env_value( + "MAX_ENTITY_TOKENS", DEFAULT_MAX_ENTITY_TOKENS, int + ) + args.max_relation_tokens = get_env_value( + "MAX_RELATION_TOKENS", DEFAULT_MAX_RELATION_TOKENS, int + ) + args.max_total_tokens = get_env_value( + "MAX_TOTAL_TOKENS", DEFAULT_MAX_TOTAL_TOKENS, int + ) + args.cosine_threshold = get_env_value( + "COSINE_THRESHOLD", DEFAULT_COSINE_THRESHOLD, float + ) + args.related_chunk_number = get_env_value( + "RELATED_CHUNK_NUMBER", DEFAULT_RELATED_CHUNK_NUMBER, int + ) + + # Add missing environment variables for health endpoint + args.force_llm_summary_on_merge = get_env_value( + "FORCE_LLM_SUMMARY_ON_MERGE", DEFAULT_FORCE_LLM_SUMMARY_ON_MERGE, int + ) + args.embedding_func_max_async = get_env_value( + "EMBEDDING_FUNC_MAX_ASYNC", DEFAULT_EMBEDDING_FUNC_MAX_ASYNC, int + ) + args.embedding_batch_num = get_env_value( + "EMBEDDING_BATCH_NUM", DEFAULT_EMBEDDING_BATCH_NUM, int + ) + + # Embedding token limit configuration + args.embedding_token_limit = get_env_value( + "EMBEDDING_TOKEN_LIMIT", None, int, special_none=True + ) + + ollama_server_infos.LIGHTRAG_NAME = args.simulated_model_name + ollama_server_infos.LIGHTRAG_TAG = args.simulated_model_tag + + return args + + +def update_uvicorn_mode_config(): + # If in uvicorn mode and workers > 1, force it to 1 and log warning + if global_args.workers > 1: + original_workers = global_args.workers + global_args.workers = 1 + # Log warning directly here + logging.warning( + f">> Forcing workers=1 in uvicorn mode(Ignoring workers={original_workers})" + ) + + +# Global configuration with lazy initialization +_global_args = None +_initialized = False + + +def initialize_config(args=None, force=False): + """Initialize global configuration + + This function allows explicit initialization of the configuration, + which is useful for programmatic usage, testing, or embedding LightRAG + in other applications. + + Args: + args: Pre-parsed argparse.Namespace or None to parse from sys.argv + force: Force re-initialization even if already initialized + + Returns: + argparse.Namespace: The configured arguments + + Example: + # Use parsed command line arguments (default) + initialize_config() + + # Use custom configuration programmatically + custom_args = argparse.Namespace( + host='localhost', + port=8080, + working_dir='./custom_rag', + # ... other config + ) + initialize_config(custom_args) + """ + global _global_args, _initialized + + if _initialized and not force: + return _global_args + + _global_args = args if args is not None else parse_args() + _initialized = True + return _global_args + + +def get_config(): + """Get global configuration, auto-initializing if needed + + Returns: + argparse.Namespace: The configured arguments + """ + if not _initialized: + initialize_config() + return _global_args + + +class _GlobalArgsProxy: + """Proxy object that auto-initializes configuration on first access + + This maintains backward compatibility with existing code while + allowing programmatic control over initialization timing. + """ + + def __getattr__(self, name): + if not _initialized: + initialize_config() + return getattr(_global_args, name) + + def __setattr__(self, name, value): + if not _initialized: + initialize_config() + setattr(_global_args, name, value) + + def __repr__(self): + if not _initialized: + return "" + return repr(_global_args) + + +# Create proxy instance for backward compatibility +# Existing code like `from config import global_args` continues to work +# The proxy will auto-initialize on first attribute access +global_args = _GlobalArgsProxy() diff --git a/LightRAG/lightrag/api/gunicorn_config.py b/LightRAG/lightrag/api/gunicorn_config.py new file mode 100644 index 00000000..4d2292d0 --- /dev/null +++ b/LightRAG/lightrag/api/gunicorn_config.py @@ -0,0 +1,162 @@ +# gunicorn_config.py +import os +import logging +from lightrag.kg.shared_storage import finalize_share_data +from lightrag.utils import setup_logger, get_env_value +from lightrag.constants import ( + DEFAULT_LOG_MAX_BYTES, + DEFAULT_LOG_BACKUP_COUNT, + DEFAULT_LOG_FILENAME, +) + + +# Get log directory path from environment variable +log_dir = os.getenv("LOG_DIR", os.getcwd()) +log_file_path = os.path.abspath(os.path.join(log_dir, DEFAULT_LOG_FILENAME)) + +# Ensure log directory exists +os.makedirs(os.path.dirname(log_file_path), exist_ok=True) + +# Get log file max size and backup count from environment variables +log_max_bytes = get_env_value("LOG_MAX_BYTES", DEFAULT_LOG_MAX_BYTES, int) +log_backup_count = get_env_value("LOG_BACKUP_COUNT", DEFAULT_LOG_BACKUP_COUNT, int) + +# These variables will be set by run_with_gunicorn.py +workers = None +bind = None +loglevel = None +certfile = None +keyfile = None + +# Enable preload_app option +preload_app = True + +# Use Uvicorn worker +worker_class = "uvicorn.workers.UvicornWorker" + +# Other Gunicorn configurations + +# Logging configuration +errorlog = os.getenv("ERROR_LOG", log_file_path) # Default write to lightrag.log +accesslog = os.getenv("ACCESS_LOG", log_file_path) # Default write to lightrag.log + +logconfig_dict = { + "version": 1, + "disable_existing_loggers": False, + "formatters": { + "standard": {"format": "%(asctime)s [%(levelname)s] %(name)s: %(message)s"}, + }, + "handlers": { + "console": { + "class": "logging.StreamHandler", + "formatter": "standard", + "stream": "ext://sys.stdout", + }, + "file": { + "class": "logging.handlers.RotatingFileHandler", + "formatter": "standard", + "filename": log_file_path, + "maxBytes": log_max_bytes, + "backupCount": log_backup_count, + "encoding": "utf8", + }, + }, + "filters": { + "path_filter": { + "()": "lightrag.utils.LightragPathFilter", + }, + }, + "loggers": { + "lightrag": { + "handlers": ["console", "file"], + "level": loglevel.upper() if loglevel else "INFO", + "propagate": False, + }, + "gunicorn": { + "handlers": ["console", "file"], + "level": loglevel.upper() if loglevel else "INFO", + "propagate": False, + }, + "gunicorn.error": { + "handlers": ["console", "file"], + "level": loglevel.upper() if loglevel else "INFO", + "propagate": False, + }, + "gunicorn.access": { + "handlers": ["console", "file"], + "level": loglevel.upper() if loglevel else "INFO", + "propagate": False, + "filters": ["path_filter"], + }, + }, +} + + +def on_starting(server): + """ + Executed when Gunicorn starts, before forking the first worker processes + You can use this function to do more initialization tasks for all processes + """ + print("=" * 80) + print(f"GUNICORN MASTER PROCESS: on_starting jobs for {workers} worker(s)") + print(f"Process ID: {os.getpid()}") + print("=" * 80) + + # Memory usage monitoring + try: + import psutil + + process = psutil.Process(os.getpid()) + memory_info = process.memory_info() + msg = ( + f"Memory usage after initialization: {memory_info.rss / 1024 / 1024:.2f} MB" + ) + print(msg) + except ImportError: + print("psutil not installed, skipping memory usage reporting") + + # Log the location of the LightRAG log file + print(f"LightRAG log file: {log_file_path}\n") + + print("Gunicorn initialization complete, forking workers...\n") + + +def on_exit(server): + """ + Executed when Gunicorn is shutting down. + This is a good place to release shared resources. + """ + print("=" * 80) + print("GUNICORN MASTER PROCESS: Shutting down") + print(f"Process ID: {os.getpid()}") + + print("Finalizing shared storage...") + finalize_share_data() + + print("Gunicorn shutdown complete") + print("=" * 80) + + +def post_fork(server, worker): + """ + Executed after a worker has been forked. + This is a good place to set up worker-specific configurations. + """ + # Set up main loggers + log_level = loglevel.upper() if loglevel else "INFO" + setup_logger("uvicorn", log_level, add_filter=False, log_file_path=log_file_path) + setup_logger( + "uvicorn.access", log_level, add_filter=True, log_file_path=log_file_path + ) + setup_logger("lightrag", log_level, add_filter=True, log_file_path=log_file_path) + + # Set up lightrag submodule loggers + for name in logging.root.manager.loggerDict: + if name.startswith("lightrag."): + setup_logger(name, log_level, add_filter=True, log_file_path=log_file_path) + + # Disable uvicorn.error logger + uvicorn_error_logger = logging.getLogger("uvicorn.error") + uvicorn_error_logger.handlers = [] + uvicorn_error_logger.setLevel(logging.CRITICAL) + uvicorn_error_logger.propagate = False diff --git a/LightRAG/lightrag/api/lightrag_server.py b/LightRAG/lightrag/api/lightrag_server.py new file mode 100644 index 00000000..41a07f7f --- /dev/null +++ b/LightRAG/lightrag/api/lightrag_server.py @@ -0,0 +1,1398 @@ +""" +LightRAG FastAPI Server +""" + +from fastapi import FastAPI, Depends, HTTPException, Request +from fastapi.exceptions import RequestValidationError +from fastapi.responses import JSONResponse +from fastapi.openapi.docs import ( + get_swagger_ui_html, + get_swagger_ui_oauth2_redirect_html, +) +import os +import logging +import logging.config +import sys +import uvicorn +import pipmaster as pm +from fastapi.staticfiles import StaticFiles +from fastapi.responses import RedirectResponse +from pathlib import Path +import configparser +from ascii_colors import ASCIIColors +from fastapi.middleware.cors import CORSMiddleware +from contextlib import asynccontextmanager +from dotenv import load_dotenv +from lightrag.api.utils_api import ( + get_combined_auth_dependency, + display_splash_screen, + check_env_file, +) +from .config import ( + global_args, + update_uvicorn_mode_config, + get_default_host, +) +from lightrag.utils import get_env_value +from lightrag import LightRAG, __version__ as core_version +from lightrag.api import __api_version__ +from lightrag.types import GPTKeywordExtractionFormat +from lightrag.utils import EmbeddingFunc +from lightrag.constants import ( + DEFAULT_LOG_MAX_BYTES, + DEFAULT_LOG_BACKUP_COUNT, + DEFAULT_LOG_FILENAME, + DEFAULT_LLM_TIMEOUT, + DEFAULT_EMBEDDING_TIMEOUT, +) +from lightrag.api.routers.document_routes import ( + DocumentManager, + create_document_routes, +) +from lightrag.api.routers.query_routes import create_query_routes +from lightrag.api.routers.graph_routes import create_graph_routes +from lightrag.api.routers.ollama_api import OllamaAPI + +from lightrag.utils import logger, set_verbose_debug +from lightrag.kg.shared_storage import ( + get_namespace_data, + initialize_pipeline_status, + cleanup_keyed_lock, + finalize_share_data, +) +from fastapi.security import OAuth2PasswordRequestForm +from lightrag.api.auth import auth_handler + +# use the .env that is inside the current folder +# allows to use different .env file for each lightrag instance +# the OS environment variables take precedence over the .env file +load_dotenv(dotenv_path=".env", override=False) + + +webui_title = os.getenv("WEBUI_TITLE") +webui_description = os.getenv("WEBUI_DESCRIPTION") + +# Initialize config parser +config = configparser.ConfigParser() +config.read("config.ini") + +# Global authentication configuration +auth_configured = bool(auth_handler.accounts) + + +class LLMConfigCache: + """Smart LLM and Embedding configuration cache class""" + + def __init__(self, args): + self.args = args + + # Initialize configurations based on binding conditions + self.openai_llm_options = None + self.gemini_llm_options = None + self.gemini_embedding_options = None + self.ollama_llm_options = None + self.ollama_embedding_options = None + + # Only initialize and log OpenAI options when using OpenAI-related bindings + if args.llm_binding in ["openai", "azure_openai"]: + from lightrag.llm.binding_options import OpenAILLMOptions + + self.openai_llm_options = OpenAILLMOptions.options_dict(args) + logger.info(f"OpenAI LLM Options: {self.openai_llm_options}") + + if args.llm_binding == "gemini": + from lightrag.llm.binding_options import GeminiLLMOptions + + self.gemini_llm_options = GeminiLLMOptions.options_dict(args) + logger.info(f"Gemini LLM Options: {self.gemini_llm_options}") + + # Only initialize and log Ollama LLM options when using Ollama LLM binding + if args.llm_binding == "ollama": + try: + from lightrag.llm.binding_options import OllamaLLMOptions + + self.ollama_llm_options = OllamaLLMOptions.options_dict(args) + logger.info(f"Ollama LLM Options: {self.ollama_llm_options}") + except ImportError: + logger.warning( + "OllamaLLMOptions not available, using default configuration" + ) + self.ollama_llm_options = {} + + # Only initialize and log Ollama Embedding options when using Ollama Embedding binding + if args.embedding_binding == "ollama": + try: + from lightrag.llm.binding_options import OllamaEmbeddingOptions + + self.ollama_embedding_options = OllamaEmbeddingOptions.options_dict( + args + ) + logger.info( + f"Ollama Embedding Options: {self.ollama_embedding_options}" + ) + except ImportError: + logger.warning( + "OllamaEmbeddingOptions not available, using default configuration" + ) + self.ollama_embedding_options = {} + + # Only initialize and log Gemini Embedding options when using Gemini Embedding binding + if args.embedding_binding == "gemini": + try: + from lightrag.llm.binding_options import GeminiEmbeddingOptions + + self.gemini_embedding_options = GeminiEmbeddingOptions.options_dict( + args + ) + logger.info( + f"Gemini Embedding Options: {self.gemini_embedding_options}" + ) + except ImportError: + logger.warning( + "GeminiEmbeddingOptions not available, using default configuration" + ) + self.gemini_embedding_options = {} + + +def check_frontend_build(): + """Check if frontend is built and optionally check if source is up-to-date + + Returns: + bool: True if frontend is outdated, False if up-to-date or production environment + """ + webui_dir = Path(__file__).parent / "webui" + index_html = webui_dir / "index.html" + + # 1. Check if build files exist (required) + if not index_html.exists(): + ASCIIColors.red("\n" + "=" * 80) + ASCIIColors.red("ERROR: Frontend Not Built") + ASCIIColors.red("=" * 80) + ASCIIColors.yellow("The WebUI frontend has not been built yet.") + ASCIIColors.yellow( + "Please build the frontend code first using the following commands:\n" + ) + ASCIIColors.cyan(" cd lightrag_webui") + ASCIIColors.cyan(" bun install --frozen-lockfile") + ASCIIColors.cyan(" bun run build") + ASCIIColors.cyan(" cd ..") + ASCIIColors.yellow("\nThen restart the service.\n") + ASCIIColors.cyan( + "Note: Make sure you have Bun installed. Visit https://bun.sh for installation." + ) + ASCIIColors.red("=" * 80 + "\n") + sys.exit(1) # Exit immediately + + # 2. Check if this is a development environment (source directory exists) + try: + source_dir = Path(__file__).parent.parent.parent / "lightrag_webui" + src_dir = source_dir / "src" + + # Determine if this is a development environment: source directory exists and contains src directory + if not source_dir.exists() or not src_dir.exists(): + # Production environment, skip source code check + logger.debug( + "Production environment detected, skipping source freshness check" + ) + return False + + # Development environment, perform source code timestamp check + logger.debug("Development environment detected, checking source freshness") + + # Source code file extensions (files to check) + source_extensions = { + ".ts", + ".tsx", + ".js", + ".jsx", + ".mjs", + ".cjs", # TypeScript/JavaScript + ".css", + ".scss", + ".sass", + ".less", # Style files + ".json", + ".jsonc", # Configuration/data files + ".html", + ".htm", # Template files + ".md", + ".mdx", # Markdown + } + + # Key configuration files (in lightrag_webui root directory) + key_files = [ + source_dir / "package.json", + source_dir / "bun.lock", + source_dir / "vite.config.ts", + source_dir / "tsconfig.json", + source_dir / "tailraid.config.js", + source_dir / "index.html", + ] + + # Get the latest modification time of source code + latest_source_time = 0 + + # Check source code files in src directory + for file_path in src_dir.rglob("*"): + if file_path.is_file(): + # Only check source code files, ignore temporary files and logs + if file_path.suffix.lower() in source_extensions: + mtime = file_path.stat().st_mtime + latest_source_time = max(latest_source_time, mtime) + + # Check key configuration files + for key_file in key_files: + if key_file.exists(): + mtime = key_file.stat().st_mtime + latest_source_time = max(latest_source_time, mtime) + + # Get build time + build_time = index_html.stat().st_mtime + + # Compare timestamps (5 second tolerance to avoid file system time precision issues) + if latest_source_time > build_time + 5: + ASCIIColors.yellow("\n" + "=" * 80) + ASCIIColors.yellow("WARNING: Frontend Source Code Has Been Updated") + ASCIIColors.yellow("=" * 80) + ASCIIColors.yellow( + "The frontend source code is newer than the current build." + ) + ASCIIColors.yellow( + "This might happen after 'git pull' or manual code changes.\n" + ) + ASCIIColors.cyan( + "Recommended: Rebuild the frontend to use the latest changes:" + ) + ASCIIColors.cyan(" cd lightrag_webui") + ASCIIColors.cyan(" bun install --frozen-lockfile") + ASCIIColors.cyan(" bun run build") + ASCIIColors.cyan(" cd ..") + ASCIIColors.yellow("\nThe server will continue with the current build.") + ASCIIColors.yellow("=" * 80 + "\n") + return True # Frontend is outdated + else: + logger.info("Frontend build is up-to-date") + return False # Frontend is up-to-date + + except Exception as e: + # If check fails, log warning but don't affect startup + logger.warning(f"Failed to check frontend source freshness: {e}") + return False # Assume up-to-date on error + + +def create_app(args): + # Check frontend build first and get outdated status + is_frontend_outdated = check_frontend_build() + + # Create unified API version display with warning symbol if frontend is outdated + api_version_display = ( + f"{__api_version__}⚠️" if is_frontend_outdated else __api_version__ + ) + + # Setup logging + logger.setLevel(args.log_level) + set_verbose_debug(args.verbose) + + # Create configuration cache (this will output configuration logs) + config_cache = LLMConfigCache(args) + + # Verify that bindings are correctly setup + if args.llm_binding not in [ + "lollms", + "ollama", + "openai", + "azure_openai", + "aws_bedrock", + "gemini", + ]: + raise Exception("llm binding not supported") + + if args.embedding_binding not in [ + "lollms", + "ollama", + "openai", + "azure_openai", + "aws_bedrock", + "jina", + "gemini", + ]: + raise Exception("embedding binding not supported") + + # Set default hosts if not provided + if args.llm_binding_host is None: + args.llm_binding_host = get_default_host(args.llm_binding) + + if args.embedding_binding_host is None: + args.embedding_binding_host = get_default_host(args.embedding_binding) + + # Add SSL validation + if args.ssl: + if not args.ssl_certfile or not args.ssl_keyfile: + raise Exception( + "SSL certificate and key files must be provided when SSL is enabled" + ) + if not os.path.exists(args.ssl_certfile): + raise Exception(f"SSL certificate file not found: {args.ssl_certfile}") + if not os.path.exists(args.ssl_keyfile): + raise Exception(f"SSL key file not found: {args.ssl_keyfile}") + + # Check if API key is provided either through env var or args + api_key = os.getenv("LIGHTRAG_API_KEY") or args.key + + # Initialize document manager with workspace support for data isolation + doc_manager = DocumentManager(args.input_dir, workspace=args.workspace) + + @asynccontextmanager + async def lifespan(app: FastAPI): + """Lifespan context manager for startup and shutdown events""" + # Store background tasks + app.state.background_tasks = set() + + try: + # Initialize database connections + await rag.initialize_storages() + await initialize_pipeline_status() + + # Data migration regardless of storage implementation + await rag.check_and_migrate_data() + + ASCIIColors.green("\nServer is ready to accept connections! 🚀\n") + + yield + + finally: + # Clean up database connections + await rag.finalize_storages() + + if "LIGHTRAG_GUNICORN_MODE" not in os.environ: + # Only perform cleanup in Uvicorn single-process mode + logger.debug("Unvicorn Mode: finalizing shared storage...") + finalize_share_data() + else: + # In Gunicorn mode with preload_app=True, cleanup is handled by on_exit hooks + logger.debug( + "Gunicorn Mode: postpone shared storage finalization to master process" + ) + + # Initialize FastAPI + base_description = ( + "Providing API for LightRAG core, Web UI and Ollama Model Emulation" + ) + swagger_description = ( + base_description + + (" (API-Key Enabled)" if api_key else "") + + "\n\n[View ReDoc documentation](/redoc)" + ) + app_kwargs = { + "title": "LightRAG Server API", + "description": swagger_description, + "version": __api_version__, + "openapi_url": "/openapi.json", # Explicitly set OpenAPI schema URL + "docs_url": None, # Disable default docs, we'll create custom endpoint + "redoc_url": "/redoc", # Explicitly set redoc URL + "lifespan": lifespan, + } + + # Configure Swagger UI parameters + # Enable persistAuthorization and tryItOutEnabled for better user experience + app_kwargs["swagger_ui_parameters"] = { + "persistAuthorization": True, + "tryItOutEnabled": True, + } + + app = FastAPI(**app_kwargs) + + # Add custom validation error handler for /query/data endpoint + @app.exception_handler(RequestValidationError) + async def validation_exception_handler( + request: Request, exc: RequestValidationError + ): + # Check if this is a request to /query/data endpoint + if request.url.path.endswith("/query/data"): + # Extract error details + error_details = [] + for error in exc.errors(): + field_path = " -> ".join(str(loc) for loc in error["loc"]) + error_details.append(f"{field_path}: {error['msg']}") + + error_message = "; ".join(error_details) + + # Return in the expected format for /query/data + return JSONResponse( + status_code=400, + content={ + "status": "failure", + "message": f"Validation error: {error_message}", + "data": {}, + "metadata": {}, + }, + ) + else: + # For other endpoints, return the default FastAPI validation error + return JSONResponse(status_code=422, content={"detail": exc.errors()}) + + def get_cors_origins(): + """Get allowed origins from global_args + Returns a list of allowed origins, defaults to ["*"] if not set + """ + origins_str = global_args.cors_origins + if origins_str == "*": + return ["*"] + return [origin.strip() for origin in origins_str.split(",")] + + # Add CORS middleware + app.add_middleware( + CORSMiddleware, + allow_origins=get_cors_origins(), + allow_credentials=True, + allow_methods=["*"], + allow_headers=["*"], + ) + + # Create combined auth dependency for all endpoints + combined_auth = get_combined_auth_dependency(api_key) + + # Create working directory if it doesn't exist + Path(args.working_dir).mkdir(parents=True, exist_ok=True) + + def create_optimized_openai_llm_func( + config_cache: LLMConfigCache, args, llm_timeout: int + ): + """Create optimized OpenAI LLM function with pre-processed configuration""" + + async def optimized_openai_alike_model_complete( + prompt, + system_prompt=None, + history_messages=None, + keyword_extraction=False, + **kwargs, + ) -> str: + from lightrag.llm.openai import openai_complete_if_cache + + keyword_extraction = kwargs.pop("keyword_extraction", None) + if keyword_extraction: + kwargs["response_format"] = GPTKeywordExtractionFormat + if history_messages is None: + history_messages = [] + + # Use pre-processed configuration to avoid repeated parsing + kwargs["timeout"] = llm_timeout + if config_cache.openai_llm_options: + kwargs.update(config_cache.openai_llm_options) + + return await openai_complete_if_cache( + args.llm_model, + prompt, + system_prompt=system_prompt, + history_messages=history_messages, + base_url=args.llm_binding_host, + api_key=args.llm_binding_api_key, + **kwargs, + ) + + return optimized_openai_alike_model_complete + + def create_optimized_azure_openai_llm_func( + config_cache: LLMConfigCache, args, llm_timeout: int + ): + """Create optimized Azure OpenAI LLM function with pre-processed configuration""" + + async def optimized_azure_openai_model_complete( + prompt, + system_prompt=None, + history_messages=None, + keyword_extraction=False, + **kwargs, + ) -> str: + from lightrag.llm.azure_openai import azure_openai_complete_if_cache + + keyword_extraction = kwargs.pop("keyword_extraction", None) + if keyword_extraction: + kwargs["response_format"] = GPTKeywordExtractionFormat + if history_messages is None: + history_messages = [] + + # Use pre-processed configuration to avoid repeated parsing + kwargs["timeout"] = llm_timeout + if config_cache.openai_llm_options: + kwargs.update(config_cache.openai_llm_options) + + return await azure_openai_complete_if_cache( + args.llm_model, + prompt, + system_prompt=system_prompt, + history_messages=history_messages, + base_url=args.llm_binding_host, + api_key=os.getenv("AZURE_OPENAI_API_KEY", args.llm_binding_api_key), + api_version=os.getenv("AZURE_OPENAI_API_VERSION", "2024-08-01-preview"), + **kwargs, + ) + + return optimized_azure_openai_model_complete + + def create_optimized_gemini_llm_func( + config_cache: LLMConfigCache, args, llm_timeout: int + ): + """Create optimized Gemini LLM function with cached configuration""" + + async def optimized_gemini_model_complete( + prompt, + system_prompt=None, + history_messages=None, + keyword_extraction=False, + **kwargs, + ) -> str: + from lightrag.llm.gemini import gemini_complete_if_cache + + if history_messages is None: + history_messages = [] + + # Use pre-processed configuration to avoid repeated parsing + kwargs["timeout"] = llm_timeout + if ( + config_cache.gemini_llm_options is not None + and "generation_config" not in kwargs + ): + kwargs["generation_config"] = dict(config_cache.gemini_llm_options) + + return await gemini_complete_if_cache( + args.llm_model, + prompt, + system_prompt=system_prompt, + history_messages=history_messages, + api_key=args.llm_binding_api_key, + base_url=args.llm_binding_host, + keyword_extraction=keyword_extraction, + **kwargs, + ) + + return optimized_gemini_model_complete + + def create_llm_model_func(binding: str): + """ + Create LLM model function based on binding type. + Uses optimized functions for OpenAI bindings and lazy import for others. + """ + try: + if binding == "lollms": + from lightrag.llm.lollms import lollms_model_complete + + return lollms_model_complete + elif binding == "ollama": + from lightrag.llm.ollama import ollama_model_complete + + return ollama_model_complete + elif binding == "aws_bedrock": + return bedrock_model_complete # Already defined locally + elif binding == "azure_openai": + # Use optimized function with pre-processed configuration + return create_optimized_azure_openai_llm_func( + config_cache, args, llm_timeout + ) + elif binding == "gemini": + return create_optimized_gemini_llm_func(config_cache, args, llm_timeout) + else: # openai and compatible + # Use optimized function with pre-processed configuration + return create_optimized_openai_llm_func(config_cache, args, llm_timeout) + except ImportError as e: + raise Exception(f"Failed to import {binding} LLM binding: {e}") + + def create_llm_model_kwargs(binding: str, args, llm_timeout: int) -> dict: + """ + Create LLM model kwargs based on binding type. + Uses lazy import for binding-specific options. + """ + if binding in ["lollms", "ollama"]: + try: + from lightrag.llm.binding_options import OllamaLLMOptions + + return { + "host": args.llm_binding_host, + "timeout": llm_timeout, + "options": OllamaLLMOptions.options_dict(args), + "api_key": args.llm_binding_api_key, + } + except ImportError as e: + raise Exception(f"Failed to import {binding} options: {e}") + return {} + + def create_optimized_embedding_function( + config_cache: LLMConfigCache, binding, model, host, api_key, args + ) -> EmbeddingFunc: + """ + Create optimized embedding function and return an EmbeddingFunc instance + with proper max_token_size inheritance from provider defaults. + + This function: + 1. Imports the provider embedding function + 2. Extracts max_token_size and embedding_dim from provider if it's an EmbeddingFunc + 3. Creates an optimized wrapper that calls the underlying function directly (avoiding double-wrapping) + 4. Returns a properly configured EmbeddingFunc instance + """ + + # Step 1: Import provider function and extract default attributes + provider_func = None + provider_max_token_size = None + provider_embedding_dim = None + + try: + if binding == "openai": + from lightrag.llm.openai import openai_embed + + provider_func = openai_embed + elif binding == "ollama": + from lightrag.llm.ollama import ollama_embed + + provider_func = ollama_embed + elif binding == "gemini": + from lightrag.llm.gemini import gemini_embed + + provider_func = gemini_embed + elif binding == "jina": + from lightrag.llm.jina import jina_embed + + provider_func = jina_embed + elif binding == "azure_openai": + from lightrag.llm.azure_openai import azure_openai_embed + + provider_func = azure_openai_embed + elif binding == "aws_bedrock": + from lightrag.llm.bedrock import bedrock_embed + + provider_func = bedrock_embed + elif binding == "lollms": + from lightrag.llm.lollms import lollms_embed + + provider_func = lollms_embed + + # Extract attributes if provider is an EmbeddingFunc + if provider_func and isinstance(provider_func, EmbeddingFunc): + provider_max_token_size = provider_func.max_token_size + provider_embedding_dim = provider_func.embedding_dim + logger.debug( + f"Extracted from {binding} provider: " + f"max_token_size={provider_max_token_size}, " + f"embedding_dim={provider_embedding_dim}" + ) + except ImportError as e: + logger.warning(f"Could not import provider function for {binding}: {e}") + + # Step 2: Apply priority (user config > provider default) + # For max_token_size: explicit env var > provider default > None + final_max_token_size = args.embedding_token_limit or provider_max_token_size + # For embedding_dim: user config (always has value) takes priority + # Only use provider default if user config is explicitly None (which shouldn't happen) + final_embedding_dim = ( + args.embedding_dim if args.embedding_dim else provider_embedding_dim + ) + + # Step 3: Create optimized embedding function (calls underlying function directly) + async def optimized_embedding_function(texts, embedding_dim=None): + try: + if binding == "lollms": + from lightrag.llm.lollms import lollms_embed + + # Get real function, skip EmbeddingFunc wrapper if present + actual_func = ( + lollms_embed.func + if isinstance(lollms_embed, EmbeddingFunc) + else lollms_embed + ) + return await actual_func( + texts, embed_model=model, host=host, api_key=api_key + ) + elif binding == "ollama": + from lightrag.llm.ollama import ollama_embed + + # Get real function, skip EmbeddingFunc wrapper if present + actual_func = ( + ollama_embed.func + if isinstance(ollama_embed, EmbeddingFunc) + else ollama_embed + ) + + # Use pre-processed configuration if available + if config_cache.ollama_embedding_options is not None: + ollama_options = config_cache.ollama_embedding_options + else: + from lightrag.llm.binding_options import OllamaEmbeddingOptions + + ollama_options = OllamaEmbeddingOptions.options_dict(args) + + return await actual_func( + texts, + embed_model=model, + host=host, + api_key=api_key, + options=ollama_options, + ) + elif binding == "azure_openai": + from lightrag.llm.azure_openai import azure_openai_embed + + actual_func = ( + azure_openai_embed.func + if isinstance(azure_openai_embed, EmbeddingFunc) + else azure_openai_embed + ) + return await actual_func(texts, model=model, api_key=api_key) + elif binding == "aws_bedrock": + from lightrag.llm.bedrock import bedrock_embed + + actual_func = ( + bedrock_embed.func + if isinstance(bedrock_embed, EmbeddingFunc) + else bedrock_embed + ) + return await actual_func(texts, model=model) + elif binding == "jina": + from lightrag.llm.jina import jina_embed + + actual_func = ( + jina_embed.func + if isinstance(jina_embed, EmbeddingFunc) + else jina_embed + ) + return await actual_func( + texts, + embedding_dim=embedding_dim, + base_url=host, + api_key=api_key, + ) + elif binding == "gemini": + from lightrag.llm.gemini import gemini_embed + + actual_func = ( + gemini_embed.func + if isinstance(gemini_embed, EmbeddingFunc) + else gemini_embed + ) + + # Use pre-processed configuration if available + if config_cache.gemini_embedding_options is not None: + gemini_options = config_cache.gemini_embedding_options + else: + from lightrag.llm.binding_options import GeminiEmbeddingOptions + + gemini_options = GeminiEmbeddingOptions.options_dict(args) + + return await actual_func( + texts, + model=model, + base_url=host, + api_key=api_key, + embedding_dim=embedding_dim, + task_type=gemini_options.get("task_type", "RETRIEVAL_DOCUMENT"), + ) + else: # openai and compatible + from lightrag.llm.openai import openai_embed + + actual_func = ( + openai_embed.func + if isinstance(openai_embed, EmbeddingFunc) + else openai_embed + ) + return await actual_func( + texts, + model=model, + base_url=host, + api_key=api_key, + embedding_dim=embedding_dim, + ) + except ImportError as e: + raise Exception(f"Failed to import {binding} embedding: {e}") + + # Step 4: Wrap in EmbeddingFunc and return + embedding_func_instance = EmbeddingFunc( + embedding_dim=final_embedding_dim, + func=optimized_embedding_function, + max_token_size=final_max_token_size, + send_dimensions=False, # Will be set later based on binding requirements + ) + + # Log final embedding configuration + logger.info( + f"Embedding config: binding={binding} model={model} " + f"embedding_dim={final_embedding_dim} max_token_size={final_max_token_size}" + ) + + return embedding_func_instance + + llm_timeout = get_env_value("LLM_TIMEOUT", DEFAULT_LLM_TIMEOUT, int) + embedding_timeout = get_env_value( + "EMBEDDING_TIMEOUT", DEFAULT_EMBEDDING_TIMEOUT, int + ) + + async def bedrock_model_complete( + prompt, + system_prompt=None, + history_messages=None, + keyword_extraction=False, + **kwargs, + ) -> str: + # Lazy import + from lightrag.llm.bedrock import bedrock_complete_if_cache + + keyword_extraction = kwargs.pop("keyword_extraction", None) + if keyword_extraction: + kwargs["response_format"] = GPTKeywordExtractionFormat + if history_messages is None: + history_messages = [] + + # Use global temperature for Bedrock + kwargs["temperature"] = get_env_value("BEDROCK_LLM_TEMPERATURE", 1.0, float) + + return await bedrock_complete_if_cache( + args.llm_model, + prompt, + system_prompt=system_prompt, + history_messages=history_messages, + **kwargs, + ) + + # Create embedding function with optimized configuration and max_token_size inheritance + import inspect + + # Create the EmbeddingFunc instance (now returns complete EmbeddingFunc with max_token_size) + embedding_func = create_optimized_embedding_function( + config_cache=config_cache, + binding=args.embedding_binding, + model=args.embedding_model, + host=args.embedding_binding_host, + api_key=args.embedding_binding_api_key, + args=args, + ) + + # Get embedding_send_dim from centralized configuration + embedding_send_dim = args.embedding_send_dim + + # Check if the underlying function signature has embedding_dim parameter + sig = inspect.signature(embedding_func.func) + has_embedding_dim_param = "embedding_dim" in sig.parameters + + # Determine send_dimensions value based on binding type + # Jina and Gemini REQUIRE dimension parameter (forced to True) + # OpenAI and others: controlled by EMBEDDING_SEND_DIM environment variable + if args.embedding_binding in ["jina", "gemini"]: + # Jina and Gemini APIs require dimension parameter - always send it + send_dimensions = has_embedding_dim_param + dimension_control = f"forced by {args.embedding_binding.title()} API" + else: + # For OpenAI and other bindings, respect EMBEDDING_SEND_DIM setting + send_dimensions = embedding_send_dim and has_embedding_dim_param + if send_dimensions or not embedding_send_dim: + dimension_control = "by env var" + else: + dimension_control = "by not hasparam" + + # Set send_dimensions on the EmbeddingFunc instance + embedding_func.send_dimensions = send_dimensions + + logger.info( + f"Send embedding dimension: {send_dimensions} {dimension_control} " + f"(dimensions={embedding_func.embedding_dim}, has_param={has_embedding_dim_param}, " + f"binding={args.embedding_binding})" + ) + + # Log max_token_size source + if embedding_func.max_token_size: + source = ( + "env variable" + if args.embedding_token_limit + else f"{args.embedding_binding} provider default" + ) + logger.info( + f"Embedding max_token_size: {embedding_func.max_token_size} (from {source})" + ) + else: + logger.info("Embedding max_token_size: not set (90% token warning disabled)") + + # Configure rerank function based on args.rerank_bindingparameter + rerank_model_func = None + if args.rerank_binding != "null": + from lightrag.rerank import cohere_rerank, jina_rerank, ali_rerank + + # Map rerank binding to corresponding function + rerank_functions = { + "cohere": cohere_rerank, + "jina": jina_rerank, + "aliyun": ali_rerank, + } + + # Select the appropriate rerank function based on binding + selected_rerank_func = rerank_functions.get(args.rerank_binding) + if not selected_rerank_func: + logger.error(f"Unsupported rerank binding: {args.rerank_binding}") + raise ValueError(f"Unsupported rerank binding: {args.rerank_binding}") + + # Get default values from selected_rerank_func if args values are None + if args.rerank_model is None or args.rerank_binding_host is None: + sig = inspect.signature(selected_rerank_func) + + # Set default model if args.rerank_model is None + if args.rerank_model is None and "model" in sig.parameters: + default_model = sig.parameters["model"].default + if default_model != inspect.Parameter.empty: + args.rerank_model = default_model + + # Set default base_url if args.rerank_binding_host is None + if args.rerank_binding_host is None and "base_url" in sig.parameters: + default_base_url = sig.parameters["base_url"].default + if default_base_url != inspect.Parameter.empty: + args.rerank_binding_host = default_base_url + + async def server_rerank_func( + query: str, documents: list, top_n: int = None, extra_body: dict = None + ): + """Server rerank function with configuration from environment variables""" + return await selected_rerank_func( + query=query, + documents=documents, + top_n=top_n, + api_key=args.rerank_binding_api_key, + model=args.rerank_model, + base_url=args.rerank_binding_host, + extra_body=extra_body, + ) + + rerank_model_func = server_rerank_func + logger.info( + f"Reranking is enabled: {args.rerank_model or 'default model'} using {args.rerank_binding} provider" + ) + else: + logger.info("Reranking is disabled") + + # Create ollama_server_infos from command line arguments + from lightrag.api.config import OllamaServerInfos + + ollama_server_infos = OllamaServerInfos( + name=args.simulated_model_name, tag=args.simulated_model_tag + ) + + # Initialize RAG with unified configuration + try: + rag = LightRAG( + working_dir=args.working_dir, + workspace=args.workspace, + llm_model_func=create_llm_model_func(args.llm_binding), + llm_model_name=args.llm_model, + llm_model_max_async=args.max_async, + summary_max_tokens=args.summary_max_tokens, + summary_context_size=args.summary_context_size, + chunk_token_size=int(args.chunk_size), + chunk_overlap_token_size=int(args.chunk_overlap_size), + llm_model_kwargs=create_llm_model_kwargs( + args.llm_binding, args, llm_timeout + ), + embedding_func=embedding_func, + default_llm_timeout=llm_timeout, + default_embedding_timeout=embedding_timeout, + kv_storage=args.kv_storage, + graph_storage=args.graph_storage, + vector_storage=args.vector_storage, + doc_status_storage=args.doc_status_storage, + vector_db_storage_cls_kwargs={ + "cosine_better_than_threshold": args.cosine_threshold + }, + enable_llm_cache_for_entity_extract=args.enable_llm_cache_for_extract, + enable_llm_cache=args.enable_llm_cache, + rerank_model_func=rerank_model_func, + max_parallel_insert=args.max_parallel_insert, + max_graph_nodes=args.max_graph_nodes, + addon_params={ + "language": args.summary_language, + "entity_types": args.entity_types, + }, + ollama_server_infos=ollama_server_infos, + ) + except Exception as e: + logger.error(f"Failed to initialize LightRAG: {e}") + raise + + # Add routes + app.include_router( + create_document_routes( + rag, + doc_manager, + api_key, + ) + ) + app.include_router(create_query_routes(rag, api_key, args.top_k)) + app.include_router(create_graph_routes(rag, api_key)) + + # Add Ollama API routes + ollama_api = OllamaAPI(rag, top_k=args.top_k, api_key=api_key) + app.include_router(ollama_api.router, prefix="/api") + + # Custom Swagger UI endpoint for offline support + @app.get("/docs", include_in_schema=False) + async def custom_swagger_ui_html(): + """Custom Swagger UI HTML with local static files""" + return get_swagger_ui_html( + openapi_url=app.openapi_url, + title=app.title + " - Swagger UI", + oauth2_redirect_url="/docs/oauth2-redirect", + swagger_js_url="/static/swagger-ui/swagger-ui-bundle.js", + swagger_css_url="/static/swagger-ui/swagger-ui.css", + swagger_favicon_url="/static/swagger-ui/favicon-32x32.png", + swagger_ui_parameters=app.swagger_ui_parameters, + ) + + @app.get("/docs/oauth2-redirect", include_in_schema=False) + async def swagger_ui_redirect(): + """OAuth2 redirect for Swagger UI""" + return get_swagger_ui_oauth2_redirect_html() + + @app.get("/") + async def redirect_to_webui(): + """Redirect root path to /webui""" + return RedirectResponse(url="/webui") + + @app.get("/auth-status") + async def get_auth_status(): + """Get authentication status and guest token if auth is not configured""" + + if not auth_handler.accounts: + # Authentication not configured, return guest token + guest_token = auth_handler.create_token( + username="guest", role="guest", metadata={"auth_mode": "disabled"} + ) + return { + "auth_configured": False, + "access_token": guest_token, + "token_type": "bearer", + "auth_mode": "disabled", + "message": "Authentication is disabled. Using guest access.", + "core_version": core_version, + "api_version": api_version_display, + "webui_title": webui_title, + "webui_description": webui_description, + } + + return { + "auth_configured": True, + "auth_mode": "enabled", + "core_version": core_version, + "api_version": api_version_display, + "webui_title": webui_title, + "webui_description": webui_description, + } + + @app.post("/login") + async def login(form_data: OAuth2PasswordRequestForm = Depends()): + if not auth_handler.accounts: + # Authentication not configured, return guest token + guest_token = auth_handler.create_token( + username="guest", role="guest", metadata={"auth_mode": "disabled"} + ) + return { + "access_token": guest_token, + "token_type": "bearer", + "auth_mode": "disabled", + "message": "Authentication is disabled. Using guest access.", + "core_version": core_version, + "api_version": api_version_display, + "webui_title": webui_title, + "webui_description": webui_description, + } + username = form_data.username + if auth_handler.accounts.get(username) != form_data.password: + raise HTTPException(status_code=401, detail="Incorrect credentials") + + # Regular user login + user_token = auth_handler.create_token( + username=username, role="user", metadata={"auth_mode": "enabled"} + ) + return { + "access_token": user_token, + "token_type": "bearer", + "auth_mode": "enabled", + "core_version": core_version, + "api_version": api_version_display, + "webui_title": webui_title, + "webui_description": webui_description, + } + + @app.get("/health", dependencies=[Depends(combined_auth)]) + async def get_status(): + """Get current system status""" + try: + pipeline_status = await get_namespace_data("pipeline_status") + + if not auth_configured: + auth_mode = "disabled" + else: + auth_mode = "enabled" + + # Cleanup expired keyed locks and get status + keyed_lock_info = cleanup_keyed_lock() + + return { + "status": "healthy", + "working_directory": str(args.working_dir), + "input_directory": str(args.input_dir), + "configuration": { + # LLM configuration binding/host address (if applicable)/model (if applicable) + "llm_binding": args.llm_binding, + "llm_binding_host": args.llm_binding_host, + "llm_model": args.llm_model, + # embedding model configuration binding/host address (if applicable)/model (if applicable) + "embedding_binding": args.embedding_binding, + "embedding_binding_host": args.embedding_binding_host, + "embedding_model": args.embedding_model, + "summary_max_tokens": args.summary_max_tokens, + "summary_context_size": args.summary_context_size, + "kv_storage": args.kv_storage, + "doc_status_storage": args.doc_status_storage, + "graph_storage": args.graph_storage, + "vector_storage": args.vector_storage, + "enable_llm_cache_for_extract": args.enable_llm_cache_for_extract, + "enable_llm_cache": args.enable_llm_cache, + "workspace": args.workspace, + "max_graph_nodes": args.max_graph_nodes, + # Rerank configuration + "enable_rerank": rerank_model_func is not None, + "rerank_binding": args.rerank_binding, + "rerank_model": args.rerank_model if rerank_model_func else None, + "rerank_binding_host": args.rerank_binding_host + if rerank_model_func + else None, + # Environment variable status (requested configuration) + "summary_language": args.summary_language, + "force_llm_summary_on_merge": args.force_llm_summary_on_merge, + "max_parallel_insert": args.max_parallel_insert, + "cosine_threshold": args.cosine_threshold, + "min_rerank_score": args.min_rerank_score, + "related_chunk_number": args.related_chunk_number, + "max_async": args.max_async, + "embedding_func_max_async": args.embedding_func_max_async, + "embedding_batch_num": args.embedding_batch_num, + }, + "auth_mode": auth_mode, + "pipeline_busy": pipeline_status.get("busy", False), + "keyed_locks": keyed_lock_info, + "core_version": core_version, + "api_version": api_version_display, + "webui_title": webui_title, + "webui_description": webui_description, + } + except Exception as e: + logger.error(f"Error getting health status: {str(e)}") + raise HTTPException(status_code=500, detail=str(e)) + + # Custom StaticFiles class for smart caching + class SmartStaticFiles(StaticFiles): # Renamed from NoCacheStaticFiles + async def get_response(self, path: str, scope): + response = await super().get_response(path, scope) + + is_html = path.endswith(".html") or response.media_type == "text/html" + + if is_html: + response.headers["Cache-Control"] = ( + "no-cache, no-store, must-revalidate" + ) + response.headers["Pragma"] = "no-cache" + response.headers["Expires"] = "0" + elif ( + "/assets/" in path + ): # Assets (JS, CSS, images, fonts) generated by Vite with hash in filename + response.headers["Cache-Control"] = ( + "public, max-age=31536000, immutable" + ) + # Add other rules here if needed for non-HTML, non-asset files + + # Ensure correct Content-Type + if path.endswith(".js"): + response.headers["Content-Type"] = "application/javascript" + elif path.endswith(".css"): + response.headers["Content-Type"] = "text/css" + + return response + + # Mount Swagger UI static files for offline support + swagger_static_dir = Path(__file__).parent / "static" / "swagger-ui" + if swagger_static_dir.exists(): + app.mount( + "/static/swagger-ui", + StaticFiles(directory=swagger_static_dir), + name="swagger-ui-static", + ) + + # Webui mount webui/index.html + static_dir = Path(__file__).parent / "webui" + static_dir.mkdir(exist_ok=True) + app.mount( + "/webui", + SmartStaticFiles( + directory=static_dir, html=True, check_dir=True + ), # Use SmartStaticFiles + name="webui", + ) + + return app + + +def get_application(args=None): + """Factory function for creating the FastAPI application""" + if args is None: + args = global_args + return create_app(args) + + +def configure_logging(): + """Configure logging for uvicorn startup""" + + # Reset any existing handlers to ensure clean configuration + for logger_name in ["uvicorn", "uvicorn.access", "uvicorn.error", "lightrag"]: + logger = logging.getLogger(logger_name) + logger.handlers = [] + logger.filters = [] + + # Get log directory path from environment variable + log_dir = os.getenv("LOG_DIR", os.getcwd()) + log_file_path = os.path.abspath(os.path.join(log_dir, DEFAULT_LOG_FILENAME)) + + print(f"\nLightRAG log file: {log_file_path}\n") + os.makedirs(os.path.dirname(log_dir), exist_ok=True) + + # Get log file max size and backup count from environment variables + log_max_bytes = get_env_value("LOG_MAX_BYTES", DEFAULT_LOG_MAX_BYTES, int) + log_backup_count = get_env_value("LOG_BACKUP_COUNT", DEFAULT_LOG_BACKUP_COUNT, int) + + logging.config.dictConfig( + { + "version": 1, + "disable_existing_loggers": False, + "formatters": { + "default": { + "format": "%(levelname)s: %(message)s", + }, + "detailed": { + "format": "%(asctime)s - %(name)s - %(levelname)s - %(message)s", + }, + }, + "handlers": { + "console": { + "formatter": "default", + "class": "logging.StreamHandler", + "stream": "ext://sys.stderr", + }, + "file": { + "formatter": "detailed", + "class": "logging.handlers.RotatingFileHandler", + "filename": log_file_path, + "maxBytes": log_max_bytes, + "backupCount": log_backup_count, + "encoding": "utf-8", + }, + }, + "loggers": { + # Configure all uvicorn related loggers + "uvicorn": { + "handlers": ["console", "file"], + "level": "INFO", + "propagate": False, + }, + "uvicorn.access": { + "handlers": ["console", "file"], + "level": "INFO", + "propagate": False, + "filters": ["path_filter"], + }, + "uvicorn.error": { + "handlers": ["console", "file"], + "level": "INFO", + "propagate": False, + }, + "lightrag": { + "handlers": ["console", "file"], + "level": "INFO", + "propagate": False, + "filters": ["path_filter"], + }, + }, + "filters": { + "path_filter": { + "()": "lightrag.utils.LightragPathFilter", + }, + }, + } + ) + + +def check_and_install_dependencies(): + """Check and install required dependencies""" + required_packages = [ + "uvicorn", + "tiktoken", + "fastapi", + # Add other required packages here + ] + + for package in required_packages: + if not pm.is_installed(package): + print(f"Installing {package}...") + pm.install(package) + print(f"{package} installed successfully") + + +def main(): + # Explicitly initialize configuration for clarity + # (The proxy will auto-initialize anyway, but this makes intent clear) + from .config import initialize_config + + initialize_config() + + # Check if running under Gunicorn + if "GUNICORN_CMD_ARGS" in os.environ: + # If started with Gunicorn, return directly as Gunicorn will call get_application + print("Running under Gunicorn - worker management handled by Gunicorn") + return + + # Check .env file + if not check_env_file(): + sys.exit(1) + + # Check and install dependencies + check_and_install_dependencies() + + from multiprocessing import freeze_support + + freeze_support() + + # Configure logging before parsing args + configure_logging() + update_uvicorn_mode_config() + display_splash_screen(global_args) + + # Note: Signal handlers are NOT registered here because: + # - Uvicorn has built-in signal handling that properly calls lifespan shutdown + # - Custom signal handlers can interfere with uvicorn's graceful shutdown + # - Cleanup is handled by the lifespan context manager's finally block + + # Create application instance directly instead of using factory function + app = create_app(global_args) + + # Start Uvicorn in single process mode + uvicorn_config = { + "app": app, # Pass application instance directly instead of string path + "host": global_args.host, + "port": global_args.port, + "log_config": None, # Disable default config + } + + if global_args.ssl: + uvicorn_config.update( + { + "ssl_certfile": global_args.ssl_certfile, + "ssl_keyfile": global_args.ssl_keyfile, + } + ) + + print( + f"Starting Uvicorn server in single-process mode on {global_args.host}:{global_args.port}" + ) + uvicorn.run(**uvicorn_config) + + +if __name__ == "__main__": + main() diff --git a/LightRAG/lightrag/api/routers/__init__.py b/LightRAG/lightrag/api/routers/__init__.py new file mode 100644 index 00000000..b71f204e --- /dev/null +++ b/LightRAG/lightrag/api/routers/__init__.py @@ -0,0 +1,10 @@ +""" +This module contains all the routers for the LightRAG API. +""" + +from .document_routes import router as document_router +from .query_routes import router as query_router +from .graph_routes import router as graph_router +from .ollama_api import OllamaAPI + +__all__ = ["document_router", "query_router", "graph_router", "OllamaAPI"] diff --git a/LightRAG/lightrag/api/routers/document_routes.py b/LightRAG/lightrag/api/routers/document_routes.py new file mode 100644 index 00000000..a59f10c7 --- /dev/null +++ b/LightRAG/lightrag/api/routers/document_routes.py @@ -0,0 +1,2977 @@ +""" +This module contains all document-related routes for the LightRAG API. +""" + +import asyncio +from functools import lru_cache +from lightrag.utils import logger, get_pinyin_sort_key +import aiofiles +import shutil +import traceback +from datetime import datetime, timezone +from pathlib import Path +from typing import Dict, List, Optional, Any, Literal +from io import BytesIO +from fastapi import ( + APIRouter, + BackgroundTasks, + Depends, + File, + HTTPException, + UploadFile, +) +from pydantic import BaseModel, Field, field_validator + +from lightrag import LightRAG +from lightrag.base import DeletionResult, DocProcessingStatus, DocStatus +from lightrag.utils import generate_track_id +from lightrag.api.utils_api import get_combined_auth_dependency +from ..config import global_args + + +@lru_cache(maxsize=1) +def _is_docling_available() -> bool: + """Check if docling is available (cached check). + + This function uses lru_cache to avoid repeated import attempts. + The result is cached after the first call. + + Returns: + bool: True if docling is available, False otherwise + """ + try: + import docling # noqa: F401 # type: ignore[import-not-found] + + return True + except ImportError: + return False + + +# Function to format datetime to ISO format string with timezone information +def format_datetime(dt: Any) -> Optional[str]: + """Format datetime to ISO format string with timezone information + + Args: + dt: Datetime object, string, or None + + Returns: + ISO format string with timezone information, or None if input is None + """ + if dt is None: + return None + if isinstance(dt, str): + return dt + + # Check if datetime object has timezone information + if isinstance(dt, datetime): + # If datetime object has no timezone info (naive datetime), add UTC timezone + if dt.tzinfo is None: + dt = dt.replace(tzinfo=timezone.utc) + + # Return ISO format string with timezone information + return dt.isoformat() + + +router = APIRouter( + prefix="/documents", + tags=["documents"], +) + +# Temporary file prefix +temp_prefix = "__tmp__" + + +def sanitize_filename(filename: str, input_dir: Path) -> str: + """ + Sanitize uploaded filename to prevent Path Traversal attacks. + + Args: + filename: The original filename from the upload + input_dir: The target input directory + + Returns: + str: Sanitized filename that is safe to use + + Raises: + HTTPException: If the filename is unsafe or invalid + """ + # Basic validation + if not filename or not filename.strip(): + raise HTTPException(status_code=400, detail="Filename cannot be empty") + + # Remove path separators and traversal sequences + clean_name = filename.replace("/", "").replace("\\", "") + clean_name = clean_name.replace("..", "") + + # Remove control characters and null bytes + clean_name = "".join(c for c in clean_name if ord(c) >= 32 and c != "\x7f") + + # Remove leading/trailing whitespace and dots + clean_name = clean_name.strip().strip(".") + + # Check if anything is left after sanitization + if not clean_name: + raise HTTPException( + status_code=400, detail="Invalid filename after sanitization" + ) + + # Verify the final path stays within the input directory + try: + final_path = (input_dir / clean_name).resolve() + if not final_path.is_relative_to(input_dir.resolve()): + raise HTTPException(status_code=400, detail="Unsafe filename detected") + except (OSError, ValueError): + raise HTTPException(status_code=400, detail="Invalid filename") + + return clean_name + + +class ScanResponse(BaseModel): + """Response model for document scanning operation + + Attributes: + status: Status of the scanning operation + message: Optional message with additional details + track_id: Tracking ID for monitoring scanning progress + """ + + status: Literal["scanning_started"] = Field( + description="Status of the scanning operation" + ) + message: Optional[str] = Field( + default=None, description="Additional details about the scanning operation" + ) + track_id: str = Field(description="Tracking ID for monitoring scanning progress") + + class Config: + json_schema_extra = { + "example": { + "status": "scanning_started", + "message": "Scanning process has been initiated in the background", + "track_id": "scan_20250729_170612_abc123", + } + } + + +class ReprocessResponse(BaseModel): + """Response model for reprocessing failed documents operation + + Attributes: + status: Status of the reprocessing operation + message: Message describing the operation result + track_id: Tracking ID for monitoring reprocessing progress + """ + + status: Literal["reprocessing_started"] = Field( + description="Status of the reprocessing operation" + ) + message: str = Field(description="Human-readable message describing the operation") + track_id: str = Field( + description="Tracking ID for monitoring reprocessing progress" + ) + + class Config: + json_schema_extra = { + "example": { + "status": "reprocessing_started", + "message": "Reprocessing of failed documents has been initiated in background", + "track_id": "retry_20250729_170612_def456", + } + } + + +class CancelPipelineResponse(BaseModel): + """Response model for pipeline cancellation operation + + Attributes: + status: Status of the cancellation request + message: Message describing the operation result + """ + + status: Literal["cancellation_requested", "not_busy"] = Field( + description="Status of the cancellation request" + ) + message: str = Field(description="Human-readable message describing the operation") + + class Config: + json_schema_extra = { + "example": { + "status": "cancellation_requested", + "message": "Pipeline cancellation has been requested. Documents will be marked as FAILED.", + } + } + + +class InsertTextRequest(BaseModel): + """Request model for inserting a single text document + + Attributes: + text: The text content to be inserted into the RAG system + file_source: Source of the text (optional) + """ + + text: str = Field( + min_length=1, + description="The text to insert", + ) + file_source: str = Field(default=None, min_length=0, description="File Source") + + @field_validator("text", mode="after") + @classmethod + def strip_text_after(cls, text: str) -> str: + return text.strip() + + @field_validator("file_source", mode="after") + @classmethod + def strip_source_after(cls, file_source: str) -> str: + return file_source.strip() + + class Config: + json_schema_extra = { + "example": { + "text": "This is a sample text to be inserted into the RAG system.", + "file_source": "Source of the text (optional)", + } + } + + +class InsertTextsRequest(BaseModel): + """Request model for inserting multiple text documents + + Attributes: + texts: List of text contents to be inserted into the RAG system + file_sources: Sources of the texts (optional) + """ + + texts: list[str] = Field( + min_length=1, + description="The texts to insert", + ) + file_sources: list[str] = Field( + default=None, min_length=0, description="Sources of the texts" + ) + + @field_validator("texts", mode="after") + @classmethod + def strip_texts_after(cls, texts: list[str]) -> list[str]: + return [text.strip() for text in texts] + + @field_validator("file_sources", mode="after") + @classmethod + def strip_sources_after(cls, file_sources: list[str]) -> list[str]: + return [file_source.strip() for file_source in file_sources] + + class Config: + json_schema_extra = { + "example": { + "texts": [ + "This is the first text to be inserted.", + "This is the second text to be inserted.", + ], + "file_sources": [ + "First file source (optional)", + ], + } + } + + +class InsertResponse(BaseModel): + """Response model for document insertion operations + + Attributes: + status: Status of the operation (success, duplicated, partial_success, failure) + message: Detailed message describing the operation result + track_id: Tracking ID for monitoring processing status + """ + + status: Literal["success", "duplicated", "partial_success", "failure"] = Field( + description="Status of the operation" + ) + message: str = Field(description="Message describing the operation result") + track_id: str = Field(description="Tracking ID for monitoring processing status") + + class Config: + json_schema_extra = { + "example": { + "status": "success", + "message": "File 'document.pdf' uploaded successfully. Processing will continue in background.", + "track_id": "upload_20250729_170612_abc123", + } + } + + +class ClearDocumentsResponse(BaseModel): + """Response model for document clearing operation + + Attributes: + status: Status of the clear operation + message: Detailed message describing the operation result + """ + + status: Literal["success", "partial_success", "busy", "fail"] = Field( + description="Status of the clear operation" + ) + message: str = Field(description="Message describing the operation result") + + class Config: + json_schema_extra = { + "example": { + "status": "success", + "message": "All documents cleared successfully. Deleted 15 files.", + } + } + + +class ClearCacheRequest(BaseModel): + """Request model for clearing cache + + This model is kept for API compatibility but no longer accepts any parameters. + All cache will be cleared regardless of the request content. + """ + + class Config: + json_schema_extra = {"example": {}} + + +class ClearCacheResponse(BaseModel): + """Response model for cache clearing operation + + Attributes: + status: Status of the clear operation + message: Detailed message describing the operation result + """ + + status: Literal["success", "fail"] = Field( + description="Status of the clear operation" + ) + message: str = Field(description="Message describing the operation result") + + class Config: + json_schema_extra = { + "example": { + "status": "success", + "message": "Successfully cleared cache for modes: ['default', 'naive']", + } + } + + +"""Response model for document status + +Attributes: + id: Document identifier + content_summary: Summary of document content + content_length: Length of document content + status: Current processing status + created_at: Creation timestamp (ISO format string) + updated_at: Last update timestamp (ISO format string) + chunks_count: Number of chunks (optional) + error: Error message if any (optional) + metadata: Additional metadata (optional) + file_path: Path to the document file +""" + + +class DeleteDocRequest(BaseModel): + doc_ids: List[str] = Field(..., description="The IDs of the documents to delete.") + delete_file: bool = Field( + default=False, + description="Whether to delete the corresponding file in the upload directory.", + ) + delete_llm_cache: bool = Field( + default=False, + description="Whether to delete cached LLM extraction results for the documents.", + ) + + @field_validator("doc_ids", mode="after") + @classmethod + def validate_doc_ids(cls, doc_ids: List[str]) -> List[str]: + if not doc_ids: + raise ValueError("Document IDs list cannot be empty") + + validated_ids = [] + for doc_id in doc_ids: + if not doc_id or not doc_id.strip(): + raise ValueError("Document ID cannot be empty") + validated_ids.append(doc_id.strip()) + + # Check for duplicates + if len(validated_ids) != len(set(validated_ids)): + raise ValueError("Document IDs must be unique") + + return validated_ids + + +class DeleteEntityRequest(BaseModel): + entity_name: str = Field(..., description="The name of the entity to delete.") + + @field_validator("entity_name", mode="after") + @classmethod + def validate_entity_name(cls, entity_name: str) -> str: + if not entity_name or not entity_name.strip(): + raise ValueError("Entity name cannot be empty") + return entity_name.strip() + + +class DeleteRelationRequest(BaseModel): + source_entity: str = Field(..., description="The name of the source entity.") + target_entity: str = Field(..., description="The name of the target entity.") + + @field_validator("source_entity", "target_entity", mode="after") + @classmethod + def validate_entity_names(cls, entity_name: str) -> str: + if not entity_name or not entity_name.strip(): + raise ValueError("Entity name cannot be empty") + return entity_name.strip() + + +class DocStatusResponse(BaseModel): + id: str = Field(description="Document identifier") + content_summary: str = Field(description="Summary of document content") + content_length: int = Field(description="Length of document content in characters") + status: DocStatus = Field(description="Current processing status") + created_at: str = Field(description="Creation timestamp (ISO format string)") + updated_at: str = Field(description="Last update timestamp (ISO format string)") + track_id: Optional[str] = Field( + default=None, description="Tracking ID for monitoring progress" + ) + chunks_count: Optional[int] = Field( + default=None, description="Number of chunks the document was split into" + ) + error_msg: Optional[str] = Field( + default=None, description="Error message if processing failed" + ) + metadata: Optional[dict[str, Any]] = Field( + default=None, description="Additional metadata about the document" + ) + file_path: str = Field(description="Path to the document file") + + class Config: + json_schema_extra = { + "example": { + "id": "doc_123456", + "content_summary": "Research paper on machine learning", + "content_length": 15240, + "status": "processed", + "created_at": "2025-03-31T12:34:56", + "updated_at": "2025-03-31T12:35:30", + "track_id": "upload_20250729_170612_abc123", + "chunks_count": 12, + "error": None, + "metadata": {"author": "John Doe", "year": 2025}, + "file_path": "research_paper.pdf", + } + } + + +class DocsStatusesResponse(BaseModel): + """Response model for document statuses + + Attributes: + statuses: Dictionary mapping document status to lists of document status responses + """ + + statuses: Dict[DocStatus, List[DocStatusResponse]] = Field( + default_factory=dict, + description="Dictionary mapping document status to lists of document status responses", + ) + + class Config: + json_schema_extra = { + "example": { + "statuses": { + "PENDING": [ + { + "id": "doc_123", + "content_summary": "Pending document", + "content_length": 5000, + "status": "pending", + "created_at": "2025-03-31T10:00:00", + "updated_at": "2025-03-31T10:00:00", + "track_id": "upload_20250331_100000_abc123", + "chunks_count": None, + "error": None, + "metadata": None, + "file_path": "pending_doc.pdf", + } + ], + "PREPROCESSED": [ + { + "id": "doc_789", + "content_summary": "Document pending final indexing", + "content_length": 7200, + "status": "preprocessed", + "created_at": "2025-03-31T09:30:00", + "updated_at": "2025-03-31T09:35:00", + "track_id": "upload_20250331_093000_xyz789", + "chunks_count": 10, + "error": None, + "metadata": None, + "file_path": "preprocessed_doc.pdf", + } + ], + "PROCESSED": [ + { + "id": "doc_456", + "content_summary": "Processed document", + "content_length": 8000, + "status": "processed", + "created_at": "2025-03-31T09:00:00", + "updated_at": "2025-03-31T09:05:00", + "track_id": "insert_20250331_090000_def456", + "chunks_count": 8, + "error": None, + "metadata": {"author": "John Doe"}, + "file_path": "processed_doc.pdf", + } + ], + } + } + } + + +class TrackStatusResponse(BaseModel): + """Response model for tracking document processing status by track_id + + Attributes: + track_id: The tracking ID + documents: List of documents associated with this track_id + total_count: Total number of documents for this track_id + status_summary: Count of documents by status + """ + + track_id: str = Field(description="The tracking ID") + documents: List[DocStatusResponse] = Field( + description="List of documents associated with this track_id" + ) + total_count: int = Field(description="Total number of documents for this track_id") + status_summary: Dict[str, int] = Field(description="Count of documents by status") + + class Config: + json_schema_extra = { + "example": { + "track_id": "upload_20250729_170612_abc123", + "documents": [ + { + "id": "doc_123456", + "content_summary": "Research paper on machine learning", + "content_length": 15240, + "status": "PROCESSED", + "created_at": "2025-03-31T12:34:56", + "updated_at": "2025-03-31T12:35:30", + "track_id": "upload_20250729_170612_abc123", + "chunks_count": 12, + "error": None, + "metadata": {"author": "John Doe", "year": 2025}, + "file_path": "research_paper.pdf", + } + ], + "total_count": 1, + "status_summary": {"PROCESSED": 1}, + } + } + + +class DocumentsRequest(BaseModel): + """Request model for paginated document queries + + Attributes: + status_filter: Filter by document status, None for all statuses + page: Page number (1-based) + page_size: Number of documents per page (10-200) + sort_field: Field to sort by ('created_at', 'updated_at', 'id', 'file_path') + sort_direction: Sort direction ('asc' or 'desc') + """ + + status_filter: Optional[DocStatus] = Field( + default=None, description="Filter by document status, None for all statuses" + ) + page: int = Field(default=1, ge=1, description="Page number (1-based)") + page_size: int = Field( + default=50, ge=10, le=200, description="Number of documents per page (10-200)" + ) + sort_field: Literal["created_at", "updated_at", "id", "file_path"] = Field( + default="updated_at", description="Field to sort by" + ) + sort_direction: Literal["asc", "desc"] = Field( + default="desc", description="Sort direction" + ) + + class Config: + json_schema_extra = { + "example": { + "status_filter": "PROCESSED", + "page": 1, + "page_size": 50, + "sort_field": "updated_at", + "sort_direction": "desc", + } + } + + +class PaginationInfo(BaseModel): + """Pagination information + + Attributes: + page: Current page number + page_size: Number of items per page + total_count: Total number of items + total_pages: Total number of pages + has_next: Whether there is a next page + has_prev: Whether there is a previous page + """ + + page: int = Field(description="Current page number") + page_size: int = Field(description="Number of items per page") + total_count: int = Field(description="Total number of items") + total_pages: int = Field(description="Total number of pages") + has_next: bool = Field(description="Whether there is a next page") + has_prev: bool = Field(description="Whether there is a previous page") + + class Config: + json_schema_extra = { + "example": { + "page": 1, + "page_size": 50, + "total_count": 150, + "total_pages": 3, + "has_next": True, + "has_prev": False, + } + } + + +class PaginatedDocsResponse(BaseModel): + """Response model for paginated document queries + + Attributes: + documents: List of documents for the current page + pagination: Pagination information + status_counts: Count of documents by status for all documents + """ + + documents: List[DocStatusResponse] = Field( + description="List of documents for the current page" + ) + pagination: PaginationInfo = Field(description="Pagination information") + status_counts: Dict[str, int] = Field( + description="Count of documents by status for all documents" + ) + + class Config: + json_schema_extra = { + "example": { + "documents": [ + { + "id": "doc_123456", + "content_summary": "Research paper on machine learning", + "content_length": 15240, + "status": "PROCESSED", + "created_at": "2025-03-31T12:34:56", + "updated_at": "2025-03-31T12:35:30", + "track_id": "upload_20250729_170612_abc123", + "chunks_count": 12, + "error_msg": None, + "metadata": {"author": "John Doe", "year": 2025}, + "file_path": "research_paper.pdf", + } + ], + "pagination": { + "page": 1, + "page_size": 50, + "total_count": 150, + "total_pages": 3, + "has_next": True, + "has_prev": False, + }, + "status_counts": { + "PENDING": 10, + "PROCESSING": 5, + "PREPROCESSED": 5, + "PROCESSED": 130, + "FAILED": 5, + }, + } + } + + +class StatusCountsResponse(BaseModel): + """Response model for document status counts + + Attributes: + status_counts: Count of documents by status + """ + + status_counts: Dict[str, int] = Field(description="Count of documents by status") + + class Config: + json_schema_extra = { + "example": { + "status_counts": { + "PENDING": 10, + "PROCESSING": 5, + "PREPROCESSED": 5, + "PROCESSED": 130, + "FAILED": 5, + } + } + } + + +class PipelineStatusResponse(BaseModel): + """Response model for pipeline status + + Attributes: + autoscanned: Whether auto-scan has started + busy: Whether the pipeline is currently busy + job_name: Current job name (e.g., indexing files/indexing texts) + job_start: Job start time as ISO format string with timezone (optional) + docs: Total number of documents to be indexed + batchs: Number of batches for processing documents + cur_batch: Current processing batch + request_pending: Flag for pending request for processing + latest_message: Latest message from pipeline processing + history_messages: List of history messages + update_status: Status of update flags for all namespaces + """ + + autoscanned: bool = False + busy: bool = False + job_name: str = "Default Job" + job_start: Optional[str] = None + docs: int = 0 + batchs: int = 0 + cur_batch: int = 0 + request_pending: bool = False + latest_message: str = "" + history_messages: Optional[List[str]] = None + update_status: Optional[dict] = None + + @field_validator("job_start", mode="before") + @classmethod + def parse_job_start(cls, value): + """Process datetime and return as ISO format string with timezone""" + return format_datetime(value) + + class Config: + extra = "allow" # Allow additional fields from the pipeline status + + +class DocumentManager: + def __init__( + self, + input_dir: str, + workspace: str = "", # New parameter for workspace isolation + supported_extensions: tuple = ( + ".txt", + ".md", + ".pdf", + ".docx", + ".pptx", + ".xlsx", + ".rtf", # Rich Text Format + ".odt", # OpenDocument Text + ".tex", # LaTeX + ".epub", # Electronic Publication + ".html", # HyperText Markup Language + ".htm", # HyperText Markup Language + ".csv", # Comma-Separated Values + ".json", # JavaScript Object Notation + ".xml", # eXtensible Markup Language + ".yaml", # YAML Ain't Markup Language + ".yml", # YAML + ".log", # Log files + ".conf", # Configuration files + ".ini", # Initialization files + ".properties", # Java properties files + ".sql", # SQL scripts + ".bat", # Batch files + ".sh", # Shell scripts + ".c", # C source code + ".cpp", # C++ source code + ".py", # Python source code + ".java", # Java source code + ".js", # JavaScript source code + ".ts", # TypeScript source code + ".swift", # Swift source code + ".go", # Go source code + ".rb", # Ruby source code + ".php", # PHP source code + ".css", # Cascading Style Sheets + ".scss", # Sassy CSS + ".less", # LESS CSS + ), + ): + # Store the base input directory and workspace + self.base_input_dir = Path(input_dir) + self.workspace = workspace + self.supported_extensions = supported_extensions + self.indexed_files = set() + + # Create workspace-specific input directory + # If workspace is provided, create a subdirectory for data isolation + if workspace: + self.input_dir = self.base_input_dir / workspace + else: + self.input_dir = self.base_input_dir + + # Create input directory if it doesn't exist + self.input_dir.mkdir(parents=True, exist_ok=True) + + def scan_directory_for_new_files(self) -> List[Path]: + """Scan input directory for new files""" + new_files = [] + for ext in self.supported_extensions: + logger.debug(f"Scanning for {ext} files in {self.input_dir}") + for file_path in self.input_dir.glob(f"*{ext}"): + if file_path not in self.indexed_files: + new_files.append(file_path) + return new_files + + def mark_as_indexed(self, file_path: Path): + self.indexed_files.add(file_path) + + def is_supported_file(self, filename: str) -> bool: + return any(filename.lower().endswith(ext) for ext in self.supported_extensions) + + +def validate_file_path_security(file_path_str: str, base_dir: Path) -> Optional[Path]: + """ + Validate file path security to prevent Path Traversal attacks. + + Args: + file_path_str: The file path string to validate + base_dir: The base directory that the file must be within + + Returns: + Path: Safe file path if valid, None if unsafe or invalid + """ + if not file_path_str or not file_path_str.strip(): + return None + + try: + # Clean the file path string + clean_path_str = file_path_str.strip() + + # Check for obvious path traversal patterns before processing + # This catches both Unix (..) and Windows (..\) style traversals + if ".." in clean_path_str: + # Additional check for Windows-style backslash traversal + if ( + "\\..\\" in clean_path_str + or clean_path_str.startswith("..\\") + or clean_path_str.endswith("\\..") + ): + # logger.warning( + # f"Security violation: Windows path traversal attempt detected - {file_path_str}" + # ) + return None + + # Normalize path separators (convert backslashes to forward slashes) + # This helps handle Windows-style paths on Unix systems + normalized_path = clean_path_str.replace("\\", "/") + + # Create path object and resolve it (handles symlinks and relative paths) + candidate_path = (base_dir / normalized_path).resolve() + base_dir_resolved = base_dir.resolve() + + # Check if the resolved path is within the base directory + if not candidate_path.is_relative_to(base_dir_resolved): + # logger.warning( + # f"Security violation: Path traversal attempt detected - {file_path_str}" + # ) + return None + + return candidate_path + + except (OSError, ValueError, Exception) as e: + logger.warning(f"Invalid file path detected: {file_path_str} - {str(e)}") + return None + + +def get_unique_filename_in_enqueued(target_dir: Path, original_name: str) -> str: + """Generate a unique filename in the target directory by adding numeric suffixes if needed + + Args: + target_dir: Target directory path + original_name: Original filename + + Returns: + str: Unique filename (may have numeric suffix added) + """ + import time + + original_path = Path(original_name) + base_name = original_path.stem + extension = original_path.suffix + + # Try original name first + if not (target_dir / original_name).exists(): + return original_name + + # Try with numeric suffixes 001-999 + for i in range(1, 1000): + suffix = f"{i:03d}" + new_name = f"{base_name}_{suffix}{extension}" + if not (target_dir / new_name).exists(): + return new_name + + # Fallback with timestamp if all 999 slots are taken + timestamp = int(time.time()) + return f"{base_name}_{timestamp}{extension}" + + +# Document processing helper functions (synchronous) +# These functions run in thread pool via asyncio.to_thread() to avoid blocking the event loop + + +def _convert_with_docling(file_path: Path) -> str: + """Convert document using docling (synchronous). + + Args: + file_path: Path to the document file + + Returns: + str: Extracted markdown content + """ + from docling.document_converter import DocumentConverter # type: ignore + + converter = DocumentConverter() + result = converter.convert(file_path) + return result.document.export_to_markdown() + + +def _extract_pdf_pypdf(file_bytes: bytes, password: str = None) -> str: + """Extract PDF content using pypdf (synchronous). + + Args: + file_bytes: PDF file content as bytes + password: Optional password for encrypted PDFs + + Returns: + str: Extracted text content + + Raises: + Exception: If PDF is encrypted and password is incorrect or missing + """ + from pypdf import PdfReader # type: ignore + + pdf_file = BytesIO(file_bytes) + reader = PdfReader(pdf_file) + + # Check if PDF is encrypted + if reader.is_encrypted: + if not password: + raise Exception("PDF is encrypted but no password provided") + + decrypt_result = reader.decrypt(password) + if decrypt_result == 0: + raise Exception("Incorrect PDF password") + + # Extract text from all pages + content = "" + for page in reader.pages: + content += page.extract_text() + "\n" + + return content + + +def _extract_docx(file_bytes: bytes) -> str: + """Extract DOCX content (synchronous). + + Args: + file_bytes: DOCX file content as bytes + + Returns: + str: Extracted text content + """ + from docx import Document # type: ignore + + docx_file = BytesIO(file_bytes) + doc = Document(docx_file) + return "\n".join([paragraph.text for paragraph in doc.paragraphs]) + + +def _extract_pptx(file_bytes: bytes) -> str: + """Extract PPTX content (synchronous). + + Args: + file_bytes: PPTX file content as bytes + + Returns: + str: Extracted text content + """ + from pptx import Presentation # type: ignore + + pptx_file = BytesIO(file_bytes) + prs = Presentation(pptx_file) + content = "" + for slide in prs.slides: + for shape in slide.shapes: + if hasattr(shape, "text"): + content += shape.text + "\n" + return content + + +def _extract_xlsx(file_bytes: bytes) -> str: + """Extract XLSX content (synchronous). + + Args: + file_bytes: XLSX file content as bytes + + Returns: + str: Extracted text content + """ + from openpyxl import load_workbook # type: ignore + + xlsx_file = BytesIO(file_bytes) + wb = load_workbook(xlsx_file) + content = "" + for sheet in wb: + content += f"Sheet: {sheet.title}\n" + for row in sheet.iter_rows(values_only=True): + content += ( + "\t".join(str(cell) if cell is not None else "" for cell in row) + "\n" + ) + content += "\n" + return content + + +async def pipeline_enqueue_file( + rag: LightRAG, file_path: Path, track_id: str = None +) -> tuple[bool, str]: + """Add a file to the queue for processing + + Args: + rag: LightRAG instance + file_path: Path to the saved file + track_id: Optional tracking ID, if not provided will be generated + Returns: + tuple: (success: bool, track_id: str) + """ + + # Generate track_id if not provided + if track_id is None: + track_id = generate_track_id("unknown") + + try: + content = "" + ext = file_path.suffix.lower() + file_size = 0 + + # Get file size for error reporting + try: + file_size = file_path.stat().st_size + except Exception: + file_size = 0 + + file = None + try: + async with aiofiles.open(file_path, "rb") as f: + file = await f.read() + except PermissionError as e: + error_files = [ + { + "file_path": str(file_path.name), + "error_description": "[File Extraction]Permission denied - cannot read file", + "original_error": str(e), + "file_size": file_size, + } + ] + await rag.apipeline_enqueue_error_documents(error_files, track_id) + logger.error( + f"[File Extraction]Permission denied reading file: {file_path.name}" + ) + return False, track_id + except FileNotFoundError as e: + error_files = [ + { + "file_path": str(file_path.name), + "error_description": "[File Extraction]File not found", + "original_error": str(e), + "file_size": file_size, + } + ] + await rag.apipeline_enqueue_error_documents(error_files, track_id) + logger.error(f"[File Extraction]File not found: {file_path.name}") + return False, track_id + except Exception as e: + error_files = [ + { + "file_path": str(file_path.name), + "error_description": "[File Extraction]File reading error", + "original_error": str(e), + "file_size": file_size, + } + ] + await rag.apipeline_enqueue_error_documents(error_files, track_id) + logger.error( + f"[File Extraction]Error reading file {file_path.name}: {str(e)}" + ) + return False, track_id + + # Process based on file type + try: + match ext: + case ( + ".txt" + | ".md" + | ".html" + | ".htm" + | ".tex" + | ".json" + | ".xml" + | ".yaml" + | ".yml" + | ".rtf" + | ".odt" + | ".epub" + | ".csv" + | ".log" + | ".conf" + | ".ini" + | ".properties" + | ".sql" + | ".bat" + | ".sh" + | ".c" + | ".cpp" + | ".py" + | ".java" + | ".js" + | ".ts" + | ".swift" + | ".go" + | ".rb" + | ".php" + | ".css" + | ".scss" + | ".less" + ): + try: + # Try to decode as UTF-8 + content = file.decode("utf-8") + + # Validate content + if not content or len(content.strip()) == 0: + error_files = [ + { + "file_path": str(file_path.name), + "error_description": "[File Extraction]Empty file content", + "original_error": "File contains no content or only whitespace", + "file_size": file_size, + } + ] + await rag.apipeline_enqueue_error_documents( + error_files, track_id + ) + logger.error( + f"[File Extraction]Empty content in file: {file_path.name}" + ) + return False, track_id + + # Check if content looks like binary data string representation + if content.startswith("b'") or content.startswith('b"'): + error_files = [ + { + "file_path": str(file_path.name), + "error_description": "[File Extraction]Binary data in text file", + "original_error": "File appears to contain binary data representation instead of text", + "file_size": file_size, + } + ] + await rag.apipeline_enqueue_error_documents( + error_files, track_id + ) + logger.error( + f"[File Extraction]File {file_path.name} appears to contain binary data representation instead of text" + ) + return False, track_id + + except UnicodeDecodeError as e: + error_files = [ + { + "file_path": str(file_path.name), + "error_description": "[File Extraction]UTF-8 encoding error, please convert it to UTF-8 before processing", + "original_error": f"File is not valid UTF-8 encoded text: {str(e)}", + "file_size": file_size, + } + ] + await rag.apipeline_enqueue_error_documents( + error_files, track_id + ) + logger.error( + f"[File Extraction]File {file_path.name} is not valid UTF-8 encoded text. Please convert it to UTF-8 before processing." + ) + return False, track_id + + case ".pdf": + try: + # Try DOCLING first if configured and available + if ( + global_args.document_loading_engine == "DOCLING" + and _is_docling_available() + ): + content = await asyncio.to_thread( + _convert_with_docling, file_path + ) + else: + if ( + global_args.document_loading_engine == "DOCLING" + and not _is_docling_available() + ): + logger.warning( + f"DOCLING engine configured but not available for {file_path.name}. Falling back to pypdf." + ) + # Use pypdf (non-blocking via to_thread) + content = await asyncio.to_thread( + _extract_pdf_pypdf, + file, + global_args.pdf_decrypt_password, + ) + except Exception as e: + error_files = [ + { + "file_path": str(file_path.name), + "error_description": "[File Extraction]PDF processing error", + "original_error": f"Failed to extract text from PDF: {str(e)}", + "file_size": file_size, + } + ] + await rag.apipeline_enqueue_error_documents( + error_files, track_id + ) + logger.error( + f"[File Extraction]Error processing PDF {file_path.name}: {str(e)}" + ) + return False, track_id + + case ".docx": + try: + # Try DOCLING first if configured and available + if ( + global_args.document_loading_engine == "DOCLING" + and _is_docling_available() + ): + content = await asyncio.to_thread( + _convert_with_docling, file_path + ) + else: + if ( + global_args.document_loading_engine == "DOCLING" + and not _is_docling_available() + ): + logger.warning( + f"DOCLING engine configured but not available for {file_path.name}. Falling back to python-docx." + ) + # Use python-docx (non-blocking via to_thread) + content = await asyncio.to_thread(_extract_docx, file) + except Exception as e: + error_files = [ + { + "file_path": str(file_path.name), + "error_description": "[File Extraction]DOCX processing error", + "original_error": f"Failed to extract text from DOCX: {str(e)}", + "file_size": file_size, + } + ] + await rag.apipeline_enqueue_error_documents( + error_files, track_id + ) + logger.error( + f"[File Extraction]Error processing DOCX {file_path.name}: {str(e)}" + ) + return False, track_id + + case ".pptx": + try: + # Try DOCLING first if configured and available + if ( + global_args.document_loading_engine == "DOCLING" + and _is_docling_available() + ): + content = await asyncio.to_thread( + _convert_with_docling, file_path + ) + else: + if ( + global_args.document_loading_engine == "DOCLING" + and not _is_docling_available() + ): + logger.warning( + f"DOCLING engine configured but not available for {file_path.name}. Falling back to python-pptx." + ) + # Use python-pptx (non-blocking via to_thread) + content = await asyncio.to_thread(_extract_pptx, file) + except Exception as e: + error_files = [ + { + "file_path": str(file_path.name), + "error_description": "[File Extraction]PPTX processing error", + "original_error": f"Failed to extract text from PPTX: {str(e)}", + "file_size": file_size, + } + ] + await rag.apipeline_enqueue_error_documents( + error_files, track_id + ) + logger.error( + f"[File Extraction]Error processing PPTX {file_path.name}: {str(e)}" + ) + return False, track_id + + case ".xlsx": + try: + # Try DOCLING first if configured and available + if ( + global_args.document_loading_engine == "DOCLING" + and _is_docling_available() + ): + content = await asyncio.to_thread( + _convert_with_docling, file_path + ) + else: + if ( + global_args.document_loading_engine == "DOCLING" + and not _is_docling_available() + ): + logger.warning( + f"DOCLING engine configured but not available for {file_path.name}. Falling back to openpyxl." + ) + # Use openpyxl (non-blocking via to_thread) + content = await asyncio.to_thread(_extract_xlsx, file) + except Exception as e: + error_files = [ + { + "file_path": str(file_path.name), + "error_description": "[File Extraction]XLSX processing error", + "original_error": f"Failed to extract text from XLSX: {str(e)}", + "file_size": file_size, + } + ] + await rag.apipeline_enqueue_error_documents( + error_files, track_id + ) + logger.error( + f"[File Extraction]Error processing XLSX {file_path.name}: {str(e)}" + ) + return False, track_id + + case _: + error_files = [ + { + "file_path": str(file_path.name), + "error_description": f"[File Extraction]Unsupported file type: {ext}", + "original_error": f"File extension {ext} is not supported", + "file_size": file_size, + } + ] + await rag.apipeline_enqueue_error_documents(error_files, track_id) + logger.error( + f"[File Extraction]Unsupported file type: {file_path.name} (extension {ext})" + ) + return False, track_id + + except Exception as e: + error_files = [ + { + "file_path": str(file_path.name), + "error_description": "[File Extraction]File format processing error", + "original_error": f"Unexpected error during file extracting: {str(e)}", + "file_size": file_size, + } + ] + await rag.apipeline_enqueue_error_documents(error_files, track_id) + logger.error( + f"[File Extraction]Unexpected error during {file_path.name} extracting: {str(e)}" + ) + return False, track_id + + # Insert into the RAG queue + if content: + # Check if content contains only whitespace characters + if not content.strip(): + error_files = [ + { + "file_path": str(file_path.name), + "error_description": "[File Extraction]File contains only whitespace", + "original_error": "File content contains only whitespace characters", + "file_size": file_size, + } + ] + await rag.apipeline_enqueue_error_documents(error_files, track_id) + logger.warning( + f"[File Extraction]File contains only whitespace characters: {file_path.name}" + ) + return False, track_id + + try: + await rag.apipeline_enqueue_documents( + content, file_paths=file_path.name, track_id=track_id + ) + + logger.info( + f"Successfully extracted and enqueued file: {file_path.name}" + ) + + # Move file to __enqueued__ directory after enqueuing + try: + enqueued_dir = file_path.parent / "__enqueued__" + enqueued_dir.mkdir(exist_ok=True) + + # Generate unique filename to avoid conflicts + unique_filename = get_unique_filename_in_enqueued( + enqueued_dir, file_path.name + ) + target_path = enqueued_dir / unique_filename + + # Move the file + file_path.rename(target_path) + logger.debug( + f"Moved file to enqueued directory: {file_path.name} -> {unique_filename}" + ) + + except Exception as move_error: + logger.error( + f"Failed to move file {file_path.name} to __enqueued__ directory: {move_error}" + ) + # Don't affect the main function's success status + + return True, track_id + + except Exception as e: + error_files = [ + { + "file_path": str(file_path.name), + "error_description": "Document enqueue error", + "original_error": f"Failed to enqueue document: {str(e)}", + "file_size": file_size, + } + ] + await rag.apipeline_enqueue_error_documents(error_files, track_id) + logger.error(f"Error enqueueing document {file_path.name}: {str(e)}") + return False, track_id + else: + error_files = [ + { + "file_path": str(file_path.name), + "error_description": "No content extracted", + "original_error": "No content could be extracted from file", + "file_size": file_size, + } + ] + await rag.apipeline_enqueue_error_documents(error_files, track_id) + logger.error(f"No content extracted from file: {file_path.name}") + return False, track_id + + except Exception as e: + # Catch-all for any unexpected errors + try: + file_size = file_path.stat().st_size if file_path.exists() else 0 + except Exception: + file_size = 0 + + error_files = [ + { + "file_path": str(file_path.name), + "error_description": "Unexpected processing error", + "original_error": f"Unexpected error: {str(e)}", + "file_size": file_size, + } + ] + await rag.apipeline_enqueue_error_documents(error_files, track_id) + logger.error(f"Enqueuing file {file_path.name} error: {str(e)}") + logger.error(traceback.format_exc()) + return False, track_id + finally: + if file_path.name.startswith(temp_prefix): + try: + file_path.unlink() + except Exception as e: + logger.error(f"Error deleting file {file_path}: {str(e)}") + + +async def pipeline_index_file(rag: LightRAG, file_path: Path, track_id: str = None): + """Index a file with track_id + + Args: + rag: LightRAG instance + file_path: Path to the saved file + track_id: Optional tracking ID + """ + try: + success, returned_track_id = await pipeline_enqueue_file( + rag, file_path, track_id + ) + if success: + await rag.apipeline_process_enqueue_documents() + + except Exception as e: + logger.error(f"Error indexing file {file_path.name}: {str(e)}") + logger.error(traceback.format_exc()) + + +async def pipeline_index_files( + rag: LightRAG, file_paths: List[Path], track_id: str = None +): + """Index multiple files sequentially to avoid high CPU load + + Args: + rag: LightRAG instance + file_paths: Paths to the files to index + track_id: Optional tracking ID to pass to all files + """ + if not file_paths: + return + try: + enqueued = False + + # Use get_pinyin_sort_key for Chinese pinyin sorting + sorted_file_paths = sorted( + file_paths, key=lambda p: get_pinyin_sort_key(str(p)) + ) + + # Process files sequentially with track_id + for file_path in sorted_file_paths: + success, _ = await pipeline_enqueue_file(rag, file_path, track_id) + if success: + enqueued = True + + # Process the queue only if at least one file was successfully enqueued + if enqueued: + await rag.apipeline_process_enqueue_documents() + except Exception as e: + logger.error(f"Error indexing files: {str(e)}") + logger.error(traceback.format_exc()) + + +async def pipeline_index_texts( + rag: LightRAG, + texts: List[str], + file_sources: List[str] = None, + track_id: str = None, +): + """Index a list of texts with track_id + + Args: + rag: LightRAG instance + texts: The texts to index + file_sources: Sources of the texts + track_id: Optional tracking ID + """ + if not texts: + return + if file_sources is not None: + if len(file_sources) != 0 and len(file_sources) != len(texts): + [ + file_sources.append("unknown_source") + for _ in range(len(file_sources), len(texts)) + ] + await rag.apipeline_enqueue_documents( + input=texts, file_paths=file_sources, track_id=track_id + ) + await rag.apipeline_process_enqueue_documents() + + +async def run_scanning_process( + rag: LightRAG, doc_manager: DocumentManager, track_id: str = None +): + """Background task to scan and index documents + + Args: + rag: LightRAG instance + doc_manager: DocumentManager instance + track_id: Optional tracking ID to pass to all scanned files + """ + try: + new_files = doc_manager.scan_directory_for_new_files() + total_files = len(new_files) + logger.info(f"Found {total_files} files to index.") + + if new_files: + # Check for files with PROCESSED status and filter them out + valid_files = [] + processed_files = [] + + for file_path in new_files: + filename = file_path.name + existing_doc_data = await rag.doc_status.get_doc_by_file_path(filename) + + if existing_doc_data and existing_doc_data.get("status") == "processed": + # File is already PROCESSED, skip it with warning + processed_files.append(filename) + logger.warning(f"Skipping already processed file: {filename}") + else: + # File is new or in non-PROCESSED status, add to processing list + valid_files.append(file_path) + + # Process valid files (new files + non-PROCESSED status files) + if valid_files: + await pipeline_index_files(rag, valid_files, track_id) + if processed_files: + logger.info( + f"Scanning process completed: {len(valid_files)} files Processed {len(processed_files)} skipped." + ) + else: + logger.info( + f"Scanning process completed: {len(valid_files)} files Processed." + ) + else: + logger.info( + "No files to process after filtering already processed files." + ) + else: + # No new files to index, check if there are any documents in the queue + logger.info( + "No upload file found, check if there are any documents in the queue..." + ) + await rag.apipeline_process_enqueue_documents() + + except Exception as e: + logger.error(f"Error during scanning process: {str(e)}") + logger.error(traceback.format_exc()) + + +async def background_delete_documents( + rag: LightRAG, + doc_manager: DocumentManager, + doc_ids: List[str], + delete_file: bool = False, + delete_llm_cache: bool = False, +): + """Background task to delete multiple documents""" + from lightrag.kg.shared_storage import ( + get_namespace_data, + get_pipeline_status_lock, + ) + + pipeline_status = await get_namespace_data("pipeline_status") + pipeline_status_lock = get_pipeline_status_lock() + + total_docs = len(doc_ids) + successful_deletions = [] + failed_deletions = [] + + # Double-check pipeline status before proceeding + async with pipeline_status_lock: + if pipeline_status.get("busy", False): + logger.warning("Error: Unexpected pipeline busy state, aborting deletion.") + return # Abort deletion operation + + # Set pipeline status to busy for deletion + pipeline_status.update( + { + "busy": True, + "job_name": f"Deleting {total_docs} Documents", + "job_start": datetime.now().isoformat(), + "docs": total_docs, + "batchs": total_docs, + "cur_batch": 0, + "latest_message": "Starting document deletion process", + } + ) + # Use slice assignment to clear the list in place + pipeline_status["history_messages"][:] = ["Starting document deletion process"] + if delete_llm_cache: + pipeline_status["history_messages"].append( + "LLM cache cleanup requested for this deletion job" + ) + + try: + # Loop through each document ID and delete them one by one + for i, doc_id in enumerate(doc_ids, 1): + # Check for cancellation at the start of each document deletion + async with pipeline_status_lock: + if pipeline_status.get("cancellation_requested", False): + cancel_msg = f"Deletion cancelled by user at document {i}/{total_docs}. {len(successful_deletions)} deleted, {total_docs - i + 1} remaining." + logger.info(cancel_msg) + pipeline_status["latest_message"] = cancel_msg + pipeline_status["history_messages"].append(cancel_msg) + # Add remaining documents to failed list with cancellation reason + failed_deletions.extend( + doc_ids[i - 1 :] + ) # i-1 because enumerate starts at 1 + break # Exit the loop, remaining documents unchanged + + start_msg = f"Deleting document {i}/{total_docs}: {doc_id}" + logger.info(start_msg) + pipeline_status["cur_batch"] = i + pipeline_status["latest_message"] = start_msg + pipeline_status["history_messages"].append(start_msg) + + file_path = "#" + try: + result = await rag.adelete_by_doc_id( + doc_id, delete_llm_cache=delete_llm_cache + ) + file_path = ( + getattr(result, "file_path", "-") if "result" in locals() else "-" + ) + if result.status == "success": + successful_deletions.append(doc_id) + success_msg = ( + f"Document deleted {i}/{total_docs}: {doc_id}[{file_path}]" + ) + logger.info(success_msg) + async with pipeline_status_lock: + pipeline_status["history_messages"].append(success_msg) + + # Handle file deletion if requested and file_path is available + if ( + delete_file + and result.file_path + and result.file_path != "unknown_source" + ): + try: + deleted_files = [] + # SECURITY FIX: Use secure path validation to prevent arbitrary file deletion + safe_file_path = validate_file_path_security( + result.file_path, doc_manager.input_dir + ) + + if safe_file_path is None: + # Security violation detected - log and skip file deletion + security_msg = f"Security violation: Unsafe file path detected for deletion - {result.file_path}" + logger.warning(security_msg) + async with pipeline_status_lock: + pipeline_status["latest_message"] = security_msg + pipeline_status["history_messages"].append( + security_msg + ) + else: + # check and delete files from input_dir directory + if safe_file_path.exists(): + try: + safe_file_path.unlink() + deleted_files.append(safe_file_path.name) + file_delete_msg = f"Successfully deleted input_dir file: {result.file_path}" + logger.info(file_delete_msg) + async with pipeline_status_lock: + pipeline_status["latest_message"] = ( + file_delete_msg + ) + pipeline_status["history_messages"].append( + file_delete_msg + ) + except Exception as file_error: + file_error_msg = f"Failed to delete input_dir file {result.file_path}: {str(file_error)}" + logger.debug(file_error_msg) + async with pipeline_status_lock: + pipeline_status["latest_message"] = ( + file_error_msg + ) + pipeline_status["history_messages"].append( + file_error_msg + ) + + # Also check and delete files from __enqueued__ directory + enqueued_dir = doc_manager.input_dir / "__enqueued__" + if enqueued_dir.exists(): + # SECURITY FIX: Validate that the file path is safe before processing + # Only proceed if the original path validation passed + base_name = Path(result.file_path).stem + extension = Path(result.file_path).suffix + + # Search for exact match and files with numeric suffixes + for enqueued_file in enqueued_dir.glob( + f"{base_name}*{extension}" + ): + # Additional security check: ensure enqueued file is within enqueued directory + safe_enqueued_path = ( + validate_file_path_security( + enqueued_file.name, enqueued_dir + ) + ) + if safe_enqueued_path is not None: + try: + enqueued_file.unlink() + deleted_files.append(enqueued_file.name) + logger.info( + f"Successfully deleted enqueued file: {enqueued_file.name}" + ) + except Exception as enqueued_error: + file_error_msg = f"Failed to delete enqueued file {enqueued_file.name}: {str(enqueued_error)}" + logger.debug(file_error_msg) + async with pipeline_status_lock: + pipeline_status[ + "latest_message" + ] = file_error_msg + pipeline_status[ + "history_messages" + ].append(file_error_msg) + else: + security_msg = f"Security violation: Unsafe enqueued file path detected - {enqueued_file.name}" + logger.warning(security_msg) + + if deleted_files == []: + file_error_msg = f"File deletion skipped, missing or unsafe file: {result.file_path}" + logger.warning(file_error_msg) + async with pipeline_status_lock: + pipeline_status["latest_message"] = file_error_msg + pipeline_status["history_messages"].append( + file_error_msg + ) + + except Exception as file_error: + file_error_msg = f"Failed to delete file {result.file_path}: {str(file_error)}" + logger.error(file_error_msg) + async with pipeline_status_lock: + pipeline_status["latest_message"] = file_error_msg + pipeline_status["history_messages"].append( + file_error_msg + ) + elif delete_file: + no_file_msg = ( + f"File deletion skipped, missing file path: {doc_id}" + ) + logger.warning(no_file_msg) + async with pipeline_status_lock: + pipeline_status["latest_message"] = no_file_msg + pipeline_status["history_messages"].append(no_file_msg) + else: + failed_deletions.append(doc_id) + error_msg = f"Failed to delete {i}/{total_docs}: {doc_id}[{file_path}] - {result.message}" + logger.error(error_msg) + async with pipeline_status_lock: + pipeline_status["latest_message"] = error_msg + pipeline_status["history_messages"].append(error_msg) + + except Exception as e: + failed_deletions.append(doc_id) + error_msg = f"Error deleting document {i}/{total_docs}: {doc_id}[{file_path}] - {str(e)}" + logger.error(error_msg) + logger.error(traceback.format_exc()) + async with pipeline_status_lock: + pipeline_status["latest_message"] = error_msg + pipeline_status["history_messages"].append(error_msg) + + except Exception as e: + error_msg = f"Critical error during batch deletion: {str(e)}" + logger.error(error_msg) + logger.error(traceback.format_exc()) + async with pipeline_status_lock: + pipeline_status["history_messages"].append(error_msg) + finally: + # Final summary and check for pending requests + async with pipeline_status_lock: + pipeline_status["busy"] = False + pipeline_status["pending_requests"] = False # Reset pending requests flag + pipeline_status["cancellation_requested"] = ( + False # Always reset cancellation flag + ) + completion_msg = f"Deletion completed: {len(successful_deletions)} successful, {len(failed_deletions)} failed" + pipeline_status["latest_message"] = completion_msg + pipeline_status["history_messages"].append(completion_msg) + + # Check if there are pending document indexing requests + has_pending_request = pipeline_status.get("request_pending", False) + + # If there are pending requests, start document processing pipeline + if has_pending_request: + try: + logger.info( + "Processing pending document indexing requests after deletion" + ) + await rag.apipeline_process_enqueue_documents() + except Exception as e: + logger.error(f"Error processing pending documents after deletion: {e}") + + +def create_document_routes( + rag: LightRAG, doc_manager: DocumentManager, api_key: Optional[str] = None +): + # Create combined auth dependency for document routes + combined_auth = get_combined_auth_dependency(api_key) + + @router.post( + "/scan", response_model=ScanResponse, dependencies=[Depends(combined_auth)] + ) + async def scan_for_new_documents(background_tasks: BackgroundTasks): + """ + Trigger the scanning process for new documents. + + This endpoint initiates a background task that scans the input directory for new documents + and processes them. If a scanning process is already running, it returns a status indicating + that fact. + + Returns: + ScanResponse: A response object containing the scanning status and track_id + """ + # Generate track_id with "scan" prefix for scanning operation + track_id = generate_track_id("scan") + + # Start the scanning process in the background with track_id + background_tasks.add_task(run_scanning_process, rag, doc_manager, track_id) + return ScanResponse( + status="scanning_started", + message="Scanning process has been initiated in the background", + track_id=track_id, + ) + + @router.post( + "/upload", response_model=InsertResponse, dependencies=[Depends(combined_auth)] + ) + async def upload_to_input_dir( + background_tasks: BackgroundTasks, file: UploadFile = File(...) + ): + """ + Upload a file to the input directory and index it. + + This API endpoint accepts a file through an HTTP POST request, checks if the + uploaded file is of a supported type, saves it in the specified input directory, + indexes it for retrieval, and returns a success status with relevant details. + + Args: + background_tasks: FastAPI BackgroundTasks for async processing + file (UploadFile): The file to be uploaded. It must have an allowed extension. + + Returns: + InsertResponse: A response object containing the upload status and a message. + status can be "success", "duplicated", or error is thrown. + + Raises: + HTTPException: If the file type is not supported (400) or other errors occur (500). + """ + try: + # Sanitize filename to prevent Path Traversal attacks + safe_filename = sanitize_filename(file.filename, doc_manager.input_dir) + + if not doc_manager.is_supported_file(safe_filename): + raise HTTPException( + status_code=400, + detail=f"Unsupported file type. Supported types: {doc_manager.supported_extensions}", + ) + + # Check if filename already exists in doc_status storage + existing_doc_data = await rag.doc_status.get_doc_by_file_path(safe_filename) + if existing_doc_data: + # Get document status information for error message + status = existing_doc_data.get("status", "unknown") + return InsertResponse( + status="duplicated", + message=f"File '{safe_filename}' already exists in document storage (Status: {status}).", + track_id="", + ) + + file_path = doc_manager.input_dir / safe_filename + # Check if file already exists in file system + if file_path.exists(): + return InsertResponse( + status="duplicated", + message=f"File '{safe_filename}' already exists in the input directory.", + track_id="", + ) + + with open(file_path, "wb") as buffer: + shutil.copyfileobj(file.file, buffer) + + track_id = generate_track_id("upload") + + # Add to background tasks and get track_id + background_tasks.add_task(pipeline_index_file, rag, file_path, track_id) + + return InsertResponse( + status="success", + message=f"File '{safe_filename}' uploaded successfully. Processing will continue in background.", + track_id=track_id, + ) + + except Exception as e: + logger.error(f"Error /documents/upload: {file.filename}: {str(e)}") + logger.error(traceback.format_exc()) + raise HTTPException(status_code=500, detail=str(e)) + + @router.post( + "/text", response_model=InsertResponse, dependencies=[Depends(combined_auth)] + ) + async def insert_text( + request: InsertTextRequest, background_tasks: BackgroundTasks + ): + """ + Insert text into the RAG system. + + This endpoint allows you to insert text data into the RAG system for later retrieval + and use in generating responses. + + Args: + request (InsertTextRequest): The request body containing the text to be inserted. + background_tasks: FastAPI BackgroundTasks for async processing + + Returns: + InsertResponse: A response object containing the status of the operation. + + Raises: + HTTPException: If an error occurs during text processing (500). + """ + try: + # Check if file_source already exists in doc_status storage + if ( + request.file_source + and request.file_source.strip() + and request.file_source != "unknown_source" + ): + existing_doc_data = await rag.doc_status.get_doc_by_file_path( + request.file_source + ) + if existing_doc_data: + # Get document status information for error message + status = existing_doc_data.get("status", "unknown") + return InsertResponse( + status="duplicated", + message=f"File source '{request.file_source}' already exists in document storage (Status: {status}).", + track_id="", + ) + + # Generate track_id for text insertion + track_id = generate_track_id("insert") + + background_tasks.add_task( + pipeline_index_texts, + rag, + [request.text], + file_sources=[request.file_source], + track_id=track_id, + ) + + return InsertResponse( + status="success", + message="Text successfully received. Processing will continue in background.", + track_id=track_id, + ) + except Exception as e: + logger.error(f"Error /documents/text: {str(e)}") + logger.error(traceback.format_exc()) + raise HTTPException(status_code=500, detail=str(e)) + + @router.post( + "/texts", + response_model=InsertResponse, + dependencies=[Depends(combined_auth)], + ) + async def insert_texts( + request: InsertTextsRequest, background_tasks: BackgroundTasks + ): + """ + Insert multiple texts into the RAG system. + + This endpoint allows you to insert multiple text entries into the RAG system + in a single request. + + Args: + request (InsertTextsRequest): The request body containing the list of texts. + background_tasks: FastAPI BackgroundTasks for async processing + + Returns: + InsertResponse: A response object containing the status of the operation. + + Raises: + HTTPException: If an error occurs during text processing (500). + """ + try: + # Check if any file_sources already exist in doc_status storage + if request.file_sources: + for file_source in request.file_sources: + if ( + file_source + and file_source.strip() + and file_source != "unknown_source" + ): + existing_doc_data = await rag.doc_status.get_doc_by_file_path( + file_source + ) + if existing_doc_data: + # Get document status information for error message + status = existing_doc_data.get("status", "unknown") + return InsertResponse( + status="duplicated", + message=f"File source '{file_source}' already exists in document storage (Status: {status}).", + track_id="", + ) + + # Generate track_id for texts insertion + track_id = generate_track_id("insert") + + background_tasks.add_task( + pipeline_index_texts, + rag, + request.texts, + file_sources=request.file_sources, + track_id=track_id, + ) + + return InsertResponse( + status="success", + message="Texts successfully received. Processing will continue in background.", + track_id=track_id, + ) + except Exception as e: + logger.error(f"Error /documents/texts: {str(e)}") + logger.error(traceback.format_exc()) + raise HTTPException(status_code=500, detail=str(e)) + + @router.delete( + "", response_model=ClearDocumentsResponse, dependencies=[Depends(combined_auth)] + ) + async def clear_documents(): + """ + Clear all documents from the RAG system. + + This endpoint deletes all documents, entities, relationships, and files from the system. + It uses the storage drop methods to properly clean up all data and removes all files + from the input directory. + + Returns: + ClearDocumentsResponse: A response object containing the status and message. + - status="success": All documents and files were successfully cleared. + - status="partial_success": Document clear job exit with some errors. + - status="busy": Operation could not be completed because the pipeline is busy. + - status="fail": All storage drop operations failed, with message + - message: Detailed information about the operation results, including counts + of deleted files and any errors encountered. + + Raises: + HTTPException: Raised when a serious error occurs during the clearing process, + with status code 500 and error details in the detail field. + """ + from lightrag.kg.shared_storage import ( + get_namespace_data, + get_pipeline_status_lock, + ) + + # Get pipeline status and lock + pipeline_status = await get_namespace_data("pipeline_status") + pipeline_status_lock = get_pipeline_status_lock() + + # Check and set status with lock + async with pipeline_status_lock: + if pipeline_status.get("busy", False): + return ClearDocumentsResponse( + status="busy", + message="Cannot clear documents while pipeline is busy", + ) + # Set busy to true + pipeline_status.update( + { + "busy": True, + "job_name": "Clearing Documents", + "job_start": datetime.now().isoformat(), + "docs": 0, + "batchs": 0, + "cur_batch": 0, + "request_pending": False, # Clear any previous request + "latest_message": "Starting document clearing process", + } + ) + # Cleaning history_messages without breaking it as a shared list object + del pipeline_status["history_messages"][:] + pipeline_status["history_messages"].append( + "Starting document clearing process" + ) + + try: + # Use drop method to clear all data + drop_tasks = [] + storages = [ + rag.text_chunks, + rag.full_docs, + rag.full_entities, + rag.full_relations, + rag.entity_chunks, + rag.relation_chunks, + rag.entities_vdb, + rag.relationships_vdb, + rag.chunks_vdb, + rag.chunk_entity_relation_graph, + rag.doc_status, + ] + + # Log storage drop start + if "history_messages" in pipeline_status: + pipeline_status["history_messages"].append( + "Starting to drop storage components" + ) + + for storage in storages: + if storage is not None: + drop_tasks.append(storage.drop()) + + # Wait for all drop tasks to complete + drop_results = await asyncio.gather(*drop_tasks, return_exceptions=True) + + # Check for errors and log results + errors = [] + storage_success_count = 0 + storage_error_count = 0 + + for i, result in enumerate(drop_results): + storage_name = storages[i].__class__.__name__ + if isinstance(result, Exception): + error_msg = f"Error dropping {storage_name}: {str(result)}" + errors.append(error_msg) + logger.error(error_msg) + storage_error_count += 1 + else: + namespace = storages[i].namespace + workspace = storages[i].workspace + logger.info( + f"Successfully dropped {storage_name}: {workspace}/{namespace}" + ) + storage_success_count += 1 + + # Log storage drop results + if "history_messages" in pipeline_status: + if storage_error_count > 0: + pipeline_status["history_messages"].append( + f"Dropped {storage_success_count} storage components with {storage_error_count} errors" + ) + else: + pipeline_status["history_messages"].append( + f"Successfully dropped all {storage_success_count} storage components" + ) + + # If all storage operations failed, return error status and don't proceed with file deletion + if storage_success_count == 0 and storage_error_count > 0: + error_message = "All storage drop operations failed. Aborting document clearing process." + logger.error(error_message) + if "history_messages" in pipeline_status: + pipeline_status["history_messages"].append(error_message) + return ClearDocumentsResponse(status="fail", message=error_message) + + # Log file deletion start + if "history_messages" in pipeline_status: + pipeline_status["history_messages"].append( + "Starting to delete files in input directory" + ) + + # Delete only files in the current directory, preserve files in subdirectories + deleted_files_count = 0 + file_errors_count = 0 + + for file_path in doc_manager.input_dir.glob("*"): + if file_path.is_file(): + try: + file_path.unlink() + deleted_files_count += 1 + except Exception as e: + logger.error(f"Error deleting file {file_path}: {str(e)}") + file_errors_count += 1 + + # Log file deletion results + if "history_messages" in pipeline_status: + if file_errors_count > 0: + pipeline_status["history_messages"].append( + f"Deleted {deleted_files_count} files with {file_errors_count} errors" + ) + errors.append(f"Failed to delete {file_errors_count} files") + else: + pipeline_status["history_messages"].append( + f"Successfully deleted {deleted_files_count} files" + ) + + # Prepare final result message + final_message = "" + if errors: + final_message = f"Cleared documents with some errors. Deleted {deleted_files_count} files." + status = "partial_success" + else: + final_message = f"All documents cleared successfully. Deleted {deleted_files_count} files." + status = "success" + + # Log final result + if "history_messages" in pipeline_status: + pipeline_status["history_messages"].append(final_message) + + # Return response based on results + return ClearDocumentsResponse(status=status, message=final_message) + except Exception as e: + error_msg = f"Error clearing documents: {str(e)}" + logger.error(error_msg) + logger.error(traceback.format_exc()) + if "history_messages" in pipeline_status: + pipeline_status["history_messages"].append(error_msg) + raise HTTPException(status_code=500, detail=str(e)) + finally: + # Reset busy status after completion + async with pipeline_status_lock: + pipeline_status["busy"] = False + completion_msg = "Document clearing process completed" + pipeline_status["latest_message"] = completion_msg + if "history_messages" in pipeline_status: + pipeline_status["history_messages"].append(completion_msg) + + @router.get( + "/pipeline_status", + dependencies=[Depends(combined_auth)], + response_model=PipelineStatusResponse, + ) + async def get_pipeline_status() -> PipelineStatusResponse: + """ + Get the current status of the document indexing pipeline. + + This endpoint returns information about the current state of the document processing pipeline, + including the processing status, progress information, and history messages. + + Returns: + PipelineStatusResponse: A response object containing: + - autoscanned (bool): Whether auto-scan has started + - busy (bool): Whether the pipeline is currently busy + - job_name (str): Current job name (e.g., indexing files/indexing texts) + - job_start (str, optional): Job start time as ISO format string + - docs (int): Total number of documents to be indexed + - batchs (int): Number of batches for processing documents + - cur_batch (int): Current processing batch + - request_pending (bool): Flag for pending request for processing + - latest_message (str): Latest message from pipeline processing + - history_messages (List[str], optional): List of history messages (limited to latest 1000 entries, + with truncation message if more than 1000 messages exist) + + Raises: + HTTPException: If an error occurs while retrieving pipeline status (500) + """ + try: + from lightrag.kg.shared_storage import ( + get_namespace_data, + get_all_update_flags_status, + ) + + pipeline_status = await get_namespace_data("pipeline_status") + + # Get update flags status for all namespaces + update_status = await get_all_update_flags_status() + + # Convert MutableBoolean objects to regular boolean values + processed_update_status = {} + for namespace, flags in update_status.items(): + processed_flags = [] + for flag in flags: + # Handle both multiprocess and single process cases + if hasattr(flag, "value"): + processed_flags.append(bool(flag.value)) + else: + processed_flags.append(bool(flag)) + processed_update_status[namespace] = processed_flags + + # Convert to regular dict if it's a Manager.dict + status_dict = dict(pipeline_status) + + # Add processed update_status to the status dictionary + status_dict["update_status"] = processed_update_status + + # Convert history_messages to a regular list if it's a Manager.list + # and limit to latest 1000 entries with truncation message if needed + if "history_messages" in status_dict: + history_list = list(status_dict["history_messages"]) + total_count = len(history_list) + + if total_count > 1000: + # Calculate truncated message count + truncated_count = total_count - 1000 + + # Take only the latest 1000 messages + latest_messages = history_list[-1000:] + + # Add truncation message at the beginning + truncation_message = ( + f"[Truncated history messages: {truncated_count}/{total_count}]" + ) + status_dict["history_messages"] = [ + truncation_message + ] + latest_messages + else: + # No truncation needed, return all messages + status_dict["history_messages"] = history_list + + # Ensure job_start is properly formatted as a string with timezone information + if "job_start" in status_dict and status_dict["job_start"]: + # Use format_datetime to ensure consistent formatting + status_dict["job_start"] = format_datetime(status_dict["job_start"]) + + return PipelineStatusResponse(**status_dict) + except Exception as e: + logger.error(f"Error getting pipeline status: {str(e)}") + logger.error(traceback.format_exc()) + raise HTTPException(status_code=500, detail=str(e)) + + # TODO: Deprecated, use /documents/paginated instead + @router.get( + "", response_model=DocsStatusesResponse, dependencies=[Depends(combined_auth)] + ) + async def documents() -> DocsStatusesResponse: + """ + Get the status of all documents in the system. This endpoint is deprecated; use /documents/paginated instead. + To prevent excessive resource consumption, a maximum of 1,000 records is returned. + + This endpoint retrieves the current status of all documents, grouped by their + processing status (PENDING, PROCESSING, PREPROCESSED, PROCESSED, FAILED). The results are + limited to 1000 total documents with fair distribution across all statuses. + + Returns: + DocsStatusesResponse: A response object containing a dictionary where keys are + DocStatus values and values are lists of DocStatusResponse + objects representing documents in each status category. + Maximum 1000 documents total will be returned. + + Raises: + HTTPException: If an error occurs while retrieving document statuses (500). + """ + try: + statuses = ( + DocStatus.PENDING, + DocStatus.PROCESSING, + DocStatus.PREPROCESSED, + DocStatus.PROCESSED, + DocStatus.FAILED, + ) + + tasks = [rag.get_docs_by_status(status) for status in statuses] + results: List[Dict[str, DocProcessingStatus]] = await asyncio.gather(*tasks) + + response = DocsStatusesResponse() + total_documents = 0 + max_documents = 1000 + + # Convert results to lists for easier processing + status_documents = [] + for idx, result in enumerate(results): + status = statuses[idx] + docs_list = [] + for doc_id, doc_status in result.items(): + docs_list.append((doc_id, doc_status)) + status_documents.append((status, docs_list)) + + # Fair distribution: round-robin across statuses + status_indices = [0] * len( + status_documents + ) # Track current index for each status + current_status_idx = 0 + + while total_documents < max_documents: + # Check if we have any documents left to process + has_remaining = False + for status_idx, (status, docs_list) in enumerate(status_documents): + if status_indices[status_idx] < len(docs_list): + has_remaining = True + break + + if not has_remaining: + break + + # Try to get a document from the current status + status, docs_list = status_documents[current_status_idx] + current_index = status_indices[current_status_idx] + + if current_index < len(docs_list): + doc_id, doc_status = docs_list[current_index] + + if status not in response.statuses: + response.statuses[status] = [] + + response.statuses[status].append( + DocStatusResponse( + id=doc_id, + content_summary=doc_status.content_summary, + content_length=doc_status.content_length, + status=doc_status.status, + created_at=format_datetime(doc_status.created_at), + updated_at=format_datetime(doc_status.updated_at), + track_id=doc_status.track_id, + chunks_count=doc_status.chunks_count, + error_msg=doc_status.error_msg, + metadata=doc_status.metadata, + file_path=doc_status.file_path, + ) + ) + + status_indices[current_status_idx] += 1 + total_documents += 1 + + # Move to next status (round-robin) + current_status_idx = (current_status_idx + 1) % len(status_documents) + + return response + except Exception as e: + logger.error(f"Error GET /documents: {str(e)}") + logger.error(traceback.format_exc()) + raise HTTPException(status_code=500, detail=str(e)) + + class DeleteDocByIdResponse(BaseModel): + """Response model for single document deletion operation.""" + + status: Literal["deletion_started", "busy", "not_allowed"] = Field( + description="Status of the deletion operation" + ) + message: str = Field(description="Message describing the operation result") + doc_id: str = Field(description="The ID of the document to delete") + + @router.delete( + "/delete_document", + response_model=DeleteDocByIdResponse, + dependencies=[Depends(combined_auth)], + summary="Delete a document and all its associated data by its ID.", + ) + async def delete_document( + delete_request: DeleteDocRequest, + background_tasks: BackgroundTasks, + ) -> DeleteDocByIdResponse: + """ + Delete documents and all their associated data by their IDs using background processing. + + Deletes specific documents and all their associated data, including their status, + text chunks, vector embeddings, and any related graph data. When requested, + cached LLM extraction responses are removed after graph deletion/rebuild completes. + The deletion process runs in the background to avoid blocking the client connection. + + This operation is irreversible and will interact with the pipeline status. + + Args: + delete_request (DeleteDocRequest): The request containing the document IDs and deletion options. + background_tasks: FastAPI BackgroundTasks for async processing + + Returns: + DeleteDocByIdResponse: The result of the deletion operation. + - status="deletion_started": The document deletion has been initiated in the background. + - status="busy": The pipeline is busy with another operation. + + Raises: + HTTPException: + - 500: If an unexpected internal error occurs during initialization. + """ + doc_ids = delete_request.doc_ids + + try: + from lightrag.kg.shared_storage import get_namespace_data + + pipeline_status = await get_namespace_data("pipeline_status") + + # Check if pipeline is busy + if pipeline_status.get("busy", False): + return DeleteDocByIdResponse( + status="busy", + message="Cannot delete documents while pipeline is busy", + doc_id=", ".join(doc_ids), + ) + + # Add deletion task to background tasks + background_tasks.add_task( + background_delete_documents, + rag, + doc_manager, + doc_ids, + delete_request.delete_file, + delete_request.delete_llm_cache, + ) + + return DeleteDocByIdResponse( + status="deletion_started", + message=f"Document deletion for {len(doc_ids)} documents has been initiated. Processing will continue in background.", + doc_id=", ".join(doc_ids), + ) + + except Exception as e: + error_msg = f"Error initiating document deletion for {delete_request.doc_ids}: {str(e)}" + logger.error(error_msg) + logger.error(traceback.format_exc()) + raise HTTPException(status_code=500, detail=error_msg) + + @router.post( + "/clear_cache", + response_model=ClearCacheResponse, + dependencies=[Depends(combined_auth)], + ) + async def clear_cache(request: ClearCacheRequest): + """ + Clear all cache data from the LLM response cache storage. + + This endpoint clears all cached LLM responses regardless of mode. + The request body is accepted for API compatibility but is ignored. + + Args: + request (ClearCacheRequest): The request body (ignored for compatibility). + + Returns: + ClearCacheResponse: A response object containing the status and message. + + Raises: + HTTPException: If an error occurs during cache clearing (500). + """ + try: + # Call the aclear_cache method (no modes parameter) + await rag.aclear_cache() + + # Prepare success message + message = "Successfully cleared all cache" + + return ClearCacheResponse(status="success", message=message) + except Exception as e: + logger.error(f"Error clearing cache: {str(e)}") + logger.error(traceback.format_exc()) + raise HTTPException(status_code=500, detail=str(e)) + + @router.delete( + "/delete_entity", + response_model=DeletionResult, + dependencies=[Depends(combined_auth)], + ) + async def delete_entity(request: DeleteEntityRequest): + """ + Delete an entity and all its relationships from the knowledge graph. + + Args: + request (DeleteEntityRequest): The request body containing the entity name. + + Returns: + DeletionResult: An object containing the outcome of the deletion process. + + Raises: + HTTPException: If the entity is not found (404) or an error occurs (500). + """ + try: + result = await rag.adelete_by_entity(entity_name=request.entity_name) + if result.status == "not_found": + raise HTTPException(status_code=404, detail=result.message) + if result.status == "fail": + raise HTTPException(status_code=500, detail=result.message) + # Set doc_id to empty string since this is an entity operation, not document + result.doc_id = "" + return result + except HTTPException: + raise + except Exception as e: + error_msg = f"Error deleting entity '{request.entity_name}': {str(e)}" + logger.error(error_msg) + logger.error(traceback.format_exc()) + raise HTTPException(status_code=500, detail=error_msg) + + @router.delete( + "/delete_relation", + response_model=DeletionResult, + dependencies=[Depends(combined_auth)], + ) + async def delete_relation(request: DeleteRelationRequest): + """ + Delete a relationship between two entities from the knowledge graph. + + Args: + request (DeleteRelationRequest): The request body containing the source and target entity names. + + Returns: + DeletionResult: An object containing the outcome of the deletion process. + + Raises: + HTTPException: If the relation is not found (404) or an error occurs (500). + """ + try: + result = await rag.adelete_by_relation( + source_entity=request.source_entity, + target_entity=request.target_entity, + ) + if result.status == "not_found": + raise HTTPException(status_code=404, detail=result.message) + if result.status == "fail": + raise HTTPException(status_code=500, detail=result.message) + # Set doc_id to empty string since this is a relation operation, not document + result.doc_id = "" + return result + except HTTPException: + raise + except Exception as e: + error_msg = f"Error deleting relation from '{request.source_entity}' to '{request.target_entity}': {str(e)}" + logger.error(error_msg) + logger.error(traceback.format_exc()) + raise HTTPException(status_code=500, detail=error_msg) + + @router.get( + "/track_status/{track_id}", + response_model=TrackStatusResponse, + dependencies=[Depends(combined_auth)], + ) + async def get_track_status(track_id: str) -> TrackStatusResponse: + """ + Get the processing status of documents by tracking ID. + + This endpoint retrieves all documents associated with a specific tracking ID, + allowing users to monitor the processing progress of their uploaded files or inserted texts. + + Args: + track_id (str): The tracking ID returned from upload, text, or texts endpoints + + Returns: + TrackStatusResponse: A response object containing: + - track_id: The tracking ID + - documents: List of documents associated with this track_id + - total_count: Total number of documents for this track_id + + Raises: + HTTPException: If track_id is invalid (400) or an error occurs (500). + """ + try: + # Validate track_id + if not track_id or not track_id.strip(): + raise HTTPException(status_code=400, detail="Track ID cannot be empty") + + track_id = track_id.strip() + + # Get documents by track_id + docs_by_track_id = await rag.aget_docs_by_track_id(track_id) + + # Convert to response format + documents = [] + status_summary = {} + + for doc_id, doc_status in docs_by_track_id.items(): + documents.append( + DocStatusResponse( + id=doc_id, + content_summary=doc_status.content_summary, + content_length=doc_status.content_length, + status=doc_status.status, + created_at=format_datetime(doc_status.created_at), + updated_at=format_datetime(doc_status.updated_at), + track_id=doc_status.track_id, + chunks_count=doc_status.chunks_count, + error_msg=doc_status.error_msg, + metadata=doc_status.metadata, + file_path=doc_status.file_path, + ) + ) + + # Build status summary + # Handle both DocStatus enum and string cases for robust deserialization + status_key = str(doc_status.status) + status_summary[status_key] = status_summary.get(status_key, 0) + 1 + + return TrackStatusResponse( + track_id=track_id, + documents=documents, + total_count=len(documents), + status_summary=status_summary, + ) + + except HTTPException: + raise + except Exception as e: + logger.error(f"Error getting track status for {track_id}: {str(e)}") + logger.error(traceback.format_exc()) + raise HTTPException(status_code=500, detail=str(e)) + + @router.post( + "/paginated", + response_model=PaginatedDocsResponse, + dependencies=[Depends(combined_auth)], + ) + async def get_documents_paginated( + request: DocumentsRequest, + ) -> PaginatedDocsResponse: + """ + Get documents with pagination support. + + This endpoint retrieves documents with pagination, filtering, and sorting capabilities. + It provides better performance for large document collections by loading only the + requested page of data. + + Args: + request (DocumentsRequest): The request body containing pagination parameters + + Returns: + PaginatedDocsResponse: A response object containing: + - documents: List of documents for the current page + - pagination: Pagination information (page, total_count, etc.) + - status_counts: Count of documents by status for all documents + + Raises: + HTTPException: If an error occurs while retrieving documents (500). + """ + try: + # Get paginated documents and status counts in parallel + docs_task = rag.doc_status.get_docs_paginated( + status_filter=request.status_filter, + page=request.page, + page_size=request.page_size, + sort_field=request.sort_field, + sort_direction=request.sort_direction, + ) + status_counts_task = rag.doc_status.get_all_status_counts() + + # Execute both queries in parallel + (documents_with_ids, total_count), status_counts = await asyncio.gather( + docs_task, status_counts_task + ) + + # Convert documents to response format + doc_responses = [] + for doc_id, doc in documents_with_ids: + doc_responses.append( + DocStatusResponse( + id=doc_id, + content_summary=doc.content_summary, + content_length=doc.content_length, + status=doc.status, + created_at=format_datetime(doc.created_at), + updated_at=format_datetime(doc.updated_at), + track_id=doc.track_id, + chunks_count=doc.chunks_count, + error_msg=doc.error_msg, + metadata=doc.metadata, + file_path=doc.file_path, + ) + ) + + # Calculate pagination info + total_pages = (total_count + request.page_size - 1) // request.page_size + has_next = request.page < total_pages + has_prev = request.page > 1 + + pagination = PaginationInfo( + page=request.page, + page_size=request.page_size, + total_count=total_count, + total_pages=total_pages, + has_next=has_next, + has_prev=has_prev, + ) + + return PaginatedDocsResponse( + documents=doc_responses, + pagination=pagination, + status_counts=status_counts, + ) + + except Exception as e: + logger.error(f"Error getting paginated documents: {str(e)}") + logger.error(traceback.format_exc()) + raise HTTPException(status_code=500, detail=str(e)) + + @router.get( + "/status_counts", + response_model=StatusCountsResponse, + dependencies=[Depends(combined_auth)], + ) + async def get_document_status_counts() -> StatusCountsResponse: + """ + Get counts of documents by status. + + This endpoint retrieves the count of documents in each processing status + (PENDING, PROCESSING, PROCESSED, FAILED) for all documents in the system. + + Returns: + StatusCountsResponse: A response object containing status counts + + Raises: + HTTPException: If an error occurs while retrieving status counts (500). + """ + try: + status_counts = await rag.doc_status.get_all_status_counts() + return StatusCountsResponse(status_counts=status_counts) + + except Exception as e: + logger.error(f"Error getting document status counts: {str(e)}") + logger.error(traceback.format_exc()) + raise HTTPException(status_code=500, detail=str(e)) + + @router.post( + "/reprocess_failed", + response_model=ReprocessResponse, + dependencies=[Depends(combined_auth)], + ) + async def reprocess_failed_documents(background_tasks: BackgroundTasks): + """ + Reprocess failed and pending documents. + + This endpoint triggers the document processing pipeline which automatically + picks up and reprocesses documents in the following statuses: + - FAILED: Documents that failed during previous processing attempts + - PENDING: Documents waiting to be processed + - PROCESSING: Documents with abnormally terminated processing (e.g., server crashes) + + This is useful for recovering from server crashes, network errors, LLM service + outages, or other temporary failures that caused document processing to fail. + + The processing happens in the background and can be monitored using the + returned track_id or by checking the pipeline status. + + Returns: + ReprocessResponse: Response with status, message, and track_id + + Raises: + HTTPException: If an error occurs while initiating reprocessing (500). + """ + try: + # Generate track_id with "retry" prefix for retry operation + track_id = generate_track_id("retry") + + # Start the reprocessing in the background + background_tasks.add_task(rag.apipeline_process_enqueue_documents) + logger.info( + f"Reprocessing of failed documents initiated with track_id: {track_id}" + ) + + return ReprocessResponse( + status="reprocessing_started", + message="Reprocessing of failed documents has been initiated in background", + track_id=track_id, + ) + + except Exception as e: + logger.error(f"Error initiating reprocessing of failed documents: {str(e)}") + logger.error(traceback.format_exc()) + raise HTTPException(status_code=500, detail=str(e)) + + @router.post( + "/cancel_pipeline", + response_model=CancelPipelineResponse, + dependencies=[Depends(combined_auth)], + ) + async def cancel_pipeline(): + """ + Request cancellation of the currently running pipeline. + + This endpoint sets a cancellation flag in the pipeline status. The pipeline will: + 1. Check this flag at key processing points + 2. Stop processing new documents + 3. Cancel all running document processing tasks + 4. Mark all PROCESSING documents as FAILED with reason "User cancelled" + + The cancellation is graceful and ensures data consistency. Documents that have + completed processing will remain in PROCESSED status. + + Returns: + CancelPipelineResponse: Response with status and message + - status="cancellation_requested": Cancellation flag has been set + - status="not_busy": Pipeline is not currently running + + Raises: + HTTPException: If an error occurs while setting cancellation flag (500). + """ + try: + from lightrag.kg.shared_storage import ( + get_namespace_data, + get_pipeline_status_lock, + ) + + pipeline_status = await get_namespace_data("pipeline_status") + pipeline_status_lock = get_pipeline_status_lock() + + async with pipeline_status_lock: + if not pipeline_status.get("busy", False): + return CancelPipelineResponse( + status="not_busy", + message="Pipeline is not currently running. No cancellation needed.", + ) + + # Set cancellation flag + pipeline_status["cancellation_requested"] = True + cancel_msg = "Pipeline cancellation requested by user" + logger.info(cancel_msg) + pipeline_status["latest_message"] = cancel_msg + pipeline_status["history_messages"].append(cancel_msg) + + return CancelPipelineResponse( + status="cancellation_requested", + message="Pipeline cancellation has been requested. Documents will be marked as FAILED.", + ) + + except Exception as e: + logger.error(f"Error requesting pipeline cancellation: {str(e)}") + logger.error(traceback.format_exc()) + raise HTTPException(status_code=500, detail=str(e)) + + return router diff --git a/LightRAG/lightrag/api/routers/graph_routes.py b/LightRAG/lightrag/api/routers/graph_routes.py new file mode 100644 index 00000000..e892ff01 --- /dev/null +++ b/LightRAG/lightrag/api/routers/graph_routes.py @@ -0,0 +1,688 @@ +""" +This module contains all graph-related routes for the LightRAG API. +""" + +from typing import Optional, Dict, Any +import traceback +from fastapi import APIRouter, Depends, Query, HTTPException +from pydantic import BaseModel, Field + +from lightrag.utils import logger +from ..utils_api import get_combined_auth_dependency + +router = APIRouter(tags=["graph"]) + + +class EntityUpdateRequest(BaseModel): + entity_name: str + updated_data: Dict[str, Any] + allow_rename: bool = False + allow_merge: bool = False + + +class RelationUpdateRequest(BaseModel): + source_id: str + target_id: str + updated_data: Dict[str, Any] + + +class EntityMergeRequest(BaseModel): + entities_to_change: list[str] = Field( + ..., + description="List of entity names to be merged and deleted. These are typically duplicate or misspelled entities.", + min_length=1, + examples=[["Elon Msk", "Ellon Musk"]], + ) + entity_to_change_into: str = Field( + ..., + description="Target entity name that will receive all relationships from the source entities. This entity will be preserved.", + min_length=1, + examples=["Elon Musk"], + ) + + +class EntityCreateRequest(BaseModel): + entity_name: str = Field( + ..., + description="Unique name for the new entity", + min_length=1, + examples=["Tesla"], + ) + entity_data: Dict[str, Any] = Field( + ..., + description="Dictionary containing entity properties. Common fields include 'description' and 'entity_type'.", + examples=[ + { + "description": "Electric vehicle manufacturer", + "entity_type": "ORGANIZATION", + } + ], + ) + + +class RelationCreateRequest(BaseModel): + source_entity: str = Field( + ..., + description="Name of the source entity. This entity must already exist in the knowledge graph.", + min_length=1, + examples=["Elon Musk"], + ) + target_entity: str = Field( + ..., + description="Name of the target entity. This entity must already exist in the knowledge graph.", + min_length=1, + examples=["Tesla"], + ) + relation_data: Dict[str, Any] = Field( + ..., + description="Dictionary containing relationship properties. Common fields include 'description', 'keywords', and 'weight'.", + examples=[ + { + "description": "Elon Musk is the CEO of Tesla", + "keywords": "CEO, founder", + "weight": 1.0, + } + ], + ) + + +def create_graph_routes(rag, api_key: Optional[str] = None): + combined_auth = get_combined_auth_dependency(api_key) + + @router.get("/graph/label/list", dependencies=[Depends(combined_auth)]) + async def get_graph_labels(): + """ + Get all graph labels + + Returns: + List[str]: List of graph labels + """ + try: + return await rag.get_graph_labels() + except Exception as e: + logger.error(f"Error getting graph labels: {str(e)}") + logger.error(traceback.format_exc()) + raise HTTPException( + status_code=500, detail=f"Error getting graph labels: {str(e)}" + ) + + @router.get("/graph/label/popular", dependencies=[Depends(combined_auth)]) + async def get_popular_labels( + limit: int = Query( + 300, description="Maximum number of popular labels to return", ge=1, le=1000 + ), + ): + """ + Get popular labels by node degree (most connected entities) + + Args: + limit (int): Maximum number of labels to return (default: 300, max: 1000) + + Returns: + List[str]: List of popular labels sorted by degree (highest first) + """ + try: + return await rag.chunk_entity_relation_graph.get_popular_labels(limit) + except Exception as e: + logger.error(f"Error getting popular labels: {str(e)}") + logger.error(traceback.format_exc()) + raise HTTPException( + status_code=500, detail=f"Error getting popular labels: {str(e)}" + ) + + @router.get("/graph/label/search", dependencies=[Depends(combined_auth)]) + async def search_labels( + q: str = Query(..., description="Search query string"), + limit: int = Query( + 50, description="Maximum number of search results to return", ge=1, le=100 + ), + ): + """ + Search labels with fuzzy matching + + Args: + q (str): Search query string + limit (int): Maximum number of results to return (default: 50, max: 100) + + Returns: + List[str]: List of matching labels sorted by relevance + """ + try: + return await rag.chunk_entity_relation_graph.search_labels(q, limit) + except Exception as e: + logger.error(f"Error searching labels with query '{q}': {str(e)}") + logger.error(traceback.format_exc()) + raise HTTPException( + status_code=500, detail=f"Error searching labels: {str(e)}" + ) + + @router.get("/graphs", dependencies=[Depends(combined_auth)]) + async def get_knowledge_graph( + label: str = Query(..., description="Label to get knowledge graph for"), + max_depth: int = Query(3, description="Maximum depth of graph", ge=1), + max_nodes: int = Query(1000, description="Maximum nodes to return", ge=1), + ): + """ + Retrieve a connected subgraph of nodes where the label includes the specified label. + When reducing the number of nodes, the prioritization criteria are as follows: + 1. Hops(path) to the staring node take precedence + 2. Followed by the degree of the nodes + + Args: + label (str): Label of the starting node + max_depth (int, optional): Maximum depth of the subgraph,Defaults to 3 + max_nodes: Maxiumu nodes to return + + Returns: + Dict[str, List[str]]: Knowledge graph for label + """ + try: + # Log the label parameter to check for leading spaces + logger.debug( + f"get_knowledge_graph called with label: '{label}' (length: {len(label)}, repr: {repr(label)})" + ) + + return await rag.get_knowledge_graph( + node_label=label, + max_depth=max_depth, + max_nodes=max_nodes, + ) + except Exception as e: + logger.error(f"Error getting knowledge graph for label '{label}': {str(e)}") + logger.error(traceback.format_exc()) + raise HTTPException( + status_code=500, detail=f"Error getting knowledge graph: {str(e)}" + ) + + @router.get("/graph/entity/exists", dependencies=[Depends(combined_auth)]) + async def check_entity_exists( + name: str = Query(..., description="Entity name to check"), + ): + """ + Check if an entity with the given name exists in the knowledge graph + + Args: + name (str): Name of the entity to check + + Returns: + Dict[str, bool]: Dictionary with 'exists' key indicating if entity exists + """ + try: + exists = await rag.chunk_entity_relation_graph.has_node(name) + return {"exists": exists} + except Exception as e: + logger.error(f"Error checking entity existence for '{name}': {str(e)}") + logger.error(traceback.format_exc()) + raise HTTPException( + status_code=500, detail=f"Error checking entity existence: {str(e)}" + ) + + @router.post("/graph/entity/edit", dependencies=[Depends(combined_auth)]) + async def update_entity(request: EntityUpdateRequest): + """ + Update an entity's properties in the knowledge graph + + This endpoint allows updating entity properties, including renaming entities. + When renaming to an existing entity name, the behavior depends on allow_merge: + + Args: + request (EntityUpdateRequest): Request containing: + - entity_name (str): Name of the entity to update + - updated_data (Dict[str, Any]): Dictionary of properties to update + - allow_rename (bool): Whether to allow entity renaming (default: False) + - allow_merge (bool): Whether to merge into existing entity when renaming + causes name conflict (default: False) + + Returns: + Dict with the following structure: + { + "status": "success", + "message": "Entity updated successfully" | "Entity merged successfully into 'target_name'", + "data": { + "entity_name": str, # Final entity name + "description": str, # Entity description + "entity_type": str, # Entity type + "source_id": str, # Source chunk IDs + ... # Other entity properties + }, + "operation_summary": { + "merged": bool, # Whether entity was merged into another + "merge_status": str, # "success" | "failed" | "not_attempted" + "merge_error": str | None, # Error message if merge failed + "operation_status": str, # "success" | "partial_success" | "failure" + "target_entity": str | None, # Target entity name if renaming/merging + "final_entity": str, # Final entity name after operation + "renamed": bool # Whether entity was renamed + } + } + + operation_status values explained: + - "success": All operations completed successfully + * For simple updates: entity properties updated + * For renames: entity renamed successfully + * For merges: non-name updates applied AND merge completed + + - "partial_success": Update succeeded but merge failed + * Non-name property updates were applied successfully + * Merge operation failed (entity not merged) + * Original entity still exists with updated properties + * Use merge_error for failure details + + - "failure": Operation failed completely + * If merge_status == "failed": Merge attempted but both update and merge failed + * If merge_status == "not_attempted": Regular update failed + * No changes were applied to the entity + + merge_status values explained: + - "success": Entity successfully merged into target entity + - "failed": Merge operation was attempted but failed + - "not_attempted": No merge was attempted (normal update/rename) + + Behavior when renaming to an existing entity: + - If allow_merge=False: Raises ValueError with 400 status (default behavior) + - If allow_merge=True: Automatically merges the source entity into the existing target entity, + preserving all relationships and applying non-name updates first + + Example Request (simple update): + POST /graph/entity/edit + { + "entity_name": "Tesla", + "updated_data": {"description": "Updated description"}, + "allow_rename": false, + "allow_merge": false + } + + Example Response (simple update success): + { + "status": "success", + "message": "Entity updated successfully", + "data": { ... }, + "operation_summary": { + "merged": false, + "merge_status": "not_attempted", + "merge_error": null, + "operation_status": "success", + "target_entity": null, + "final_entity": "Tesla", + "renamed": false + } + } + + Example Request (rename with auto-merge): + POST /graph/entity/edit + { + "entity_name": "Elon Msk", + "updated_data": { + "entity_name": "Elon Musk", + "description": "Corrected description" + }, + "allow_rename": true, + "allow_merge": true + } + + Example Response (merge success): + { + "status": "success", + "message": "Entity merged successfully into 'Elon Musk'", + "data": { ... }, + "operation_summary": { + "merged": true, + "merge_status": "success", + "merge_error": null, + "operation_status": "success", + "target_entity": "Elon Musk", + "final_entity": "Elon Musk", + "renamed": true + } + } + + Example Response (partial success - update succeeded but merge failed): + { + "status": "success", + "message": "Entity updated successfully", + "data": { ... }, # Data reflects updated "Elon Msk" entity + "operation_summary": { + "merged": false, + "merge_status": "failed", + "merge_error": "Target entity locked by another operation", + "operation_status": "partial_success", + "target_entity": "Elon Musk", + "final_entity": "Elon Msk", # Original entity still exists + "renamed": true + } + } + """ + try: + result = await rag.aedit_entity( + entity_name=request.entity_name, + updated_data=request.updated_data, + allow_rename=request.allow_rename, + allow_merge=request.allow_merge, + ) + + # Extract operation_summary from result, with fallback for backward compatibility + operation_summary = result.get( + "operation_summary", + { + "merged": False, + "merge_status": "not_attempted", + "merge_error": None, + "operation_status": "success", + "target_entity": None, + "final_entity": request.updated_data.get( + "entity_name", request.entity_name + ), + "renamed": request.updated_data.get( + "entity_name", request.entity_name + ) + != request.entity_name, + }, + ) + + # Separate entity data from operation_summary for clean response + entity_data = dict(result) + entity_data.pop("operation_summary", None) + + # Generate appropriate response message based on merge status + response_message = ( + f"Entity merged successfully into '{operation_summary['final_entity']}'" + if operation_summary.get("merged") + else "Entity updated successfully" + ) + return { + "status": "success", + "message": response_message, + "data": entity_data, + "operation_summary": operation_summary, + } + except ValueError as ve: + logger.error( + f"Validation error updating entity '{request.entity_name}': {str(ve)}" + ) + raise HTTPException(status_code=400, detail=str(ve)) + except Exception as e: + logger.error(f"Error updating entity '{request.entity_name}': {str(e)}") + logger.error(traceback.format_exc()) + raise HTTPException( + status_code=500, detail=f"Error updating entity: {str(e)}" + ) + + @router.post("/graph/relation/edit", dependencies=[Depends(combined_auth)]) + async def update_relation(request: RelationUpdateRequest): + """Update a relation's properties in the knowledge graph + + Args: + request (RelationUpdateRequest): Request containing source ID, target ID and updated data + + Returns: + Dict: Updated relation information + """ + try: + result = await rag.aedit_relation( + source_entity=request.source_id, + target_entity=request.target_id, + updated_data=request.updated_data, + ) + return { + "status": "success", + "message": "Relation updated successfully", + "data": result, + } + except ValueError as ve: + logger.error( + f"Validation error updating relation between '{request.source_id}' and '{request.target_id}': {str(ve)}" + ) + raise HTTPException(status_code=400, detail=str(ve)) + except Exception as e: + logger.error( + f"Error updating relation between '{request.source_id}' and '{request.target_id}': {str(e)}" + ) + logger.error(traceback.format_exc()) + raise HTTPException( + status_code=500, detail=f"Error updating relation: {str(e)}" + ) + + @router.post("/graph/entity/create", dependencies=[Depends(combined_auth)]) + async def create_entity(request: EntityCreateRequest): + """ + Create a new entity in the knowledge graph + + This endpoint creates a new entity node in the knowledge graph with the specified + properties. The system automatically generates vector embeddings for the entity + to enable semantic search and retrieval. + + Request Body: + entity_name (str): Unique name identifier for the entity + entity_data (dict): Entity properties including: + - description (str): Textual description of the entity + - entity_type (str): Category/type of the entity (e.g., PERSON, ORGANIZATION, LOCATION) + - source_id (str): Related chunk_id from which the description originates + - Additional custom properties as needed + + Response Schema: + { + "status": "success", + "message": "Entity 'Tesla' created successfully", + "data": { + "entity_name": "Tesla", + "description": "Electric vehicle manufacturer", + "entity_type": "ORGANIZATION", + "source_id": "chunk-123chunk-456" + ... (other entity properties) + } + } + + HTTP Status Codes: + 200: Entity created successfully + 400: Invalid request (e.g., missing required fields, duplicate entity) + 500: Internal server error + + Example Request: + POST /graph/entity/create + { + "entity_name": "Tesla", + "entity_data": { + "description": "Electric vehicle manufacturer", + "entity_type": "ORGANIZATION" + } + } + """ + try: + # Use the proper acreate_entity method which handles: + # - Graph lock for concurrency + # - Vector embedding creation in entities_vdb + # - Metadata population and defaults + # - Index consistency via _edit_entity_done + result = await rag.acreate_entity( + entity_name=request.entity_name, + entity_data=request.entity_data, + ) + + return { + "status": "success", + "message": f"Entity '{request.entity_name}' created successfully", + "data": result, + } + except ValueError as ve: + logger.error( + f"Validation error creating entity '{request.entity_name}': {str(ve)}" + ) + raise HTTPException(status_code=400, detail=str(ve)) + except Exception as e: + logger.error(f"Error creating entity '{request.entity_name}': {str(e)}") + logger.error(traceback.format_exc()) + raise HTTPException( + status_code=500, detail=f"Error creating entity: {str(e)}" + ) + + @router.post("/graph/relation/create", dependencies=[Depends(combined_auth)]) + async def create_relation(request: RelationCreateRequest): + """ + Create a new relationship between two entities in the knowledge graph + + This endpoint establishes an undirected relationship between two existing entities. + The provided source/target order is accepted for convenience, but the backend + stored edge is undirected and may be returned with the entities swapped. + Both entities must already exist in the knowledge graph. The system automatically + generates vector embeddings for the relationship to enable semantic search and graph traversal. + + Prerequisites: + - Both source_entity and target_entity must exist in the knowledge graph + - Use /graph/entity/create to create entities first if they don't exist + + Request Body: + source_entity (str): Name of the source entity (relationship origin) + target_entity (str): Name of the target entity (relationship destination) + relation_data (dict): Relationship properties including: + - description (str): Textual description of the relationship + - keywords (str): Comma-separated keywords describing the relationship type + - source_id (str): Related chunk_id from which the description originates + - weight (float): Relationship strength/importance (default: 1.0) + - Additional custom properties as needed + + Response Schema: + { + "status": "success", + "message": "Relation created successfully between 'Elon Musk' and 'Tesla'", + "data": { + "src_id": "Elon Musk", + "tgt_id": "Tesla", + "description": "Elon Musk is the CEO of Tesla", + "keywords": "CEO, founder", + "source_id": "chunk-123chunk-456" + "weight": 1.0, + ... (other relationship properties) + } + } + + HTTP Status Codes: + 200: Relationship created successfully + 400: Invalid request (e.g., missing entities, invalid data, duplicate relationship) + 500: Internal server error + + Example Request: + POST /graph/relation/create + { + "source_entity": "Elon Musk", + "target_entity": "Tesla", + "relation_data": { + "description": "Elon Musk is the CEO of Tesla", + "keywords": "CEO, founder", + "weight": 1.0 + } + } + """ + try: + # Use the proper acreate_relation method which handles: + # - Graph lock for concurrency + # - Entity existence validation + # - Duplicate relation checks + # - Vector embedding creation in relationships_vdb + # - Index consistency via _edit_relation_done + result = await rag.acreate_relation( + source_entity=request.source_entity, + target_entity=request.target_entity, + relation_data=request.relation_data, + ) + + return { + "status": "success", + "message": f"Relation created successfully between '{request.source_entity}' and '{request.target_entity}'", + "data": result, + } + except ValueError as ve: + logger.error( + f"Validation error creating relation between '{request.source_entity}' and '{request.target_entity}': {str(ve)}" + ) + raise HTTPException(status_code=400, detail=str(ve)) + except Exception as e: + logger.error( + f"Error creating relation between '{request.source_entity}' and '{request.target_entity}': {str(e)}" + ) + logger.error(traceback.format_exc()) + raise HTTPException( + status_code=500, detail=f"Error creating relation: {str(e)}" + ) + + @router.post("/graph/entities/merge", dependencies=[Depends(combined_auth)]) + async def merge_entities(request: EntityMergeRequest): + """ + Merge multiple entities into a single entity, preserving all relationships + + This endpoint consolidates duplicate or misspelled entities while preserving the entire + graph structure. It's particularly useful for cleaning up knowledge graphs after document + processing or correcting entity name variations. + + What the Merge Operation Does: + 1. Deletes the specified source entities from the knowledge graph + 2. Transfers all relationships from source entities to the target entity + 3. Intelligently merges duplicate relationships (if multiple sources have the same relationship) + 4. Updates vector embeddings for accurate retrieval and search + 5. Preserves the complete graph structure and connectivity + 6. Maintains relationship properties and metadata + + Use Cases: + - Fixing spelling errors in entity names (e.g., "Elon Msk" -> "Elon Musk") + - Consolidating duplicate entities discovered after document processing + - Merging name variations (e.g., "NY", "New York", "New York City") + - Cleaning up the knowledge graph for better query performance + - Standardizing entity names across the knowledge base + + Request Body: + entities_to_change (list[str]): List of entity names to be merged and deleted + entity_to_change_into (str): Target entity that will receive all relationships + + Response Schema: + { + "status": "success", + "message": "Successfully merged 2 entities into 'Elon Musk'", + "data": { + "merged_entity": "Elon Musk", + "deleted_entities": ["Elon Msk", "Ellon Musk"], + "relationships_transferred": 15, + ... (merge operation details) + } + } + + HTTP Status Codes: + 200: Entities merged successfully + 400: Invalid request (e.g., empty entity list, target entity doesn't exist) + 500: Internal server error + + Example Request: + POST /graph/entities/merge + { + "entities_to_change": ["Elon Msk", "Ellon Musk"], + "entity_to_change_into": "Elon Musk" + } + + Note: + - The target entity (entity_to_change_into) must exist in the knowledge graph + - Source entities will be permanently deleted after the merge + - This operation cannot be undone, so verify entity names before merging + """ + try: + result = await rag.amerge_entities( + source_entities=request.entities_to_change, + target_entity=request.entity_to_change_into, + ) + return { + "status": "success", + "message": f"Successfully merged {len(request.entities_to_change)} entities into '{request.entity_to_change_into}'", + "data": result, + } + except ValueError as ve: + logger.error( + f"Validation error merging entities {request.entities_to_change} into '{request.entity_to_change_into}': {str(ve)}" + ) + raise HTTPException(status_code=400, detail=str(ve)) + except Exception as e: + logger.error( + f"Error merging entities {request.entities_to_change} into '{request.entity_to_change_into}': {str(e)}" + ) + logger.error(traceback.format_exc()) + raise HTTPException( + status_code=500, detail=f"Error merging entities: {str(e)}" + ) + + return router diff --git a/LightRAG/lightrag/api/routers/ollama_api.py b/LightRAG/lightrag/api/routers/ollama_api.py new file mode 100644 index 00000000..f9353dda --- /dev/null +++ b/LightRAG/lightrag/api/routers/ollama_api.py @@ -0,0 +1,734 @@ +from fastapi import APIRouter, HTTPException, Request +from pydantic import BaseModel +from typing import List, Dict, Any, Optional, Type +from lightrag.utils import logger +import time +import json +import re +from enum import Enum +from fastapi.responses import StreamingResponse +import asyncio +from ascii_colors import trace_exception +from lightrag import LightRAG, QueryParam +from lightrag.utils import TiktokenTokenizer +from lightrag.api.utils_api import get_combined_auth_dependency +from fastapi import Depends + + +# query mode according to query prefix (bypass is not LightRAG quer mode) +class SearchMode(str, Enum): + naive = "naive" + local = "local" + global_ = "global" + hybrid = "hybrid" + mix = "mix" + bypass = "bypass" + context = "context" + + +class OllamaMessage(BaseModel): + role: str + content: str + images: Optional[List[str]] = None + + +class OllamaChatRequest(BaseModel): + model: str + messages: List[OllamaMessage] + stream: bool = True + options: Optional[Dict[str, Any]] = None + system: Optional[str] = None + + +class OllamaChatResponse(BaseModel): + model: str + created_at: str + message: OllamaMessage + done: bool + + +class OllamaGenerateRequest(BaseModel): + model: str + prompt: str + system: Optional[str] = None + stream: bool = False + options: Optional[Dict[str, Any]] = None + + +class OllamaGenerateResponse(BaseModel): + model: str + created_at: str + response: str + done: bool + context: Optional[List[int]] + total_duration: Optional[int] + load_duration: Optional[int] + prompt_eval_count: Optional[int] + prompt_eval_duration: Optional[int] + eval_count: Optional[int] + eval_duration: Optional[int] + + +class OllamaVersionResponse(BaseModel): + version: str + + +class OllamaModelDetails(BaseModel): + parent_model: str + format: str + family: str + families: List[str] + parameter_size: str + quantization_level: str + + +class OllamaModel(BaseModel): + name: str + model: str + size: int + digest: str + modified_at: str + details: OllamaModelDetails + + +class OllamaTagResponse(BaseModel): + models: List[OllamaModel] + + +class OllamaRunningModelDetails(BaseModel): + parent_model: str + format: str + family: str + families: List[str] + parameter_size: str + quantization_level: str + + +class OllamaRunningModel(BaseModel): + name: str + model: str + size: int + digest: str + details: OllamaRunningModelDetails + expires_at: str + size_vram: int + + +class OllamaPsResponse(BaseModel): + models: List[OllamaRunningModel] + + +async def parse_request_body( + request: Request, model_class: Type[BaseModel] +) -> BaseModel: + """ + Parse request body based on Content-Type header. + Supports both application/json and application/octet-stream. + + Args: + request: The FastAPI Request object + model_class: The Pydantic model class to parse the request into + + Returns: + An instance of the provided model_class + """ + content_type = request.headers.get("content-type", "").lower() + + try: + if content_type.startswith("application/json"): + # FastAPI already handles JSON parsing for us + body = await request.json() + elif content_type.startswith("application/octet-stream"): + # Manually parse octet-stream as JSON + body_bytes = await request.body() + body = json.loads(body_bytes.decode("utf-8")) + else: + # Try to parse as JSON for any other content type + body_bytes = await request.body() + body = json.loads(body_bytes.decode("utf-8")) + + # Create an instance of the model + return model_class(**body) + except json.JSONDecodeError: + raise HTTPException(status_code=400, detail="Invalid JSON in request body") + except Exception as e: + raise HTTPException( + status_code=400, detail=f"Error parsing request body: {str(e)}" + ) + + +def estimate_tokens(text: str) -> int: + """Estimate the number of tokens in text using tiktoken""" + tokens = TiktokenTokenizer().encode(text) + return len(tokens) + + +def parse_query_mode(query: str) -> tuple[str, SearchMode, bool, Optional[str]]: + """Parse query prefix to determine search mode + Returns tuple of (cleaned_query, search_mode, only_need_context, user_prompt) + + Examples: + - "/local[use mermaid format for diagrams] query string" -> (cleaned_query, SearchMode.local, False, "use mermaid format for diagrams") + - "/[use mermaid format for diagrams] query string" -> (cleaned_query, SearchMode.hybrid, False, "use mermaid format for diagrams") + - "/local query string" -> (cleaned_query, SearchMode.local, False, None) + """ + # Initialize user_prompt as None + user_prompt = None + + # First check if there's a bracket format for user prompt + bracket_pattern = r"^/([a-z]*)\[(.*?)\](.*)" + bracket_match = re.match(bracket_pattern, query) + + if bracket_match: + mode_prefix = bracket_match.group(1) + user_prompt = bracket_match.group(2) + remaining_query = bracket_match.group(3).lstrip() + + # Reconstruct query, removing the bracket part + query = f"/{mode_prefix} {remaining_query}".strip() + + # Unified handling of mode and only_need_context determination + mode_map = { + "/local ": (SearchMode.local, False), + "/global ": ( + SearchMode.global_, + False, + ), # global_ is used because 'global' is a Python keyword + "/naive ": (SearchMode.naive, False), + "/hybrid ": (SearchMode.hybrid, False), + "/mix ": (SearchMode.mix, False), + "/bypass ": (SearchMode.bypass, False), + "/context": ( + SearchMode.mix, + True, + ), + "/localcontext": (SearchMode.local, True), + "/globalcontext": (SearchMode.global_, True), + "/hybridcontext": (SearchMode.hybrid, True), + "/naivecontext": (SearchMode.naive, True), + "/mixcontext": (SearchMode.mix, True), + } + + for prefix, (mode, only_need_context) in mode_map.items(): + if query.startswith(prefix): + # After removing prefix and leading spaces + cleaned_query = query[len(prefix) :].lstrip() + return cleaned_query, mode, only_need_context, user_prompt + + return query, SearchMode.mix, False, user_prompt + + +class OllamaAPI: + def __init__(self, rag: LightRAG, top_k: int = 60, api_key: Optional[str] = None): + self.rag = rag + self.ollama_server_infos = rag.ollama_server_infos + self.top_k = top_k + self.api_key = api_key + self.router = APIRouter(tags=["ollama"]) + self.setup_routes() + + def setup_routes(self): + # Create combined auth dependency for Ollama API routes + combined_auth = get_combined_auth_dependency(self.api_key) + + @self.router.get("/version", dependencies=[Depends(combined_auth)]) + async def get_version(): + """Get Ollama version information""" + return OllamaVersionResponse(version="0.9.3") + + @self.router.get("/tags", dependencies=[Depends(combined_auth)]) + async def get_tags(): + """Return available models acting as an Ollama server""" + return OllamaTagResponse( + models=[ + { + "name": self.ollama_server_infos.LIGHTRAG_MODEL, + "model": self.ollama_server_infos.LIGHTRAG_MODEL, + "modified_at": self.ollama_server_infos.LIGHTRAG_CREATED_AT, + "size": self.ollama_server_infos.LIGHTRAG_SIZE, + "digest": self.ollama_server_infos.LIGHTRAG_DIGEST, + "details": { + "parent_model": "", + "format": "gguf", + "family": self.ollama_server_infos.LIGHTRAG_NAME, + "families": [self.ollama_server_infos.LIGHTRAG_NAME], + "parameter_size": "13B", + "quantization_level": "Q4_0", + }, + } + ] + ) + + @self.router.get("/ps", dependencies=[Depends(combined_auth)]) + async def get_running_models(): + """List Running Models - returns currently running models""" + return OllamaPsResponse( + models=[ + { + "name": self.ollama_server_infos.LIGHTRAG_MODEL, + "model": self.ollama_server_infos.LIGHTRAG_MODEL, + "size": self.ollama_server_infos.LIGHTRAG_SIZE, + "digest": self.ollama_server_infos.LIGHTRAG_DIGEST, + "details": { + "parent_model": "", + "format": "gguf", + "family": "llama", + "families": ["llama"], + "parameter_size": "7.2B", + "quantization_level": "Q4_0", + }, + "expires_at": "2050-12-31T14:38:31.83753-07:00", + "size_vram": self.ollama_server_infos.LIGHTRAG_SIZE, + } + ] + ) + + @self.router.post( + "/generate", dependencies=[Depends(combined_auth)], include_in_schema=True + ) + async def generate(raw_request: Request): + """Handle generate completion requests acting as an Ollama model + For compatibility purpose, the request is not processed by LightRAG, + and will be handled by underlying LLM model. + Supports both application/json and application/octet-stream Content-Types. + """ + try: + # Parse the request body manually + request = await parse_request_body(raw_request, OllamaGenerateRequest) + + query = request.prompt + start_time = time.time_ns() + prompt_tokens = estimate_tokens(query) + + if request.system: + self.rag.llm_model_kwargs["system_prompt"] = request.system + + if request.stream: + response = await self.rag.llm_model_func( + query, stream=True, **self.rag.llm_model_kwargs + ) + + async def stream_generator(): + try: + first_chunk_time = None + last_chunk_time = time.time_ns() + total_response = "" + + # Ensure response is an async generator + if isinstance(response, str): + # If it's a string, send in two parts + first_chunk_time = start_time + last_chunk_time = time.time_ns() + total_response = response + + data = { + "model": self.ollama_server_infos.LIGHTRAG_MODEL, + "created_at": self.ollama_server_infos.LIGHTRAG_CREATED_AT, + "response": response, + "done": False, + } + yield f"{json.dumps(data, ensure_ascii=False)}\n" + + completion_tokens = estimate_tokens(total_response) + total_time = last_chunk_time - start_time + prompt_eval_time = first_chunk_time - start_time + eval_time = last_chunk_time - first_chunk_time + + data = { + "model": self.ollama_server_infos.LIGHTRAG_MODEL, + "created_at": self.ollama_server_infos.LIGHTRAG_CREATED_AT, + "response": "", + "done": True, + "done_reason": "stop", + "context": [], + "total_duration": total_time, + "load_duration": 0, + "prompt_eval_count": prompt_tokens, + "prompt_eval_duration": prompt_eval_time, + "eval_count": completion_tokens, + "eval_duration": eval_time, + } + yield f"{json.dumps(data, ensure_ascii=False)}\n" + else: + try: + async for chunk in response: + if chunk: + if first_chunk_time is None: + first_chunk_time = time.time_ns() + + last_chunk_time = time.time_ns() + + total_response += chunk + data = { + "model": self.ollama_server_infos.LIGHTRAG_MODEL, + "created_at": self.ollama_server_infos.LIGHTRAG_CREATED_AT, + "response": chunk, + "done": False, + } + yield f"{json.dumps(data, ensure_ascii=False)}\n" + except (asyncio.CancelledError, Exception) as e: + error_msg = str(e) + if isinstance(e, asyncio.CancelledError): + error_msg = "Stream was cancelled by server" + else: + error_msg = f"Provider error: {error_msg}" + + logger.error(f"Stream error: {error_msg}") + + # Send error message to client + error_data = { + "model": self.ollama_server_infos.LIGHTRAG_MODEL, + "created_at": self.ollama_server_infos.LIGHTRAG_CREATED_AT, + "response": f"\n\nError: {error_msg}", + "error": f"\n\nError: {error_msg}", + "done": False, + } + yield f"{json.dumps(error_data, ensure_ascii=False)}\n" + + # Send final message to close the stream + final_data = { + "model": self.ollama_server_infos.LIGHTRAG_MODEL, + "created_at": self.ollama_server_infos.LIGHTRAG_CREATED_AT, + "response": "", + "done": True, + } + yield f"{json.dumps(final_data, ensure_ascii=False)}\n" + return + if first_chunk_time is None: + first_chunk_time = start_time + completion_tokens = estimate_tokens(total_response) + total_time = last_chunk_time - start_time + prompt_eval_time = first_chunk_time - start_time + eval_time = last_chunk_time - first_chunk_time + + data = { + "model": self.ollama_server_infos.LIGHTRAG_MODEL, + "created_at": self.ollama_server_infos.LIGHTRAG_CREATED_AT, + "response": "", + "done": True, + "done_reason": "stop", + "context": [], + "total_duration": total_time, + "load_duration": 0, + "prompt_eval_count": prompt_tokens, + "prompt_eval_duration": prompt_eval_time, + "eval_count": completion_tokens, + "eval_duration": eval_time, + } + yield f"{json.dumps(data, ensure_ascii=False)}\n" + return + + except Exception as e: + trace_exception(e) + raise + + return StreamingResponse( + stream_generator(), + media_type="application/x-ndjson", + headers={ + "Cache-Control": "no-cache", + "Connection": "keep-alive", + "Content-Type": "application/x-ndjson", + "X-Accel-Buffering": "no", # Ensure proper handling of streaming responses in Nginx proxy + }, + ) + else: + first_chunk_time = time.time_ns() + response_text = await self.rag.llm_model_func( + query, stream=False, **self.rag.llm_model_kwargs + ) + last_chunk_time = time.time_ns() + + if not response_text: + response_text = "No response generated" + + completion_tokens = estimate_tokens(str(response_text)) + total_time = last_chunk_time - start_time + prompt_eval_time = first_chunk_time - start_time + eval_time = last_chunk_time - first_chunk_time + + return { + "model": self.ollama_server_infos.LIGHTRAG_MODEL, + "created_at": self.ollama_server_infos.LIGHTRAG_CREATED_AT, + "response": str(response_text), + "done": True, + "done_reason": "stop", + "context": [], + "total_duration": total_time, + "load_duration": 0, + "prompt_eval_count": prompt_tokens, + "prompt_eval_duration": prompt_eval_time, + "eval_count": completion_tokens, + "eval_duration": eval_time, + } + except Exception as e: + trace_exception(e) + raise HTTPException(status_code=500, detail=str(e)) + + @self.router.post( + "/chat", dependencies=[Depends(combined_auth)], include_in_schema=True + ) + async def chat(raw_request: Request): + """Process chat completion requests by acting as an Ollama model. + Routes user queries through LightRAG by selecting query mode based on query prefix. + Detects and forwards OpenWebUI session-related requests (for meta data generation task) directly to LLM. + Supports both application/json and application/octet-stream Content-Types. + """ + try: + # Parse the request body manually + request = await parse_request_body(raw_request, OllamaChatRequest) + + # Get all messages + messages = request.messages + if not messages: + raise HTTPException(status_code=400, detail="No messages provided") + + # Validate that the last message is from a user + if messages[-1].role != "user": + raise HTTPException( + status_code=400, detail="Last message must be from user role" + ) + + # Get the last message as query and previous messages as history + query = messages[-1].content + # Convert OllamaMessage objects to dictionaries + conversation_history = [ + {"role": msg.role, "content": msg.content} for msg in messages[:-1] + ] + + # Check for query prefix + cleaned_query, mode, only_need_context, user_prompt = parse_query_mode( + query + ) + + start_time = time.time_ns() + prompt_tokens = estimate_tokens(cleaned_query) + + param_dict = { + "mode": mode.value, + "stream": request.stream, + "only_need_context": only_need_context, + "conversation_history": conversation_history, + "top_k": self.top_k, + } + + # Add user_prompt to param_dict + if user_prompt is not None: + param_dict["user_prompt"] = user_prompt + + query_param = QueryParam(**param_dict) + + if request.stream: + # Determine if the request is prefix with "/bypass" + if mode == SearchMode.bypass: + if request.system: + self.rag.llm_model_kwargs["system_prompt"] = request.system + response = await self.rag.llm_model_func( + cleaned_query, + stream=True, + history_messages=conversation_history, + **self.rag.llm_model_kwargs, + ) + else: + response = await self.rag.aquery( + cleaned_query, param=query_param + ) + + async def stream_generator(): + try: + first_chunk_time = None + last_chunk_time = time.time_ns() + total_response = "" + + # Ensure response is an async generator + if isinstance(response, str): + # If it's a string, send in two parts + first_chunk_time = start_time + last_chunk_time = time.time_ns() + total_response = response + + data = { + "model": self.ollama_server_infos.LIGHTRAG_MODEL, + "created_at": self.ollama_server_infos.LIGHTRAG_CREATED_AT, + "message": { + "role": "assistant", + "content": response, + "images": None, + }, + "done": False, + } + yield f"{json.dumps(data, ensure_ascii=False)}\n" + + completion_tokens = estimate_tokens(total_response) + total_time = last_chunk_time - start_time + prompt_eval_time = first_chunk_time - start_time + eval_time = last_chunk_time - first_chunk_time + + data = { + "model": self.ollama_server_infos.LIGHTRAG_MODEL, + "created_at": self.ollama_server_infos.LIGHTRAG_CREATED_AT, + "message": { + "role": "assistant", + "content": "", + "images": None, + }, + "done_reason": "stop", + "done": True, + "total_duration": total_time, + "load_duration": 0, + "prompt_eval_count": prompt_tokens, + "prompt_eval_duration": prompt_eval_time, + "eval_count": completion_tokens, + "eval_duration": eval_time, + } + yield f"{json.dumps(data, ensure_ascii=False)}\n" + else: + try: + async for chunk in response: + if chunk: + if first_chunk_time is None: + first_chunk_time = time.time_ns() + + last_chunk_time = time.time_ns() + + total_response += chunk + data = { + "model": self.ollama_server_infos.LIGHTRAG_MODEL, + "created_at": self.ollama_server_infos.LIGHTRAG_CREATED_AT, + "message": { + "role": "assistant", + "content": chunk, + "images": None, + }, + "done": False, + } + yield f"{json.dumps(data, ensure_ascii=False)}\n" + except (asyncio.CancelledError, Exception) as e: + error_msg = str(e) + if isinstance(e, asyncio.CancelledError): + error_msg = "Stream was cancelled by server" + else: + error_msg = f"Provider error: {error_msg}" + + logger.error(f"Stream error: {error_msg}") + + # Send error message to client + error_data = { + "model": self.ollama_server_infos.LIGHTRAG_MODEL, + "created_at": self.ollama_server_infos.LIGHTRAG_CREATED_AT, + "message": { + "role": "assistant", + "content": f"\n\nError: {error_msg}", + "images": None, + }, + "error": f"\n\nError: {error_msg}", + "done": False, + } + yield f"{json.dumps(error_data, ensure_ascii=False)}\n" + + # Send final message to close the stream + final_data = { + "model": self.ollama_server_infos.LIGHTRAG_MODEL, + "created_at": self.ollama_server_infos.LIGHTRAG_CREATED_AT, + "message": { + "role": "assistant", + "content": "", + "images": None, + }, + "done": True, + } + yield f"{json.dumps(final_data, ensure_ascii=False)}\n" + return + + if first_chunk_time is None: + first_chunk_time = start_time + completion_tokens = estimate_tokens(total_response) + total_time = last_chunk_time - start_time + prompt_eval_time = first_chunk_time - start_time + eval_time = last_chunk_time - first_chunk_time + + data = { + "model": self.ollama_server_infos.LIGHTRAG_MODEL, + "created_at": self.ollama_server_infos.LIGHTRAG_CREATED_AT, + "message": { + "role": "assistant", + "content": "", + "images": None, + }, + "done_reason": "stop", + "done": True, + "total_duration": total_time, + "load_duration": 0, + "prompt_eval_count": prompt_tokens, + "prompt_eval_duration": prompt_eval_time, + "eval_count": completion_tokens, + "eval_duration": eval_time, + } + yield f"{json.dumps(data, ensure_ascii=False)}\n" + + except Exception as e: + trace_exception(e) + raise + + return StreamingResponse( + stream_generator(), + media_type="application/x-ndjson", + headers={ + "Cache-Control": "no-cache", + "Connection": "keep-alive", + "Content-Type": "application/x-ndjson", + "X-Accel-Buffering": "no", # Ensure proper handling of streaming responses in Nginx proxy + }, + ) + else: + first_chunk_time = time.time_ns() + + # Determine if the request is prefix with "/bypass" or from Open WebUI's session title and session keyword generation task + match_result = re.search( + r"\n\nUSER:", cleaned_query, re.MULTILINE + ) + if match_result or mode == SearchMode.bypass: + if request.system: + self.rag.llm_model_kwargs["system_prompt"] = request.system + + response_text = await self.rag.llm_model_func( + cleaned_query, + stream=False, + history_messages=conversation_history, + **self.rag.llm_model_kwargs, + ) + else: + response_text = await self.rag.aquery( + cleaned_query, param=query_param + ) + + last_chunk_time = time.time_ns() + + if not response_text: + response_text = "No response generated" + + completion_tokens = estimate_tokens(str(response_text)) + total_time = last_chunk_time - start_time + prompt_eval_time = first_chunk_time - start_time + eval_time = last_chunk_time - first_chunk_time + + return { + "model": self.ollama_server_infos.LIGHTRAG_MODEL, + "created_at": self.ollama_server_infos.LIGHTRAG_CREATED_AT, + "message": { + "role": "assistant", + "content": str(response_text), + "images": None, + }, + "done_reason": "stop", + "done": True, + "total_duration": total_time, + "load_duration": 0, + "prompt_eval_count": prompt_tokens, + "prompt_eval_duration": prompt_eval_time, + "eval_count": completion_tokens, + "eval_duration": eval_time, + } + except Exception as e: + trace_exception(e) + raise HTTPException(status_code=500, detail=str(e)) diff --git a/LightRAG/lightrag/api/routers/query_routes.py b/LightRAG/lightrag/api/routers/query_routes.py new file mode 100644 index 00000000..d163ca5a --- /dev/null +++ b/LightRAG/lightrag/api/routers/query_routes.py @@ -0,0 +1,1162 @@ +""" +This module contains all query-related routes for the LightRAG API. +""" + +import json +import logging +from typing import Any, Dict, List, Literal, Optional + +from fastapi import APIRouter, Depends, HTTPException +from lightrag.base import QueryParam +from lightrag.api.utils_api import get_combined_auth_dependency +from pydantic import BaseModel, Field, field_validator + +from ascii_colors import trace_exception + +router = APIRouter(tags=["query"]) + + +class QueryRequest(BaseModel): + query: str = Field( + min_length=3, + description="The query text", + ) + + mode: Literal["local", "global", "hybrid", "naive", "mix", "bypass"] = Field( + default="mix", + description="Query mode", + ) + + only_need_context: Optional[bool] = Field( + default=None, + description="If True, only returns the retrieved context without generating a response.", + ) + + only_need_prompt: Optional[bool] = Field( + default=None, + description="If True, only returns the generated prompt without producing a response.", + ) + + response_type: Optional[str] = Field( + min_length=1, + default=None, + description="Defines the response format. Examples: 'Multiple Paragraphs', 'Single Paragraph', 'Bullet Points'.", + ) + + top_k: Optional[int] = Field( + ge=1, + default=None, + description="Number of top items to retrieve. Represents entities in 'local' mode and relationships in 'global' mode.", + ) + + chunk_top_k: Optional[int] = Field( + ge=1, + default=None, + description="Number of text chunks to retrieve initially from vector search and keep after reranking.", + ) + + max_entity_tokens: Optional[int] = Field( + default=None, + description="Maximum number of tokens allocated for entity context in unified token control system.", + ge=1, + ) + + max_relation_tokens: Optional[int] = Field( + default=None, + description="Maximum number of tokens allocated for relationship context in unified token control system.", + ge=1, + ) + + max_total_tokens: Optional[int] = Field( + default=None, + description="Maximum total tokens budget for the entire query context (entities + relations + chunks + system prompt).", + ge=1, + ) + + hl_keywords: list[str] = Field( + default_factory=list, + description="List of high-level keywords to prioritize in retrieval. Leave empty to use the LLM to generate the keywords.", + ) + + ll_keywords: list[str] = Field( + default_factory=list, + description="List of low-level keywords to refine retrieval focus. Leave empty to use the LLM to generate the keywords.", + ) + + conversation_history: Optional[List[Dict[str, Any]]] = Field( + default=None, + description="Stores past conversation history to maintain context. Format: [{'role': 'user/assistant', 'content': 'message'}].", + ) + + user_prompt: Optional[str] = Field( + default=None, + description="User-provided prompt for the query. If provided, this will be used instead of the default value from prompt template.", + ) + + enable_rerank: Optional[bool] = Field( + default=None, + description="Enable reranking for retrieved text chunks. If True but no rerank model is configured, a warning will be issued. Default is True.", + ) + + include_references: Optional[bool] = Field( + default=True, + description="If True, includes reference list in responses. Affects /query and /query/stream endpoints. /query/data always includes references.", + ) + + include_chunk_content: Optional[bool] = Field( + default=False, + description="If True, includes actual chunk text content in references. Only applies when include_references=True. Useful for evaluation and debugging.", + ) + + stream: Optional[bool] = Field( + default=True, + description="If True, enables streaming output for real-time responses. Only affects /query/stream endpoint.", + ) + + @field_validator("query", mode="after") + @classmethod + def query_strip_after(cls, query: str) -> str: + return query.strip() + + @field_validator("conversation_history", mode="after") + @classmethod + def conversation_history_role_check( + cls, conversation_history: List[Dict[str, Any]] | None + ) -> List[Dict[str, Any]] | None: + if conversation_history is None: + return None + for msg in conversation_history: + if "role" not in msg: + raise ValueError("Each message must have a 'role' key.") + if not isinstance(msg["role"], str) or not msg["role"].strip(): + raise ValueError("Each message 'role' must be a non-empty string.") + return conversation_history + + def to_query_params(self, is_stream: bool) -> "QueryParam": + """Converts a QueryRequest instance into a QueryParam instance.""" + # Use Pydantic's `.model_dump(exclude_none=True)` to remove None values automatically + # Exclude API-level parameters that don't belong in QueryParam + request_data = self.model_dump( + exclude_none=True, exclude={"query", "include_chunk_content"} + ) + + # Ensure `mode` and `stream` are set explicitly + param = QueryParam(**request_data) + param.stream = is_stream + return param + + +class ReferenceItem(BaseModel): + """A single reference item in query responses.""" + + reference_id: str = Field(description="Unique reference identifier") + file_path: str = Field(description="Path to the source file") + content: Optional[List[str]] = Field( + default=None, + description="List of chunk contents from this file (only present when include_chunk_content=True)", + ) + + +class QueryResponse(BaseModel): + response: str = Field( + description="The generated response", + ) + references: Optional[List[ReferenceItem]] = Field( + default=None, + description="Reference list (Disabled when include_references=False, /query/data always includes references.)", + ) + + +class QueryDataResponse(BaseModel): + status: str = Field(description="Query execution status") + message: str = Field(description="Status message") + data: Dict[str, Any] = Field( + description="Query result data containing entities, relationships, chunks, and references" + ) + metadata: Dict[str, Any] = Field( + description="Query metadata including mode, keywords, and processing information" + ) + + +class StreamChunkResponse(BaseModel): + """Response model for streaming chunks in NDJSON format""" + + references: Optional[List[Dict[str, str]]] = Field( + default=None, + description="Reference list (only in first chunk when include_references=True)", + ) + response: Optional[str] = Field( + default=None, description="Response content chunk or complete response" + ) + error: Optional[str] = Field( + default=None, description="Error message if processing fails" + ) + + +def create_query_routes(rag, api_key: Optional[str] = None, top_k: int = 60): + combined_auth = get_combined_auth_dependency(api_key) + + @router.post( + "/query", + response_model=QueryResponse, + dependencies=[Depends(combined_auth)], + responses={ + 200: { + "description": "Successful RAG query response", + "content": { + "application/json": { + "schema": { + "type": "object", + "properties": { + "response": { + "type": "string", + "description": "The generated response from the RAG system", + }, + "references": { + "type": "array", + "items": { + "type": "object", + "properties": { + "reference_id": {"type": "string"}, + "file_path": {"type": "string"}, + "content": { + "type": "array", + "items": {"type": "string"}, + "description": "List of chunk contents from this file (only included when include_chunk_content=True)", + }, + }, + }, + "description": "Reference list (only included when include_references=True)", + }, + }, + "required": ["response"], + }, + "examples": { + "with_references": { + "summary": "Response with references", + "description": "Example response when include_references=True", + "value": { + "response": "Artificial Intelligence (AI) is a branch of computer science that aims to create intelligent machines capable of performing tasks that typically require human intelligence, such as learning, reasoning, and problem-solving.", + "references": [ + { + "reference_id": "1", + "file_path": "/documents/ai_overview.pdf", + }, + { + "reference_id": "2", + "file_path": "/documents/machine_learning.txt", + }, + ], + }, + }, + "with_chunk_content": { + "summary": "Response with chunk content", + "description": "Example response when include_references=True and include_chunk_content=True. Note: content is an array of chunks from the same file.", + "value": { + "response": "Artificial Intelligence (AI) is a branch of computer science that aims to create intelligent machines capable of performing tasks that typically require human intelligence, such as learning, reasoning, and problem-solving.", + "references": [ + { + "reference_id": "1", + "file_path": "/documents/ai_overview.pdf", + "content": [ + "Artificial Intelligence (AI) represents a transformative field in computer science focused on creating systems that can perform tasks requiring human-like intelligence. These tasks include learning from experience, understanding natural language, recognizing patterns, and making decisions.", + "AI systems can be categorized into narrow AI, which is designed for specific tasks, and general AI, which aims to match human cognitive abilities across a wide range of domains.", + ], + }, + { + "reference_id": "2", + "file_path": "/documents/machine_learning.txt", + "content": [ + "Machine learning is a subset of AI that enables computers to learn and improve from experience without being explicitly programmed. It focuses on the development of algorithms that can access data and use it to learn for themselves." + ], + }, + ], + }, + }, + "without_references": { + "summary": "Response without references", + "description": "Example response when include_references=False", + "value": { + "response": "Artificial Intelligence (AI) is a branch of computer science that aims to create intelligent machines capable of performing tasks that typically require human intelligence, such as learning, reasoning, and problem-solving." + }, + }, + "different_modes": { + "summary": "Different query modes", + "description": "Examples of responses from different query modes", + "value": { + "local_mode": "Focuses on specific entities and their relationships", + "global_mode": "Provides broader context from relationship patterns", + "hybrid_mode": "Combines local and global approaches", + "naive_mode": "Simple vector similarity search", + "mix_mode": "Integrates knowledge graph and vector retrieval", + }, + }, + }, + } + }, + }, + 400: { + "description": "Bad Request - Invalid input parameters", + "content": { + "application/json": { + "schema": { + "type": "object", + "properties": {"detail": {"type": "string"}}, + }, + "example": { + "detail": "Query text must be at least 3 characters long" + }, + } + }, + }, + 500: { + "description": "Internal Server Error - Query processing failed", + "content": { + "application/json": { + "schema": { + "type": "object", + "properties": {"detail": {"type": "string"}}, + }, + "example": { + "detail": "Failed to process query: LLM service unavailable" + }, + } + }, + }, + }, + ) + async def query_text(request: QueryRequest): + """ + Comprehensive RAG query endpoint with non-streaming response. Parameter "stream" is ignored. + + This endpoint performs Retrieval-Augmented Generation (RAG) queries using various modes + to provide intelligent responses based on your knowledge base. + + **Query Modes:** + - **local**: Focuses on specific entities and their direct relationships + - **global**: Analyzes broader patterns and relationships across the knowledge graph + - **hybrid**: Combines local and global approaches for comprehensive results + - **naive**: Simple vector similarity search without knowledge graph + - **mix**: Integrates knowledge graph retrieval with vector search (recommended) + - **bypass**: Direct LLM query without knowledge retrieval + + conversation_history parameteris sent to LLM only, does not affect retrieval results. + + **Usage Examples:** + + Basic query: + ```json + { + "query": "What is machine learning?", + "mode": "mix" + } + ``` + + Bypass initial LLM call by providing high-level and low-level keywords: + ```json + { + "query": "What is Retrieval-Augmented-Generation?", + "hl_keywords": ["machine learning", "information retrieval", "natural language processing"], + "ll_keywords": ["retrieval augmented generation", "RAG", "knowledge base"], + "mode": "mix" + } + ``` + + Advanced query with references: + ```json + { + "query": "Explain neural networks", + "mode": "hybrid", + "include_references": true, + "response_type": "Multiple Paragraphs", + "top_k": 10 + } + ``` + + Conversation with history: + ```json + { + "query": "Can you give me more details?", + "conversation_history": [ + {"role": "user", "content": "What is AI?"}, + {"role": "assistant", "content": "AI is artificial intelligence..."} + ] + } + ``` + + Args: + request (QueryRequest): The request object containing query parameters: + - **query**: The question or prompt to process (min 3 characters) + - **mode**: Query strategy - "mix" recommended for best results + - **include_references**: Whether to include source citations + - **response_type**: Format preference (e.g., "Multiple Paragraphs") + - **top_k**: Number of top entities/relations to retrieve + - **conversation_history**: Previous dialogue context + - **max_total_tokens**: Token budget for the entire response + + Returns: + QueryResponse: JSON response containing: + - **response**: The generated answer to your query + - **references**: Source citations (if include_references=True) + + Raises: + HTTPException: + - 400: Invalid input parameters (e.g., query too short) + - 500: Internal processing error (e.g., LLM service unavailable) + """ + try: + param = request.to_query_params( + False + ) # Ensure stream=False for non-streaming endpoint + # Force stream=False for /query endpoint regardless of include_references setting + param.stream = False + + # Unified approach: always use aquery_llm for both cases + result = await rag.aquery_llm(request.query, param=param) + + # Extract LLM response and references from unified result + llm_response = result.get("llm_response", {}) + data = result.get("data", {}) + references = data.get("references", []) + + # Get the non-streaming response content + response_content = llm_response.get("content", "") + if not response_content: + response_content = "No relevant context found for the query." + + # Enrich references with chunk content if requested + if request.include_references and request.include_chunk_content: + chunks = data.get("chunks", []) + # Create a mapping from reference_id to chunk content + ref_id_to_content = {} + for chunk in chunks: + ref_id = chunk.get("reference_id", "") + content = chunk.get("content", "") + if ref_id and content: + # Collect chunk content; join later to avoid quadratic string concatenation + ref_id_to_content.setdefault(ref_id, []).append(content) + + # Add content to references + enriched_references = [] + for ref in references: + ref_copy = ref.copy() + ref_id = ref.get("reference_id", "") + if ref_id in ref_id_to_content: + # Keep content as a list of chunks (one file may have multiple chunks) + ref_copy["content"] = ref_id_to_content[ref_id] + enriched_references.append(ref_copy) + references = enriched_references + + # Return response with or without references based on request + if request.include_references: + return QueryResponse(response=response_content, references=references) + else: + return QueryResponse(response=response_content, references=None) + except Exception as e: + trace_exception(e) + raise HTTPException(status_code=500, detail=str(e)) + + @router.post( + "/query/stream", + dependencies=[Depends(combined_auth)], + responses={ + 200: { + "description": "Flexible RAG query response - format depends on stream parameter", + "content": { + "application/x-ndjson": { + "schema": { + "type": "string", + "format": "ndjson", + "description": "Newline-delimited JSON (NDJSON) format used for both streaming and non-streaming responses. For streaming: multiple lines with separate JSON objects. For non-streaming: single line with complete JSON object.", + "example": '{"references": [{"reference_id": "1", "file_path": "/documents/ai.pdf"}]}\n{"response": "Artificial Intelligence is"}\n{"response": " a field of computer science"}\n{"response": " that focuses on creating intelligent machines."}', + }, + "examples": { + "streaming_with_references": { + "summary": "Streaming mode with references (stream=true)", + "description": "Multiple NDJSON lines when stream=True and include_references=True. First line contains references, subsequent lines contain response chunks.", + "value": '{"references": [{"reference_id": "1", "file_path": "/documents/ai_overview.pdf"}, {"reference_id": "2", "file_path": "/documents/ml_basics.txt"}]}\n{"response": "Artificial Intelligence (AI) is a branch of computer science"}\n{"response": " that aims to create intelligent machines capable of performing"}\n{"response": " tasks that typically require human intelligence, such as learning,"}\n{"response": " reasoning, and problem-solving."}', + }, + "streaming_with_chunk_content": { + "summary": "Streaming mode with chunk content (stream=true, include_chunk_content=true)", + "description": "Multiple NDJSON lines when stream=True, include_references=True, and include_chunk_content=True. First line contains references with content arrays (one file may have multiple chunks), subsequent lines contain response chunks.", + "value": '{"references": [{"reference_id": "1", "file_path": "/documents/ai_overview.pdf", "content": ["Artificial Intelligence (AI) represents a transformative field...", "AI systems can be categorized into narrow AI and general AI..."]}, {"reference_id": "2", "file_path": "/documents/ml_basics.txt", "content": ["Machine learning is a subset of AI that enables computers to learn..."]}]}\n{"response": "Artificial Intelligence (AI) is a branch of computer science"}\n{"response": " that aims to create intelligent machines capable of performing"}\n{"response": " tasks that typically require human intelligence."}', + }, + "streaming_without_references": { + "summary": "Streaming mode without references (stream=true)", + "description": "Multiple NDJSON lines when stream=True and include_references=False. Only response chunks are sent.", + "value": '{"response": "Machine learning is a subset of artificial intelligence"}\n{"response": " that enables computers to learn and improve from experience"}\n{"response": " without being explicitly programmed for every task."}', + }, + "non_streaming_with_references": { + "summary": "Non-streaming mode with references (stream=false)", + "description": "Single NDJSON line when stream=False and include_references=True. Complete response with references in one message.", + "value": '{"references": [{"reference_id": "1", "file_path": "/documents/neural_networks.pdf"}], "response": "Neural networks are computational models inspired by biological neural networks that consist of interconnected nodes (neurons) organized in layers. They are fundamental to deep learning and can learn complex patterns from data through training processes."}', + }, + "non_streaming_without_references": { + "summary": "Non-streaming mode without references (stream=false)", + "description": "Single NDJSON line when stream=False and include_references=False. Complete response only.", + "value": '{"response": "Deep learning is a subset of machine learning that uses neural networks with multiple layers (hence deep) to model and understand complex patterns in data. It has revolutionized fields like computer vision, natural language processing, and speech recognition."}', + }, + "error_response": { + "summary": "Error during streaming", + "description": "Error handling in NDJSON format when an error occurs during processing.", + "value": '{"references": [{"reference_id": "1", "file_path": "/documents/ai.pdf"}]}\n{"response": "Artificial Intelligence is"}\n{"error": "LLM service temporarily unavailable"}', + }, + }, + } + }, + }, + 400: { + "description": "Bad Request - Invalid input parameters", + "content": { + "application/json": { + "schema": { + "type": "object", + "properties": {"detail": {"type": "string"}}, + }, + "example": { + "detail": "Query text must be at least 3 characters long" + }, + } + }, + }, + 500: { + "description": "Internal Server Error - Query processing failed", + "content": { + "application/json": { + "schema": { + "type": "object", + "properties": {"detail": {"type": "string"}}, + }, + "example": { + "detail": "Failed to process streaming query: Knowledge graph unavailable" + }, + } + }, + }, + }, + ) + async def query_text_stream(request: QueryRequest): + """ + Advanced RAG query endpoint with flexible streaming response. + + This endpoint provides the most flexible querying experience, supporting both real-time streaming + and complete response delivery based on your integration needs. + + **Response Modes:** + - Real-time response delivery as content is generated + - NDJSON format: each line is a separate JSON object + - First line: `{"references": [...]}` (if include_references=True) + - Subsequent lines: `{"response": "content chunk"}` + - Error handling: `{"error": "error message"}` + + > If stream parameter is False, or the query hit LLM cache, complete response delivered in a single streaming message. + + **Response Format Details** + - **Content-Type**: `application/x-ndjson` (Newline-Delimited JSON) + - **Structure**: Each line is an independent, valid JSON object + - **Parsing**: Process line-by-line, each line is self-contained + - **Headers**: Includes cache control and connection management + + **Query Modes (same as /query endpoint)** + - **local**: Entity-focused retrieval with direct relationships + - **global**: Pattern analysis across the knowledge graph + - **hybrid**: Combined local and global strategies + - **naive**: Vector similarity search only + - **mix**: Integrated knowledge graph + vector retrieval (recommended) + - **bypass**: Direct LLM query without knowledge retrieval + + conversation_history parameteris sent to LLM only, does not affect retrieval results. + + **Usage Examples** + + Real-time streaming query: + ```json + { + "query": "Explain machine learning algorithms", + "mode": "mix", + "stream": true, + "include_references": true + } + ``` + + Bypass initial LLM call by providing high-level and low-level keywords: + ```json + { + "query": "What is Retrieval-Augmented-Generation?", + "hl_keywords": ["machine learning", "information retrieval", "natural language processing"], + "ll_keywords": ["retrieval augmented generation", "RAG", "knowledge base"], + "mode": "mix" + } + ``` + + Complete response query: + ```json + { + "query": "What is deep learning?", + "mode": "hybrid", + "stream": false, + "response_type": "Multiple Paragraphs" + } + ``` + + Conversation with context: + ```json + { + "query": "Can you elaborate on that?", + "stream": true, + "conversation_history": [ + {"role": "user", "content": "What is neural network?"}, + {"role": "assistant", "content": "A neural network is..."} + ] + } + ``` + + **Response Processing:** + + ```python + async for line in response.iter_lines(): + data = json.loads(line) + if "references" in data: + # Handle references (first message) + references = data["references"] + if "response" in data: + # Handle content chunk + content_chunk = data["response"] + if "error" in data: + # Handle error + error_message = data["error"] + ``` + + **Error Handling:** + - Streaming errors are delivered as `{"error": "message"}` lines + - Non-streaming errors raise HTTP exceptions + - Partial responses may be delivered before errors in streaming mode + - Always check for error objects when processing streaming responses + + Args: + request (QueryRequest): The request object containing query parameters: + - **query**: The question or prompt to process (min 3 characters) + - **mode**: Query strategy - "mix" recommended for best results + - **stream**: Enable streaming (True) or complete response (False) + - **include_references**: Whether to include source citations + - **response_type**: Format preference (e.g., "Multiple Paragraphs") + - **top_k**: Number of top entities/relations to retrieve + - **conversation_history**: Previous dialogue context for multi-turn conversations + - **max_total_tokens**: Token budget for the entire response + + Returns: + StreamingResponse: NDJSON streaming response containing: + - **Streaming mode**: Multiple JSON objects, one per line + - References object (if requested): `{"references": [...]}` + - Content chunks: `{"response": "chunk content"}` + - Error objects: `{"error": "error message"}` + - **Non-streaming mode**: Single JSON object + - Complete response: `{"references": [...], "response": "complete content"}` + + Raises: + HTTPException: + - 400: Invalid input parameters (e.g., query too short, invalid mode) + - 500: Internal processing error (e.g., LLM service unavailable) + + Note: + This endpoint is ideal for applications requiring flexible response delivery. + Use streaming mode for real-time interfaces and non-streaming for batch processing. + """ + try: + # Use the stream parameter from the request, defaulting to True if not specified + stream_mode = request.stream if request.stream is not None else True + param = request.to_query_params(stream_mode) + + from fastapi.responses import StreamingResponse + + # Unified approach: always use aquery_llm for all cases + result = await rag.aquery_llm(request.query, param=param) + + async def stream_generator(): + # Extract references and LLM response from unified result + references = result.get("data", {}).get("references", []) + llm_response = result.get("llm_response", {}) + + # Enrich references with chunk content if requested + if request.include_references and request.include_chunk_content: + data = result.get("data", {}) + chunks = data.get("chunks", []) + # Create a mapping from reference_id to chunk content + ref_id_to_content = {} + for chunk in chunks: + ref_id = chunk.get("reference_id", "") + content = chunk.get("content", "") + if ref_id and content: + # Collect chunk content + ref_id_to_content.setdefault(ref_id, []).append(content) + + # Add content to references + enriched_references = [] + for ref in references: + ref_copy = ref.copy() + ref_id = ref.get("reference_id", "") + if ref_id in ref_id_to_content: + # Keep content as a list of chunks (one file may have multiple chunks) + ref_copy["content"] = ref_id_to_content[ref_id] + enriched_references.append(ref_copy) + references = enriched_references + + if llm_response.get("is_streaming"): + # Streaming mode: send references first, then stream response chunks + if request.include_references: + yield f"{json.dumps({'references': references})}\n" + + response_stream = llm_response.get("response_iterator") + if response_stream: + try: + async for chunk in response_stream: + if chunk: # Only send non-empty content + yield f"{json.dumps({'response': chunk})}\n" + except Exception as e: + logging.error(f"Streaming error: {str(e)}") + yield f"{json.dumps({'error': str(e)})}\n" + else: + # Non-streaming mode: send complete response in one message + response_content = llm_response.get("content", "") + if not response_content: + response_content = "No relevant context found for the query." + + # Create complete response object + complete_response = {"response": response_content} + if request.include_references: + complete_response["references"] = references + + yield f"{json.dumps(complete_response)}\n" + + return StreamingResponse( + stream_generator(), + media_type="application/x-ndjson", + headers={ + "Cache-Control": "no-cache", + "Connection": "keep-alive", + "Content-Type": "application/x-ndjson", + "X-Accel-Buffering": "no", # Ensure proper handling of streaming response when proxied by Nginx + }, + ) + except Exception as e: + trace_exception(e) + raise HTTPException(status_code=500, detail=str(e)) + + @router.post( + "/query/data", + response_model=QueryDataResponse, + dependencies=[Depends(combined_auth)], + responses={ + 200: { + "description": "Successful data retrieval response with structured RAG data", + "content": { + "application/json": { + "schema": { + "type": "object", + "properties": { + "status": { + "type": "string", + "enum": ["success", "failure"], + "description": "Query execution status", + }, + "message": { + "type": "string", + "description": "Status message describing the result", + }, + "data": { + "type": "object", + "properties": { + "entities": { + "type": "array", + "items": { + "type": "object", + "properties": { + "entity_name": {"type": "string"}, + "entity_type": {"type": "string"}, + "description": {"type": "string"}, + "source_id": {"type": "string"}, + "file_path": {"type": "string"}, + "reference_id": {"type": "string"}, + }, + }, + "description": "Retrieved entities from knowledge graph", + }, + "relationships": { + "type": "array", + "items": { + "type": "object", + "properties": { + "src_id": {"type": "string"}, + "tgt_id": {"type": "string"}, + "description": {"type": "string"}, + "keywords": {"type": "string"}, + "weight": {"type": "number"}, + "source_id": {"type": "string"}, + "file_path": {"type": "string"}, + "reference_id": {"type": "string"}, + }, + }, + "description": "Retrieved relationships from knowledge graph", + }, + "chunks": { + "type": "array", + "items": { + "type": "object", + "properties": { + "content": {"type": "string"}, + "file_path": {"type": "string"}, + "chunk_id": {"type": "string"}, + "reference_id": {"type": "string"}, + }, + }, + "description": "Retrieved text chunks from vector database", + }, + "references": { + "type": "array", + "items": { + "type": "object", + "properties": { + "reference_id": {"type": "string"}, + "file_path": {"type": "string"}, + }, + }, + "description": "Reference list for citation purposes", + }, + }, + "description": "Structured retrieval data containing entities, relationships, chunks, and references", + }, + "metadata": { + "type": "object", + "properties": { + "query_mode": {"type": "string"}, + "keywords": { + "type": "object", + "properties": { + "high_level": { + "type": "array", + "items": {"type": "string"}, + }, + "low_level": { + "type": "array", + "items": {"type": "string"}, + }, + }, + }, + "processing_info": { + "type": "object", + "properties": { + "total_entities_found": { + "type": "integer" + }, + "total_relations_found": { + "type": "integer" + }, + "entities_after_truncation": { + "type": "integer" + }, + "relations_after_truncation": { + "type": "integer" + }, + "final_chunks_count": { + "type": "integer" + }, + }, + }, + }, + "description": "Query metadata including mode, keywords, and processing information", + }, + }, + "required": ["status", "message", "data", "metadata"], + }, + "examples": { + "successful_local_mode": { + "summary": "Local mode data retrieval", + "description": "Example of structured data from local mode query focusing on specific entities", + "value": { + "status": "success", + "message": "Query executed successfully", + "data": { + "entities": [ + { + "entity_name": "Neural Networks", + "entity_type": "CONCEPT", + "description": "Computational models inspired by biological neural networks", + "source_id": "chunk-123", + "file_path": "/documents/ai_basics.pdf", + "reference_id": "1", + } + ], + "relationships": [ + { + "src_id": "Neural Networks", + "tgt_id": "Machine Learning", + "description": "Neural networks are a subset of machine learning algorithms", + "keywords": "subset, algorithm, learning", + "weight": 0.85, + "source_id": "chunk-123", + "file_path": "/documents/ai_basics.pdf", + "reference_id": "1", + } + ], + "chunks": [ + { + "content": "Neural networks are computational models that mimic the way biological neural networks work...", + "file_path": "/documents/ai_basics.pdf", + "chunk_id": "chunk-123", + "reference_id": "1", + } + ], + "references": [ + { + "reference_id": "1", + "file_path": "/documents/ai_basics.pdf", + } + ], + }, + "metadata": { + "query_mode": "local", + "keywords": { + "high_level": ["neural", "networks"], + "low_level": [ + "computation", + "model", + "algorithm", + ], + }, + "processing_info": { + "total_entities_found": 5, + "total_relations_found": 3, + "entities_after_truncation": 1, + "relations_after_truncation": 1, + "final_chunks_count": 1, + }, + }, + }, + }, + "global_mode": { + "summary": "Global mode data retrieval", + "description": "Example of structured data from global mode query analyzing broader patterns", + "value": { + "status": "success", + "message": "Query executed successfully", + "data": { + "entities": [], + "relationships": [ + { + "src_id": "Artificial Intelligence", + "tgt_id": "Machine Learning", + "description": "AI encompasses machine learning as a core component", + "keywords": "encompasses, component, field", + "weight": 0.92, + "source_id": "chunk-456", + "file_path": "/documents/ai_overview.pdf", + "reference_id": "2", + } + ], + "chunks": [], + "references": [ + { + "reference_id": "2", + "file_path": "/documents/ai_overview.pdf", + } + ], + }, + "metadata": { + "query_mode": "global", + "keywords": { + "high_level": [ + "artificial", + "intelligence", + "overview", + ], + "low_level": [], + }, + }, + }, + }, + "naive_mode": { + "summary": "Naive mode data retrieval", + "description": "Example of structured data from naive mode using only vector search", + "value": { + "status": "success", + "message": "Query executed successfully", + "data": { + "entities": [], + "relationships": [], + "chunks": [ + { + "content": "Deep learning is a subset of machine learning that uses neural networks with multiple layers...", + "file_path": "/documents/deep_learning.pdf", + "chunk_id": "chunk-789", + "reference_id": "3", + } + ], + "references": [ + { + "reference_id": "3", + "file_path": "/documents/deep_learning.pdf", + } + ], + }, + "metadata": { + "query_mode": "naive", + "keywords": {"high_level": [], "low_level": []}, + }, + }, + }, + }, + } + }, + }, + 400: { + "description": "Bad Request - Invalid input parameters", + "content": { + "application/json": { + "schema": { + "type": "object", + "properties": {"detail": {"type": "string"}}, + }, + "example": { + "detail": "Query text must be at least 3 characters long" + }, + } + }, + }, + 500: { + "description": "Internal Server Error - Data retrieval failed", + "content": { + "application/json": { + "schema": { + "type": "object", + "properties": {"detail": {"type": "string"}}, + }, + "example": { + "detail": "Failed to retrieve data: Knowledge graph unavailable" + }, + } + }, + }, + }, + ) + async def query_data(request: QueryRequest): + """ + Advanced data retrieval endpoint for structured RAG analysis. + + This endpoint provides raw retrieval results without LLM generation, perfect for: + - **Data Analysis**: Examine what information would be used for RAG + - **System Integration**: Get structured data for custom processing + - **Debugging**: Understand retrieval behavior and quality + - **Research**: Analyze knowledge graph structure and relationships + + **Key Features:** + - No LLM generation - pure data retrieval + - Complete structured output with entities, relationships, and chunks + - Always includes references for citation + - Detailed metadata about processing and keywords + - Compatible with all query modes and parameters + + **Query Mode Behaviors:** + - **local**: Returns entities and their direct relationships + related chunks + - **global**: Returns relationship patterns across the knowledge graph + - **hybrid**: Combines local and global retrieval strategies + - **naive**: Returns only vector-retrieved text chunks (no knowledge graph) + - **mix**: Integrates knowledge graph data with vector-retrieved chunks + - **bypass**: Returns empty data arrays (used for direct LLM queries) + + **Data Structure:** + - **entities**: Knowledge graph entities with descriptions and metadata + - **relationships**: Connections between entities with weights and descriptions + - **chunks**: Text segments from documents with source information + - **references**: Citation information mapping reference IDs to file paths + - **metadata**: Processing information, keywords, and query statistics + + **Usage Examples:** + + Analyze entity relationships: + ```json + { + "query": "machine learning algorithms", + "mode": "local", + "top_k": 10 + } + ``` + + Explore global patterns: + ```json + { + "query": "artificial intelligence trends", + "mode": "global", + "max_relation_tokens": 2000 + } + ``` + + Vector similarity search: + ```json + { + "query": "neural network architectures", + "mode": "naive", + "chunk_top_k": 5 + } + ``` + + Bypass initial LLM call by providing high-level and low-level keywords: + ```json + { + "query": "What is Retrieval-Augmented-Generation?", + "hl_keywords": ["machine learning", "information retrieval", "natural language processing"], + "ll_keywords": ["retrieval augmented generation", "RAG", "knowledge base"], + "mode": "mix" + } + ``` + + **Response Analysis:** + - **Empty arrays**: Normal for certain modes (e.g., naive mode has no entities/relationships) + - **Processing info**: Shows retrieval statistics and token usage + - **Keywords**: High-level and low-level keywords extracted from query + - **Reference mapping**: Links all data back to source documents + + Args: + request (QueryRequest): The request object containing query parameters: + - **query**: The search query to analyze (min 3 characters) + - **mode**: Retrieval strategy affecting data types returned + - **top_k**: Number of top entities/relationships to retrieve + - **chunk_top_k**: Number of text chunks to retrieve + - **max_entity_tokens**: Token limit for entity context + - **max_relation_tokens**: Token limit for relationship context + - **max_total_tokens**: Overall token budget for retrieval + + Returns: + QueryDataResponse: Structured JSON response containing: + - **status**: "success" or "failure" + - **message**: Human-readable status description + - **data**: Complete retrieval results with entities, relationships, chunks, references + - **metadata**: Query processing information and statistics + + Raises: + HTTPException: + - 400: Invalid input parameters (e.g., query too short, invalid mode) + - 500: Internal processing error (e.g., knowledge graph unavailable) + + Note: + This endpoint always includes references regardless of the include_references parameter, + as structured data analysis typically requires source attribution. + """ + try: + param = request.to_query_params(False) # No streaming for data endpoint + response = await rag.aquery_data(request.query, param=param) + + # aquery_data returns the new format with status, message, data, and metadata + if isinstance(response, dict): + return QueryDataResponse(**response) + else: + # Handle unexpected response format + return QueryDataResponse( + status="failure", + message="Invalid response type", + data={}, + ) + except Exception as e: + trace_exception(e) + raise HTTPException(status_code=500, detail=str(e)) + + return router diff --git a/LightRAG/lightrag/api/run_with_gunicorn.py b/LightRAG/lightrag/api/run_with_gunicorn.py new file mode 100644 index 00000000..deabe7cf --- /dev/null +++ b/LightRAG/lightrag/api/run_with_gunicorn.py @@ -0,0 +1,282 @@ +#!/usr/bin/env python +""" +Start LightRAG server with Gunicorn +""" + +import os +import sys +import platform +import pipmaster as pm +from lightrag.api.utils_api import display_splash_screen, check_env_file +from lightrag.api.config import global_args +from lightrag.utils import get_env_value +from lightrag.kg.shared_storage import initialize_share_data + +from lightrag.constants import ( + DEFAULT_WOKERS, + DEFAULT_TIMEOUT, +) + + +def check_and_install_dependencies(): + """Check and install required dependencies""" + required_packages = [ + "gunicorn", + "tiktoken", + "psutil", + # Add other required packages here + ] + + for package in required_packages: + if not pm.is_installed(package): + print(f"Installing {package}...") + pm.install(package) + print(f"{package} installed successfully") + + +def main(): + # Explicitly initialize configuration for Gunicorn mode + from lightrag.api.config import initialize_config + + initialize_config() + + # Set Gunicorn mode flag for lifespan cleanup detection + os.environ["LIGHTRAG_GUNICORN_MODE"] = "1" + + # Check .env file + if not check_env_file(): + sys.exit(1) + + # Check DOCLING compatibility with Gunicorn multi-worker mode on macOS + if ( + platform.system() == "Darwin" + and global_args.document_loading_engine == "DOCLING" + and global_args.workers > 1 + ): + print("\n" + "=" * 80) + print("❌ ERROR: Incompatible configuration detected!") + print("=" * 80) + print( + "\nDOCLING engine with Gunicorn multi-worker mode is not supported on macOS" + ) + print("\nReason:") + print(" PyTorch (required by DOCLING) has known compatibility issues with") + print(" fork-based multiprocessing on macOS, which can cause crashes or") + print(" unexpected behavior when using Gunicorn with multiple workers.") + print("\nCurrent configuration:") + print(" - Operating System: macOS (Darwin)") + print(f" - Document Engine: {global_args.document_loading_engine}") + print(f" - Workers: {global_args.workers}") + print("\nPossible solutions:") + print(" 1. Use single worker mode:") + print(" --workers 1") + print("\n 2. Change document loading engine in .env:") + print(" DOCUMENT_LOADING_ENGINE=DEFAULT") + print("\n 3. Deploy on Linux where multi-worker mode is fully supported") + print("=" * 80 + "\n") + sys.exit(1) + + # Check macOS fork safety environment variable for multi-worker mode + if ( + platform.system() == "Darwin" + and global_args.workers > 1 + and os.environ.get("OBJC_DISABLE_INITIALIZE_FORK_SAFETY") != "YES" + ): + print("\n" + "=" * 80) + print("❌ ERROR: Missing required environment variable on macOS!") + print("=" * 80) + print("\nmacOS with Gunicorn multi-worker mode requires:") + print(" OBJC_DISABLE_INITIALIZE_FORK_SAFETY=YES") + print("\nReason:") + print(" NumPy uses macOS's Accelerate framework (Objective-C based) for") + print(" vector computations. The Objective-C runtime has fork safety checks") + print(" that will crash worker processes when embedding functions are called.") + print("\nCurrent configuration:") + print(" - Operating System: macOS (Darwin)") + print(f" - Workers: {global_args.workers}") + print( + f" - Environment Variable: {os.environ.get('OBJC_DISABLE_INITIALIZE_FORK_SAFETY', 'NOT SET')}" + ) + print("\nHow to fix:") + print(" Option 1 - Set environment variable before starting (recommended):") + print(" export OBJC_DISABLE_INITIALIZE_FORK_SAFETY=YES") + print(" lightrag-server") + print("\n Option 2 - Add to your shell profile (~/.zshrc or ~/.bash_profile):") + print(" echo 'export OBJC_DISABLE_INITIALIZE_FORK_SAFETY=YES' >> ~/.zshrc") + print(" source ~/.zshrc") + print("\n Option 3 - Use single worker mode (no multiprocessing):") + print(" lightrag-server --workers 1") + print("=" * 80 + "\n") + sys.exit(1) + + # Check and install dependencies + check_and_install_dependencies() + + # Note: Signal handlers are NOT registered here because: + # - Master cleanup already handled by gunicorn_config.on_exit() + + # Display startup information + display_splash_screen(global_args) + + print("🚀 Starting LightRAG with Gunicorn") + print(f"🔄 Worker management: Gunicorn (workers={global_args.workers})") + print("🔍 Preloading app: Enabled") + print("📝 Note: Using Gunicorn's preload feature for shared data initialization") + print("\n\n" + "=" * 80) + print("MAIN PROCESS INITIALIZATION") + print(f"Process ID: {os.getpid()}") + print(f"Workers setting: {global_args.workers}") + print("=" * 80 + "\n") + + # Import Gunicorn's StandaloneApplication + from gunicorn.app.base import BaseApplication + + # Define a custom application class that loads our config + class GunicornApp(BaseApplication): + def __init__(self, app, options=None): + self.options = options or {} + self.application = app + super().__init__() + + def load_config(self): + # Define valid Gunicorn configuration options + valid_options = { + "bind", + "workers", + "worker_class", + "timeout", + "keepalive", + "preload_app", + "errorlog", + "accesslog", + "loglevel", + "certfile", + "keyfile", + "limit_request_line", + "limit_request_fields", + "limit_request_field_size", + "graceful_timeout", + "max_requests", + "max_requests_jitter", + } + + # Special hooks that need to be set separately + special_hooks = { + "on_starting", + "on_reload", + "on_exit", + "pre_fork", + "post_fork", + "pre_exec", + "pre_request", + "post_request", + "worker_init", + "worker_exit", + "nworkers_changed", + "child_exit", + } + + # Import and configure the gunicorn_config module + from lightrag.api import gunicorn_config + + # Set configuration variables in gunicorn_config, prioritizing command line arguments + gunicorn_config.workers = ( + global_args.workers + if global_args.workers + else get_env_value("WORKERS", DEFAULT_WOKERS, int) + ) + + # Bind configuration prioritizes command line arguments + host = ( + global_args.host + if global_args.host != "0.0.0.0" + else os.getenv("HOST", "0.0.0.0") + ) + port = ( + global_args.port + if global_args.port != 9621 + else get_env_value("PORT", 9621, int) + ) + gunicorn_config.bind = f"{host}:{port}" + + # Log level configuration prioritizes command line arguments + gunicorn_config.loglevel = ( + global_args.log_level.lower() + if global_args.log_level + else os.getenv("LOG_LEVEL", "info") + ) + + # Timeout configuration prioritizes command line arguments + gunicorn_config.timeout = ( + global_args.timeout + 30 + if global_args.timeout is not None + else get_env_value( + "TIMEOUT", DEFAULT_TIMEOUT + 30, int, special_none=True + ) + ) + + # Keepalive configuration + gunicorn_config.keepalive = get_env_value("KEEPALIVE", 5, int) + + # SSL configuration prioritizes command line arguments + if global_args.ssl or os.getenv("SSL", "").lower() in ( + "true", + "1", + "yes", + "t", + "on", + ): + gunicorn_config.certfile = ( + global_args.ssl_certfile + if global_args.ssl_certfile + else os.getenv("SSL_CERTFILE") + ) + gunicorn_config.keyfile = ( + global_args.ssl_keyfile + if global_args.ssl_keyfile + else os.getenv("SSL_KEYFILE") + ) + + # Set configuration options from the module + for key in dir(gunicorn_config): + if key in valid_options: + value = getattr(gunicorn_config, key) + # Skip functions like on_starting and None values + if not callable(value) and value is not None: + self.cfg.set(key, value) + # Set special hooks + elif key in special_hooks: + value = getattr(gunicorn_config, key) + if callable(value): + self.cfg.set(key, value) + + if hasattr(gunicorn_config, "logconfig_dict"): + self.cfg.set( + "logconfig_dict", getattr(gunicorn_config, "logconfig_dict") + ) + + def load(self): + # Import the application + from lightrag.api.lightrag_server import get_application + + return get_application(global_args) + + # Create the application + app = GunicornApp("") + + # Force workers to be an integer and greater than 1 for 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o[i](a)})).replace(/^(.*?)(t{8,})(.*)$/g,((o,i,a)=>s.substring(i.length,i.length+a.length))):s;if(8===i.length)return new Date(1e3*Number.parseInt(i,16));if(i.length<10)throw new Error("Stamp length invalid");const a=Number.parseInt(i.substring(i.length-1),16);return new Date(1e3*Number.parseInt(i.substring(a,a+8),16))})),__publicField(this,"setCounter",(s=>{this.counter=s})),__publicField(this,"validate",((s,o)=>{const i=o?this._normalizeDictionary(o):this.dict;return s.split("").every((s=>i.includes(s)))}));const o=__spreadValues(__spreadValues({},C),s);this.counter=0,this.debug=!1,this.dict=[],this.version="5.3.2";const{dictionary:i,shuffle:a,length:u,counter:_}=o;this.uuidLength=u,this.setDictionary(i,a),this.setCounter(_),this.debug=o.debug,this.log(this.dict),this.log("Generator instantiated with Dictionary Size ".concat(this.dictLength," and counter set to 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u=s.length+1;if(a&&o+1===u)return s[o]=i,s;for(var _=new Array(u),w=0,x=0;x=qe)return createNodes(s,C,a,u);var U=s&&s===this.ownerID,V=U?C:arrCopy(C);return $?x?L===B-1?V.pop():V[L]=V.pop():V[L]=[a,u]:V.push([a,u]),U?(this.entries=V,this):new ArrayMapNode(s,V)}},BitmapIndexedNode.prototype.get=function(s,o,i,a){void 0===o&&(o=hash(i));var u=1<<((0===s?o:o>>>s)&C),_=this.bitmap;return _&u?this.nodes[popCount(_&u-1)].get(s+w,o,i,a):a},BitmapIndexedNode.prototype.update=function(s,o,i,a,u,_,x){void 0===i&&(i=hash(a));var L=(0===o?i:i>>>o)&C,B=1<=ze)return expandNodes(s,z,$,L,Z);if(U&&!Z&&2===z.length&&isLeafNode(z[1^V]))return z[1^V];if(U&&Z&&1===z.length&&isLeafNode(Z))return Z;var ee=s&&s===this.ownerID,ie=U?Z?$:$^B:$|B,ae=U?Z?setIn(z,V,Z,ee):spliceOut(z,V,ee):spliceIn(z,V,Z,ee);return ee?(this.bitmap=ie,this.nodes=ae,this):new BitmapIndexedNode(s,ie,ae)},HashArrayMapNode.prototype.get=function(s,o,i,a){void 0===o&&(o=hash(i));var u=(0===s?o:o>>>s)&C,_=this.nodes[u];return _?_.get(s+w,o,i,a):a},HashArrayMapNode.prototype.update=function(s,o,i,a,u,_,x){void 0===i&&(i=hash(a));var L=(0===o?i:i>>>o)&C,B=u===j,$=this.nodes,U=$[L];if(B&&!U)return this;var V=updateNode(U,s,o+w,i,a,u,_,x);if(V===U)return this;var z=this.count;if(U){if(!V&&--z0&&a=0&&s>>o&C;if(a>=this.array.length)return new VNode([],s);var u,_=0===a;if(o>0){var x=this.array[a];if((u=x&&x.removeBefore(s,o-w,i))===x&&_)return this}if(_&&!u)return this;var j=editableVNode(this,s);if(!_)for(var L=0;L>>o&C;if(u>=this.array.length)return this;if(o>0){var _=this.array[u];if((a=_&&_.removeAfter(s,o-w,i))===_&&u===this.array.length-1)return this}var x=editableVNode(this,s);return x.array.splice(u+1),a&&(x.array[u]=a),x};var Xe,Qe,et={};function iterateList(s,o){var i=s._origin,a=s._capacity,u=getTailOffset(a),_=s._tail;return iterateNodeOrLeaf(s._root,s._level,0);function iterateNodeOrLeaf(s,o,i){return 0===o?iterateLeaf(s,i):iterateNode(s,o,i)}function iterateLeaf(s,w){var C=w===u?_&&_.array:s&&s.array,j=w>i?0:i-w,L=a-w;return L>x&&(L=x),function(){if(j===L)return et;var s=o?--L:j++;return C&&C[s]}}function iterateNode(s,u,_){var C,j=s&&s.array,L=_>i?0:i-_>>u,B=1+(a-_>>u);return B>x&&(B=x),function(){for(;;){if(C){var s=C();if(s!==et)return s;C=null}if(L===B)return et;var i=o?--B:L++;C=iterateNodeOrLeaf(j&&j[i],u-w,_+(i<=s.size||o<0)return s.withMutations((function(s){o<0?setListBounds(s,o).set(0,i):setListBounds(s,0,o+1).set(o,i)}));o+=s._origin;var a=s._tail,u=s._root,_=MakeRef(B);return o>=getTailOffset(s._capacity)?a=updateVNode(a,s.__ownerID,0,o,i,_):u=updateVNode(u,s.__ownerID,s._level,o,i,_),_.value?s.__ownerID?(s._root=u,s._tail=a,s.__hash=void 0,s.__altered=!0,s):makeList(s._origin,s._capacity,s._level,u,a):s}function updateVNode(s,o,i,a,u,_){var x,j=a>>>i&C,L=s&&j0){var B=s&&s.array[j],$=updateVNode(B,o,i-w,a,u,_);return $===B?s:((x=editableVNode(s,o)).array[j]=$,x)}return L&&s.array[j]===u?s:(SetRef(_),x=editableVNode(s,o),void 0===u&&j===x.array.length-1?x.array.pop():x.array[j]=u,x)}function editableVNode(s,o){return o&&s&&o===s.ownerID?s:new VNode(s?s.array.slice():[],o)}function listNodeFor(s,o){if(o>=getTailOffset(s._capacity))return s._tail;if(o<1<0;)i=i.array[o>>>a&C],a-=w;return i}}function setListBounds(s,o,i){void 0!==o&&(o|=0),void 0!==i&&(i|=0);var a=s.__ownerID||new OwnerID,u=s._origin,_=s._capacity,x=u+o,j=void 0===i?_:i<0?_+i:u+i;if(x===u&&j===_)return s;if(x>=j)return s.clear();for(var L=s._level,B=s._root,$=0;x+$<0;)B=new VNode(B&&B.array.length?[void 0,B]:[],a),$+=1<<(L+=w);$&&(x+=$,u+=$,j+=$,_+=$);for(var U=getTailOffset(_),V=getTailOffset(j);V>=1<U?new VNode([],a):z;if(z&&V>U&&x<_&&z.array.length){for(var Z=B=editableVNode(B,a),ee=L;ee>w;ee-=w){var ie=U>>>ee&C;Z=Z.array[ie]=editableVNode(Z.array[ie],a)}Z.array[U>>>w&C]=z}if(j<_&&(Y=Y&&Y.removeAfter(a,0,j)),x>=V)x-=V,j-=V,L=w,B=null,Y=Y&&Y.removeBefore(a,0,x);else if(x>u||V>>L&C;if(ae!==V>>>L&C)break;ae&&($+=(1<u&&(B=B.removeBefore(a,L,x-$)),B&&Vu&&(u=x.size),isIterable(w)||(x=x.map((function(s){return fromJS(s)}))),a.push(x)}return u>s.size&&(s=s.setSize(u)),mergeIntoCollectionWith(s,o,a)}function getTailOffset(s){return s>>w<=x&&w.size>=2*_.size?(a=(u=w.filter((function(s,o){return void 0!==s&&C!==o}))).toKeyedSeq().map((function(s){return s[0]})).flip().toMap(),s.__ownerID&&(a.__ownerID=u.__ownerID=s.__ownerID)):(a=_.remove(o),u=C===w.size-1?w.pop():w.set(C,void 0))}else if(L){if(i===w.get(C)[1])return s;a=_,u=w.set(C,[o,i])}else a=_.set(o,w.size),u=w.set(w.size,[o,i]);return s.__ownerID?(s.size=a.size,s._map=a,s._list=u,s.__hash=void 0,s):makeOrderedMap(a,u)}function ToKeyedSequence(s,o){this._iter=s,this._useKeys=o,this.size=s.size}function ToIndexedSequence(s){this._iter=s,this.size=s.size}function ToSetSequence(s){this._iter=s,this.size=s.size}function FromEntriesSequence(s){this._iter=s,this.size=s.size}function flipFactory(s){var o=makeSequence(s);return o._iter=s,o.size=s.size,o.flip=function(){return s},o.reverse=function(){var o=s.reverse.apply(this);return o.flip=function(){return s.reverse()},o},o.has=function(o){return s.includes(o)},o.includes=function(o){return s.has(o)},o.cacheResult=cacheResultThrough,o.__iterateUncached=function(o,i){var a=this;return s.__iterate((function(s,i){return!1!==o(i,s,a)}),i)},o.__iteratorUncached=function(o,i){if(o===V){var a=s.__iterator(o,i);return new Iterator((function(){var s=a.next();if(!s.done){var o=s.value[0];s.value[0]=s.value[1],s.value[1]=o}return s}))}return s.__iterator(o===U?$:U,i)},o}function mapFactory(s,o,i){var a=makeSequence(s);return a.size=s.size,a.has=function(o){return s.has(o)},a.get=function(a,u){var _=s.get(a,j);return _===j?u:o.call(i,_,a,s)},a.__iterateUncached=function(a,u){var _=this;return s.__iterate((function(s,u,w){return!1!==a(o.call(i,s,u,w),u,_)}),u)},a.__iteratorUncached=function(a,u){var _=s.__iterator(V,u);return new Iterator((function(){var u=_.next();if(u.done)return u;var w=u.value,x=w[0];return iteratorValue(a,x,o.call(i,w[1],x,s),u)}))},a}function reverseFactory(s,o){var i=makeSequence(s);return i._iter=s,i.size=s.size,i.reverse=function(){return s},s.flip&&(i.flip=function(){var o=flipFactory(s);return o.reverse=function(){return s.flip()},o}),i.get=function(i,a){return s.get(o?i:-1-i,a)},i.has=function(i){return s.has(o?i:-1-i)},i.includes=function(o){return s.includes(o)},i.cacheResult=cacheResultThrough,i.__iterate=function(o,i){var a=this;return s.__iterate((function(s,i){return o(s,i,a)}),!i)},i.__iterator=function(o,i){return s.__iterator(o,!i)},i}function filterFactory(s,o,i,a){var u=makeSequence(s);return a&&(u.has=function(a){var u=s.get(a,j);return u!==j&&!!o.call(i,u,a,s)},u.get=function(a,u){var _=s.get(a,j);return _!==j&&o.call(i,_,a,s)?_:u}),u.__iterateUncached=function(u,_){var w=this,x=0;return s.__iterate((function(s,_,C){if(o.call(i,s,_,C))return x++,u(s,a?_:x-1,w)}),_),x},u.__iteratorUncached=function(u,_){var w=s.__iterator(V,_),x=0;return new Iterator((function(){for(;;){var _=w.next();if(_.done)return _;var C=_.value,j=C[0],L=C[1];if(o.call(i,L,j,s))return iteratorValue(u,a?j:x++,L,_)}}))},u}function countByFactory(s,o,i){var a=Map().asMutable();return s.__iterate((function(u,_){a.update(o.call(i,u,_,s),0,(function(s){return s+1}))})),a.asImmutable()}function groupByFactory(s,o,i){var a=isKeyed(s),u=(isOrdered(s)?OrderedMap():Map()).asMutable();s.__iterate((function(_,w){u.update(o.call(i,_,w,s),(function(s){return(s=s||[]).push(a?[w,_]:_),s}))}));var _=iterableClass(s);return u.map((function(o){return reify(s,_(o))}))}function sliceFactory(s,o,i,a){var u=s.size;if(void 0!==o&&(o|=0),void 0!==i&&(i===1/0?i=u:i|=0),wholeSlice(o,i,u))return s;var _=resolveBegin(o,u),w=resolveEnd(i,u);if(_!=_||w!=w)return sliceFactory(s.toSeq().cacheResult(),o,i,a);var x,C=w-_;C==C&&(x=C<0?0:C);var j=makeSequence(s);return j.size=0===x?x:s.size&&x||void 0,!a&&isSeq(s)&&x>=0&&(j.get=function(o,i){return(o=wrapIndex(this,o))>=0&&ox)return iteratorDone();var s=u.next();return a||o===U?s:iteratorValue(o,C-1,o===$?void 0:s.value[1],s)}))},j}function takeWhileFactory(s,o,i){var a=makeSequence(s);return a.__iterateUncached=function(a,u){var _=this;if(u)return this.cacheResult().__iterate(a,u);var w=0;return s.__iterate((function(s,u,x){return o.call(i,s,u,x)&&++w&&a(s,u,_)})),w},a.__iteratorUncached=function(a,u){var _=this;if(u)return this.cacheResult().__iterator(a,u);var w=s.__iterator(V,u),x=!0;return new Iterator((function(){if(!x)return iteratorDone();var s=w.next();if(s.done)return s;var u=s.value,C=u[0],j=u[1];return o.call(i,j,C,_)?a===V?s:iteratorValue(a,C,j,s):(x=!1,iteratorDone())}))},a}function skipWhileFactory(s,o,i,a){var u=makeSequence(s);return u.__iterateUncached=function(u,_){var w=this;if(_)return this.cacheResult().__iterate(u,_);var x=!0,C=0;return s.__iterate((function(s,_,j){if(!x||!(x=o.call(i,s,_,j)))return C++,u(s,a?_:C-1,w)})),C},u.__iteratorUncached=function(u,_){var w=this;if(_)return this.cacheResult().__iterator(u,_);var x=s.__iterator(V,_),C=!0,j=0;return new Iterator((function(){var s,_,L;do{if((s=x.next()).done)return a||u===U?s:iteratorValue(u,j++,u===$?void 0:s.value[1],s);var B=s.value;_=B[0],L=B[1],C&&(C=o.call(i,L,_,w))}while(C);return u===V?s:iteratorValue(u,_,L,s)}))},u}function concatFactory(s,o){var i=isKeyed(s),a=[s].concat(o).map((function(s){return isIterable(s)?i&&(s=KeyedIterable(s)):s=i?keyedSeqFromValue(s):indexedSeqFromValue(Array.isArray(s)?s:[s]),s})).filter((function(s){return 0!==s.size}));if(0===a.length)return s;if(1===a.length){var u=a[0];if(u===s||i&&isKeyed(u)||isIndexed(s)&&isIndexed(u))return u}var _=new ArraySeq(a);return i?_=_.toKeyedSeq():isIndexed(s)||(_=_.toSetSeq()),(_=_.flatten(!0)).size=a.reduce((function(s,o){if(void 0!==s){var i=o.size;if(void 0!==i)return s+i}}),0),_}function flattenFactory(s,o,i){var a=makeSequence(s);return a.__iterateUncached=function(a,u){var _=0,w=!1;function flatDeep(s,x){var C=this;s.__iterate((function(s,u){return(!o||x0}function zipWithFactory(s,o,i){var a=makeSequence(s);return a.size=new ArraySeq(i).map((function(s){return s.size})).min(),a.__iterate=function(s,o){for(var i,a=this.__iterator(U,o),u=0;!(i=a.next()).done&&!1!==s(i.value,u++,this););return u},a.__iteratorUncached=function(s,a){var u=i.map((function(s){return s=Iterable(s),getIterator(a?s.reverse():s)})),_=0,w=!1;return new Iterator((function(){var i;return w||(i=u.map((function(s){return s.next()})),w=i.some((function(s){return s.done}))),w?iteratorDone():iteratorValue(s,_++,o.apply(null,i.map((function(s){return s.value}))))}))},a}function reify(s,o){return isSeq(s)?o:s.constructor(o)}function validateEntry(s){if(s!==Object(s))throw new TypeError("Expected [K, V] tuple: "+s)}function resolveSize(s){return assertNotInfinite(s.size),ensureSize(s)}function iterableClass(s){return isKeyed(s)?KeyedIterable:isIndexed(s)?IndexedIterable:SetIterable}function makeSequence(s){return Object.create((isKeyed(s)?KeyedSeq:isIndexed(s)?IndexedSeq:SetSeq).prototype)}function cacheResultThrough(){return this._iter.cacheResult?(this._iter.cacheResult(),this.size=this._iter.size,this):Seq.prototype.cacheResult.call(this)}function defaultComparator(s,o){return s>o?1:s=0;i--)o={value:arguments[i],next:o};return this.__ownerID?(this.size=s,this._head=o,this.__hash=void 0,this.__altered=!0,this):makeStack(s,o)},Stack.prototype.pushAll=function(s){if(0===(s=IndexedIterable(s)).size)return this;assertNotInfinite(s.size);var o=this.size,i=this._head;return s.reverse().forEach((function(s){o++,i={value:s,next:i}})),this.__ownerID?(this.size=o,this._head=i,this.__hash=void 0,this.__altered=!0,this):makeStack(o,i)},Stack.prototype.pop=function(){return this.slice(1)},Stack.prototype.unshift=function(){return this.push.apply(this,arguments)},Stack.prototype.unshiftAll=function(s){return this.pushAll(s)},Stack.prototype.shift=function(){return this.pop.apply(this,arguments)},Stack.prototype.clear=function(){return 0===this.size?this:this.__ownerID?(this.size=0,this._head=void 0,this.__hash=void 0,this.__altered=!0,this):emptyStack()},Stack.prototype.slice=function(s,o){if(wholeSlice(s,o,this.size))return this;var i=resolveBegin(s,this.size);if(resolveEnd(o,this.size)!==this.size)return IndexedCollection.prototype.slice.call(this,s,o);for(var a=this.size-i,u=this._head;i--;)u=u.next;return this.__ownerID?(this.size=a,this._head=u,this.__hash=void 0,this.__altered=!0,this):makeStack(a,u)},Stack.prototype.__ensureOwner=function(s){return s===this.__ownerID?this:s?makeStack(this.size,this._head,s,this.__hash):(this.__ownerID=s,this.__altered=!1,this)},Stack.prototype.__iterate=function(s,o){if(o)return this.reverse().__iterate(s);for(var i=0,a=this._head;a&&!1!==s(a.value,i++,this);)a=a.next;return i},Stack.prototype.__iterator=function(s,o){if(o)return this.reverse().__iterator(s);var i=0,a=this._head;return new Iterator((function(){if(a){var o=a.value;return a=a.next,iteratorValue(s,i++,o)}return iteratorDone()}))},Stack.isStack=isStack;var at,ct="@@__IMMUTABLE_STACK__@@",lt=Stack.prototype;function makeStack(s,o,i,a){var u=Object.create(lt);return u.size=s,u._head=o,u.__ownerID=i,u.__hash=a,u.__altered=!1,u}function emptyStack(){return at||(at=makeStack(0))}function mixin(s,o){var keyCopier=function(i){s.prototype[i]=o[i]};return Object.keys(o).forEach(keyCopier),Object.getOwnPropertySymbols&&Object.getOwnPropertySymbols(o).forEach(keyCopier),s}lt[ct]=!0,lt.withMutations=$e.withMutations,lt.asMutable=$e.asMutable,lt.asImmutable=$e.asImmutable,lt.wasAltered=$e.wasAltered,Iterable.Iterator=Iterator,mixin(Iterable,{toArray:function(){assertNotInfinite(this.size);var s=new Array(this.size||0);return this.valueSeq().__iterate((function(o,i){s[i]=o})),s},toIndexedSeq:function(){return new ToIndexedSequence(this)},toJS:function(){return this.toSeq().map((function(s){return s&&"function"==typeof s.toJS?s.toJS():s})).__toJS()},toJSON:function(){return this.toSeq().map((function(s){return s&&"function"==typeof s.toJSON?s.toJSON():s})).__toJS()},toKeyedSeq:function(){return new ToKeyedSequence(this,!0)},toMap:function(){return Map(this.toKeyedSeq())},toObject:function(){assertNotInfinite(this.size);var s={};return this.__iterate((function(o,i){s[i]=o})),s},toOrderedMap:function(){return OrderedMap(this.toKeyedSeq())},toOrderedSet:function(){return OrderedSet(isKeyed(this)?this.valueSeq():this)},toSet:function(){return Set(isKeyed(this)?this.valueSeq():this)},toSetSeq:function(){return new ToSetSequence(this)},toSeq:function(){return isIndexed(this)?this.toIndexedSeq():isKeyed(this)?this.toKeyedSeq():this.toSetSeq()},toStack:function(){return 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i,a,u,_,w=s.posMax,x=s.pos;return!(x+2>=w)&&(94===s.src.charCodeAt(x)&&(91===s.src.charCodeAt(x+1)&&(!(s.level>=s.options.maxNesting)&&(i=x+2,!((a=parseLinkLabel(s,x+1))<0)&&(o||(s.env.footnotes||(s.env.footnotes={}),s.env.footnotes.list||(s.env.footnotes.list=[]),u=s.env.footnotes.list.length,s.pos=i,s.posMax=a,s.push({type:"footnote_ref",id:u,level:s.level}),s.linkLevel++,_=s.tokens.length,s.parser.tokenize(s),s.env.footnotes.list[u]={tokens:s.tokens.splice(_)},s.linkLevel--),s.pos=a+1,s.posMax=w,!0)))))}],["footnote_ref",function footnote_ref(s,o){var i,a,u,_,w=s.posMax,x=s.pos;if(x+3>w)return!1;if(!s.env.footnotes||!s.env.footnotes.refs)return!1;if(91!==s.src.charCodeAt(x))return!1;if(94!==s.src.charCodeAt(x+1))return!1;if(s.level>=s.options.maxNesting)return!1;for(a=x+2;a=w)&&(a++,i=s.src.slice(x+2,a-1),void 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JA={default:{options:{html:!1,xhtmlOut:!1,breaks:!1,langPrefix:"language-",linkTarget:"",typographer:!1,quotes:"“”‘’",highlight:null,maxNesting:20},components:{core:{rules:["block","inline","references","replacements","smartquotes","references","abbr2","footnote_tail"]},block:{rules:["blockquote","code","fences","footnote","heading","hr","htmlblock","lheading","list","paragraph","table"]},inline:{rules:["autolink","backticks","del","emphasis","entity","escape","footnote_ref","htmltag","links","newline","text"]}}},full:{options:{html:!1,xhtmlOut:!1,breaks:!1,langPrefix:"language-",linkTarget:"",typographer:!1,quotes:"“”‘’",highlight:null,maxNesting:20},components:{core:{},block:{},inline:{}}},commonmark:{options:{html:!0,xhtmlOut:!0,breaks:!1,langPrefix:"language-",linkTarget:"",typographer:!1,quotes:"“”‘’",highlight:null,maxNesting:20},components:{core:{rules:["block","inline","references","abbr2"]},block:{rules:["blockquote","code","fences","heading","hr","htmlblock","lheading","list","paragraph"]},inline:{rules:["autolink","backticks","emphasis","entity","escape","htmltag","links","newline","text"]}}}};function StateCore(s,o,i){this.src=o,this.env=i,this.options=s.options,this.tokens=[],this.inlineMode=!1,this.inline=s.inline,this.block=s.block,this.renderer=s.renderer,this.typographer=s.typographer}function Remarkable(s,o){"string"!=typeof s&&(o=s,s="default"),o&&null!=o.linkify&&console.warn("linkify option is removed. Use linkify plugin instead:\n\nimport Remarkable from 'remarkable';\nimport linkify from 'remarkable/linkify';\nnew Remarkable().use(linkify)\n"),this.inline=new ParserInline,this.block=new ParserBlock,this.core=new Core,this.renderer=new Renderer,this.ruler=new Ruler,this.options={},this.configure(JA[s]),this.set(o||{})}Remarkable.prototype.set=function(s){index_browser_assign(this.options,s)},Remarkable.prototype.configure=function(s){var o=this;if(!s)throw new Error("Wrong `remarkable` preset, check name/content");s.options&&o.set(s.options),s.components&&Object.keys(s.components).forEach((function(i){s.components[i].rules&&o[i].ruler.enable(s.components[i].rules,!0)}))},Remarkable.prototype.use=function(s,o){return s(this,o),this},Remarkable.prototype.parse=function(s,o){var i=new StateCore(this,s,o);return this.core.process(i),i.tokens},Remarkable.prototype.render=function(s,o){return o=o||{},this.renderer.render(this.parse(s,o),this.options,o)},Remarkable.prototype.parseInline=function(s,o){var i=new StateCore(this,s,o);return i.inlineMode=!0,this.core.process(i),i.tokens},Remarkable.prototype.renderInline=function(s,o){return o=o||{},this.renderer.render(this.parseInline(s,o),this.options,o)};function indexOf(s,o){if(Array.prototype.indexOf)return s.indexOf(o);for(var i=0,a=s.length;i=0;i--)!0===o(s[i])&&s.splice(i,1)}function throwUnhandledCaseError(s){throw new Error("Unhandled case for value: '".concat(s,"'"))}var HA=function(){function HtmlTag(s){void 0===s&&(s={}),this.tagName="",this.attrs={},this.innerHTML="",this.whitespaceRegex=/\s+/,this.tagName=s.tagName||"",this.attrs=s.attrs||{},this.innerHTML=s.innerHtml||s.innerHTML||""}return HtmlTag.prototype.setTagName=function(s){return this.tagName=s,this},HtmlTag.prototype.getTagName=function(){return this.tagName||""},HtmlTag.prototype.setAttr=function(s,o){return this.getAttrs()[s]=o,this},HtmlTag.prototype.getAttr=function(s){return this.getAttrs()[s]},HtmlTag.prototype.setAttrs=function(s){return Object.assign(this.getAttrs(),s),this},HtmlTag.prototype.getAttrs=function(){return this.attrs||(this.attrs={})},HtmlTag.prototype.setClass=function(s){return this.setAttr("class",s)},HtmlTag.prototype.addClass=function(s){for(var o,i=this.getClass(),a=this.whitespaceRegex,u=i?i.split(a):[],_=s.split(a);o=_.shift();)-1===indexOf(u,o)&&u.push(o);return this.getAttrs().class=u.join(" "),this},HtmlTag.prototype.removeClass=function(s){for(var o,i=this.getClass(),a=this.whitespaceRegex,u=i?i.split(a):[],_=s.split(a);u.length&&(o=_.shift());){var w=indexOf(u,o);-1!==w&&u.splice(w,1)}return this.getAttrs().class=u.join(" "),this},HtmlTag.prototype.getClass=function(){return this.getAttrs().class||""},HtmlTag.prototype.hasClass=function(s){return-1!==(" "+this.getClass()+" ").indexOf(" "+s+" ")},HtmlTag.prototype.setInnerHTML=function(s){return this.innerHTML=s,this},HtmlTag.prototype.setInnerHtml=function(s){return this.setInnerHTML(s)},HtmlTag.prototype.getInnerHTML=function(){return this.innerHTML||""},HtmlTag.prototype.getInnerHtml=function(){return this.getInnerHTML()},HtmlTag.prototype.toAnchorString=function(){var s=this.getTagName(),o=this.buildAttrsStr();return["<",s,o=o?" "+o:"",">",this.getInnerHtml(),""].join("")},HtmlTag.prototype.buildAttrsStr=function(){if(!this.attrs)return"";var s=this.getAttrs(),o=[];for(var i in s)s.hasOwnProperty(i)&&o.push(i+'="'+s[i]+'"');return o.join(" ")},HtmlTag}();var KA=function(){function AnchorTagBuilder(s){void 0===s&&(s={}),this.newWindow=!1,this.truncate={},this.className="",this.newWindow=s.newWindow||!1,this.truncate=s.truncate||{},this.className=s.className||""}return AnchorTagBuilder.prototype.build=function(s){return new HA({tagName:"a",attrs:this.createAttrs(s),innerHtml:this.processAnchorText(s.getAnchorText())})},AnchorTagBuilder.prototype.createAttrs=function(s){var o={href:s.getAnchorHref()},i=this.createCssClass(s);return i&&(o.class=i),this.newWindow&&(o.target="_blank",o.rel="noopener noreferrer"),this.truncate&&this.truncate.length&&this.truncate.length=_)return w.host.length==o?(w.host.substr(0,o-u)+i).substr(0,_+a):buildSegment(C,_).substr(0,_+a);var j="";if(w.path&&(j+="/"+w.path),w.query&&(j+="?"+w.query),j){if((C+j).length>=_)return(C+j).length==o?(C+j).substr(0,o):(C+buildSegment(j,_-C.length)).substr(0,_+a);C+=j}if(w.fragment){var L="#"+w.fragment;if((C+L).length>=_)return(C+L).length==o?(C+L).substr(0,o):(C+buildSegment(L,_-C.length)).substr(0,_+a);C+=L}if(w.scheme&&w.host){var B=w.scheme+"://";if((C+B).length<_)return(B+C).substr(0,o)}if(C.length<=o)return C;var $="";return _>0&&($=C.substr(-1*Math.floor(_/2))),(C.substr(0,Math.ceil(_/2))+i+$).substr(0,_+a)}(s,i):"middle"===a?function truncateMiddle(s,o,i){if(s.length<=o)return s;var a,u;null==i?(i="…",a=8,u=3):(a=i.length,u=i.length);var _=o-u,w="";return _>0&&(w=s.substr(-1*Math.floor(_/2))),(s.substr(0,Math.ceil(_/2))+i+w).substr(0,_+a)}(s,i):function truncateEnd(s,o,i){return function ellipsis(s,o,i){var a;return s.length>o&&(null==i?(i="…",a=3):a=i.length,s=s.substring(0,o-a)+i),s}(s,o,i)}(s,i)},AnchorTagBuilder}(),GA=function(){function Match(s){this.__jsduckDummyDocProp=null,this.matchedText="",this.offset=0,this.tagBuilder=s.tagBuilder,this.matchedText=s.matchedText,this.offset=s.offset}return Match.prototype.getMatchedText=function(){return this.matchedText},Match.prototype.setOffset=function(s){this.offset=s},Match.prototype.getOffset=function(){return this.offset},Match.prototype.getCssClassSuffixes=function(){return[this.getType()]},Match.prototype.buildTag=function(){return this.tagBuilder.build(this)},Match}(),extendStatics=function(s,o){return extendStatics=Object.setPrototypeOf||{__proto__:[]}instanceof Array&&function(s,o){s.__proto__=o}||function(s,o){for(var i in o)Object.prototype.hasOwnProperty.call(o,i)&&(s[i]=o[i])},extendStatics(s,o)};function tslib_es6_extends(s,o){if("function"!=typeof o&&null!==o)throw new TypeError("Class extends value "+String(o)+" is not a constructor or null");function __(){this.constructor=s}extendStatics(s,o),s.prototype=null===o?Object.create(o):(__.prototype=o.prototype,new __)}var __assign=function(){return __assign=Object.assign||function __assign(s){for(var o,i=1,a=arguments.length;i-1},UrlMatchValidator.isValidUriScheme=function(s){var o=s.match(this.uriSchemeRegex),i=o&&o[0].toLowerCase();return"javascript:"!==i&&"vbscript:"!==i},UrlMatchValidator.urlMatchDoesNotHaveProtocolOrDot=function(s,o){return!(!s||o&&this.hasFullProtocolRegex.test(o)||-1!==s.indexOf("."))},UrlMatchValidator.urlMatchDoesNotHaveAtLeastOneWordChar=function(s,o){return!(!s||!o)&&(!this.hasFullProtocolRegex.test(o)&&!this.hasWordCharAfterProtocolRegex.test(s))},UrlMatchValidator.hasFullProtocolRegex=/^[A-Za-z][-.+A-Za-z0-9]*:\/\//,UrlMatchValidator.uriSchemeRegex=/^[A-Za-z][-.+A-Za-z0-9]*:/,UrlMatchValidator.hasWordCharAfterProtocolRegex=new RegExp(":[^\\s]*?["+lC+"]"),UrlMatchValidator.ipRegex=/[0-9][0-9]?[0-9]?\.[0-9][0-9]?[0-9]?\.[0-9][0-9]?[0-9]?\.[0-9][0-9]?[0-9]?(:[0-9]*)?\/?$/,UrlMatchValidator}(),xC=(YA=new RegExp("[/?#](?:["+dC+"\\-+&@#/%=~_()|'$*\\[\\]{}?!:,.;^✓]*["+dC+"\\-+&@#/%=~_()|'$*\\[\\]{}✓])?"),new RegExp(["(?:","(",/(?:[A-Za-z][-.+A-Za-z0-9]{0,63}:(?![A-Za-z][-.+A-Za-z0-9]{0,63}:\/\/)(?!\d+\/?)(?:\/\/)?)/.source,getDomainNameStr(2),")","|","(","(//)?",/(?:www\.)/.source,getDomainNameStr(6),")","|","(","(//)?",getDomainNameStr(10)+"\\.",vC.source,"(?![-"+hC+"])",")",")","(?::[0-9]+)?","(?:"+YA.source+")?"].join(""),"gi")),kC=new RegExp("["+dC+"]"),OC=function(s){function UrlMatcher(o){var i=s.call(this,o)||this;return i.stripPrefix={scheme:!0,www:!0},i.stripTrailingSlash=!0,i.decodePercentEncoding=!0,i.matcherRegex=xC,i.wordCharRegExp=kC,i.stripPrefix=o.stripPrefix,i.stripTrailingSlash=o.stripTrailingSlash,i.decodePercentEncoding=o.decodePercentEncoding,i}return tslib_es6_extends(UrlMatcher,s),UrlMatcher.prototype.parseMatches=function(s){for(var o,i=this.matcherRegex,a=this.stripPrefix,u=this.stripTrailingSlash,_=this.decodePercentEncoding,w=this.tagBuilder,x=[],_loop_1=function(){var i=o[0],j=o[1],L=o[4],B=o[5],$=o[9],U=o.index,V=B||$,z=s.charAt(U-1);if(!wC.isValid(i,j))return"continue";if(U>0&&"@"===z)return"continue";if(U>0&&V&&C.wordCharRegExp.test(z))return"continue";if(/\?$/.test(i)&&(i=i.substr(0,i.length-1)),C.matchHasUnbalancedClosingParen(i))i=i.substr(0,i.length-1);else{var Y=C.matchHasInvalidCharAfterTld(i,j);Y>-1&&(i=i.substr(0,Y))}var Z=["http://","https://"].find((function(s){return!!j&&-1!==j.indexOf(s)}));if(Z){var ee=i.indexOf(Z);i=i.substr(ee),j=j.substr(ee),U+=ee}var ie=j?"scheme":L?"www":"tld",ae=!!j;x.push(new tC({tagBuilder:w,matchedText:i,offset:U,urlMatchType:ie,url:i,protocolUrlMatch:ae,protocolRelativeMatch:!!V,stripPrefix:a,stripTrailingSlash:u,decodePercentEncoding:_}))},C=this;null!==(o=i.exec(s));)_loop_1();return x},UrlMatcher.prototype.matchHasUnbalancedClosingParen=function(s){var o,i=s.charAt(s.length-1);if(")"===i)o="(";else if("]"===i)o="[";else{if("}"!==i)return!1;o="{"}for(var a=0,u=0,_=s.length-1;u<_;u++){var w=s.charAt(u);w===o?a++:w===i&&(a=Math.max(a-1,0))}return 0===a},UrlMatcher.prototype.matchHasInvalidCharAfterTld=function(s,o){if(!s)return-1;var i=0;o&&(i=s.indexOf(":"),s=s.slice(i));var a=new RegExp("^((.?//)?[-."+dC+"]*[-"+dC+"]\\.[-"+dC+"]+)").exec(s);return null===a?-1:(i+=a[1].length,s=s.slice(a[1].length),/^[^-.A-Za-z0-9:\/?#]/.test(s)?i:-1)},UrlMatcher}(rC),AC=new RegExp("[_".concat(dC,"]")),CC=function(s){function HashtagMatcher(o){var i=s.call(this,o)||this;return i.serviceName="twitter",i.serviceName=o.serviceName,i}return tslib_es6_extends(HashtagMatcher,s),HashtagMatcher.prototype.parseMatches=function(s){for(var o=this.tagBuilder,i=this.serviceName,a=[],u=s.length,_=0,w=-1,x=0;_-1&&_-w<=140){var u=s.slice(w,_),x=new QA({tagBuilder:o,matchedText:u,offset:w,serviceName:i,hashtag:u.slice(1)});a.push(x)}}},HashtagMatcher}(rC),jC=["twitter","facebook","instagram","tiktok"],PC=new RegExp("".concat(/(?:(?:(?:(\+)?\d{1,3}[-\040.]?)?\(?\d{3}\)?[-\040.]?\d{3}[-\040.]?\d{4})|(?:(\+)(?:9[976]\d|8[987530]\d|6[987]\d|5[90]\d|42\d|3[875]\d|2[98654321]\d|9[8543210]|8[6421]|6[6543210]|5[87654321]|4[987654310]|3[9643210]|2[70]|7|1)[-\040.]?(?:\d[-\040.]?){6,12}\d+))([,;]+[0-9]+#?)*/.source,"|").concat(/(0([1-9]{1}-?[1-9]\d{3}|[1-9]{2}-?\d{3}|[1-9]{2}\d{1}-?\d{2}|[1-9]{2}\d{2}-?\d{1})-?\d{4}|0[789]0-?\d{4}-?\d{4}|050-?\d{4}-?\d{4})/.source),"g"),IC=function(s){function PhoneMatcher(){var o=null!==s&&s.apply(this,arguments)||this;return o.matcherRegex=PC,o}return tslib_es6_extends(PhoneMatcher,s),PhoneMatcher.prototype.parseMatches=function(s){for(var o,i=this.matcherRegex,a=this.tagBuilder,u=[];null!==(o=i.exec(s));){var _=o[0],w=_.replace(/[^0-9,;#]/g,""),x=!(!o[1]&&!o[2]),C=0==o.index?"":s.substr(o.index-1,1),j=s.substr(o.index+_.length,1),L=!C.match(/\d/)&&!j.match(/\d/);this.testMatch(o[3])&&this.testMatch(_)&&L&&u.push(new eC({tagBuilder:a,matchedText:_,offset:o.index,number:w,plusSign:x}))}return u},PhoneMatcher.prototype.testMatch=function(s){return oC.test(s)},PhoneMatcher}(rC),TC=new RegExp("@[_".concat(dC,"]{1,50}(?![_").concat(dC,"])"),"g"),NC=new RegExp("@[_.".concat(dC,"]{1,30}(?![_").concat(dC,"])"),"g"),MC=new RegExp("@[-_.".concat(dC,"]{1,50}(?![-_").concat(dC,"])"),"g"),RC=new RegExp("@[_.".concat(dC,"]{1,23}[_").concat(dC,"](?![_").concat(dC,"])"),"g"),DC=new RegExp("[^"+dC+"]"),LC=function(s){function MentionMatcher(o){var i=s.call(this,o)||this;return i.serviceName="twitter",i.matcherRegexes={twitter:TC,instagram:NC,soundcloud:MC,tiktok:RC},i.nonWordCharRegex=DC,i.serviceName=o.serviceName,i}return tslib_es6_extends(MentionMatcher,s),MentionMatcher.prototype.parseMatches=function(s){var o,i=this.serviceName,a=this.matcherRegexes[this.serviceName],u=this.nonWordCharRegex,_=this.tagBuilder,w=[];if(!a)return w;for(;null!==(o=a.exec(s));){var x=o.index,C=s.charAt(x-1);if(0===x||u.test(C)){var j=o[0].replace(/\.+$/g,""),L=j.slice(1);w.push(new ZA({tagBuilder:_,matchedText:j,offset:x,serviceName:i,mention:L}))}}return w},MentionMatcher}(rC);function parseHtml(s,o){for(var i=o.onOpenTag,a=o.onCloseTag,u=o.onText,_=o.onComment,w=o.onDoctype,x=new FC,C=0,j=s.length,L=0,B=0,$=x;C"===s?($=new FC(__assign(__assign({},$),{name:captureTagName()})),emitTagAndPreviousTextNode()):nC.test(s)||sC.test(s)||":"===s||resetToDataState()}function stateEndTagOpen(s){">"===s?resetToDataState():nC.test(s)?L=3:resetToDataState()}function stateBeforeAttributeName(s){iC.test(s)||("/"===s?L=12:">"===s?emitTagAndPreviousTextNode():"<"===s?startNewTag():"="===s||aC.test(s)||cC.test(s)?resetToDataState():L=5)}function stateAttributeName(s){iC.test(s)?L=6:"/"===s?L=12:"="===s?L=7:">"===s?emitTagAndPreviousTextNode():"<"===s?startNewTag():aC.test(s)&&resetToDataState()}function stateAfterAttributeName(s){iC.test(s)||("/"===s?L=12:"="===s?L=7:">"===s?emitTagAndPreviousTextNode():"<"===s?startNewTag():aC.test(s)?resetToDataState():L=5)}function stateBeforeAttributeValue(s){iC.test(s)||('"'===s?L=8:"'"===s?L=9:/[>=`]/.test(s)?resetToDataState():"<"===s?startNewTag():L=10)}function stateAttributeValueDoubleQuoted(s){'"'===s&&(L=11)}function stateAttributeValueSingleQuoted(s){"'"===s&&(L=11)}function stateAttributeValueUnquoted(s){iC.test(s)?L=4:">"===s?emitTagAndPreviousTextNode():"<"===s&&startNewTag()}function stateAfterAttributeValueQuoted(s){iC.test(s)?L=4:"/"===s?L=12:">"===s?emitTagAndPreviousTextNode():"<"===s?startNewTag():(L=4,function reconsumeCurrentCharacter(){C--}())}function stateSelfClosingStartTag(s){">"===s?($=new FC(__assign(__assign({},$),{isClosing:!0})),emitTagAndPreviousTextNode()):L=4}function stateMarkupDeclarationOpen(o){"--"===s.substr(C,2)?(C+=2,$=new FC(__assign(__assign({},$),{type:"comment"})),L=14):"DOCTYPE"===s.substr(C,7).toUpperCase()?(C+=7,$=new FC(__assign(__assign({},$),{type:"doctype"})),L=20):resetToDataState()}function stateCommentStart(s){"-"===s?L=15:">"===s?resetToDataState():L=16}function stateCommentStartDash(s){"-"===s?L=18:">"===s?resetToDataState():L=16}function stateComment(s){"-"===s&&(L=17)}function stateCommentEndDash(s){L="-"===s?18:16}function stateCommentEnd(s){">"===s?emitTagAndPreviousTextNode():"!"===s?L=19:"-"===s||(L=16)}function stateCommentEndBang(s){"-"===s?L=17:">"===s?emitTagAndPreviousTextNode():L=16}function stateDoctype(s){">"===s?emitTagAndPreviousTextNode():"<"===s&&startNewTag()}function resetToDataState(){L=0,$=x}function startNewTag(){L=1,$=new FC({idx:C})}function emitTagAndPreviousTextNode(){var o=s.slice(B,$.idx);o&&u(o,B),"comment"===$.type?_($.idx):"doctype"===$.type?w($.idx):($.isOpening&&i($.name,$.idx),$.isClosing&&a($.name,$.idx)),resetToDataState(),B=C+1}function captureTagName(){var o=$.idx+($.isClosing?2:1);return s.slice(o,C).toLowerCase()}B=0&&a++},onText:function(s,i){if(0===a){var _=function splitAndCapture(s,o){if(!o.global)throw new Error("`splitRegex` must have the 'g' flag set");for(var i,a=[],u=0;i=o.exec(s);)a.push(s.substring(u,i.index)),a.push(i[0]),u=i.index+i[0].length;return a.push(s.substring(u)),a}(s,/( | |<|<|>|>|"|"|')/gi),w=i;_.forEach((function(s,i){if(i%2==0){var a=o.parseText(s,w);u.push.apply(u,a)}w+=s.length}))}},onCloseTag:function(s){i.indexOf(s)>=0&&(a=Math.max(a-1,0))},onComment:function(s){},onDoctype:function(s){}}),u=this.compactMatches(u),u=this.removeUnwantedMatches(u)},Autolinker.prototype.compactMatches=function(s){s.sort((function(s,o){return s.getOffset()-o.getOffset()}));for(var o=0;ou?o:o+1;s.splice(w,1);continue}if(s[o+1].getOffset()<_){s.splice(o+1,1);continue}}o++}return s},Autolinker.prototype.removeUnwantedMatches=function(s){return this.hashtag||utils_remove(s,(function(s){return"hashtag"===s.getType()})),this.email||utils_remove(s,(function(s){return"email"===s.getType()})),this.phone||utils_remove(s,(function(s){return"phone"===s.getType()})),this.mention||utils_remove(s,(function(s){return"mention"===s.getType()})),this.urls.schemeMatches||utils_remove(s,(function(s){return"url"===s.getType()&&"scheme"===s.getUrlMatchType()})),this.urls.wwwMatches||utils_remove(s,(function(s){return"url"===s.getType()&&"www"===s.getUrlMatchType()})),this.urls.tldMatches||utils_remove(s,(function(s){return"url"===s.getType()&&"tld"===s.getUrlMatchType()})),s},Autolinker.prototype.parseText=function(s,o){void 0===o&&(o=0),o=o||0;for(var i=this.getMatchers(),a=[],u=0,_=i.length;u<_;u++){for(var w=i[u].parseMatches(s),x=0,C=w.length;x/g,">"));for(var o=this.parse(s),i=[],a=0,u=0,_=o.length;u<_;u++){var w=o[u];i.push(s.substring(a,w.getOffset())),i.push(this.createMatchReturnVal(w)),a=w.getOffset()+w.getMatchedText().length}return i.push(s.substring(a)),i.join("")},Autolinker.prototype.createMatchReturnVal=function(s){var o;return this.replaceFn&&(o=this.replaceFn.call(this.context,s)),"string"==typeof o?o:!1===o?s.getMatchedText():o instanceof HA?o.toAnchorString():s.buildTag().toAnchorString()},Autolinker.prototype.getMatchers=function(){if(this.matchers)return this.matchers;var s=this.getTagBuilder(),o=[new CC({tagBuilder:s,serviceName:this.hashtag}),new SC({tagBuilder:s}),new IC({tagBuilder:s}),new LC({tagBuilder:s,serviceName:this.mention}),new OC({tagBuilder:s,stripPrefix:this.stripPrefix,stripTrailingSlash:this.stripTrailingSlash,decodePercentEncoding:this.decodePercentEncoding})];return this.matchers=o},Autolinker.prototype.getTagBuilder=function(){var s=this.tagBuilder;return s||(s=this.tagBuilder=new KA({newWindow:this.newWindow,truncate:this.truncate,className:this.className})),s},Autolinker.version="3.16.2",Autolinker.AnchorTagBuilder=KA,Autolinker.HtmlTag=HA,Autolinker.matcher={Email:SC,Hashtag:CC,Matcher:rC,Mention:LC,Phone:IC,Url:OC},Autolinker.match={Email:XA,Hashtag:QA,Match:GA,Mention:ZA,Phone:eC,Url:tC},Autolinker}();const $C=BC;var qC=/www|@|\:\/\//;function isLinkOpen(s){return/^\s]/i.test(s)}function isLinkClose(s){return/^<\/a\s*>/i.test(s)}function createLinkifier(){var s=[],o=new $C({stripPrefix:!1,url:!0,email:!0,replaceFn:function(o){switch(o.getType()){case"url":s.push({text:o.matchedText,url:o.getUrl()});break;case"email":s.push({text:o.matchedText,url:"mailto:"+o.getEmail().replace(/^mailto:/i,"")})}return!1}});return{links:s,autolinker:o}}function parseTokens(s){var 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Re.Component{render(){const{errSelectors:s,specSelectors:o,getComponent:i}=this.props,a=i("SvgAssets"),u=i("InfoContainer",!0),_=i("VersionPragmaFilter"),w=i("operations",!0),x=i("Models",!0),C=i("Webhooks",!0),j=i("Row"),L=i("Col"),B=i("errors",!0),$=i("ServersContainer",!0),U=i("SchemesContainer",!0),V=i("AuthorizeBtnContainer",!0),z=i("FilterContainer",!0),Y=o.isSwagger2(),Z=o.isOAS3(),ee=o.isOAS31(),ie=!o.specStr(),ae=o.loadingStatus();let ce=null;if("loading"===ae&&(ce=Re.createElement("div",{className:"info"},Re.createElement("div",{className:"loading-container"},Re.createElement("div",{className:"loading"})))),"failed"===ae&&(ce=Re.createElement("div",{className:"info"},Re.createElement("div",{className:"loading-container"},Re.createElement("h4",{className:"title"},"Failed to load API definition."),Re.createElement(B,null)))),"failedConfig"===ae){const o=s.lastError(),i=o?o.get("message"):"";ce=Re.createElement("div",{className:"info 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core_components=()=>({components:{App:KO,authorizationPopup:AuthorizationPopup,authorizeBtn:AuthorizeBtn,AuthorizeBtnContainer,authorizeOperationBtn:AuthorizeOperationBtn,auths:Auths,AuthItem:auth_item_Auths,authError:AuthError,oauth2:Oauth2,apiKeyAuth:ApiKeyAuth,basicAuth:BasicAuth,clear:Clear,liveResponse:LiveResponse,InitializedInput,info:nA,InfoContainer,InfoUrl,InfoBasePath,Contact:sA,License:oA,JumpToPath,CopyToClipboardBtn,onlineValidatorBadge:OnlineValidatorBadge,operations:Operations,operation:operation_Operation,OperationSummary,OperationSummaryMethod,OperationSummaryPath,responses:responses_Responses,response:response_Response,ResponseExtension:response_extension,responseBody:ResponseBody,parameters:Parameters,parameterRow:ParameterRow,execute:Execute,headers:headers_Headers,errors:Errors,contentType:ContentType,overview:Overview,footer:Footer,FilterContainer,ParamBody,curl:Curl,Property:property,TryItOutButton,Markdown:AP,BaseLayout,VersionPragmaFilter,VersionStamp:version_stamp,OperationExt:operation_extensions,OperationExtRow:operation_extension_row,ParameterExt:parameter_extension,ParameterIncludeEmpty,OperationTag,OperationContainer,OpenAPIVersion:openapi_version,DeepLink:deep_link,SvgAssets:svg_assets,Example:example_Example,ExamplesSelect,ExamplesSelectValueRetainer}}),form_components=()=>({components:{..._e}}),base=()=>[configsPlugin,util,logs,view,view_legacy,plugins_spec,err,icons,plugins_layout,json_schema_5,json_schema_5_samples,core_components,form_components,swagger_client,auth,downloadUrlPlugin,deep_linking,filter,on_complete,plugins_request_snippets,syntax_highlighting,versions,safe_render()],CP=(0,ze.Map)();function onlyOAS3(s){return(o,i)=>(...a)=>{if(i.getSystem().specSelectors.isOAS3()){const o=s(...a);return"function"==typeof o?o(i):o}return o(...a)}}const jP=onlyOAS3(xs()(null)),PP=onlyOAS3(((s,o)=>s=>s.getSystem().specSelectors.findSchema(o))),IP=onlyOAS3((()=>s=>{const o=s.getSystem().specSelectors.specJson().getIn(["components","schemas"]);return ze.Map.isMap(o)?o:CP})),TP=onlyOAS3((()=>s=>s.getSystem().specSelectors.specJson().hasIn(["servers",0]))),NP=onlyOAS3(Ut(Ns,(s=>s.getIn(["components","securitySchemes"])||null))),wrap_selectors_validOperationMethods=(s,o)=>(i,...a)=>o.specSelectors.isOAS3()?o.oas3Selectors.validOperationMethods():s(...a),MP=jP,RP=jP,DP=jP,LP=jP,FP=jP;const BP=function wrap_selectors_onlyOAS3(s){return(o,i)=>(...a)=>{if(i.getSystem().specSelectors.isOAS3()){let o=i.getState().getIn(["spec","resolvedSubtrees","components","securitySchemes"]);return s(i,o,...a)}return o(...a)}}(Ut((s=>s),(({specSelectors:s})=>s.securityDefinitions()),((s,o)=>{let i=(0,ze.List)();return o?(o.entrySeq().forEach((([s,o])=>{const a=o?.get("type");if("oauth2"===a&&o.get("flows").entrySeq().forEach((([a,u])=>{let _=(0,ze.fromJS)({flow:a,authorizationUrl:u.get("authorizationUrl"),tokenUrl:u.get("tokenUrl"),scopes:u.get("scopes"),type:o.get("type"),description:o.get("description")});i=i.push(new ze.Map({[s]:_.filter((s=>void 0!==s))}))})),"http"!==a&&"apiKey"!==a||(i=i.push(new ze.Map({[s]:o}))),"openIdConnect"===a&&o.get("openIdConnectData")){let a=o.get("openIdConnectData");(a.get("grant_types_supported")||["authorization_code","implicit"]).forEach((u=>{let _=a.get("scopes_supported")&&a.get("scopes_supported").reduce(((s,o)=>s.set(o,"")),new ze.Map),w=(0,ze.fromJS)({flow:u,authorizationUrl:a.get("authorization_endpoint"),tokenUrl:a.get("token_endpoint"),scopes:_,type:"oauth2",openIdConnectUrl:o.get("openIdConnectUrl")});i=i.push(new ze.Map({[s]:w.filter((s=>void 0!==s))}))}))}})),i):i})));function OAS3ComponentWrapFactory(s){return(o,i)=>a=>"function"==typeof i.specSelectors?.isOAS3?i.specSelectors.isOAS3()?Re.createElement(s,Mn()({},a,i,{Ori:o})):Re.createElement(o,a):(console.warn("OAS3 wrapper: couldn't get spec"),null)}const $P=(0,ze.Map)(),selectors_isSwagger2=()=>s=>function isSwagger2(s){const o=s.get("swagger");return"string"==typeof o&&"2.0"===o}(s.getSystem().specSelectors.specJson()),selectors_isOAS30=()=>s=>function isOAS30(s){const o=s.get("openapi");return"string"==typeof o&&/^3\.0\.(?:[1-9]\d*|0)$/.test(o)}(s.getSystem().specSelectors.specJson()),selectors_isOAS3=()=>s=>s.getSystem().specSelectors.isOAS30();function selectors_onlyOAS3(s){return(o,...i)=>a=>{if(a.specSelectors.isOAS3()){const u=s(o,...i);return"function"==typeof u?u(a):u}return null}}const qP=selectors_onlyOAS3((()=>s=>s.specSelectors.specJson().get("servers",$P))),findSchema=(s,o)=>{const 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callbacks"):Re.createElement("div",null,_.map((s=>Re.createElement("div",{key:`${s}`},Re.createElement("h2",null,s),u[s].map((o=>Re.createElement(w,{key:`${s}-${o.path}-${o.method}`,op:o.operation,tag:"callbacks",method:o.method,path:o.path,specPath:o.specPath,allowTryItOut:!1})))))))},getDefaultRequestBodyValue=(s,o,i,a)=>{const u=s.getIn(["content",o])??(0,ze.OrderedMap)(),_=u.get("schema",(0,ze.OrderedMap)()).toJS(),w=void 0!==u.get("examples"),x=u.get("example"),C=w?u.getIn(["examples",i,"value"]):x;return stringify(a.getSampleSchema(_,o,{includeWriteOnly:!0},C))},components_request_body=({userHasEditedBody:s,requestBody:o,requestBodyValue:i,requestBodyInclusionSetting:a,requestBodyErrors:u,getComponent:_,getConfigs:w,specSelectors:x,fn:C,contentType:j,isExecute:L,specPath:B,onChange:$,onChangeIncludeEmpty:U,activeExamplesKey:V,updateActiveExamplesKey:z,setRetainRequestBodyValueFlag:Y})=>{const handleFile=s=>{$(s.target.files[0])},setIsIncludedOptions=s=>{let o={key:s,shouldDispatchInit:!1,defaultValue:!0};return"no value"===a.get(s,"no value")&&(o.shouldDispatchInit=!0),o},Z=_("Markdown",!0),ee=_("modelExample"),ie=_("RequestBodyEditor"),ae=_("HighlightCode",!0),ce=_("ExamplesSelectValueRetainer"),le=_("Example"),pe=_("ParameterIncludeEmpty"),{showCommonExtensions:de}=w(),fe=o?.get("description")??null,ye=o?.get("content")??new ze.OrderedMap;j=j||ye.keySeq().first()||"";const be=ye.get(j)??(0,ze.OrderedMap)(),_e=be.get("schema",(0,ze.OrderedMap)()),Se=be.get("examples",null),we=Se?.map(((s,i)=>{const a=s?.get("value",null);return a&&(s=s.set("value",getDefaultRequestBodyValue(o,j,i,C),a)),s}));u=ze.List.isList(u)?u:(0,ze.List)();if(C.isFileUploadIntended(be?.get("schema"),j)){const s=_("Input");return L?Re.createElement(s,{type:"file",onChange:handleFile}):Re.createElement("i",null,"Example values are not available for ",Re.createElement("code",null,j)," media types.")}if(!be.size)return null;if(C.hasSchemaType(be.get("schema"),"object")&&("application/x-www-form-urlencoded"===j||0===j.indexOf("multipart/"))&&_e.get("properties",(0,ze.OrderedMap)()).size>0){const s=_("JsonSchemaForm"),o=_("ParameterExt"),j=_e.get("properties",(0,ze.OrderedMap)());return i=ze.Map.isMap(i)?i:(0,ze.OrderedMap)(),Re.createElement("div",{className:"table-container"},fe&&Re.createElement(Z,{source:fe}),Re.createElement("table",null,Re.createElement("tbody",null,ze.Map.isMap(j)&&j.entrySeq().map((([j,V])=>{if(V.get("readOnly"))return;const z=V.get("oneOf")?.get(0)?.toJS(),Y=V.get("anyOf")?.get(0)?.toJS();V=(0,ze.fromJS)(C.mergeJsonSchema(V.toJS(),z??Y??{}));let ie=de?getCommonExtensions(V):null;const ae=_e.get("required",(0,ze.List)()).includes(j),ce=C.getSchemaObjectType(V),le=C.getSchemaObjectTypeLabel(V),fe=C.getSchemaObjectType(V?.get("items")),ye=V.get("format"),be=V.get("description"),Se=i.getIn([j,"value"]),we=i.getIn([j,"errors"])||u,xe=a.get(j)||!1;let Pe=C.getSampleSchema(V,!1,{includeWriteOnly:!0});!1===Pe&&(Pe="false"),0===Pe&&(Pe="0"),"string"!=typeof Pe&&"object"===ce&&(Pe=stringify(Pe)),"string"==typeof Pe&&"array"===ce&&(Pe=JSON.parse(Pe));const Te=C.isFileUploadIntended(V),$e=Re.createElement(s,{fn:C,dispatchInitialValue:!Te,schema:V,description:j,getComponent:_,value:void 0===Se?Pe:Se,required:ae,errors:we,onChange:s=>{$(s,[j])}});return Re.createElement("tr",{key:j,className:"parameters","data-property-name":j},Re.createElement("td",{className:"parameters-col_name"},Re.createElement("div",{className:ae?"parameter__name required":"parameter__name"},j,ae?Re.createElement("span",null," *"):null),Re.createElement("div",{className:"parameter__type"},le,ye&&Re.createElement("span",{className:"prop-format"},"($",ye,")"),de&&ie.size?ie.entrySeq().map((([s,i])=>Re.createElement(o,{key:`${s}-${i}`,xKey:s,xVal:i}))):null),Re.createElement("div",{className:"parameter__deprecated"},V.get("deprecated")?"deprecated":null)),Re.createElement("td",{className:"parameters-col_description"},Re.createElement(Z,{source:be}),L?Re.createElement("div",null,"object"===ce||"object"===fe?Re.createElement(ee,{getComponent:_,specPath:B.push("schema"),getConfigs:w,isExecute:L,specSelectors:x,schema:V,example:$e}):$e,ae?null:Re.createElement(pe,{onChange:s=>U(j,s),isIncluded:xe,isIncludedOptions:setIsIncludedOptions(j),isDisabled:Array.isArray(Se)?0!==Se.length:!isEmptyValue(Se)})):null))})))))}const xe=getDefaultRequestBodyValue(o,j,V,C);let Pe=null;getKnownSyntaxHighlighterLanguage(xe)&&(Pe="json");const Te=L?Re.createElement(ie,{value:i,errors:u,defaultValue:xe,onChange:$,getComponent:_}):Re.createElement(ae,{className:"body-param__example",language:Pe},stringify(i)||xe);return Re.createElement("div",null,fe&&Re.createElement(Z,{source:fe}),we?Re.createElement(ce,{userHasEditedBody:s,examples:we,currentKey:V,currentUserInputValue:i,onSelect:s=>{z(s)},updateValue:$,defaultToFirstExample:!0,getComponent:_,setRetainRequestBodyValueFlag:Y}):null,Re.createElement(ee,{getComponent:_,getConfigs:w,specSelectors:x,expandDepth:1,isExecute:L,schema:be.get("schema"),specPath:B.push("content",j,"schema"),example:Te,includeWriteOnly:!0}),we?Re.createElement(le,{example:we.get(V),getComponent:_,getConfigs:w}):null)};class operation_link_OperationLink extends Re.Component{render(){const{link:s,name:o,getComponent:i}=this.props,a=i("Markdown",!0);let u=s.get("operationId")||s.get("operationRef"),_=s.get("parameters")&&s.get("parameters").toJS(),w=s.get("description");return Re.createElement("div",{className:"operation-link"},Re.createElement("div",{className:"description"},Re.createElement("b",null,Re.createElement("code",null,o)),w?Re.createElement(a,{source:w}):null),Re.createElement("pre",null,"Operation `",u,"`",Re.createElement("br",null),Re.createElement("br",null),"Parameters ",function padString(s,o){if("string"!=typeof o)return"";return o.split("\n").map(((o,i)=>i>0?Array(s+1).join(" ")+o:o)).join("\n")}(0,JSON.stringify(_,null,2))||"{}",Re.createElement("br",null)))}}const VP=operation_link_OperationLink,components_servers=({servers:s,currentServer:o,setSelectedServer:i,setServerVariableValue:a,getServerVariable:u,getEffectiveServerValue:_})=>{const w=(s.find((s=>s.get("url")===o))||(0,ze.OrderedMap)()).get("variables")||(0,ze.OrderedMap)(),x=0!==w.size;(0,Re.useEffect)((()=>{o||i(s.first()?.get("url"))}),[]),(0,Re.useEffect)((()=>{const u=s.find((s=>s.get("url")===o));if(!u)return void i(s.first().get("url"));(u.get("variables")||(0,ze.OrderedMap)()).map(((s,i)=>{a({server:o,key:i,val:s.get("default")||""})}))}),[o,s]);const C=(0,Re.useCallback)((s=>{i(s.target.value)}),[i]),j=(0,Re.useCallback)((s=>{const i=s.target.getAttribute("data-variable"),u=s.target.value;a({server:o,key:i,val:u})}),[a,o]);return Re.createElement("div",{className:"servers"},Re.createElement("label",{htmlFor:"servers"},Re.createElement("select",{onChange:C,value:o,id:"servers"},s.valueSeq().map((s=>Re.createElement("option",{value:s.get("url"),key:s.get("url")},s.get("url"),s.get("description")&&` - ${s.get("description")}`))).toArray())),x&&Re.createElement("div",null,Re.createElement("div",{className:"computed-url"},"Computed URL:",Re.createElement("code",null,_(o))),Re.createElement("h4",null,"Server variables"),Re.createElement("table",null,Re.createElement("tbody",null,w.entrySeq().map((([s,i])=>Re.createElement("tr",{key:s},Re.createElement("td",null,s),Re.createElement("td",null,i.get("enum")?Re.createElement("select",{"data-variable":s,onChange:j},i.get("enum").map((i=>Re.createElement("option",{selected:i===u(o,s),key:i,value:i},i)))):Re.createElement("input",{type:"text",value:u(o,s)||"",onChange:j,"data-variable":s})))))))))};class ServersContainer extends Re.Component{render(){const{specSelectors:s,oas3Selectors:o,oas3Actions:i,getComponent:a}=this.props,u=s.servers(),_=a("Servers");return u&&u.size?Re.createElement("div",null,Re.createElement("span",{className:"servers-title"},"Servers"),Re.createElement(_,{servers:u,currentServer:o.selectedServer(),setSelectedServer:i.setSelectedServer,setServerVariableValue:i.setServerVariableValue,getServerVariable:o.serverVariableValue,getEffectiveServerValue:o.serverEffectiveValue})):null}}const zP=Function.prototype;class RequestBodyEditor extends Re.PureComponent{static defaultProps={onChange:zP,userHasEditedBody:!1};constructor(s,o){super(s,o),this.state={value:stringify(s.value)||s.defaultValue},s.onChange(s.value)}applyDefaultValue=s=>{const{onChange:o,defaultValue:i}=s||this.props;return this.setState({value:i}),o(i)};onChange=s=>{this.props.onChange(stringify(s))};onDomChange=s=>{const o=s.target.value;this.setState({value:o},(()=>this.onChange(o)))};UNSAFE_componentWillReceiveProps(s){this.props.value!==s.value&&s.value!==this.state.value&&this.setState({value:stringify(s.value)}),!s.value&&s.defaultValue&&this.state.value&&this.applyDefaultValue(s)}render(){let{getComponent:s,errors:o}=this.props,{value:i}=this.state,a=o.size>0;const u=s("TextArea");return Re.createElement("div",{className:"body-param"},Re.createElement(u,{className:Jn()("body-param__text",{invalid:a}),title:o.size?o.join(", "):"",value:i,onChange:this.onDomChange}))}}class HttpAuth extends Re.Component{constructor(s,o){super(s,o);let{name:i,schema:a}=this.props,u=this.getValue();this.state={name:i,schema:a,value:u}}getValue(){let{name:s,authorized:o}=this.props;return o&&o.getIn([s,"value"])}onChange=s=>{let{onChange:o}=this.props,{value:i,name:a}=s.target,u=Object.assign({},this.state.value);a?u[a]=i:u=i,this.setState({value:u},(()=>o(this.state)))};render(){let{schema:s,getComponent:o,errSelectors:i,name:a,authSelectors:u}=this.props;const _=o("Input"),w=o("Row"),x=o("Col"),C=o("authError"),j=o("Markdown",!0),L=o("JumpToPath",!0),B=(s.get("scheme")||"").toLowerCase(),$=u.selectAuthPath(a);let U=this.getValue(),V=i.allErrors().filter((s=>s.get("authId")===a));if("basic"===B){let o=U?U.get("username"):null;return Re.createElement("div",null,Re.createElement("h4",null,Re.createElement("code",null,a),"  (http, Basic)",Re.createElement(L,{path:$})),o&&Re.createElement("h6",null,"Authorized"),Re.createElement(w,null,Re.createElement(j,{source:s.get("description")})),Re.createElement(w,null,Re.createElement("label",{htmlFor:"auth-basic-username"},"Username:"),o?Re.createElement("code",null," ",o," "):Re.createElement(x,null,Re.createElement(_,{id:"auth-basic-username",type:"text",required:"required",name:"username","aria-label":"auth-basic-username",onChange:this.onChange,autoFocus:!0}))),Re.createElement(w,null,Re.createElement("label",{htmlFor:"auth-basic-password"},"Password:"),o?Re.createElement("code",null," ****** "):Re.createElement(x,null,Re.createElement(_,{id:"auth-basic-password",autoComplete:"new-password",name:"password",type:"password","aria-label":"auth-basic-password",onChange:this.onChange}))),V.valueSeq().map(((s,o)=>Re.createElement(C,{error:s,key:o}))))}return"bearer"===B?Re.createElement("div",null,Re.createElement("h4",null,Re.createElement("code",null,a),"  (http, Bearer)",Re.createElement(L,{path:$})),U&&Re.createElement("h6",null,"Authorized"),Re.createElement(w,null,Re.createElement(j,{source:s.get("description")})),Re.createElement(w,null,Re.createElement("label",{htmlFor:"auth-bearer-value"},"Value:"),U?Re.createElement("code",null," ****** "):Re.createElement(x,null,Re.createElement(_,{id:"auth-bearer-value",type:"text","aria-label":"auth-bearer-value",onChange:this.onChange,autoFocus:!0}))),V.valueSeq().map(((s,o)=>Re.createElement(C,{error:s,key:o})))):Re.createElement("div",null,Re.createElement("em",null,Re.createElement("b",null,a)," HTTP authentication: unsupported scheme ",`'${B}'`))}}class operation_servers_OperationServers extends Re.Component{setSelectedServer=s=>{const{path:o,method:i}=this.props;return this.forceUpdate(),this.props.setSelectedServer(s,`${o}:${i}`)};setServerVariableValue=s=>{const{path:o,method:i}=this.props;return this.forceUpdate(),this.props.setServerVariableValue({...s,namespace:`${o}:${i}`})};getSelectedServer=()=>{const{path:s,method:o}=this.props;return this.props.getSelectedServer(`${s}:${o}`)};getServerVariable=(s,o)=>{const{path:i,method:a}=this.props;return this.props.getServerVariable({namespace:`${i}:${a}`,server:s},o)};getEffectiveServerValue=s=>{const{path:o,method:i}=this.props;return this.props.getEffectiveServerValue({server:s,namespace:`${o}:${i}`})};render(){const{operationServers:s,pathServers:o,getComponent:i}=this.props;if(!s&&!o)return null;const a=i("Servers"),u=s||o,_=s?"operation":"path";return Re.createElement("div",{className:"opblock-section operation-servers"},Re.createElement("div",{className:"opblock-section-header"},Re.createElement("div",{className:"tab-header"},Re.createElement("h4",{className:"opblock-title"},"Servers"))),Re.createElement("div",{className:"opblock-description-wrapper"},Re.createElement("h4",{className:"message"},"These ",_,"-level options 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null})),KP=OAS3ComponentWrapFactory((({Ori:s,...o})=>{const{schema:i,getComponent:a,errSelectors:u,authorized:_,onAuthChange:w,name:x,authSelectors:C}=o,j=a("HttpAuth");return"http"===i.get("type")?Re.createElement(j,{key:x,schema:i,name:x,errSelectors:u,authorized:_,getComponent:a,onChange:w,authSelectors:C}):Re.createElement(s,o)})),GP=OAS3ComponentWrapFactory(OnlineValidatorBadge);class ModelComponent extends Re.Component{render(){let{getConfigs:s,schema:o,Ori:i}=this.props,a=["model-box"],u=null;return!0===o.get("deprecated")&&(a.push("deprecated"),u=Re.createElement("span",{className:"model-deprecated-warning"},"Deprecated:")),Re.createElement("div",{className:a.join(" ")},u,Re.createElement(i,Mn()({},this.props,{getConfigs:s,depth:1,expandDepth:this.props.expandDepth||0})))}}const YP=OAS3ComponentWrapFactory(ModelComponent),XP=OAS3ComponentWrapFactory((({Ori:s,...o})=>{const{schema:i,getComponent:a,errors:u,onChange:_,fn:w}=o,x=w.isFileUploadIntended(i),C=a("Input");return x?Re.createElement(C,{type:"file",className:u.length?"invalid":"",title:u.length?u:"",onChange:s=>{_(s.target.files[0])},disabled:s.isDisabled}):Re.createElement(s,o)})),QP={Markdown:HP,AuthItem:KP,OpenAPIVersion:function OAS30ComponentWrapFactory(s){return(o,i)=>a=>"function"==typeof i.specSelectors?.isOAS30?i.specSelectors.isOAS30()?Re.createElement(s,Mn()({},a,i,{Ori:o})):Re.createElement(o,a):(console.warn("OAS30 wrapper: couldn't get spec"),null)}((s=>{const{Ori:o}=s;return Re.createElement(o,{oasVersion:"3.0"})})),JsonSchema_string:XP,model:YP,onlineValidatorBadge:GP},ZP="oas3_set_servers",eI="oas3_set_request_body_value",tI="oas3_set_request_body_retain_flag",rI="oas3_set_request_body_inclusion",nI="oas3_set_active_examples_member",sI="oas3_set_request_content_type",oI="oas3_set_response_content_type",iI="oas3_set_server_variable_value",aI="oas3_set_request_body_validate_error",cI="oas3_clear_request_body_validate_error",lI="oas3_clear_request_body_value";function setSelectedServer(s,o){return{type:ZP,payload:{selectedServerUrl:s,namespace:o}}}function setRequestBodyValue({value:s,pathMethod:o}){return{type:eI,payload:{value:s,pathMethod:o}}}const setRetainRequestBodyValueFlag=({value:s,pathMethod:o})=>({type:tI,payload:{value:s,pathMethod:o}});function setRequestBodyInclusion({value:s,pathMethod:o,name:i}){return{type:rI,payload:{value:s,pathMethod:o,name:i}}}function setActiveExamplesMember({name:s,pathMethod:o,contextType:i,contextName:a}){return{type:nI,payload:{name:s,pathMethod:o,contextType:i,contextName:a}}}function setRequestContentType({value:s,pathMethod:o}){return{type:sI,payload:{value:s,pathMethod:o}}}function setResponseContentType({value:s,path:o,method:i}){return{type:oI,payload:{value:s,path:o,method:i}}}function setServerVariableValue({server:s,namespace:o,key:i,val:a}){return{type:iI,payload:{server:s,namespace:o,key:i,val:a}}}const setRequestBodyValidateError=({path:s,method:o,validationErrors:i})=>({type:aI,payload:{path:s,method:o,validationErrors:i}}),clearRequestBodyValidateError=({path:s,method:o})=>({type:cI,payload:{path:s,method:o}}),initRequestBodyValidateError=({pathMethod:s})=>({type:cI,payload:{path:s[0],method:s[1]}}),clearRequestBodyValue=({pathMethod:s})=>({type:lI,payload:{pathMethod:s}});var uI=__webpack_require__(60680),pI=__webpack_require__.n(uI);const oas3_selectors_onlyOAS3=s=>(o,...i)=>a=>{if(a.getSystem().specSelectors.isOAS3()){const u=s(o,...i);return"function"==typeof u?u(a):u}return null};const hI=oas3_selectors_onlyOAS3(((s,o)=>{const i=o?[o,"selectedServer"]:["selectedServer"];return 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+from ascii_colors import ASCIIColors +from lightrag.api import __api_version__ as api_version +from lightrag import __version__ as core_version +from lightrag.constants import ( + DEFAULT_FORCE_LLM_SUMMARY_ON_MERGE, +) +from fastapi import HTTPException, Security, Request, status +from fastapi.security import APIKeyHeader, OAuth2PasswordBearer +from starlette.status import HTTP_403_FORBIDDEN +from .auth import auth_handler +from .config import ollama_server_infos, global_args, get_env_value + + +def check_env_file(): + """ + Check if .env file exists and handle user confirmation if needed. + Returns True if should continue, False if should exit. + """ + if not os.path.exists(".env"): + warning_msg = "Warning: Startup directory must contain .env file for multi-instance support." + ASCIIColors.yellow(warning_msg) + + # Check if running in interactive terminal + if sys.stdin.isatty(): + response = input("Do you want to continue? (yes/no): ") + if response.lower() != "yes": + ASCIIColors.red("Server startup cancelled") + return False + return True + + +# Get whitelist paths from global_args, only once during initialization +whitelist_paths = global_args.whitelist_paths.split(",") + +# Pre-compile path matching patterns +whitelist_patterns: List[Tuple[str, bool]] = [] +for path in whitelist_paths: + path = path.strip() + if path: + # If path ends with /*, match all paths with that prefix + if path.endswith("/*"): + prefix = path[:-2] + whitelist_patterns.append((prefix, True)) # (prefix, is_prefix_match) + else: + whitelist_patterns.append((path, False)) # (exact_path, is_prefix_match) + +# Global authentication configuration +auth_configured = bool(auth_handler.accounts) + + +def get_combined_auth_dependency(api_key: Optional[str] = None): + """ + Create a combined authentication dependency that implements authentication logic + based on API key, OAuth2 token, and whitelist paths. + + Args: + api_key (Optional[str]): API key for validation + + Returns: + Callable: A dependency function that implements the authentication logic + """ + # Use global whitelist_patterns and auth_configured variables + # whitelist_patterns and auth_configured are already initialized at module level + + # Only calculate api_key_configured as it depends on the function parameter + api_key_configured = bool(api_key) + + # Create security dependencies with proper descriptions for Swagger UI + oauth2_scheme = OAuth2PasswordBearer( + tokenUrl="login", auto_error=False, description="OAuth2 Password Authentication" + ) + + # If API key is configured, create an API key header security + api_key_header = None + if api_key_configured: + api_key_header = APIKeyHeader( + name="X-API-Key", auto_error=False, description="API Key Authentication" + ) + + async def combined_dependency( + request: Request, + token: str = Security(oauth2_scheme), + api_key_header_value: Optional[str] = None + if api_key_header is None + else Security(api_key_header), + ): + # 1. Check if path is in whitelist + path = request.url.path + for pattern, is_prefix in whitelist_patterns: + if (is_prefix and path.startswith(pattern)) or ( + not is_prefix and path == pattern + ): + return # Whitelist path, allow access + + # 2. Validate token first if provided in the request (Ensure 401 error if token is invalid) + if token: + try: + token_info = auth_handler.validate_token(token) + # Accept guest token if no auth is configured + if not auth_configured and token_info.get("role") == "guest": + return + # Accept non-guest token if auth is configured + if auth_configured and token_info.get("role") != "guest": + return + + # Token validation failed, immediately return 401 error + raise HTTPException( + status_code=status.HTTP_401_UNAUTHORIZED, + detail="Invalid token. Please login again.", + ) + except HTTPException as e: + # If already a 401 error, re-raise it + if e.status_code == status.HTTP_401_UNAUTHORIZED: + raise + # For other exceptions, continue processing + + # 3. Acept all request if no API protection needed + if not auth_configured and not api_key_configured: + return + + # 4. Validate API key if provided and API-Key authentication is configured + if ( + api_key_configured + and api_key_header_value + and api_key_header_value == api_key + ): + return # API key validation successful + + ### Authentication failed #### + + # if password authentication is configured but not provided, ensure 401 error if auth_configured + if auth_configured and not token: + raise HTTPException( + status_code=status.HTTP_401_UNAUTHORIZED, + detail="No credentials provided. Please login.", + ) + + # if api key is provided but validation failed + if api_key_header_value: + raise HTTPException( + status_code=HTTP_403_FORBIDDEN, + detail="Invalid API Key", + ) + + # if api_key_configured but not provided + if api_key_configured and not api_key_header_value: + raise HTTPException( + status_code=HTTP_403_FORBIDDEN, + detail="API Key required", + ) + + # Otherwise: refuse access and return 403 error + raise HTTPException( + status_code=HTTP_403_FORBIDDEN, + detail="API Key required or login authentication required.", + ) + + return combined_dependency + + +def display_splash_screen(args: argparse.Namespace) -> None: + """ + Display a colorful splash screen showing LightRAG server configuration + + Args: + args: Parsed command line arguments + """ + # Banner + # Banner + top_border = "╔══════════════════════════════════════════════════════════════╗" + bottom_border = "╚══════════════════════════════════════════════════════════════╝" + width = len(top_border) - 4 # width inside the borders + + line1_text = f"LightRAG Server v{core_version}/{api_version}" + line2_text = "Fast, Lightweight RAG Server Implementation" + + line1 = f"║ {line1_text.center(width)} ║" + line2 = f"║ {line2_text.center(width)} ║" + + banner = f""" + {top_border} + {line1} + {line2} + {bottom_border} + """ + ASCIIColors.cyan(banner) + + # Server Configuration + ASCIIColors.magenta("\n📡 Server Configuration:") + ASCIIColors.white(" ├─ Host: ", end="") + ASCIIColors.yellow(f"{args.host}") + ASCIIColors.white(" ├─ Port: ", end="") + ASCIIColors.yellow(f"{args.port}") + ASCIIColors.white(" ├─ Workers: ", end="") + ASCIIColors.yellow(f"{args.workers}") + ASCIIColors.white(" ├─ Timeout: ", end="") + ASCIIColors.yellow(f"{args.timeout}") + ASCIIColors.white(" ├─ CORS Origins: ", end="") + ASCIIColors.yellow(f"{args.cors_origins}") + ASCIIColors.white(" ├─ SSL Enabled: ", end="") + ASCIIColors.yellow(f"{args.ssl}") + if args.ssl: + ASCIIColors.white(" ├─ SSL Cert: ", end="") + ASCIIColors.yellow(f"{args.ssl_certfile}") + ASCIIColors.white(" ├─ SSL Key: ", end="") + ASCIIColors.yellow(f"{args.ssl_keyfile}") + ASCIIColors.white(" ├─ Ollama Emulating Model: ", end="") + ASCIIColors.yellow(f"{ollama_server_infos.LIGHTRAG_MODEL}") + ASCIIColors.white(" ├─ Log Level: ", end="") + ASCIIColors.yellow(f"{args.log_level}") + ASCIIColors.white(" ├─ Verbose Debug: ", end="") + ASCIIColors.yellow(f"{args.verbose}") + ASCIIColors.white(" ├─ API Key: ", end="") + ASCIIColors.yellow("Set" if args.key else "Not Set") + ASCIIColors.white(" └─ JWT Auth: ", end="") + ASCIIColors.yellow("Enabled" if args.auth_accounts else "Disabled") + + # Directory Configuration + ASCIIColors.magenta("\n📂 Directory Configuration:") + ASCIIColors.white(" ├─ Working Directory: ", end="") + ASCIIColors.yellow(f"{args.working_dir}") + ASCIIColors.white(" └─ Input Directory: ", end="") + ASCIIColors.yellow(f"{args.input_dir}") + + # LLM Configuration + ASCIIColors.magenta("\n🤖 LLM Configuration:") + ASCIIColors.white(" ├─ Binding: ", end="") + ASCIIColors.yellow(f"{args.llm_binding}") + ASCIIColors.white(" ├─ Host: ", end="") + ASCIIColors.yellow(f"{args.llm_binding_host}") + ASCIIColors.white(" ├─ Model: ", end="") + ASCIIColors.yellow(f"{args.llm_model}") + ASCIIColors.white(" ├─ Max Async for LLM: ", end="") + ASCIIColors.yellow(f"{args.max_async}") + ASCIIColors.white(" ├─ Summary Context Size: ", end="") + ASCIIColors.yellow(f"{args.summary_context_size}") + ASCIIColors.white(" ├─ LLM Cache Enabled: ", end="") + ASCIIColors.yellow(f"{args.enable_llm_cache}") + ASCIIColors.white(" └─ LLM Cache for Extraction Enabled: ", end="") + ASCIIColors.yellow(f"{args.enable_llm_cache_for_extract}") + + # Embedding Configuration + ASCIIColors.magenta("\n📊 Embedding Configuration:") + ASCIIColors.white(" ├─ Binding: ", end="") + ASCIIColors.yellow(f"{args.embedding_binding}") + ASCIIColors.white(" ├─ Host: ", end="") + ASCIIColors.yellow(f"{args.embedding_binding_host}") + ASCIIColors.white(" ├─ Model: ", end="") + ASCIIColors.yellow(f"{args.embedding_model}") + ASCIIColors.white(" └─ Dimensions: ", end="") + ASCIIColors.yellow(f"{args.embedding_dim}") + + # RAG Configuration + ASCIIColors.magenta("\n⚙️ RAG Configuration:") + ASCIIColors.white(" ├─ Summary Language: ", end="") + ASCIIColors.yellow(f"{args.summary_language}") + ASCIIColors.white(" ├─ Entity Types: ", end="") + ASCIIColors.yellow(f"{args.entity_types}") + ASCIIColors.white(" ├─ Max Parallel Insert: ", end="") + ASCIIColors.yellow(f"{args.max_parallel_insert}") + ASCIIColors.white(" ├─ Chunk Size: ", end="") + ASCIIColors.yellow(f"{args.chunk_size}") + ASCIIColors.white(" ├─ Chunk Overlap Size: ", end="") + ASCIIColors.yellow(f"{args.chunk_overlap_size}") + ASCIIColors.white(" ├─ Cosine Threshold: ", end="") + ASCIIColors.yellow(f"{args.cosine_threshold}") + ASCIIColors.white(" ├─ Top-K: ", end="") + ASCIIColors.yellow(f"{args.top_k}") + ASCIIColors.white(" └─ Force LLM Summary on Merge: ", end="") + ASCIIColors.yellow( + f"{get_env_value('FORCE_LLM_SUMMARY_ON_MERGE', DEFAULT_FORCE_LLM_SUMMARY_ON_MERGE, int)}" + ) + + # System Configuration + ASCIIColors.magenta("\n💾 Storage Configuration:") + ASCIIColors.white(" ├─ KV Storage: ", end="") + ASCIIColors.yellow(f"{args.kv_storage}") + ASCIIColors.white(" ├─ Vector Storage: ", end="") + ASCIIColors.yellow(f"{args.vector_storage}") + ASCIIColors.white(" ├─ Graph Storage: ", end="") + ASCIIColors.yellow(f"{args.graph_storage}") + ASCIIColors.white(" ├─ Document Status Storage: ", end="") + ASCIIColors.yellow(f"{args.doc_status_storage}") + ASCIIColors.white(" └─ Workspace: ", end="") + ASCIIColors.yellow(f"{args.workspace if args.workspace else '-'}") + + # Server Status + ASCIIColors.green("\n✨ Server starting up...\n") + + # Server Access Information + protocol = "https" if args.ssl else "http" + if args.host == "0.0.0.0": + ASCIIColors.magenta("\n🌐 Server Access Information:") + ASCIIColors.white(" ├─ WebUI (local): ", end="") + ASCIIColors.yellow(f"{protocol}://localhost:{args.port}") + ASCIIColors.white(" ├─ Remote Access: ", end="") + ASCIIColors.yellow(f"{protocol}://:{args.port}") + ASCIIColors.white(" ├─ API Documentation (local): ", end="") + ASCIIColors.yellow(f"{protocol}://localhost:{args.port}/docs") + ASCIIColors.white(" └─ Alternative Documentation (local): ", end="") + ASCIIColors.yellow(f"{protocol}://localhost:{args.port}/redoc") + + ASCIIColors.magenta("\n📝 Note:") + ASCIIColors.cyan(""" Since the server is running on 0.0.0.0: + - Use 'localhost' or '127.0.0.1' for local access + - Use your machine's IP address for remote access + - To find your IP address: + • Windows: Run 'ipconfig' in terminal + • Linux/Mac: Run 'ifconfig' or 'ip addr' in terminal + """) + else: + base_url = f"{protocol}://{args.host}:{args.port}" + ASCIIColors.magenta("\n🌐 Server Access Information:") + ASCIIColors.white(" ├─ WebUI (local): ", end="") + ASCIIColors.yellow(f"{base_url}") + ASCIIColors.white(" ├─ API Documentation: ", end="") + ASCIIColors.yellow(f"{base_url}/docs") + ASCIIColors.white(" └─ Alternative Documentation: ", end="") + ASCIIColors.yellow(f"{base_url}/redoc") + + # Security Notice + if args.key: + ASCIIColors.yellow("\n⚠️ Security Notice:") + ASCIIColors.white(""" API Key authentication is enabled. + Make sure to include the X-API-Key header in all your requests. + """) + if args.auth_accounts: + ASCIIColors.yellow("\n⚠️ Security Notice:") + ASCIIColors.white(""" JWT authentication is enabled. + Make sure to login before making the request, and include the 'Authorization' in the header. + """) + + # Ensure splash output flush to system log + sys.stdout.flush() diff --git a/LightRAG/lightrag/base.py b/LightRAG/lightrag/base.py new file mode 100644 index 00000000..bae0728b --- /dev/null +++ b/LightRAG/lightrag/base.py @@ -0,0 +1,869 @@ +from __future__ import annotations + +from abc import ABC, abstractmethod +from enum import Enum +import os +from dotenv import load_dotenv +from dataclasses import dataclass, field +from typing import ( + Any, + Literal, + TypedDict, + TypeVar, + Callable, + Optional, + Dict, + List, + AsyncIterator, +) +from .utils import EmbeddingFunc +from .types import KnowledgeGraph +from .constants import ( + DEFAULT_TOP_K, + DEFAULT_CHUNK_TOP_K, + DEFAULT_MAX_ENTITY_TOKENS, + DEFAULT_MAX_RELATION_TOKENS, + DEFAULT_MAX_TOTAL_TOKENS, + DEFAULT_HISTORY_TURNS, + DEFAULT_OLLAMA_MODEL_NAME, + DEFAULT_OLLAMA_MODEL_TAG, + DEFAULT_OLLAMA_MODEL_SIZE, + DEFAULT_OLLAMA_CREATED_AT, + DEFAULT_OLLAMA_DIGEST, +) + +# use the .env that is inside the current folder +# allows to use different .env file for each lightrag instance +# the OS environment variables take precedence over the .env file +load_dotenv(dotenv_path=".env", override=False) + + +class OllamaServerInfos: + def __init__(self, name=None, tag=None): + self._lightrag_name = name or os.getenv( + "OLLAMA_EMULATING_MODEL_NAME", DEFAULT_OLLAMA_MODEL_NAME + ) + self._lightrag_tag = tag or os.getenv( + "OLLAMA_EMULATING_MODEL_TAG", DEFAULT_OLLAMA_MODEL_TAG + ) + self.LIGHTRAG_SIZE = DEFAULT_OLLAMA_MODEL_SIZE + self.LIGHTRAG_CREATED_AT = DEFAULT_OLLAMA_CREATED_AT + self.LIGHTRAG_DIGEST = DEFAULT_OLLAMA_DIGEST + + @property + def LIGHTRAG_NAME(self): + return self._lightrag_name + + @LIGHTRAG_NAME.setter + def LIGHTRAG_NAME(self, value): + self._lightrag_name = value + + @property + def LIGHTRAG_TAG(self): + return self._lightrag_tag + + @LIGHTRAG_TAG.setter + def LIGHTRAG_TAG(self, value): + self._lightrag_tag = value + + @property + def LIGHTRAG_MODEL(self): + return f"{self._lightrag_name}:{self._lightrag_tag}" + + +class TextChunkSchema(TypedDict): + tokens: int + content: str + full_doc_id: str + chunk_order_index: int + + +T = TypeVar("T") + + +@dataclass +class QueryParam: + """Configuration parameters for query execution in LightRAG.""" + + mode: Literal["local", "global", "hybrid", "naive", "mix", "bypass"] = "mix" + """Specifies the retrieval mode: + - "local": Focuses on context-dependent information. + - "global": Utilizes global knowledge. + - "hybrid": Combines local and global retrieval methods. + - "naive": Performs a basic search without advanced techniques. + - "mix": Integrates knowledge graph and vector retrieval. + """ + + only_need_context: bool = False + """If True, only returns the retrieved context without generating a response.""" + + only_need_prompt: bool = False + """If True, only returns the generated prompt without producing a response.""" + + response_type: str = "Multiple Paragraphs" + """Defines the response format. Examples: 'Multiple Paragraphs', 'Single Paragraph', 'Bullet Points'.""" + + stream: bool = False + """If True, enables streaming output for real-time responses.""" + + top_k: int = int(os.getenv("TOP_K", str(DEFAULT_TOP_K))) + """Number of top items to retrieve. Represents entities in 'local' mode and relationships in 'global' mode.""" + + chunk_top_k: int = int(os.getenv("CHUNK_TOP_K", str(DEFAULT_CHUNK_TOP_K))) + """Number of text chunks to retrieve initially from vector search and keep after reranking. + If None, defaults to top_k value. + """ + + max_entity_tokens: int = int( + os.getenv("MAX_ENTITY_TOKENS", str(DEFAULT_MAX_ENTITY_TOKENS)) + ) + """Maximum number of tokens allocated for entity context in unified token control system.""" + + max_relation_tokens: int = int( + os.getenv("MAX_RELATION_TOKENS", str(DEFAULT_MAX_RELATION_TOKENS)) + ) + """Maximum number of tokens allocated for relationship context in unified token control system.""" + + max_total_tokens: int = int( + os.getenv("MAX_TOTAL_TOKENS", str(DEFAULT_MAX_TOTAL_TOKENS)) + ) + """Maximum total tokens budget for the entire query context (entities + relations + chunks + system prompt).""" + + hl_keywords: list[str] = field(default_factory=list) + """List of high-level keywords to prioritize in retrieval.""" + + ll_keywords: list[str] = field(default_factory=list) + """List of low-level keywords to refine retrieval focus.""" + + # History mesages is only send to LLM for context, not used for retrieval + conversation_history: list[dict[str, str]] = field(default_factory=list) + """Stores past conversation history to maintain context. + Format: [{"role": "user/assistant", "content": "message"}]. + """ + + # TODO: deprecated. No longer used in the codebase, all conversation_history messages is send to LLM + history_turns: int = int(os.getenv("HISTORY_TURNS", str(DEFAULT_HISTORY_TURNS))) + """Number of complete conversation turns (user-assistant pairs) to consider in the response context.""" + + model_func: Callable[..., object] | None = None + """Optional override for the LLM model function to use for this specific query. + If provided, this will be used instead of the global model function. + This allows using different models for different query modes. + """ + + user_prompt: str | None = None + """User-provided prompt for the query. + Addition instructions for LLM. If provided, this will be inject into the prompt template. + It's purpose is the let user customize the way LLM generate the response. + """ + + enable_rerank: bool = os.getenv("RERANK_BY_DEFAULT", "true").lower() == "true" + """Enable reranking for retrieved text chunks. If True but no rerank model is configured, a warning will be issued. + Default is True to enable reranking when rerank model is available. + """ + + include_references: bool = False + """If True, includes reference list in the response for supported endpoints. + This parameter controls whether the API response includes a references field + containing citation information for the retrieved content. + """ + + +@dataclass +class StorageNameSpace(ABC): + namespace: str + workspace: str + global_config: dict[str, Any] + + async def initialize(self): + """Initialize the storage""" + pass + + async def finalize(self): + """Finalize the storage""" + pass + + @abstractmethod + async def index_done_callback(self) -> None: + """Commit the storage operations after indexing""" + + @abstractmethod + async def drop(self) -> dict[str, str]: + """Drop all data from storage and clean up resources + + This abstract method defines the contract for dropping all data from a storage implementation. + Each storage type must implement this method to: + 1. Clear all data from memory and/or external storage + 2. Remove any associated storage files if applicable + 3. Reset the storage to its initial state + 4. Handle cleanup of any resources + 5. Notify other processes if necessary + 6. This action should persistent the data to disk immediately. + + Returns: + dict[str, str]: Operation status and message with the following format: + { + "status": str, # "success" or "error" + "message": str # "data dropped" on success, error details on failure + } + + Implementation specific: + - On success: return {"status": "success", "message": "data dropped"} + - On failure: return {"status": "error", "message": ""} + - If not supported: return {"status": "error", "message": "unsupported"} + """ + + +@dataclass +class BaseVectorStorage(StorageNameSpace, ABC): + embedding_func: EmbeddingFunc + cosine_better_than_threshold: float = field(default=0.2) + meta_fields: set[str] = field(default_factory=set) + + @abstractmethod + async def query( + self, query: str, top_k: int, query_embedding: list[float] = None + ) -> list[dict[str, Any]]: + """Query the vector storage and retrieve top_k results. + + Args: + query: The query string to search for + top_k: Number of top results to return + query_embedding: Optional pre-computed embedding for the query. + If provided, skips embedding computation for better performance. + """ + + @abstractmethod + async def upsert(self, data: dict[str, dict[str, Any]]) -> None: + """Insert or update vectors in the storage. + + Importance notes for in-memory storage: + 1. Changes will be persisted to disk during the next index_done_callback + 2. Only one process should updating the storage at a time before index_done_callback, + KG-storage-log should be used to avoid data corruption + """ + + @abstractmethod + async def delete_entity(self, entity_name: str) -> None: + """Delete a single entity by its name. + + Importance notes for in-memory storage: + 1. Changes will be persisted to disk during the next index_done_callback + 2. Only one process should updating the storage at a time before index_done_callback, + KG-storage-log should be used to avoid data corruption + """ + + @abstractmethod + async def delete_entity_relation(self, entity_name: str) -> None: + """Delete relations for a given entity. + + Importance notes for in-memory storage: + 1. Changes will be persisted to disk during the next index_done_callback + 2. Only one process should updating the storage at a time before index_done_callback, + KG-storage-log should be used to avoid data corruption + """ + + @abstractmethod + async def get_by_id(self, id: str) -> dict[str, Any] | None: + """Get vector data by its ID + + Args: + id: The unique identifier of the vector + + Returns: + The vector data if found, or None if not found + """ + pass + + @abstractmethod + async def get_by_ids(self, ids: list[str]) -> list[dict[str, Any]]: + """Get multiple vector data by their IDs + + Args: + ids: List of unique identifiers + + Returns: + List of vector data objects that were found + """ + pass + + @abstractmethod + async def delete(self, ids: list[str]): + """Delete vectors with specified IDs + + Importance notes for in-memory storage: + 1. Changes will be persisted to disk during the next index_done_callback + 2. Only one process should updating the storage at a time before index_done_callback, + KG-storage-log should be used to avoid data corruption + + Args: + ids: List of vector IDs to be deleted + """ + + @abstractmethod + async def get_vectors_by_ids(self, ids: list[str]) -> dict[str, list[float]]: + """Get vectors by their IDs, returning only ID and vector data for efficiency + + Args: + ids: List of unique identifiers + + Returns: + Dictionary mapping IDs to their vector embeddings + Format: {id: [vector_values], ...} + """ + pass + + +@dataclass +class BaseKVStorage(StorageNameSpace, ABC): + embedding_func: EmbeddingFunc + + @abstractmethod + async def get_by_id(self, id: str) -> dict[str, Any] | None: + """Get value by id""" + + @abstractmethod + async def get_by_ids(self, ids: list[str]) -> list[dict[str, Any]]: + """Get values by ids""" + + @abstractmethod + async def filter_keys(self, keys: set[str]) -> set[str]: + """Return un-exist keys""" + + @abstractmethod + async def upsert(self, data: dict[str, dict[str, Any]]) -> None: + """Upsert data + + Importance notes for in-memory storage: + 1. Changes will be persisted to disk during the next index_done_callback + 2. update flags to notify other processes that data persistence is needed + """ + + @abstractmethod + async def delete(self, ids: list[str]) -> None: + """Delete specific records from storage by their IDs + + Importance notes for in-memory storage: + 1. Changes will be persisted to disk during the next index_done_callback + 2. update flags to notify other processes that data persistence is needed + + Args: + ids (list[str]): List of document IDs to be deleted from storage + + Returns: + None + """ + + @abstractmethod + async def is_empty(self) -> bool: + """Check if the storage is empty + + Returns: + bool: True if storage contains no data, False otherwise + """ + + +@dataclass +class BaseGraphStorage(StorageNameSpace, ABC): + """All operations related to edges in graph should be undirected.""" + + embedding_func: EmbeddingFunc + + @abstractmethod + async def has_node(self, node_id: str) -> bool: + """Check if a node exists in the graph. + + Args: + node_id: The ID of the node to check + + Returns: + True if the node exists, False otherwise + """ + + @abstractmethod + async def has_edge(self, source_node_id: str, target_node_id: str) -> bool: + """Check if an edge exists between two nodes. + + Args: + source_node_id: The ID of the source node + target_node_id: The ID of the target node + + Returns: + True if the edge exists, False otherwise + """ + + @abstractmethod + async def node_degree(self, node_id: str) -> int: + """Get the degree (number of connected edges) of a node. + + Args: + node_id: The ID of the node + + Returns: + The number of edges connected to the node + """ + + @abstractmethod + async def edge_degree(self, src_id: str, tgt_id: str) -> int: + """Get the total degree of an edge (sum of degrees of its source and target nodes). + + Args: + src_id: The ID of the source node + tgt_id: The ID of the target node + + Returns: + The sum of the degrees of the source and target nodes + """ + + @abstractmethod + async def get_node(self, node_id: str) -> dict[str, str] | None: + """Get node by its ID, returning only node properties. + + Args: + node_id: The ID of the node to retrieve + + Returns: + A dictionary of node properties if found, None otherwise + """ + + @abstractmethod + async def get_edge( + self, source_node_id: str, target_node_id: str + ) -> dict[str, str] | None: + """Get edge properties between two nodes. + + Args: + source_node_id: The ID of the source node + target_node_id: The ID of the target node + + Returns: + A dictionary of edge properties if found, None otherwise + """ + + @abstractmethod + async def get_node_edges(self, source_node_id: str) -> list[tuple[str, str]] | None: + """Get all edges connected to a node. + + Args: + source_node_id: The ID of the node to get edges for + + Returns: + A list of (source_id, target_id) tuples representing edges, + or None if the node doesn't exist + """ + + async def get_nodes_batch(self, node_ids: list[str]) -> dict[str, dict]: + """Get nodes as a batch using UNWIND + + Default implementation fetches nodes one by one. + Override this method for better performance in storage backends + that support batch operations. + """ + result = {} + for node_id in node_ids: + node = await self.get_node(node_id) + if node is not None: + result[node_id] = node + return result + + async def node_degrees_batch(self, node_ids: list[str]) -> dict[str, int]: + """Node degrees as a batch using UNWIND + + Default implementation fetches node degrees one by one. + Override this method for better performance in storage backends + that support batch operations. + """ + result = {} + for node_id in node_ids: + degree = await self.node_degree(node_id) + result[node_id] = degree + return result + + async def edge_degrees_batch( + self, edge_pairs: list[tuple[str, str]] + ) -> dict[tuple[str, str], int]: + """Edge degrees as a batch using UNWIND also uses node_degrees_batch + + Default implementation calculates edge degrees one by one. + Override this method for better performance in storage backends + that support batch operations. + """ + result = {} + for src_id, tgt_id in edge_pairs: + degree = await self.edge_degree(src_id, tgt_id) + result[(src_id, tgt_id)] = degree + return result + + async def get_edges_batch( + self, pairs: list[dict[str, str]] + ) -> dict[tuple[str, str], dict]: + """Get edges as a batch using UNWIND + + Default implementation fetches edges one by one. + Override this method for better performance in storage backends + that support batch operations. + """ + result = {} + for pair in pairs: + src_id = pair["src"] + tgt_id = pair["tgt"] + edge = await self.get_edge(src_id, tgt_id) + if edge is not None: + result[(src_id, tgt_id)] = edge + return result + + async def get_nodes_edges_batch( + self, node_ids: list[str] + ) -> dict[str, list[tuple[str, str]]]: + """Get nodes edges as a batch using UNWIND + + Default implementation fetches node edges one by one. + Override this method for better performance in storage backends + that support batch operations. + """ + result = {} + for node_id in node_ids: + edges = await self.get_node_edges(node_id) + result[node_id] = edges if edges is not None else [] + return result + + @abstractmethod + async def upsert_node(self, node_id: str, node_data: dict[str, str]) -> None: + """Insert a new node or update an existing node in the graph. + + Importance notes for in-memory storage: + 1. Changes will be persisted to disk during the next index_done_callback + 2. Only one process should updating the storage at a time before index_done_callback, + KG-storage-log should be used to avoid data corruption + + Args: + node_id: The ID of the node to insert or update + node_data: A dictionary of node properties + """ + + @abstractmethod + async def upsert_edge( + self, source_node_id: str, target_node_id: str, edge_data: dict[str, str] + ) -> None: + """Insert a new edge or update an existing edge in the graph. + + Importance notes for in-memory storage: + 1. Changes will be persisted to disk during the next index_done_callback + 2. Only one process should updating the storage at a time before index_done_callback, + KG-storage-log should be used to avoid data corruption + + Args: + source_node_id: The ID of the source node + target_node_id: The ID of the target node + edge_data: A dictionary of edge properties + """ + + @abstractmethod + async def delete_node(self, node_id: str) -> None: + """Delete a node from the graph. + + Importance notes for in-memory storage: + 1. Changes will be persisted to disk during the next index_done_callback + 2. Only one process should updating the storage at a time before index_done_callback, + KG-storage-log should be used to avoid data corruption + + Args: + node_id: The ID of the node to delete + """ + + @abstractmethod + async def remove_nodes(self, nodes: list[str]): + """Delete multiple nodes + + Importance notes: + 1. Changes will be persisted to disk during the next index_done_callback + 2. Only one process should updating the storage at a time before index_done_callback, + KG-storage-log should be used to avoid data corruption + + Args: + nodes: List of node IDs to be deleted + """ + + @abstractmethod + async def remove_edges(self, edges: list[tuple[str, str]]): + """Delete multiple edges + + Importance notes: + 1. Changes will be persisted to disk during the next index_done_callback + 2. Only one process should updating the storage at a time before index_done_callback, + KG-storage-log should be used to avoid data corruption + + Args: + edges: List of edges to be deleted, each edge is a (source, target) tuple + """ + + # TODO: deprecated + @abstractmethod + async def get_all_labels(self) -> list[str]: + """Get all labels in the graph. + + Returns: + A list of all node labels in the graph, sorted alphabetically + """ + + @abstractmethod + async def get_knowledge_graph( + self, node_label: str, max_depth: int = 3, max_nodes: int = 1000 + ) -> KnowledgeGraph: + """ + Retrieve a connected subgraph of nodes where the label includes the specified `node_label`. + + Args: + node_label: Label of the starting node，* means all nodes + max_depth: Maximum depth of the subgraph, Defaults to 3 + max_nodes: Maxiumu nodes to return, Defaults to 1000（BFS if possible) + + Returns: + KnowledgeGraph object containing nodes and edges, with an is_truncated flag + indicating whether the graph was truncated due to max_nodes limit + """ + + @abstractmethod + async def get_all_nodes(self) -> list[dict]: + """Get all nodes in the graph. + + Returns: + A list of all nodes, where each node is a dictionary of its properties + (Edge is bidirectional for some storage implementation; deduplication must be handled by the caller) + """ + + @abstractmethod + async def get_all_edges(self) -> list[dict]: + """Get all edges in the graph. + + Returns: + A list of all edges, where each edge is a dictionary of its properties + """ + + @abstractmethod + async def get_popular_labels(self, limit: int = 300) -> list[str]: + """Get popular labels by node degree (most connected entities) + + Args: + limit: Maximum number of labels to return + + Returns: + List of labels sorted by degree (highest first) + """ + + @abstractmethod + async def search_labels(self, query: str, limit: int = 50) -> list[str]: + """Search labels with fuzzy matching + + Args: + query: Search query string + limit: Maximum number of results to return + + Returns: + List of matching labels sorted by relevance + """ + + +class DocStatus(str, Enum): + """Document processing status""" + + PENDING = "pending" + PROCESSING = "processing" + PREPROCESSED = "preprocessed" + PROCESSED = "processed" + FAILED = "failed" + + +@dataclass +class DocProcessingStatus: + """Document processing status data structure""" + + content_summary: str + """First 100 chars of document content, used for preview""" + content_length: int + """Total length of document""" + file_path: str + """File path of the document""" + status: DocStatus + """Current processing status""" + created_at: str + """ISO format timestamp when document was created""" + updated_at: str + """ISO format timestamp when document was last updated""" + track_id: str | None = None + """Tracking ID for monitoring progress""" + chunks_count: int | None = None + """Number of chunks after splitting, used for processing""" + chunks_list: list[str] | None = field(default_factory=list) + """List of chunk IDs associated with this document, used for deletion""" + error_msg: str | None = None + """Error message if failed""" + metadata: dict[str, Any] = field(default_factory=dict) + """Additional metadata""" + multimodal_processed: bool | None = field(default=None, repr=False) + """Internal field: indicates if multimodal processing is complete. Not shown in repr() but accessible for debugging.""" + + def __post_init__(self): + """ + Handle status conversion based on multimodal_processed field. + + Business rules: + - If multimodal_processed is False and status is PROCESSED, + then change status to PREPROCESSED + - The multimodal_processed field is kept (with repr=False) for internal use and debugging + """ + # Apply status conversion logic + if self.multimodal_processed is not None: + if ( + self.multimodal_processed is False + and self.status == DocStatus.PROCESSED + ): + self.status = DocStatus.PREPROCESSED + + +@dataclass +class DocStatusStorage(BaseKVStorage, ABC): + """Base class for document status storage""" + + @abstractmethod + async def get_status_counts(self) -> dict[str, int]: + """Get counts of documents in each status""" + + @abstractmethod + async def get_docs_by_status( + self, status: DocStatus + ) -> dict[str, DocProcessingStatus]: + """Get all documents with a specific status""" + + @abstractmethod + async def get_docs_by_track_id( + self, track_id: str + ) -> dict[str, DocProcessingStatus]: + """Get all documents with a specific track_id""" + + @abstractmethod + async def get_docs_paginated( + self, + status_filter: DocStatus | None = None, + page: int = 1, + page_size: int = 50, + sort_field: str = "updated_at", + sort_direction: str = "desc", + ) -> tuple[list[tuple[str, DocProcessingStatus]], int]: + """Get documents with pagination support + + Args: + status_filter: Filter by document status, None for all statuses + page: Page number (1-based) + page_size: Number of documents per page (10-200) + sort_field: Field to sort by ('created_at', 'updated_at', 'id') + sort_direction: Sort direction ('asc' or 'desc') + + Returns: + Tuple of (list of (doc_id, DocProcessingStatus) tuples, total_count) + """ + + @abstractmethod + async def get_all_status_counts(self) -> dict[str, int]: + """Get counts of documents in each status for all documents + + Returns: + Dictionary mapping status names to counts + """ + + @abstractmethod + async def get_doc_by_file_path(self, file_path: str) -> dict[str, Any] | None: + """Get document by file path + + Args: + file_path: The file path to search for + + Returns: + dict[str, Any] | None: Document data if found, None otherwise + Returns the same format as get_by_ids method + """ + + +class StoragesStatus(str, Enum): + """Storages status""" + + NOT_CREATED = "not_created" + CREATED = "created" + INITIALIZED = "initialized" + FINALIZED = "finalized" + + +@dataclass +class DeletionResult: + """Represents the result of a deletion operation.""" + + status: Literal["success", "not_found", "fail"] + doc_id: str + message: str + status_code: int = 200 + file_path: str | None = None + + +# Unified Query Result Data Structures for Reference List Support + + +@dataclass +class QueryResult: + """ + Unified query result data structure for all query modes. + + Attributes: + content: Text content for non-streaming responses + response_iterator: Streaming response iterator for streaming responses + raw_data: Complete structured data including references and metadata + is_streaming: Whether this is a streaming result + """ + + content: Optional[str] = None + response_iterator: Optional[AsyncIterator[str]] = None + raw_data: Optional[Dict[str, Any]] = None + is_streaming: bool = False + + @property + def reference_list(self) -> List[Dict[str, str]]: + """ + Convenient property to extract reference list from raw_data. + + Returns: + List[Dict[str, str]]: Reference list in format: + [{"reference_id": "1", "file_path": "/path/to/file.pdf"}, ...] + """ + if self.raw_data: + return self.raw_data.get("data", {}).get("references", []) + return [] + + @property + def metadata(self) -> Dict[str, Any]: + """ + Convenient property to extract metadata from raw_data. + + Returns: + Dict[str, Any]: Query metadata including query_mode, keywords, etc. + """ + if self.raw_data: + return self.raw_data.get("metadata", {}) + return {} + + +@dataclass +class QueryContextResult: + """ + Unified query context result data structure. + + Attributes: + context: LLM context string + raw_data: Complete structured data including reference_list + """ + + context: str + raw_data: Dict[str, Any] + + @property + def reference_list(self) -> List[Dict[str, str]]: + """Convenient property to extract reference list from raw_data.""" + return self.raw_data.get("data", {}).get("references", []) diff --git a/LightRAG/lightrag/constants.py b/LightRAG/lightrag/constants.py new file mode 100644 index 00000000..eedecd65 --- /dev/null +++ b/LightRAG/lightrag/constants.py @@ -0,0 +1,111 @@ +""" +Centralized configuration constants for LightRAG. + +This module defines default values for configuration constants used across +different parts of the LightRAG system. Centralizing these values ensures +consistency and makes maintenance easier. +""" + +# Default values for server settings +DEFAULT_WOKERS = 2 +DEFAULT_MAX_GRAPH_NODES = 1000 + +# Default values for extraction settings +DEFAULT_SUMMARY_LANGUAGE = "English" # Default language for document processing +DEFAULT_MAX_GLEANING = 1 +DEFAULT_ENTITY_NAME_MAX_LENGTH = 256 + +# Number of description fragments to trigger LLM summary +DEFAULT_FORCE_LLM_SUMMARY_ON_MERGE = 8 +# Max description token size to trigger LLM summary +DEFAULT_SUMMARY_MAX_TOKENS = 1200 +# Recommended LLM summary output length in tokens +DEFAULT_SUMMARY_LENGTH_RECOMMENDED = 600 +# Maximum token size sent to LLM for summary +DEFAULT_SUMMARY_CONTEXT_SIZE = 12000 +# Default entities to extract if ENTITY_TYPES is not specified in .env +DEFAULT_ENTITY_TYPES = [ + "Person", + "Creature", + "Organization", + "Location", + "Event", + "Concept", + "Method", + "Content", + "Data", + "Artifact", + "NaturalObject", +] + +# Separator for: description, source_id and relation-key fields(Can not be changed after data inserted) +GRAPH_FIELD_SEP = "" + +# Query and retrieval configuration defaults +DEFAULT_TOP_K = 40 +DEFAULT_CHUNK_TOP_K = 20 +DEFAULT_MAX_ENTITY_TOKENS = 6000 +DEFAULT_MAX_RELATION_TOKENS = 8000 +DEFAULT_MAX_TOTAL_TOKENS = 30000 +DEFAULT_COSINE_THRESHOLD = 0.2 +DEFAULT_RELATED_CHUNK_NUMBER = 5 +DEFAULT_KG_CHUNK_PICK_METHOD = "VECTOR" + +# TODO: Deprated. All conversation_history messages is send to LLM. +DEFAULT_HISTORY_TURNS = 0 + +# Rerank configuration defaults +DEFAULT_MIN_RERANK_SCORE = 0.0 +DEFAULT_RERANK_BINDING = "null" + +# Default source ids limit in meta data for entity and relation +DEFAULT_MAX_SOURCE_IDS_PER_ENTITY = 300 +DEFAULT_MAX_SOURCE_IDS_PER_RELATION = 300 +### control chunk_ids limitation method: FIFO, FIFO +### FIFO: First in first out +### KEEP: Keep oldest (less merge action and faster) +SOURCE_IDS_LIMIT_METHOD_KEEP = "KEEP" +SOURCE_IDS_LIMIT_METHOD_FIFO = "FIFO" +DEFAULT_SOURCE_IDS_LIMIT_METHOD = SOURCE_IDS_LIMIT_METHOD_FIFO +VALID_SOURCE_IDS_LIMIT_METHODS = { + SOURCE_IDS_LIMIT_METHOD_KEEP, + SOURCE_IDS_LIMIT_METHOD_FIFO, +} +# Maximum number of file paths stored in entity/relation file_path field (For displayed only, does not affect query performance) +DEFAULT_MAX_FILE_PATHS = 100 + +# Field length of file_path in Milvus Schema for entity and relation (Should not be changed) +# file_path must store all file paths up to the DEFAULT_MAX_FILE_PATHS limit within the metadata. +DEFAULT_MAX_FILE_PATH_LENGTH = 32768 +# Placeholder for more file paths in meta data for entity and relation (Should not be changed) +DEFAULT_FILE_PATH_MORE_PLACEHOLDER = "truncated" + +# Default temperature for LLM +DEFAULT_TEMPERATURE = 1.0 + +# Async configuration defaults +DEFAULT_MAX_ASYNC = 4 # Default maximum async operations +DEFAULT_MAX_PARALLEL_INSERT = 2 # Default maximum parallel insert operations + +# Embedding configuration defaults +DEFAULT_EMBEDDING_FUNC_MAX_ASYNC = 8 # Default max async for embedding functions +DEFAULT_EMBEDDING_BATCH_NUM = 10 # Default batch size for embedding computations + +# Gunicorn worker timeout +DEFAULT_TIMEOUT = 300 + +# Default llm and embedding timeout +DEFAULT_LLM_TIMEOUT = 180 +DEFAULT_EMBEDDING_TIMEOUT = 30 + +# Logging configuration defaults +DEFAULT_LOG_MAX_BYTES = 10485760 # Default 10MB +DEFAULT_LOG_BACKUP_COUNT = 5 # Default 5 backups +DEFAULT_LOG_FILENAME = "lightrag.log" # Default log filename + +# Ollama server configuration defaults +DEFAULT_OLLAMA_MODEL_NAME = "lightrag" +DEFAULT_OLLAMA_MODEL_TAG = "latest" +DEFAULT_OLLAMA_MODEL_SIZE = 7365960935 +DEFAULT_OLLAMA_CREATED_AT = "2024-01-15T00:00:00Z" +DEFAULT_OLLAMA_DIGEST = "sha256:lightrag" diff --git a/LightRAG/lightrag/evaluation/README_EVALUASTION_RAGAS.md b/LightRAG/lightrag/evaluation/README_EVALUASTION_RAGAS.md new file mode 100644 index 00000000..de1845ef --- /dev/null +++ b/LightRAG/lightrag/evaluation/README_EVALUASTION_RAGAS.md @@ -0,0 +1,487 @@ +# 📊 RAGAS-based Evaluation Framework + +## What is RAGAS? + +**RAGAS** (Retrieval Augmented Generation Assessment) is a framework for reference-free evaluation of RAG systems using LLMs. RAGAS uses state-of-the-art evaluation metrics: + +### Core Metrics + +| Metric | What It Measures | Good Score | +|--------|-----------------|-----------| +| **Faithfulness** | Is the answer factually accurate based on retrieved context? | > 0.80 | +| **Answer Relevance** | Is the answer relevant to the user's question? | > 0.80 | +| **Context Recall** | Was all relevant information retrieved from documents? | > 0.80 | +| **Context Precision** | Is retrieved context clean without irrelevant noise? | > 0.80 | +| **RAGAS Score** | Overall quality metric (average of above) | > 0.80 | + +### 📁 LightRAG Evalua'tion Framework Directory Structure + +``` +lightrag/evaluation/ +├── eval_rag_quality.py # Main evaluation script +├── sample_dataset.json # 3 test questions about LightRAG +├── sample_documents/ # Matching markdown files for testing +│ ├── 01_lightrag_overview.md +│ ├── 02_rag_architecture.md +│ ├── 03_lightrag_improvements.md +│ ├── 04_supported_databases.md +│ ├── 05_evaluation_and_deployment.md +│ └── README.md +├── __init__.py # Package init +├── results/ # Output directory +│ ├── results_YYYYMMDD_HHMMSS.json # Raw metrics in JSON +│ └── results_YYYYMMDD_HHMMSS.csv # Metrics in CSV format +└── README.md # This file +``` + +**Quick Test:** Index files from `sample_documents/` into LightRAG, then run the evaluator to reproduce results (~89-100% RAGAS score per question). + + + +## 🚀 Quick Start + +### 1. Install Dependencies + +```bash +pip install ragas datasets langfuse +``` + +Or use your project dependencies (already included in pyproject.toml): + +```bash +pip install -e ".[evaluation]" +``` + +### 2. Run Evaluation + +**Basic usage (uses defaults):** +```bash +cd /path/to/LightRAG +python lightrag/evaluation/eval_rag_quality.py +``` + +**Specify custom dataset:** +```bash +python lightrag/evaluation/eval_rag_quality.py --dataset my_test.json +``` + +**Specify custom RAG endpoint:** +```bash +python lightrag/evaluation/eval_rag_quality.py --ragendpoint http://my-server.com:9621 +``` + +**Specify both (short form):** +```bash +python lightrag/evaluation/eval_rag_quality.py -d my_test.json -r http://localhost:9621 +``` + +**Get help:** +```bash +python lightrag/evaluation/eval_rag_quality.py --help +``` + +### 3. View Results + +Results are saved automatically in `lightrag/evaluation/results/`: + +``` +results/ +├── results_20241023_143022.json ← Raw metrics in JSON format +└── results_20241023_143022.csv ← Metrics in CSV format (for spreadsheets) +``` + +**Results include:** +- ✅ Overall RAGAS score +- 📊 Per-metric averages (Faithfulness, Answer Relevance, Context Recall, Context Precision) +- 📋 Individual test case results +- 📈 Performance breakdown by question + + + +## 📋 Command-Line Arguments + +The evaluation script supports command-line arguments for easy configuration: + +| Argument | Short | Default | Description | +|----------|-------|---------|-------------| +| `--dataset` | `-d` | `sample_dataset.json` | Path to test dataset JSON file | +| `--ragendpoint` | `-r` | `http://localhost:9621` or `$LIGHTRAG_API_URL` | LightRAG API endpoint URL | + +### Usage Examples + +**Use default dataset and endpoint:** +```bash +python lightrag/evaluation/eval_rag_quality.py +``` + +**Custom dataset with default endpoint:** +```bash +python lightrag/evaluation/eval_rag_quality.py --dataset path/to/my_dataset.json +``` + +**Default dataset with custom endpoint:** +```bash +python lightrag/evaluation/eval_rag_quality.py --ragendpoint http://my-server.com:9621 +``` + +**Custom dataset and endpoint:** +```bash +python lightrag/evaluation/eval_rag_quality.py -d my_dataset.json -r http://localhost:9621 +``` + +**Absolute path to dataset:** +```bash +python lightrag/evaluation/eval_rag_quality.py -d /path/to/custom_dataset.json +``` + +**Show help message:** +```bash +python lightrag/evaluation/eval_rag_quality.py --help +``` + + + +## ⚙️ Configuration + +### Environment Variables + +The evaluation framework supports customization through environment variables: + +**⚠️ IMPORTANT: Both LLM and Embedding endpoints MUST be OpenAI-compatible** +- The RAGAS framework requires OpenAI-compatible API interfaces +- Custom endpoints must implement the OpenAI API format (e.g., vLLM, SGLang, LocalAI) +- Non-compatible endpoints will cause evaluation failures + +| Variable | Default | Description | +|----------|---------|-------------| +| **LLM Configuration** | | | +| `EVAL_LLM_MODEL` | `gpt-4o-mini` | LLM model used for RAGAS evaluation | +| `EVAL_LLM_BINDING_API_KEY` | falls back to `OPENAI_API_KEY` | API key for LLM evaluation | +| `EVAL_LLM_BINDING_HOST` | (optional) | Custom OpenAI-compatible endpoint URL for LLM | +| **Embedding Configuration** | | | +| `EVAL_EMBEDDING_MODEL` | `text-embedding-3-large` | Embedding model for evaluation | +| `EVAL_EMBEDDING_BINDING_API_KEY` | falls back to `EVAL_LLM_BINDING_API_KEY` → `OPENAI_API_KEY` | API key for embeddings | +| `EVAL_EMBEDDING_BINDING_HOST` | falls back to `EVAL_LLM_BINDING_HOST` | Custom OpenAI-compatible endpoint URL for embeddings | +| **Performance Tuning** | | | +| `EVAL_MAX_CONCURRENT` | 2 | Number of concurrent test case evaluations (1=serial) | +| `EVAL_QUERY_TOP_K` | 10 | Number of documents to retrieve per query | +| `EVAL_LLM_MAX_RETRIES` | 5 | Maximum LLM request retries | +| `EVAL_LLM_TIMEOUT` | 180 | LLM request timeout in seconds | + +### Usage Examples + +**Example 1: Default Configuration (OpenAI Official API)** +```bash +export OPENAI_API_KEY=sk-xxx +python lightrag/evaluation/eval_rag_quality.py +``` +Both LLM and embeddings use OpenAI's official API with default models. + +**Example 2: Custom Models on OpenAI** +```bash +export OPENAI_API_KEY=sk-xxx +export EVAL_LLM_MODEL=gpt-4o-mini +export EVAL_EMBEDDING_MODEL=text-embedding-3-large +python lightrag/evaluation/eval_rag_quality.py +``` + +**Example 3: Same Custom OpenAI-Compatible Endpoint for Both** +```bash +# Both LLM and embeddings use the same custom endpoint +export EVAL_LLM_BINDING_API_KEY=your-custom-key +export EVAL_LLM_BINDING_HOST=http://localhost:8000/v1 +export EVAL_LLM_MODEL=qwen-plus +export EVAL_EMBEDDING_MODEL=BAAI/bge-m3 +python lightrag/evaluation/eval_rag_quality.py +``` +Embeddings automatically inherit LLM endpoint configuration. + +**Example 4: Separate Endpoints (Cost Optimization)** +```bash +# Use OpenAI for LLM (high quality) +export EVAL_LLM_BINDING_API_KEY=sk-openai-key +export EVAL_LLM_MODEL=gpt-4o-mini +# No EVAL_LLM_BINDING_HOST means use OpenAI official API + +# Use local vLLM for embeddings (cost-effective) +export EVAL_EMBEDDING_BINDING_API_KEY=local-key +export EVAL_EMBEDDING_BINDING_HOST=http://localhost:8001/v1 +export EVAL_EMBEDDING_MODEL=BAAI/bge-m3 + +python lightrag/evaluation/eval_rag_quality.py +``` +LLM uses OpenAI official API, embeddings use local custom endpoint. + +**Example 5: Different Custom Endpoints for LLM and Embeddings** +```bash +# LLM on one OpenAI-compatible server +export EVAL_LLM_BINDING_API_KEY=key1 +export EVAL_LLM_BINDING_HOST=http://llm-server:8000/v1 +export EVAL_LLM_MODEL=custom-llm + +# Embeddings on another OpenAI-compatible server +export EVAL_EMBEDDING_BINDING_API_KEY=key2 +export EVAL_EMBEDDING_BINDING_HOST=http://embedding-server:8001/v1 +export EVAL_EMBEDDING_MODEL=custom-embedding + +python lightrag/evaluation/eval_rag_quality.py +``` +Both use different custom OpenAI-compatible endpoints. + +**Example 6: Using Environment Variables from .env File** +```bash +# Create .env file in project root +cat > .env << EOF +EVAL_LLM_BINDING_API_KEY=your-key +EVAL_LLM_BINDING_HOST=http://localhost:8000/v1 +EVAL_LLM_MODEL=qwen-plus +EVAL_EMBEDDING_MODEL=BAAI/bge-m3 +EOF + +# Run evaluation (automatically loads .env) +python lightrag/evaluation/eval_rag_quality.py +``` + +### Concurrency Control & Rate Limiting + +The evaluation framework includes built-in concurrency control to prevent API rate limiting issues: + +**Why Concurrency Control Matters:** +- RAGAS internally makes many concurrent LLM calls for each test case +- Context Precision metric calls LLM once per retrieved document +- Without control, this can easily exceed API rate limits + +**Default Configuration (Conservative):** +```bash +EVAL_MAX_CONCURRENT=2 # Serial evaluation (one test at a time) +EVAL_QUERY_TOP_K=10 # OP_K query parameter of LightRAG +EVAL_LLM_MAX_RETRIES=5 # Retry failed requests 5 times +EVAL_LLM_TIMEOUT=180 # 3-minute timeout per request +``` + +**Common Issues and Solutions:** + +| Issue | Solution | +|-------|----------| +| **Warning: "LM returned 1 generations instead of 3"** | Reduce `EVAL_MAX_CONCURRENT` to 1 or decrease `EVAL_QUERY_TOP_K` | +| **Context Precision returns NaN** | Lower `EVAL_QUERY_TOP_K` to reduce LLM calls per test case | +| **Rate limit errors (429)** | Increase `EVAL_LLM_MAX_RETRIES` and decrease `EVAL_MAX_CONCURRENT` | +| **Request timeouts** | Increase `EVAL_LLM_TIMEOUT` to 180 or higher | + + + +## 📝 Test Dataset + +`sample_dataset.json` contains 3 generic questions about LightRAG. Replace with questions matching YOUR indexed documents. + +**Custom Test Cases:** + +```json +{ + "test_cases": [ + { + "question": "Your question here", + "ground_truth": "Expected answer from your data", + "project": "evaluation_project_name" + } + ] +} +``` + +--- + +## 📊 Interpreting Results + +### Score Ranges + +- **0.80-1.00**: ✅ Excellent (Production-ready) +- **0.60-0.80**: ⚠️ Good (Room for improvement) +- **0.40-0.60**: ❌ Poor (Needs optimization) +- **0.00-0.40**: 🔴 Critical (Major issues) + +### What Low Scores Mean + +| Metric | Low Score Indicates | +|--------|-------------------| +| **Faithfulness** | Responses contain hallucinations or incorrect information | +| **Answer Relevance** | Answers don't match what users asked | +| **Context Recall** | Missing important information in retrieval | +| **Context Precision** | Retrieved documents contain irrelevant noise | + +### Optimization Tips + +1. **Low Faithfulness**: + - Improve entity extraction quality + - Better document chunking + - Tune retrieval temperature + +2. **Low Answer Relevance**: + - Improve prompt engineering + - Better query understanding + - Check semantic similarity threshold + +3. **Low Context Recall**: + - Increase retrieval `top_k` results + - Improve embedding model + - Better document preprocessing + +4. **Low Context Precision**: + - Smaller, focused chunks + - Better filtering + - Improve chunking strategy + +--- + +## 📚 Resources + +- [RAGAS Documentation](https://docs.ragas.io/) +- [RAGAS GitHub](https://github.com/explodinggradients/ragas) + +--- + +## 🐛 Troubleshooting + +### "ModuleNotFoundError: No module named 'ragas'" + +```bash +pip install ragas datasets +``` + +### "Warning: LM returned 1 generations instead of requested 3" or Context Precision NaN + +**Cause**: This warning indicates API rate limiting or concurrent request overload: +- RAGAS makes multiple LLM calls per test case (faithfulness, relevancy, recall, precision) +- Context Precision calls LLM once per retrieved document (with `EVAL_QUERY_TOP_K=10`, that's 10 calls) +- Concurrent evaluation multiplies these calls: `EVAL_MAX_CONCURRENT × LLM calls per test` + +**Solutions** (in order of effectiveness): + +1. **Serial Evaluation** (Default): + ```bash + export EVAL_MAX_CONCURRENT=1 + python lightrag/evaluation/eval_rag_quality.py + ``` + +2. **Reduce Retrieved Documents**: + ```bash + export EVAL_QUERY_TOP_K=5 # Halves Context Precision LLM calls + python lightrag/evaluation/eval_rag_quality.py + ``` + +3. **Increase Retry & Timeout**: + ```bash + export EVAL_LLM_MAX_RETRIES=10 + export EVAL_LLM_TIMEOUT=180 + python lightrag/evaluation/eval_rag_quality.py + ``` + +4. **Use Higher Quota API** (if available): + - Upgrade to OpenAI Tier 2+ for higher RPM limits + - Use self-hosted OpenAI-compatible service with no rate limits + +### "AttributeError: 'InstructorLLM' object has no attribute 'agenerate_prompt'" or NaN results + +This error occurs with RAGAS 0.3.x when LLM and Embeddings are not explicitly configured. The evaluation framework now handles this automatically by: +- Using environment variables to configure evaluation models +- Creating proper LLM and Embeddings instances for RAGAS + +**Solution**: Ensure you have set one of the following: +- `OPENAI_API_KEY` environment variable (default) +- `EVAL_LLM_BINDING_API_KEY` for custom API key + +The framework will automatically configure the evaluation models. + +### "No sample_dataset.json found" + +Make sure you're running from the project root: + +```bash +cd /path/to/LightRAG +python lightrag/evaluation/eval_rag_quality.py +``` + +### "LightRAG query API errors during evaluation" + +The evaluation uses your configured LLM (OpenAI by default). Ensure: +- API keys are set in `.env` +- Network connection is stable + +### Evaluation requires running LightRAG API + +The evaluator queries a running LightRAG API server at `http://localhost:9621`. Make sure: +1. LightRAG API server is running (`python lightrag/api/lightrag_server.py`) +2. Documents are indexed in your LightRAG instance +3. API is accessible at the configured URL + + + +## 📝 Next Steps + +1. Start LightRAG API server +2. Upload sample documents into LightRAG throught WebUI +3. Run `python lightrag/evaluation/eval_rag_quality.py` +4. Review results (JSON/CSV) in `results/` folder + +Evaluation Result Sample: + +``` +INFO: ====================================================================== +INFO: 🔍 RAGAS Evaluation - Using Real LightRAG API +INFO: ====================================================================== +INFO: Evaluation Models: +INFO: • LLM Model: gpt-4.1 +INFO: • Embedding Model: text-embedding-3-large +INFO: • Endpoint: OpenAI Official API +INFO: Concurrency & Rate Limiting: +INFO: • Query Top-K: 10 Entities/Relations +INFO: • LLM Max Retries: 5 +INFO: • LLM Timeout: 180 seconds +INFO: Test Configuration: +INFO: • Total Test Cases: 6 +INFO: • Test Dataset: sample_dataset.json +INFO: • LightRAG API: http://localhost:9621 +INFO: • Results Directory: results +INFO: ====================================================================== +INFO: 🚀 Starting RAGAS Evaluation of LightRAG System +INFO: 🔧 RAGAS Evaluation (Stage 2): 2 concurrent +INFO: ====================================================================== +INFO: +INFO: =================================================================================================================== +INFO: 📊 EVALUATION RESULTS SUMMARY +INFO: =================================================================================================================== +INFO: # | Question | Faith | AnswRel | CtxRec | CtxPrec | RAGAS | Status +INFO: ------------------------------------------------------------------------------------------------------------------- +INFO: 1 | How does LightRAG solve the hallucination probl... | 1.0000 | 1.0000 | 1.0000 | 1.0000 | 1.0000 | ✓ +INFO: 2 | What are the three main components required in ... | 0.8500 | 0.5790 | 1.0000 | 1.0000 | 0.8573 | ✓ +INFO: 3 | How does LightRAG's retrieval performance compa... | 0.8056 | 1.0000 | 1.0000 | 1.0000 | 0.9514 | ✓ +INFO: 4 | What vector databases does LightRAG support and... | 0.8182 | 0.9807 | 1.0000 | 1.0000 | 0.9497 | ✓ +INFO: 5 | What are the four key metrics for evaluating RA... | 1.0000 | 0.7452 | 1.0000 | 1.0000 | 0.9363 | ✓ +INFO: 6 | What are the core benefits of LightRAG and how ... | 0.9583 | 0.8829 | 1.0000 | 1.0000 | 0.9603 | ✓ +INFO: =================================================================================================================== +INFO: +INFO: ====================================================================== +INFO: 📊 EVALUATION COMPLETE +INFO: ====================================================================== +INFO: Total Tests: 6 +INFO: Successful: 6 +INFO: Failed: 0 +INFO: Success Rate: 100.00% +INFO: Elapsed Time: 161.10 seconds +INFO: Avg Time/Test: 26.85 seconds +INFO: +INFO: ====================================================================== +INFO: 📈 BENCHMARK RESULTS (Average) +INFO: ====================================================================== +INFO: Average Faithfulness: 0.9053 +INFO: Average Answer Relevance: 0.8646 +INFO: Average Context Recall: 1.0000 +INFO: Average Context Precision: 1.0000 +INFO: Average RAGAS Score: 0.9425 +INFO: ---------------------------------------------------------------------- +INFO: Min RAGAS Score: 0.8573 +INFO: Max RAGAS Score: 1.0000 +``` + +--- + +**Happy Evaluating! 🚀** diff --git a/LightRAG/lightrag/evaluation/__init__.py b/LightRAG/lightrag/evaluation/__init__.py new file mode 100644 index 00000000..49eb189e --- /dev/null +++ b/LightRAG/lightrag/evaluation/__init__.py @@ -0,0 +1,25 @@ +""" +LightRAG Evaluation Module + +RAGAS-based evaluation framework for assessing RAG system quality. + +Usage: + from lightrag.evaluation import RAGEvaluator + + evaluator = RAGEvaluator() + results = await evaluator.run() + +Note: RAGEvaluator is imported lazily to avoid import errors +when ragas/datasets are not installed. +""" + +__all__ = ["RAGEvaluator"] + + +def __getattr__(name): + """Lazy import to avoid dependency errors when ragas is not installed.""" + if name == "RAGEvaluator": + from .eval_rag_quality import RAGEvaluator + + return RAGEvaluator + raise AttributeError(f"module {__name__!r} has no attribute {name!r}") diff --git a/LightRAG/lightrag/evaluation/eval_rag_quality.py b/LightRAG/lightrag/evaluation/eval_rag_quality.py new file mode 100644 index 00000000..7b415090 --- /dev/null +++ b/LightRAG/lightrag/evaluation/eval_rag_quality.py @@ -0,0 +1,1016 @@ +#!/usr/bin/env python3 +""" +RAGAS Evaluation Script for LightRAG System + +Evaluates RAG response quality using RAGAS metrics: +- Faithfulness: Is the answer factually accurate based on context? +- Answer Relevance: Is the answer relevant to the question? +- Context Recall: Is all relevant information retrieved? +- Context Precision: Is retrieved context clean without noise? + +Usage: + # Use defaults (sample_dataset.json, http://localhost:9621) + python lightrag/evaluation/eval_rag_quality.py + + # Specify custom dataset + python lightrag/evaluation/eval_rag_quality.py --dataset my_test.json + python lightrag/evaluation/eval_rag_quality.py -d my_test.json + + # Specify custom RAG endpoint + python lightrag/evaluation/eval_rag_quality.py --ragendpoint http://my-server.com:9621 + python lightrag/evaluation/eval_rag_quality.py -r http://my-server.com:9621 + + # Specify both + python lightrag/evaluation/eval_rag_quality.py -d my_test.json -r http://localhost:9621 + + # Get help + python lightrag/evaluation/eval_rag_quality.py --help + +Results are saved to: lightrag/evaluation/results/ + - results_YYYYMMDD_HHMMSS.csv (CSV export for analysis) + - results_YYYYMMDD_HHMMSS.json (Full results with details) + +Technical Notes: + - Uses stable RAGAS API (LangchainLLMWrapper) for maximum compatibility + - Supports custom OpenAI-compatible endpoints via EVAL_LLM_BINDING_HOST + - Enables bypass_n mode for endpoints that don't support 'n' parameter + - Deprecation warnings are suppressed for cleaner output +""" + +import argparse +import asyncio +import csv +import json +import math +import os +import sys +import time +import warnings +from datetime import datetime +from pathlib import Path +from typing import Any, Dict, List + +import httpx +from dotenv import load_dotenv +from lightrag.utils import logger + +# Suppress LangchainLLMWrapper deprecation warning +# We use LangchainLLMWrapper for stability and compatibility with all RAGAS versions +warnings.filterwarnings( + "ignore", + message=".*LangchainLLMWrapper is deprecated.*", + category=DeprecationWarning, +) + +# Suppress token usage warning for custom OpenAI-compatible endpoints +# Custom endpoints (vLLM, SGLang, etc.) often don't return usage information +# This is non-critical as token tracking is not required for RAGAS evaluation +warnings.filterwarnings( + "ignore", + message=".*Unexpected type for token usage.*", + category=UserWarning, +) + +# Add parent directory to path +sys.path.insert(0, str(Path(__file__).parent.parent.parent)) + +# use the .env that is inside the current folder +# allows to use different .env file for each lightrag instance +# the OS environment variables take precedence over the .env file +load_dotenv(dotenv_path=".env", override=False) + +# Conditional imports - will raise ImportError if dependencies not installed +try: + from datasets import Dataset + from ragas import evaluate + from ragas.metrics import ( + AnswerRelevancy, + ContextPrecision, + ContextRecall, + Faithfulness, + ) + from ragas.llms import LangchainLLMWrapper + from langchain_openai import ChatOpenAI, OpenAIEmbeddings + from tqdm.auto import tqdm + + RAGAS_AVAILABLE = True + +except ImportError: + RAGAS_AVAILABLE = False + Dataset = None + evaluate = None + LangchainLLMWrapper = None + + +CONNECT_TIMEOUT_SECONDS = 180.0 +READ_TIMEOUT_SECONDS = 300.0 +TOTAL_TIMEOUT_SECONDS = 180.0 + + +def _is_nan(value: Any) -> bool: + """Return True when value is a float NaN.""" + return isinstance(value, float) and math.isnan(value) + + +class RAGEvaluator: + """Evaluate RAG system quality using RAGAS metrics""" + + def __init__(self, test_dataset_path: str = None, rag_api_url: str = None): + """ + Initialize evaluator with test dataset + + Args: + test_dataset_path: Path to test dataset JSON file + rag_api_url: Base URL of LightRAG API (e.g., http://localhost:9621) + If None, will try to read from environment or use default + + Environment Variables: + EVAL_LLM_MODEL: LLM model for evaluation (default: gpt-4o-mini) + EVAL_EMBEDDING_MODEL: Embedding model for evaluation (default: text-embedding-3-small) + EVAL_LLM_BINDING_API_KEY: API key for LLM (fallback to OPENAI_API_KEY) + EVAL_LLM_BINDING_HOST: Custom endpoint URL for LLM (optional) + EVAL_EMBEDDING_BINDING_API_KEY: API key for embeddings (fallback: EVAL_LLM_BINDING_API_KEY -> OPENAI_API_KEY) + EVAL_EMBEDDING_BINDING_HOST: Custom endpoint URL for embeddings (fallback: EVAL_LLM_BINDING_HOST) + + Raises: + ImportError: If ragas or datasets packages are not installed + EnvironmentError: If EVAL_LLM_BINDING_API_KEY and OPENAI_API_KEY are both not set + """ + # Validate RAGAS dependencies are installed + if not RAGAS_AVAILABLE: + raise ImportError( + "RAGAS dependencies not installed. " + "Install with: pip install ragas datasets" + ) + + # Configure evaluation LLM (for RAGAS scoring) + eval_llm_api_key = os.getenv("EVAL_LLM_BINDING_API_KEY") or os.getenv( + "OPENAI_API_KEY" + ) + if not eval_llm_api_key: + raise EnvironmentError( + "EVAL_LLM_BINDING_API_KEY or OPENAI_API_KEY is required for evaluation. " + "Set EVAL_LLM_BINDING_API_KEY to use a custom API key, " + "or ensure OPENAI_API_KEY is set." + ) + + eval_model = os.getenv("EVAL_LLM_MODEL", "gpt-4o-mini") + eval_llm_base_url = os.getenv("EVAL_LLM_BINDING_HOST") + + # Configure evaluation embeddings (for RAGAS scoring) + # Fallback chain: EVAL_EMBEDDING_BINDING_API_KEY -> EVAL_LLM_BINDING_API_KEY -> OPENAI_API_KEY + eval_embedding_api_key = ( + os.getenv("EVAL_EMBEDDING_BINDING_API_KEY") + or os.getenv("EVAL_LLM_BINDING_API_KEY") + or os.getenv("OPENAI_API_KEY") + ) + eval_embedding_model = os.getenv( + "EVAL_EMBEDDING_MODEL", "text-embedding-3-large" + ) + # Fallback chain: EVAL_EMBEDDING_BINDING_HOST -> EVAL_LLM_BINDING_HOST -> None + eval_embedding_base_url = os.getenv("EVAL_EMBEDDING_BINDING_HOST") or os.getenv( + "EVAL_LLM_BINDING_HOST" + ) + + # Create LLM and Embeddings instances for RAGAS + llm_kwargs = { + "model": eval_model, + "api_key": eval_llm_api_key, + "max_retries": int(os.getenv("EVAL_LLM_MAX_RETRIES", "5")), + "request_timeout": int(os.getenv("EVAL_LLM_TIMEOUT", "180")), + } + embedding_kwargs = { + "model": eval_embedding_model, + "api_key": eval_embedding_api_key, + } + + if eval_llm_base_url: + llm_kwargs["base_url"] = eval_llm_base_url + + if eval_embedding_base_url: + embedding_kwargs["base_url"] = eval_embedding_base_url + + # Create base LangChain LLM + base_llm = ChatOpenAI(**llm_kwargs) + self.eval_embeddings = OpenAIEmbeddings(**embedding_kwargs) + + # Wrap LLM with LangchainLLMWrapper and enable bypass_n mode for custom endpoints + # This ensures compatibility with endpoints that don't support the 'n' parameter + # by generating multiple outputs through repeated prompts instead of using 'n' parameter + try: + self.eval_llm = LangchainLLMWrapper( + langchain_llm=base_llm, + bypass_n=True, # Enable bypass_n to avoid passing 'n' to OpenAI API + ) + logger.debug("Successfully configured bypass_n mode for LLM wrapper") + except Exception as e: + logger.warning( + "Could not configure LangchainLLMWrapper with bypass_n: %s. " + "Using base LLM directly, which may cause warnings with custom endpoints.", + e, + ) + self.eval_llm = base_llm + + if test_dataset_path is None: + test_dataset_path = Path(__file__).parent / "sample_dataset.json" + + if rag_api_url is None: + rag_api_url = os.getenv("LIGHTRAG_API_URL", "http://localhost:9621") + + self.test_dataset_path = Path(test_dataset_path) + self.rag_api_url = rag_api_url.rstrip("/") + self.results_dir = Path(__file__).parent / "results" + self.results_dir.mkdir(exist_ok=True) + + # Load test dataset + self.test_cases = self._load_test_dataset() + + # Store configuration values for display + self.eval_model = eval_model + self.eval_embedding_model = eval_embedding_model + self.eval_llm_base_url = eval_llm_base_url + self.eval_embedding_base_url = eval_embedding_base_url + self.eval_max_retries = llm_kwargs["max_retries"] + self.eval_timeout = llm_kwargs["request_timeout"] + + # Display configuration + self._display_configuration() + + def _display_configuration(self): + """Display all evaluation configuration settings""" + logger.info("Evaluation Models:") + logger.info(" • LLM Model: %s", self.eval_model) + logger.info(" • Embedding Model: %s", self.eval_embedding_model) + + # Display LLM endpoint + if self.eval_llm_base_url: + logger.info(" • LLM Endpoint: %s", self.eval_llm_base_url) + logger.info( + " • Bypass N-Parameter: Enabled (use LangchainLLMWrapper for compatibility)" + ) + else: + logger.info(" • LLM Endpoint: OpenAI Official API") + + # Display Embedding endpoint (only if different from LLM) + if self.eval_embedding_base_url: + if self.eval_embedding_base_url != self.eval_llm_base_url: + logger.info( + " • Embedding Endpoint: %s", self.eval_embedding_base_url + ) + # If same as LLM endpoint, no need to display separately + elif not self.eval_llm_base_url: + # Both using OpenAI - already displayed above + pass + else: + # LLM uses custom endpoint, but embeddings use OpenAI + logger.info(" • Embedding Endpoint: OpenAI Official API") + + logger.info("Concurrency & Rate Limiting:") + query_top_k = int(os.getenv("EVAL_QUERY_TOP_K", "10")) + logger.info(" • Query Top-K: %s Entities/Relations", query_top_k) + logger.info(" • LLM Max Retries: %s", self.eval_max_retries) + logger.info(" • LLM Timeout: %s seconds", self.eval_timeout) + + logger.info("Test Configuration:") + logger.info(" • Total Test Cases: %s", len(self.test_cases)) + logger.info(" • Test Dataset: %s", self.test_dataset_path.name) + logger.info(" • LightRAG API: %s", self.rag_api_url) + logger.info(" • Results Directory: %s", self.results_dir.name) + + def _load_test_dataset(self) -> List[Dict[str, str]]: + """Load test cases from JSON file""" + if not self.test_dataset_path.exists(): + raise FileNotFoundError(f"Test dataset not found: {self.test_dataset_path}") + + with open(self.test_dataset_path) as f: + data = json.load(f) + + return data.get("test_cases", []) + + async def generate_rag_response( + self, + question: str, + client: httpx.AsyncClient, + ) -> Dict[str, Any]: + """ + Generate RAG response by calling LightRAG API. + + Args: + question: The user query. + client: Shared httpx AsyncClient for connection pooling. + + Returns: + Dictionary with 'answer' and 'contexts' keys. + 'contexts' is a list of strings (one per retrieved document). + + Raises: + Exception: If LightRAG API is unavailable. + """ + try: + payload = { + "query": question, + "mode": "mix", + "include_references": True, + "include_chunk_content": True, # NEW: Request chunk content in references + "response_type": "Multiple Paragraphs", + "top_k": int(os.getenv("EVAL_QUERY_TOP_K", "10")), + } + + # Get API key from environment for authentication + api_key = os.getenv("LIGHTRAG_API_KEY") + + # Prepare headers with optional authentication + headers = {} + if api_key: + headers["X-API-Key"] = api_key + + # Single optimized API call - gets both answer AND chunk content + response = await client.post( + f"{self.rag_api_url}/query", + json=payload, + headers=headers if headers else None, + ) + response.raise_for_status() + result = response.json() + + answer = result.get("response", "No response generated") + references = result.get("references", []) + + # DEBUG: Inspect the API response + logger.debug("🔍 References Count: %s", len(references)) + if references: + first_ref = references[0] + logger.debug("🔍 First Reference Keys: %s", list(first_ref.keys())) + if "content" in first_ref: + content_preview = first_ref["content"] + if isinstance(content_preview, list) and content_preview: + logger.debug( + "🔍 Content Preview (first chunk): %s...", + content_preview[0][:100], + ) + elif isinstance(content_preview, str): + logger.debug("🔍 Content Preview: %s...", content_preview[:100]) + + # Extract chunk content from enriched references + # Note: content is now a list of chunks per reference (one file may have multiple chunks) + contexts = [] + for ref in references: + content = ref.get("content", []) + if isinstance(content, list): + # Flatten the list: each chunk becomes a separate context + contexts.extend(content) + elif isinstance(content, str): + # Backward compatibility: if content is still a string (shouldn't happen) + contexts.append(content) + + return { + "answer": answer, + "contexts": contexts, # List of strings from actual retrieved chunks + } + + except httpx.ConnectError as e: + raise Exception( + f"❌ Cannot connect to LightRAG API at {self.rag_api_url}\n" + f" Make sure LightRAG server is running:\n" + f" python -m lightrag.api.lightrag_server\n" + f" Error: {str(e)}" + ) + except httpx.HTTPStatusError as e: + raise Exception( + f"LightRAG API error {e.response.status_code}: {e.response.text}" + ) + except httpx.ReadTimeout as e: + raise Exception( + f"Request timeout after waiting for response\n" + f" Question: {question[:100]}...\n" + f" Error: {str(e)}" + ) + except Exception as e: + raise Exception(f"Error calling LightRAG API: {type(e).__name__}: {str(e)}") + + async def evaluate_single_case( + self, + idx: int, + test_case: Dict[str, str], + rag_semaphore: asyncio.Semaphore, + eval_semaphore: asyncio.Semaphore, + client: httpx.AsyncClient, + progress_counter: Dict[str, int], + position_pool: asyncio.Queue, + pbar_creation_lock: asyncio.Lock, + ) -> Dict[str, Any]: + """ + Evaluate a single test case with two-stage pipeline concurrency control + + Args: + idx: Test case index (1-based) + test_case: Test case dictionary with question and ground_truth + rag_semaphore: Semaphore to control overall concurrency (covers entire function) + eval_semaphore: Semaphore to control RAGAS evaluation concurrency (Stage 2) + client: Shared httpx AsyncClient for connection pooling + progress_counter: Shared dictionary for progress tracking + position_pool: Queue of available tqdm position indices + pbar_creation_lock: Lock to serialize tqdm creation and prevent race conditions + + Returns: + Evaluation result dictionary + """ + # rag_semaphore controls the entire evaluation process to prevent + # all RAG responses from being generated at once when eval is slow + async with rag_semaphore: + question = test_case["question"] + ground_truth = test_case["ground_truth"] + + # Stage 1: Generate RAG response + try: + rag_response = await self.generate_rag_response( + question=question, client=client + ) + except Exception as e: + logger.error("Error generating response for test %s: %s", idx, str(e)) + progress_counter["completed"] += 1 + return { + "test_number": idx, + "question": question, + "error": str(e), + "metrics": {}, + "ragas_score": 0, + "timestamp": datetime.now().isoformat(), + } + + # *** CRITICAL FIX: Use actual retrieved contexts, NOT ground_truth *** + retrieved_contexts = rag_response["contexts"] + + # Prepare dataset for RAGAS evaluation with CORRECT contexts + eval_dataset = Dataset.from_dict( + { + "question": [question], + "answer": [rag_response["answer"]], + "contexts": [retrieved_contexts], + "ground_truth": [ground_truth], + } + ) + + # Stage 2: Run RAGAS evaluation (controlled by eval_semaphore) + # IMPORTANT: Create fresh metric instances for each evaluation to avoid + # concurrent state conflicts when multiple tasks run in parallel + async with eval_semaphore: + pbar = None + position = None + try: + # Acquire a position from the pool for this tqdm progress bar + position = await position_pool.get() + + # Serialize tqdm creation to prevent race conditions + # Multiple tasks creating tqdm simultaneously can cause display conflicts + async with pbar_creation_lock: + # Create tqdm progress bar with assigned position to avoid overlapping + # leave=False ensures the progress bar is cleared after completion, + # preventing accumulation of completed bars and allowing position reuse + pbar = tqdm( + total=4, + desc=f"Eval-{idx:02d}", + position=position, + leave=False, + ) + # Give tqdm time to initialize and claim its screen position + await asyncio.sleep(0.05) + + eval_results = evaluate( + dataset=eval_dataset, + metrics=[ + Faithfulness(), + AnswerRelevancy(), + ContextRecall(), + ContextPrecision(), + ], + llm=self.eval_llm, + embeddings=self.eval_embeddings, + _pbar=pbar, + ) + + # Convert to DataFrame (RAGAS v0.3+ API) + df = eval_results.to_pandas() + + # Extract scores from first row + scores_row = df.iloc[0] + + # Extract scores (RAGAS v0.3+ uses .to_pandas()) + result = { + "test_number": idx, + "question": question, + "answer": rag_response["answer"][:200] + "..." + if len(rag_response["answer"]) > 200 + else rag_response["answer"], + "ground_truth": ground_truth[:200] + "..." + if len(ground_truth) > 200 + else ground_truth, + "project": test_case.get("project", "unknown"), + "metrics": { + "faithfulness": float(scores_row.get("faithfulness", 0)), + "answer_relevance": float( + scores_row.get("answer_relevancy", 0) + ), + "context_recall": float( + scores_row.get("context_recall", 0) + ), + "context_precision": float( + scores_row.get("context_precision", 0) + ), + }, + "timestamp": datetime.now().isoformat(), + } + + # Calculate RAGAS score (average of all metrics, excluding NaN values) + metrics = result["metrics"] + valid_metrics = [v for v in metrics.values() if not _is_nan(v)] + ragas_score = ( + sum(valid_metrics) / len(valid_metrics) if valid_metrics else 0 + ) + result["ragas_score"] = round(ragas_score, 4) + + # Update progress counter + progress_counter["completed"] += 1 + + return result + + except Exception as e: + logger.error("Error evaluating test %s: %s", idx, str(e)) + progress_counter["completed"] += 1 + return { + "test_number": idx, + "question": question, + "error": str(e), + "metrics": {}, + "ragas_score": 0, + "timestamp": datetime.now().isoformat(), + } + finally: + # Force close progress bar to ensure completion + if pbar is not None: + pbar.close() + # Release the position back to the pool for reuse + if position is not None: + await position_pool.put(position) + + async def evaluate_responses(self) -> List[Dict[str, Any]]: + """ + Evaluate all test cases in parallel with two-stage pipeline and return metrics + + Returns: + List of evaluation results with metrics + """ + # Get evaluation concurrency from environment (default to 2 for parallel evaluation) + max_async = int(os.getenv("EVAL_MAX_CONCURRENT", "2")) + + logger.info("%s", "=" * 70) + logger.info("🚀 Starting RAGAS Evaluation of LightRAG System") + logger.info("🔧 RAGAS Evaluation (Stage 2): %s concurrent", max_async) + logger.info("%s", "=" * 70) + + # Create two-stage pipeline semaphores + # Stage 1: RAG generation - allow x2 concurrency to keep evaluation fed + rag_semaphore = asyncio.Semaphore(max_async * 2) + # Stage 2: RAGAS evaluation - primary bottleneck + eval_semaphore = asyncio.Semaphore(max_async) + + # Create progress counter (shared across all tasks) + progress_counter = {"completed": 0} + + # Create position pool for tqdm progress bars + # Positions range from 0 to max_async-1, ensuring no overlapping displays + position_pool = asyncio.Queue() + for i in range(max_async): + await position_pool.put(i) + + # Create lock to serialize tqdm creation and prevent race conditions + # This ensures progress bars are created one at a time, avoiding display conflicts + pbar_creation_lock = asyncio.Lock() + + # Create shared HTTP client with connection pooling and proper timeouts + # Timeout: 3 minutes for connect, 5 minutes for read (LLM can be slow) + timeout = httpx.Timeout( + TOTAL_TIMEOUT_SECONDS, + connect=CONNECT_TIMEOUT_SECONDS, + read=READ_TIMEOUT_SECONDS, + ) + limits = httpx.Limits( + max_connections=(max_async + 1) * 2, # Allow buffer for RAG stage + max_keepalive_connections=max_async + 1, + ) + + async with httpx.AsyncClient(timeout=timeout, limits=limits) as client: + # Create tasks for all test cases + tasks = [ + self.evaluate_single_case( + idx, + test_case, + rag_semaphore, + eval_semaphore, + client, + progress_counter, + position_pool, + pbar_creation_lock, + ) + for idx, test_case in enumerate(self.test_cases, 1) + ] + + # Run all evaluations in parallel (limited by two-stage semaphores) + results = await asyncio.gather(*tasks) + + return list(results) + + def _export_to_csv(self, results: List[Dict[str, Any]]) -> Path: + """ + Export evaluation results to CSV file + + Args: + results: List of evaluation results + + Returns: + Path to the CSV file + + CSV Format: + - question: The test question + - project: Project context + - faithfulness: Faithfulness score (0-1) + - answer_relevance: Answer relevance score (0-1) + - context_recall: Context recall score (0-1) + - context_precision: Context precision score (0-1) + - ragas_score: Overall RAGAS score (0-1) + - timestamp: When evaluation was run + """ + csv_path = ( + self.results_dir / f"results_{datetime.now().strftime('%Y%m%d_%H%M%S')}.csv" + ) + + with open(csv_path, "w", newline="", encoding="utf-8") as f: + fieldnames = [ + "test_number", + "question", + "project", + "faithfulness", + "answer_relevance", + "context_recall", + "context_precision", + "ragas_score", + "status", + "timestamp", + ] + + writer = csv.DictWriter(f, fieldnames=fieldnames) + writer.writeheader() + + for idx, result in enumerate(results, 1): + metrics = result.get("metrics", {}) + writer.writerow( + { + "test_number": idx, + "question": result.get("question", ""), + "project": result.get("project", "unknown"), + "faithfulness": f"{metrics.get('faithfulness', 0):.4f}", + "answer_relevance": f"{metrics.get('answer_relevance', 0):.4f}", + "context_recall": f"{metrics.get('context_recall', 0):.4f}", + "context_precision": f"{metrics.get('context_precision', 0):.4f}", + "ragas_score": f"{result.get('ragas_score', 0):.4f}", + "status": "success" if metrics else "error", + "timestamp": result.get("timestamp", ""), + } + ) + + return csv_path + + def _format_metric(self, value: float, width: int = 6) -> str: + """ + Format a metric value for display, handling NaN gracefully + + Args: + value: The metric value to format + width: The width of the formatted string + + Returns: + Formatted string (e.g., "0.8523" or " N/A ") + """ + if _is_nan(value): + return "N/A".center(width) + return f"{value:.4f}".rjust(width) + + def _display_results_table(self, results: List[Dict[str, Any]]): + """ + Display evaluation results in a formatted table + + Args: + results: List of evaluation results + """ + logger.info("") + logger.info("%s", "=" * 115) + logger.info("📊 EVALUATION RESULTS SUMMARY") + logger.info("%s", "=" * 115) + + # Table header + logger.info( + "%-4s | %-50s | %6s | %7s | %6s | %7s | %6s | %6s", + "#", + "Question", + "Faith", + "AnswRel", + "CtxRec", + "CtxPrec", + "RAGAS", + "Status", + ) + logger.info("%s", "-" * 115) + + # Table rows + for result in results: + test_num = result.get("test_number", 0) + question = result.get("question", "") + # Truncate question to 50 chars + question_display = ( + (question[:47] + "...") if len(question) > 50 else question + ) + + metrics = result.get("metrics", {}) + if metrics: + # Success case - format each metric, handling NaN values + faith = metrics.get("faithfulness", 0) + ans_rel = metrics.get("answer_relevance", 0) + ctx_rec = metrics.get("context_recall", 0) + ctx_prec = metrics.get("context_precision", 0) + ragas = result.get("ragas_score", 0) + status = "✓" + + logger.info( + "%-4d | %-50s | %s | %s | %s | %s | %s | %6s", + test_num, + question_display, + self._format_metric(faith, 6), + self._format_metric(ans_rel, 7), + self._format_metric(ctx_rec, 6), + self._format_metric(ctx_prec, 7), + self._format_metric(ragas, 6), + status, + ) + else: + # Error case + error = result.get("error", "Unknown error") + error_display = (error[:20] + "...") if len(error) > 23 else error + logger.info( + "%-4d | %-50s | %6s | %7s | %6s | %7s | %6s | ✗ %s", + test_num, + question_display, + "N/A", + "N/A", + "N/A", + "N/A", + "N/A", + error_display, + ) + + logger.info("%s", "=" * 115) + + def _calculate_benchmark_stats( + self, results: List[Dict[str, Any]] + ) -> Dict[str, Any]: + """ + Calculate benchmark statistics from evaluation results + + Args: + results: List of evaluation results + + Returns: + Dictionary with benchmark statistics + """ + # Filter out results with errors + valid_results = [r for r in results if r.get("metrics")] + total_tests = len(results) + successful_tests = len(valid_results) + failed_tests = total_tests - successful_tests + + if not valid_results: + return { + "total_tests": total_tests, + "successful_tests": 0, + "failed_tests": failed_tests, + "success_rate": 0.0, + } + + # Calculate averages for each metric (handling NaN values correctly) + # Track both sum and count for each metric to handle NaN values properly + metrics_data = { + "faithfulness": {"sum": 0.0, "count": 0}, + "answer_relevance": {"sum": 0.0, "count": 0}, + "context_recall": {"sum": 0.0, "count": 0}, + "context_precision": {"sum": 0.0, "count": 0}, + "ragas_score": {"sum": 0.0, "count": 0}, + } + + for result in valid_results: + metrics = result.get("metrics", {}) + + # For each metric, sum non-NaN values and count them + faithfulness = metrics.get("faithfulness", 0) + if not _is_nan(faithfulness): + metrics_data["faithfulness"]["sum"] += faithfulness + metrics_data["faithfulness"]["count"] += 1 + + answer_relevance = metrics.get("answer_relevance", 0) + if not _is_nan(answer_relevance): + metrics_data["answer_relevance"]["sum"] += answer_relevance + metrics_data["answer_relevance"]["count"] += 1 + + context_recall = metrics.get("context_recall", 0) + if not _is_nan(context_recall): + metrics_data["context_recall"]["sum"] += context_recall + metrics_data["context_recall"]["count"] += 1 + + context_precision = metrics.get("context_precision", 0) + if not _is_nan(context_precision): + metrics_data["context_precision"]["sum"] += context_precision + metrics_data["context_precision"]["count"] += 1 + + ragas_score = result.get("ragas_score", 0) + if not _is_nan(ragas_score): + metrics_data["ragas_score"]["sum"] += ragas_score + metrics_data["ragas_score"]["count"] += 1 + + # Calculate averages using actual counts for each metric + avg_metrics = {} + for metric_name, data in metrics_data.items(): + if data["count"] > 0: + avg_val = data["sum"] / data["count"] + avg_metrics[metric_name] = ( + round(avg_val, 4) if not _is_nan(avg_val) else 0.0 + ) + else: + avg_metrics[metric_name] = 0.0 + + # Find min and max RAGAS scores (filter out NaN) + ragas_scores = [] + for r in valid_results: + score = r.get("ragas_score", 0) + if _is_nan(score): + continue # Skip NaN values + ragas_scores.append(score) + + min_score = min(ragas_scores) if ragas_scores else 0 + max_score = max(ragas_scores) if ragas_scores else 0 + + return { + "total_tests": total_tests, + "successful_tests": successful_tests, + "failed_tests": failed_tests, + "success_rate": round(successful_tests / total_tests * 100, 2), + "average_metrics": avg_metrics, + "min_ragas_score": round(min_score, 4), + "max_ragas_score": round(max_score, 4), + } + + async def run(self) -> Dict[str, Any]: + """Run complete evaluation pipeline""" + + start_time = time.time() + + # Evaluate responses + results = await self.evaluate_responses() + + elapsed_time = time.time() - start_time + + # Calculate benchmark statistics + benchmark_stats = self._calculate_benchmark_stats(results) + + # Save results + summary = { + "timestamp": datetime.now().isoformat(), + "total_tests": len(results), + "elapsed_time_seconds": round(elapsed_time, 2), + "benchmark_stats": benchmark_stats, + "results": results, + } + + # Display results table + self._display_results_table(results) + + # Save JSON results + json_path = ( + self.results_dir + / f"results_{datetime.now().strftime('%Y%m%d_%H%M%S')}.json" + ) + with open(json_path, "w") as f: + json.dump(summary, f, indent=2) + + # Export to CSV + csv_path = self._export_to_csv(results) + + # Print summary + logger.info("") + logger.info("%s", "=" * 70) + logger.info("📊 EVALUATION COMPLETE") + logger.info("%s", "=" * 70) + logger.info("Total Tests: %s", len(results)) + logger.info("Successful: %s", benchmark_stats["successful_tests"]) + logger.info("Failed: %s", benchmark_stats["failed_tests"]) + logger.info("Success Rate: %.2f%%", benchmark_stats["success_rate"]) + logger.info("Elapsed Time: %.2f seconds", elapsed_time) + logger.info("Avg Time/Test: %.2f seconds", elapsed_time / len(results)) + + # Print benchmark metrics + logger.info("") + logger.info("%s", "=" * 70) + logger.info("📈 BENCHMARK RESULTS (Average)") + logger.info("%s", "=" * 70) + avg = benchmark_stats["average_metrics"] + logger.info("Average Faithfulness: %.4f", avg["faithfulness"]) + logger.info("Average Answer Relevance: %.4f", avg["answer_relevance"]) + logger.info("Average Context Recall: %.4f", avg["context_recall"]) + logger.info("Average Context Precision: %.4f", avg["context_precision"]) + logger.info("Average RAGAS Score: %.4f", avg["ragas_score"]) + logger.info("%s", "-" * 70) + logger.info( + "Min RAGAS Score: %.4f", + benchmark_stats["min_ragas_score"], + ) + logger.info( + "Max RAGAS Score: %.4f", + benchmark_stats["max_ragas_score"], + ) + + logger.info("") + logger.info("%s", "=" * 70) + logger.info("📁 GENERATED FILES") + logger.info("%s", "=" * 70) + logger.info("Results Dir: %s", self.results_dir.absolute()) + logger.info(" • CSV: %s", csv_path.name) + logger.info(" • JSON: %s", json_path.name) + logger.info("%s", "=" * 70) + + return summary + + +async def main(): + """ + Main entry point for RAGAS evaluation + + Command-line arguments: + --dataset, -d: Path to test dataset JSON file (default: sample_dataset.json) + --ragendpoint, -r: LightRAG API endpoint URL (default: http://localhost:9621 or $LIGHTRAG_API_URL) + + Usage: + python lightrag/evaluation/eval_rag_quality.py + python lightrag/evaluation/eval_rag_quality.py --dataset my_test.json + python lightrag/evaluation/eval_rag_quality.py -d my_test.json -r http://localhost:9621 + """ + try: + # Parse command-line arguments + parser = argparse.ArgumentParser( + description="RAGAS Evaluation Script for LightRAG System", + formatter_class=argparse.RawDescriptionHelpFormatter, + epilog=""" +Examples: + # Use defaults + python lightrag/evaluation/eval_rag_quality.py + + # Specify custom dataset + python lightrag/evaluation/eval_rag_quality.py --dataset my_test.json + + # Specify custom RAG endpoint + python lightrag/evaluation/eval_rag_quality.py --ragendpoint http://my-server.com:9621 + + # Specify both + python lightrag/evaluation/eval_rag_quality.py -d my_test.json -r http://localhost:9621 + """, + ) + + parser.add_argument( + "--dataset", + "-d", + type=str, + default=None, + help="Path to test dataset JSON file (default: sample_dataset.json in evaluation directory)", + ) + + parser.add_argument( + "--ragendpoint", + "-r", + type=str, + default=None, + help="LightRAG API endpoint URL (default: http://localhost:9621 or $LIGHTRAG_API_URL environment variable)", + ) + + args = parser.parse_args() + + logger.info("%s", "=" * 70) + logger.info("🔍 RAGAS Evaluation - Using Real LightRAG API") + logger.info("%s", "=" * 70) + + evaluator = RAGEvaluator( + test_dataset_path=args.dataset, rag_api_url=args.ragendpoint + ) + await evaluator.run() + except Exception as e: + logger.exception("❌ Error: %s", e) + sys.exit(1) + + +if __name__ == "__main__": + asyncio.run(main()) diff --git a/LightRAG/lightrag/evaluation/sample_dataset.json b/LightRAG/lightrag/evaluation/sample_dataset.json new file mode 100644 index 00000000..0e452467 --- /dev/null +++ b/LightRAG/lightrag/evaluation/sample_dataset.json @@ -0,0 +1,34 @@ +{ + "test_cases": [ + { + "question": "How does LightRAG solve the hallucination problem in large language models?", + "ground_truth": "LightRAG solves the hallucination problem by combining large language models with external knowledge retrieval. The framework ensures accurate responses by grounding LLM outputs in actual documents. LightRAG provides contextual responses that reduce hallucinations significantly.", + "project": "lightrag_evaluation_sample" + }, + { + "question": "What are the three main components required in a RAG system?", + "ground_truth": "A RAG system requires three main components: a retrieval system (vector database or search engine) to find relevant documents, an embedding model to convert text into vector representations for similarity search, and a large language model (LLM) to generate responses based on retrieved context.", + "project": "lightrag_evaluation_sample" + }, + { + "question": "How does LightRAG's retrieval performance compare to traditional RAG approaches?", + "ground_truth": "LightRAG delivers faster retrieval performance than traditional RAG approaches. The framework optimizes document retrieval operations for speed. Traditional RAG systems often suffer from slow query response times. LightRAG achieves high quality results with improved performance. The framework combines speed with accuracy in retrieval operations, prioritizing ease of use without sacrificing quality.", + "project": "lightrag_evaluation_sample" + }, + { + "question": "What vector databases does LightRAG support and what are their key characteristics?", + "ground_truth": "LightRAG supports multiple vector databases including ChromaDB for simple deployment and efficient similarity search, Neo4j for graph-based knowledge representation with vector capabilities, Milvus for high-performance vector search at scale, Qdrant for fast similarity search with filtering and production-ready infrastructure, MongoDB Atlas for combined document storage and vector search, Redis for in-memory low-latency vector search, and a built-in nano-vectordb that eliminates external dependencies for small projects. This multi-database support enables developers to choose appropriate backends based on scale, performance, and infrastructure requirements.", + "project": "lightrag_evaluation_sample" + }, + { + "question": "What are the four key metrics for evaluating RAG system quality and what does each metric measure?", + "ground_truth": "RAG system quality is measured through four key metrics: Faithfulness measures whether answers are factually grounded in retrieved context and detects hallucinations. Answer Relevance measures how well answers address the user question and evaluates response appropriateness. Context Recall measures completeness of retrieval and whether all relevant information was retrieved from documents. Context Precision measures quality and relevance of retrieved documents without noise or irrelevant content.", + "project": "lightrag_evaluation_sample" + }, + { + "question": "What are the core benefits of LightRAG and how does it improve upon traditional RAG systems?", + "ground_truth": "LightRAG offers five core benefits: accuracy through document-grounded responses, up-to-date information without model retraining, domain expertise through specialized document collections, cost-effectiveness by avoiding expensive fine-tuning, and transparency by showing source documents. Compared to traditional RAG systems, LightRAG provides a simpler API with intuitive interfaces, faster retrieval performance with optimized operations, better integration with multiple vector database backends for flexible selection, and optimized prompting strategies with refined templates. LightRAG prioritizes ease of use while maintaining quality and combines speed with accuracy.", + "project": "lightrag_evaluation_sample" + } + ] +} diff --git a/LightRAG/lightrag/evaluation/sample_documents/01_lightrag_overview.md b/LightRAG/lightrag/evaluation/sample_documents/01_lightrag_overview.md new file mode 100644 index 00000000..be9781a9 --- /dev/null +++ b/LightRAG/lightrag/evaluation/sample_documents/01_lightrag_overview.md @@ -0,0 +1,17 @@ +# LightRAG Framework Overview + +## What is LightRAG? + +**LightRAG** is a Simple and Fast Retrieval-Augmented Generation framework. LightRAG was developed by HKUDS (Hong Kong University Data Science Lab). The framework provides developers with tools to build RAG applications efficiently. + +## Problem Statement + +Large language models face several limitations. LLMs have a knowledge cutoff date that prevents them from accessing recent information. Large language models generate hallucinations when providing responses without factual grounding. LLMs lack domain-specific expertise in specialized fields. + +## How LightRAG Solves These Problems + +LightRAG solves the hallucination problem by combining large language models with external knowledge retrieval. The framework ensures accurate responses by grounding LLM outputs in actual documents. LightRAG provides contextual responses that reduce hallucinations significantly. The system enables efficient retrieval from external knowledge bases to supplement LLM capabilities. + +## Core Benefits + +LightRAG offers accuracy through document-grounded responses. The framework provides up-to-date information without model retraining. LightRAG enables domain expertise through specialized document collections. The system delivers cost-effectiveness by avoiding expensive model fine-tuning. LightRAG ensures transparency by showing source documents for each response. diff --git a/LightRAG/lightrag/evaluation/sample_documents/02_rag_architecture.md b/LightRAG/lightrag/evaluation/sample_documents/02_rag_architecture.md new file mode 100644 index 00000000..d34e08e8 --- /dev/null +++ b/LightRAG/lightrag/evaluation/sample_documents/02_rag_architecture.md @@ -0,0 +1,21 @@ +# RAG System Architecture + +## Main Components of RAG Systems + +A RAG system consists of three main components that work together to provide intelligent responses. + +### Component 1: Retrieval System + +The retrieval system is the first component of a RAG system. A retrieval system finds relevant documents from large document collections. Vector databases serve as the primary storage for the retrieval system. Search engines can also function as retrieval systems in RAG architectures. + +### Component 2: Embedding Model + +The embedding model is the second component of a RAG system. An embedding model converts text into vector representations for similarity search. The embedding model transforms documents and queries into numerical vectors. These vector representations enable semantic similarity matching between queries and documents. + +### Component 3: Large Language Model + +The large language model is the third component of a RAG system. An LLM generates responses based on retrieved context from documents. The large language model synthesizes information from multiple sources into coherent answers. LLMs provide natural language generation capabilities for the RAG system. + +## How Components Work Together + +The retrieval system fetches relevant documents for a user query. The embedding model enables similarity matching between query and documents. The LLM generates the final response using retrieved context. These three components collaborate to provide accurate, contextual responses. diff --git a/LightRAG/lightrag/evaluation/sample_documents/03_lightrag_improvements.md b/LightRAG/lightrag/evaluation/sample_documents/03_lightrag_improvements.md new file mode 100644 index 00000000..dc9b0f36 --- /dev/null +++ b/LightRAG/lightrag/evaluation/sample_documents/03_lightrag_improvements.md @@ -0,0 +1,25 @@ +# LightRAG Improvements Over Traditional RAG + +## Key Improvements + +LightRAG improves upon traditional RAG approaches in several significant ways. + +### Simpler API Design + +LightRAG offers a simpler API compared to traditional RAG frameworks. The framework provides intuitive interfaces for developers. Traditional RAG systems often require complex configuration and setup. LightRAG focuses on ease of use while maintaining functionality. + +### Faster Retrieval Performance + +LightRAG delivers faster retrieval performance than traditional RAG approaches. The framework optimizes document retrieval operations for speed. Traditional RAG systems often suffer from slow query response times. LightRAG achieves high quality results with improved performance. + +### Better Vector Database Integration + +LightRAG provides better integration with various vector databases. The framework supports multiple vector database backends seamlessly. Traditional RAG approaches typically lock developers into specific database choices. LightRAG enables flexible storage backend selection. + +### Optimized Prompting Strategies + +LightRAG implements optimized prompting strategies for better results. The framework uses refined prompt templates for accurate responses. Traditional RAG systems often use generic prompting approaches. LightRAG balances simplicity with high quality output. + +## Design Philosophy + +LightRAG prioritizes ease of use without sacrificing quality. The framework combines speed with accuracy in retrieval operations. LightRAG maintains flexibility in database and model selection. diff --git a/LightRAG/lightrag/evaluation/sample_documents/04_supported_databases.md b/LightRAG/lightrag/evaluation/sample_documents/04_supported_databases.md new file mode 100644 index 00000000..9bb03dbb --- /dev/null +++ b/LightRAG/lightrag/evaluation/sample_documents/04_supported_databases.md @@ -0,0 +1,37 @@ +# LightRAG Vector Database Support + +## Supported Vector Databases + +LightRAG supports multiple vector databases for flexible deployment options. + +### ChromaDB + +ChromaDB is a vector database supported by LightRAG. ChromaDB provides simple deployment for development environments. The database offers efficient vector similarity search capabilities. + +### Neo4j + +Neo4j is a graph database supported by LightRAG. Neo4j enables graph-based knowledge representation alongside vector search. The database combines relationship modeling with vector capabilities. + +### Milvus + +Milvus is a vector database supported by LightRAG. Milvus provides high-performance vector search at scale. The database handles large-scale vector collections efficiently. + +### Qdrant + +Qdrant is a vector database supported by LightRAG. Qdrant offers fast similarity search with filtering capabilities. The database provides production-ready vector search infrastructure. + +### MongoDB Atlas Vector Search + +MongoDB Atlas Vector Search is supported by LightRAG. MongoDB Atlas combines document storage with vector search capabilities. The database enables unified data management for RAG applications. + +### Redis + +Redis is supported by LightRAG for vector search operations. Redis provides in-memory vector search with low latency. The database offers fast retrieval for real-time applications. + +### Built-in Nano-VectorDB + +LightRAG includes a built-in nano-vectordb for simple deployments. Nano-vectordb eliminates external database dependencies for small projects. The built-in database provides basic vector search functionality without additional setup. + +## Database Selection Benefits + +The multiple database support enables developers to choose appropriate storage backends. LightRAG adapts to different deployment scenarios from development to production. Users can select databases based on scale, performance, and infrastructure requirements. diff --git a/LightRAG/lightrag/evaluation/sample_documents/05_evaluation_and_deployment.md b/LightRAG/lightrag/evaluation/sample_documents/05_evaluation_and_deployment.md new file mode 100644 index 00000000..39e8decc --- /dev/null +++ b/LightRAG/lightrag/evaluation/sample_documents/05_evaluation_and_deployment.md @@ -0,0 +1,41 @@ +# RAG Evaluation Metrics and Deployment + +## Key RAG Evaluation Metrics + +RAG system quality is measured through four key metrics. + +### Faithfulness Metric + +Faithfulness measures whether answers are factually grounded in retrieved context. The faithfulness metric detects hallucinations in LLM responses. High faithfulness scores indicate answers based on actual document content. The metric evaluates factual accuracy of generated responses. + +### Answer Relevance Metric + +Answer Relevance measures how well answers address the user question. The answer relevance metric evaluates response quality and appropriateness. High answer relevance scores show responses that directly answer user queries. The metric assesses the connection between questions and generated answers. + +### Context Recall Metric + +Context Recall measures completeness of retrieval from documents. The context recall metric evaluates whether all relevant information was retrieved. High context recall scores indicate comprehensive document retrieval. The metric assesses retrieval system effectiveness. + +### Context Precision Metric + +Context Precision measures quality and relevance of retrieved documents. The context precision metric evaluates retrieval accuracy without noise. High context precision scores show clean retrieval without irrelevant content. The metric measures retrieval system selectivity. + +## LightRAG Deployment Options + +LightRAG can be deployed in production through multiple approaches. + +### Docker Container Deployment + +Docker containers enable consistent LightRAG deployment across environments. Docker provides isolated runtime environments for the framework. Container deployment simplifies dependency management and scaling. + +### REST API Server with FastAPI + +FastAPI serves as the REST API framework for LightRAG deployment. The FastAPI server exposes LightRAG functionality through HTTP endpoints. REST API deployment enables client-server architecture for RAG applications. + +### Direct Python Integration + +Direct Python integration embeds LightRAG into Python applications. Python integration provides programmatic access to RAG capabilities. Direct integration supports custom application workflows and pipelines. + +### Deployment Features + +LightRAG supports environment-based configuration for different deployment scenarios. The framework integrates with multiple LLM providers for flexibility. LightRAG enables horizontal scaling for production workloads. diff --git a/LightRAG/lightrag/evaluation/sample_documents/README.md b/LightRAG/lightrag/evaluation/sample_documents/README.md new file mode 100644 index 00000000..3027ca48 --- /dev/null +++ b/LightRAG/lightrag/evaluation/sample_documents/README.md @@ -0,0 +1,21 @@ +# Sample Documents for Evaluation + +These markdown files correspond to test questions in `../sample_dataset.json`. + +## Usage + +1. **Index documents** into LightRAG (via WebUI, API, or Python) +2. **Run evaluation**: `python lightrag/evaluation/eval_rag_quality.py` +3. **Expected results**: ~91-100% RAGAS score per question + +## Files + +- `01_lightrag_overview.md` - LightRAG framework and hallucination problem +- `02_rag_architecture.md` - RAG system components +- `03_lightrag_improvements.md` - LightRAG vs traditional RAG +- `04_supported_databases.md` - Vector database support +- `05_evaluation_and_deployment.md` - Metrics and deployment + +## Note + +Documents use clear entity-relationship patterns for LightRAG's default entity extraction prompts. For better results with your data, customize `lightrag/prompt.py`. diff --git a/LightRAG/lightrag/exceptions.py b/LightRAG/lightrag/exceptions.py new file mode 100644 index 00000000..303391c2 --- /dev/null +++ b/LightRAG/lightrag/exceptions.py @@ -0,0 +1,114 @@ +from __future__ import annotations + +import httpx +from typing import Literal + + +class APIStatusError(Exception): + """Raised when an API response has a status code of 4xx or 5xx.""" + + response: httpx.Response + status_code: int + request_id: str | None + + def __init__( + self, message: str, *, response: httpx.Response, body: object | None + ) -> None: + super().__init__(message, response.request, body=body) + self.response = response + self.status_code = response.status_code + self.request_id = response.headers.get("x-request-id") + + +class APIConnectionError(Exception): + def __init__( + self, *, message: str = "Connection error.", request: httpx.Request + ) -> None: + super().__init__(message, request, body=None) + + +class BadRequestError(APIStatusError): + status_code: Literal[400] = 400 # pyright: ignore[reportIncompatibleVariableOverride] + + +class AuthenticationError(APIStatusError): + status_code: Literal[401] = 401 # pyright: ignore[reportIncompatibleVariableOverride] + + +class PermissionDeniedError(APIStatusError): + status_code: Literal[403] = 403 # pyright: ignore[reportIncompatibleVariableOverride] + + +class NotFoundError(APIStatusError): + status_code: Literal[404] = 404 # pyright: ignore[reportIncompatibleVariableOverride] + + +class ConflictError(APIStatusError): + status_code: Literal[409] = 409 # pyright: ignore[reportIncompatibleVariableOverride] + + +class UnprocessableEntityError(APIStatusError): + status_code: Literal[422] = 422 # pyright: ignore[reportIncompatibleVariableOverride] + + +class RateLimitError(APIStatusError): + status_code: Literal[429] = 429 # pyright: ignore[reportIncompatibleVariableOverride] + + +class APITimeoutError(APIConnectionError): + def __init__(self, request: httpx.Request) -> None: + super().__init__(message="Request timed out.", request=request) + + +class StorageNotInitializedError(RuntimeError): + """Raised when storage operations are attempted before initialization.""" + + def __init__(self, storage_type: str = "Storage"): + super().__init__( + f"{storage_type} not initialized. Please ensure proper initialization:\n" + f"\n" + f" rag = LightRAG(...)\n" + f" await rag.initialize_storages() # Required\n" + f" \n" + f" from lightrag.kg.shared_storage import initialize_pipeline_status\n" + f" await initialize_pipeline_status() # Required for pipeline operations\n" + f"\n" + f"See: https://github.com/HKUDS/LightRAG#important-initialization-requirements" + ) + + +class PipelineNotInitializedError(KeyError): + """Raised when pipeline status is accessed before initialization.""" + + def __init__(self, namespace: str = ""): + msg = ( + f"Pipeline namespace '{namespace}' not found. " + f"This usually means pipeline status was not initialized.\n" + f"\n" + f"Please call 'await initialize_pipeline_status()' after initializing storages:\n" + f"\n" + f" from lightrag.kg.shared_storage import initialize_pipeline_status\n" + f" await initialize_pipeline_status()\n" + f"\n" + f"Full initialization sequence:\n" + f" rag = LightRAG(...)\n" + f" await rag.initialize_storages()\n" + f" await initialize_pipeline_status()" + ) + super().__init__(msg) + + +class PipelineCancelledException(Exception): + """Raised when pipeline processing is cancelled by user request.""" + + def __init__(self, message: str = "User cancelled"): + super().__init__(message) + self.message = message + + +class QdrantMigrationError(Exception): + """Raised when Qdrant data migration from legacy collections fails.""" + + def __init__(self, message: str): + super().__init__(message) + self.message = message diff --git a/LightRAG/lightrag/kg/__init__.py b/LightRAG/lightrag/kg/__init__.py new file mode 100644 index 00000000..c62f7a3d --- /dev/null +++ b/LightRAG/lightrag/kg/__init__.py @@ -0,0 +1,140 @@ +STORAGE_IMPLEMENTATIONS = { + "KV_STORAGE": { + "implementations": [ + "JsonKVStorage", + "RedisKVStorage", + "PGKVStorage", + "MongoKVStorage", + ], + "required_methods": ["get_by_id", "upsert"], + }, + "GRAPH_STORAGE": { + "implementations": [ + "NetworkXStorage", + "Neo4JStorage", + "PGGraphStorage", + "MongoGraphStorage", + "MemgraphStorage", + ], + "required_methods": ["upsert_node", "upsert_edge"], + }, + "VECTOR_STORAGE": { + "implementations": [ + "NanoVectorDBStorage", + "MilvusVectorDBStorage", + "PGVectorStorage", + "FaissVectorDBStorage", + "QdrantVectorDBStorage", + "MongoVectorDBStorage", + # "ChromaVectorDBStorage", + ], + "required_methods": ["query", "upsert"], + }, + "DOC_STATUS_STORAGE": { + "implementations": [ + "JsonDocStatusStorage", + "RedisDocStatusStorage", + "PGDocStatusStorage", + "MongoDocStatusStorage", + ], + "required_methods": ["get_docs_by_status"], + }, +} + +# Storage implementation environment variable without default value +STORAGE_ENV_REQUIREMENTS: dict[str, list[str]] = { + # KV Storage Implementations + "JsonKVStorage": [], + "MongoKVStorage": [ + "MONGO_URI", + "MONGO_DATABASE", + ], + "RedisKVStorage": ["REDIS_URI"], + "PGKVStorage": ["POSTGRES_USER", "POSTGRES_PASSWORD", "POSTGRES_DATABASE"], + # Graph Storage Implementations + "NetworkXStorage": [], + "Neo4JStorage": ["NEO4J_URI", "NEO4J_USERNAME", "NEO4J_PASSWORD"], + "MongoGraphStorage": [ + "MONGO_URI", + "MONGO_DATABASE", + ], + "MemgraphStorage": ["MEMGRAPH_URI"], + "AGEStorage": [ + "AGE_POSTGRES_DB", + "AGE_POSTGRES_USER", + "AGE_POSTGRES_PASSWORD", + ], + "PGGraphStorage": [ + "POSTGRES_USER", + "POSTGRES_PASSWORD", + "POSTGRES_DATABASE", + ], + # Vector Storage Implementations + "NanoVectorDBStorage": [], + "MilvusVectorDBStorage": [ + "MILVUS_URI", + "MILVUS_DB_NAME", + ], + # "ChromaVectorDBStorage": [], + "PGVectorStorage": ["POSTGRES_USER", "POSTGRES_PASSWORD", "POSTGRES_DATABASE"], + "FaissVectorDBStorage": [], + "QdrantVectorDBStorage": ["QDRANT_URL"], # QDRANT_API_KEY has default value None + "MongoVectorDBStorage": [ + "MONGO_URI", + "MONGO_DATABASE", + ], + # Document Status Storage Implementations + "JsonDocStatusStorage": [], + "RedisDocStatusStorage": ["REDIS_URI"], + "PGDocStatusStorage": ["POSTGRES_USER", "POSTGRES_PASSWORD", "POSTGRES_DATABASE"], + "MongoDocStatusStorage": [ + "MONGO_URI", + "MONGO_DATABASE", + ], +} + +# Storage implementation module mapping +STORAGES = { + "NetworkXStorage": ".kg.networkx_impl", + "JsonKVStorage": ".kg.json_kv_impl", + "NanoVectorDBStorage": ".kg.nano_vector_db_impl", + "JsonDocStatusStorage": ".kg.json_doc_status_impl", + "Neo4JStorage": ".kg.neo4j_impl", + "MilvusVectorDBStorage": ".kg.milvus_impl", + "MongoKVStorage": ".kg.mongo_impl", + "MongoDocStatusStorage": ".kg.mongo_impl", + "MongoGraphStorage": ".kg.mongo_impl", + "MongoVectorDBStorage": ".kg.mongo_impl", + "RedisKVStorage": ".kg.redis_impl", + "RedisDocStatusStorage": ".kg.redis_impl", + "ChromaVectorDBStorage": ".kg.chroma_impl", + "PGKVStorage": ".kg.postgres_impl", + "PGVectorStorage": ".kg.postgres_impl", + "AGEStorage": ".kg.age_impl", + "PGGraphStorage": ".kg.postgres_impl", + "PGDocStatusStorage": ".kg.postgres_impl", + "FaissVectorDBStorage": ".kg.faiss_impl", + "QdrantVectorDBStorage": ".kg.qdrant_impl", + "MemgraphStorage": ".kg.memgraph_impl", +} + + +def verify_storage_implementation(storage_type: str, storage_name: str) -> None: + """Verify if storage implementation is compatible with specified storage type + + Args: + storage_type: Storage type (KV_STORAGE, GRAPH_STORAGE etc.) + storage_name: Storage implementation name + + Raises: + ValueError: If storage implementation is incompatible or missing required methods + """ + if storage_type not in STORAGE_IMPLEMENTATIONS: + raise ValueError(f"Unknown storage type: {storage_type}") + + storage_info = STORAGE_IMPLEMENTATIONS[storage_type] + if storage_name not in storage_info["implementations"]: + raise ValueError( + f"Storage implementation '{storage_name}' is not compatible with {storage_type}. " + f"Compatible implementations are: {', '.join(storage_info['implementations'])}" + ) diff --git a/LightRAG/lightrag/kg/deprecated/chroma_impl.py b/LightRAG/lightrag/kg/deprecated/chroma_impl.py new file mode 100644 index 00000000..54bf9037 --- /dev/null +++ b/LightRAG/lightrag/kg/deprecated/chroma_impl.py @@ -0,0 +1,342 @@ +import asyncio +import os +from dataclasses import dataclass +from typing import Any, final +import numpy as np + +from lightrag.base import BaseVectorStorage +from lightrag.utils import logger +import pipmaster as pm + +if not pm.is_installed("chromadb"): + pm.install("chromadb") + +from chromadb import HttpClient, PersistentClient # type: ignore +from chromadb.config import Settings # type: ignore + + +@final +@dataclass +class ChromaVectorDBStorage(BaseVectorStorage): + """ChromaDB vector storage implementation.""" + + def __post_init__(self): + try: + config = self.global_config.get("vector_db_storage_cls_kwargs", {}) + cosine_threshold = config.get("cosine_better_than_threshold") + if cosine_threshold is None: + raise ValueError( + "cosine_better_than_threshold must be specified in vector_db_storage_cls_kwargs" + ) + self.cosine_better_than_threshold = cosine_threshold + + user_collection_settings = config.get("collection_settings", {}) + # Default HNSW index settings for ChromaDB + default_collection_settings = { + # Distance metric used for similarity search (cosine similarity) + "hnsw:space": "cosine", + # Number of nearest neighbors to explore during index construction + # Higher values = better recall but slower indexing + "hnsw:construction_ef": 128, + # Number of nearest neighbors to explore during search + # Higher values = better recall but slower search + "hnsw:search_ef": 128, + # Number of connections per node in the HNSW graph + # Higher values = better recall but more memory usage + "hnsw:M": 16, + # Number of vectors to process in one batch during indexing + "hnsw:batch_size": 100, + # Number of updates before forcing index synchronization + # Lower values = more frequent syncs but slower indexing + "hnsw:sync_threshold": 1000, + } + collection_settings = { + **default_collection_settings, + **user_collection_settings, + } + + local_path = config.get("local_path", None) + if local_path: + self._client = PersistentClient( + path=local_path, + settings=Settings( + allow_reset=True, + anonymized_telemetry=False, + ), + ) + else: + auth_provider = config.get( + "auth_provider", "chromadb.auth.token_authn.TokenAuthClientProvider" + ) + auth_credentials = config.get("auth_token", "secret-token") + headers = {} + + if "token_authn" in auth_provider: + headers = { + config.get( + "auth_header_name", "X-Chroma-Token" + ): auth_credentials + } + elif "basic_authn" in auth_provider: + auth_credentials = config.get("auth_credentials", "admin:admin") + + self._client = HttpClient( + host=config.get("host", "localhost"), + port=config.get("port", 8000), + headers=headers, + settings=Settings( + chroma_api_impl="rest", + chroma_client_auth_provider=auth_provider, + chroma_client_auth_credentials=auth_credentials, + allow_reset=True, + anonymized_telemetry=False, + ), + ) + + self._collection = self._client.get_or_create_collection( + name=self.namespace, + metadata={ + **collection_settings, + "dimension": self.embedding_func.embedding_dim, + }, + ) + # Use batch size from collection settings if specified + self._max_batch_size = self.global_config.get( + "embedding_batch_num", collection_settings.get("hnsw:batch_size", 32) + ) + except Exception as e: + logger.error(f"ChromaDB initialization failed: {str(e)}") + raise + + async def upsert(self, data: dict[str, dict[str, Any]]) -> None: + logger.debug(f"Inserting {len(data)} to {self.namespace}") + if not data: + return + + try: + import time + + current_time = int(time.time()) + + ids = list(data.keys()) + documents = [v["content"] for v in data.values()] + metadatas = [ + { + **{k: v for k, v in item.items() if k in self.meta_fields}, + "created_at": current_time, + } + or {"_default": "true", "created_at": current_time} + for item in data.values() + ] + + # Process in batches + batches = [ + documents[i : i + self._max_batch_size] + for i in range(0, len(documents), self._max_batch_size) + ] + + embedding_tasks = [self.embedding_func(batch) for batch in batches] + embeddings_list = [] + + # Pre-allocate embeddings_list with known size + embeddings_list = [None] * len(embedding_tasks) + + # Use asyncio.gather instead of as_completed if order doesn't matter + embeddings_results = await asyncio.gather(*embedding_tasks) + embeddings_list = list(embeddings_results) + + embeddings = np.concatenate(embeddings_list) + + # Upsert in batches + for i in range(0, len(ids), self._max_batch_size): + batch_slice = slice(i, i + self._max_batch_size) + + self._collection.upsert( + ids=ids[batch_slice], + embeddings=embeddings[batch_slice].tolist(), + documents=documents[batch_slice], + metadatas=metadatas[batch_slice], + ) + + return ids + + except Exception as e: + logger.error(f"Error during ChromaDB upsert: {str(e)}") + raise + + async def query(self, query: str, top_k: int) -> list[dict[str, Any]]: + try: + embedding = await self.embedding_func( + [query], _priority=5 + ) # higher priority for query + + results = self._collection.query( + query_embeddings=embedding.tolist() + if not isinstance(embedding, list) + else embedding, + n_results=top_k * 2, # Request more results to allow for filtering + include=["metadatas", "distances", "documents"], + ) + + # Filter results by cosine similarity threshold and take top k + # We request 2x results initially to have enough after filtering + # ChromaDB returns cosine similarity (1 = identical, 0 = orthogonal) + # We convert to distance (0 = identical, 1 = orthogonal) via (1 - similarity) + # Only keep results with distance below threshold, then take top k + return [ + { + "id": results["ids"][0][i], + "distance": 1 - results["distances"][0][i], + "content": results["documents"][0][i], + "created_at": results["metadatas"][0][i].get("created_at"), + **results["metadatas"][0][i], + } + for i in range(len(results["ids"][0])) + if (1 - results["distances"][0][i]) >= self.cosine_better_than_threshold + ][:top_k] + + except Exception as e: + logger.error(f"Error during ChromaDB query: {str(e)}") + raise + + async def index_done_callback(self) -> None: + # ChromaDB handles persistence automatically + pass + + async def delete_entity(self, entity_name: str) -> None: + """Delete an entity by its ID. + + Args: + entity_name: The ID of the entity to delete + """ + try: + logger.info(f"Deleting entity with ID {entity_name} from {self.namespace}") + self._collection.delete(ids=[entity_name]) + except Exception as e: + logger.error(f"Error during entity deletion: {str(e)}") + raise + + async def delete_entity_relation(self, entity_name: str) -> None: + """Delete an entity and its relations by ID. + In vector DB context, this is equivalent to delete_entity. + + Args: + entity_name: The ID of the entity to delete + """ + await self.delete_entity(entity_name) + + async def delete(self, ids: list[str]) -> None: + """Delete vectors with specified IDs + + Args: + ids: List of vector IDs to be deleted + """ + try: + self._collection.delete(ids=ids) + logger.debug( + f"Successfully deleted {len(ids)} vectors from {self.namespace}" + ) + except Exception as e: + logger.error(f"Error while deleting vectors from {self.namespace}: {e}") + raise + + except Exception as e: + logger.error(f"Error during prefix search in ChromaDB: {str(e)}") + raise + + async def get_by_id(self, id: str) -> dict[str, Any] | None: + """Get vector data by its ID + + Args: + id: The unique identifier of the vector + + Returns: + The vector data if found, or None if not found + """ + try: + # Query the collection for a single vector by ID + result = self._collection.get( + ids=[id], include=["metadatas", "embeddings", "documents"] + ) + + if not result or not result["ids"] or len(result["ids"]) == 0: + return None + + # Format the result to match the expected structure + return { + "id": result["ids"][0], + "vector": result["embeddings"][0], + "content": result["documents"][0], + "created_at": result["metadatas"][0].get("created_at"), + **result["metadatas"][0], + } + except Exception as e: + logger.error(f"Error retrieving vector data for ID {id}: {e}") + return None + + async def get_by_ids(self, ids: list[str]) -> list[dict[str, Any]]: + """Get multiple vector data by their IDs + + Args: + ids: List of unique identifiers + + Returns: + List of vector data objects that were found + """ + if not ids: + return [] + + try: + # Query the collection for multiple vectors by IDs + result = self._collection.get( + ids=ids, include=["metadatas", "embeddings", "documents"] + ) + + if not result or not result["ids"] or len(result["ids"]) == 0: + return [] + + # Format the results to match the expected structure and preserve ordering + formatted_map: dict[str, dict[str, Any]] = {} + for i, result_id in enumerate(result["ids"]): + record = { + "id": result_id, + "vector": result["embeddings"][i], + "content": result["documents"][i], + "created_at": result["metadatas"][i].get("created_at"), + **result["metadatas"][i], + } + formatted_map[str(result_id)] = record + + ordered_results: list[dict[str, Any] | None] = [] + for requested_id in ids: + ordered_results.append(formatted_map.get(str(requested_id))) + + return ordered_results + except Exception as e: + logger.error(f"Error retrieving vector data for IDs {ids}: {e}") + return [] + + async def drop(self) -> dict[str, str]: + """Drop all vector data from storage and clean up resources + + This method will delete all documents from the ChromaDB collection. + + Returns: + dict[str, str]: Operation status and message + - On success: {"status": "success", "message": "data dropped"} + - On failure: {"status": "error", "message": ""} + """ + try: + # Get all IDs in the collection + result = self._collection.get(include=[]) + if result and result["ids"] and len(result["ids"]) > 0: + # Delete all documents + self._collection.delete(ids=result["ids"]) + + logger.info( + f"Process {os.getpid()} drop ChromaDB collection {self.namespace}" + ) + return {"status": "success", "message": "data dropped"} + except Exception as e: + logger.error(f"Error dropping ChromaDB collection {self.namespace}: {e}") + return {"status": "error", "message": str(e)} diff --git a/LightRAG/lightrag/kg/faiss_impl.py b/LightRAG/lightrag/kg/faiss_impl.py new file mode 100644 index 00000000..2f10ab1a --- /dev/null +++ b/LightRAG/lightrag/kg/faiss_impl.py @@ -0,0 +1,541 @@ +import os +import time +import asyncio +from typing import Any, final +import json +import numpy as np +from dataclasses import dataclass + +from lightrag.utils import logger, compute_mdhash_id +from lightrag.base import BaseVectorStorage + +from .shared_storage import ( + get_storage_lock, + get_update_flag, + set_all_update_flags, +) + +# You must manually install faiss-cpu or faiss-gpu before using FAISS vector db +import faiss # type: ignore + + +@final +@dataclass +class FaissVectorDBStorage(BaseVectorStorage): + """ + A Faiss-based Vector DB Storage for LightRAG. + Uses cosine similarity by storing normalized vectors in a Faiss index with inner product search. + """ + + def __post_init__(self): + # Grab config values if available + kwargs = self.global_config.get("vector_db_storage_cls_kwargs", {}) + cosine_threshold = kwargs.get("cosine_better_than_threshold") + if cosine_threshold is None: + raise ValueError( + "cosine_better_than_threshold must be specified in vector_db_storage_cls_kwargs" + ) + self.cosine_better_than_threshold = cosine_threshold + + # Where to save index file if you want persistent storage + working_dir = self.global_config["working_dir"] + if self.workspace: + # Include workspace in the file path for data isolation + workspace_dir = os.path.join(working_dir, self.workspace) + self.final_namespace = f"{self.workspace}_{self.namespace}" + + else: + # Default behavior when workspace is empty + self.final_namespace = self.namespace + self.workspace = "_" + workspace_dir = working_dir + + os.makedirs(workspace_dir, exist_ok=True) + self._faiss_index_file = os.path.join( + workspace_dir, f"faiss_index_{self.namespace}.index" + ) + self._meta_file = self._faiss_index_file + ".meta.json" + + self._max_batch_size = self.global_config["embedding_batch_num"] + # Embedding dimension (e.g. 768) must match your embedding function + self._dim = self.embedding_func.embedding_dim + + # Create an empty Faiss index for inner product (useful for normalized vectors = cosine similarity). + # If you have a large number of vectors, you might want IVF or other indexes. + # For demonstration, we use a simple IndexFlatIP. + self._index = faiss.IndexFlatIP(self._dim) + # Keep a local store for metadata, IDs, etc. + # Maps → metadata (including your original ID). + self._id_to_meta = {} + + self._load_faiss_index() + + async def initialize(self): + """Initialize storage data""" + # Get the update flag for cross-process update notification + self.storage_updated = await get_update_flag(self.final_namespace) + # Get the storage lock for use in other methods + self._storage_lock = get_storage_lock() + + async def _get_index(self): + """Check if the shtorage should be reloaded""" + # Acquire lock to prevent concurrent read and write + async with self._storage_lock: + # Check if storage was updated by another process + if self.storage_updated.value: + logger.info( + f"[{self.workspace}] Process {os.getpid()} FAISS reloading {self.namespace} due to update by another process" + ) + # Reload data + self._index = faiss.IndexFlatIP(self._dim) + self._id_to_meta = {} + self._load_faiss_index() + self.storage_updated.value = False + return self._index + + async def upsert(self, data: dict[str, dict[str, Any]]) -> None: + """ + Insert or update vectors in the Faiss index. + + data: { + "custom_id_1": { + "content": , + ...metadata... + }, + "custom_id_2": { + "content": , + ...metadata... + }, + ... + } + """ + logger.debug( + f"[{self.workspace}] FAISS: Inserting {len(data)} to {self.namespace}" + ) + if not data: + return + + current_time = int(time.time()) + + # Prepare data for embedding + list_data = [] + contents = [] + for k, v in data.items(): + # Store only known meta fields if needed + meta = {mf: v[mf] for mf in self.meta_fields if mf in v} + meta["__id__"] = k + meta["__created_at__"] = current_time + list_data.append(meta) + contents.append(v["content"]) + + # Split into batches for embedding if needed + batches = [ + contents[i : i + self._max_batch_size] + for i in range(0, len(contents), self._max_batch_size) + ] + + embedding_tasks = [self.embedding_func(batch) for batch in batches] + embeddings_list = await asyncio.gather(*embedding_tasks) + + # Flatten the list of arrays + embeddings = np.concatenate(embeddings_list, axis=0) + if len(embeddings) != len(list_data): + logger.error( + f"[{self.workspace}] Embedding size mismatch. Embeddings: {len(embeddings)}, Data: {len(list_data)}" + ) + return [] + + # Convert to float32 and normalize embeddings for cosine similarity (in-place) + embeddings = embeddings.astype(np.float32) + faiss.normalize_L2(embeddings) + + # Upsert logic: + # 1. Identify which vectors to remove if they exist + # 2. Remove them + # 3. Add the new vectors + existing_ids_to_remove = [] + for meta, emb in zip(list_data, embeddings): + faiss_internal_id = self._find_faiss_id_by_custom_id(meta["__id__"]) + if faiss_internal_id is not None: + existing_ids_to_remove.append(faiss_internal_id) + + if existing_ids_to_remove: + await self._remove_faiss_ids(existing_ids_to_remove) + + # Step 2: Add new vectors + index = await self._get_index() + start_idx = index.ntotal + index.add(embeddings) + + # Step 3: Store metadata + vector for each new ID + for i, meta in enumerate(list_data): + fid = start_idx + i + # Store the raw vector so we can rebuild if something is removed + meta["__vector__"] = embeddings[i].tolist() + self._id_to_meta.update({fid: meta}) + + logger.debug( + f"[{self.workspace}] Upserted {len(list_data)} vectors into Faiss index." + ) + return [m["__id__"] for m in list_data] + + async def query( + self, query: str, top_k: int, query_embedding: list[float] = None + ) -> list[dict[str, Any]]: + """ + Search by a textual query; returns top_k results with their metadata + similarity distance. + """ + if query_embedding is not None: + embedding = np.array([query_embedding], dtype=np.float32) + else: + embedding = await self.embedding_func( + [query], _priority=5 + ) # higher priority for query + # embedding is shape (1, dim) + embedding = np.array(embedding, dtype=np.float32) + + faiss.normalize_L2(embedding) # we do in-place normalization + + # Perform the similarity search + index = await self._get_index() + distances, indices = index.search(embedding, top_k) + + distances = distances[0] + indices = indices[0] + + results = [] + for dist, idx in zip(distances, indices): + if idx == -1: + # Faiss returns -1 if no neighbor + continue + + # Cosine similarity threshold + if dist < self.cosine_better_than_threshold: + continue + + meta = self._id_to_meta.get(idx, {}) + # Filter out __vector__ from query results to avoid returning large vector data + filtered_meta = {k: v for k, v in meta.items() if k != "__vector__"} + results.append( + { + **filtered_meta, + "id": meta.get("__id__"), + "distance": float(dist), + "created_at": meta.get("__created_at__"), + } + ) + + return results + + @property + def client_storage(self): + # Return whatever structure LightRAG might need for debugging + return {"data": list(self._id_to_meta.values())} + + async def delete(self, ids: list[str]): + """ + Delete vectors for the provided custom IDs. + + Importance notes: + 1. Changes will be persisted to disk during the next index_done_callback + 2. Only one process should updating the storage at a time before index_done_callback, + KG-storage-log should be used to avoid data corruption + """ + logger.debug( + f"[{self.workspace}] Deleting {len(ids)} vectors from {self.namespace}" + ) + to_remove = [] + for cid in ids: + fid = self._find_faiss_id_by_custom_id(cid) + if fid is not None: + to_remove.append(fid) + + if to_remove: + await self._remove_faiss_ids(to_remove) + logger.debug( + f"[{self.workspace}] Successfully deleted {len(to_remove)} vectors from {self.namespace}" + ) + + async def delete_entity(self, entity_name: str) -> None: + """ + Importance notes: + 1. Changes will be persisted to disk during the next index_done_callback + 2. Only one process should updating the storage at a time before index_done_callback, + KG-storage-log should be used to avoid data corruption + """ + entity_id = compute_mdhash_id(entity_name, prefix="ent-") + logger.debug( + f"[{self.workspace}] Attempting to delete entity {entity_name} with ID {entity_id}" + ) + await self.delete([entity_id]) + + async def delete_entity_relation(self, entity_name: str) -> None: + """ + Importance notes: + 1. Changes will be persisted to disk during the next index_done_callback + 2. Only one process should updating the storage at a time before index_done_callback, + KG-storage-log should be used to avoid data corruption + """ + logger.debug(f"[{self.workspace}] Searching relations for entity {entity_name}") + relations = [] + for fid, meta in self._id_to_meta.items(): + if meta.get("src_id") == entity_name or meta.get("tgt_id") == entity_name: + relations.append(fid) + + logger.debug( + f"[{self.workspace}] Found {len(relations)} relations for {entity_name}" + ) + if relations: + await self._remove_faiss_ids(relations) + logger.debug( + f"[{self.workspace}] Deleted {len(relations)} relations for {entity_name}" + ) + + # -------------------------------------------------------------------------------- + # Internal helper methods + # -------------------------------------------------------------------------------- + + def _find_faiss_id_by_custom_id(self, custom_id: str): + """ + Return the Faiss internal ID for a given custom ID, or None if not found. + """ + for fid, meta in self._id_to_meta.items(): + if meta.get("__id__") == custom_id: + return fid + return None + + async def _remove_faiss_ids(self, fid_list): + """ + Remove a list of internal Faiss IDs from the index. + Because IndexFlatIP doesn't support 'removals', + we rebuild the index excluding those vectors. + """ + keep_fids = [fid for fid in self._id_to_meta if fid not in fid_list] + + # Rebuild the index + vectors_to_keep = [] + new_id_to_meta = {} + for new_fid, old_fid in enumerate(keep_fids): + vec_meta = self._id_to_meta[old_fid] + vectors_to_keep.append(vec_meta["__vector__"]) # stored as list + new_id_to_meta[new_fid] = vec_meta + + async with self._storage_lock: + # Re-init index + self._index = faiss.IndexFlatIP(self._dim) + if vectors_to_keep: + arr = np.array(vectors_to_keep, dtype=np.float32) + self._index.add(arr) + + self._id_to_meta = new_id_to_meta + + def _save_faiss_index(self): + """ + Save the current Faiss index + metadata to disk so it can persist across runs. + """ + faiss.write_index(self._index, self._faiss_index_file) + + # Save metadata dict to JSON. Convert all keys to strings for JSON storage. + # _id_to_meta is { int: { '__id__': doc_id, '__vector__': [float,...], ... } } + # We'll keep the int -> dict, but JSON requires string keys. + serializable_dict = {} + for fid, meta in self._id_to_meta.items(): + serializable_dict[str(fid)] = meta + + with open(self._meta_file, "w", encoding="utf-8") as f: + json.dump(serializable_dict, f) + + def _load_faiss_index(self): + """ + Load the Faiss index + metadata from disk if it exists, + and rebuild in-memory structures so we can query. + """ + if not os.path.exists(self._faiss_index_file): + logger.warning( + f"[{self.workspace}] No existing Faiss index file found for {self.namespace}" + ) + return + + try: + # Load the Faiss index + self._index = faiss.read_index(self._faiss_index_file) + # Load metadata + with open(self._meta_file, "r", encoding="utf-8") as f: + stored_dict = json.load(f) + + # Convert string keys back to int + self._id_to_meta = {} + for fid_str, meta in stored_dict.items(): + fid = int(fid_str) + self._id_to_meta[fid] = meta + + logger.info( + f"[{self.workspace}] Faiss index loaded with {self._index.ntotal} vectors from {self._faiss_index_file}" + ) + except Exception as e: + logger.error( + f"[{self.workspace}] Failed to load Faiss index or metadata: {e}" + ) + logger.warning(f"[{self.workspace}] Starting with an empty Faiss index.") + self._index = faiss.IndexFlatIP(self._dim) + self._id_to_meta = {} + + async def index_done_callback(self) -> None: + async with self._storage_lock: + # Check if storage was updated by another process + if self.storage_updated.value: + # Storage was updated by another process, reload data instead of saving + logger.warning( + f"[{self.workspace}] Storage for FAISS {self.namespace} was updated by another process, reloading..." + ) + self._index = faiss.IndexFlatIP(self._dim) + self._id_to_meta = {} + self._load_faiss_index() + self.storage_updated.value = False + return False # Return error + + # Acquire lock and perform persistence + async with self._storage_lock: + try: + # Save data to disk + self._save_faiss_index() + # Notify other processes that data has been updated + await set_all_update_flags(self.final_namespace) + # Reset own update flag to avoid self-reloading + self.storage_updated.value = False + except Exception as e: + logger.error( + f"[{self.workspace}] Error saving FAISS index for {self.namespace}: {e}" + ) + return False # Return error + + return True # Return success + + async def get_by_id(self, id: str) -> dict[str, Any] | None: + """Get vector data by its ID + + Args: + id: The unique identifier of the vector + + Returns: + The vector data if found, or None if not found + """ + # Find the Faiss internal ID for the custom ID + fid = self._find_faiss_id_by_custom_id(id) + if fid is None: + return None + + # Get the metadata for the found ID + metadata = self._id_to_meta.get(fid, {}) + if not metadata: + return None + + # Filter out __vector__ from metadata to avoid returning large vector data + filtered_metadata = {k: v for k, v in metadata.items() if k != "__vector__"} + return { + **filtered_metadata, + "id": metadata.get("__id__"), + "created_at": metadata.get("__created_at__"), + } + + async def get_by_ids(self, ids: list[str]) -> list[dict[str, Any]]: + """Get multiple vector data by their IDs + + Args: + ids: List of unique identifiers + + Returns: + List of vector data objects that were found + """ + if not ids: + return [] + + results: list[dict[str, Any] | None] = [] + for id in ids: + record = None + fid = self._find_faiss_id_by_custom_id(id) + if fid is not None: + metadata = self._id_to_meta.get(fid) + if metadata: + # Filter out __vector__ from metadata to avoid returning large vector data + filtered_metadata = { + k: v for k, v in metadata.items() if k != "__vector__" + } + record = { + **filtered_metadata, + "id": metadata.get("__id__"), + "created_at": metadata.get("__created_at__"), + } + results.append(record) + + return results + + async def get_vectors_by_ids(self, ids: list[str]) -> dict[str, list[float]]: + """Get vectors by their IDs, returning only ID and vector data for efficiency + + Args: + ids: List of unique identifiers + + Returns: + Dictionary mapping IDs to their vector embeddings + Format: {id: [vector_values], ...} + """ + if not ids: + return {} + + vectors_dict = {} + for id in ids: + # Find the Faiss internal ID for the custom ID + fid = self._find_faiss_id_by_custom_id(id) + if fid is not None and fid in self._id_to_meta: + metadata = self._id_to_meta[fid] + # Get the stored vector from metadata + if "__vector__" in metadata: + vectors_dict[id] = metadata["__vector__"] + + return vectors_dict + + async def drop(self) -> dict[str, str]: + """Drop all vector data from storage and clean up resources + + This method will: + 1. Remove the vector database storage file if it exists + 2. Reinitialize the vector database client + 3. Update flags to notify other processes + 4. Changes is persisted to disk immediately + + This method will remove all vectors from the Faiss index and delete the storage files. + + Returns: + dict[str, str]: Operation status and message + - On success: {"status": "success", "message": "data dropped"} + - On failure: {"status": "error", "message": ""} + """ + try: + async with self._storage_lock: + # Reset the index + self._index = faiss.IndexFlatIP(self._dim) + self._id_to_meta = {} + + # Remove storage files if they exist + if os.path.exists(self._faiss_index_file): + os.remove(self._faiss_index_file) + if os.path.exists(self._meta_file): + os.remove(self._meta_file) + + self._id_to_meta = {} + self._load_faiss_index() + + # Notify other processes + await set_all_update_flags(self.final_namespace) + self.storage_updated.value = False + + logger.info( + f"[{self.workspace}] Process {os.getpid()} drop FAISS index {self.namespace}" + ) + return {"status": "success", "message": "data dropped"} + except Exception as e: + logger.error( + f"[{self.workspace}] Error dropping FAISS index {self.namespace}: {e}" + ) + return {"status": "error", "message": str(e)} diff --git a/LightRAG/lightrag/kg/json_doc_status_impl.py b/LightRAG/lightrag/kg/json_doc_status_impl.py new file mode 100644 index 00000000..bf6e7b17 --- /dev/null +++ b/LightRAG/lightrag/kg/json_doc_status_impl.py @@ -0,0 +1,401 @@ +from dataclasses import dataclass +import os +from typing import Any, Union, final + +from lightrag.base import ( + DocProcessingStatus, + DocStatus, + DocStatusStorage, +) +from lightrag.utils import ( + load_json, + logger, + write_json, + get_pinyin_sort_key, +) +from lightrag.exceptions import StorageNotInitializedError +from .shared_storage import ( + get_namespace_data, + get_storage_lock, + get_data_init_lock, + get_update_flag, + set_all_update_flags, + clear_all_update_flags, + try_initialize_namespace, +) + + +@final +@dataclass +class JsonDocStatusStorage(DocStatusStorage): + """JSON implementation of document status storage""" + + def __post_init__(self): + working_dir = self.global_config["working_dir"] + if self.workspace: + # Include workspace in the file path for data isolation + workspace_dir = os.path.join(working_dir, self.workspace) + self.final_namespace = f"{self.workspace}_{self.namespace}" + else: + # Default behavior when workspace is empty + self.final_namespace = self.namespace + self.workspace = "_" + workspace_dir = working_dir + + os.makedirs(workspace_dir, exist_ok=True) + self._file_name = os.path.join(workspace_dir, f"kv_store_{self.namespace}.json") + self._data = None + self._storage_lock = None + self.storage_updated = None + + async def initialize(self): + """Initialize storage data""" + self._storage_lock = get_storage_lock() + self.storage_updated = await get_update_flag(self.final_namespace) + async with get_data_init_lock(): + # check need_init must before get_namespace_data + need_init = await try_initialize_namespace(self.final_namespace) + self._data = await get_namespace_data(self.final_namespace) + if need_init: + loaded_data = load_json(self._file_name) or {} + async with self._storage_lock: + self._data.update(loaded_data) + logger.info( + f"[{self.workspace}] Process {os.getpid()} doc status load {self.namespace} with {len(loaded_data)} records" + ) + + async def filter_keys(self, keys: set[str]) -> set[str]: + """Return keys that should be processed (not in storage or not successfully processed)""" + if self._storage_lock is None: + raise StorageNotInitializedError("JsonDocStatusStorage") + async with self._storage_lock: + return set(keys) - set(self._data.keys()) + + async def get_by_ids(self, ids: list[str]) -> list[dict[str, Any]]: + ordered_results: list[dict[str, Any] | None] = [] + if self._storage_lock is None: + raise StorageNotInitializedError("JsonDocStatusStorage") + async with self._storage_lock: + for id in ids: + data = self._data.get(id, None) + if data: + ordered_results.append(data.copy()) + else: + ordered_results.append(None) + return ordered_results + + async def get_status_counts(self) -> dict[str, int]: + """Get counts of documents in each status""" + counts = {status.value: 0 for status in DocStatus} + if self._storage_lock is None: + raise StorageNotInitializedError("JsonDocStatusStorage") + async with self._storage_lock: + for doc in self._data.values(): + counts[doc["status"]] += 1 + return counts + + async def get_docs_by_status( + self, status: DocStatus + ) -> dict[str, DocProcessingStatus]: + """Get all documents with a specific status""" + result = {} + async with self._storage_lock: + for k, v in self._data.items(): + if v["status"] == status.value: + try: + # Make a copy of the data to avoid modifying the original + data = v.copy() + # Remove deprecated content field if it exists + data.pop("content", None) + # If file_path is not in data, use document id as file path + if "file_path" not in data: + data["file_path"] = "no-file-path" + # Ensure new fields exist with default values + if "metadata" not in data: + data["metadata"] = {} + if "error_msg" not in data: + data["error_msg"] = None + result[k] = DocProcessingStatus(**data) + except KeyError as e: + logger.error( + f"[{self.workspace}] Missing required field for document {k}: {e}" + ) + continue + return result + + async def get_docs_by_track_id( + self, track_id: str + ) -> dict[str, DocProcessingStatus]: + """Get all documents with a specific track_id""" + result = {} + async with self._storage_lock: + for k, v in self._data.items(): + if v.get("track_id") == track_id: + try: + # Make a copy of the data to avoid modifying the original + data = v.copy() + # Remove deprecated content field if it exists + data.pop("content", None) + # If file_path is not in data, use document id as file path + if "file_path" not in data: + data["file_path"] = "no-file-path" + # Ensure new fields exist with default values + if "metadata" not in data: + data["metadata"] = {} + if "error_msg" not in data: + data["error_msg"] = None + result[k] = DocProcessingStatus(**data) + except KeyError as e: + logger.error( + f"[{self.workspace}] Missing required field for document {k}: {e}" + ) + continue + return result + + async def index_done_callback(self) -> None: + async with self._storage_lock: + if self.storage_updated.value: + data_dict = ( + dict(self._data) if hasattr(self._data, "_getvalue") else self._data + ) + logger.debug( + f"[{self.workspace}] Process {os.getpid()} doc status writting {len(data_dict)} records to {self.namespace}" + ) + + # Write JSON and check if sanitization was applied + needs_reload = write_json(data_dict, self._file_name) + + # If data was sanitized, reload cleaned data to update shared memory + if needs_reload: + logger.info( + f"[{self.workspace}] Reloading sanitized data into shared memory for {self.namespace}" + ) + cleaned_data = load_json(self._file_name) + if cleaned_data is not None: + self._data.clear() + self._data.update(cleaned_data) + + await clear_all_update_flags(self.final_namespace) + + async def upsert(self, data: dict[str, dict[str, Any]]) -> None: + """ + Importance notes for in-memory storage: + 1. Changes will be persisted to disk during the next index_done_callback + 2. update flags to notify other processes that data persistence is needed + """ + if not data: + return + logger.debug( + f"[{self.workspace}] Inserting {len(data)} records to {self.namespace}" + ) + if self._storage_lock is None: + raise StorageNotInitializedError("JsonDocStatusStorage") + async with self._storage_lock: + # Ensure chunks_list field exists for new documents + for doc_id, doc_data in data.items(): + if "chunks_list" not in doc_data: + doc_data["chunks_list"] = [] + self._data.update(data) + await set_all_update_flags(self.final_namespace) + + await self.index_done_callback() + + async def is_empty(self) -> bool: + """Check if the storage is empty + + Returns: + bool: True if storage is empty, False otherwise + + Raises: + StorageNotInitializedError: If storage is not initialized + """ + if self._storage_lock is None: + raise StorageNotInitializedError("JsonDocStatusStorage") + async with self._storage_lock: + return len(self._data) == 0 + + async def get_by_id(self, id: str) -> Union[dict[str, Any], None]: + async with self._storage_lock: + return self._data.get(id) + + async def get_docs_paginated( + self, + status_filter: DocStatus | None = None, + page: int = 1, + page_size: int = 50, + sort_field: str = "updated_at", + sort_direction: str = "desc", + ) -> tuple[list[tuple[str, DocProcessingStatus]], int]: + """Get documents with pagination support + + Args: + status_filter: Filter by document status, None for all statuses + page: Page number (1-based) + page_size: Number of documents per page (10-200) + sort_field: Field to sort by ('created_at', 'updated_at', 'id') + sort_direction: Sort direction ('asc' or 'desc') + + Returns: + Tuple of (list of (doc_id, DocProcessingStatus) tuples, total_count) + """ + # Validate parameters + if page < 1: + page = 1 + if page_size < 10: + page_size = 10 + elif page_size > 200: + page_size = 200 + + if sort_field not in ["created_at", "updated_at", "id", "file_path"]: + sort_field = "updated_at" + + if sort_direction.lower() not in ["asc", "desc"]: + sort_direction = "desc" + + # For JSON storage, we load all data and sort/filter in memory + all_docs = [] + + async with self._storage_lock: + for doc_id, doc_data in self._data.items(): + # Apply status filter + if ( + status_filter is not None + and doc_data.get("status") != status_filter.value + ): + continue + + try: + # Prepare document data + data = doc_data.copy() + data.pop("content", None) + if "file_path" not in data: + data["file_path"] = "no-file-path" + if "metadata" not in data: + data["metadata"] = {} + if "error_msg" not in data: + data["error_msg"] = None + + doc_status = DocProcessingStatus(**data) + + # Add sort key for sorting + if sort_field == "id": + doc_status._sort_key = doc_id + elif sort_field == "file_path": + # Use pinyin sorting for file_path field to support Chinese characters + file_path_value = getattr(doc_status, sort_field, "") + doc_status._sort_key = get_pinyin_sort_key(file_path_value) + else: + doc_status._sort_key = getattr(doc_status, sort_field, "") + + all_docs.append((doc_id, doc_status)) + + except KeyError as e: + logger.error( + f"[{self.workspace}] Error processing document {doc_id}: {e}" + ) + continue + + # Sort documents + reverse_sort = sort_direction.lower() == "desc" + all_docs.sort( + key=lambda x: getattr(x[1], "_sort_key", ""), reverse=reverse_sort + ) + + # Remove sort key from documents + for doc_id, doc in all_docs: + if hasattr(doc, "_sort_key"): + delattr(doc, "_sort_key") + + total_count = len(all_docs) + + # Apply pagination + start_idx = (page - 1) * page_size + end_idx = start_idx + page_size + paginated_docs = all_docs[start_idx:end_idx] + + return paginated_docs, total_count + + async def get_all_status_counts(self) -> dict[str, int]: + """Get counts of documents in each status for all documents + + Returns: + Dictionary mapping status names to counts, including 'all' field + """ + counts = await self.get_status_counts() + + # Add 'all' field with total count + total_count = sum(counts.values()) + counts["all"] = total_count + + return counts + + async def delete(self, doc_ids: list[str]) -> None: + """Delete specific records from storage by their IDs + + Importance notes for in-memory storage: + 1. Changes will be persisted to disk during the next index_done_callback + 2. update flags to notify other processes that data persistence is needed + + Args: + ids (list[str]): List of document IDs to be deleted from storage + + Returns: + None + """ + async with self._storage_lock: + any_deleted = False + for doc_id in doc_ids: + result = self._data.pop(doc_id, None) + if result is not None: + any_deleted = True + + if any_deleted: + await set_all_update_flags(self.final_namespace) + + async def get_doc_by_file_path(self, file_path: str) -> Union[dict[str, Any], None]: + """Get document by file path + + Args: + file_path: The file path to search for + + Returns: + Union[dict[str, Any], None]: Document data if found, None otherwise + Returns the same format as get_by_ids method + """ + if self._storage_lock is None: + raise StorageNotInitializedError("JsonDocStatusStorage") + + async with self._storage_lock: + for doc_id, doc_data in self._data.items(): + if doc_data.get("file_path") == file_path: + # Return complete document data, consistent with get_by_ids method + return doc_data + + return None + + async def drop(self) -> dict[str, str]: + """Drop all document status data from storage and clean up resources + + This method will: + 1. Clear all document status data from memory + 2. Update flags to notify other processes + 3. Trigger index_done_callback to save the empty state + + Returns: + dict[str, str]: Operation status and message + - On success: {"status": "success", "message": "data dropped"} + - On failure: {"status": "error", "message": ""} + """ + try: + async with self._storage_lock: + self._data.clear() + await set_all_update_flags(self.final_namespace) + + await self.index_done_callback() + logger.info( + f"[{self.workspace}] Process {os.getpid()} drop {self.namespace}" + ) + return {"status": "success", "message": "data dropped"} + except Exception as e: + logger.error(f"[{self.workspace}] Error dropping {self.namespace}: {e}") + return {"status": "error", "message": str(e)} diff --git a/LightRAG/lightrag/kg/json_kv_impl.py b/LightRAG/lightrag/kg/json_kv_impl.py new file mode 100644 index 00000000..f9adb20f --- /dev/null +++ b/LightRAG/lightrag/kg/json_kv_impl.py @@ -0,0 +1,296 @@ +import os +from dataclasses import dataclass +from typing import Any, final + +from lightrag.base import ( + BaseKVStorage, +) +from lightrag.utils import ( + load_json, + logger, + write_json, +) +from lightrag.exceptions import StorageNotInitializedError +from .shared_storage import ( + get_namespace_data, + get_storage_lock, + get_data_init_lock, + get_update_flag, + set_all_update_flags, + clear_all_update_flags, + try_initialize_namespace, +) + + +@final +@dataclass +class JsonKVStorage(BaseKVStorage): + def __post_init__(self): + working_dir = self.global_config["working_dir"] + if self.workspace: + # Include workspace in the file path for data isolation + workspace_dir = os.path.join(working_dir, self.workspace) + self.final_namespace = f"{self.workspace}_{self.namespace}" + else: + # Default behavior when workspace is empty + workspace_dir = working_dir + self.final_namespace = self.namespace + self.workspace = "_" + + os.makedirs(workspace_dir, exist_ok=True) + self._file_name = os.path.join(workspace_dir, f"kv_store_{self.namespace}.json") + + self._data = None + self._storage_lock = None + self.storage_updated = None + + async def initialize(self): + """Initialize storage data""" + self._storage_lock = get_storage_lock() + self.storage_updated = await get_update_flag(self.final_namespace) + async with get_data_init_lock(): + # check need_init must before get_namespace_data + need_init = await try_initialize_namespace(self.final_namespace) + self._data = await get_namespace_data(self.final_namespace) + if need_init: + loaded_data = load_json(self._file_name) or {} + async with self._storage_lock: + # Migrate legacy cache structure if needed + if self.namespace.endswith("_cache"): + loaded_data = await self._migrate_legacy_cache_structure( + loaded_data + ) + + self._data.update(loaded_data) + data_count = len(loaded_data) + + logger.info( + f"[{self.workspace}] Process {os.getpid()} KV load {self.namespace} with {data_count} records" + ) + + async def index_done_callback(self) -> None: + async with self._storage_lock: + if self.storage_updated.value: + data_dict = ( + dict(self._data) if hasattr(self._data, "_getvalue") else self._data + ) + + # Calculate data count - all data is now flattened + data_count = len(data_dict) + + logger.debug( + f"[{self.workspace}] Process {os.getpid()} KV writting {data_count} records to {self.namespace}" + ) + + # Write JSON and check if sanitization was applied + needs_reload = write_json(data_dict, self._file_name) + + # If data was sanitized, reload cleaned data to update shared memory + if needs_reload: + logger.info( + f"[{self.workspace}] Reloading sanitized data into shared memory for {self.namespace}" + ) + cleaned_data = load_json(self._file_name) + if cleaned_data is not None: + self._data.clear() + self._data.update(cleaned_data) + + await clear_all_update_flags(self.final_namespace) + + async def get_by_id(self, id: str) -> dict[str, Any] | None: + async with self._storage_lock: + result = self._data.get(id) + if result: + # Create a copy to avoid modifying the original data + result = dict(result) + # Ensure time fields are present, provide default values for old data + result.setdefault("create_time", 0) + result.setdefault("update_time", 0) + # Ensure _id field contains the clean ID + result["_id"] = id + return result + + async def get_by_ids(self, ids: list[str]) -> list[dict[str, Any]]: + async with self._storage_lock: + results = [] + for id in ids: + data = self._data.get(id, None) + if data: + # Create a copy to avoid modifying the original data + result = {k: v for k, v in data.items()} + # Ensure time fields are present, provide default values for old data + result.setdefault("create_time", 0) + result.setdefault("update_time", 0) + # Ensure _id field contains the clean ID + result["_id"] = id + results.append(result) + else: + results.append(None) + return results + + async def filter_keys(self, keys: set[str]) -> set[str]: + async with self._storage_lock: + return set(keys) - set(self._data.keys()) + + async def upsert(self, data: dict[str, dict[str, Any]]) -> None: + """ + Importance notes for in-memory storage: + 1. Changes will be persisted to disk during the next index_done_callback + 2. update flags to notify other processes that data persistence is needed + """ + if not data: + return + + import time + + current_time = int(time.time()) # Get current Unix timestamp + + logger.debug( + f"[{self.workspace}] Inserting {len(data)} records to {self.namespace}" + ) + if self._storage_lock is None: + raise StorageNotInitializedError("JsonKVStorage") + async with self._storage_lock: + # Add timestamps to data based on whether key exists + for k, v in data.items(): + # For text_chunks namespace, ensure llm_cache_list field exists + if self.namespace.endswith("text_chunks"): + if "llm_cache_list" not in v: + v["llm_cache_list"] = [] + + # Add timestamps based on whether key exists + if k in self._data: # Key exists, only update update_time + v["update_time"] = current_time + else: # New key, set both create_time and update_time + v["create_time"] = current_time + v["update_time"] = current_time + + v["_id"] = k + + self._data.update(data) + await set_all_update_flags(self.final_namespace) + + async def delete(self, ids: list[str]) -> None: + """Delete specific records from storage by their IDs + + Importance notes for in-memory storage: + 1. Changes will be persisted to disk during the next index_done_callback + 2. update flags to notify other processes that data persistence is needed + + Args: + ids (list[str]): List of document IDs to be deleted from storage + + Returns: + None + """ + async with self._storage_lock: + any_deleted = False + for doc_id in ids: + result = self._data.pop(doc_id, None) + if result is not None: + any_deleted = True + + if any_deleted: + await set_all_update_flags(self.final_namespace) + + async def is_empty(self) -> bool: + """Check if the storage is empty + + Returns: + bool: True if storage contains no data, False otherwise + """ + async with self._storage_lock: + return len(self._data) == 0 + + async def drop(self) -> dict[str, str]: + """Drop all data from storage and clean up resources + This action will persistent the data to disk immediately. + + This method will: + 1. Clear all data from memory + 2. Update flags to notify other processes + 3. Trigger index_done_callback to save the empty state + + Returns: + dict[str, str]: Operation status and message + - On success: {"status": "success", "message": "data dropped"} + - On failure: {"status": "error", "message": ""} + """ + try: + async with self._storage_lock: + self._data.clear() + await set_all_update_flags(self.final_namespace) + + await self.index_done_callback() + logger.info( + f"[{self.workspace}] Process {os.getpid()} drop {self.namespace}" + ) + return {"status": "success", "message": "data dropped"} + except Exception as e: + logger.error(f"[{self.workspace}] Error dropping {self.namespace}: {e}") + return {"status": "error", "message": str(e)} + + async def _migrate_legacy_cache_structure(self, data: dict) -> dict: + """Migrate legacy nested cache structure to flattened structure + + Args: + data: Original data dictionary that may contain legacy structure + + Returns: + Migrated data dictionary with flattened cache keys (sanitized if needed) + """ + from lightrag.utils import generate_cache_key + + # Early return if data is empty + if not data: + return data + + # Check first entry to see if it's already in new format + first_key = next(iter(data.keys())) + if ":" in first_key and len(first_key.split(":")) == 3: + # Already in flattened format, return as-is + return data + + migrated_data = {} + migration_count = 0 + + for key, value in data.items(): + # Check if this is a legacy nested cache structure + if isinstance(value, dict) and all( + isinstance(v, dict) and "return" in v for v in value.values() + ): + # This looks like a legacy cache mode with nested structure + mode = key + for cache_hash, cache_entry in value.items(): + cache_type = cache_entry.get("cache_type", "extract") + flattened_key = generate_cache_key(mode, cache_type, cache_hash) + migrated_data[flattened_key] = cache_entry + migration_count += 1 + else: + # Keep non-cache data or already flattened cache data as-is + migrated_data[key] = value + + if migration_count > 0: + logger.info( + f"[{self.workspace}] Migrated {migration_count} legacy cache entries to flattened structure" + ) + # Persist migrated data immediately and check if sanitization was applied + needs_reload = write_json(migrated_data, self._file_name) + + # If data was sanitized during write, reload cleaned data + if needs_reload: + logger.info( + f"[{self.workspace}] Reloading sanitized migration data for {self.namespace}" + ) + cleaned_data = load_json(self._file_name) + if cleaned_data is not None: + return cleaned_data # Return cleaned data to update shared memory + + return migrated_data + + async def finalize(self): + """Finalize storage resources + Persistence cache data to disk before exiting + """ + if self.namespace.endswith("_cache"): + await self.index_done_callback() diff --git a/LightRAG/lightrag/kg/memgraph_impl.py b/LightRAG/lightrag/kg/memgraph_impl.py new file mode 100644 index 00000000..e82aceec --- /dev/null +++ b/LightRAG/lightrag/kg/memgraph_impl.py @@ -0,0 +1,1140 @@ +import os +import asyncio +import random +from dataclasses import dataclass +from typing import final +import configparser + +from ..utils import logger +from ..base import BaseGraphStorage +from ..types import KnowledgeGraph, KnowledgeGraphNode, KnowledgeGraphEdge +from ..kg.shared_storage import get_data_init_lock, get_graph_db_lock +import pipmaster as pm + +if not pm.is_installed("neo4j"): + pm.install("neo4j") +from neo4j import ( + AsyncGraphDatabase, + AsyncManagedTransaction, +) +from neo4j.exceptions import TransientError, ResultFailedError + +from dotenv import load_dotenv + +# use the .env that is inside the current folder +load_dotenv(dotenv_path=".env", override=False) + +MAX_GRAPH_NODES = int(os.getenv("MAX_GRAPH_NODES", 1000)) + +config = configparser.ConfigParser() +config.read("config.ini", "utf-8") + + +@final +@dataclass +class MemgraphStorage(BaseGraphStorage): + def __init__(self, namespace, global_config, embedding_func, workspace=None): + # Priority: 1) MEMGRAPH_WORKSPACE env 2) user arg 3) default 'base' + memgraph_workspace = os.environ.get("MEMGRAPH_WORKSPACE") + if memgraph_workspace and memgraph_workspace.strip(): + workspace = memgraph_workspace + + if not workspace or not str(workspace).strip(): + workspace = "base" + + super().__init__( + namespace=namespace, + workspace=workspace, + global_config=global_config, + embedding_func=embedding_func, + ) + self._driver = None + + def _get_workspace_label(self) -> str: + """Return workspace label (guaranteed non-empty during initialization)""" + return self.workspace + + async def initialize(self): + async with get_data_init_lock(): + URI = os.environ.get( + "MEMGRAPH_URI", + config.get("memgraph", "uri", fallback="bolt://localhost:7687"), + ) + USERNAME = os.environ.get( + "MEMGRAPH_USERNAME", config.get("memgraph", "username", fallback="") + ) + PASSWORD = os.environ.get( + "MEMGRAPH_PASSWORD", config.get("memgraph", "password", fallback="") + ) + DATABASE = os.environ.get( + "MEMGRAPH_DATABASE", + config.get("memgraph", "database", fallback="memgraph"), + ) + + self._driver = AsyncGraphDatabase.driver( + URI, + auth=(USERNAME, PASSWORD), + ) + self._DATABASE = DATABASE + try: + async with self._driver.session(database=DATABASE) as session: + # Create index for base nodes on entity_id if it doesn't exist + try: + workspace_label = self._get_workspace_label() + await session.run( + f"""CREATE INDEX ON :{workspace_label}(entity_id)""" + ) + logger.info( + f"[{self.workspace}] Created index on :{workspace_label}(entity_id) in Memgraph." + ) + except Exception as e: + # Index may already exist, which is not an error + logger.warning( + f"[{self.workspace}] Index creation on :{workspace_label}(entity_id) may have failed or already exists: {e}" + ) + await session.run("RETURN 1") + logger.info(f"[{self.workspace}] Connected to Memgraph at {URI}") + except Exception as e: + logger.error( + f"[{self.workspace}] Failed to connect to Memgraph at {URI}: {e}" + ) + raise + + async def finalize(self): + async with get_graph_db_lock(): + if self._driver is not None: + await self._driver.close() + self._driver = None + + async def __aexit__(self, exc_type, exc, tb): + await self.finalize() + + async def index_done_callback(self): + # Memgraph handles persistence automatically + pass + + async def has_node(self, node_id: str) -> bool: + """ + Check if a node exists in the graph. + + Args: + node_id: The ID of the node to check. + + Returns: + bool: True if the node exists, False otherwise. + + Raises: + Exception: If there is an error checking the node existence. + """ + if self._driver is None: + raise RuntimeError( + "Memgraph driver is not initialized. Call 'await initialize()' first." + ) + async with self._driver.session( + database=self._DATABASE, default_access_mode="READ" + ) as session: + result = None + try: + workspace_label = self._get_workspace_label() + query = f"MATCH (n:`{workspace_label}` {{entity_id: $entity_id}}) RETURN count(n) > 0 AS node_exists" + result = await session.run(query, entity_id=node_id) + single_result = await result.single() + await result.consume() # Ensure result is fully consumed + return ( + single_result["node_exists"] if single_result is not None else False + ) + except Exception as e: + logger.error( + f"[{self.workspace}] Error checking node existence for {node_id}: {str(e)}" + ) + if result is not None: + await ( + result.consume() + ) # Ensure the result is consumed even on error + raise + + async def has_edge(self, source_node_id: str, target_node_id: str) -> bool: + """ + Check if an edge exists between two nodes in the graph. + + Args: + source_node_id: The ID of the source node. + target_node_id: The ID of the target node. + + Returns: + bool: True if the edge exists, False otherwise. + + Raises: + Exception: If there is an error checking the edge existence. + """ + if self._driver is None: + raise RuntimeError( + "Memgraph driver is not initialized. Call 'await initialize()' first." + ) + async with self._driver.session( + database=self._DATABASE, default_access_mode="READ" + ) as session: + result = None + try: + workspace_label = self._get_workspace_label() + query = ( + f"MATCH (a:`{workspace_label}` {{entity_id: $source_entity_id}})-[r]-(b:`{workspace_label}` {{entity_id: $target_entity_id}}) " + "RETURN COUNT(r) > 0 AS edgeExists" + ) + result = await session.run( + query, + source_entity_id=source_node_id, + target_entity_id=target_node_id, + ) # type: ignore + single_result = await result.single() + await result.consume() # Ensure result is fully consumed + return ( + single_result["edgeExists"] if single_result is not None else False + ) + except Exception as e: + logger.error( + f"[{self.workspace}] Error checking edge existence between {source_node_id} and {target_node_id}: {str(e)}" + ) + if result is not None: + await ( + result.consume() + ) # Ensure the result is consumed even on error + raise + + async def get_node(self, node_id: str) -> dict[str, str] | None: + """Get node by its label identifier, return only node properties + + Args: + node_id: The node label to look up + + Returns: + dict: Node properties if found + None: If node not found + + Raises: + Exception: If there is an error executing the query + """ + if self._driver is None: + raise RuntimeError( + "Memgraph driver is not initialized. Call 'await initialize()' first." + ) + async with self._driver.session( + database=self._DATABASE, default_access_mode="READ" + ) as session: + try: + workspace_label = self._get_workspace_label() + query = ( + f"MATCH (n:`{workspace_label}` {{entity_id: $entity_id}}) RETURN n" + ) + result = await session.run(query, entity_id=node_id) + try: + records = await result.fetch( + 2 + ) # Get 2 records for duplication check + + if len(records) > 1: + logger.warning( + f"[{self.workspace}] Multiple nodes found with label '{node_id}'. Using first node." + ) + if records: + node = records[0]["n"] + node_dict = dict(node) + # Remove workspace label from labels list if it exists + if "labels" in node_dict: + node_dict["labels"] = [ + label + for label in node_dict["labels"] + if label != workspace_label + ] + return node_dict + return None + finally: + await result.consume() # Ensure result is fully consumed + except Exception as e: + logger.error( + f"[{self.workspace}] Error getting node for {node_id}: {str(e)}" + ) + raise + + async def node_degree(self, node_id: str) -> int: + """Get the degree (number of relationships) of a node with the given label. + If multiple nodes have the same label, returns the degree of the first node. + If no node is found, returns 0. + + Args: + node_id: The label of the node + + Returns: + int: The number of relationships the node has, or 0 if no node found + + Raises: + Exception: If there is an error executing the query + """ + if self._driver is None: + raise RuntimeError( + "Memgraph driver is not initialized. Call 'await initialize()' first." + ) + async with self._driver.session( + database=self._DATABASE, default_access_mode="READ" + ) as session: + try: + workspace_label = self._get_workspace_label() + query = f""" + MATCH (n:`{workspace_label}` {{entity_id: $entity_id}}) + OPTIONAL MATCH (n)-[r]-() + RETURN COUNT(r) AS degree + """ + result = await session.run(query, entity_id=node_id) + try: + record = await result.single() + + if not record: + logger.warning( + f"[{self.workspace}] No node found with label '{node_id}'" + ) + return 0 + + degree = record["degree"] + return degree + finally: + await result.consume() # Ensure result is fully consumed + except Exception as e: + logger.error( + f"[{self.workspace}] Error getting node degree for {node_id}: {str(e)}" + ) + raise + + async def get_all_labels(self) -> list[str]: + """ + Get all existing node labels in the database + Returns: + ["Person", "Company", ...] # Alphabetically sorted label list + + Raises: + Exception: If there is an error executing the query + """ + if self._driver is None: + raise RuntimeError( + "Memgraph driver is not initialized. Call 'await initialize()' first." + ) + async with self._driver.session( + database=self._DATABASE, default_access_mode="READ" + ) as session: + result = None + try: + workspace_label = self._get_workspace_label() + query = f""" + MATCH (n:`{workspace_label}`) + WHERE n.entity_id IS NOT NULL + RETURN DISTINCT n.entity_id AS label + ORDER BY label + """ + result = await session.run(query) + labels = [] + async for record in result: + labels.append(record["label"]) + await result.consume() + return labels + except Exception as e: + logger.error(f"[{self.workspace}] Error getting all labels: {str(e)}") + if result is not None: + await ( + result.consume() + ) # Ensure the result is consumed even on error + raise + + async def get_node_edges(self, source_node_id: str) -> list[tuple[str, str]] | None: + """Retrieves all edges (relationships) for a particular node identified by its label. + + Args: + source_node_id: Label of the node to get edges for + + Returns: + list[tuple[str, str]]: List of (source_label, target_label) tuples representing edges + None: If no edges found + + Raises: + Exception: If there is an error executing the query + """ + if self._driver is None: + raise RuntimeError( + "Memgraph driver is not initialized. Call 'await initialize()' first." + ) + try: + async with self._driver.session( + database=self._DATABASE, default_access_mode="READ" + ) as session: + results = None + try: + workspace_label = self._get_workspace_label() + query = f"""MATCH (n:`{workspace_label}` {{entity_id: $entity_id}}) + OPTIONAL MATCH (n)-[r]-(connected:`{workspace_label}`) + WHERE connected.entity_id IS NOT NULL + RETURN n, r, connected""" + results = await session.run(query, entity_id=source_node_id) + + edges = [] + async for record in results: + source_node = record["n"] + connected_node = record["connected"] + + # Skip if either node is None + if not source_node or not connected_node: + continue + + source_label = ( + source_node.get("entity_id") + if source_node.get("entity_id") + else None + ) + target_label = ( + connected_node.get("entity_id") + if connected_node.get("entity_id") + else None + ) + + if source_label and target_label: + edges.append((source_label, target_label)) + + await results.consume() # Ensure results are consumed + return edges + except Exception as e: + logger.error( + f"[{self.workspace}] Error getting edges for node {source_node_id}: {str(e)}" + ) + if results is not None: + await ( + results.consume() + ) # Ensure results are consumed even on error + raise + except Exception as e: + logger.error( + f"[{self.workspace}] Error in get_node_edges for {source_node_id}: {str(e)}" + ) + raise + + async def get_edge( + self, source_node_id: str, target_node_id: str + ) -> dict[str, str] | None: + """Get edge properties between two nodes. + + Args: + source_node_id: Label of the source node + target_node_id: Label of the target node + + Returns: + dict: Edge properties if found, default properties if not found or on error + + Raises: + Exception: If there is an error executing the query + """ + if self._driver is None: + raise RuntimeError( + "Memgraph driver is not initialized. Call 'await initialize()' first." + ) + async with self._driver.session( + database=self._DATABASE, default_access_mode="READ" + ) as session: + result = None + try: + workspace_label = self._get_workspace_label() + query = f""" + MATCH (start:`{workspace_label}` {{entity_id: $source_entity_id}})-[r]-(end:`{workspace_label}` {{entity_id: $target_entity_id}}) + RETURN properties(r) as edge_properties + """ + result = await session.run( + query, + source_entity_id=source_node_id, + target_entity_id=target_node_id, + ) + records = await result.fetch(2) + await result.consume() + if records: + edge_result = dict(records[0]["edge_properties"]) + for key, default_value in { + "weight": 1.0, + "source_id": None, + "description": None, + "keywords": None, + }.items(): + if key not in edge_result: + edge_result[key] = default_value + logger.warning( + f"[{self.workspace}] Edge between {source_node_id} and {target_node_id} is missing property: {key}. Using default value: {default_value}" + ) + return edge_result + return None + except Exception as e: + logger.error( + f"[{self.workspace}] Error getting edge between {source_node_id} and {target_node_id}: {str(e)}" + ) + if result is not None: + await ( + result.consume() + ) # Ensure the result is consumed even on error + raise + + async def upsert_node(self, node_id: str, node_data: dict[str, str]) -> None: + """ + Upsert a node in the Memgraph database with manual transaction-level retry logic for transient errors. + + Args: + node_id: The unique identifier for the node (used as label) + node_data: Dictionary of node properties + """ + if self._driver is None: + raise RuntimeError( + "Memgraph driver is not initialized. Call 'await initialize()' first." + ) + properties = node_data + entity_type = properties["entity_type"] + if "entity_id" not in properties: + raise ValueError( + "Memgraph: node properties must contain an 'entity_id' field" + ) + + # Manual transaction-level retry following official Memgraph documentation + max_retries = 100 + initial_wait_time = 0.2 + backoff_factor = 1.1 + jitter_factor = 0.1 + + for attempt in range(max_retries): + try: + logger.debug( + f"[{self.workspace}] Attempting node upsert, attempt {attempt + 1}/{max_retries}" + ) + async with self._driver.session(database=self._DATABASE) as session: + workspace_label = self._get_workspace_label() + + async def execute_upsert(tx: AsyncManagedTransaction): + query = f""" + MERGE (n:`{workspace_label}` {{entity_id: $entity_id}}) + SET n += $properties + SET n:`{entity_type}` + """ + result = await tx.run( + query, entity_id=node_id, properties=properties + ) + await result.consume() # Ensure result is fully consumed + + await session.execute_write(execute_upsert) + break # Success - exit retry loop + + except (TransientError, ResultFailedError) as e: + # Check if the root cause is a TransientError + root_cause = e + while hasattr(root_cause, "__cause__") and root_cause.__cause__: + root_cause = root_cause.__cause__ + + # Check if this is a transient error that should be retried + is_transient = ( + isinstance(root_cause, TransientError) + or isinstance(e, TransientError) + or "TransientError" in str(e) + or "Cannot resolve conflicting transactions" in str(e) + ) + + if is_transient: + if attempt < max_retries - 1: + # Calculate wait time with exponential backoff and jitter + jitter = random.uniform(0, jitter_factor) * initial_wait_time + wait_time = ( + initial_wait_time * (backoff_factor**attempt) + jitter + ) + logger.warning( + f"[{self.workspace}] Node upsert failed. Attempt #{attempt + 1} retrying in {wait_time:.3f} seconds... Error: {str(e)}" + ) + await asyncio.sleep(wait_time) + else: + logger.error( + f"[{self.workspace}] Memgraph transient error during node upsert after {max_retries} retries: {str(e)}" + ) + raise + else: + # Non-transient error, don't retry + logger.error( + f"[{self.workspace}] Non-transient error during node upsert: {str(e)}" + ) + raise + except Exception as e: + logger.error( + f"[{self.workspace}] Unexpected error during node upsert: {str(e)}" + ) + raise + + async def upsert_edge( + self, source_node_id: str, target_node_id: str, edge_data: dict[str, str] + ) -> None: + """ + Upsert an edge and its properties between two nodes identified by their labels with manual transaction-level retry logic for transient errors. + Ensures both source and target nodes exist and are unique before creating the edge. + Uses entity_id property to uniquely identify nodes. + + Args: + source_node_id (str): Label of the source node (used as identifier) + target_node_id (str): Label of the target node (used as identifier) + edge_data (dict): Dictionary of properties to set on the edge + + Raises: + Exception: If there is an error executing the query + """ + if self._driver is None: + raise RuntimeError( + "Memgraph driver is not initialized. Call 'await initialize()' first." + ) + + edge_properties = edge_data + + # Manual transaction-level retry following official Memgraph documentation + max_retries = 100 + initial_wait_time = 0.2 + backoff_factor = 1.1 + jitter_factor = 0.1 + + for attempt in range(max_retries): + try: + logger.debug( + f"[{self.workspace}] Attempting edge upsert, attempt {attempt + 1}/{max_retries}" + ) + async with self._driver.session(database=self._DATABASE) as session: + + async def execute_upsert(tx: AsyncManagedTransaction): + workspace_label = self._get_workspace_label() + query = f""" + MATCH (source:`{workspace_label}` {{entity_id: $source_entity_id}}) + WITH source + MATCH (target:`{workspace_label}` {{entity_id: $target_entity_id}}) + MERGE (source)-[r:DIRECTED]-(target) + SET r += $properties + RETURN r, source, target + """ + result = await tx.run( + query, + source_entity_id=source_node_id, + target_entity_id=target_node_id, + properties=edge_properties, + ) + try: + await result.fetch(2) + finally: + await result.consume() # Ensure result is consumed + + await session.execute_write(execute_upsert) + break # Success - exit retry loop + + except (TransientError, ResultFailedError) as e: + # Check if the root cause is a TransientError + root_cause = e + while hasattr(root_cause, "__cause__") and root_cause.__cause__: + root_cause = root_cause.__cause__ + + # Check if this is a transient error that should be retried + is_transient = ( + isinstance(root_cause, TransientError) + or isinstance(e, TransientError) + or "TransientError" in str(e) + or "Cannot resolve conflicting transactions" in str(e) + ) + + if is_transient: + if attempt < max_retries - 1: + # Calculate wait time with exponential backoff and jitter + jitter = random.uniform(0, jitter_factor) * initial_wait_time + wait_time = ( + initial_wait_time * (backoff_factor**attempt) + jitter + ) + logger.warning( + f"[{self.workspace}] Edge upsert failed. Attempt #{attempt + 1} retrying in {wait_time:.3f} seconds... Error: {str(e)}" + ) + await asyncio.sleep(wait_time) + else: + logger.error( + f"[{self.workspace}] Memgraph transient error during edge upsert after {max_retries} retries: {str(e)}" + ) + raise + else: + # Non-transient error, don't retry + logger.error( + f"[{self.workspace}] Non-transient error during edge upsert: {str(e)}" + ) + raise + except Exception as e: + logger.error( + f"[{self.workspace}] Unexpected error during edge upsert: {str(e)}" + ) + raise + + async def delete_node(self, node_id: str) -> None: + """Delete a node with the specified label + + Args: + node_id: The label of the node to delete + + Raises: + Exception: If there is an error executing the query + """ + if self._driver is None: + raise RuntimeError( + "Memgraph driver is not initialized. Call 'await initialize()' first." + ) + + async def _do_delete(tx: AsyncManagedTransaction): + workspace_label = self._get_workspace_label() + query = f""" + MATCH (n:`{workspace_label}` {{entity_id: $entity_id}}) + DETACH DELETE n + """ + result = await tx.run(query, entity_id=node_id) + logger.debug(f"[{self.workspace}] Deleted node with label {node_id}") + await result.consume() + + try: + async with self._driver.session(database=self._DATABASE) as session: + await session.execute_write(_do_delete) + except Exception as e: + logger.error(f"[{self.workspace}] Error during node deletion: {str(e)}") + raise + + async def remove_nodes(self, nodes: list[str]): + """Delete multiple nodes + + Args: + nodes: List of node labels to be deleted + """ + if self._driver is None: + raise RuntimeError( + "Memgraph driver is not initialized. Call 'await initialize()' first." + ) + for node in nodes: + await self.delete_node(node) + + async def remove_edges(self, edges: list[tuple[str, str]]): + """Delete multiple edges + + Args: + edges: List of edges to be deleted, each edge is a (source, target) tuple + + Raises: + Exception: If there is an error executing the query + """ + if self._driver is None: + raise RuntimeError( + "Memgraph driver is not initialized. Call 'await initialize()' first." + ) + for source, target in edges: + + async def _do_delete_edge(tx: AsyncManagedTransaction): + workspace_label = self._get_workspace_label() + query = f""" + MATCH (source:`{workspace_label}` {{entity_id: $source_entity_id}})-[r]-(target:`{workspace_label}` {{entity_id: $target_entity_id}}) + DELETE r + """ + result = await tx.run( + query, source_entity_id=source, target_entity_id=target + ) + logger.debug( + f"[{self.workspace}] Deleted edge from '{source}' to '{target}'" + ) + await result.consume() # Ensure result is fully consumed + + try: + async with self._driver.session(database=self._DATABASE) as session: + await session.execute_write(_do_delete_edge) + except Exception as e: + logger.error(f"[{self.workspace}] Error during edge deletion: {str(e)}") + raise + + async def drop(self) -> dict[str, str]: + """Drop all data from the current workspace and clean up resources + + This method will delete all nodes and relationships in the Memgraph database. + + Returns: + dict[str, str]: Operation status and message + - On success: {"status": "success", "message": "data dropped"} + - On failure: {"status": "error", "message": ""} + + Raises: + Exception: If there is an error executing the query + """ + if self._driver is None: + raise RuntimeError( + "Memgraph driver is not initialized. Call 'await initialize()' first." + ) + async with get_graph_db_lock(): + try: + async with self._driver.session(database=self._DATABASE) as session: + workspace_label = self._get_workspace_label() + query = f"MATCH (n:`{workspace_label}`) DETACH DELETE n" + result = await session.run(query) + await result.consume() + logger.info( + f"[{self.workspace}] Dropped workspace {workspace_label} from Memgraph database {self._DATABASE}" + ) + return {"status": "success", "message": "workspace data dropped"} + except Exception as e: + logger.error( + f"[{self.workspace}] Error dropping workspace {workspace_label} from Memgraph database {self._DATABASE}: {e}" + ) + return {"status": "error", "message": str(e)} + + async def edge_degree(self, src_id: str, tgt_id: str) -> int: + """Get the total degree (sum of relationships) of two nodes. + + Args: + src_id: Label of the source node + tgt_id: Label of the target node + + Returns: + int: Sum of the degrees of both nodes + """ + if self._driver is None: + raise RuntimeError( + "Memgraph driver is not initialized. Call 'await initialize()' first." + ) + src_degree = await self.node_degree(src_id) + trg_degree = await self.node_degree(tgt_id) + + # Convert None to 0 for addition + src_degree = 0 if src_degree is None else src_degree + trg_degree = 0 if trg_degree is None else trg_degree + + degrees = int(src_degree) + int(trg_degree) + return degrees + + async def get_knowledge_graph( + self, + node_label: str, + max_depth: int = 3, + max_nodes: int = None, + ) -> KnowledgeGraph: + """ + Retrieve a connected subgraph of nodes where the label includes the specified `node_label`. + + Args: + node_label: Label of the starting node, * means all nodes + max_depth: Maximum depth of the subgraph, Defaults to 3 + max_nodes: Maximum nodes to return by BFS, Defaults to 1000 + + Returns: + KnowledgeGraph object containing nodes and edges, with an is_truncated flag + indicating whether the graph was truncated due to max_nodes limit + """ + # Get max_nodes from global_config if not provided + if max_nodes is None: + max_nodes = self.global_config.get("max_graph_nodes", 1000) + else: + # Limit max_nodes to not exceed global_config max_graph_nodes + max_nodes = min(max_nodes, self.global_config.get("max_graph_nodes", 1000)) + + workspace_label = self._get_workspace_label() + result = KnowledgeGraph() + seen_nodes = set() + seen_edges = set() + + async with self._driver.session( + database=self._DATABASE, default_access_mode="READ" + ) as session: + try: + if node_label == "*": + # First check total node count to determine if graph is truncated + count_query = ( + f"MATCH (n:`{workspace_label}`) RETURN count(n) as total" + ) + count_result = None + try: + count_result = await session.run(count_query) + count_record = await count_result.single() + + if count_record and count_record["total"] > max_nodes: + result.is_truncated = True + logger.info( + f"Graph truncated: {count_record['total']} nodes found, limited to {max_nodes}" + ) + finally: + if count_result: + await count_result.consume() + + # Run main query to get nodes with highest degree + main_query = f""" + MATCH (n:`{workspace_label}`) + OPTIONAL MATCH (n)-[r]-() + WITH n, COALESCE(count(r), 0) AS degree + ORDER BY degree DESC + LIMIT $max_nodes + WITH collect({{node: n}}) AS filtered_nodes + UNWIND filtered_nodes AS node_info + WITH collect(node_info.node) AS kept_nodes, filtered_nodes + OPTIONAL MATCH (a)-[r]-(b) + WHERE a IN kept_nodes AND b IN kept_nodes + RETURN filtered_nodes AS node_info, + collect(DISTINCT r) AS relationships + """ + result_set = None + try: + result_set = await session.run( + main_query, + {"max_nodes": max_nodes}, + ) + record = await result_set.single() + finally: + if result_set: + await result_set.consume() + + else: + # Run subgraph query for specific node_label + subgraph_query = f""" + MATCH (start:`{workspace_label}`) + WHERE start.entity_id = $entity_id + + MATCH path = (start)-[*BFS 0..{max_depth}]-(end:`{workspace_label}`) + WHERE ALL(n IN nodes(path) WHERE '{workspace_label}' IN labels(n)) + WITH collect(DISTINCT end) + start AS all_nodes_unlimited + WITH + CASE + WHEN size(all_nodes_unlimited) <= $max_nodes THEN all_nodes_unlimited + ELSE all_nodes_unlimited[0..$max_nodes] + END AS limited_nodes, + size(all_nodes_unlimited) > $max_nodes AS is_truncated + + UNWIND limited_nodes AS n + MATCH (n)-[r]-(m) + WHERE m IN limited_nodes + WITH collect(DISTINCT n) AS limited_nodes, collect(DISTINCT r) AS relationships, is_truncated + + RETURN + [node IN limited_nodes | {{node: node}}] AS node_info, + relationships, + is_truncated + """ + + result_set = None + try: + result_set = await session.run( + subgraph_query, + { + "entity_id": node_label, + "max_nodes": max_nodes, + }, + ) + record = await result_set.single() + + # If no record found, return empty KnowledgeGraph + if not record: + logger.debug( + f"[{self.workspace}] No nodes found for entity_id: {node_label}" + ) + return result + + # Check if the result was truncated + if record.get("is_truncated"): + result.is_truncated = True + logger.info( + f"[{self.workspace}] Graph truncated: breadth-first search limited to {max_nodes} nodes" + ) + + finally: + if result_set: + await result_set.consume() + + if record: + for node_info in record["node_info"]: + node = node_info["node"] + node_id = node.id + if node_id not in seen_nodes: + result.nodes.append( + KnowledgeGraphNode( + id=f"{node_id}", + labels=[node.get("entity_id")], + properties=dict(node), + ) + ) + seen_nodes.add(node_id) + + for rel in record["relationships"]: + edge_id = rel.id + if edge_id not in seen_edges: + start = rel.start_node + end = rel.end_node + result.edges.append( + KnowledgeGraphEdge( + id=f"{edge_id}", + type=rel.type, + source=f"{start.id}", + target=f"{end.id}", + properties=dict(rel), + ) + ) + seen_edges.add(edge_id) + + logger.info( + f"[{self.workspace}] Subgraph query successful | Node count: {len(result.nodes)} | Edge count: {len(result.edges)}" + ) + + except Exception as e: + logger.warning( + f"[{self.workspace}] Memgraph error during subgraph query: {str(e)}" + ) + + return result + + async def get_all_nodes(self) -> list[dict]: + """Get all nodes in the graph. + + Returns: + A list of all nodes, where each node is a dictionary of its properties + """ + if self._driver is None: + raise RuntimeError( + "Memgraph driver is not initialized. Call 'await initialize()' first." + ) + workspace_label = self._get_workspace_label() + async with self._driver.session( + database=self._DATABASE, default_access_mode="READ" + ) as session: + query = f""" + MATCH (n:`{workspace_label}`) + RETURN n + """ + result = await session.run(query) + nodes = [] + async for record in result: + node = record["n"] + node_dict = dict(node) + # Add node id (entity_id) to the dictionary for easier access + node_dict["id"] = node_dict.get("entity_id") + nodes.append(node_dict) + await result.consume() + return nodes + + async def get_all_edges(self) -> list[dict]: + """Get all edges in the graph. + + Returns: + A list of all edges, where each edge is a dictionary of its properties + """ + if self._driver is None: + raise RuntimeError( + "Memgraph driver is not initialized. Call 'await initialize()' first." + ) + workspace_label = self._get_workspace_label() + async with self._driver.session( + database=self._DATABASE, default_access_mode="READ" + ) as session: + query = f""" + MATCH (a:`{workspace_label}`)-[r]-(b:`{workspace_label}`) + RETURN DISTINCT a.entity_id AS source, b.entity_id AS target, properties(r) AS properties + """ + result = await session.run(query) + edges = [] + async for record in result: + edge_properties = record["properties"] + edge_properties["source"] = record["source"] + edge_properties["target"] = record["target"] + edges.append(edge_properties) + await result.consume() + return edges + + async def get_popular_labels(self, limit: int = 300) -> list[str]: + """Get popular labels by node degree (most connected entities) + + Args: + limit: Maximum number of labels to return + + Returns: + List of labels sorted by degree (highest first) + """ + if self._driver is None: + raise RuntimeError( + "Memgraph driver is not initialized. Call 'await initialize()' first." + ) + + result = None + try: + workspace_label = self._get_workspace_label() + async with self._driver.session( + database=self._DATABASE, default_access_mode="READ" + ) as session: + query = f""" + MATCH (n:`{workspace_label}`) + WHERE n.entity_id IS NOT NULL + OPTIONAL MATCH (n)-[r]-() + WITH n.entity_id AS label, count(r) AS degree + ORDER BY degree DESC, label ASC + LIMIT {limit} + RETURN label + """ + result = await session.run(query) + labels = [] + async for record in result: + labels.append(record["label"]) + await result.consume() + + logger.debug( + f"[{self.workspace}] Retrieved {len(labels)} popular labels (limit: {limit})" + ) + return labels + except Exception as e: + logger.error(f"[{self.workspace}] Error getting popular labels: {str(e)}") + if result is not None: + await result.consume() + return [] + + async def search_labels(self, query: str, limit: int = 50) -> list[str]: + """Search labels with fuzzy matching + + Args: + query: Search query string + limit: Maximum number of results to return + + Returns: + List of matching labels sorted by relevance + """ + if self._driver is None: + raise RuntimeError( + "Memgraph driver is not initialized. Call 'await initialize()' first." + ) + + query_lower = query.lower().strip() + + if not query_lower: + return [] + + result = None + try: + workspace_label = self._get_workspace_label() + async with self._driver.session( + database=self._DATABASE, default_access_mode="READ" + ) as session: + cypher_query = f""" + MATCH (n:`{workspace_label}`) + WHERE n.entity_id IS NOT NULL + WITH n.entity_id AS label, toLower(n.entity_id) AS label_lower + WHERE label_lower CONTAINS $query_lower + WITH label, label_lower, + CASE + WHEN label_lower = $query_lower THEN 1000 + WHEN label_lower STARTS WITH $query_lower THEN 500 + ELSE 100 - size(label) + END AS score + ORDER BY score DESC, label ASC + LIMIT {limit} + RETURN label + """ + + result = await session.run(cypher_query, query_lower=query_lower) + labels = [] + async for record in result: + labels.append(record["label"]) + await result.consume() + + logger.debug( + f"[{self.workspace}] Search query '{query}' returned {len(labels)} results (limit: {limit})" + ) + return labels + except Exception as e: + logger.error(f"[{self.workspace}] Error searching labels: {str(e)}") + if result is not None: + await result.consume() + return [] diff --git a/LightRAG/lightrag/kg/milvus_impl.py b/LightRAG/lightrag/kg/milvus_impl.py new file mode 100644 index 00000000..3c621c06 --- /dev/null +++ b/LightRAG/lightrag/kg/milvus_impl.py @@ -0,0 +1,1371 @@ +import asyncio +import os +from typing import Any, final +from dataclasses import dataclass +import numpy as np +from lightrag.utils import logger, compute_mdhash_id +from ..base import BaseVectorStorage +from ..constants import DEFAULT_MAX_FILE_PATH_LENGTH +from ..kg.shared_storage import get_data_init_lock, get_storage_lock +import pipmaster as pm + +if not pm.is_installed("pymilvus"): + pm.install("pymilvus>=2.6.2") + +import configparser +from pymilvus import MilvusClient, DataType, CollectionSchema, FieldSchema # type: ignore + +config = configparser.ConfigParser() +config.read("config.ini", "utf-8") + + +@final +@dataclass +class MilvusVectorDBStorage(BaseVectorStorage): + def _create_schema_for_namespace(self) -> CollectionSchema: + """Create schema based on the current instance's namespace""" + + # Get vector dimension from embedding_func + dimension = self.embedding_func.embedding_dim + + # Base fields (common to all collections) + base_fields = [ + FieldSchema( + name="id", dtype=DataType.VARCHAR, max_length=64, is_primary=True + ), + FieldSchema(name="vector", dtype=DataType.FLOAT_VECTOR, dim=dimension), + FieldSchema(name="created_at", dtype=DataType.INT64), + ] + + # Determine specific fields based on namespace + if self.namespace.endswith("entities"): + specific_fields = [ + FieldSchema( + name="entity_name", + dtype=DataType.VARCHAR, + max_length=512, + nullable=True, + ), + FieldSchema( + name="file_path", + dtype=DataType.VARCHAR, + max_length=DEFAULT_MAX_FILE_PATH_LENGTH, + nullable=True, + ), + ] + description = "LightRAG entities vector storage" + + elif self.namespace.endswith("relationships"): + specific_fields = [ + FieldSchema( + name="src_id", dtype=DataType.VARCHAR, max_length=512, nullable=True + ), + FieldSchema( + name="tgt_id", dtype=DataType.VARCHAR, max_length=512, nullable=True + ), + FieldSchema( + name="file_path", + dtype=DataType.VARCHAR, + max_length=DEFAULT_MAX_FILE_PATH_LENGTH, + nullable=True, + ), + ] + description = "LightRAG relationships vector storage" + + elif self.namespace.endswith("chunks"): + specific_fields = [ + FieldSchema( + name="full_doc_id", + dtype=DataType.VARCHAR, + max_length=64, + nullable=True, + ), + FieldSchema( + name="file_path", + dtype=DataType.VARCHAR, + max_length=DEFAULT_MAX_FILE_PATH_LENGTH, + nullable=True, + ), + ] + description = "LightRAG chunks vector storage" + + else: + # Default generic schema (backward compatibility) + specific_fields = [ + FieldSchema( + name="file_path", + dtype=DataType.VARCHAR, + max_length=DEFAULT_MAX_FILE_PATH_LENGTH, + nullable=True, + ), + ] + description = "LightRAG generic vector storage" + + # Merge all fields + all_fields = base_fields + specific_fields + + return CollectionSchema( + fields=all_fields, + description=description, + enable_dynamic_field=True, # Support dynamic fields + ) + + def _get_index_params(self): + """Get IndexParams in a version-compatible way""" + try: + # Try to use client's prepare_index_params method (most common) + if hasattr(self._client, "prepare_index_params"): + return self._client.prepare_index_params() + except Exception: + pass + + try: + # Try to import IndexParams from different possible locations + from pymilvus.client.prepare import IndexParams + + return IndexParams() + except ImportError: + pass + + try: + from pymilvus.client.types import IndexParams + + return IndexParams() + except ImportError: + pass + + try: + from pymilvus import IndexParams + + return IndexParams() + except ImportError: + pass + + # If all else fails, return None to use fallback method + return None + + def _create_vector_index_fallback(self): + """Fallback method to create vector index using direct API""" + try: + self._client.create_index( + collection_name=self.final_namespace, + field_name="vector", + index_params={ + "index_type": "HNSW", + "metric_type": "COSINE", + "params": {"M": 16, "efConstruction": 256}, + }, + ) + logger.debug( + f"[{self.workspace}] Created vector index using fallback method" + ) + except Exception as e: + logger.warning( + f"[{self.workspace}] Failed to create vector index using fallback method: {e}" + ) + + def _create_scalar_index_fallback(self, field_name: str, index_type: str): + """Fallback method to create scalar index using direct API""" + # Skip unsupported index types + if index_type == "SORTED": + logger.info( + f"[{self.workspace}] Skipping SORTED index for {field_name} (not supported in this Milvus version)" + ) + return + + try: + self._client.create_index( + collection_name=self.final_namespace, + field_name=field_name, + index_params={"index_type": index_type}, + ) + logger.debug( + f"[{self.workspace}] Created {field_name} index using fallback method" + ) + except Exception as e: + logger.info( + f"[{self.workspace}] Could not create {field_name} index using fallback method: {e}" + ) + + def _create_indexes_after_collection(self): + """Create indexes after collection is created""" + try: + # Try to get IndexParams in a version-compatible way + IndexParamsClass = self._get_index_params() + + if IndexParamsClass is not None: + # Use IndexParams approach if available + try: + # Create vector index first (required for most operations) + vector_index = IndexParamsClass + vector_index.add_index( + field_name="vector", + index_type="HNSW", + metric_type="COSINE", + params={"M": 16, "efConstruction": 256}, + ) + self._client.create_index( + collection_name=self.final_namespace, index_params=vector_index + ) + logger.debug( + f"[{self.workspace}] Created vector index using IndexParams" + ) + except Exception as e: + logger.debug( + f"[{self.workspace}] IndexParams method failed for vector index: {e}" + ) + self._create_vector_index_fallback() + + # Create scalar indexes based on namespace + if self.namespace.endswith("entities"): + # Create indexes for entity fields + try: + entity_name_index = self._get_index_params() + entity_name_index.add_index( + field_name="entity_name", index_type="INVERTED" + ) + self._client.create_index( + collection_name=self.final_namespace, + index_params=entity_name_index, + ) + except Exception as e: + logger.debug( + f"[{self.workspace}] IndexParams method failed for entity_name: {e}" + ) + self._create_scalar_index_fallback("entity_name", "INVERTED") + + elif self.namespace.endswith("relationships"): + # Create indexes for relationship fields + try: + src_id_index = self._get_index_params() + src_id_index.add_index( + field_name="src_id", index_type="INVERTED" + ) + self._client.create_index( + collection_name=self.final_namespace, + index_params=src_id_index, + ) + except Exception as e: + logger.debug( + f"[{self.workspace}] IndexParams method failed for src_id: {e}" + ) + self._create_scalar_index_fallback("src_id", "INVERTED") + + try: + tgt_id_index = self._get_index_params() + tgt_id_index.add_index( + field_name="tgt_id", index_type="INVERTED" + ) + self._client.create_index( + collection_name=self.final_namespace, + index_params=tgt_id_index, + ) + except Exception as e: + logger.debug( + f"[{self.workspace}] IndexParams method failed for tgt_id: {e}" + ) + self._create_scalar_index_fallback("tgt_id", "INVERTED") + + elif self.namespace.endswith("chunks"): + # Create indexes for chunk fields + try: + doc_id_index = self._get_index_params() + doc_id_index.add_index( + field_name="full_doc_id", index_type="INVERTED" + ) + self._client.create_index( + collection_name=self.final_namespace, + index_params=doc_id_index, + ) + except Exception as e: + logger.debug( + f"[{self.workspace}] IndexParams method failed for full_doc_id: {e}" + ) + self._create_scalar_index_fallback("full_doc_id", "INVERTED") + + # No common indexes needed + + else: + # Fallback to direct API calls if IndexParams is not available + logger.info( + f"[{self.workspace}] IndexParams not available, using fallback methods for {self.namespace}" + ) + + # Create vector index using fallback + self._create_vector_index_fallback() + + # Create scalar indexes using fallback + if self.namespace.endswith("entities"): + self._create_scalar_index_fallback("entity_name", "INVERTED") + elif self.namespace.endswith("relationships"): + self._create_scalar_index_fallback("src_id", "INVERTED") + self._create_scalar_index_fallback("tgt_id", "INVERTED") + elif self.namespace.endswith("chunks"): + self._create_scalar_index_fallback("full_doc_id", "INVERTED") + + logger.info( + f"[{self.workspace}] Created indexes for collection: {self.namespace}" + ) + + except Exception as e: + logger.warning( + f"[{self.workspace}] Failed to create some indexes for {self.namespace}: {e}" + ) + + def _get_required_fields_for_namespace(self) -> dict: + """Get required core field definitions for current namespace""" + + # Base fields (common to all types) + base_fields = { + "id": {"type": "VarChar", "is_primary": True}, + "vector": {"type": "FloatVector"}, + "created_at": {"type": "Int64"}, + } + + # Add specific fields based on namespace + if self.namespace.endswith("entities"): + specific_fields = { + "entity_name": {"type": "VarChar"}, + "file_path": {"type": "VarChar"}, + } + elif self.namespace.endswith("relationships"): + specific_fields = { + "src_id": {"type": "VarChar"}, + "tgt_id": {"type": "VarChar"}, + "file_path": {"type": "VarChar"}, + } + elif self.namespace.endswith("chunks"): + specific_fields = { + "full_doc_id": {"type": "VarChar"}, + "file_path": {"type": "VarChar"}, + } + else: + specific_fields = { + "file_path": {"type": "VarChar"}, + } + + return {**base_fields, **specific_fields} + + def _is_field_compatible(self, existing_field: dict, expected_config: dict) -> bool: + """Check compatibility of a single field""" + field_name = existing_field.get("name", "unknown") + existing_type = existing_field.get("type") + expected_type = expected_config.get("type") + + logger.debug( + f"[{self.workspace}] Checking field '{field_name}': existing_type={existing_type} (type={type(existing_type)}), expected_type={expected_type}" + ) + + # Convert DataType enum values to string names if needed + original_existing_type = existing_type + if hasattr(existing_type, "name"): + existing_type = existing_type.name + logger.debug( + f"[{self.workspace}] Converted enum to name: {original_existing_type} -> {existing_type}" + ) + elif isinstance(existing_type, int): + # Map common Milvus internal type codes to type names for backward compatibility + type_mapping = { + 21: "VarChar", + 101: "FloatVector", + 5: "Int64", + 9: "Double", + } + mapped_type = type_mapping.get(existing_type, str(existing_type)) + logger.debug( + f"[{self.workspace}] Mapped numeric type: {existing_type} -> {mapped_type}" + ) + existing_type = mapped_type + + # Normalize type names for comparison + type_aliases = { + "VARCHAR": "VarChar", + "String": "VarChar", + "FLOAT_VECTOR": "FloatVector", + "INT64": "Int64", + "BigInt": "Int64", + "DOUBLE": "Double", + "Float": "Double", + } + + original_existing = existing_type + original_expected = expected_type + existing_type = type_aliases.get(existing_type, existing_type) + expected_type = type_aliases.get(expected_type, expected_type) + + if original_existing != existing_type or original_expected != expected_type: + logger.debug( + f"[{self.workspace}] Applied aliases: {original_existing} -> {existing_type}, {original_expected} -> {expected_type}" + ) + + # Basic type compatibility check + type_compatible = existing_type == expected_type + logger.debug( + f"[{self.workspace}] Type compatibility for '{field_name}': {existing_type} == {expected_type} -> {type_compatible}" + ) + + if not type_compatible: + logger.warning( + f"[{self.workspace}] Type mismatch for field '{field_name}': expected {expected_type}, got {existing_type}" + ) + return False + + # Primary key check - be more flexible about primary key detection + if expected_config.get("is_primary"): + # Check multiple possible field names for primary key status + is_primary = ( + existing_field.get("is_primary_key", False) + or existing_field.get("is_primary", False) + or existing_field.get("primary_key", False) + ) + logger.debug( + f"[{self.workspace}] Primary key check for '{field_name}': expected=True, actual={is_primary}" + ) + logger.debug( + f"[{self.workspace}] Raw field data for '{field_name}': {existing_field}" + ) + + # For ID field, be more lenient - if it's the ID field, assume it should be primary + if field_name == "id" and not is_primary: + logger.info( + f"[{self.workspace}] ID field '{field_name}' not marked as primary in existing collection, but treating as compatible" + ) + # Don't fail for ID field primary key mismatch + elif not is_primary: + logger.warning( + f"[{self.workspace}] Primary key mismatch for field '{field_name}': expected primary key, but field is not primary" + ) + return False + + logger.debug(f"[{self.workspace}] Field '{field_name}' is compatible") + return True + + def _check_vector_dimension(self, collection_info: dict): + """Check vector dimension compatibility""" + current_dimension = self.embedding_func.embedding_dim + + # Find vector field dimension + for field in collection_info.get("fields", []): + if field.get("name") == "vector": + field_type = field.get("type") + + # Extract type name from DataType enum or string + type_name = None + if hasattr(field_type, "name"): + type_name = field_type.name + elif isinstance(field_type, str): + type_name = field_type + else: + type_name = str(field_type) + + # Check if it's a vector type (supports multiple formats) + if type_name in ["FloatVector", "FLOAT_VECTOR"]: + existing_dimension = field.get("params", {}).get("dim") + + # Convert both to int for comparison to handle type mismatches + # (Milvus API may return string "1024" vs int 1024) + try: + existing_dim_int = ( + int(existing_dimension) + if existing_dimension is not None + else None + ) + current_dim_int = ( + int(current_dimension) + if current_dimension is not None + else None + ) + except (TypeError, ValueError) as e: + logger.error( + f"[{self.workspace}] Failed to parse dimensions: existing={existing_dimension} (type={type(existing_dimension)}), " + f"current={current_dimension} (type={type(current_dimension)}), error={e}" + ) + raise ValueError( + f"Invalid dimension values for collection '{self.final_namespace}': " + f"existing={existing_dimension}, current={current_dimension}" + ) from e + + if existing_dim_int != current_dim_int: + raise ValueError( + f"Vector dimension mismatch for collection '{self.final_namespace}': " + f"existing={existing_dim_int}, current={current_dim_int}" + ) + + logger.debug( + f"[{self.workspace}] Vector dimension check passed: {current_dim_int}" + ) + return + + # If no vector field found, this might be an old collection created with simple schema + logger.warning( + f"[{self.workspace}] Vector field not found in collection '{self.namespace}'. This might be an old collection created with simple schema." + ) + logger.warning( + f"[{self.workspace}] Consider recreating the collection for optimal performance." + ) + return + + def _check_file_path_length_restriction(self, collection_info: dict) -> bool: + """Check if collection has file_path length restrictions that need migration + + Returns: + bool: True if migration is needed, False otherwise + """ + existing_fields = { + field["name"]: field for field in collection_info.get("fields", []) + } + + # Check if file_path field exists and has length restrictions + if "file_path" in existing_fields: + file_path_field = existing_fields["file_path"] + # Get max_length from field params + max_length = file_path_field.get("params", {}).get("max_length") + + if max_length and max_length < DEFAULT_MAX_FILE_PATH_LENGTH: + logger.info( + f"[{self.workspace}] Collection {self.namespace} has file_path max_length={max_length}, " + f"needs migration to {DEFAULT_MAX_FILE_PATH_LENGTH}" + ) + return True + + return False + + def _check_schema_compatibility(self, collection_info: dict): + """Check schema field compatibility and detect migration needs""" + existing_fields = { + field["name"]: field for field in collection_info.get("fields", []) + } + + # Check if this is an old collection created with simple schema + has_vector_field = any( + field.get("name") == "vector" for field in collection_info.get("fields", []) + ) + + if not has_vector_field: + logger.warning( + f"[{self.workspace}] Collection {self.namespace} appears to be created with old simple schema (no vector field)" + ) + logger.warning( + f"[{self.workspace}] This collection will work but may have suboptimal performance" + ) + logger.warning( + f"[{self.workspace}] Consider recreating the collection for optimal performance" + ) + return + + # Check if migration is needed for file_path length restrictions + if self._check_file_path_length_restriction(collection_info): + logger.info( + f"[{self.workspace}] Starting automatic migration for collection {self.namespace}" + ) + self._migrate_collection_schema() + return + + # For collections with vector field, check basic compatibility + # Only check for critical incompatibilities, not missing optional fields + critical_fields = {"id": {"type": "VarChar", "is_primary": True}} + + incompatible_fields = [] + + for field_name, expected_config in critical_fields.items(): + if field_name in existing_fields: + existing_field = existing_fields[field_name] + if not self._is_field_compatible(existing_field, expected_config): + incompatible_fields.append( + f"{field_name}: expected {expected_config['type']}, " + f"got {existing_field.get('type')}" + ) + + if incompatible_fields: + raise ValueError( + f"Critical schema incompatibility in collection '{self.final_namespace}': {incompatible_fields}" + ) + + # Get all expected fields for informational purposes + expected_fields = self._get_required_fields_for_namespace() + missing_fields = [ + field for field in expected_fields if field not in existing_fields + ] + + if missing_fields: + logger.info( + f"[{self.workspace}] Collection {self.namespace} missing optional fields: {missing_fields}" + ) + logger.info( + "These fields would be available in a newly created collection for better performance" + ) + + logger.debug( + f"[{self.workspace}] Schema compatibility check passed for {self.namespace}" + ) + + def _migrate_collection_schema(self): + """Migrate collection schema using query_iterator - completely solves query window limitations""" + original_collection_name = self.final_namespace + temp_collection_name = f"{self.final_namespace}_temp" + iterator = None + + try: + logger.info( + f"[{self.workspace}] Starting iterator-based schema migration for {self.namespace}" + ) + + # Step 1: Create temporary collection with new schema + logger.info( + f"[{self.workspace}] Step 1: Creating temporary collection: {temp_collection_name}" + ) + # Temporarily update final_namespace for index creation + self.final_namespace = temp_collection_name + new_schema = self._create_schema_for_namespace() + self._client.create_collection( + collection_name=temp_collection_name, schema=new_schema + ) + try: + self._create_indexes_after_collection() + except Exception as index_error: + logger.warning( + f"[{self.workspace}] Failed to create indexes for new collection: {index_error}" + ) + # Continue with migration even if index creation fails + + # Load the new collection + self._client.load_collection(temp_collection_name) + + # Step 2: Copy data using query_iterator (solves query window limitation) + logger.info( + f"[{self.workspace}] Step 2: Copying data using query_iterator from: {original_collection_name}" + ) + + # Create query iterator + try: + iterator = self._client.query_iterator( + collection_name=original_collection_name, + batch_size=2000, # Adjustable batch size for optimal performance + output_fields=["*"], # Get all fields + ) + logger.debug(f"[{self.workspace}] Query iterator created successfully") + except Exception as iterator_error: + logger.error( + f"[{self.workspace}] Failed to create query iterator: {iterator_error}" + ) + raise + + # Iterate through all data + total_migrated = 0 + batch_number = 1 + + while True: + try: + batch_data = iterator.next() + if not batch_data: + # No more data available + break + + # Insert batch data to new collection + try: + self._client.insert( + collection_name=temp_collection_name, data=batch_data + ) + total_migrated += len(batch_data) + + logger.info( + f"[{self.workspace}] Iterator batch {batch_number}: " + f"processed {len(batch_data)} records, total migrated: {total_migrated}" + ) + batch_number += 1 + + except Exception as batch_error: + logger.error( + f"[{self.workspace}] Failed to insert iterator batch {batch_number}: {batch_error}" + ) + raise + + except Exception as next_error: + logger.error( + f"[{self.workspace}] Iterator next() failed at batch {batch_number}: {next_error}" + ) + raise + + if total_migrated > 0: + logger.info( + f"[{self.workspace}] Successfully migrated {total_migrated} records using iterator" + ) + else: + logger.info( + f"[{self.workspace}] No data found in original collection, migration completed" + ) + + # Step 3: Rename origin collection (keep for safety) + logger.info( + f"[{self.workspace}] Step 3: Rename origin collection to {original_collection_name}_old" + ) + try: + self._client.rename_collection( + original_collection_name, f"{original_collection_name}_old" + ) + except Exception as rename_error: + try: + logger.warning( + f"[{self.workspace}] Try to drop origin collection instead" + ) + self._client.drop_collection(original_collection_name) + except Exception as e: + logger.error( + f"[{self.workspace}] Rename operation failed: {rename_error}" + ) + raise e + + # Step 4: Rename temporary collection to original name + logger.info( + f"[{self.workspace}] Step 4: Renaming collection {temp_collection_name} -> {original_collection_name}" + ) + try: + self._client.rename_collection( + temp_collection_name, original_collection_name + ) + logger.info(f"[{self.workspace}] Rename operation completed") + except Exception as rename_error: + logger.error( + f"[{self.workspace}] Rename operation failed: {rename_error}" + ) + raise RuntimeError( + f"Failed to rename collection: {rename_error}" + ) from rename_error + + # Restore final_namespace + self.final_namespace = original_collection_name + + except Exception as e: + logger.error( + f"[{self.workspace}] Iterator-based migration failed for {self.namespace}: {e}" + ) + + # Attempt cleanup of temporary collection if it exists + try: + if self._client and self._client.has_collection(temp_collection_name): + logger.info( + f"[{self.workspace}] Cleaning up failed migration temporary collection" + ) + self._client.drop_collection(temp_collection_name) + except Exception as cleanup_error: + logger.warning( + f"[{self.workspace}] Failed to cleanup temporary collection: {cleanup_error}" + ) + + # Re-raise the original error + raise RuntimeError( + f"Iterator-based migration failed for collection {self.namespace}: {e}" + ) from e + + finally: + # Ensure iterator is properly closed + if iterator: + try: + iterator.close() + logger.debug( + f"[{self.workspace}] Query iterator closed successfully" + ) + except Exception as close_error: + logger.warning( + f"[{self.workspace}] Failed to close query iterator: {close_error}" + ) + + def _validate_collection_compatibility(self): + """Validate existing collection's dimension and schema compatibility""" + try: + collection_info = self._client.describe_collection(self.final_namespace) + + # 1. Check vector dimension + self._check_vector_dimension(collection_info) + + # 2. Check schema compatibility + self._check_schema_compatibility(collection_info) + + logger.info( + f"[{self.workspace}] VectorDB Collection '{self.namespace}' compatibility validation passed" + ) + + except Exception as e: + logger.error( + f"[{self.workspace}] Collection compatibility validation failed for {self.namespace}: {e}" + ) + raise + + def _ensure_collection_loaded(self): + """Ensure the collection is loaded into memory for search operations""" + try: + # Check if collection exists first + if not self._client.has_collection(self.final_namespace): + logger.error( + f"[{self.workspace}] Collection {self.namespace} does not exist" + ) + raise ValueError(f"Collection {self.final_namespace} does not exist") + + # Load the collection if it's not already loaded + # In Milvus, collections need to be loaded before they can be searched + self._client.load_collection(self.final_namespace) + # logger.debug(f"[{self.workspace}] Collection {self.namespace} loaded successfully") + + except Exception as e: + logger.error( + f"[{self.workspace}] Failed to load collection {self.namespace}: {e}" + ) + raise + + def _create_collection_if_not_exist(self): + """Create collection if not exists and check existing collection compatibility""" + + try: + # Check if our specific collection exists + collection_exists = self._client.has_collection(self.final_namespace) + logger.info( + f"[{self.workspace}] VectorDB collection '{self.namespace}' exists check: {collection_exists}" + ) + + if collection_exists: + # Double-check by trying to describe the collection + try: + self._client.describe_collection(self.final_namespace) + self._validate_collection_compatibility() + # Ensure the collection is loaded after validation + self._ensure_collection_loaded() + return + except Exception as validation_error: + # CRITICAL: Collection exists but validation failed + # This indicates potential data migration failure or incompatible schema + # Stop execution to prevent data loss and require manual intervention + logger.error( + f"[{self.workspace}] CRITICAL ERROR: Collection '{self.namespace}' exists but validation failed!" + ) + logger.error( + f"[{self.workspace}] This indicates potential data migration failure or schema incompatibility." + ) + logger.error( + f"[{self.workspace}] Validation error: {validation_error}" + ) + logger.error(f"[{self.workspace}] MANUAL INTERVENTION REQUIRED:") + logger.error( + f"[{self.workspace}] 1. Check the existing collection schema and data integrity" + ) + logger.error( + f"[{self.workspace}] 2. Backup existing data if needed" + ) + logger.error( + f"[{self.workspace}] 3. Manually resolve schema compatibility issues" + ) + logger.error( + f"[{self.workspace}] 4. Consider dropping and recreating the collection if data is not critical" + ) + logger.error( + f"[{self.workspace}] Program execution stopped to prevent potential data loss." + ) + + # Raise a specific exception to stop execution + raise RuntimeError( + f"Collection validation failed for '{self.final_namespace}'. " + f"Data migration failure detected. Manual intervention required to prevent data loss. " + f"Original error: {validation_error}" + ) + + # Collection doesn't exist, create new collection + logger.info(f"[{self.workspace}] Creating new collection: {self.namespace}") + schema = self._create_schema_for_namespace() + + # Create collection with schema only first + self._client.create_collection( + collection_name=self.final_namespace, schema=schema + ) + + # Then create indexes + self._create_indexes_after_collection() + + # Load the newly created collection + self._ensure_collection_loaded() + + logger.info( + f"[{self.workspace}] Successfully created Milvus collection: {self.namespace}" + ) + + except RuntimeError: + # Re-raise RuntimeError (validation failures) without modification + # These are critical errors that should stop execution + raise + + except Exception as e: + logger.error( + f"[{self.workspace}] Error in _create_collection_if_not_exist for {self.namespace}: {e}" + ) + + # If there's any error (other than validation failure), try to force create the collection + logger.info( + f"[{self.workspace}] Attempting to force create collection {self.namespace}..." + ) + try: + # Try to drop the collection first if it exists in a bad state + try: + if self._client.has_collection(self.final_namespace): + logger.info( + f"[{self.workspace}] Dropping potentially corrupted collection {self.namespace}" + ) + self._client.drop_collection(self.final_namespace) + except Exception as drop_error: + logger.warning( + f"[{self.workspace}] Could not drop collection {self.namespace}: {drop_error}" + ) + + # Create fresh collection + schema = self._create_schema_for_namespace() + self._client.create_collection( + collection_name=self.final_namespace, schema=schema + ) + self._create_indexes_after_collection() + + # Load the newly created collection + self._ensure_collection_loaded() + + logger.info( + f"[{self.workspace}] Successfully force-created collection {self.namespace}" + ) + + except Exception as create_error: + logger.error( + f"[{self.workspace}] Failed to force-create collection {self.namespace}: {create_error}" + ) + raise + + def __post_init__(self): + # Check for MILVUS_WORKSPACE environment variable first (higher priority) + # This allows administrators to force a specific workspace for all Milvus storage instances + milvus_workspace = os.environ.get("MILVUS_WORKSPACE") + if milvus_workspace and milvus_workspace.strip(): + # Use environment variable value, overriding the passed workspace parameter + effective_workspace = milvus_workspace.strip() + logger.info( + f"Using MILVUS_WORKSPACE environment variable: '{effective_workspace}' (overriding passed workspace: '{self.workspace}')" + ) + else: + # Use the workspace parameter passed during initialization + effective_workspace = self.workspace + if effective_workspace: + logger.debug( + f"Using passed workspace parameter: '{effective_workspace}'" + ) + + # Build final_namespace with workspace prefix for data isolation + # Keep original namespace unchanged for type detection logic + if effective_workspace: + self.final_namespace = f"{effective_workspace}_{self.namespace}" + logger.debug( + f"Final namespace with workspace prefix: '{self.final_namespace}'" + ) + else: + # When workspace is empty, final_namespace equals original namespace + self.final_namespace = self.namespace + logger.debug(f"Final namespace (no workspace): '{self.final_namespace}'") + self.workspace = "_" + + kwargs = self.global_config.get("vector_db_storage_cls_kwargs", {}) + cosine_threshold = kwargs.get("cosine_better_than_threshold") + if cosine_threshold is None: + raise ValueError( + "cosine_better_than_threshold must be specified in vector_db_storage_cls_kwargs" + ) + self.cosine_better_than_threshold = cosine_threshold + + # Ensure created_at is in meta_fields + if "created_at" not in self.meta_fields: + self.meta_fields.add("created_at") + + # Initialize client as None - will be created in initialize() method + self._client = None + self._max_batch_size = self.global_config["embedding_batch_num"] + self._initialized = False + + async def initialize(self): + """Initialize Milvus collection""" + async with get_data_init_lock(): + if self._initialized: + return + + try: + # Create MilvusClient if not already created + if self._client is None: + self._client = MilvusClient( + uri=os.environ.get( + "MILVUS_URI", + config.get( + "milvus", + "uri", + fallback=os.path.join( + self.global_config["working_dir"], "milvus_lite.db" + ), + ), + ), + user=os.environ.get( + "MILVUS_USER", config.get("milvus", "user", fallback=None) + ), + password=os.environ.get( + "MILVUS_PASSWORD", + config.get("milvus", "password", fallback=None), + ), + token=os.environ.get( + "MILVUS_TOKEN", config.get("milvus", "token", fallback=None) + ), + db_name=os.environ.get( + "MILVUS_DB_NAME", + config.get("milvus", "db_name", fallback=None), + ), + ) + logger.debug( + f"[{self.workspace}] MilvusClient created successfully" + ) + + # Create collection and check compatibility + self._create_collection_if_not_exist() + self._initialized = True + logger.info( + f"[{self.workspace}] Milvus collection '{self.namespace}' initialized successfully" + ) + except Exception as e: + logger.error( + f"[{self.workspace}] Failed to initialize Milvus collection '{self.namespace}': {e}" + ) + raise + + async def upsert(self, data: dict[str, dict[str, Any]]) -> None: + # logger.debug(f"[{self.workspace}] Inserting {len(data)} to {self.namespace}") + if not data: + return + + # Ensure collection is loaded before upserting + self._ensure_collection_loaded() + + import time + + current_time = int(time.time()) + + list_data: list[dict[str, Any]] = [ + { + "id": k, + "created_at": current_time, + **{k1: v1 for k1, v1 in v.items() if k1 in self.meta_fields}, + } + for k, v in data.items() + ] + contents = [v["content"] for v in data.values()] + batches = [ + contents[i : i + self._max_batch_size] + for i in range(0, len(contents), self._max_batch_size) + ] + + embedding_tasks = [self.embedding_func(batch) for batch in batches] + embeddings_list = await asyncio.gather(*embedding_tasks) + + embeddings = np.concatenate(embeddings_list) + for i, d in enumerate(list_data): + d["vector"] = embeddings[i] + results = self._client.upsert( + collection_name=self.final_namespace, data=list_data + ) + return results + + async def query( + self, query: str, top_k: int, query_embedding: list[float] = None + ) -> list[dict[str, Any]]: + # Ensure collection is loaded before querying + self._ensure_collection_loaded() + + # Use provided embedding or compute it + if query_embedding is not None: + embedding = [query_embedding] # Milvus expects a list of embeddings + else: + embedding = await self.embedding_func( + [query], _priority=5 + ) # higher priority for query + + # Include all meta_fields (created_at is now always included) + output_fields = list(self.meta_fields) + + results = self._client.search( + collection_name=self.final_namespace, + data=embedding, + limit=top_k, + output_fields=output_fields, + search_params={ + "metric_type": "COSINE", + "params": {"radius": self.cosine_better_than_threshold}, + }, + ) + return [ + { + **dp["entity"], + "id": dp["id"], + "distance": dp["distance"], + "created_at": dp.get("created_at"), + } + for dp in results[0] + ] + + async def index_done_callback(self) -> None: + # Milvus handles persistence automatically + pass + + async def delete_entity(self, entity_name: str) -> None: + """Delete an entity from the vector database + + Args: + entity_name: The name of the entity to delete + """ + try: + # Compute entity ID from name + entity_id = compute_mdhash_id(entity_name, prefix="ent-") + logger.debug( + f"[{self.workspace}] Attempting to delete entity {entity_name} with ID {entity_id}" + ) + + # Delete the entity from Milvus collection + result = self._client.delete( + collection_name=self.final_namespace, pks=[entity_id] + ) + + if result and result.get("delete_count", 0) > 0: + logger.debug( + f"[{self.workspace}] Successfully deleted entity {entity_name}" + ) + else: + logger.debug( + f"[{self.workspace}] Entity {entity_name} not found in storage" + ) + + except Exception as e: + logger.error(f"[{self.workspace}] Error deleting entity {entity_name}: {e}") + + async def delete_entity_relation(self, entity_name: str) -> None: + """Delete all relations associated with an entity + + Args: + entity_name: The name of the entity whose relations should be deleted + """ + try: + # Ensure collection is loaded before querying + self._ensure_collection_loaded() + + # Search for relations where entity is either source or target + expr = f'src_id == "{entity_name}" or tgt_id == "{entity_name}"' + + # Find all relations involving this entity + results = self._client.query( + collection_name=self.final_namespace, filter=expr, output_fields=["id"] + ) + + if not results or len(results) == 0: + logger.debug( + f"[{self.workspace}] No relations found for entity {entity_name}" + ) + return + + # Extract IDs of relations to delete + relation_ids = [item["id"] for item in results] + logger.debug( + f"[{self.workspace}] Found {len(relation_ids)} relations for entity {entity_name}" + ) + + # Delete the relations + if relation_ids: + delete_result = self._client.delete( + collection_name=self.final_namespace, pks=relation_ids + ) + + logger.debug( + f"[{self.workspace}] Deleted {delete_result.get('delete_count', 0)} relations for {entity_name}" + ) + + except Exception as e: + logger.error( + f"[{self.workspace}] Error deleting relations for {entity_name}: {e}" + ) + + async def delete(self, ids: list[str]) -> None: + """Delete vectors with specified IDs + + Args: + ids: List of vector IDs to be deleted + """ + try: + # Ensure collection is loaded before deleting + self._ensure_collection_loaded() + + # Delete vectors by IDs + result = self._client.delete(collection_name=self.final_namespace, pks=ids) + + if result and result.get("delete_count", 0) > 0: + logger.debug( + f"[{self.workspace}] Successfully deleted {result.get('delete_count', 0)} vectors from {self.namespace}" + ) + else: + logger.debug( + f"[{self.workspace}] No vectors were deleted from {self.namespace}" + ) + + except Exception as e: + logger.error( + f"[{self.workspace}] Error while deleting vectors from {self.namespace}: {e}" + ) + + async def get_by_id(self, id: str) -> dict[str, Any] | None: + """Get vector data by its ID + + Args: + id: The unique identifier of the vector + + Returns: + The vector data if found, or None if not found + """ + try: + # Ensure collection is loaded before querying + self._ensure_collection_loaded() + + # Include all meta_fields (created_at is now always included) plus id + output_fields = list(self.meta_fields) + ["id"] + + # Query Milvus for a specific ID + result = self._client.query( + collection_name=self.final_namespace, + filter=f'id == "{id}"', + output_fields=output_fields, + ) + + if not result or len(result) == 0: + return None + + return result[0] + except Exception as e: + logger.error( + f"[{self.workspace}] Error retrieving vector data for ID {id}: {e}" + ) + return None + + async def get_by_ids(self, ids: list[str]) -> list[dict[str, Any]]: + """Get multiple vector data by their IDs + + Args: + ids: List of unique identifiers + + Returns: + List of vector data objects that were found + """ + if not ids: + return [] + + try: + # Ensure collection is loaded before querying + self._ensure_collection_loaded() + + # Include all meta_fields (created_at is now always included) plus id + output_fields = list(self.meta_fields) + ["id"] + + # Prepare the ID filter expression + id_list = '", "'.join(ids) + filter_expr = f'id in ["{id_list}"]' + + # Query Milvus with the filter + result = self._client.query( + collection_name=self.final_namespace, + filter=filter_expr, + output_fields=output_fields, + ) + + if not result: + return [] + + result_map: dict[str, dict[str, Any]] = {} + for row in result: + if not row: + continue + row_id = row.get("id") + if row_id is not None: + result_map[str(row_id)] = row + + ordered_results: list[dict[str, Any] | None] = [] + for requested_id in ids: + ordered_results.append(result_map.get(str(requested_id))) + + return ordered_results + except Exception as e: + logger.error( + f"[{self.workspace}] Error retrieving vector data for IDs {ids}: {e}" + ) + return [] + + async def get_vectors_by_ids(self, ids: list[str]) -> dict[str, list[float]]: + """Get vectors by their IDs, returning only ID and vector data for efficiency + + Args: + ids: List of unique identifiers + + Returns: + Dictionary mapping IDs to their vector embeddings + Format: {id: [vector_values], ...} + """ + if not ids: + return {} + + try: + # Ensure collection is loaded before querying + self._ensure_collection_loaded() + + # Prepare the ID filter expression + id_list = '", "'.join(ids) + filter_expr = f'id in ["{id_list}"]' + + # Query Milvus with the filter, requesting only vector field + result = self._client.query( + collection_name=self.final_namespace, + filter=filter_expr, + output_fields=["vector"], + ) + + vectors_dict = {} + for item in result: + if item and "vector" in item and "id" in item: + # Convert numpy array to list if needed + vector_data = item["vector"] + if isinstance(vector_data, np.ndarray): + vector_data = vector_data.tolist() + vectors_dict[item["id"]] = vector_data + + return vectors_dict + except Exception as e: + logger.error( + f"[{self.workspace}] Error retrieving vectors by IDs from {self.namespace}: {e}" + ) + return {} + + async def drop(self) -> dict[str, str]: + """Drop all vector data from storage and clean up resources + + This method will delete all data from the Milvus collection. + + Returns: + dict[str, str]: Operation status and message + - On success: {"status": "success", "message": "data dropped"} + - On failure: {"status": "error", "message": ""} + """ + async with get_storage_lock(): + try: + # Drop the collection and recreate it + if self._client.has_collection(self.final_namespace): + self._client.drop_collection(self.final_namespace) + + # Recreate the collection + self._create_collection_if_not_exist() + + logger.info( + f"[{self.workspace}] Process {os.getpid()} drop Milvus collection {self.namespace}" + ) + return {"status": "success", "message": "data dropped"} + except Exception as e: + logger.error( + f"[{self.workspace}] Error dropping Milvus collection {self.namespace}: {e}" + ) + return {"status": "error", "message": str(e)} diff --git a/LightRAG/lightrag/kg/mongo_impl.py b/LightRAG/lightrag/kg/mongo_impl.py new file mode 100644 index 00000000..30452c74 --- /dev/null +++ b/LightRAG/lightrag/kg/mongo_impl.py @@ -0,0 +1,2487 @@ +import os +import re +import time +from dataclasses import dataclass, field +import numpy as np +import configparser +import asyncio + +from typing import Any, Union, final + +from ..base import ( + BaseGraphStorage, + BaseKVStorage, + BaseVectorStorage, + DocProcessingStatus, + DocStatus, + DocStatusStorage, +) +from ..utils import logger, compute_mdhash_id +from ..types import KnowledgeGraph, KnowledgeGraphNode, KnowledgeGraphEdge +from ..constants import GRAPH_FIELD_SEP +from ..kg.shared_storage import get_data_init_lock, get_storage_lock, get_graph_db_lock + +import pipmaster as pm + +if not pm.is_installed("pymongo"): + pm.install("pymongo") + +from pymongo import AsyncMongoClient # type: ignore +from pymongo import UpdateOne # type: ignore +from pymongo.asynchronous.database import AsyncDatabase # type: ignore +from pymongo.asynchronous.collection import AsyncCollection # type: ignore +from pymongo.operations import SearchIndexModel # type: ignore +from pymongo.errors import PyMongoError # type: ignore + +config = configparser.ConfigParser() +config.read("config.ini", "utf-8") + +GRAPH_BFS_MODE = os.getenv("MONGO_GRAPH_BFS_MODE", "bidirectional") + + +class ClientManager: + _instances = {"db": None, "ref_count": 0} + _lock = asyncio.Lock() + + @classmethod + async def get_client(cls) -> AsyncMongoClient: + async with cls._lock: + if cls._instances["db"] is None: + uri = os.environ.get( + "MONGO_URI", + config.get( + "mongodb", + "uri", + fallback="mongodb://root:root@localhost:27017/", + ), + ) + database_name = os.environ.get( + "MONGO_DATABASE", + config.get("mongodb", "database", fallback="LightRAG"), + ) + client = AsyncMongoClient(uri) + db = client.get_database(database_name) + cls._instances["db"] = db + cls._instances["ref_count"] = 0 + cls._instances["ref_count"] += 1 + return cls._instances["db"] + + @classmethod + async def release_client(cls, db: AsyncDatabase): + async with cls._lock: + if db is not None: + if db is cls._instances["db"]: + cls._instances["ref_count"] -= 1 + if cls._instances["ref_count"] == 0: + cls._instances["db"] = None + + +@final +@dataclass +class MongoKVStorage(BaseKVStorage): + db: AsyncDatabase = field(default=None) + _data: AsyncCollection = field(default=None) + + def __init__(self, namespace, global_config, embedding_func, workspace=None): + super().__init__( + namespace=namespace, + workspace=workspace or "", + global_config=global_config, + embedding_func=embedding_func, + ) + self.__post_init__() + + def __post_init__(self): + # Check for MONGODB_WORKSPACE environment variable first (higher priority) + # This allows administrators to force a specific workspace for all MongoDB storage instances + mongodb_workspace = os.environ.get("MONGODB_WORKSPACE") + if mongodb_workspace and mongodb_workspace.strip(): + # Use environment variable value, overriding the passed workspace parameter + effective_workspace = mongodb_workspace.strip() + logger.info( + f"Using MONGODB_WORKSPACE environment variable: '{effective_workspace}' (overriding passed workspace: '{self.workspace}')" + ) + else: + # Use the workspace parameter passed during initialization + effective_workspace = self.workspace + if effective_workspace: + logger.debug( + f"Using passed workspace parameter: '{effective_workspace}'" + ) + + # Build final_namespace with workspace prefix for data isolation + # Keep original namespace unchanged for type detection logic + if effective_workspace: + self.final_namespace = f"{effective_workspace}_{self.namespace}" + self.workspace = effective_workspace + logger.debug( + f"Final namespace with workspace prefix: '{self.final_namespace}'" + ) + else: + # When workspace is empty, final_namespace equals original namespace + self.final_namespace = self.namespace + self.workspace = "_" + logger.debug( + f"[{self.workspace}] Final namespace (no workspace): '{self.namespace}'" + ) + + self._collection_name = self.final_namespace + + async def initialize(self): + async with get_data_init_lock(): + if self.db is None: + self.db = await ClientManager.get_client() + + self._data = await get_or_create_collection(self.db, self._collection_name) + logger.debug( + f"[{self.workspace}] Use MongoDB as KV {self._collection_name}" + ) + + async def finalize(self): + async with get_storage_lock(): + if self.db is not None: + await ClientManager.release_client(self.db) + self.db = None + self._data = None + + async def get_by_id(self, id: str) -> dict[str, Any] | None: + # Unified handling for flattened keys + doc = await self._data.find_one({"_id": id}) + if doc: + # Ensure time fields are present, provide default values for old data + doc.setdefault("create_time", 0) + doc.setdefault("update_time", 0) + return doc + + async def get_by_ids(self, ids: list[str]) -> list[dict[str, Any]]: + cursor = self._data.find({"_id": {"$in": ids}}) + docs = await cursor.to_list(length=None) + + doc_map: dict[str, dict[str, Any]] = {} + for doc in docs: + if not doc: + continue + doc.setdefault("create_time", 0) + doc.setdefault("update_time", 0) + doc_map[str(doc.get("_id"))] = doc + + ordered_results: list[dict[str, Any] | None] = [] + for id_value in ids: + ordered_results.append(doc_map.get(str(id_value))) + return ordered_results + + async def filter_keys(self, keys: set[str]) -> set[str]: + cursor = self._data.find({"_id": {"$in": list(keys)}}, {"_id": 1}) + existing_ids = {str(x["_id"]) async for x in cursor} + return keys - existing_ids + + async def upsert(self, data: dict[str, dict[str, Any]]) -> None: + logger.debug(f"[{self.workspace}] Inserting {len(data)} to {self.namespace}") + if not data: + return + + # Unified handling for all namespaces with flattened keys + # Use bulk_write for better performance + + operations = [] + current_time = int(time.time()) # Get current Unix timestamp + + for k, v in data.items(): + # For text_chunks namespace, ensure llm_cache_list field exists + if self.namespace.endswith("text_chunks"): + if "llm_cache_list" not in v: + v["llm_cache_list"] = [] + + # Create a copy of v for $set operation, excluding create_time to avoid conflicts + v_for_set = v.copy() + v_for_set["_id"] = k # Use flattened key as _id + v_for_set["update_time"] = current_time # Always update update_time + + # Remove create_time from $set to avoid conflict with $setOnInsert + v_for_set.pop("create_time", None) + + operations.append( + UpdateOne( + {"_id": k}, + { + "$set": v_for_set, # Update all fields except create_time + "$setOnInsert": { + "create_time": current_time + }, # Set create_time only on insert + }, + upsert=True, + ) + ) + + if operations: + await self._data.bulk_write(operations) + + async def index_done_callback(self) -> None: + # Mongo handles persistence automatically + pass + + async def is_empty(self) -> bool: + """Check if the storage is empty for the current workspace and namespace + + Returns: + bool: True if storage is empty, False otherwise + """ + try: + # Use count_documents with limit 1 for efficiency + count = await self._data.count_documents({}, limit=1) + return count == 0 + except PyMongoError as e: + logger.error(f"[{self.workspace}] Error checking if storage is empty: {e}") + return True + + async def delete(self, ids: list[str]) -> None: + """Delete documents with specified IDs + + Args: + ids: List of document IDs to be deleted + """ + if not ids: + return + + # Convert to list if it's a set (MongoDB BSON cannot encode sets) + if isinstance(ids, set): + ids = list(ids) + + try: + result = await self._data.delete_many({"_id": {"$in": ids}}) + logger.info( + f"[{self.workspace}] Deleted {result.deleted_count} documents from {self.namespace}" + ) + except PyMongoError as e: + logger.error( + f"[{self.workspace}] Error deleting documents from {self.namespace}: {e}" + ) + + async def drop(self) -> dict[str, str]: + """Drop the storage by removing all documents in the collection. + + Returns: + dict[str, str]: Status of the operation with keys 'status' and 'message' + """ + async with get_storage_lock(): + try: + result = await self._data.delete_many({}) + deleted_count = result.deleted_count + + logger.info( + f"[{self.workspace}] Dropped {deleted_count} documents from doc status {self._collection_name}" + ) + return { + "status": "success", + "message": f"{deleted_count} documents dropped", + } + except PyMongoError as e: + logger.error( + f"[{self.workspace}] Error dropping doc status {self._collection_name}: {e}" + ) + return {"status": "error", "message": str(e)} + + +@final +@dataclass +class MongoDocStatusStorage(DocStatusStorage): + db: AsyncDatabase = field(default=None) + _data: AsyncCollection = field(default=None) + + def _prepare_doc_status_data(self, doc: dict[str, Any]) -> dict[str, Any]: + """Normalize and migrate a raw Mongo document to DocProcessingStatus-compatible dict.""" + # Make a copy of the data to avoid modifying the original + data = doc.copy() + # Remove deprecated content field if it exists + data.pop("content", None) + # Remove MongoDB _id field if it exists + data.pop("_id", None) + # If file_path is not in data, use document id as file path + if "file_path" not in data: + data["file_path"] = "no-file-path" + # Ensure new fields exist with default values + if "metadata" not in data: + data["metadata"] = {} + if "error_msg" not in data: + data["error_msg"] = None + # Backward compatibility: migrate legacy 'error' field to 'error_msg' + if "error" in data: + if "error_msg" not in data or data["error_msg"] in (None, ""): + data["error_msg"] = data.pop("error") + else: + data.pop("error", None) + return data + + def __init__(self, namespace, global_config, embedding_func, workspace=None): + super().__init__( + namespace=namespace, + workspace=workspace or "", + global_config=global_config, + embedding_func=embedding_func, + ) + self.__post_init__() + + def __post_init__(self): + # Check for MONGODB_WORKSPACE environment variable first (higher priority) + # This allows administrators to force a specific workspace for all MongoDB storage instances + mongodb_workspace = os.environ.get("MONGODB_WORKSPACE") + if mongodb_workspace and mongodb_workspace.strip(): + # Use environment variable value, overriding the passed workspace parameter + effective_workspace = mongodb_workspace.strip() + logger.info( + f"Using MONGODB_WORKSPACE environment variable: '{effective_workspace}' (overriding passed workspace: '{self.workspace}')" + ) + else: + # Use the workspace parameter passed during initialization + effective_workspace = self.workspace + if effective_workspace: + logger.debug( + f"Using passed workspace parameter: '{effective_workspace}'" + ) + + # Build final_namespace with workspace prefix for data isolation + # Keep original namespace unchanged for type detection logic + if effective_workspace: + self.final_namespace = f"{effective_workspace}_{self.namespace}" + self.workspace = effective_workspace + logger.debug( + f"Final namespace with workspace prefix: '{self.final_namespace}'" + ) + else: + # When workspace is empty, final_namespace equals original namespace + self.final_namespace = self.namespace + self.workspace = "_" + logger.debug(f"Final namespace (no workspace): '{self.final_namespace}'") + + self._collection_name = self.final_namespace + + async def initialize(self): + async with get_data_init_lock(): + if self.db is None: + self.db = await ClientManager.get_client() + + self._data = await get_or_create_collection(self.db, self._collection_name) + + # Create and migrate all indexes including Chinese collation for file_path + await self.create_and_migrate_indexes_if_not_exists() + + logger.debug( + f"[{self.workspace}] Use MongoDB as DocStatus {self._collection_name}" + ) + + async def finalize(self): + async with get_storage_lock(): + if self.db is not None: + await ClientManager.release_client(self.db) + self.db = None + self._data = None + + async def get_by_id(self, id: str) -> Union[dict[str, Any], None]: + return await self._data.find_one({"_id": id}) + + async def get_by_ids(self, ids: list[str]) -> list[dict[str, Any]]: + cursor = self._data.find({"_id": {"$in": ids}}) + docs = await cursor.to_list(length=None) + + doc_map: dict[str, dict[str, Any]] = {} + for doc in docs: + if not doc: + continue + doc_map[str(doc.get("_id"))] = doc + + ordered_results: list[dict[str, Any] | None] = [] + for id_value in ids: + ordered_results.append(doc_map.get(str(id_value))) + return ordered_results + + async def filter_keys(self, data: set[str]) -> set[str]: + cursor = self._data.find({"_id": {"$in": list(data)}}, {"_id": 1}) + existing_ids = {str(x["_id"]) async for x in cursor} + return data - existing_ids + + async def upsert(self, data: dict[str, dict[str, Any]]) -> None: + logger.debug(f"[{self.workspace}] Inserting {len(data)} to {self.namespace}") + if not data: + return + update_tasks: list[Any] = [] + for k, v in data.items(): + # Ensure chunks_list field exists and is an array + if "chunks_list" not in v: + v["chunks_list"] = [] + data[k]["_id"] = k + update_tasks.append( + self._data.update_one({"_id": k}, {"$set": v}, upsert=True) + ) + await asyncio.gather(*update_tasks) + + async def get_status_counts(self) -> dict[str, int]: + """Get counts of documents in each status""" + pipeline = [{"$group": {"_id": "$status", "count": {"$sum": 1}}}] + cursor = await self._data.aggregate(pipeline, allowDiskUse=True) + result = await cursor.to_list() + counts = {} + for doc in result: + counts[doc["_id"]] = doc["count"] + return counts + + async def get_docs_by_status( + self, status: DocStatus + ) -> dict[str, DocProcessingStatus]: + """Get all documents with a specific status""" + cursor = self._data.find({"status": status.value}) + result = await cursor.to_list() + processed_result = {} + for doc in result: + try: + data = self._prepare_doc_status_data(doc) + processed_result[doc["_id"]] = DocProcessingStatus(**data) + except KeyError as e: + logger.error( + f"[{self.workspace}] Missing required field for document {doc['_id']}: {e}" + ) + continue + return processed_result + + async def get_docs_by_track_id( + self, track_id: str + ) -> dict[str, DocProcessingStatus]: + """Get all documents with a specific track_id""" + cursor = self._data.find({"track_id": track_id}) + result = await cursor.to_list() + processed_result = {} + for doc in result: + try: + data = self._prepare_doc_status_data(doc) + processed_result[doc["_id"]] = DocProcessingStatus(**data) + except KeyError as e: + logger.error( + f"[{self.workspace}] Missing required field for document {doc['_id']}: {e}" + ) + continue + return processed_result + + async def index_done_callback(self) -> None: + # Mongo handles persistence automatically + pass + + async def is_empty(self) -> bool: + """Check if the storage is empty for the current workspace and namespace + + Returns: + bool: True if storage is empty, False otherwise + """ + try: + # Use count_documents with limit 1 for efficiency + count = await self._data.count_documents({}, limit=1) + return count == 0 + except PyMongoError as e: + logger.error(f"[{self.workspace}] Error checking if storage is empty: {e}") + return True + + async def drop(self) -> dict[str, str]: + """Drop the storage by removing all documents in the collection. + + Returns: + dict[str, str]: Status of the operation with keys 'status' and 'message' + """ + async with get_storage_lock(): + try: + result = await self._data.delete_many({}) + deleted_count = result.deleted_count + + logger.info( + f"[{self.workspace}] Dropped {deleted_count} documents from doc status {self._collection_name}" + ) + return { + "status": "success", + "message": f"{deleted_count} documents dropped", + } + except PyMongoError as e: + logger.error( + f"[{self.workspace}] Error dropping doc status {self._collection_name}: {e}" + ) + return {"status": "error", "message": str(e)} + + async def delete(self, ids: list[str]) -> None: + await self._data.delete_many({"_id": {"$in": ids}}) + + async def create_and_migrate_indexes_if_not_exists(self): + """Create indexes to optimize pagination queries and migrate file_path indexes for Chinese collation""" + try: + # Get indexes for the current collection only + indexes_cursor = await self._data.list_indexes() + existing_indexes = await indexes_cursor.to_list(length=None) + existing_index_names = {idx.get("name", "") for idx in existing_indexes} + + # Define collation configuration for Chinese pinyin sorting + collation_config = {"locale": "zh", "numericOrdering": True} + + # Use workspace-specific index names to avoid cross-workspace conflicts + workspace_prefix = f"{self.workspace}_" if self.workspace != "_" else "" + + # 1. Define all indexes needed with workspace-specific names + all_indexes = [ + # Original pagination indexes + { + "name": f"{workspace_prefix}status_updated_at", + "keys": [("status", 1), ("updated_at", -1)], + }, + { + "name": f"{workspace_prefix}status_created_at", + "keys": [("status", 1), ("created_at", -1)], + }, + {"name": f"{workspace_prefix}updated_at", "keys": [("updated_at", -1)]}, + {"name": f"{workspace_prefix}created_at", "keys": [("created_at", -1)]}, + {"name": f"{workspace_prefix}id", "keys": [("_id", 1)]}, + {"name": f"{workspace_prefix}track_id", "keys": [("track_id", 1)]}, + # New file_path indexes with Chinese collation and workspace-specific names + { + "name": f"{workspace_prefix}file_path_zh_collation", + "keys": [("file_path", 1)], + "collation": collation_config, + }, + { + "name": f"{workspace_prefix}status_file_path_zh_collation", + "keys": [("status", 1), ("file_path", 1)], + "collation": collation_config, + }, + ] + + # 2. Handle legacy index cleanup: only drop old indexes that exist in THIS collection + legacy_index_names = [ + "file_path_zh_collation", + "status_file_path_zh_collation", + "status_updated_at", + "status_created_at", + "updated_at", + "created_at", + "id", + "track_id", + ] + + for legacy_name in legacy_index_names: + if ( + legacy_name in existing_index_names + and legacy_name + != f"{workspace_prefix}{legacy_name.replace(workspace_prefix, '')}" + ): + try: + await self._data.drop_index(legacy_name) + logger.debug( + f"[{self.workspace}] Migrated: dropped legacy index '{legacy_name}' from collection {self._collection_name}" + ) + existing_index_names.discard(legacy_name) + except PyMongoError as drop_error: + logger.warning( + f"[{self.workspace}] Failed to drop legacy index '{legacy_name}' from collection {self._collection_name}: {drop_error}" + ) + + # 3. Create all needed indexes with workspace-specific names + for index_info in all_indexes: + index_name = index_info["name"] + if index_name not in existing_index_names: + create_kwargs = {"name": index_name} + if "collation" in index_info: + create_kwargs["collation"] = index_info["collation"] + + try: + await self._data.create_index( + index_info["keys"], **create_kwargs + ) + logger.debug( + f"[{self.workspace}] Created index '{index_name}' for collection {self._collection_name}" + ) + except PyMongoError as create_error: + # If creation still fails, log the error but continue with other indexes + logger.error( + f"[{self.workspace}] Failed to create index '{index_name}' for collection {self._collection_name}: {create_error}" + ) + else: + logger.debug( + f"[{self.workspace}] Index '{index_name}' already exists for collection {self._collection_name}" + ) + + except PyMongoError as e: + logger.error( + f"[{self.workspace}] Error creating/migrating indexes for {self._collection_name}: {e}" + ) + + async def get_docs_paginated( + self, + status_filter: DocStatus | None = None, + page: int = 1, + page_size: int = 50, + sort_field: str = "updated_at", + sort_direction: str = "desc", + ) -> tuple[list[tuple[str, DocProcessingStatus]], int]: + """Get documents with pagination support + + Args: + status_filter: Filter by document status, None for all statuses + page: Page number (1-based) + page_size: Number of documents per page (10-200) + sort_field: Field to sort by ('created_at', 'updated_at', '_id') + sort_direction: Sort direction ('asc' or 'desc') + + Returns: + Tuple of (list of (doc_id, DocProcessingStatus) tuples, total_count) + """ + # Validate parameters + if page < 1: + page = 1 + if page_size < 10: + page_size = 10 + elif page_size > 200: + page_size = 200 + + if sort_field not in ["created_at", "updated_at", "_id", "file_path"]: + sort_field = "updated_at" + + if sort_direction.lower() not in ["asc", "desc"]: + sort_direction = "desc" + + # Build query filter + query_filter = {} + if status_filter is not None: + query_filter["status"] = status_filter.value + + # Get total count + total_count = await self._data.count_documents(query_filter) + + # Calculate skip value + skip = (page - 1) * page_size + + # Build sort criteria + sort_direction_value = 1 if sort_direction.lower() == "asc" else -1 + sort_criteria = [(sort_field, sort_direction_value)] + + # Query for paginated data with Chinese collation for file_path sorting + if sort_field == "file_path": + # Use Chinese collation for pinyin sorting + cursor = ( + self._data.find(query_filter) + .sort(sort_criteria) + .collation({"locale": "zh", "numericOrdering": True}) + .skip(skip) + .limit(page_size) + ) + else: + # Use default sorting for other fields + cursor = ( + self._data.find(query_filter) + .sort(sort_criteria) + .skip(skip) + .limit(page_size) + ) + result = await cursor.to_list(length=page_size) + + # Convert to (doc_id, DocProcessingStatus) tuples + documents = [] + for doc in result: + try: + doc_id = doc["_id"] + + data = self._prepare_doc_status_data(doc) + + doc_status = DocProcessingStatus(**data) + documents.append((doc_id, doc_status)) + except KeyError as e: + logger.error( + f"[{self.workspace}] Missing required field for document {doc['_id']}: {e}" + ) + continue + + return documents, total_count + + async def get_all_status_counts(self) -> dict[str, int]: + """Get counts of documents in each status for all documents + + Returns: + Dictionary mapping status names to counts, including 'all' field + """ + pipeline = [{"$group": {"_id": "$status", "count": {"$sum": 1}}}] + cursor = await self._data.aggregate(pipeline, allowDiskUse=True) + result = await cursor.to_list() + + counts = {} + total_count = 0 + for doc in result: + counts[doc["_id"]] = doc["count"] + total_count += doc["count"] + + # Add 'all' field with total count + counts["all"] = total_count + + return counts + + async def get_doc_by_file_path(self, file_path: str) -> Union[dict[str, Any], None]: + """Get document by file path + + Args: + file_path: The file path to search for + + Returns: + Union[dict[str, Any], None]: Document data if found, None otherwise + Returns the same format as get_by_id method + """ + return await self._data.find_one({"file_path": file_path}) + + +@final +@dataclass +class MongoGraphStorage(BaseGraphStorage): + """ + A concrete implementation using MongoDB's $graphLookup to demonstrate multi-hop queries. + """ + + db: AsyncDatabase = field(default=None) + # node collection storing node_id, node_properties + collection: AsyncCollection = field(default=None) + # edge collection storing source_node_id, target_node_id, and edge_properties + edgeCollection: AsyncCollection = field(default=None) + + def __init__(self, namespace, global_config, embedding_func, workspace=None): + super().__init__( + namespace=namespace, + workspace=workspace or "", + global_config=global_config, + embedding_func=embedding_func, + ) + # Check for MONGODB_WORKSPACE environment variable first (higher priority) + # This allows administrators to force a specific workspace for all MongoDB storage instances + mongodb_workspace = os.environ.get("MONGODB_WORKSPACE") + if mongodb_workspace and mongodb_workspace.strip(): + # Use environment variable value, overriding the passed workspace parameter + effective_workspace = mongodb_workspace.strip() + logger.info( + f"Using MONGODB_WORKSPACE environment variable: '{effective_workspace}' (overriding passed workspace: '{self.workspace}')" + ) + else: + # Use the workspace parameter passed during initialization + effective_workspace = self.workspace + if effective_workspace: + logger.debug( + f"Using passed workspace parameter: '{effective_workspace}'" + ) + + # Build final_namespace with workspace prefix for data isolation + # Keep original namespace unchanged for type detection logic + if effective_workspace: + self.final_namespace = f"{effective_workspace}_{self.namespace}" + self.workspace = effective_workspace + logger.debug( + f"Final namespace with workspace prefix: '{self.final_namespace}'" + ) + else: + # When workspace is empty, final_namespace equals original namespace + self.final_namespace = self.namespace + self.workspace = "_" + logger.debug(f"Final namespace (no workspace): '{self.final_namespace}'") + + self._collection_name = self.final_namespace + self._edge_collection_name = f"{self._collection_name}_edges" + + async def initialize(self): + async with get_data_init_lock(): + if self.db is None: + self.db = await ClientManager.get_client() + + self.collection = await get_or_create_collection( + self.db, self._collection_name + ) + self.edge_collection = await get_or_create_collection( + self.db, self._edge_collection_name + ) + + # Create Atlas Search index for better search performance if possible + await self.create_search_index_if_not_exists() + + logger.debug( + f"[{self.workspace}] Use MongoDB as KG {self._collection_name}" + ) + + async def finalize(self): + async with get_graph_db_lock(): + if self.db is not None: + await ClientManager.release_client(self.db) + self.db = None + self.collection = None + self.edge_collection = None + + # Sample entity document + # "source_ids" is Array representation of "source_id" split by GRAPH_FIELD_SEP + + # { + # "_id" : "CompanyA", + # "entity_id" : "CompanyA", + # "entity_type" : "Organization", + # "description" : "A major technology company", + # "source_id" : "chunk-eeec0036b909839e8ec4fa150c939eec", + # "source_ids": ["chunk-eeec0036b909839e8ec4fa150c939eec"], + # "file_path" : "custom_kg", + # "created_at" : 1749904575 + # } + + # Sample relation document + # { + # "_id" : ObjectId("6856ac6e7c6bad9b5470b678"), // MongoDB build-in ObjectId + # "description" : "CompanyA develops ProductX", + # "source_node_id" : "CompanyA", + # "target_node_id" : "ProductX", + # "relationship": "Develops", // To distinguish multiple same-target relations + # "weight" : Double("1"), + # "keywords" : "develop, produce", + # "source_id" : "chunk-eeec0036b909839e8ec4fa150c939eec", + # "source_ids": ["chunk-eeec0036b909839e8ec4fa150c939eec"], + # "file_path" : "custom_kg", + # "created_at" : 1749904575 + # } + + # + # ------------------------------------------------------------------------- + # BASIC QUERIES + # ------------------------------------------------------------------------- + # + + async def has_node(self, node_id: str) -> bool: + """ + Check if node_id is present in the collection by looking up its doc. + No real need for $graphLookup here, but let's keep it direct. + """ + doc = await self.collection.find_one({"_id": node_id}, {"_id": 1}) + return doc is not None + + async def has_edge(self, source_node_id: str, target_node_id: str) -> bool: + """ + Check if there's a direct single-hop edge between source_node_id and target_node_id. + """ + doc = await self.edge_collection.find_one( + { + "$or": [ + { + "source_node_id": source_node_id, + "target_node_id": target_node_id, + }, + { + "source_node_id": target_node_id, + "target_node_id": source_node_id, + }, + ] + }, + {"_id": 1}, + ) + return doc is not None + + # + # ------------------------------------------------------------------------- + # DEGREES + # ------------------------------------------------------------------------- + # + + async def node_degree(self, node_id: str) -> int: + """ + Returns the total number of edges connected to node_id (both inbound and outbound). + """ + return await self.edge_collection.count_documents( + {"$or": [{"source_node_id": node_id}, {"target_node_id": node_id}]} + ) + + async def edge_degree(self, src_id: str, tgt_id: str) -> int: + """Get the total degree (sum of relationships) of two nodes. + + Args: + src_id: Label of the source node + tgt_id: Label of the target node + + Returns: + int: Sum of the degrees of both nodes + """ + src_degree = await self.node_degree(src_id) + trg_degree = await self.node_degree(tgt_id) + + return src_degree + trg_degree + + # + # ------------------------------------------------------------------------- + # GETTERS + # ------------------------------------------------------------------------- + # + + async def get_node(self, node_id: str) -> dict[str, str] | None: + """ + Return the full node document, or None if missing. + """ + return await self.collection.find_one({"_id": node_id}) + + async def get_edge( + self, source_node_id: str, target_node_id: str + ) -> dict[str, str] | None: + return await self.edge_collection.find_one( + { + "$or": [ + { + "source_node_id": source_node_id, + "target_node_id": target_node_id, + }, + { + "source_node_id": target_node_id, + "target_node_id": source_node_id, + }, + ] + } + ) + + async def get_node_edges(self, source_node_id: str) -> list[tuple[str, str]] | None: + """ + Retrieves all edges (relationships) for a particular node identified by its label. + + Args: + source_node_id: Label of the node to get edges for + + Returns: + list[tuple[str, str]]: List of (source_label, target_label) tuples representing edges + None: If no edges found + """ + cursor = self.edge_collection.find( + { + "$or": [ + {"source_node_id": source_node_id}, + {"target_node_id": source_node_id}, + ] + }, + {"source_node_id": 1, "target_node_id": 1}, + ) + + return [ + (e.get("source_node_id"), e.get("target_node_id")) async for e in cursor + ] + + async def get_nodes_batch(self, node_ids: list[str]) -> dict[str, dict]: + result = {} + + async for doc in self.collection.find({"_id": {"$in": node_ids}}): + result[doc.get("_id")] = doc + return result + + async def node_degrees_batch(self, node_ids: list[str]) -> dict[str, int]: + # merge the outbound and inbound results with the same "_id" and sum the "degree" + merged_results = {} + + # Outbound degrees + outbound_pipeline = [ + {"$match": {"source_node_id": {"$in": node_ids}}}, + {"$group": {"_id": "$source_node_id", "degree": {"$sum": 1}}}, + ] + + cursor = await self.edge_collection.aggregate( + outbound_pipeline, allowDiskUse=True + ) + async for doc in cursor: + merged_results[doc.get("_id")] = doc.get("degree") + + # Inbound degrees + inbound_pipeline = [ + {"$match": {"target_node_id": {"$in": node_ids}}}, + {"$group": {"_id": "$target_node_id", "degree": {"$sum": 1}}}, + ] + + cursor = await self.edge_collection.aggregate( + inbound_pipeline, allowDiskUse=True + ) + async for doc in cursor: + merged_results[doc.get("_id")] = merged_results.get( + doc.get("_id"), 0 + ) + doc.get("degree") + + return merged_results + + async def get_nodes_edges_batch( + self, node_ids: list[str] + ) -> dict[str, list[tuple[str, str]]]: + """ + Batch retrieve edges for multiple nodes. + For each node, returns both outgoing and incoming edges to properly represent + the undirected graph nature. + + Args: + node_ids: List of node IDs (entity_id) for which to retrieve edges. + + Returns: + A dictionary mapping each node ID to its list of edge tuples (source, target). + For each node, the list includes both: + - Outgoing edges: (queried_node, connected_node) + - Incoming edges: (connected_node, queried_node) + """ + result = {node_id: [] for node_id in node_ids} + + # Query outgoing edges (where node is the source) + outgoing_cursor = self.edge_collection.find( + {"source_node_id": {"$in": node_ids}}, + {"source_node_id": 1, "target_node_id": 1}, + ) + async for edge in outgoing_cursor: + source = edge["source_node_id"] + target = edge["target_node_id"] + result[source].append((source, target)) + + # Query incoming edges (where node is the target) + incoming_cursor = self.edge_collection.find( + {"target_node_id": {"$in": node_ids}}, + {"source_node_id": 1, "target_node_id": 1}, + ) + async for edge in incoming_cursor: + source = edge["source_node_id"] + target = edge["target_node_id"] + result[target].append((source, target)) + + return result + + # + # ------------------------------------------------------------------------- + # UPSERTS + # ------------------------------------------------------------------------- + # + + async def upsert_node(self, node_id: str, node_data: dict[str, str]) -> None: + """ + Insert or update a node document. + """ + update_doc = {"$set": {**node_data}} + if node_data.get("source_id", ""): + update_doc["$set"]["source_ids"] = node_data["source_id"].split( + GRAPH_FIELD_SEP + ) + + await self.collection.update_one({"_id": node_id}, update_doc, upsert=True) + + async def upsert_edge( + self, source_node_id: str, target_node_id: str, edge_data: dict[str, str] + ) -> None: + """ + Upsert an edge between source_node_id and target_node_id with optional 'relation'. + If an edge with the same target exists, we remove it and re-insert with updated data. + """ + # Ensure source node exists + await self.upsert_node(source_node_id, {}) + + update_doc = {"$set": edge_data} + if edge_data.get("source_id", ""): + update_doc["$set"]["source_ids"] = edge_data["source_id"].split( + GRAPH_FIELD_SEP + ) + + edge_data["source_node_id"] = source_node_id + edge_data["target_node_id"] = target_node_id + + await self.edge_collection.update_one( + { + "$or": [ + { + "source_node_id": source_node_id, + "target_node_id": target_node_id, + }, + { + "source_node_id": target_node_id, + "target_node_id": source_node_id, + }, + ] + }, + update_doc, + upsert=True, + ) + + # + # ------------------------------------------------------------------------- + # DELETION + # ------------------------------------------------------------------------- + # + + async def delete_node(self, node_id: str) -> None: + """ + 1) Remove node's doc entirely. + 2) Remove inbound & outbound edges from any doc that references node_id. + """ + # Remove all edges + await self.edge_collection.delete_many( + {"$or": [{"source_node_id": node_id}, {"target_node_id": node_id}]} + ) + + # Remove the node doc + await self.collection.delete_one({"_id": node_id}) + + # + # ------------------------------------------------------------------------- + # QUERY + # ------------------------------------------------------------------------- + # + + async def get_all_labels(self) -> list[str]: + """ + Get all existing node _id in the database + Returns: + [id1, id2, ...] # Alphabetically sorted id list + """ + + # Use aggregation with allowDiskUse for large datasets + pipeline = [{"$project": {"_id": 1}}, {"$sort": {"_id": 1}}] + cursor = await self.collection.aggregate(pipeline, allowDiskUse=True) + labels = [] + async for doc in cursor: + labels.append(doc["_id"]) + return labels + + def _construct_graph_node( + self, node_id, node_data: dict[str, str] + ) -> KnowledgeGraphNode: + return KnowledgeGraphNode( + id=node_id, + labels=[node_id], + properties={ + k: v + for k, v in node_data.items() + if k + not in [ + "_id", + "connected_edges", + "source_ids", + "edge_count", + ] + }, + ) + + def _construct_graph_edge(self, edge_id: str, edge: dict[str, str]): + return KnowledgeGraphEdge( + id=edge_id, + type=edge.get("relationship", ""), + source=edge["source_node_id"], + target=edge["target_node_id"], + properties={ + k: v + for k, v in edge.items() + if k + not in [ + "_id", + "source_node_id", + "target_node_id", + "relationship", + "source_ids", + ] + }, + ) + + async def get_knowledge_graph_all_by_degree( + self, max_depth: int, max_nodes: int + ) -> KnowledgeGraph: + """ + It's possible that the node with one or multiple relationships is retrieved, + while its neighbor is not. Then this node might seem like disconnected in UI. + """ + + total_node_count = await self.collection.count_documents({}) + result = KnowledgeGraph() + seen_edges = set() + + result.is_truncated = total_node_count > max_nodes + if result.is_truncated: + # Get all node_ids ranked by degree if max_nodes exceeds total node count + pipeline = [ + {"$project": {"source_node_id": 1, "_id": 0}}, + {"$group": {"_id": "$source_node_id", "degree": {"$sum": 1}}}, + { + "$unionWith": { + "coll": self._edge_collection_name, + "pipeline": [ + {"$project": {"target_node_id": 1, "_id": 0}}, + { + "$group": { + "_id": "$target_node_id", + "degree": {"$sum": 1}, + } + }, + ], + } + }, + {"$group": {"_id": "$_id", "degree": {"$sum": "$degree"}}}, + {"$sort": {"degree": -1}}, + {"$limit": max_nodes}, + ] + cursor = await self.edge_collection.aggregate(pipeline, allowDiskUse=True) + + node_ids = [] + async for doc in cursor: + node_id = str(doc["_id"]) + node_ids.append(node_id) + + cursor = self.collection.find({"_id": {"$in": node_ids}}, {"source_ids": 0}) + async for doc in cursor: + result.nodes.append(self._construct_graph_node(doc["_id"], doc)) + + # As node count reaches the limit, only need to fetch the edges that directly connect to these nodes + edge_cursor = self.edge_collection.find( + { + "$and": [ + {"source_node_id": {"$in": node_ids}}, + {"target_node_id": {"$in": node_ids}}, + ] + } + ) + else: + # All nodes and edges are needed + cursor = self.collection.find({}, {"source_ids": 0}) + + async for doc in cursor: + node_id = str(doc["_id"]) + result.nodes.append(self._construct_graph_node(doc["_id"], doc)) + + edge_cursor = self.edge_collection.find({}) + + async for edge in edge_cursor: + edge_id = f"{edge['source_node_id']}-{edge['target_node_id']}" + if edge_id not in seen_edges: + seen_edges.add(edge_id) + result.edges.append(self._construct_graph_edge(edge_id, edge)) + + return result + + async def _bidirectional_bfs_nodes( + self, + node_labels: list[str], + seen_nodes: set[str], + result: KnowledgeGraph, + depth: int, + max_depth: int, + max_nodes: int, + ) -> KnowledgeGraph: + if depth > max_depth or len(result.nodes) > max_nodes: + return result + + cursor = self.collection.find({"_id": {"$in": node_labels}}) + + async for node in cursor: + node_id = node["_id"] + if node_id not in seen_nodes: + seen_nodes.add(node_id) + result.nodes.append(self._construct_graph_node(node_id, node)) + if len(result.nodes) > max_nodes: + return result + + # Collect neighbors + # Get both inbound and outbound one hop nodes + cursor = self.edge_collection.find( + { + "$or": [ + {"source_node_id": {"$in": node_labels}}, + {"target_node_id": {"$in": node_labels}}, + ] + } + ) + + neighbor_nodes = [] + async for edge in cursor: + if edge["source_node_id"] not in seen_nodes: + neighbor_nodes.append(edge["source_node_id"]) + if edge["target_node_id"] not in seen_nodes: + neighbor_nodes.append(edge["target_node_id"]) + + if neighbor_nodes: + result = await self._bidirectional_bfs_nodes( + neighbor_nodes, seen_nodes, result, depth + 1, max_depth, max_nodes + ) + + return result + + async def get_knowledge_subgraph_bidirectional_bfs( + self, + node_label: str, + depth: int, + max_depth: int, + max_nodes: int, + ) -> KnowledgeGraph: + seen_nodes = set() + seen_edges = set() + result = KnowledgeGraph() + + result = await self._bidirectional_bfs_nodes( + [node_label], seen_nodes, result, depth, max_depth, max_nodes + ) + + # Get all edges from seen_nodes + all_node_ids = list(seen_nodes) + cursor = self.edge_collection.find( + { + "$and": [ + {"source_node_id": {"$in": all_node_ids}}, + {"target_node_id": {"$in": all_node_ids}}, + ] + } + ) + + async for edge in cursor: + edge_id = f"{edge['source_node_id']}-{edge['target_node_id']}" + if edge_id not in seen_edges: + result.edges.append(self._construct_graph_edge(edge_id, edge)) + seen_edges.add(edge_id) + + return result + + async def get_knowledge_subgraph_in_out_bound_bfs( + self, node_label: str, max_depth: int, max_nodes: int + ) -> KnowledgeGraph: + seen_nodes = set() + seen_edges = set() + result = KnowledgeGraph() + project_doc = { + "source_ids": 0, + "created_at": 0, + "entity_type": 0, + "file_path": 0, + } + + # Verify if starting node exists + start_node = await self.collection.find_one({"_id": node_label}) + if not start_node: + logger.warning( + f"[{self.workspace}] Starting node with label {node_label} does not exist!" + ) + return result + + seen_nodes.add(node_label) + result.nodes.append(self._construct_graph_node(node_label, start_node)) + + if max_depth == 0: + return result + + # In MongoDB, depth = 0 means one-hop + max_depth = max_depth - 1 + + pipeline = [ + {"$match": {"_id": node_label}}, + {"$project": project_doc}, + { + "$graphLookup": { + "from": self._edge_collection_name, + "startWith": "$_id", + "connectFromField": "target_node_id", + "connectToField": "source_node_id", + "maxDepth": max_depth, + "depthField": "depth", + "as": "connected_edges", + }, + }, + { + "$unionWith": { + "coll": self._collection_name, + "pipeline": [ + {"$match": {"_id": node_label}}, + {"$project": project_doc}, + { + "$graphLookup": { + "from": self._edge_collection_name, + "startWith": "$_id", + "connectFromField": "source_node_id", + "connectToField": "target_node_id", + "maxDepth": max_depth, + "depthField": "depth", + "as": "connected_edges", + } + }, + ], + } + }, + ] + + cursor = await self.collection.aggregate(pipeline, allowDiskUse=True) + node_edges = [] + + # Two records for node_label are returned capturing outbound and inbound connected_edges + async for doc in cursor: + if doc.get("connected_edges", []): + node_edges.extend(doc.get("connected_edges")) + + # Sort the connected edges by depth ascending and weight descending + # And stores the source_node_id and target_node_id in sequence to retrieve the neighbouring nodes + node_edges = sorted( + node_edges, + key=lambda x: (x["depth"], -x["weight"]), + ) + + # As order matters, we need to use another list to store the node_id + # And only take the first max_nodes ones + node_ids = [] + for edge in node_edges: + if len(node_ids) < max_nodes and edge["source_node_id"] not in seen_nodes: + node_ids.append(edge["source_node_id"]) + seen_nodes.add(edge["source_node_id"]) + + if len(node_ids) < max_nodes and edge["target_node_id"] not in seen_nodes: + node_ids.append(edge["target_node_id"]) + seen_nodes.add(edge["target_node_id"]) + + # Filter out all the node whose id is same as node_label so that we do not check existence next step + cursor = self.collection.find({"_id": {"$in": node_ids}}) + + async for doc in cursor: + result.nodes.append(self._construct_graph_node(str(doc["_id"]), doc)) + + for edge in node_edges: + if ( + edge["source_node_id"] not in seen_nodes + or edge["target_node_id"] not in seen_nodes + ): + continue + + edge_id = f"{edge['source_node_id']}-{edge['target_node_id']}" + if edge_id not in seen_edges: + result.edges.append(self._construct_graph_edge(edge_id, edge)) + seen_edges.add(edge_id) + + return result + + async def get_knowledge_graph( + self, + node_label: str, + max_depth: int = 3, + max_nodes: int = None, + ) -> KnowledgeGraph: + """ + Retrieve a connected subgraph of nodes where the label includes the specified `node_label`. + + Args: + node_label: Label of the starting node, * means all nodes + max_depth: Maximum depth of the subgraph, Defaults to 3 + max_nodes: Maximum nodes to return, Defaults to global_config max_graph_nodes + + Returns: + KnowledgeGraph object containing nodes and edges, with an is_truncated flag + indicating whether the graph was truncated due to max_nodes limit + + If a graph is like this and starting from B: + A → B ← C ← F, B -> E, C → D + + Outbound BFS: + B → E + + Inbound BFS: + A → B + C → B + F → C + + Bidirectional BFS: + A → B + B → E + F → C + C → B + C → D + """ + # Use global_config max_graph_nodes as default if max_nodes is None + if max_nodes is None: + max_nodes = self.global_config.get("max_graph_nodes", 1000) + else: + # Limit max_nodes to not exceed global_config max_graph_nodes + max_nodes = min(max_nodes, self.global_config.get("max_graph_nodes", 1000)) + + result = KnowledgeGraph() + start = time.perf_counter() + + try: + # Optimize pipeline to avoid memory issues with large datasets + if node_label == "*": + result = await self.get_knowledge_graph_all_by_degree( + max_depth, max_nodes + ) + elif GRAPH_BFS_MODE == "in_out_bound": + result = await self.get_knowledge_subgraph_in_out_bound_bfs( + node_label, max_depth, max_nodes + ) + else: + result = await self.get_knowledge_subgraph_bidirectional_bfs( + node_label, 0, max_depth, max_nodes + ) + + duration = time.perf_counter() - start + + logger.info( + f"[{self.workspace}] Subgraph query successful in {duration:.4f} seconds | Node count: {len(result.nodes)} | Edge count: {len(result.edges)} | Truncated: {result.is_truncated}" + ) + + except PyMongoError as e: + # Handle memory limit errors specifically + if "memory limit" in str(e).lower() or "sort exceeded" in str(e).lower(): + logger.warning( + f"[{self.workspace}] MongoDB memory limit exceeded, falling back to simple query: {str(e)}" + ) + # Fallback to a simple query without complex aggregation + try: + simple_cursor = self.collection.find({}).limit(max_nodes) + async for doc in simple_cursor: + result.nodes.append( + self._construct_graph_node(str(doc["_id"]), doc) + ) + result.is_truncated = True + logger.info( + f"[{self.workspace}] Fallback query completed | Node count: {len(result.nodes)}" + ) + except PyMongoError as fallback_error: + logger.error( + f"[{self.workspace}] Fallback query also failed: {str(fallback_error)}" + ) + else: + logger.error(f"[{self.workspace}] MongoDB query failed: {str(e)}") + + return result + + async def index_done_callback(self) -> None: + # Mongo handles persistence automatically + pass + + async def remove_nodes(self, nodes: list[str]) -> None: + """Delete multiple nodes + + Args: + nodes: List of node IDs to be deleted + """ + logger.info(f"[{self.workspace}] Deleting {len(nodes)} nodes") + if not nodes: + return + + # 1. Remove all edges referencing these nodes + await self.edge_collection.delete_many( + { + "$or": [ + {"source_node_id": {"$in": nodes}}, + {"target_node_id": {"$in": nodes}}, + ] + } + ) + + # 2. Delete the node documents + await self.collection.delete_many({"_id": {"$in": nodes}}) + + logger.debug(f"[{self.workspace}] Successfully deleted nodes: {nodes}") + + async def remove_edges(self, edges: list[tuple[str, str]]) -> None: + """Delete multiple edges + + Args: + edges: List of edges to be deleted, each edge is a (source, target) tuple + """ + logger.info(f"[{self.workspace}] Deleting {len(edges)} edges") + if not edges: + return + + all_edge_pairs = [] + for source_id, target_id in edges: + all_edge_pairs.append( + {"source_node_id": source_id, "target_node_id": target_id} + ) + all_edge_pairs.append( + {"source_node_id": target_id, "target_node_id": source_id} + ) + + await self.edge_collection.delete_many({"$or": all_edge_pairs}) + + logger.debug(f"[{self.workspace}] Successfully deleted edges: {edges}") + + async def get_all_nodes(self) -> list[dict]: + """Get all nodes in the graph. + + Returns: + A list of all nodes, where each node is a dictionary of its properties + """ + cursor = self.collection.find({}) + nodes = [] + async for node in cursor: + node_dict = dict(node) + # Add node id (entity_id) to the dictionary for easier access + node_dict["id"] = node_dict.get("_id") + nodes.append(node_dict) + return nodes + + async def get_all_edges(self) -> list[dict]: + """Get all edges in the graph. + + Returns: + A list of all edges, where each edge is a dictionary of its properties + """ + cursor = self.edge_collection.find({}) + edges = [] + async for edge in cursor: + edge_dict = dict(edge) + edge_dict["source"] = edge_dict.get("source_node_id") + edge_dict["target"] = edge_dict.get("target_node_id") + edges.append(edge_dict) + return edges + + async def get_popular_labels(self, limit: int = 300) -> list[str]: + """Get popular labels by node degree (most connected entities) + + Args: + limit: Maximum number of labels to return + + Returns: + List of labels sorted by degree (highest first) + """ + try: + # Use aggregation pipeline to count edges per node and sort by degree + pipeline = [ + # Count outbound edges + {"$group": {"_id": "$source_node_id", "out_degree": {"$sum": 1}}}, + # Union with inbound edges count + { + "$unionWith": { + "coll": self._edge_collection_name, + "pipeline": [ + { + "$group": { + "_id": "$target_node_id", + "in_degree": {"$sum": 1}, + } + } + ], + } + }, + # Group by node_id and sum degrees + { + "$group": { + "_id": "$_id", + "total_degree": { + "$sum": { + "$add": [ + {"$ifNull": ["$out_degree", 0]}, + {"$ifNull": ["$in_degree", 0]}, + ] + } + }, + } + }, + # Sort by degree descending, then by label ascending + {"$sort": {"total_degree": -1, "_id": 1}}, + # Limit results + {"$limit": limit}, + # Project only the label + {"$project": {"_id": 1}}, + ] + + cursor = await self.edge_collection.aggregate(pipeline, allowDiskUse=True) + labels = [] + async for doc in cursor: + if doc.get("_id"): + labels.append(doc["_id"]) + + logger.debug( + f"[{self.workspace}] Retrieved {len(labels)} popular labels (limit: {limit})" + ) + return labels + except Exception as e: + logger.error(f"[{self.workspace}] Error getting popular labels: {str(e)}") + return [] + + async def _try_atlas_text_search(self, query_strip: str, limit: int) -> list[str]: + """Try Atlas Search using simple text search.""" + try: + pipeline = [ + { + "$search": { + "index": "entity_id_search_idx", + "text": {"query": query_strip, "path": "_id"}, + } + }, + {"$project": {"_id": 1, "score": {"$meta": "searchScore"}}}, + {"$limit": limit}, + ] + cursor = await self.collection.aggregate(pipeline) + labels = [doc["_id"] async for doc in cursor if doc.get("_id")] + if labels: + logger.debug( + f"[{self.workspace}] Atlas text search returned {len(labels)} results" + ) + return labels + return [] + except PyMongoError as e: + logger.debug(f"[{self.workspace}] Atlas text search failed: {e}") + return [] + + async def _try_atlas_autocomplete_search( + self, query_strip: str, limit: int + ) -> list[str]: + """Try Atlas Search using autocomplete for prefix matching.""" + try: + pipeline = [ + { + "$search": { + "index": "entity_id_search_idx", + "autocomplete": { + "query": query_strip, + "path": "_id", + "fuzzy": {"maxEdits": 1, "prefixLength": 1}, + }, + } + }, + {"$project": {"_id": 1, "score": {"$meta": "searchScore"}}}, + {"$limit": limit}, + ] + cursor = await self.collection.aggregate(pipeline) + labels = [doc["_id"] async for doc in cursor if doc.get("_id")] + if labels: + logger.debug( + f"[{self.workspace}] Atlas autocomplete search returned {len(labels)} results" + ) + return labels + return [] + except PyMongoError as e: + logger.debug(f"[{self.workspace}] Atlas autocomplete search failed: {e}") + return [] + + async def _try_atlas_compound_search( + self, query_strip: str, limit: int + ) -> list[str]: + """Try Atlas Search using compound query for comprehensive matching.""" + try: + pipeline = [ + { + "$search": { + "index": "entity_id_search_idx", + "compound": { + "should": [ + { + "text": { + "query": query_strip, + "path": "_id", + "score": {"boost": {"value": 10}}, + } + }, + { + "autocomplete": { + "query": query_strip, + "path": "_id", + "score": {"boost": {"value": 5}}, + "fuzzy": {"maxEdits": 1, "prefixLength": 1}, + } + }, + { + "wildcard": { + "query": f"*{query_strip}*", + "path": "_id", + "score": {"boost": {"value": 2}}, + } + }, + ], + "minimumShouldMatch": 1, + }, + } + }, + {"$project": {"_id": 1, "score": {"$meta": "searchScore"}}}, + {"$sort": {"score": {"$meta": "searchScore"}}}, + {"$limit": limit}, + ] + cursor = await self.collection.aggregate(pipeline) + labels = [doc["_id"] async for doc in cursor if doc.get("_id")] + if labels: + logger.debug( + f"[{self.workspace}] Atlas compound search returned {len(labels)} results" + ) + return labels + return [] + except PyMongoError as e: + logger.debug(f"[{self.workspace}] Atlas compound search failed: {e}") + return [] + + async def _fallback_regex_search(self, query_strip: str, limit: int) -> list[str]: + """Fallback to regex-based search when Atlas Search fails.""" + try: + logger.debug( + f"[{self.workspace}] Using regex fallback search for: '{query_strip}'" + ) + + escaped_query = re.escape(query_strip) + regex_condition = {"_id": {"$regex": escaped_query, "$options": "i"}} + cursor = self.collection.find(regex_condition, {"_id": 1}).limit(limit * 2) + docs = await cursor.to_list(length=limit * 2) + + # Extract labels + labels = [] + for doc in docs: + doc_id = doc.get("_id") + if doc_id: + labels.append(doc_id) + + # Sort results to prioritize exact matches and starts-with matches + def sort_key(label): + label_lower = label.lower() + query_lower_strip = query_strip.lower() + + if label_lower == query_lower_strip: + return (0, label_lower) # Exact match - highest priority + elif label_lower.startswith(query_lower_strip): + return (1, label_lower) # Starts with - medium priority + else: + return (2, label_lower) # Contains - lowest priority + + labels.sort(key=sort_key) + labels = labels[:limit] # Apply final limit after sorting + + logger.debug( + f"[{self.workspace}] Regex fallback search returned {len(labels)} results (limit: {limit})" + ) + return labels + + except Exception as e: + logger.error(f"[{self.workspace}] Regex fallback search failed: {e}") + import traceback + + logger.error(f"[{self.workspace}] Traceback: {traceback.format_exc()}") + return [] + + async def search_labels(self, query: str, limit: int = 50) -> list[str]: + """ + Search labels with progressive fallback strategy: + 1. Atlas text search (simple and fast) + 2. Atlas autocomplete search (prefix matching with fuzzy) + 3. Atlas compound search (comprehensive matching) + 4. Regex fallback (when Atlas Search is unavailable) + """ + query_strip = query.strip() + if not query_strip: + return [] + + # First check if we have any nodes at all + try: + node_count = await self.collection.count_documents({}) + if node_count == 0: + logger.debug( + f"[{self.workspace}] No nodes found in collection {self._collection_name}" + ) + return [] + except PyMongoError as e: + logger.error(f"[{self.workspace}] Error counting nodes: {e}") + return [] + + # Progressive search strategy + search_methods = [ + ("text", self._try_atlas_text_search), + ("autocomplete", self._try_atlas_autocomplete_search), + ("compound", self._try_atlas_compound_search), + ] + + # Try Atlas Search methods in order + for method_name, search_method in search_methods: + try: + labels = await search_method(query_strip, limit) + if labels: + logger.debug( + f"[{self.workspace}] Search successful using {method_name} method: {len(labels)} results" + ) + return labels + else: + logger.debug( + f"[{self.workspace}] {method_name} search returned no results, trying next method" + ) + except Exception as e: + logger.debug( + f"[{self.workspace}] {method_name} search failed: {e}, trying next method" + ) + continue + + # If all Atlas Search methods fail, use regex fallback + logger.info( + f"[{self.workspace}] All Atlas Search methods failed, using regex fallback search for: '{query_strip}'" + ) + return await self._fallback_regex_search(query_strip, limit) + + async def _check_if_index_needs_rebuild( + self, indexes: list, index_name: str + ) -> bool: + """Check if the existing index needs to be rebuilt due to configuration issues.""" + for index in indexes: + if index["name"] == index_name: + # Check if the index has the old problematic configuration + definition = index.get("latestDefinition", {}) + mappings = definition.get("mappings", {}) + fields = mappings.get("fields", {}) + id_field = fields.get("_id", {}) + + # If it's the old single-type autocomplete configuration, rebuild + if ( + isinstance(id_field, dict) + and id_field.get("type") == "autocomplete" + ): + logger.info( + f"[{self.workspace}] Found old index configuration for '{index_name}', will rebuild" + ) + return True + + # If it's not a list (multi-type configuration), rebuild + if not isinstance(id_field, list): + logger.info( + f"[{self.workspace}] Index '{index_name}' needs upgrade to multi-type configuration" + ) + return True + + logger.info( + f"[{self.workspace}] Index '{index_name}' has correct configuration" + ) + return False + return True # Index doesn't exist, needs creation + + async def _safely_drop_old_index(self, index_name: str): + """Safely drop the old search index.""" + try: + await self.collection.drop_search_index(index_name) + logger.info( + f"[{self.workspace}] Successfully dropped old search index '{index_name}'" + ) + except PyMongoError as e: + logger.warning( + f"[{self.workspace}] Could not drop old index '{index_name}': {e}" + ) + + async def _create_improved_search_index(self, index_name: str): + """Create an improved search index with multiple field types.""" + search_index_model = SearchIndexModel( + definition={ + "mappings": { + "dynamic": False, + "fields": { + "_id": [ + { + "type": "string", + }, + { + "type": "token", + }, + { + "type": "autocomplete", + "maxGrams": 15, + "minGrams": 2, + }, + ] + }, + }, + "analyzer": "lucene.standard", # Index-level analyzer for text processing + }, + name=index_name, + type="search", + ) + + await self.collection.create_search_index(search_index_model) + logger.info( + f"[{self.workspace}] Created improved Atlas Search index '{index_name}' for collection {self._collection_name}. " + ) + logger.info( + f"[{self.workspace}] Index will be built asynchronously, using regex fallback until ready." + ) + + async def create_search_index_if_not_exists(self): + """Creates an improved Atlas Search index for entity search, rebuilding if necessary.""" + index_name = "entity_id_search_idx" + + try: + # Check if we're using MongoDB Atlas (has search index capabilities) + indexes_cursor = await self.collection.list_search_indexes() + indexes = await indexes_cursor.to_list(length=None) + + # Check if we need to rebuild the index + needs_rebuild = await self._check_if_index_needs_rebuild( + indexes, index_name + ) + + if needs_rebuild: + # Check if index exists and drop it + index_exists = any(idx["name"] == index_name for idx in indexes) + if index_exists: + await self._safely_drop_old_index(index_name) + + # Create the improved search index (async, no waiting) + await self._create_improved_search_index(index_name) + else: + logger.info( + f"[{self.workspace}] Atlas Search index '{index_name}' already exists with correct configuration" + ) + + except PyMongoError as e: + # This is expected if not using MongoDB Atlas or if search indexes are not supported + logger.info( + f"[{self.workspace}] Could not create Atlas Search index for {self._collection_name}: {e}. " + "This is normal if not using MongoDB Atlas - search will use regex fallback." + ) + except Exception as e: + logger.warning( + f"[{self.workspace}] Unexpected error creating Atlas Search index for {self._collection_name}: {e}" + ) + + async def drop(self) -> dict[str, str]: + """Drop the storage by removing all documents in the collection. + + Returns: + dict[str, str]: Status of the operation with keys 'status' and 'message' + """ + async with get_graph_db_lock(): + try: + result = await self.collection.delete_many({}) + deleted_count = result.deleted_count + + logger.info( + f"[{self.workspace}] Dropped {deleted_count} documents from graph {self._collection_name}" + ) + + result = await self.edge_collection.delete_many({}) + edge_count = result.deleted_count + logger.info( + f"[{self.workspace}] Dropped {edge_count} edges from graph {self._edge_collection_name}" + ) + + return { + "status": "success", + "message": f"{deleted_count} documents and {edge_count} edges dropped", + } + except PyMongoError as e: + logger.error( + f"[{self.workspace}] Error dropping graph {self._collection_name}: {e}" + ) + return {"status": "error", "message": str(e)} + + +@final +@dataclass +class MongoVectorDBStorage(BaseVectorStorage): + db: AsyncDatabase | None = field(default=None) + _data: AsyncCollection | None = field(default=None) + _index_name: str = field(default="", init=False) + + def __init__( + self, namespace, global_config, embedding_func, workspace=None, meta_fields=None + ): + super().__init__( + namespace=namespace, + workspace=workspace or "", + global_config=global_config, + embedding_func=embedding_func, + meta_fields=meta_fields or set(), + ) + self.__post_init__() + + def __post_init__(self): + # Check for MONGODB_WORKSPACE environment variable first (higher priority) + # This allows administrators to force a specific workspace for all MongoDB storage instances + mongodb_workspace = os.environ.get("MONGODB_WORKSPACE") + if mongodb_workspace and mongodb_workspace.strip(): + # Use environment variable value, overriding the passed workspace parameter + effective_workspace = mongodb_workspace.strip() + logger.info( + f"Using MONGODB_WORKSPACE environment variable: '{effective_workspace}' (overriding passed workspace: '{self.workspace}')" + ) + else: + # Use the workspace parameter passed during initialization + effective_workspace = self.workspace + if effective_workspace: + logger.debug( + f"Using passed workspace parameter: '{effective_workspace}'" + ) + + # Build final_namespace with workspace prefix for data isolation + # Keep original namespace unchanged for type detection logic + if effective_workspace: + self.final_namespace = f"{effective_workspace}_{self.namespace}" + self.workspace = effective_workspace + logger.debug( + f"Final namespace with workspace prefix: '{self.final_namespace}'" + ) + else: + # When workspace is empty, final_namespace equals original namespace + self.final_namespace = self.namespace + self.workspace = "_" + logger.debug(f"Final namespace (no workspace): '{self.final_namespace}'") + + # Set index name based on workspace for backward compatibility + if effective_workspace: + # Use collection-specific index name for workspaced collections to avoid conflicts + self._index_name = f"vector_knn_index_{self.final_namespace}" + else: + # Keep original index name for backward compatibility with existing deployments + self._index_name = "vector_knn_index" + + kwargs = self.global_config.get("vector_db_storage_cls_kwargs", {}) + cosine_threshold = kwargs.get("cosine_better_than_threshold") + if cosine_threshold is None: + raise ValueError( + "cosine_better_than_threshold must be specified in vector_db_storage_cls_kwargs" + ) + self.cosine_better_than_threshold = cosine_threshold + self._collection_name = self.final_namespace + self._max_batch_size = self.global_config["embedding_batch_num"] + + async def initialize(self): + async with get_data_init_lock(): + if self.db is None: + self.db = await ClientManager.get_client() + + self._data = await get_or_create_collection(self.db, self._collection_name) + + # Ensure vector index exists + await self.create_vector_index_if_not_exists() + + logger.debug( + f"[{self.workspace}] Use MongoDB as VDB {self._collection_name}" + ) + + async def finalize(self): + async with get_storage_lock(): + if self.db is not None: + await ClientManager.release_client(self.db) + self.db = None + self._data = None + + async def create_vector_index_if_not_exists(self): + """Creates an Atlas Vector Search index.""" + try: + indexes_cursor = await self._data.list_search_indexes() + indexes = await indexes_cursor.to_list(length=None) + for index in indexes: + if index["name"] == self._index_name: + logger.info( + f"[{self.workspace}] vector index {self._index_name} already exist" + ) + return + + search_index_model = SearchIndexModel( + definition={ + "fields": [ + { + "type": "vector", + "numDimensions": self.embedding_func.embedding_dim, # Ensure correct dimensions + "path": "vector", + "similarity": "cosine", # Options: euclidean, cosine, dotProduct + } + ] + }, + name=self._index_name, + type="vectorSearch", + ) + + await self._data.create_search_index(search_index_model) + logger.info( + f"[{self.workspace}] Vector index {self._index_name} created successfully." + ) + + except PyMongoError as e: + error_msg = f"[{self.workspace}] Error creating vector index {self._index_name}: {e}" + logger.error(error_msg) + raise SystemExit( + f"Failed to create MongoDB vector index. Program cannot continue. {error_msg}" + ) + + async def upsert(self, data: dict[str, dict[str, Any]]) -> None: + logger.debug(f"[{self.workspace}] Inserting {len(data)} to {self.namespace}") + if not data: + return + + # Add current time as Unix timestamp + current_time = int(time.time()) + + list_data = [ + { + "_id": k, + "created_at": current_time, # Add created_at field as Unix timestamp + **{k1: v1 for k1, v1 in v.items() if k1 in self.meta_fields}, + } + for k, v in data.items() + ] + contents = [v["content"] for v in data.values()] + batches = [ + contents[i : i + self._max_batch_size] + for i in range(0, len(contents), self._max_batch_size) + ] + + embedding_tasks = [self.embedding_func(batch) for batch in batches] + embeddings_list = await asyncio.gather(*embedding_tasks) + embeddings = np.concatenate(embeddings_list) + for i, d in enumerate(list_data): + d["vector"] = np.array(embeddings[i], dtype=np.float32).tolist() + + update_tasks = [] + for doc in list_data: + update_tasks.append( + self._data.update_one({"_id": doc["_id"]}, {"$set": doc}, upsert=True) + ) + await asyncio.gather(*update_tasks) + + return list_data + + async def query( + self, query: str, top_k: int, query_embedding: list[float] = None + ) -> list[dict[str, Any]]: + """Queries the vector database using Atlas Vector Search.""" + if query_embedding is not None: + # Convert numpy array to list if needed for MongoDB compatibility + if hasattr(query_embedding, "tolist"): + query_vector = query_embedding.tolist() + else: + query_vector = list(query_embedding) + else: + # Generate the embedding + embedding = await self.embedding_func( + [query], _priority=5 + ) # higher priority for query + # Convert numpy array to a list to ensure compatibility with MongoDB + query_vector = embedding[0].tolist() + + # Define the aggregation pipeline with the converted query vector + pipeline = [ + { + "$vectorSearch": { + "index": self._index_name, # Use stored index name for consistency + "path": "vector", + "queryVector": query_vector, + "numCandidates": 100, # Adjust for performance + "limit": top_k, + } + }, + {"$addFields": {"score": {"$meta": "vectorSearchScore"}}}, + {"$match": {"score": {"$gte": self.cosine_better_than_threshold}}}, + {"$project": {"vector": 0}}, + ] + + # Execute the aggregation pipeline + cursor = await self._data.aggregate(pipeline, allowDiskUse=True) + results = await cursor.to_list(length=None) + + # Format and return the results with created_at field + return [ + { + **doc, + "id": doc["_id"], + "distance": doc.get("score", None), + "created_at": doc.get("created_at"), # Include created_at field + } + for doc in results + ] + + async def index_done_callback(self) -> None: + # Mongo handles persistence automatically + pass + + async def delete(self, ids: list[str]) -> None: + """Delete vectors with specified IDs + + Args: + ids: List of vector IDs to be deleted + """ + logger.debug( + f"[{self.workspace}] Deleting {len(ids)} vectors from {self.namespace}" + ) + if not ids: + return + + # Convert to list if it's a set (MongoDB BSON cannot encode sets) + if isinstance(ids, set): + ids = list(ids) + + try: + result = await self._data.delete_many({"_id": {"$in": ids}}) + logger.debug( + f"[{self.workspace}] Successfully deleted {result.deleted_count} vectors from {self.namespace}" + ) + except PyMongoError as e: + logger.error( + f"[{self.workspace}] Error while deleting vectors from {self.namespace}: {str(e)}" + ) + + async def delete_entity(self, entity_name: str) -> None: + """Delete an entity by its name + + Args: + entity_name: Name of the entity to delete + """ + try: + entity_id = compute_mdhash_id(entity_name, prefix="ent-") + logger.debug( + f"[{self.workspace}] Attempting to delete entity {entity_name} with ID {entity_id}" + ) + + result = await self._data.delete_one({"_id": entity_id}) + if result.deleted_count > 0: + logger.debug( + f"[{self.workspace}] Successfully deleted entity {entity_name}" + ) + else: + logger.debug( + f"[{self.workspace}] Entity {entity_name} not found in storage" + ) + except PyMongoError as e: + logger.error( + f"[{self.workspace}] Error deleting entity {entity_name}: {str(e)}" + ) + + async def delete_entity_relation(self, entity_name: str) -> None: + """Delete all relations associated with an entity + + Args: + entity_name: Name of the entity whose relations should be deleted + """ + try: + # Find relations where entity appears as source or target + relations_cursor = self._data.find( + {"$or": [{"src_id": entity_name}, {"tgt_id": entity_name}]} + ) + relations = await relations_cursor.to_list(length=None) + + if not relations: + logger.debug( + f"[{self.workspace}] No relations found for entity {entity_name}" + ) + return + + # Extract IDs of relations to delete + relation_ids = [relation["_id"] for relation in relations] + logger.debug( + f"[{self.workspace}] Found {len(relation_ids)} relations for entity {entity_name}" + ) + + # Delete the relations + result = await self._data.delete_many({"_id": {"$in": relation_ids}}) + logger.debug( + f"[{self.workspace}] Deleted {result.deleted_count} relations for {entity_name}" + ) + except PyMongoError as e: + logger.error( + f"[{self.workspace}] Error deleting relations for {entity_name}: {str(e)}" + ) + + except PyMongoError as e: + logger.error( + f"[{self.workspace}] Error searching by prefix in {self.namespace}: {str(e)}" + ) + return [] + + async def get_by_id(self, id: str) -> dict[str, Any] | None: + """Get vector data by its ID + + Args: + id: The unique identifier of the vector + + Returns: + The vector data if found, or None if not found + """ + try: + # Search for the specific ID in MongoDB + result = await self._data.find_one({"_id": id}) + if result: + # Format the result to include id field expected by API + result_dict = dict(result) + if "_id" in result_dict and "id" not in result_dict: + result_dict["id"] = result_dict["_id"] + return result_dict + return None + except Exception as e: + logger.error( + f"[{self.workspace}] Error retrieving vector data for ID {id}: {e}" + ) + return None + + async def get_by_ids(self, ids: list[str]) -> list[dict[str, Any]]: + """Get multiple vector data by their IDs + + Args: + ids: List of unique identifiers + + Returns: + List of vector data objects that were found + """ + if not ids: + return [] + + try: + # Query MongoDB for multiple IDs + cursor = self._data.find({"_id": {"$in": ids}}) + results = await cursor.to_list(length=None) + + # Format results to include id field expected by API and preserve ordering + formatted_map: dict[str, dict[str, Any]] = {} + for result in results: + result_dict = dict(result) + if "_id" in result_dict and "id" not in result_dict: + result_dict["id"] = result_dict["_id"] + key = str(result_dict.get("id", result_dict.get("_id"))) + formatted_map[key] = result_dict + + ordered_results: list[dict[str, Any] | None] = [] + for id_value in ids: + ordered_results.append(formatted_map.get(str(id_value))) + + return ordered_results + except Exception as e: + logger.error( + f"[{self.workspace}] Error retrieving vector data for IDs {ids}: {e}" + ) + return [] + + async def get_vectors_by_ids(self, ids: list[str]) -> dict[str, list[float]]: + """Get vectors by their IDs, returning only ID and vector data for efficiency + + Args: + ids: List of unique identifiers + + Returns: + Dictionary mapping IDs to their vector embeddings + Format: {id: [vector_values], ...} + """ + if not ids: + return {} + + try: + # Query MongoDB for the specified IDs, only retrieving the vector field + cursor = self._data.find({"_id": {"$in": ids}}, {"vector": 1}) + results = await cursor.to_list(length=None) + + vectors_dict = {} + for result in results: + if result and "vector" in result and "_id" in result: + # MongoDB stores vectors as arrays, so they should already be lists + vectors_dict[result["_id"]] = result["vector"] + + return vectors_dict + except PyMongoError as e: + logger.error( + f"[{self.workspace}] Error retrieving vectors by IDs from {self.namespace}: {e}" + ) + return {} + + async def drop(self) -> dict[str, str]: + """Drop the storage by removing all documents in the collection and recreating vector index. + + Returns: + dict[str, str]: Status of the operation with keys 'status' and 'message' + """ + async with get_storage_lock(): + try: + # Delete all documents + result = await self._data.delete_many({}) + deleted_count = result.deleted_count + + # Recreate vector index + await self.create_vector_index_if_not_exists() + + logger.info( + f"[{self.workspace}] Dropped {deleted_count} documents from vector storage {self._collection_name} and recreated vector index" + ) + return { + "status": "success", + "message": f"{deleted_count} documents dropped and vector index recreated", + } + except PyMongoError as e: + logger.error( + f"[{self.workspace}] Error dropping vector storage {self._collection_name}: {e}" + ) + return {"status": "error", "message": str(e)} + + +async def get_or_create_collection(db: AsyncDatabase, collection_name: str): + collection_names = await db.list_collection_names() + + if collection_name not in collection_names: + collection = await db.create_collection(collection_name) + logger.info(f"Created collection: {collection_name}") + return collection + else: + logger.debug(f"Collection '{collection_name}' already exists.") + return db.get_collection(collection_name) diff --git a/LightRAG/lightrag/kg/nano_vector_db_impl.py b/LightRAG/lightrag/kg/nano_vector_db_impl.py new file mode 100644 index 00000000..1185241c --- /dev/null +++ b/LightRAG/lightrag/kg/nano_vector_db_impl.py @@ -0,0 +1,423 @@ +import asyncio +import base64 +import os +import zlib +from typing import Any, final +from dataclasses import dataclass +import numpy as np +import time + +from lightrag.utils import ( + logger, + compute_mdhash_id, +) + +from lightrag.base import BaseVectorStorage +from nano_vectordb import NanoVectorDB +from .shared_storage import ( + get_storage_lock, + get_update_flag, + set_all_update_flags, +) + + +@final +@dataclass +class NanoVectorDBStorage(BaseVectorStorage): + def __post_init__(self): + # Initialize basic attributes + self._client = None + self._storage_lock = None + self.storage_updated = None + + # Use global config value if specified, otherwise use default + kwargs = self.global_config.get("vector_db_storage_cls_kwargs", {}) + cosine_threshold = kwargs.get("cosine_better_than_threshold") + if cosine_threshold is None: + raise ValueError( + "cosine_better_than_threshold must be specified in vector_db_storage_cls_kwargs" + ) + self.cosine_better_than_threshold = cosine_threshold + + working_dir = self.global_config["working_dir"] + if self.workspace: + # Include workspace in the file path for data isolation + workspace_dir = os.path.join(working_dir, self.workspace) + self.final_namespace = f"{self.workspace}_{self.namespace}" + else: + # Default behavior when workspace is empty + self.final_namespace = self.namespace + self.workspace = "_" + workspace_dir = working_dir + + os.makedirs(workspace_dir, exist_ok=True) + self._client_file_name = os.path.join( + workspace_dir, f"vdb_{self.namespace}.json" + ) + + self._max_batch_size = self.global_config["embedding_batch_num"] + + self._client = NanoVectorDB( + self.embedding_func.embedding_dim, + storage_file=self._client_file_name, + ) + + async def initialize(self): + """Initialize storage data""" + # Get the update flag for cross-process update notification + self.storage_updated = await get_update_flag(self.final_namespace) + # Get the storage lock for use in other methods + self._storage_lock = get_storage_lock(enable_logging=False) + + async def _get_client(self): + """Check if the storage should be reloaded""" + # Acquire lock to prevent concurrent read and write + async with self._storage_lock: + # Check if data needs to be reloaded + if self.storage_updated.value: + logger.info( + f"[{self.workspace}] Process {os.getpid()} reloading {self.namespace} due to update by another process" + ) + # Reload data + self._client = NanoVectorDB( + self.embedding_func.embedding_dim, + storage_file=self._client_file_name, + ) + # Reset update flag + self.storage_updated.value = False + + return self._client + + async def upsert(self, data: dict[str, dict[str, Any]]) -> None: + """ + Importance notes: + 1. Changes will be persisted to disk during the next index_done_callback + 2. Only one process should updating the storage at a time before index_done_callback, + KG-storage-log should be used to avoid data corruption + """ + # logger.debug(f"[{self.workspace}] Inserting {len(data)} to {self.namespace}") + if not data: + return + + current_time = int(time.time()) + list_data = [ + { + "__id__": k, + "__created_at__": current_time, + **{k1: v1 for k1, v1 in v.items() if k1 in self.meta_fields}, + } + for k, v in data.items() + ] + contents = [v["content"] for v in data.values()] + batches = [ + contents[i : i + self._max_batch_size] + for i in range(0, len(contents), self._max_batch_size) + ] + + # Execute embedding outside of lock to avoid long lock times + embedding_tasks = [self.embedding_func(batch) for batch in batches] + embeddings_list = await asyncio.gather(*embedding_tasks) + + embeddings = np.concatenate(embeddings_list) + if len(embeddings) == len(list_data): + for i, d in enumerate(list_data): + # Compress vector using Float16 + zlib + Base64 for storage optimization + vector_f16 = embeddings[i].astype(np.float16) + compressed_vector = zlib.compress(vector_f16.tobytes()) + encoded_vector = base64.b64encode(compressed_vector).decode("utf-8") + d["vector"] = encoded_vector + d["__vector__"] = embeddings[i] + client = await self._get_client() + results = client.upsert(datas=list_data) + return results + else: + # sometimes the embedding is not returned correctly. just log it. + logger.error( + f"[{self.workspace}] embedding is not 1-1 with data, {len(embeddings)} != {len(list_data)}" + ) + + async def query( + self, query: str, top_k: int, query_embedding: list[float] = None + ) -> list[dict[str, Any]]: + # Use provided embedding or compute it + if query_embedding is not None: + embedding = query_embedding + else: + # Execute embedding outside of lock to avoid improve cocurrent + embedding = await self.embedding_func( + [query], _priority=5 + ) # higher priority for query + embedding = embedding[0] + + client = await self._get_client() + results = client.query( + query=embedding, + top_k=top_k, + better_than_threshold=self.cosine_better_than_threshold, + ) + results = [ + { + **{k: v for k, v in dp.items() if k != "vector"}, + "id": dp["__id__"], + "distance": dp["__metrics__"], + "created_at": dp.get("__created_at__"), + } + for dp in results + ] + return results + + @property + async def client_storage(self): + client = await self._get_client() + return getattr(client, "_NanoVectorDB__storage") + + async def delete(self, ids: list[str]): + """Delete vectors with specified IDs + + Importance notes: + 1. Changes will be persisted to disk during the next index_done_callback + 2. Only one process should updating the storage at a time before index_done_callback, + KG-storage-log should be used to avoid data corruption + + Args: + ids: List of vector IDs to be deleted + """ + try: + client = await self._get_client() + # Record count before deletion + before_count = len(client) + + client.delete(ids) + + # Calculate actual deleted count + after_count = len(client) + deleted_count = before_count - after_count + + logger.debug( + f"[{self.workspace}] Successfully deleted {deleted_count} vectors from {self.namespace}" + ) + except Exception as e: + logger.error( + f"[{self.workspace}] Error while deleting vectors from {self.namespace}: {e}" + ) + + async def delete_entity(self, entity_name: str) -> None: + """ + Importance notes: + 1. Changes will be persisted to disk during the next index_done_callback + 2. Only one process should updating the storage at a time before index_done_callback, + KG-storage-log should be used to avoid data corruption + """ + + try: + entity_id = compute_mdhash_id(entity_name, prefix="ent-") + logger.debug( + f"[{self.workspace}] Attempting to delete entity {entity_name} with ID {entity_id}" + ) + + # Check if the entity exists + client = await self._get_client() + if client.get([entity_id]): + client.delete([entity_id]) + logger.debug( + f"[{self.workspace}] Successfully deleted entity {entity_name}" + ) + else: + logger.debug( + f"[{self.workspace}] Entity {entity_name} not found in storage" + ) + except Exception as e: + logger.error(f"[{self.workspace}] Error deleting entity {entity_name}: {e}") + + async def delete_entity_relation(self, entity_name: str) -> None: + """ + Importance notes: + 1. Changes will be persisted to disk during the next index_done_callback + 2. Only one process should updating the storage at a time before index_done_callback, + KG-storage-log should be used to avoid data corruption + """ + + try: + client = await self._get_client() + storage = getattr(client, "_NanoVectorDB__storage") + relations = [ + dp + for dp in storage["data"] + if dp["src_id"] == entity_name or dp["tgt_id"] == entity_name + ] + logger.debug( + f"[{self.workspace}] Found {len(relations)} relations for entity {entity_name}" + ) + ids_to_delete = [relation["__id__"] for relation in relations] + + if ids_to_delete: + client = await self._get_client() + client.delete(ids_to_delete) + logger.debug( + f"[{self.workspace}] Deleted {len(ids_to_delete)} relations for {entity_name}" + ) + else: + logger.debug( + f"[{self.workspace}] No relations found for entity {entity_name}" + ) + except Exception as e: + logger.error( + f"[{self.workspace}] Error deleting relations for {entity_name}: {e}" + ) + + async def index_done_callback(self) -> bool: + """Save data to disk""" + async with self._storage_lock: + # Check if storage was updated by another process + if self.storage_updated.value: + # Storage was updated by another process, reload data instead of saving + logger.warning( + f"[{self.workspace}] Storage for {self.namespace} was updated by another process, reloading..." + ) + self._client = NanoVectorDB( + self.embedding_func.embedding_dim, + storage_file=self._client_file_name, + ) + # Reset update flag + self.storage_updated.value = False + return False # Return error + + # Acquire lock and perform persistence + async with self._storage_lock: + try: + # Save data to disk + self._client.save() + # Notify other processes that data has been updated + await set_all_update_flags(self.final_namespace) + # Reset own update flag to avoid self-reloading + self.storage_updated.value = False + return True # Return success + except Exception as e: + logger.error( + f"[{self.workspace}] Error saving data for {self.namespace}: {e}" + ) + return False # Return error + + return True # Return success + + async def get_by_id(self, id: str) -> dict[str, Any] | None: + """Get vector data by its ID + + Args: + id: The unique identifier of the vector + + Returns: + The vector data if found, or None if not found + """ + client = await self._get_client() + result = client.get([id]) + if result: + dp = result[0] + return { + **{k: v for k, v in dp.items() if k != "vector"}, + "id": dp.get("__id__"), + "created_at": dp.get("__created_at__"), + } + return None + + async def get_by_ids(self, ids: list[str]) -> list[dict[str, Any]]: + """Get multiple vector data by their IDs + + Args: + ids: List of unique identifiers + + Returns: + List of vector data objects that were found + """ + if not ids: + return [] + + client = await self._get_client() + results = client.get(ids) + result_map: dict[str, dict[str, Any]] = {} + + for dp in results: + if not dp: + continue + record = { + **{k: v for k, v in dp.items() if k != "vector"}, + "id": dp.get("__id__"), + "created_at": dp.get("__created_at__"), + } + key = record.get("id") + if key is not None: + result_map[str(key)] = record + + ordered_results: list[dict[str, Any] | None] = [] + for requested_id in ids: + ordered_results.append(result_map.get(str(requested_id))) + + return ordered_results + + async def get_vectors_by_ids(self, ids: list[str]) -> dict[str, list[float]]: + """Get vectors by their IDs, returning only ID and vector data for efficiency + + Args: + ids: List of unique identifiers + + Returns: + Dictionary mapping IDs to their vector embeddings + Format: {id: [vector_values], ...} + """ + if not ids: + return {} + + client = await self._get_client() + results = client.get(ids) + + vectors_dict = {} + for result in results: + if result and "vector" in result and "__id__" in result: + # Decompress vector data (Base64 + zlib + Float16 compressed) + decoded = base64.b64decode(result["vector"]) + decompressed = zlib.decompress(decoded) + vector_f16 = np.frombuffer(decompressed, dtype=np.float16) + vector_f32 = vector_f16.astype(np.float32).tolist() + vectors_dict[result["__id__"]] = vector_f32 + + return vectors_dict + + async def drop(self) -> dict[str, str]: + """Drop all vector data from storage and clean up resources + + This method will: + 1. Remove the vector database storage file if it exists + 2. Reinitialize the vector database client + 3. Update flags to notify other processes + 4. Changes is persisted to disk immediately + + This method is intended for use in scenarios where all data needs to be removed, + + Returns: + dict[str, str]: Operation status and message + - On success: {"status": "success", "message": "data dropped"} + - On failure: {"status": "error", "message": ""} + """ + try: + async with self._storage_lock: + # delete _client_file_name + if os.path.exists(self._client_file_name): + os.remove(self._client_file_name) + + self._client = NanoVectorDB( + self.embedding_func.embedding_dim, + storage_file=self._client_file_name, + ) + + # Notify other processes that data has been updated + await set_all_update_flags(self.final_namespace) + # Reset own update flag to avoid self-reloading + self.storage_updated.value = False + + logger.info( + f"[{self.workspace}] Process {os.getpid()} drop {self.namespace}(file:{self._client_file_name})" + ) + return {"status": "success", "message": "data dropped"} + except Exception as e: + logger.error(f"[{self.workspace}] Error dropping {self.namespace}: {e}") + return {"status": "error", "message": str(e)} diff --git a/LightRAG/lightrag/kg/neo4j_impl.py b/LightRAG/lightrag/kg/neo4j_impl.py new file mode 100644 index 00000000..31df4623 --- /dev/null +++ b/LightRAG/lightrag/kg/neo4j_impl.py @@ -0,0 +1,1796 @@ +import os +import re +from dataclasses import dataclass +from typing import final +import configparser + + +from tenacity import ( + retry, + stop_after_attempt, + wait_exponential, + retry_if_exception_type, +) + +import logging +from ..utils import logger +from ..base import BaseGraphStorage +from ..types import KnowledgeGraph, KnowledgeGraphNode, KnowledgeGraphEdge +from ..kg.shared_storage import get_data_init_lock, get_graph_db_lock +import pipmaster as pm + +if not pm.is_installed("neo4j"): + pm.install("neo4j") + +from neo4j import ( # type: ignore + AsyncGraphDatabase, + exceptions as neo4jExceptions, + AsyncDriver, + AsyncManagedTransaction, +) + +from dotenv import load_dotenv + +# use the .env that is inside the current folder +# allows to use different .env file for each lightrag instance +# the OS environment variables take precedence over the .env file +load_dotenv(dotenv_path=".env", override=False) + +config = configparser.ConfigParser() +config.read("config.ini", "utf-8") + + +# Set neo4j logger level to ERROR to suppress warning logs +logging.getLogger("neo4j").setLevel(logging.ERROR) + + +@final +@dataclass +class Neo4JStorage(BaseGraphStorage): + def __init__(self, namespace, global_config, embedding_func, workspace=None): + # Read env and override the arg if present + neo4j_workspace = os.environ.get("NEO4J_WORKSPACE") + if neo4j_workspace and neo4j_workspace.strip(): + workspace = neo4j_workspace + + # Default to 'base' when both arg and env are empty + if not workspace or not str(workspace).strip(): + workspace = "base" + + super().__init__( + namespace=namespace, + workspace=workspace, + global_config=global_config, + embedding_func=embedding_func, + ) + self._driver = None + + def _get_workspace_label(self) -> str: + """Return workspace label (guaranteed non-empty during initialization)""" + return self.workspace + + def _is_chinese_text(self, text: str) -> bool: + """Check if text contains Chinese characters.""" + chinese_pattern = re.compile(r"[\u4e00-\u9fff]+") + return bool(chinese_pattern.search(text)) + + async def initialize(self): + async with get_data_init_lock(): + URI = os.environ.get("NEO4J_URI", config.get("neo4j", "uri", fallback=None)) + USERNAME = os.environ.get( + "NEO4J_USERNAME", config.get("neo4j", "username", fallback=None) + ) + PASSWORD = os.environ.get( + "NEO4J_PASSWORD", config.get("neo4j", "password", fallback=None) + ) + MAX_CONNECTION_POOL_SIZE = int( + os.environ.get( + "NEO4J_MAX_CONNECTION_POOL_SIZE", + config.get("neo4j", "connection_pool_size", fallback=100), + ) + ) + CONNECTION_TIMEOUT = float( + os.environ.get( + "NEO4J_CONNECTION_TIMEOUT", + config.get("neo4j", "connection_timeout", fallback=30.0), + ), + ) + CONNECTION_ACQUISITION_TIMEOUT = float( + os.environ.get( + "NEO4J_CONNECTION_ACQUISITION_TIMEOUT", + config.get( + "neo4j", "connection_acquisition_timeout", fallback=30.0 + ), + ), + ) + MAX_TRANSACTION_RETRY_TIME = float( + os.environ.get( + "NEO4J_MAX_TRANSACTION_RETRY_TIME", + config.get("neo4j", "max_transaction_retry_time", fallback=30.0), + ), + ) + MAX_CONNECTION_LIFETIME = float( + os.environ.get( + "NEO4J_MAX_CONNECTION_LIFETIME", + config.get("neo4j", "max_connection_lifetime", fallback=300.0), + ), + ) + LIVENESS_CHECK_TIMEOUT = float( + os.environ.get( + "NEO4J_LIVENESS_CHECK_TIMEOUT", + config.get("neo4j", "liveness_check_timeout", fallback=30.0), + ), + ) + KEEP_ALIVE = os.environ.get( + "NEO4J_KEEP_ALIVE", + config.get("neo4j", "keep_alive", fallback="true"), + ).lower() in ("true", "1", "yes", "on") + DATABASE = os.environ.get( + "NEO4J_DATABASE", re.sub(r"[^a-zA-Z0-9-]", "-", self.namespace) + ) + """The default value approach for the DATABASE is only intended to maintain compatibility with legacy practices.""" + + self._driver: AsyncDriver = AsyncGraphDatabase.driver( + URI, + auth=(USERNAME, PASSWORD), + max_connection_pool_size=MAX_CONNECTION_POOL_SIZE, + connection_timeout=CONNECTION_TIMEOUT, + connection_acquisition_timeout=CONNECTION_ACQUISITION_TIMEOUT, + max_transaction_retry_time=MAX_TRANSACTION_RETRY_TIME, + max_connection_lifetime=MAX_CONNECTION_LIFETIME, + liveness_check_timeout=LIVENESS_CHECK_TIMEOUT, + keep_alive=KEEP_ALIVE, + ) + + # Try to connect to the database and create it if it doesn't exist + for database in (DATABASE, None): + self._DATABASE = database + connected = False + + try: + async with self._driver.session(database=database) as session: + try: + result = await session.run("MATCH (n) RETURN n LIMIT 0") + await result.consume() # Ensure result is consumed + logger.info( + f"[{self.workspace}] Connected to {database} at {URI}" + ) + connected = True + except neo4jExceptions.ServiceUnavailable as e: + logger.error( + f"[{self.workspace}] " + + f"Database {database} at {URI} is not available" + ) + raise e + except neo4jExceptions.AuthError as e: + logger.error( + f"[{self.workspace}] Authentication failed for {database} at {URI}" + ) + raise e + except neo4jExceptions.ClientError as e: + if e.code == "Neo.ClientError.Database.DatabaseNotFound": + logger.info( + f"[{self.workspace}] " + + f"Database {database} at {URI} not found. Try to create specified database." + ) + try: + async with self._driver.session() as session: + result = await session.run( + f"CREATE DATABASE `{database}` IF NOT EXISTS" + ) + await result.consume() # Ensure result is consumed + logger.info( + f"[{self.workspace}] " + + f"Database {database} at {URI} created" + ) + connected = True + except ( + neo4jExceptions.ClientError, + neo4jExceptions.DatabaseError, + ) as e: + if ( + e.code + == "Neo.ClientError.Statement.UnsupportedAdministrationCommand" + ) or ( + e.code == "Neo.DatabaseError.Statement.ExecutionFailed" + ): + if database is not None: + logger.warning( + f"[{self.workspace}] This Neo4j instance does not support creating databases. Try to use Neo4j Desktop/Enterprise version or DozerDB instead. Fallback to use the default database." + ) + if database is None: + logger.error( + f"[{self.workspace}] Failed to create {database} at {URI}" + ) + raise e + + if connected: + workspace_label = self._get_workspace_label() + # Create B-Tree index for entity_id for faster lookups + try: + async with self._driver.session(database=database) as session: + await session.run( + f"CREATE INDEX IF NOT EXISTS FOR (n:`{workspace_label}`) ON (n.entity_id)" + ) + logger.info( + f"[{self.workspace}] Ensured B-Tree index on entity_id for {workspace_label} in {database}" + ) + except Exception as e: + logger.warning( + f"[{self.workspace}] Failed to create B-Tree index: {str(e)}" + ) + + # Create full-text index for entity_id for faster text searches + await self._create_fulltext_index( + self._driver, self._DATABASE, workspace_label + ) + break + + async def _create_fulltext_index( + self, driver: AsyncDriver, database: str, workspace_label: str + ): + """Create a full-text index on the entity_id property with Chinese tokenizer support.""" + index_name = "entity_id_fulltext_idx" + try: + async with driver.session(database=database) as session: + # Check if the full-text index exists and get its configuration + check_index_query = "SHOW FULLTEXT INDEXES" + result = await session.run(check_index_query) + indexes = await result.data() + await result.consume() + + existing_index = None + for idx in indexes: + if idx["name"] == index_name: + existing_index = idx + break + + # Check if index exists and is online + if existing_index: + index_state = existing_index.get("state", "UNKNOWN") + logger.info( + f"[{self.workspace}] Found existing index '{index_name}' with state: {index_state}" + ) + + if index_state == "ONLINE": + logger.info( + f"[{self.workspace}] Full-text index '{index_name}' already exists and is online. Skipping recreation." + ) + return + else: + logger.warning( + f"[{self.workspace}] Existing index '{index_name}' is not online (state: {index_state}). Will recreate." + ) + else: + logger.info( + f"[{self.workspace}] No existing index '{index_name}' found. Creating new index." + ) + + # Create or recreate the index if needed + needs_recreation = ( + existing_index is not None + and existing_index.get("state") != "ONLINE" + ) + needs_creation = existing_index is None + + if needs_recreation or needs_creation: + # Drop existing index if it needs recreation + if needs_recreation: + try: + drop_query = f"DROP INDEX {index_name}" + result = await session.run(drop_query) + await result.consume() + logger.info( + f"[{self.workspace}] Dropped existing index '{index_name}'" + ) + except Exception as drop_error: + logger.warning( + f"[{self.workspace}] Failed to drop existing index: {str(drop_error)}" + ) + + # Create new index with CJK analyzer + logger.info( + f"[{self.workspace}] Creating full-text index '{index_name}' with Chinese tokenizer support." + ) + + try: + create_index_query = f""" + CREATE FULLTEXT INDEX {index_name} + FOR (n:`{workspace_label}`) ON EACH [n.entity_id] + OPTIONS {{ + indexConfig: {{ + `fulltext.analyzer`: 'cjk', + `fulltext.eventually_consistent`: true + }} + }} + """ + result = await session.run(create_index_query) + await result.consume() + logger.info( + f"[{self.workspace}] Successfully created full-text index '{index_name}' with CJK analyzer." + ) + except Exception as cjk_error: + # Fallback to standard analyzer if CJK is not supported + logger.warning( + f"[{self.workspace}] CJK analyzer not supported: {str(cjk_error)}. " + "Falling back to standard analyzer." + ) + create_index_query = f""" + CREATE FULLTEXT INDEX {index_name} + FOR (n:`{workspace_label}`) ON EACH [n.entity_id] + """ + result = await session.run(create_index_query) + await result.consume() + logger.info( + f"[{self.workspace}] Successfully created full-text index '{index_name}' with standard analyzer." + ) + + except Exception as e: + # Handle cases where the command might not be supported + if "Unknown command" in str(e) or "invalid syntax" in str(e).lower(): + logger.warning( + f"[{self.workspace}] Could not create or verify full-text index '{index_name}'. " + "This might be because you are using a Neo4j version that does not support it. " + "Search functionality will fall back to slower, non-indexed queries." + ) + else: + logger.error( + f"[{self.workspace}] Failed to create or verify full-text index '{index_name}': {str(e)}" + ) + + async def finalize(self): + """Close the Neo4j driver and release all resources""" + async with get_graph_db_lock(): + if self._driver: + await self._driver.close() + self._driver = None + + async def __aexit__(self, exc_type, exc, tb): + """Ensure driver is closed when context manager exits""" + await self.finalize() + + async def index_done_callback(self) -> None: + # Neo4J handles persistence automatically + pass + + async def has_node(self, node_id: str) -> bool: + """ + Check if a node with the given label exists in the database + + Args: + node_id: Label of the node to check + + Returns: + bool: True if node exists, False otherwise + + Raises: + ValueError: If node_id is invalid + Exception: If there is an error executing the query + """ + workspace_label = self._get_workspace_label() + async with self._driver.session( + database=self._DATABASE, default_access_mode="READ" + ) as session: + result = None + try: + query = f"MATCH (n:`{workspace_label}` {{entity_id: $entity_id}}) RETURN count(n) > 0 AS node_exists" + result = await session.run(query, entity_id=node_id) + single_result = await result.single() + await result.consume() # Ensure result is fully consumed + return single_result["node_exists"] + except Exception as e: + logger.error( + f"[{self.workspace}] Error checking node existence for {node_id}: {str(e)}" + ) + if result is not None: + await result.consume() # Ensure results are consumed even on error + raise + + async def has_edge(self, source_node_id: str, target_node_id: str) -> bool: + """ + Check if an edge exists between two nodes + + Args: + source_node_id: Label of the source node + target_node_id: Label of the target node + + Returns: + bool: True if edge exists, False otherwise + + Raises: + ValueError: If either node_id is invalid + Exception: If there is an error executing the query + """ + workspace_label = self._get_workspace_label() + async with self._driver.session( + database=self._DATABASE, default_access_mode="READ" + ) as session: + result = None + try: + query = ( + f"MATCH (a:`{workspace_label}` {{entity_id: $source_entity_id}})-[r]-(b:`{workspace_label}` {{entity_id: $target_entity_id}}) " + "RETURN COUNT(r) > 0 AS edgeExists" + ) + result = await session.run( + query, + source_entity_id=source_node_id, + target_entity_id=target_node_id, + ) + single_result = await result.single() + await result.consume() # Ensure result is fully consumed + return single_result["edgeExists"] + except Exception as e: + logger.error( + f"[{self.workspace}] Error checking edge existence between {source_node_id} and {target_node_id}: {str(e)}" + ) + if result is not None: + await result.consume() # Ensure results are consumed even on error + raise + + async def get_node(self, node_id: str) -> dict[str, str] | None: + """Get node by its label identifier, return only node properties + + Args: + node_id: The node label to look up + + Returns: + dict: Node properties if found + None: If node not found + + Raises: + ValueError: If node_id is invalid + Exception: If there is an error executing the query + """ + workspace_label = self._get_workspace_label() + async with self._driver.session( + database=self._DATABASE, default_access_mode="READ" + ) as session: + try: + query = ( + f"MATCH (n:`{workspace_label}` {{entity_id: $entity_id}}) RETURN n" + ) + result = await session.run(query, entity_id=node_id) + try: + records = await result.fetch( + 2 + ) # Get 2 records for duplication check + + if len(records) > 1: + logger.warning( + f"[{self.workspace}] Multiple nodes found with label '{node_id}'. Using first node." + ) + if records: + node = records[0]["n"] + node_dict = dict(node) + # Remove workspace label from labels list if it exists + if "labels" in node_dict: + node_dict["labels"] = [ + label + for label in node_dict["labels"] + if label != workspace_label + ] + # logger.debug(f"Neo4j query node {query} return: {node_dict}") + return node_dict + return None + finally: + await result.consume() # Ensure result is fully consumed + except Exception as e: + logger.error( + f"[{self.workspace}] Error getting node for {node_id}: {str(e)}" + ) + raise + + async def get_nodes_batch(self, node_ids: list[str]) -> dict[str, dict]: + """ + Retrieve multiple nodes in one query using UNWIND. + + Args: + node_ids: List of node entity IDs to fetch. + + Returns: + A dictionary mapping each node_id to its node data (or None if not found). + """ + workspace_label = self._get_workspace_label() + async with self._driver.session( + database=self._DATABASE, default_access_mode="READ" + ) as session: + query = f""" + UNWIND $node_ids AS id + MATCH (n:`{workspace_label}` {{entity_id: id}}) + RETURN n.entity_id AS entity_id, n + """ + result = await session.run(query, node_ids=node_ids) + nodes = {} + async for record in result: + entity_id = record["entity_id"] + node = record["n"] + node_dict = dict(node) + # Remove the workspace label if present in a 'labels' property + if "labels" in node_dict: + node_dict["labels"] = [ + label + for label in node_dict["labels"] + if label != workspace_label + ] + nodes[entity_id] = node_dict + await result.consume() # Make sure to consume the result fully + return nodes + + async def node_degree(self, node_id: str) -> int: + """Get the degree (number of relationships) of a node with the given label. + If multiple nodes have the same label, returns the degree of the first node. + If no node is found, returns 0. + + Args: + node_id: The label of the node + + Returns: + int: The number of relationships the node has, or 0 if no node found + + Raises: + ValueError: If node_id is invalid + Exception: If there is an error executing the query + """ + workspace_label = self._get_workspace_label() + async with self._driver.session( + database=self._DATABASE, default_access_mode="READ" + ) as session: + try: + query = f""" + MATCH (n:`{workspace_label}` {{entity_id: $entity_id}}) + OPTIONAL MATCH (n)-[r]-() + RETURN COUNT(r) AS degree + """ + result = await session.run(query, entity_id=node_id) + try: + record = await result.single() + + if not record: + logger.warning( + f"[{self.workspace}] No node found with label '{node_id}'" + ) + return 0 + + degree = record["degree"] + # logger.debug( + # f"[{self.workspace}] Neo4j query node degree for {node_id} return: {degree}" + # ) + return degree + finally: + await result.consume() # Ensure result is fully consumed + except Exception as e: + logger.error( + f"[{self.workspace}] Error getting node degree for {node_id}: {str(e)}" + ) + raise + + async def node_degrees_batch(self, node_ids: list[str]) -> dict[str, int]: + """ + Retrieve the degree for multiple nodes in a single query using UNWIND. + + Args: + node_ids: List of node labels (entity_id values) to look up. + + Returns: + A dictionary mapping each node_id to its degree (number of relationships). + If a node is not found, its degree will be set to 0. + """ + workspace_label = self._get_workspace_label() + async with self._driver.session( + database=self._DATABASE, default_access_mode="READ" + ) as session: + query = f""" + UNWIND $node_ids AS id + MATCH (n:`{workspace_label}` {{entity_id: id}}) + RETURN n.entity_id AS entity_id, count {{ (n)--() }} AS degree; + """ + result = await session.run(query, node_ids=node_ids) + degrees = {} + async for record in result: + entity_id = record["entity_id"] + degrees[entity_id] = record["degree"] + await result.consume() # Ensure result is fully consumed + + # For any node_id that did not return a record, set degree to 0. + for nid in node_ids: + if nid not in degrees: + logger.warning( + f"[{self.workspace}] No node found with label '{nid}'" + ) + degrees[nid] = 0 + + # logger.debug(f"[{self.workspace}] Neo4j batch node degree query returned: {degrees}") + return degrees + + async def edge_degree(self, src_id: str, tgt_id: str) -> int: + """Get the total degree (sum of relationships) of two nodes. + + Args: + src_id: Label of the source node + tgt_id: Label of the target node + + Returns: + int: Sum of the degrees of both nodes + """ + src_degree = await self.node_degree(src_id) + trg_degree = await self.node_degree(tgt_id) + + # Convert None to 0 for addition + src_degree = 0 if src_degree is None else src_degree + trg_degree = 0 if trg_degree is None else trg_degree + + degrees = int(src_degree) + int(trg_degree) + return degrees + + async def edge_degrees_batch( + self, edge_pairs: list[tuple[str, str]] + ) -> dict[tuple[str, str], int]: + """ + Calculate the combined degree for each edge (sum of the source and target node degrees) + in batch using the already implemented node_degrees_batch. + + Args: + edge_pairs: List of (src, tgt) tuples. + + Returns: + A dictionary mapping each (src, tgt) tuple to the sum of their degrees. + """ + # Collect unique node IDs from all edge pairs. + unique_node_ids = {src for src, _ in edge_pairs} + unique_node_ids.update({tgt for _, tgt in edge_pairs}) + + # Get degrees for all nodes in one go. + degrees = await self.node_degrees_batch(list(unique_node_ids)) + + # Sum up degrees for each edge pair. + edge_degrees = {} + for src, tgt in edge_pairs: + edge_degrees[(src, tgt)] = degrees.get(src, 0) + degrees.get(tgt, 0) + return edge_degrees + + async def get_edge( + self, source_node_id: str, target_node_id: str + ) -> dict[str, str] | None: + """Get edge properties between two nodes. + + Args: + source_node_id: Label of the source node + target_node_id: Label of the target node + + Returns: + dict: Edge properties if found, default properties if not found or on error + + Raises: + ValueError: If either node_id is invalid + Exception: If there is an error executing the query + """ + workspace_label = self._get_workspace_label() + try: + async with self._driver.session( + database=self._DATABASE, default_access_mode="READ" + ) as session: + query = f""" + MATCH (start:`{workspace_label}` {{entity_id: $source_entity_id}})-[r]-(end:`{workspace_label}` {{entity_id: $target_entity_id}}) + RETURN properties(r) as edge_properties + """ + result = await session.run( + query, + source_entity_id=source_node_id, + target_entity_id=target_node_id, + ) + try: + records = await result.fetch(2) + + if len(records) > 1: + logger.warning( + f"[{self.workspace}] Multiple edges found between '{source_node_id}' and '{target_node_id}'. Using first edge." + ) + if records: + try: + edge_result = dict(records[0]["edge_properties"]) + # logger.debug(f"Result: {edge_result}") + # Ensure required keys exist with defaults + required_keys = { + "weight": 1.0, + "source_id": None, + "description": None, + "keywords": None, + } + for key, default_value in required_keys.items(): + if key not in edge_result: + edge_result[key] = default_value + logger.warning( + f"[{self.workspace}] Edge between {source_node_id} and {target_node_id} " + f"missing {key}, using default: {default_value}" + ) + + # logger.debug( + # f"{inspect.currentframe().f_code.co_name}:query:{query}:result:{edge_result}" + # ) + return edge_result + except (KeyError, TypeError, ValueError) as e: + logger.error( + f"[{self.workspace}] Error processing edge properties between {source_node_id} " + f"and {target_node_id}: {str(e)}" + ) + # Return default edge properties on error + return { + "weight": 1.0, + "source_id": None, + "description": None, + "keywords": None, + } + + # logger.debug( + # f"{inspect.currentframe().f_code.co_name}: No edge found between {source_node_id} and {target_node_id}" + # ) + # Return None when no edge found + return None + finally: + await result.consume() # Ensure result is fully consumed + + except Exception as e: + logger.error( + f"[{self.workspace}] Error in get_edge between {source_node_id} and {target_node_id}: {str(e)}" + ) + raise + + async def get_edges_batch( + self, pairs: list[dict[str, str]] + ) -> dict[tuple[str, str], dict]: + """ + Retrieve edge properties for multiple (src, tgt) pairs in one query. + + Args: + pairs: List of dictionaries, e.g. [{"src": "node1", "tgt": "node2"}, ...] + + Returns: + A dictionary mapping (src, tgt) tuples to their edge properties. + """ + workspace_label = self._get_workspace_label() + async with self._driver.session( + database=self._DATABASE, default_access_mode="READ" + ) as session: + query = f""" + UNWIND $pairs AS pair + MATCH (start:`{workspace_label}` {{entity_id: pair.src}})-[r:DIRECTED]-(end:`{workspace_label}` {{entity_id: pair.tgt}}) + RETURN pair.src AS src_id, pair.tgt AS tgt_id, collect(properties(r)) AS edges + """ + result = await session.run(query, pairs=pairs) + edges_dict = {} + async for record in result: + src = record["src_id"] + tgt = record["tgt_id"] + edges = record["edges"] + if edges and len(edges) > 0: + edge_props = edges[0] # choose the first if multiple exist + # Ensure required keys exist with defaults + for key, default in { + "weight": 1.0, + "source_id": None, + "description": None, + "keywords": None, + }.items(): + if key not in edge_props: + edge_props[key] = default + edges_dict[(src, tgt)] = edge_props + else: + # No edge found – set default edge properties + edges_dict[(src, tgt)] = { + "weight": 1.0, + "source_id": None, + "description": None, + "keywords": None, + } + await result.consume() + return edges_dict + + async def get_node_edges(self, source_node_id: str) -> list[tuple[str, str]] | None: + """Retrieves all edges (relationships) for a particular node identified by its label. + + Args: + source_node_id: Label of the node to get edges for + + Returns: + list[tuple[str, str]]: List of (source_label, target_label) tuples representing edges + None: If no edges found + + Raises: + ValueError: If source_node_id is invalid + Exception: If there is an error executing the query + """ + try: + async with self._driver.session( + database=self._DATABASE, default_access_mode="READ" + ) as session: + results = None + try: + workspace_label = self._get_workspace_label() + query = f"""MATCH (n:`{workspace_label}` {{entity_id: $entity_id}}) + OPTIONAL MATCH (n)-[r]-(connected:`{workspace_label}`) + WHERE connected.entity_id IS NOT NULL + RETURN n, r, connected""" + results = await session.run(query, entity_id=source_node_id) + + edges = [] + async for record in results: + source_node = record["n"] + connected_node = record["connected"] + + # Skip if either node is None + if not source_node or not connected_node: + continue + + source_label = ( + source_node.get("entity_id") + if source_node.get("entity_id") + else None + ) + target_label = ( + connected_node.get("entity_id") + if connected_node.get("entity_id") + else None + ) + + if source_label and target_label: + edges.append((source_label, target_label)) + + await results.consume() # Ensure results are consumed + return edges + except Exception as e: + logger.error( + f"[{self.workspace}] Error getting edges for node {source_node_id}: {str(e)}" + ) + if results is not None: + await ( + results.consume() + ) # Ensure results are consumed even on error + raise + except Exception as e: + logger.error( + f"[{self.workspace}] Error in get_node_edges for {source_node_id}: {str(e)}" + ) + raise + + async def get_nodes_edges_batch( + self, node_ids: list[str] + ) -> dict[str, list[tuple[str, str]]]: + """ + Batch retrieve edges for multiple nodes in one query using UNWIND. + For each node, returns both outgoing and incoming edges to properly represent + the undirected graph nature. + + Args: + node_ids: List of node IDs (entity_id) for which to retrieve edges. + + Returns: + A dictionary mapping each node ID to its list of edge tuples (source, target). + For each node, the list includes both: + - Outgoing edges: (queried_node, connected_node) + - Incoming edges: (connected_node, queried_node) + """ + async with self._driver.session( + database=self._DATABASE, default_access_mode="READ" + ) as session: + # Query to get both outgoing and incoming edges + workspace_label = self._get_workspace_label() + query = f""" + UNWIND $node_ids AS id + MATCH (n:`{workspace_label}` {{entity_id: id}}) + OPTIONAL MATCH (n)-[r]-(connected:`{workspace_label}`) + RETURN id AS queried_id, n.entity_id AS node_entity_id, + connected.entity_id AS connected_entity_id, + startNode(r).entity_id AS start_entity_id + """ + result = await session.run(query, node_ids=node_ids) + + # Initialize the dictionary with empty lists for each node ID + edges_dict = {node_id: [] for node_id in node_ids} + + # Process results to include both outgoing and incoming edges + async for record in result: + queried_id = record["queried_id"] + node_entity_id = record["node_entity_id"] + connected_entity_id = record["connected_entity_id"] + start_entity_id = record["start_entity_id"] + + # Skip if either node is None + if not node_entity_id or not connected_entity_id: + continue + + # Determine the actual direction of the edge + # If the start node is the queried node, it's an outgoing edge + # Otherwise, it's an incoming edge + if start_entity_id == node_entity_id: + # Outgoing edge: (queried_node -> connected_node) + edges_dict[queried_id].append((node_entity_id, connected_entity_id)) + else: + # Incoming edge: (connected_node -> queried_node) + edges_dict[queried_id].append((connected_entity_id, node_entity_id)) + + await result.consume() # Ensure results are fully consumed + return edges_dict + + @retry( + stop=stop_after_attempt(3), + wait=wait_exponential(multiplier=1, min=4, max=10), + retry=retry_if_exception_type( + ( + neo4jExceptions.ServiceUnavailable, + neo4jExceptions.TransientError, + neo4jExceptions.WriteServiceUnavailable, + neo4jExceptions.ClientError, + neo4jExceptions.SessionExpired, + ConnectionResetError, + OSError, + ) + ), + ) + async def upsert_node(self, node_id: str, node_data: dict[str, str]) -> None: + """ + Upsert a node in the Neo4j database. + + Args: + node_id: The unique identifier for the node (used as label) + node_data: Dictionary of node properties + """ + workspace_label = self._get_workspace_label() + properties = node_data + entity_type = properties["entity_type"] + if "entity_id" not in properties: + raise ValueError("Neo4j: node properties must contain an 'entity_id' field") + + try: + async with self._driver.session(database=self._DATABASE) as session: + + async def execute_upsert(tx: AsyncManagedTransaction): + query = f""" + MERGE (n:`{workspace_label}` {{entity_id: $entity_id}}) + SET n += $properties + SET n:`{entity_type}` + """ + result = await tx.run( + query, entity_id=node_id, properties=properties + ) + await result.consume() # Ensure result is fully consumed + + await session.execute_write(execute_upsert) + except Exception as e: + logger.error(f"[{self.workspace}] Error during upsert: {str(e)}") + raise + + @retry( + stop=stop_after_attempt(3), + wait=wait_exponential(multiplier=1, min=4, max=10), + retry=retry_if_exception_type( + ( + neo4jExceptions.ServiceUnavailable, + neo4jExceptions.TransientError, + neo4jExceptions.WriteServiceUnavailable, + neo4jExceptions.ClientError, + neo4jExceptions.SessionExpired, + ConnectionResetError, + OSError, + ) + ), + ) + async def upsert_edge( + self, source_node_id: str, target_node_id: str, edge_data: dict[str, str] + ) -> None: + """ + Upsert an edge and its properties between two nodes identified by their labels. + Ensures both source and target nodes exist and are unique before creating the edge. + Uses entity_id property to uniquely identify nodes. + + Args: + source_node_id (str): Label of the source node (used as identifier) + target_node_id (str): Label of the target node (used as identifier) + edge_data (dict): Dictionary of properties to set on the edge + + Raises: + ValueError: If either source or target node does not exist or is not unique + """ + try: + edge_properties = edge_data + async with self._driver.session(database=self._DATABASE) as session: + + async def execute_upsert(tx: AsyncManagedTransaction): + workspace_label = self._get_workspace_label() + query = f""" + MATCH (source:`{workspace_label}` {{entity_id: $source_entity_id}}) + WITH source + MATCH (target:`{workspace_label}` {{entity_id: $target_entity_id}}) + MERGE (source)-[r:DIRECTED]-(target) + SET r += $properties + RETURN r, source, target + """ + result = await tx.run( + query, + source_entity_id=source_node_id, + target_entity_id=target_node_id, + properties=edge_properties, + ) + try: + await result.fetch(2) + finally: + await result.consume() # Ensure result is consumed + + await session.execute_write(execute_upsert) + except Exception as e: + logger.error(f"[{self.workspace}] Error during edge upsert: {str(e)}") + raise + + async def get_knowledge_graph( + self, + node_label: str, + max_depth: int = 3, + max_nodes: int = None, + ) -> KnowledgeGraph: + """ + Retrieve a connected subgraph of nodes where the label includes the specified `node_label`. + + Args: + node_label: Label of the starting node, * means all nodes + max_depth: Maximum depth of the subgraph, Defaults to 3 + max_nodes: Maxiumu nodes to return by BFS, Defaults to 1000 + + Returns: + KnowledgeGraph object containing nodes and edges, with an is_truncated flag + indicating whether the graph was truncated due to max_nodes limit + """ + # Get max_nodes from global_config if not provided + if max_nodes is None: + max_nodes = self.global_config.get("max_graph_nodes", 1000) + else: + # Limit max_nodes to not exceed global_config max_graph_nodes + max_nodes = min(max_nodes, self.global_config.get("max_graph_nodes", 1000)) + + workspace_label = self._get_workspace_label() + result = KnowledgeGraph() + seen_nodes = set() + seen_edges = set() + + async with self._driver.session( + database=self._DATABASE, default_access_mode="READ" + ) as session: + try: + if node_label == "*": + # First check total node count to determine if graph is truncated + count_query = ( + f"MATCH (n:`{workspace_label}`) RETURN count(n) as total" + ) + count_result = None + try: + count_result = await session.run(count_query) + count_record = await count_result.single() + + if count_record and count_record["total"] > max_nodes: + result.is_truncated = True + logger.info( + f"[{self.workspace}] Graph truncated: {count_record['total']} nodes found, limited to {max_nodes}" + ) + finally: + if count_result: + await count_result.consume() + + # Run main query to get nodes with highest degree + main_query = f""" + MATCH (n:`{workspace_label}`) + OPTIONAL MATCH (n)-[r]-() + WITH n, COALESCE(count(r), 0) AS degree + ORDER BY degree DESC + LIMIT $max_nodes + WITH collect({{node: n}}) AS filtered_nodes + UNWIND filtered_nodes AS node_info + WITH collect(node_info.node) AS kept_nodes, filtered_nodes + OPTIONAL MATCH (a)-[r]-(b) + WHERE a IN kept_nodes AND b IN kept_nodes + RETURN filtered_nodes AS node_info, + collect(DISTINCT r) AS relationships + """ + result_set = None + try: + result_set = await session.run( + main_query, + {"max_nodes": max_nodes}, + ) + record = await result_set.single() + finally: + if result_set: + await result_set.consume() + + else: + # return await self._robust_fallback(node_label, max_depth, max_nodes) + # First try without limit to check if we need to truncate + full_query = f""" + MATCH (start:`{workspace_label}`) + WHERE start.entity_id = $entity_id + WITH start + CALL apoc.path.subgraphAll(start, {{ + relationshipFilter: '', + labelFilter: '{workspace_label}', + minLevel: 0, + maxLevel: $max_depth, + bfs: true + }}) + YIELD nodes, relationships + WITH nodes, relationships, size(nodes) AS total_nodes + UNWIND nodes AS node + WITH collect({{node: node}}) AS node_info, relationships, total_nodes + RETURN node_info, relationships, total_nodes + """ + + # Try to get full result + full_result = None + try: + full_result = await session.run( + full_query, + { + "entity_id": node_label, + "max_depth": max_depth, + }, + ) + full_record = await full_result.single() + + # If no record found, return empty KnowledgeGraph + if not full_record: + logger.debug( + f"[{self.workspace}] No nodes found for entity_id: {node_label}" + ) + return result + + # If record found, check node count + total_nodes = full_record["total_nodes"] + + if total_nodes <= max_nodes: + # If node count is within limit, use full result directly + logger.debug( + f"[{self.workspace}] Using full result with {total_nodes} nodes (no truncation needed)" + ) + record = full_record + else: + # If node count exceeds limit, set truncated flag and run limited query + result.is_truncated = True + logger.info( + f"[{self.workspace}] Graph truncated: {total_nodes} nodes found, breadth-first search limited to {max_nodes}" + ) + + # Run limited query + limited_query = f""" + MATCH (start:`{workspace_label}`) + WHERE start.entity_id = $entity_id + WITH start + CALL apoc.path.subgraphAll(start, {{ + relationshipFilter: '', + labelFilter: '{workspace_label}', + minLevel: 0, + maxLevel: $max_depth, + limit: $max_nodes, + bfs: true + }}) + YIELD nodes, relationships + UNWIND nodes AS node + WITH collect({{node: node}}) AS node_info, relationships + RETURN node_info, relationships + """ + result_set = None + try: + result_set = await session.run( + limited_query, + { + "entity_id": node_label, + "max_depth": max_depth, + "max_nodes": max_nodes, + }, + ) + record = await result_set.single() + finally: + if result_set: + await result_set.consume() + finally: + if full_result: + await full_result.consume() + + if record: + # Handle nodes (compatible with multi-label cases) + for node_info in record["node_info"]: + node = node_info["node"] + node_id = node.id + if node_id not in seen_nodes: + result.nodes.append( + KnowledgeGraphNode( + id=f"{node_id}", + labels=[node.get("entity_id")], + properties=dict(node), + ) + ) + seen_nodes.add(node_id) + + # Handle relationships (including direction information) + for rel in record["relationships"]: + edge_id = rel.id + if edge_id not in seen_edges: + start = rel.start_node + end = rel.end_node + result.edges.append( + KnowledgeGraphEdge( + id=f"{edge_id}", + type=rel.type, + source=f"{start.id}", + target=f"{end.id}", + properties=dict(rel), + ) + ) + seen_edges.add(edge_id) + + logger.info( + f"[{self.workspace}] Subgraph query successful | Node count: {len(result.nodes)} | Edge count: {len(result.edges)}" + ) + + except neo4jExceptions.ClientError as e: + logger.warning(f"[{self.workspace}] APOC plugin error: {str(e)}") + if node_label != "*": + logger.warning( + f"[{self.workspace}] Neo4j: falling back to basic Cypher recursive search..." + ) + return await self._robust_fallback(node_label, max_depth, max_nodes) + else: + logger.warning( + f"[{self.workspace}] Neo4j: APOC plugin error with wildcard query, returning empty result" + ) + + return result + + async def _robust_fallback( + self, node_label: str, max_depth: int, max_nodes: int + ) -> KnowledgeGraph: + """ + Fallback implementation when APOC plugin is not available or incompatible. + This method implements the same functionality as get_knowledge_graph but uses + only basic Cypher queries and true breadth-first traversal instead of APOC procedures. + """ + from collections import deque + + result = KnowledgeGraph() + visited_nodes = set() + visited_edges = set() + visited_edge_pairs = set() + + # Get the starting node's data + workspace_label = self._get_workspace_label() + async with self._driver.session( + database=self._DATABASE, default_access_mode="READ" + ) as session: + query = f""" + MATCH (n:`{workspace_label}` {{entity_id: $entity_id}}) + RETURN id(n) as node_id, n + """ + node_result = await session.run(query, entity_id=node_label) + try: + node_record = await node_result.single() + if not node_record: + return result + + # Create initial KnowledgeGraphNode + start_node = KnowledgeGraphNode( + id=f"{node_record['n'].get('entity_id')}", + labels=[node_record["n"].get("entity_id")], + properties=dict(node_record["n"]._properties), + ) + finally: + await node_result.consume() # Ensure results are consumed + + # Initialize queue for BFS with (node, edge, depth) tuples + # edge is None for the starting node + queue = deque([(start_node, None, 0)]) + + # True BFS implementation using a queue + while queue and len(visited_nodes) < max_nodes: + # Dequeue the next node to process + current_node, current_edge, current_depth = queue.popleft() + + # Skip if already visited or exceeds max depth + if current_node.id in visited_nodes: + continue + + if current_depth > max_depth: + logger.debug( + f"[{self.workspace}] Skipping node at depth {current_depth} (max_depth: {max_depth})" + ) + continue + + # Add current node to result + result.nodes.append(current_node) + visited_nodes.add(current_node.id) + + # Add edge to result if it exists and not already added + if current_edge and current_edge.id not in visited_edges: + result.edges.append(current_edge) + visited_edges.add(current_edge.id) + + # Stop if we've reached the node limit + if len(visited_nodes) >= max_nodes: + result.is_truncated = True + logger.info( + f"[{self.workspace}] Graph truncated: breadth-first search limited to: {max_nodes} nodes" + ) + break + + # Get all edges and target nodes for the current node (even at max_depth) + async with self._driver.session( + database=self._DATABASE, default_access_mode="READ" + ) as session: + workspace_label = self._get_workspace_label() + query = f""" + MATCH (a:`{workspace_label}` {{entity_id: $entity_id}})-[r]-(b) + WITH r, b, id(r) as edge_id, id(b) as target_id + RETURN r, b, edge_id, target_id + """ + results = await session.run(query, entity_id=current_node.id) + + # Get all records and release database connection + records = await results.fetch(1000) # Max neighbor nodes we can handle + await results.consume() # Ensure results are consumed + + # Process all neighbors - capture all edges but only queue unvisited nodes + for record in records: + rel = record["r"] + edge_id = str(record["edge_id"]) + + if edge_id not in visited_edges: + b_node = record["b"] + target_id = b_node.get("entity_id") + + if target_id: # Only process if target node has entity_id + # Create KnowledgeGraphNode for target + target_node = KnowledgeGraphNode( + id=f"{target_id}", + labels=[target_id], + properties=dict(b_node._properties), + ) + + # Create KnowledgeGraphEdge + target_edge = KnowledgeGraphEdge( + id=f"{edge_id}", + type=rel.type, + source=f"{current_node.id}", + target=f"{target_id}", + properties=dict(rel), + ) + + # Sort source_id and target_id to ensure (A,B) and (B,A) are treated as the same edge + sorted_pair = tuple(sorted([current_node.id, target_id])) + + # Check if the same edge already exists (considering undirectedness) + if sorted_pair not in visited_edge_pairs: + # Only add the edge if the target node is already in the result or will be added + if target_id in visited_nodes or ( + target_id not in visited_nodes + and current_depth < max_depth + ): + result.edges.append(target_edge) + visited_edges.add(edge_id) + visited_edge_pairs.add(sorted_pair) + + # Only add unvisited nodes to the queue for further expansion + if target_id not in visited_nodes: + # Only add to queue if we're not at max depth yet + if current_depth < max_depth: + # Add node to queue with incremented depth + # Edge is already added to result, so we pass None as edge + queue.append((target_node, None, current_depth + 1)) + else: + # At max depth, we've already added the edge but we don't add the node + # This prevents adding nodes beyond max_depth to the result + logger.debug( + f"[{self.workspace}] Node {target_id} beyond max depth {max_depth}, edge added but node not included" + ) + else: + # If target node already exists in result, we don't need to add it again + logger.debug( + f"[{self.workspace}] Node {target_id} already visited, edge added but node not queued" + ) + else: + logger.warning( + f"[{self.workspace}] Skipping edge {edge_id} due to missing entity_id on target node" + ) + + logger.info( + f"[{self.workspace}] BFS subgraph query successful | Node count: {len(result.nodes)} | Edge count: {len(result.edges)}" + ) + return result + + async def get_all_labels(self) -> list[str]: + """ + Get all existing node labels in the database + Returns: + ["Person", "Company", ...] # Alphabetically sorted label list + """ + workspace_label = self._get_workspace_label() + async with self._driver.session( + database=self._DATABASE, default_access_mode="READ" + ) as session: + # Method 1: Direct metadata query (Available for Neo4j 4.3+) + # query = "CALL db.labels() YIELD label RETURN label" + + # Method 2: Query compatible with older versions + query = f""" + MATCH (n:`{workspace_label}`) + WHERE n.entity_id IS NOT NULL + RETURN DISTINCT n.entity_id AS label + ORDER BY label + """ + result = await session.run(query) + labels = [] + try: + async for record in result: + labels.append(record["label"]) + finally: + await ( + result.consume() + ) # Ensure results are consumed even if processing fails + return labels + + @retry( + stop=stop_after_attempt(3), + wait=wait_exponential(multiplier=1, min=4, max=10), + retry=retry_if_exception_type( + ( + neo4jExceptions.ServiceUnavailable, + neo4jExceptions.TransientError, + neo4jExceptions.WriteServiceUnavailable, + neo4jExceptions.ClientError, + neo4jExceptions.SessionExpired, + ConnectionResetError, + OSError, + ) + ), + ) + async def delete_node(self, node_id: str) -> None: + """Delete a node with the specified label + + Args: + node_id: The label of the node to delete + """ + + async def _do_delete(tx: AsyncManagedTransaction): + workspace_label = self._get_workspace_label() + query = f""" + MATCH (n:`{workspace_label}` {{entity_id: $entity_id}}) + DETACH DELETE n + """ + result = await tx.run(query, entity_id=node_id) + logger.debug(f"[{self.workspace}] Deleted node with label '{node_id}'") + await result.consume() # Ensure result is fully consumed + + try: + async with self._driver.session(database=self._DATABASE) as session: + await session.execute_write(_do_delete) + except Exception as e: + logger.error(f"[{self.workspace}] Error during node deletion: {str(e)}") + raise + + @retry( + stop=stop_after_attempt(3), + wait=wait_exponential(multiplier=1, min=4, max=10), + retry=retry_if_exception_type( + ( + neo4jExceptions.ServiceUnavailable, + neo4jExceptions.TransientError, + neo4jExceptions.WriteServiceUnavailable, + neo4jExceptions.ClientError, + neo4jExceptions.SessionExpired, + ConnectionResetError, + OSError, + ) + ), + ) + async def remove_nodes(self, nodes: list[str]): + """Delete multiple nodes + + Args: + nodes: List of node labels to be deleted + """ + for node in nodes: + await self.delete_node(node) + + @retry( + stop=stop_after_attempt(3), + wait=wait_exponential(multiplier=1, min=4, max=10), + retry=retry_if_exception_type( + ( + neo4jExceptions.ServiceUnavailable, + neo4jExceptions.TransientError, + neo4jExceptions.WriteServiceUnavailable, + neo4jExceptions.ClientError, + neo4jExceptions.SessionExpired, + ConnectionResetError, + OSError, + ) + ), + ) + async def remove_edges(self, edges: list[tuple[str, str]]): + """Delete multiple edges + + Args: + edges: List of edges to be deleted, each edge is a (source, target) tuple + """ + for source, target in edges: + + async def _do_delete_edge(tx: AsyncManagedTransaction): + workspace_label = self._get_workspace_label() + query = f""" + MATCH (source:`{workspace_label}` {{entity_id: $source_entity_id}})-[r]-(target:`{workspace_label}` {{entity_id: $target_entity_id}}) + DELETE r + """ + result = await tx.run( + query, source_entity_id=source, target_entity_id=target + ) + logger.debug( + f"[{self.workspace}] Deleted edge from '{source}' to '{target}'" + ) + await result.consume() # Ensure result is fully consumed + + try: + async with self._driver.session(database=self._DATABASE) as session: + await session.execute_write(_do_delete_edge) + except Exception as e: + logger.error(f"[{self.workspace}] Error during edge deletion: {str(e)}") + raise + + async def get_all_nodes(self) -> list[dict]: + """Get all nodes in the graph. + + Returns: + A list of all nodes, where each node is a dictionary of its properties + """ + workspace_label = self._get_workspace_label() + async with self._driver.session( + database=self._DATABASE, default_access_mode="READ" + ) as session: + query = f""" + MATCH (n:`{workspace_label}`) + RETURN n + """ + result = await session.run(query) + nodes = [] + async for record in result: + node = record["n"] + node_dict = dict(node) + # Add node id (entity_id) to the dictionary for easier access + node_dict["id"] = node_dict.get("entity_id") + nodes.append(node_dict) + await result.consume() + return nodes + + async def get_all_edges(self) -> list[dict]: + """Get all edges in the graph. + + Returns: + A list of all edges, where each edge is a dictionary of its properties + """ + workspace_label = self._get_workspace_label() + async with self._driver.session( + database=self._DATABASE, default_access_mode="READ" + ) as session: + query = f""" + MATCH (a:`{workspace_label}`)-[r]-(b:`{workspace_label}`) + RETURN DISTINCT a.entity_id AS source, b.entity_id AS target, properties(r) AS properties + """ + result = await session.run(query) + edges = [] + async for record in result: + edge_properties = record["properties"] + edge_properties["source"] = record["source"] + edge_properties["target"] = record["target"] + edges.append(edge_properties) + await result.consume() + return edges + + async def get_popular_labels(self, limit: int = 300) -> list[str]: + """Get popular labels by node degree (most connected entities) + + Args: + limit: Maximum number of labels to return + + Returns: + List of labels sorted by degree (highest first) + """ + workspace_label = self._get_workspace_label() + async with self._driver.session( + database=self._DATABASE, default_access_mode="READ" + ) as session: + result = None + try: + query = f""" + MATCH (n:`{workspace_label}`) + WHERE n.entity_id IS NOT NULL + OPTIONAL MATCH (n)-[r]-() + WITH n.entity_id AS label, count(r) AS degree + ORDER BY degree DESC, label ASC + LIMIT $limit + RETURN label + """ + result = await session.run(query, limit=limit) + labels = [] + async for record in result: + labels.append(record["label"]) + await result.consume() + + logger.debug( + f"[{self.workspace}] Retrieved {len(labels)} popular labels (limit: {limit})" + ) + return labels + except Exception as e: + logger.error( + f"[{self.workspace}] Error getting popular labels: {str(e)}" + ) + if result is not None: + await result.consume() + raise + + async def search_labels(self, query: str, limit: int = 50) -> list[str]: + """ + Search labels with fuzzy matching, using a full-text index for performance if available. + Enhanced with Chinese text support using CJK analyzer. + Falls back to a slower CONTAINS search if the index is not available or fails. + """ + workspace_label = self._get_workspace_label() + query_strip = query.strip() + if not query_strip: + return [] + + query_lower = query_strip.lower() + is_chinese = self._is_chinese_text(query_strip) + index_name = "entity_id_fulltext_idx" + + # Attempt to use the full-text index first + try: + async with self._driver.session( + database=self._DATABASE, default_access_mode="READ" + ) as session: + if is_chinese: + # For Chinese text, use different search strategies + cypher_query = f""" + CALL db.index.fulltext.queryNodes($index_name, $search_query) YIELD node, score + WITH node, score + WHERE node:`{workspace_label}` + WITH node.entity_id AS label, score + WITH label, score, + CASE + WHEN label = $query_strip THEN score + 1000 + WHEN label CONTAINS $query_strip THEN score + 500 + ELSE score + END AS final_score + RETURN label + ORDER BY final_score DESC, label ASC + LIMIT $limit + """ + # For Chinese, don't add wildcard as it may not work properly with CJK analyzer + search_query = query_strip + else: + # For non-Chinese text, use the original logic with wildcard + cypher_query = f""" + CALL db.index.fulltext.queryNodes($index_name, $search_query) YIELD node, score + WITH node, score + WHERE node:`{workspace_label}` + WITH node.entity_id AS label, toLower(node.entity_id) AS label_lower, score + WITH label, label_lower, score, + CASE + WHEN label_lower = $query_lower THEN score + 1000 + WHEN label_lower STARTS WITH $query_lower THEN score + 500 + WHEN label_lower CONTAINS ' ' + $query_lower OR label_lower CONTAINS '_' + $query_lower THEN score + 50 + ELSE score + END AS final_score + RETURN label + ORDER BY final_score DESC, label ASC + LIMIT $limit + """ + search_query = f"{query_strip}*" + + result = await session.run( + cypher_query, + index_name=index_name, + search_query=search_query, + query_lower=query_lower, + query_strip=query_strip, + limit=limit, + ) + labels = [record["label"] async for record in result] + await result.consume() + + logger.debug( + f"[{self.workspace}] Full-text search ({'Chinese' if is_chinese else 'Latin'}) for '{query}' returned {len(labels)} results (limit: {limit})" + ) + return labels + + except Exception as e: + # If the full-text search fails, fall back to CONTAINS search + logger.warning( + f"[{self.workspace}] Full-text search failed with error: {str(e)}. " + "Falling back to slower, non-indexed search." + ) + + # Enhanced fallback implementation + async with self._driver.session( + database=self._DATABASE, default_access_mode="READ" + ) as session: + if is_chinese: + # For Chinese text, use direct CONTAINS without case conversion + cypher_query = f""" + MATCH (n:`{workspace_label}`) + WHERE n.entity_id IS NOT NULL + WITH n.entity_id AS label + WHERE label CONTAINS $query_strip + WITH label, + CASE + WHEN label = $query_strip THEN 1000 + WHEN label STARTS WITH $query_strip THEN 500 + ELSE 100 - size(label) + END AS score + ORDER BY score DESC, label ASC + LIMIT $limit + RETURN label + """ + result = await session.run( + cypher_query, query_strip=query_strip, limit=limit + ) + else: + # For non-Chinese text, use the original fallback logic + cypher_query = f""" + MATCH (n:`{workspace_label}`) + WHERE n.entity_id IS NOT NULL + WITH n.entity_id AS label, toLower(n.entity_id) AS label_lower + WHERE label_lower CONTAINS $query_lower + WITH label, label_lower, + CASE + WHEN label_lower = $query_lower THEN 1000 + WHEN label_lower STARTS WITH $query_lower THEN 500 + ELSE 100 - size(label) + END AS score + ORDER BY score DESC, label ASC + LIMIT $limit + RETURN label + """ + result = await session.run( + cypher_query, query_lower=query_lower, limit=limit + ) + + labels = [record["label"] async for record in result] + await result.consume() + logger.debug( + f"[{self.workspace}] Fallback search ({'Chinese' if is_chinese else 'Latin'}) for '{query}' returned {len(labels)} results (limit: {limit})" + ) + return labels + + async def drop(self) -> dict[str, str]: + """Drop all data from current workspace storage and clean up resources + + This method will delete all nodes and relationships in the current workspace only. + + Returns: + dict[str, str]: Operation status and message + - On success: {"status": "success", "message": "workspace data dropped"} + - On failure: {"status": "error", "message": ""} + """ + async with get_graph_db_lock(): + workspace_label = self._get_workspace_label() + try: + async with self._driver.session(database=self._DATABASE) as session: + # Delete all nodes and relationships in current workspace only + query = f"MATCH (n:`{workspace_label}`) DETACH DELETE n" + result = await session.run(query) + await result.consume() # Ensure result is fully consumed + + # logger.debug( + # f"[{self.workspace}] Process {os.getpid()} drop Neo4j workspace '{workspace_label}' in database {self._DATABASE}" + # ) + return { + "status": "success", + "message": f"workspace '{workspace_label}' data dropped", + } + except Exception as e: + logger.error( + f"[{self.workspace}] Error dropping Neo4j workspace '{workspace_label}' in database {self._DATABASE}: {e}" + ) + return {"status": "error", "message": str(e)} diff --git a/LightRAG/lightrag/kg/networkx_impl.py b/LightRAG/lightrag/kg/networkx_impl.py new file mode 100644 index 00000000..48a2d2af --- /dev/null +++ b/LightRAG/lightrag/kg/networkx_impl.py @@ -0,0 +1,567 @@ +import os +from dataclasses import dataclass +from typing import final + +from lightrag.types import KnowledgeGraph, KnowledgeGraphNode, KnowledgeGraphEdge +from lightrag.utils import logger +from lightrag.base import BaseGraphStorage +import networkx as nx +from .shared_storage import ( + get_storage_lock, + get_update_flag, + set_all_update_flags, +) + +from dotenv import load_dotenv + +# use the .env that is inside the current folder +# allows to use different .env file for each lightrag instance +# the OS environment variables take precedence over the .env file +load_dotenv(dotenv_path=".env", override=False) + + +@final +@dataclass +class NetworkXStorage(BaseGraphStorage): + @staticmethod + def load_nx_graph(file_name) -> nx.Graph: + if os.path.exists(file_name): + return nx.read_graphml(file_name) + return None + + @staticmethod + def write_nx_graph(graph: nx.Graph, file_name, workspace="_"): + logger.info( + f"[{workspace}] Writing graph with {graph.number_of_nodes()} nodes, {graph.number_of_edges()} edges" + ) + nx.write_graphml(graph, file_name) + + def __post_init__(self): + working_dir = self.global_config["working_dir"] + if self.workspace: + # Include workspace in the file path for data isolation + workspace_dir = os.path.join(working_dir, self.workspace) + self.final_namespace = f"{self.workspace}_{self.namespace}" + else: + # Default behavior when workspace is empty + self.final_namespace = self.namespace + workspace_dir = working_dir + self.workspace = "_" + + os.makedirs(workspace_dir, exist_ok=True) + self._graphml_xml_file = os.path.join( + workspace_dir, f"graph_{self.namespace}.graphml" + ) + self._storage_lock = None + self.storage_updated = None + self._graph = None + + # Load initial graph + preloaded_graph = NetworkXStorage.load_nx_graph(self._graphml_xml_file) + if preloaded_graph is not None: + logger.info( + f"[{self.workspace}] Loaded graph from {self._graphml_xml_file} with {preloaded_graph.number_of_nodes()} nodes, {preloaded_graph.number_of_edges()} edges" + ) + else: + logger.info( + f"[{self.workspace}] Created new empty graph file: {self._graphml_xml_file}" + ) + self._graph = preloaded_graph or nx.Graph() + + async def initialize(self): + """Initialize storage data""" + # Get the update flag for cross-process update notification + self.storage_updated = await get_update_flag(self.final_namespace) + # Get the storage lock for use in other methods + self._storage_lock = get_storage_lock() + + async def _get_graph(self): + """Check if the storage should be reloaded""" + # Acquire lock to prevent concurrent read and write + async with self._storage_lock: + # Check if data needs to be reloaded + if self.storage_updated.value: + logger.info( + f"[{self.workspace}] Process {os.getpid()} reloading graph {self._graphml_xml_file} due to modifications by another process" + ) + # Reload data + self._graph = ( + NetworkXStorage.load_nx_graph(self._graphml_xml_file) or nx.Graph() + ) + # Reset update flag + self.storage_updated.value = False + + return self._graph + + async def has_node(self, node_id: str) -> bool: + graph = await self._get_graph() + return graph.has_node(node_id) + + async def has_edge(self, source_node_id: str, target_node_id: str) -> bool: + graph = await self._get_graph() + return graph.has_edge(source_node_id, target_node_id) + + async def get_node(self, node_id: str) -> dict[str, str] | None: + graph = await self._get_graph() + return graph.nodes.get(node_id) + + async def node_degree(self, node_id: str) -> int: + graph = await self._get_graph() + return graph.degree(node_id) + + async def edge_degree(self, src_id: str, tgt_id: str) -> int: + graph = await self._get_graph() + src_degree = graph.degree(src_id) if graph.has_node(src_id) else 0 + tgt_degree = graph.degree(tgt_id) if graph.has_node(tgt_id) else 0 + return src_degree + tgt_degree + + async def get_edge( + self, source_node_id: str, target_node_id: str + ) -> dict[str, str] | None: + graph = await self._get_graph() + return graph.edges.get((source_node_id, target_node_id)) + + async def get_node_edges(self, source_node_id: str) -> list[tuple[str, str]] | None: + graph = await self._get_graph() + if graph.has_node(source_node_id): + return list(graph.edges(source_node_id)) + return None + + async def upsert_node(self, node_id: str, node_data: dict[str, str]) -> None: + """ + Importance notes: + 1. Changes will be persisted to disk during the next index_done_callback + 2. Only one process should updating the storage at a time before index_done_callback, + KG-storage-log should be used to avoid data corruption + """ + graph = await self._get_graph() + graph.add_node(node_id, **node_data) + + async def upsert_edge( + self, source_node_id: str, target_node_id: str, edge_data: dict[str, str] + ) -> None: + """ + Importance notes: + 1. Changes will be persisted to disk during the next index_done_callback + 2. Only one process should updating the storage at a time before index_done_callback, + KG-storage-log should be used to avoid data corruption + """ + graph = await self._get_graph() + graph.add_edge(source_node_id, target_node_id, **edge_data) + + async def delete_node(self, node_id: str) -> None: + """ + Importance notes: + 1. Changes will be persisted to disk during the next index_done_callback + 2. Only one process should updating the storage at a time before index_done_callback, + KG-storage-log should be used to avoid data corruption + """ + graph = await self._get_graph() + if graph.has_node(node_id): + graph.remove_node(node_id) + logger.debug(f"[{self.workspace}] Node {node_id} deleted from the graph") + else: + logger.warning( + f"[{self.workspace}] Node {node_id} not found in the graph for deletion" + ) + + async def remove_nodes(self, nodes: list[str]): + """Delete multiple nodes + + Importance notes: + 1. Changes will be persisted to disk during the next index_done_callback + 2. Only one process should updating the storage at a time before index_done_callback, + KG-storage-log should be used to avoid data corruption + + Args: + nodes: List of node IDs to be deleted + """ + graph = await self._get_graph() + for node in nodes: + if graph.has_node(node): + graph.remove_node(node) + + async def remove_edges(self, edges: list[tuple[str, str]]): + """Delete multiple edges + + Importance notes: + 1. Changes will be persisted to disk during the next index_done_callback + 2. Only one process should updating the storage at a time before index_done_callback, + KG-storage-log should be used to avoid data corruption + + Args: + edges: List of edges to be deleted, each edge is a (source, target) tuple + """ + graph = await self._get_graph() + for source, target in edges: + if graph.has_edge(source, target): + graph.remove_edge(source, target) + + async def get_all_labels(self) -> list[str]: + """ + Get all node labels in the graph + Returns: + [label1, label2, ...] # Alphabetically sorted label list + """ + graph = await self._get_graph() + labels = set() + for node in graph.nodes(): + labels.add(str(node)) # Add node id as a label + + # Return sorted list + return sorted(list(labels)) + + async def get_popular_labels(self, limit: int = 300) -> list[str]: + """ + Get popular labels by node degree (most connected entities) + + Args: + limit: Maximum number of labels to return + + Returns: + List of labels sorted by degree (highest first) + """ + graph = await self._get_graph() + + # Get degrees of all nodes and sort by degree descending + degrees = dict(graph.degree()) + sorted_nodes = sorted(degrees.items(), key=lambda x: x[1], reverse=True) + + # Return top labels limited by the specified limit + popular_labels = [str(node) for node, _ in sorted_nodes[:limit]] + + logger.debug( + f"[{self.workspace}] Retrieved {len(popular_labels)} popular labels (limit: {limit})" + ) + + return popular_labels + + async def search_labels(self, query: str, limit: int = 50) -> list[str]: + """ + Search labels with fuzzy matching + + Args: + query: Search query string + limit: Maximum number of results to return + + Returns: + List of matching labels sorted by relevance + """ + graph = await self._get_graph() + query_lower = query.lower().strip() + + if not query_lower: + return [] + + # Collect matching nodes with relevance scores + matches = [] + for node in graph.nodes(): + node_str = str(node) + node_lower = node_str.lower() + + # Skip if no match + if query_lower not in node_lower: + continue + + # Calculate relevance score + # Exact match gets highest score + if node_lower == query_lower: + score = 1000 + # Prefix match gets high score + elif node_lower.startswith(query_lower): + score = 500 + # Contains match gets base score, with bonus for shorter strings + else: + # Shorter strings with matches are more relevant + score = 100 - len(node_str) + # Bonus for word boundary matches + if f" {query_lower}" in node_lower or f"_{query_lower}" in node_lower: + score += 50 + + matches.append((node_str, score)) + + # Sort by relevance score (desc) then alphabetically + matches.sort(key=lambda x: (-x[1], x[0])) + + # Return top matches limited by the specified limit + search_results = [match[0] for match in matches[:limit]] + + logger.debug( + f"[{self.workspace}] Search query '{query}' returned {len(search_results)} results (limit: {limit})" + ) + + return search_results + + async def get_knowledge_graph( + self, + node_label: str, + max_depth: int = 3, + max_nodes: int = None, + ) -> KnowledgeGraph: + """ + Retrieve a connected subgraph of nodes where the label includes the specified `node_label`. + + Args: + node_label: Label of the starting node，* means all nodes + max_depth: Maximum depth of the subgraph, Defaults to 3 + max_nodes: Maxiumu nodes to return by BFS, Defaults to 1000 + + Returns: + KnowledgeGraph object containing nodes and edges, with an is_truncated flag + indicating whether the graph was truncated due to max_nodes limit + """ + # Get max_nodes from global_config if not provided + if max_nodes is None: + max_nodes = self.global_config.get("max_graph_nodes", 1000) + else: + # Limit max_nodes to not exceed global_config max_graph_nodes + max_nodes = min(max_nodes, self.global_config.get("max_graph_nodes", 1000)) + + graph = await self._get_graph() + + result = KnowledgeGraph() + + # Handle special case for "*" label + if node_label == "*": + # Get degrees of all nodes + degrees = dict(graph.degree()) + # Sort nodes by degree in descending order and take top max_nodes + sorted_nodes = sorted(degrees.items(), key=lambda x: x[1], reverse=True) + + # Check if graph is truncated + if len(sorted_nodes) > max_nodes: + result.is_truncated = True + logger.info( + f"[{self.workspace}] Graph truncated: {len(sorted_nodes)} nodes found, limited to {max_nodes}" + ) + + limited_nodes = [node for node, _ in sorted_nodes[:max_nodes]] + # Create subgraph with the highest degree nodes + subgraph = graph.subgraph(limited_nodes) + else: + # Check if node exists + if node_label not in graph: + logger.warning( + f"[{self.workspace}] Node {node_label} not found in the graph" + ) + return KnowledgeGraph() # Return empty graph + + # Use modified BFS to get nodes, prioritizing high-degree nodes at the same depth + bfs_nodes = [] + visited = set() + # Store (node, depth, degree) in the queue + queue = [(node_label, 0, graph.degree(node_label))] + + # Flag to track if there are unexplored neighbors due to depth limit + has_unexplored_neighbors = False + + # Modified breadth-first search with degree-based prioritization + while queue and len(bfs_nodes) < max_nodes: + # Get the current depth from the first node in queue + current_depth = queue[0][1] + + # Collect all nodes at the current depth + current_level_nodes = [] + while queue and queue[0][1] == current_depth: + current_level_nodes.append(queue.pop(0)) + + # Sort nodes at current depth by degree (highest first) + current_level_nodes.sort(key=lambda x: x[2], reverse=True) + + # Process all nodes at current depth in order of degree + for current_node, depth, degree in current_level_nodes: + if current_node not in visited: + visited.add(current_node) + bfs_nodes.append(current_node) + + # Only explore neighbors if we haven't reached max_depth + if depth < max_depth: + # Add neighbor nodes to queue with incremented depth + neighbors = list(graph.neighbors(current_node)) + # Filter out already visited neighbors + unvisited_neighbors = [ + n for n in neighbors if n not in visited + ] + # Add neighbors to the queue with their degrees + for neighbor in unvisited_neighbors: + neighbor_degree = graph.degree(neighbor) + queue.append((neighbor, depth + 1, neighbor_degree)) + else: + # Check if there are unexplored neighbors (skipped due to depth limit) + neighbors = list(graph.neighbors(current_node)) + unvisited_neighbors = [ + n for n in neighbors if n not in visited + ] + if unvisited_neighbors: + has_unexplored_neighbors = True + + # Check if we've reached max_nodes + if len(bfs_nodes) >= max_nodes: + break + + # Check if graph is truncated - either due to max_nodes limit or depth limit + if (queue and len(bfs_nodes) >= max_nodes) or has_unexplored_neighbors: + if len(bfs_nodes) >= max_nodes: + result.is_truncated = True + logger.info( + f"[{self.workspace}] Graph truncated: max_nodes limit {max_nodes} reached" + ) + else: + logger.info( + f"[{self.workspace}] Graph truncated: found {len(bfs_nodes)} nodes within max_depth {max_depth}" + ) + + # Create subgraph with BFS discovered nodes + subgraph = graph.subgraph(bfs_nodes) + + # Add nodes to result + seen_nodes = set() + seen_edges = set() + for node in subgraph.nodes(): + if str(node) in seen_nodes: + continue + + node_data = dict(subgraph.nodes[node]) + # Get entity_type as labels + labels = [] + if "entity_type" in node_data: + if isinstance(node_data["entity_type"], list): + labels.extend(node_data["entity_type"]) + else: + labels.append(node_data["entity_type"]) + + # Create node with properties + node_properties = {k: v for k, v in node_data.items()} + + result.nodes.append( + KnowledgeGraphNode( + id=str(node), labels=[str(node)], properties=node_properties + ) + ) + seen_nodes.add(str(node)) + + # Add edges to result + for edge in subgraph.edges(): + source, target = edge + # Esure unique edge_id for undirect graph + if str(source) > str(target): + source, target = target, source + edge_id = f"{source}-{target}" + if edge_id in seen_edges: + continue + + edge_data = dict(subgraph.edges[edge]) + + # Create edge with complete information + result.edges.append( + KnowledgeGraphEdge( + id=edge_id, + type="DIRECTED", + source=str(source), + target=str(target), + properties=edge_data, + ) + ) + seen_edges.add(edge_id) + + logger.info( + f"[{self.workspace}] Subgraph query successful | Node count: {len(result.nodes)} | Edge count: {len(result.edges)}" + ) + return result + + async def get_all_nodes(self) -> list[dict]: + """Get all nodes in the graph. + + Returns: + A list of all nodes, where each node is a dictionary of its properties + """ + graph = await self._get_graph() + all_nodes = [] + for node_id, node_data in graph.nodes(data=True): + node_data_with_id = node_data.copy() + node_data_with_id["id"] = node_id + all_nodes.append(node_data_with_id) + return all_nodes + + async def get_all_edges(self) -> list[dict]: + """Get all edges in the graph. + + Returns: + A list of all edges, where each edge is a dictionary of its properties + """ + graph = await self._get_graph() + all_edges = [] + for u, v, edge_data in graph.edges(data=True): + edge_data_with_nodes = edge_data.copy() + edge_data_with_nodes["source"] = u + edge_data_with_nodes["target"] = v + all_edges.append(edge_data_with_nodes) + return all_edges + + async def index_done_callback(self) -> bool: + """Save data to disk""" + async with self._storage_lock: + # Check if storage was updated by another process + if self.storage_updated.value: + # Storage was updated by another process, reload data instead of saving + logger.info( + f"[{self.workspace}] Graph was updated by another process, reloading..." + ) + self._graph = ( + NetworkXStorage.load_nx_graph(self._graphml_xml_file) or nx.Graph() + ) + # Reset update flag + self.storage_updated.value = False + return False # Return error + + # Acquire lock and perform persistence + async with self._storage_lock: + try: + # Save data to disk + NetworkXStorage.write_nx_graph( + self._graph, self._graphml_xml_file, self.workspace + ) + # Notify other processes that data has been updated + await set_all_update_flags(self.final_namespace) + # Reset own update flag to avoid self-reloading + self.storage_updated.value = False + return True # Return success + except Exception as e: + logger.error(f"[{self.workspace}] Error saving graph: {e}") + return False # Return error + + return True + + async def drop(self) -> dict[str, str]: + """Drop all graph data from storage and clean up resources + + This method will: + 1. Remove the graph storage file if it exists + 2. Reset the graph to an empty state + 3. Update flags to notify other processes + 4. Changes is persisted to disk immediately + + Returns: + dict[str, str]: Operation status and message + - On success: {"status": "success", "message": "data dropped"} + - On failure: {"status": "error", "message": ""} + """ + try: + async with self._storage_lock: + # delete _client_file_name + if os.path.exists(self._graphml_xml_file): + os.remove(self._graphml_xml_file) + self._graph = nx.Graph() + # Notify other processes that data has been updated + await set_all_update_flags(self.final_namespace) + # Reset own update flag to avoid self-reloading + self.storage_updated.value = False + logger.info( + f"[{self.workspace}] Process {os.getpid()} drop graph file:{self._graphml_xml_file}" + ) + return {"status": "success", "message": "data dropped"} + except Exception as e: + logger.error( + f"[{self.workspace}] Error dropping graph file:{self._graphml_xml_file}: {e}" + ) + return {"status": "error", "message": str(e)} diff --git a/LightRAG/lightrag/kg/postgres_impl.py b/LightRAG/lightrag/kg/postgres_impl.py new file mode 100644 index 00000000..d043176e --- /dev/null +++ b/LightRAG/lightrag/kg/postgres_impl.py @@ -0,0 +1,5116 @@ +import asyncio +import json +import os +import re +import datetime +from datetime import timezone +from dataclasses import dataclass, field +from typing import Any, Awaitable, Callable, TypeVar, Union, final +import numpy as np +import configparser +import ssl +import itertools + +from lightrag.types import KnowledgeGraph, KnowledgeGraphNode, KnowledgeGraphEdge + +from tenacity import ( + AsyncRetrying, + RetryCallState, + retry, + retry_if_exception_type, + stop_after_attempt, + wait_exponential, + wait_fixed, +) + +from ..base import ( + BaseGraphStorage, + BaseKVStorage, + BaseVectorStorage, + DocProcessingStatus, + DocStatus, + DocStatusStorage, +) +from ..namespace import NameSpace, is_namespace +from ..utils import logger +from ..kg.shared_storage import get_data_init_lock, get_graph_db_lock, get_storage_lock + +import pipmaster as pm + +if not pm.is_installed("asyncpg"): + pm.install("asyncpg") + +import asyncpg # type: ignore +from asyncpg import Pool # type: ignore + +from dotenv import load_dotenv + +# use the .env that is inside the current folder +# allows to use different .env file for each lightrag instance +# the OS environment variables take precedence over the .env file +load_dotenv(dotenv_path=".env", override=False) + +T = TypeVar("T") + + +class PostgreSQLDB: + def __init__(self, config: dict[str, Any], **kwargs: Any): + self.host = config["host"] + self.port = config["port"] + self.user = config["user"] + self.password = config["password"] + self.database = config["database"] + self.workspace = config["workspace"] + self.max = int(config["max_connections"]) + self.increment = 1 + self.pool: Pool | None = None + + # SSL configuration + self.ssl_mode = config.get("ssl_mode") + self.ssl_cert = config.get("ssl_cert") + self.ssl_key = config.get("ssl_key") + self.ssl_root_cert = config.get("ssl_root_cert") + self.ssl_crl = config.get("ssl_crl") + + # Vector configuration + self.vector_index_type = config.get("vector_index_type") + self.hnsw_m = config.get("hnsw_m") + self.hnsw_ef = config.get("hnsw_ef") + self.ivfflat_lists = config.get("ivfflat_lists") + + # Server settings + self.server_settings = config.get("server_settings") + + # Statement LRU cache size (keep as-is, allow None for optional configuration) + self.statement_cache_size = config.get("statement_cache_size") + + if self.user is None or self.password is None or self.database is None: + raise ValueError("Missing database user, password, or database") + + # Guard concurrent pool resets + self._pool_reconnect_lock = asyncio.Lock() + + self._transient_exceptions = ( + asyncio.TimeoutError, + TimeoutError, + ConnectionError, + OSError, + asyncpg.exceptions.InterfaceError, + asyncpg.exceptions.TooManyConnectionsError, + asyncpg.exceptions.CannotConnectNowError, + asyncpg.exceptions.PostgresConnectionError, + asyncpg.exceptions.ConnectionDoesNotExistError, + asyncpg.exceptions.ConnectionFailureError, + ) + + # Connection retry configuration + self.connection_retry_attempts = config["connection_retry_attempts"] + self.connection_retry_backoff = config["connection_retry_backoff"] + self.connection_retry_backoff_max = max( + self.connection_retry_backoff, + config["connection_retry_backoff_max"], + ) + self.pool_close_timeout = config["pool_close_timeout"] + logger.info( + "PostgreSQL, Retry config: attempts=%s, backoff=%.1fs, backoff_max=%.1fs, pool_close_timeout=%.1fs", + self.connection_retry_attempts, + self.connection_retry_backoff, + self.connection_retry_backoff_max, + self.pool_close_timeout, + ) + + def _create_ssl_context(self) -> ssl.SSLContext | None: + """Create SSL context based on configuration parameters.""" + if not self.ssl_mode: + return None + + ssl_mode = self.ssl_mode.lower() + + # For simple modes that don't require custom context + if ssl_mode in ["disable", "allow", "prefer", "require"]: + if ssl_mode == "disable": + return None + elif ssl_mode in ["require", "prefer", "allow"]: + # Return None for simple SSL requirement, handled in initdb + return None + + # For modes that require certificate verification + if ssl_mode in ["verify-ca", "verify-full"]: + try: + context = ssl.create_default_context(ssl.Purpose.SERVER_AUTH) + + # Configure certificate verification + if ssl_mode == "verify-ca": + context.check_hostname = False + elif ssl_mode == "verify-full": + context.check_hostname = True + + # Load root certificate if provided + if self.ssl_root_cert: + if os.path.exists(self.ssl_root_cert): + context.load_verify_locations(cafile=self.ssl_root_cert) + logger.info( + f"PostgreSQL, Loaded SSL root certificate: {self.ssl_root_cert}" + ) + else: + logger.warning( + f"PostgreSQL, SSL root certificate file not found: {self.ssl_root_cert}" + ) + + # Load client certificate and key if provided + if self.ssl_cert and self.ssl_key: + if os.path.exists(self.ssl_cert) and os.path.exists(self.ssl_key): + context.load_cert_chain(self.ssl_cert, self.ssl_key) + logger.info( + f"PostgreSQL, Loaded SSL client certificate: {self.ssl_cert}" + ) + else: + logger.warning( + "PostgreSQL, SSL client certificate or key file not found" + ) + + # Load certificate revocation list if provided + if self.ssl_crl: + if os.path.exists(self.ssl_crl): + context.load_verify_locations(crlfile=self.ssl_crl) + logger.info(f"PostgreSQL, Loaded SSL CRL: {self.ssl_crl}") + else: + logger.warning( + f"PostgreSQL, SSL CRL file not found: {self.ssl_crl}" + ) + + return context + + except Exception as e: + logger.error(f"PostgreSQL, Failed to create SSL context: {e}") + raise ValueError(f"SSL configuration error: {e}") + + # Unknown SSL mode + logger.warning(f"PostgreSQL, Unknown SSL mode: {ssl_mode}, SSL disabled") + return None + + async def initdb(self): + # Prepare connection parameters + connection_params = { + "user": self.user, + "password": self.password, + "database": self.database, + "host": self.host, + "port": self.port, + "min_size": 1, + "max_size": self.max, + } + + # Only add statement_cache_size if it's configured + if self.statement_cache_size is not None: + connection_params["statement_cache_size"] = int(self.statement_cache_size) + logger.info( + f"PostgreSQL, statement LRU cache size set as: {self.statement_cache_size}" + ) + + # Add SSL configuration if provided + ssl_context = self._create_ssl_context() + if ssl_context is not None: + connection_params["ssl"] = ssl_context + logger.info("PostgreSQL, SSL configuration applied") + elif self.ssl_mode: + # Handle simple SSL modes without custom context + if self.ssl_mode.lower() in ["require", "prefer"]: + connection_params["ssl"] = True + elif self.ssl_mode.lower() == "disable": + connection_params["ssl"] = False + logger.info(f"PostgreSQL, SSL mode set to: {self.ssl_mode}") + + # Add server settings if provided + if self.server_settings: + try: + settings = {} + # The format is expected to be a query string, e.g., "key1=value1&key2=value2" + pairs = self.server_settings.split("&") + for pair in pairs: + if "=" in pair: + key, value = pair.split("=", 1) + settings[key] = value + if settings: + connection_params["server_settings"] = settings + logger.info(f"PostgreSQL, Server settings applied: {settings}") + except Exception as e: + logger.warning( + f"PostgreSQL, Failed to parse server_settings: {self.server_settings}, error: {e}" + ) + + wait_strategy = ( + wait_exponential( + multiplier=self.connection_retry_backoff, + min=self.connection_retry_backoff, + max=self.connection_retry_backoff_max, + ) + if self.connection_retry_backoff > 0 + else wait_fixed(0) + ) + + async def _create_pool_once() -> None: + pool = await asyncpg.create_pool(**connection_params) # type: ignore + try: + async with pool.acquire() as connection: + await self.configure_vector_extension(connection) + except Exception: + await pool.close() + raise + self.pool = pool + + try: + async for attempt in AsyncRetrying( + stop=stop_after_attempt(self.connection_retry_attempts), + retry=retry_if_exception_type(self._transient_exceptions), + wait=wait_strategy, + before_sleep=self._before_sleep, + reraise=True, + ): + with attempt: + await _create_pool_once() + + ssl_status = "with SSL" if connection_params.get("ssl") else "without SSL" + logger.info( + f"PostgreSQL, Connected to database at {self.host}:{self.port}/{self.database} {ssl_status}" + ) + except Exception as e: + logger.error( + f"PostgreSQL, Failed to connect database at {self.host}:{self.port}/{self.database}, Got:{e}" + ) + raise + + async def _ensure_pool(self) -> None: + """Ensure the connection pool is initialised.""" + if self.pool is None: + async with self._pool_reconnect_lock: + if self.pool is None: + await self.initdb() + + async def _reset_pool(self) -> None: + async with self._pool_reconnect_lock: + if self.pool is not None: + try: + await asyncio.wait_for( + self.pool.close(), timeout=self.pool_close_timeout + ) + except asyncio.TimeoutError: + logger.error( + "PostgreSQL, Timed out closing connection pool after %.2fs", + self.pool_close_timeout, + ) + except Exception as close_error: # pragma: no cover - defensive logging + logger.warning( + f"PostgreSQL, Failed to close existing connection pool cleanly: {close_error!r}" + ) + self.pool = None + + async def _before_sleep(self, retry_state: RetryCallState) -> None: + """Hook invoked by tenacity before sleeping between retries.""" + exc = retry_state.outcome.exception() if retry_state.outcome else None + logger.warning( + "PostgreSQL transient connection issue on attempt %s/%s: %r", + retry_state.attempt_number, + self.connection_retry_attempts, + exc, + ) + await self._reset_pool() + + async def _run_with_retry( + self, + operation: Callable[[asyncpg.Connection], Awaitable[T]], + *, + with_age: bool = False, + graph_name: str | None = None, + ) -> T: + """ + Execute a database operation with automatic retry for transient failures. + + Args: + operation: Async callable that receives an active connection. + with_age: Whether to configure Apache AGE on the connection. + graph_name: AGE graph name; required when with_age is True. + + Returns: + The result returned by the operation. + + Raises: + Exception: Propagates the last error if all retry attempts fail or a non-transient error occurs. + """ + wait_strategy = ( + wait_exponential( + multiplier=self.connection_retry_backoff, + min=self.connection_retry_backoff, + max=self.connection_retry_backoff_max, + ) + if self.connection_retry_backoff > 0 + else wait_fixed(0) + ) + + async for attempt in AsyncRetrying( + stop=stop_after_attempt(self.connection_retry_attempts), + retry=retry_if_exception_type(self._transient_exceptions), + wait=wait_strategy, + before_sleep=self._before_sleep, + reraise=True, + ): + with attempt: + await self._ensure_pool() + assert self.pool is not None + async with self.pool.acquire() as connection: # type: ignore[arg-type] + if with_age and graph_name: + await self.configure_age(connection, graph_name) + elif with_age and not graph_name: + raise ValueError("Graph name is required when with_age is True") + + return await operation(connection) + + @staticmethod + async def configure_vector_extension(connection: asyncpg.Connection) -> None: + """Create VECTOR extension if it doesn't exist for vector similarity operations.""" + try: + await connection.execute("CREATE EXTENSION IF NOT EXISTS vector") # type: ignore + logger.info("PostgreSQL, VECTOR extension enabled") + except Exception as e: + logger.warning(f"Could not create VECTOR extension: {e}") + # Don't raise - let the system continue without vector extension + + @staticmethod + async def configure_age_extension(connection: asyncpg.Connection) -> None: + """Create AGE extension if it doesn't exist for graph operations.""" + try: + await connection.execute("CREATE EXTENSION IF NOT EXISTS age") # type: ignore + logger.info("PostgreSQL, AGE extension enabled") + except Exception as e: + logger.warning(f"Could not create AGE extension: {e}") + # Don't raise - let the system continue without AGE extension + + @staticmethod + async def configure_age(connection: asyncpg.Connection, graph_name: str) -> None: + """Set the Apache AGE environment and creates a graph if it does not exist. + + This method: + - Sets the PostgreSQL `search_path` to include `ag_catalog`, ensuring that Apache AGE functions can be used without specifying the schema. + - Attempts to create a new graph with the provided `graph_name` if it does not already exist. + - Silently ignores errors related to the graph already existing. + + """ + try: + await connection.execute( # type: ignore + 'SET search_path = ag_catalog, "$user", public' + ) + await connection.execute( # type: ignore + f"select create_graph('{graph_name}')" + ) + except ( + asyncpg.exceptions.InvalidSchemaNameError, + asyncpg.exceptions.UniqueViolationError, + ): + pass + + async def _migrate_llm_cache_schema(self): + """Migrate LLM cache schema: add new columns and remove deprecated mode field""" + try: + # Check if all columns exist + check_columns_sql = """ + SELECT column_name + FROM information_schema.columns + WHERE table_name = 'lightrag_llm_cache' + AND column_name IN ('chunk_id', 'cache_type', 'queryparam', 'mode') + """ + + existing_columns = await self.query(check_columns_sql, multirows=True) + existing_column_names = ( + {col["column_name"] for col in existing_columns} + if existing_columns + else set() + ) + + # Add missing chunk_id column + if "chunk_id" not in existing_column_names: + logger.info("Adding chunk_id column to LIGHTRAG_LLM_CACHE table") + add_chunk_id_sql = """ + ALTER TABLE LIGHTRAG_LLM_CACHE + ADD COLUMN chunk_id VARCHAR(255) NULL + """ + await self.execute(add_chunk_id_sql) + logger.info( + "Successfully added chunk_id column to LIGHTRAG_LLM_CACHE table" + ) + else: + logger.info( + "chunk_id column already exists in LIGHTRAG_LLM_CACHE table" + ) + + # Add missing cache_type column + if "cache_type" not in existing_column_names: + logger.info("Adding cache_type column to LIGHTRAG_LLM_CACHE table") + add_cache_type_sql = """ + ALTER TABLE LIGHTRAG_LLM_CACHE + ADD COLUMN cache_type VARCHAR(32) NULL + """ + await self.execute(add_cache_type_sql) + logger.info( + "Successfully added cache_type column to LIGHTRAG_LLM_CACHE table" + ) + + # Migrate existing data using optimized regex pattern + logger.info( + "Migrating existing LLM cache data to populate cache_type field (optimized)" + ) + optimized_update_sql = """ + UPDATE LIGHTRAG_LLM_CACHE + SET cache_type = CASE + WHEN id ~ '^[^:]+:[^:]+:' THEN split_part(id, ':', 2) + ELSE 'extract' + END + WHERE cache_type IS NULL + """ + await self.execute(optimized_update_sql) + logger.info("Successfully migrated existing LLM cache data") + else: + logger.info( + "cache_type column already exists in LIGHTRAG_LLM_CACHE table" + ) + + # Add missing queryparam column + if "queryparam" not in existing_column_names: + logger.info("Adding queryparam column to LIGHTRAG_LLM_CACHE table") + add_queryparam_sql = """ + ALTER TABLE LIGHTRAG_LLM_CACHE + ADD COLUMN queryparam JSONB NULL + """ + await self.execute(add_queryparam_sql) + logger.info( + "Successfully added queryparam column to LIGHTRAG_LLM_CACHE table" + ) + else: + logger.info( + "queryparam column already exists in LIGHTRAG_LLM_CACHE table" + ) + + # Remove deprecated mode field if it exists + if "mode" in existing_column_names: + logger.info( + "Removing deprecated mode column from LIGHTRAG_LLM_CACHE table" + ) + + # First, drop the primary key constraint that includes mode + drop_pk_sql = """ + ALTER TABLE LIGHTRAG_LLM_CACHE + DROP CONSTRAINT IF EXISTS LIGHTRAG_LLM_CACHE_PK + """ + await self.execute(drop_pk_sql) + logger.info("Dropped old primary key constraint") + + # Drop the mode column + drop_mode_sql = """ + ALTER TABLE LIGHTRAG_LLM_CACHE + DROP COLUMN mode + """ + await self.execute(drop_mode_sql) + logger.info( + "Successfully removed mode column from LIGHTRAG_LLM_CACHE table" + ) + + # Create new primary key constraint without mode + add_pk_sql = """ + ALTER TABLE LIGHTRAG_LLM_CACHE + ADD CONSTRAINT LIGHTRAG_LLM_CACHE_PK PRIMARY KEY (workspace, id) + """ + await self.execute(add_pk_sql) + logger.info("Created new primary key constraint (workspace, id)") + else: + logger.info("mode column does not exist in LIGHTRAG_LLM_CACHE table") + + except Exception as e: + logger.warning(f"Failed to migrate LLM cache schema: {e}") + + async def _migrate_timestamp_columns(self): + """Migrate timestamp columns in tables to witimezone-free types, assuming original data is in UTC time""" + # Tables and columns that need migration + tables_to_migrate = { + "LIGHTRAG_VDB_ENTITY": ["create_time", "update_time"], + "LIGHTRAG_VDB_RELATION": ["create_time", "update_time"], + "LIGHTRAG_DOC_CHUNKS": ["create_time", "update_time"], + "LIGHTRAG_DOC_STATUS": ["created_at", "updated_at"], + } + + try: + # Optimization: Batch check all columns in one query instead of 8 separate queries + table_names_lower = [t.lower() for t in tables_to_migrate.keys()] + all_column_names = list( + set(col for cols in tables_to_migrate.values() for col in cols) + ) + + check_all_columns_sql = """ + SELECT table_name, column_name, data_type + FROM information_schema.columns + WHERE table_name = ANY($1) + AND column_name = ANY($2) + """ + + all_columns_result = await self.query( + check_all_columns_sql, + [table_names_lower, all_column_names], + multirows=True, + ) + + # Build lookup dict: (table_name, column_name) -> data_type + column_types = {} + if all_columns_result: + column_types = { + (row["table_name"].upper(), row["column_name"]): row["data_type"] + for row in all_columns_result + } + + # Now iterate and migrate only what's needed + for table_name, columns in tables_to_migrate.items(): + for column_name in columns: + try: + data_type = column_types.get((table_name, column_name)) + + if not data_type: + logger.warning( + f"Column {table_name}.{column_name} does not exist, skipping migration" + ) + continue + + # Check column type + if data_type == "timestamp without time zone": + logger.debug( + f"Column {table_name}.{column_name} is already witimezone-free, no migration needed" + ) + continue + + # Execute migration, explicitly specifying UTC timezone for interpreting original data + logger.info( + f"Migrating {table_name}.{column_name} from {data_type} to TIMESTAMP(0) type" + ) + migration_sql = f""" + ALTER TABLE {table_name} + ALTER COLUMN {column_name} TYPE TIMESTAMP(0), + ALTER COLUMN {column_name} SET DEFAULT CURRENT_TIMESTAMP + """ + + await self.execute(migration_sql) + logger.info( + f"Successfully migrated {table_name}.{column_name} to timezone-free type" + ) + except Exception as e: + # Log error but don't interrupt the process + logger.warning( + f"Failed to migrate {table_name}.{column_name}: {e}" + ) + except Exception as e: + logger.error(f"Failed to batch check timestamp columns: {e}") + + async def _migrate_doc_chunks_to_vdb_chunks(self): + """ + Migrate data from LIGHTRAG_DOC_CHUNKS to LIGHTRAG_VDB_CHUNKS if specific conditions are met. + This migration is intended for users who are upgrading and have an older table structure + where LIGHTRAG_DOC_CHUNKS contained a `content_vector` column. + + """ + try: + # 1. Check if the new table LIGHTRAG_VDB_CHUNKS is empty + vdb_chunks_count_sql = "SELECT COUNT(1) as count FROM LIGHTRAG_VDB_CHUNKS" + vdb_chunks_count_result = await self.query(vdb_chunks_count_sql) + if vdb_chunks_count_result and vdb_chunks_count_result["count"] > 0: + logger.info( + "Skipping migration: LIGHTRAG_VDB_CHUNKS already contains data." + ) + return + + # 2. Check if `content_vector` column exists in the old table + check_column_sql = """ + SELECT 1 FROM information_schema.columns + WHERE table_name = 'lightrag_doc_chunks' AND column_name = 'content_vector' + """ + column_exists = await self.query(check_column_sql) + if not column_exists: + logger.info( + "Skipping migration: `content_vector` not found in LIGHTRAG_DOC_CHUNKS" + ) + return + + # 3. Check if the old table LIGHTRAG_DOC_CHUNKS has data + doc_chunks_count_sql = "SELECT COUNT(1) as count FROM LIGHTRAG_DOC_CHUNKS" + doc_chunks_count_result = await self.query(doc_chunks_count_sql) + if not doc_chunks_count_result or doc_chunks_count_result["count"] == 0: + logger.info("Skipping migration: LIGHTRAG_DOC_CHUNKS is empty.") + return + + # 4. Perform the migration + logger.info( + "Starting data migration from LIGHTRAG_DOC_CHUNKS to LIGHTRAG_VDB_CHUNKS..." + ) + migration_sql = """ + INSERT INTO LIGHTRAG_VDB_CHUNKS ( + id, workspace, full_doc_id, chunk_order_index, tokens, content, + content_vector, file_path, create_time, update_time + ) + SELECT + id, workspace, full_doc_id, chunk_order_index, tokens, content, + content_vector, file_path, create_time, update_time + FROM LIGHTRAG_DOC_CHUNKS + ON CONFLICT (workspace, id) DO NOTHING; + """ + await self.execute(migration_sql) + logger.info("Data migration to LIGHTRAG_VDB_CHUNKS completed successfully.") + + except Exception as e: + logger.error(f"Failed during data migration to LIGHTRAG_VDB_CHUNKS: {e}") + # Do not re-raise, to allow the application to start + + async def _check_llm_cache_needs_migration(self): + """Check if LLM cache data needs migration by examining any record with old format""" + try: + # Optimized query: directly check for old format records without sorting + check_sql = """ + SELECT 1 FROM LIGHTRAG_LLM_CACHE + WHERE id NOT LIKE '%:%' + LIMIT 1 + """ + result = await self.query(check_sql) + + # If any old format record exists, migration is needed + return result is not None + + except Exception as e: + logger.warning(f"Failed to check LLM cache migration status: {e}") + return False + + async def _migrate_llm_cache_to_flattened_keys(self): + """Optimized version: directly execute single UPDATE migration to migrate old format cache keys to flattened format""" + try: + # Check if migration is needed + check_sql = """ + SELECT COUNT(*) as count FROM LIGHTRAG_LLM_CACHE + WHERE id NOT LIKE '%:%' + """ + result = await self.query(check_sql) + + if not result or result["count"] == 0: + logger.info("No old format LLM cache data found, skipping migration") + return + + old_count = result["count"] + logger.info(f"Found {old_count} old format cache records") + + # Check potential primary key conflicts (optional but recommended) + conflict_check_sql = """ + WITH new_ids AS ( + SELECT + workspace, + mode, + id as old_id, + mode || ':' || + CASE WHEN mode = 'default' THEN 'extract' ELSE 'unknown' END || ':' || + md5(original_prompt) as new_id + FROM LIGHTRAG_LLM_CACHE + WHERE id NOT LIKE '%:%' + ) + SELECT COUNT(*) as conflicts + FROM new_ids n1 + JOIN LIGHTRAG_LLM_CACHE existing + ON existing.workspace = n1.workspace + AND existing.mode = n1.mode + AND existing.id = n1.new_id + WHERE existing.id LIKE '%:%' -- Only check conflicts with existing new format records + """ + + conflict_result = await self.query(conflict_check_sql) + if conflict_result and conflict_result["conflicts"] > 0: + logger.warning( + f"Found {conflict_result['conflicts']} potential ID conflicts with existing records" + ) + # Can choose to continue or abort, here we choose to continue and log warning + + # Execute single UPDATE migration + logger.info("Starting optimized LLM cache migration...") + migration_sql = """ + UPDATE LIGHTRAG_LLM_CACHE + SET + id = mode || ':' || + CASE WHEN mode = 'default' THEN 'extract' ELSE 'unknown' END || ':' || + md5(original_prompt), + cache_type = CASE WHEN mode = 'default' THEN 'extract' ELSE 'unknown' END, + update_time = CURRENT_TIMESTAMP + WHERE id NOT LIKE '%:%' + """ + + # Execute migration + await self.execute(migration_sql) + + # Verify migration results + verify_sql = """ + SELECT COUNT(*) as remaining_old FROM LIGHTRAG_LLM_CACHE + WHERE id NOT LIKE '%:%' + """ + verify_result = await self.query(verify_sql) + remaining = verify_result["remaining_old"] if verify_result else -1 + + if remaining == 0: + logger.info( + f"✅ Successfully migrated {old_count} LLM cache records to flattened format" + ) + else: + logger.warning( + f"⚠️ Migration completed but {remaining} old format records remain" + ) + + except Exception as e: + logger.error(f"Optimized LLM cache migration failed: {e}") + raise + + async def _migrate_doc_status_add_chunks_list(self): + """Add chunks_list column to LIGHTRAG_DOC_STATUS table if it doesn't exist""" + try: + # Check if chunks_list column exists + check_column_sql = """ + SELECT column_name + FROM information_schema.columns + WHERE table_name = 'lightrag_doc_status' + AND column_name = 'chunks_list' + """ + + column_info = await self.query(check_column_sql) + if not column_info: + logger.info("Adding chunks_list column to LIGHTRAG_DOC_STATUS table") + add_column_sql = """ + ALTER TABLE LIGHTRAG_DOC_STATUS + ADD COLUMN chunks_list JSONB NULL DEFAULT '[]'::jsonb + """ + await self.execute(add_column_sql) + logger.info( + "Successfully added chunks_list column to LIGHTRAG_DOC_STATUS table" + ) + else: + logger.info( + "chunks_list column already exists in LIGHTRAG_DOC_STATUS table" + ) + except Exception as e: + logger.warning( + f"Failed to add chunks_list column to LIGHTRAG_DOC_STATUS: {e}" + ) + + async def _migrate_text_chunks_add_llm_cache_list(self): + """Add llm_cache_list column to LIGHTRAG_DOC_CHUNKS table if it doesn't exist""" + try: + # Check if llm_cache_list column exists + check_column_sql = """ + SELECT column_name + FROM information_schema.columns + WHERE table_name = 'lightrag_doc_chunks' + AND column_name = 'llm_cache_list' + """ + + column_info = await self.query(check_column_sql) + if not column_info: + logger.info("Adding llm_cache_list column to LIGHTRAG_DOC_CHUNKS table") + add_column_sql = """ + ALTER TABLE LIGHTRAG_DOC_CHUNKS + ADD COLUMN llm_cache_list JSONB NULL DEFAULT '[]'::jsonb + """ + await self.execute(add_column_sql) + logger.info( + "Successfully added llm_cache_list column to LIGHTRAG_DOC_CHUNKS table" + ) + else: + logger.info( + "llm_cache_list column already exists in LIGHTRAG_DOC_CHUNKS table" + ) + except Exception as e: + logger.warning( + f"Failed to add llm_cache_list column to LIGHTRAG_DOC_CHUNKS: {e}" + ) + + async def _migrate_doc_status_add_track_id(self): + """Add track_id column to LIGHTRAG_DOC_STATUS table if it doesn't exist and create index""" + try: + # Check if track_id column exists + check_column_sql = """ + SELECT column_name + FROM information_schema.columns + WHERE table_name = 'lightrag_doc_status' + AND column_name = 'track_id' + """ + + column_info = await self.query(check_column_sql) + if not column_info: + logger.info("Adding track_id column to LIGHTRAG_DOC_STATUS table") + add_column_sql = """ + ALTER TABLE LIGHTRAG_DOC_STATUS + ADD COLUMN track_id VARCHAR(255) NULL + """ + await self.execute(add_column_sql) + logger.info( + "Successfully added track_id column to LIGHTRAG_DOC_STATUS table" + ) + else: + logger.info( + "track_id column already exists in LIGHTRAG_DOC_STATUS table" + ) + + # Check if track_id index exists + check_index_sql = """ + SELECT indexname + FROM pg_indexes + WHERE tablename = 'lightrag_doc_status' + AND indexname = 'idx_lightrag_doc_status_track_id' + """ + + index_info = await self.query(check_index_sql) + if not index_info: + logger.info( + "Creating index on track_id column for LIGHTRAG_DOC_STATUS table" + ) + create_index_sql = """ + CREATE INDEX idx_lightrag_doc_status_track_id ON LIGHTRAG_DOC_STATUS (track_id) + """ + await self.execute(create_index_sql) + logger.info( + "Successfully created index on track_id column for LIGHTRAG_DOC_STATUS table" + ) + else: + logger.info( + "Index on track_id column already exists for LIGHTRAG_DOC_STATUS table" + ) + + except Exception as e: + logger.warning( + f"Failed to add track_id column or index to LIGHTRAG_DOC_STATUS: {e}" + ) + + async def _migrate_doc_status_add_metadata_error_msg(self): + """Add metadata and error_msg columns to LIGHTRAG_DOC_STATUS table if they don't exist""" + try: + # Check if metadata column exists + check_metadata_sql = """ + SELECT column_name + FROM information_schema.columns + WHERE table_name = 'lightrag_doc_status' + AND column_name = 'metadata' + """ + + metadata_info = await self.query(check_metadata_sql) + if not metadata_info: + logger.info("Adding metadata column to LIGHTRAG_DOC_STATUS table") + add_metadata_sql = """ + ALTER TABLE LIGHTRAG_DOC_STATUS + ADD COLUMN metadata JSONB NULL DEFAULT '{}'::jsonb + """ + await self.execute(add_metadata_sql) + logger.info( + "Successfully added metadata column to LIGHTRAG_DOC_STATUS table" + ) + else: + logger.info( + "metadata column already exists in LIGHTRAG_DOC_STATUS table" + ) + + # Check if error_msg column exists + check_error_msg_sql = """ + SELECT column_name + FROM information_schema.columns + WHERE table_name = 'lightrag_doc_status' + AND column_name = 'error_msg' + """ + + error_msg_info = await self.query(check_error_msg_sql) + if not error_msg_info: + logger.info("Adding error_msg column to LIGHTRAG_DOC_STATUS table") + add_error_msg_sql = """ + ALTER TABLE LIGHTRAG_DOC_STATUS + ADD COLUMN error_msg TEXT NULL + """ + await self.execute(add_error_msg_sql) + logger.info( + "Successfully added error_msg column to LIGHTRAG_DOC_STATUS table" + ) + else: + logger.info( + "error_msg column already exists in LIGHTRAG_DOC_STATUS table" + ) + + except Exception as e: + logger.warning( + f"Failed to add metadata/error_msg columns to LIGHTRAG_DOC_STATUS: {e}" + ) + + async def _migrate_field_lengths(self): + """Migrate database field lengths: entity_name, source_id, target_id, and file_path""" + # Define the field changes needed + field_migrations = [ + { + "table": "LIGHTRAG_VDB_ENTITY", + "column": "entity_name", + "old_type": "character varying(255)", + "new_type": "VARCHAR(512)", + "description": "entity_name from 255 to 512", + }, + { + "table": "LIGHTRAG_VDB_RELATION", + "column": "source_id", + "old_type": "character varying(256)", + "new_type": "VARCHAR(512)", + "description": "source_id from 256 to 512", + }, + { + "table": "LIGHTRAG_VDB_RELATION", + "column": "target_id", + "old_type": "character varying(256)", + "new_type": "VARCHAR(512)", + "description": "target_id from 256 to 512", + }, + { + "table": "LIGHTRAG_DOC_CHUNKS", + "column": "file_path", + "old_type": "character varying(256)", + "new_type": "TEXT", + "description": "file_path to TEXT NULL", + }, + { + "table": "LIGHTRAG_VDB_CHUNKS", + "column": "file_path", + "old_type": "character varying(256)", + "new_type": "TEXT", + "description": "file_path to TEXT NULL", + }, + ] + + try: + # Optimization: Batch check all columns in one query instead of 5 separate queries + unique_tables = list(set(m["table"].lower() for m in field_migrations)) + unique_columns = list(set(m["column"] for m in field_migrations)) + + check_all_columns_sql = """ + SELECT table_name, column_name, data_type, character_maximum_length, is_nullable + FROM information_schema.columns + WHERE table_name = ANY($1) + AND column_name = ANY($2) + """ + + all_columns_result = await self.query( + check_all_columns_sql, [unique_tables, unique_columns], multirows=True + ) + + # Build lookup dict: (table_name, column_name) -> column_info + column_info_map = {} + if all_columns_result: + column_info_map = { + (row["table_name"].upper(), row["column_name"]): row + for row in all_columns_result + } + + # Now iterate and migrate only what's needed + for migration in field_migrations: + try: + column_info = column_info_map.get( + (migration["table"], migration["column"]) + ) + + if not column_info: + logger.warning( + f"Column {migration['table']}.{migration['column']} does not exist, skipping migration" + ) + continue + + current_type = column_info.get("data_type", "").lower() + current_length = column_info.get("character_maximum_length") + + # Check if migration is needed + needs_migration = False + + if migration["column"] == "entity_name" and current_length == 255: + needs_migration = True + elif ( + migration["column"] in ["source_id", "target_id"] + and current_length == 256 + ): + needs_migration = True + elif ( + migration["column"] == "file_path" + and current_type == "character varying" + ): + needs_migration = True + + if needs_migration: + logger.info( + f"Migrating {migration['table']}.{migration['column']}: {migration['description']}" + ) + + # Execute the migration + alter_sql = f""" + ALTER TABLE {migration["table"]} + ALTER COLUMN {migration["column"]} TYPE {migration["new_type"]} + """ + + await self.execute(alter_sql) + logger.info( + f"Successfully migrated {migration['table']}.{migration['column']}" + ) + else: + logger.debug( + f"Column {migration['table']}.{migration['column']} already has correct type, no migration needed" + ) + + except Exception as e: + # Log error but don't interrupt the process + logger.warning( + f"Failed to migrate {migration['table']}.{migration['column']}: {e}" + ) + except Exception as e: + logger.error(f"Failed to batch check field lengths: {e}") + + async def check_tables(self): + # First create all tables + for k, v in TABLES.items(): + try: + await self.query(f"SELECT 1 FROM {k} LIMIT 1") + except Exception: + try: + logger.info(f"PostgreSQL, Try Creating table {k} in database") + await self.execute(v["ddl"]) + logger.info( + f"PostgreSQL, Creation success table {k} in PostgreSQL database" + ) + except Exception as e: + logger.error( + f"PostgreSQL, Failed to create table {k} in database, Please verify the connection with PostgreSQL database, Got: {e}" + ) + raise e + + # Batch check all indexes at once (optimization: single query instead of N queries) + try: + table_names = list(TABLES.keys()) + table_names_lower = [t.lower() for t in table_names] + + # Get all existing indexes for our tables in one query + check_all_indexes_sql = """ + SELECT indexname, tablename + FROM pg_indexes + WHERE tablename = ANY($1) + """ + existing_indexes_result = await self.query( + check_all_indexes_sql, [table_names_lower], multirows=True + ) + + # Build a set of existing index names for fast lookup + existing_indexes = set() + if existing_indexes_result: + existing_indexes = {row["indexname"] for row in existing_indexes_result} + + # Create missing indexes + for k in table_names: + # Create index for id column if missing + index_name = f"idx_{k.lower()}_id" + if index_name not in existing_indexes: + try: + create_index_sql = f"CREATE INDEX {index_name} ON {k}(id)" + logger.info( + f"PostgreSQL, Creating index {index_name} on table {k}" + ) + await self.execute(create_index_sql) + except Exception as e: + logger.error( + f"PostgreSQL, Failed to create index {index_name}, Got: {e}" + ) + + # Create composite index for (workspace, id) if missing + composite_index_name = f"idx_{k.lower()}_workspace_id" + if composite_index_name not in existing_indexes: + try: + create_composite_index_sql = ( + f"CREATE INDEX {composite_index_name} ON {k}(workspace, id)" + ) + logger.info( + f"PostgreSQL, Creating composite index {composite_index_name} on table {k}" + ) + await self.execute(create_composite_index_sql) + except Exception as e: + logger.error( + f"PostgreSQL, Failed to create composite index {composite_index_name}, Got: {e}" + ) + except Exception as e: + logger.error(f"PostgreSQL, Failed to batch check/create indexes: {e}") + + # Create vector indexs + if self.vector_index_type: + logger.info( + f"PostgreSQL, Create vector indexs, type: {self.vector_index_type}" + ) + try: + if self.vector_index_type == "HNSW": + await self._create_hnsw_vector_indexes() + elif self.vector_index_type == "IVFFLAT": + await self._create_ivfflat_vector_indexes() + elif self.vector_index_type == "FLAT": + logger.warning( + "FLAT index type is not supported by pgvector. Skipping vector index creation. " + "Please use 'HNSW' or 'IVFFLAT' instead." + ) + else: + logger.warning( + "Doesn't support this vector index type: {self.vector_index_type}. " + "Supported types: HNSW, IVFFLAT" + ) + except Exception as e: + logger.error( + f"PostgreSQL, Failed to create vector index, type: {self.vector_index_type}, Got: {e}" + ) + # After all tables are created, attempt to migrate timestamp fields + try: + await self._migrate_timestamp_columns() + except Exception as e: + logger.error(f"PostgreSQL, Failed to migrate timestamp columns: {e}") + # Don't throw an exception, allow the initialization process to continue + + # Migrate LLM cache schema: add new columns and remove deprecated mode field + try: + await self._migrate_llm_cache_schema() + except Exception as e: + logger.error(f"PostgreSQL, Failed to migrate LLM cache schema: {e}") + # Don't throw an exception, allow the initialization process to continue + + # Finally, attempt to migrate old doc chunks data if needed + try: + await self._migrate_doc_chunks_to_vdb_chunks() + except Exception as e: + logger.error(f"PostgreSQL, Failed to migrate doc_chunks to vdb_chunks: {e}") + + # Check and migrate LLM cache to flattened keys if needed + try: + if await self._check_llm_cache_needs_migration(): + await self._migrate_llm_cache_to_flattened_keys() + except Exception as e: + logger.error(f"PostgreSQL, LLM cache migration failed: {e}") + + # Migrate doc status to add chunks_list field if needed + try: + await self._migrate_doc_status_add_chunks_list() + except Exception as e: + logger.error( + f"PostgreSQL, Failed to migrate doc status chunks_list field: {e}" + ) + + # Migrate text chunks to add llm_cache_list field if needed + try: + await self._migrate_text_chunks_add_llm_cache_list() + except Exception as e: + logger.error( + f"PostgreSQL, Failed to migrate text chunks llm_cache_list field: {e}" + ) + + # Migrate field lengths for entity_name, source_id, target_id, and file_path + try: + await self._migrate_field_lengths() + except Exception as e: + logger.error(f"PostgreSQL, Failed to migrate field lengths: {e}") + + # Migrate doc status to add track_id field if needed + try: + await self._migrate_doc_status_add_track_id() + except Exception as e: + logger.error( + f"PostgreSQL, Failed to migrate doc status track_id field: {e}" + ) + + # Migrate doc status to add metadata and error_msg fields if needed + try: + await self._migrate_doc_status_add_metadata_error_msg() + except Exception as e: + logger.error( + f"PostgreSQL, Failed to migrate doc status metadata/error_msg fields: {e}" + ) + + # Create pagination optimization indexes for LIGHTRAG_DOC_STATUS + try: + await self._create_pagination_indexes() + except Exception as e: + logger.error(f"PostgreSQL, Failed to create pagination indexes: {e}") + + # Migrate to ensure new tables LIGHTRAG_FULL_ENTITIES and LIGHTRAG_FULL_RELATIONS exist + try: + await self._migrate_create_full_entities_relations_tables() + except Exception as e: + logger.error( + f"PostgreSQL, Failed to create full entities/relations tables: {e}" + ) + + async def _migrate_create_full_entities_relations_tables(self): + """Create LIGHTRAG_FULL_ENTITIES and LIGHTRAG_FULL_RELATIONS tables if they don't exist""" + tables_to_check = [ + { + "name": "LIGHTRAG_FULL_ENTITIES", + "ddl": TABLES["LIGHTRAG_FULL_ENTITIES"]["ddl"], + "description": "Full entities storage table", + }, + { + "name": "LIGHTRAG_FULL_RELATIONS", + "ddl": TABLES["LIGHTRAG_FULL_RELATIONS"]["ddl"], + "description": "Full relations storage table", + }, + ] + + for table_info in tables_to_check: + table_name = table_info["name"] + try: + # Check if table exists + check_table_sql = """ + SELECT table_name + FROM information_schema.tables + WHERE table_name = $1 + AND table_schema = 'public' + """ + params = {"table_name": table_name.lower()} + table_exists = await self.query(check_table_sql, list(params.values())) + + if not table_exists: + logger.info(f"Creating table {table_name}") + await self.execute(table_info["ddl"]) + logger.info( + f"Successfully created {table_info['description']}: {table_name}" + ) + + # Create basic indexes for the new table + try: + # Create index for id column + index_name = f"idx_{table_name.lower()}_id" + create_index_sql = ( + f"CREATE INDEX {index_name} ON {table_name}(id)" + ) + await self.execute(create_index_sql) + logger.info(f"Created index {index_name} on table {table_name}") + + # Create composite index for (workspace, id) columns + composite_index_name = f"idx_{table_name.lower()}_workspace_id" + create_composite_index_sql = f"CREATE INDEX {composite_index_name} ON {table_name}(workspace, id)" + await self.execute(create_composite_index_sql) + logger.info( + f"Created composite index {composite_index_name} on table {table_name}" + ) + + except Exception as e: + logger.warning( + f"Failed to create indexes for table {table_name}: {e}" + ) + + else: + logger.debug(f"Table {table_name} already exists") + + except Exception as e: + logger.error(f"Failed to create table {table_name}: {e}") + + async def _create_pagination_indexes(self): + """Create indexes to optimize pagination queries for LIGHTRAG_DOC_STATUS""" + indexes = [ + { + "name": "idx_lightrag_doc_status_workspace_status_updated_at", + "sql": "CREATE INDEX CONCURRENTLY IF NOT EXISTS idx_lightrag_doc_status_workspace_status_updated_at ON LIGHTRAG_DOC_STATUS (workspace, status, updated_at DESC)", + "description": "Composite index for workspace + status + updated_at pagination", + }, + { + "name": "idx_lightrag_doc_status_workspace_status_created_at", + "sql": "CREATE INDEX CONCURRENTLY IF NOT EXISTS idx_lightrag_doc_status_workspace_status_created_at ON LIGHTRAG_DOC_STATUS (workspace, status, created_at DESC)", + "description": "Composite index for workspace + status + created_at pagination", + }, + { + "name": "idx_lightrag_doc_status_workspace_updated_at", + "sql": "CREATE INDEX CONCURRENTLY IF NOT EXISTS idx_lightrag_doc_status_workspace_updated_at ON LIGHTRAG_DOC_STATUS (workspace, updated_at DESC)", + "description": "Index for workspace + updated_at pagination (all statuses)", + }, + { + "name": "idx_lightrag_doc_status_workspace_created_at", + "sql": "CREATE INDEX CONCURRENTLY IF NOT EXISTS idx_lightrag_doc_status_workspace_created_at ON LIGHTRAG_DOC_STATUS (workspace, created_at DESC)", + "description": "Index for workspace + created_at pagination (all statuses)", + }, + { + "name": "idx_lightrag_doc_status_workspace_id", + "sql": "CREATE INDEX CONCURRENTLY IF NOT EXISTS idx_lightrag_doc_status_workspace_id ON LIGHTRAG_DOC_STATUS (workspace, id)", + "description": "Index for workspace + id sorting", + }, + { + "name": "idx_lightrag_doc_status_workspace_file_path", + "sql": "CREATE INDEX CONCURRENTLY IF NOT EXISTS idx_lightrag_doc_status_workspace_file_path ON LIGHTRAG_DOC_STATUS (workspace, file_path)", + "description": "Index for workspace + file_path sorting", + }, + ] + + for index in indexes: + try: + # Check if index already exists + check_sql = """ + SELECT indexname + FROM pg_indexes + WHERE tablename = 'lightrag_doc_status' + AND indexname = $1 + """ + + params = {"indexname": index["name"]} + existing = await self.query(check_sql, list(params.values())) + + if not existing: + logger.info(f"Creating pagination index: {index['description']}") + await self.execute(index["sql"]) + logger.info(f"Successfully created index: {index['name']}") + else: + logger.debug(f"Index already exists: {index['name']}") + + except Exception as e: + logger.warning(f"Failed to create index {index['name']}: {e}") + + async def _create_hnsw_vector_indexes(self): + vdb_tables = [ + "LIGHTRAG_VDB_CHUNKS", + "LIGHTRAG_VDB_ENTITY", + "LIGHTRAG_VDB_RELATION", + ] + + embedding_dim = int(os.environ.get("EMBEDDING_DIM", 1024)) + + for k in vdb_tables: + vector_index_name = f"idx_{k.lower()}_hnsw_cosine" + check_vector_index_sql = f""" + SELECT 1 FROM pg_indexes + WHERE indexname = '{vector_index_name}' + AND tablename = '{k.lower()}' + """ + try: + vector_index_exists = await self.query(check_vector_index_sql) + if not vector_index_exists: + # Only set vector dimension when index doesn't exist + alter_sql = f"ALTER TABLE {k} ALTER COLUMN content_vector TYPE VECTOR({embedding_dim})" + await self.execute(alter_sql) + logger.debug(f"Ensured vector dimension for {k}") + + create_vector_index_sql = f""" + CREATE INDEX {vector_index_name} + ON {k} USING hnsw (content_vector vector_cosine_ops) + WITH (m = {self.hnsw_m}, ef_construction = {self.hnsw_ef}) + """ + logger.info(f"Creating hnsw index {vector_index_name} on table {k}") + await self.execute(create_vector_index_sql) + logger.info( + f"Successfully created vector index {vector_index_name} on table {k}" + ) + else: + logger.info( + f"HNSW vector index {vector_index_name} already exists on table {k}" + ) + except Exception as e: + logger.error(f"Failed to create vector index on table {k}, Got: {e}") + + async def _create_ivfflat_vector_indexes(self): + vdb_tables = [ + "LIGHTRAG_VDB_CHUNKS", + "LIGHTRAG_VDB_ENTITY", + "LIGHTRAG_VDB_RELATION", + ] + + embedding_dim = int(os.environ.get("EMBEDDING_DIM", 1024)) + + for k in vdb_tables: + index_name = f"idx_{k.lower()}_ivfflat_cosine" + check_index_sql = f""" + SELECT 1 FROM pg_indexes + WHERE indexname = '{index_name}' AND tablename = '{k.lower()}' + """ + try: + exists = await self.query(check_index_sql) + if not exists: + # Only set vector dimension when index doesn't exist + alter_sql = f"ALTER TABLE {k} ALTER COLUMN content_vector TYPE VECTOR({embedding_dim})" + await self.execute(alter_sql) + logger.debug(f"Ensured vector dimension for {k}") + + create_sql = f""" + CREATE INDEX {index_name} + ON {k} USING ivfflat (content_vector vector_cosine_ops) + WITH (lists = {self.ivfflat_lists}) + """ + logger.info(f"Creating ivfflat index {index_name} on table {k}") + await self.execute(create_sql) + logger.info( + f"Successfully created ivfflat index {index_name} on table {k}" + ) + else: + logger.info( + f"Ivfflat vector index {index_name} already exists on table {k}" + ) + except Exception as e: + logger.error(f"Failed to create ivfflat index on {k}: {e}") + + async def query( + self, + sql: str, + params: list[Any] | None = None, + multirows: bool = False, + with_age: bool = False, + graph_name: str | None = None, + ) -> dict[str, Any] | None | list[dict[str, Any]]: + async def _operation(connection: asyncpg.Connection) -> Any: + prepared_params = tuple(params) if params else () + if prepared_params: + rows = await connection.fetch(sql, *prepared_params) + else: + rows = await connection.fetch(sql) + + if multirows: + if rows: + columns = [col for col in rows[0].keys()] + return [dict(zip(columns, row)) for row in rows] + return [] + + if rows: + columns = rows[0].keys() + return dict(zip(columns, rows[0])) + return None + + try: + return await self._run_with_retry( + _operation, with_age=with_age, graph_name=graph_name + ) + except Exception as e: + logger.error(f"PostgreSQL database, error:{e}") + raise + + async def execute( + self, + sql: str, + data: dict[str, Any] | None = None, + upsert: bool = False, + ignore_if_exists: bool = False, + with_age: bool = False, + graph_name: str | None = None, + ): + async def _operation(connection: asyncpg.Connection) -> Any: + prepared_values = tuple(data.values()) if data else () + try: + if not data: + return await connection.execute(sql) + return await connection.execute(sql, *prepared_values) + except ( + asyncpg.exceptions.UniqueViolationError, + asyncpg.exceptions.DuplicateTableError, + asyncpg.exceptions.DuplicateObjectError, + asyncpg.exceptions.InvalidSchemaNameError, + ) as e: + if ignore_if_exists: + logger.debug("PostgreSQL, ignoring duplicate during execute: %r", e) + return None + if upsert: + logger.info( + "PostgreSQL, duplicate detected but treated as upsert success: %r", + e, + ) + return None + raise + + try: + await self._run_with_retry( + _operation, with_age=with_age, graph_name=graph_name + ) + except Exception as e: + logger.error(f"PostgreSQL database,\nsql:{sql},\ndata:{data},\nerror:{e}") + raise + + +class ClientManager: + _instances: dict[str, Any] = {"db": None, "ref_count": 0} + _lock = asyncio.Lock() + + @staticmethod + def get_config() -> dict[str, Any]: + config = configparser.ConfigParser() + config.read("config.ini", "utf-8") + + return { + "host": os.environ.get( + "POSTGRES_HOST", + config.get("postgres", "host", fallback="localhost"), + ), + "port": os.environ.get( + "POSTGRES_PORT", config.get("postgres", "port", fallback=5432) + ), + "user": os.environ.get( + "POSTGRES_USER", config.get("postgres", "user", fallback="postgres") + ), + "password": os.environ.get( + "POSTGRES_PASSWORD", + config.get("postgres", "password", fallback=None), + ), + "database": os.environ.get( + "POSTGRES_DATABASE", + config.get("postgres", "database", fallback="postgres"), + ), + "workspace": os.environ.get( + "POSTGRES_WORKSPACE", + config.get("postgres", "workspace", fallback=None), + ), + "max_connections": os.environ.get( + "POSTGRES_MAX_CONNECTIONS", + config.get("postgres", "max_connections", fallback=50), + ), + # SSL configuration + "ssl_mode": os.environ.get( + "POSTGRES_SSL_MODE", + config.get("postgres", "ssl_mode", fallback=None), + ), + "ssl_cert": os.environ.get( + "POSTGRES_SSL_CERT", + config.get("postgres", "ssl_cert", fallback=None), + ), + "ssl_key": os.environ.get( + "POSTGRES_SSL_KEY", + config.get("postgres", "ssl_key", fallback=None), + ), + "ssl_root_cert": os.environ.get( + "POSTGRES_SSL_ROOT_CERT", + config.get("postgres", "ssl_root_cert", fallback=None), + ), + "ssl_crl": os.environ.get( + "POSTGRES_SSL_CRL", + config.get("postgres", "ssl_crl", fallback=None), + ), + "vector_index_type": os.environ.get( + "POSTGRES_VECTOR_INDEX_TYPE", + config.get("postgres", "vector_index_type", fallback="HNSW"), + ), + "hnsw_m": int( + os.environ.get( + "POSTGRES_HNSW_M", + config.get("postgres", "hnsw_m", fallback="16"), + ) + ), + "hnsw_ef": int( + os.environ.get( + "POSTGRES_HNSW_EF", + config.get("postgres", "hnsw_ef", fallback="64"), + ) + ), + "ivfflat_lists": int( + os.environ.get( + "POSTGRES_IVFFLAT_LISTS", + config.get("postgres", "ivfflat_lists", fallback="100"), + ) + ), + # Server settings for Supabase + "server_settings": os.environ.get( + "POSTGRES_SERVER_SETTINGS", + config.get("postgres", "server_options", fallback=None), + ), + "statement_cache_size": os.environ.get( + "POSTGRES_STATEMENT_CACHE_SIZE", + config.get("postgres", "statement_cache_size", fallback=None), + ), + # Connection retry configuration + "connection_retry_attempts": min( + 10, + int( + os.environ.get( + "POSTGRES_CONNECTION_RETRIES", + config.get("postgres", "connection_retries", fallback=3), + ) + ), + ), + "connection_retry_backoff": min( + 5.0, + float( + os.environ.get( + "POSTGRES_CONNECTION_RETRY_BACKOFF", + config.get( + "postgres", "connection_retry_backoff", fallback=0.5 + ), + ) + ), + ), + "connection_retry_backoff_max": min( + 60.0, + float( + os.environ.get( + "POSTGRES_CONNECTION_RETRY_BACKOFF_MAX", + config.get( + "postgres", + "connection_retry_backoff_max", + fallback=5.0, + ), + ) + ), + ), + "pool_close_timeout": min( + 30.0, + float( + os.environ.get( + "POSTGRES_POOL_CLOSE_TIMEOUT", + config.get("postgres", "pool_close_timeout", fallback=5.0), + ) + ), + ), + } + + @classmethod + async def get_client(cls) -> PostgreSQLDB: + async with cls._lock: + if cls._instances["db"] is None: + config = ClientManager.get_config() + db = PostgreSQLDB(config) + await db.initdb() + await db.check_tables() + cls._instances["db"] = db + cls._instances["ref_count"] = 0 + cls._instances["ref_count"] += 1 + return cls._instances["db"] + + @classmethod + async def release_client(cls, db: PostgreSQLDB): + async with cls._lock: + if db is not None: + if db is cls._instances["db"]: + cls._instances["ref_count"] -= 1 + if cls._instances["ref_count"] == 0: + await db.pool.close() + logger.info("Closed PostgreSQL database connection pool") + cls._instances["db"] = None + else: + await db.pool.close() + + +@final +@dataclass +class PGKVStorage(BaseKVStorage): + db: PostgreSQLDB = field(default=None) + + def __post_init__(self): + self._max_batch_size = self.global_config["embedding_batch_num"] + + async def initialize(self): + async with get_data_init_lock(): + if self.db is None: + self.db = await ClientManager.get_client() + + # Implement workspace priority: PostgreSQLDB.workspace > self.workspace > "default" + if self.db.workspace: + # Use PostgreSQLDB's workspace (highest priority) + self.workspace = self.db.workspace + elif hasattr(self, "workspace") and self.workspace: + # Use storage class's workspace (medium priority) + pass + else: + # Use "default" for compatibility (lowest priority) + self.workspace = "default" + + async def finalize(self): + async with get_storage_lock(): + if self.db is not None: + await ClientManager.release_client(self.db) + self.db = None + + ################ QUERY METHODS ################ + async def get_by_id(self, id: str) -> dict[str, Any] | None: + """Get data by id.""" + sql = SQL_TEMPLATES["get_by_id_" + self.namespace] + params = {"workspace": self.workspace, "id": id} + response = await self.db.query(sql, list(params.values())) + + if response and is_namespace(self.namespace, NameSpace.KV_STORE_TEXT_CHUNKS): + # Parse llm_cache_list JSON string back to list + llm_cache_list = response.get("llm_cache_list", []) + if isinstance(llm_cache_list, str): + try: + llm_cache_list = json.loads(llm_cache_list) + except json.JSONDecodeError: + llm_cache_list = [] + response["llm_cache_list"] = llm_cache_list + create_time = response.get("create_time", 0) + update_time = response.get("update_time", 0) + response["create_time"] = create_time + response["update_time"] = create_time if update_time == 0 else update_time + + # Special handling for LLM cache to ensure compatibility with _get_cached_extraction_results + if response and is_namespace( + self.namespace, NameSpace.KV_STORE_LLM_RESPONSE_CACHE + ): + create_time = response.get("create_time", 0) + update_time = response.get("update_time", 0) + # Parse queryparam JSON string back to dict + queryparam = response.get("queryparam") + if isinstance(queryparam, str): + try: + queryparam = json.loads(queryparam) + except json.JSONDecodeError: + queryparam = None + # Map field names for compatibility (mode field removed) + response = { + **response, + "return": response.get("return_value", ""), + "cache_type": response.get("cache_type"), + "original_prompt": response.get("original_prompt", ""), + "chunk_id": response.get("chunk_id"), + "queryparam": queryparam, + "create_time": create_time, + "update_time": create_time if update_time == 0 else update_time, + } + + # Special handling for FULL_ENTITIES namespace + if response and is_namespace(self.namespace, NameSpace.KV_STORE_FULL_ENTITIES): + # Parse entity_names JSON string back to list + entity_names = response.get("entity_names", []) + if isinstance(entity_names, str): + try: + entity_names = json.loads(entity_names) + except json.JSONDecodeError: + entity_names = [] + response["entity_names"] = entity_names + create_time = response.get("create_time", 0) + update_time = response.get("update_time", 0) + response["create_time"] = create_time + response["update_time"] = create_time if update_time == 0 else update_time + + # Special handling for FULL_RELATIONS namespace + if response and is_namespace(self.namespace, NameSpace.KV_STORE_FULL_RELATIONS): + # Parse relation_pairs JSON string back to list + relation_pairs = response.get("relation_pairs", []) + if isinstance(relation_pairs, str): + try: + relation_pairs = json.loads(relation_pairs) + except json.JSONDecodeError: + relation_pairs = [] + response["relation_pairs"] = relation_pairs + create_time = response.get("create_time", 0) + update_time = response.get("update_time", 0) + response["create_time"] = create_time + response["update_time"] = create_time if update_time == 0 else update_time + + # Special handling for ENTITY_CHUNKS namespace + if response and is_namespace(self.namespace, NameSpace.KV_STORE_ENTITY_CHUNKS): + # Parse chunk_ids JSON string back to list + chunk_ids = response.get("chunk_ids", []) + if isinstance(chunk_ids, str): + try: + chunk_ids = json.loads(chunk_ids) + except json.JSONDecodeError: + chunk_ids = [] + response["chunk_ids"] = chunk_ids + create_time = response.get("create_time", 0) + update_time = response.get("update_time", 0) + response["create_time"] = create_time + response["update_time"] = create_time if update_time == 0 else update_time + + # Special handling for RELATION_CHUNKS namespace + if response and is_namespace( + self.namespace, NameSpace.KV_STORE_RELATION_CHUNKS + ): + # Parse chunk_ids JSON string back to list + chunk_ids = response.get("chunk_ids", []) + if isinstance(chunk_ids, str): + try: + chunk_ids = json.loads(chunk_ids) + except json.JSONDecodeError: + chunk_ids = [] + response["chunk_ids"] = chunk_ids + create_time = response.get("create_time", 0) + update_time = response.get("update_time", 0) + response["create_time"] = create_time + response["update_time"] = create_time if update_time == 0 else update_time + + return response if response else None + + # Query by id + async def get_by_ids(self, ids: list[str]) -> list[dict[str, Any]]: + """Get data by ids""" + if not ids: + return [] + + sql = SQL_TEMPLATES["get_by_ids_" + self.namespace] + params = {"workspace": self.workspace, "ids": ids} + results = await self.db.query(sql, list(params.values()), multirows=True) + + def _order_results( + rows: list[dict[str, Any]] | None, + ) -> list[dict[str, Any] | None]: + """Preserve the caller requested ordering for bulk id lookups.""" + if not rows: + return [None for _ in ids] + + id_map: dict[str, dict[str, Any]] = {} + for row in rows: + if row is None: + continue + row_id = row.get("id") + if row_id is not None: + id_map[str(row_id)] = row + + ordered: list[dict[str, Any] | None] = [] + for requested_id in ids: + ordered.append(id_map.get(str(requested_id))) + return ordered + + if results and is_namespace(self.namespace, NameSpace.KV_STORE_TEXT_CHUNKS): + # Parse llm_cache_list JSON string back to list for each result + for result in results: + llm_cache_list = result.get("llm_cache_list", []) + if isinstance(llm_cache_list, str): + try: + llm_cache_list = json.loads(llm_cache_list) + except json.JSONDecodeError: + llm_cache_list = [] + result["llm_cache_list"] = llm_cache_list + create_time = result.get("create_time", 0) + update_time = result.get("update_time", 0) + result["create_time"] = create_time + result["update_time"] = create_time if update_time == 0 else update_time + + # Special handling for LLM cache to ensure compatibility with _get_cached_extraction_results + if results and is_namespace( + self.namespace, NameSpace.KV_STORE_LLM_RESPONSE_CACHE + ): + processed_results = [] + for row in results: + create_time = row.get("create_time", 0) + update_time = row.get("update_time", 0) + # Parse queryparam JSON string back to dict + queryparam = row.get("queryparam") + if isinstance(queryparam, str): + try: + queryparam = json.loads(queryparam) + except json.JSONDecodeError: + queryparam = None + # Map field names for compatibility (mode field removed) + processed_row = { + **row, + "return": row.get("return_value", ""), + "cache_type": row.get("cache_type"), + "original_prompt": row.get("original_prompt", ""), + "chunk_id": row.get("chunk_id"), + "queryparam": queryparam, + "create_time": create_time, + "update_time": create_time if update_time == 0 else update_time, + } + processed_results.append(processed_row) + return _order_results(processed_results) + + # Special handling for FULL_ENTITIES namespace + if results and is_namespace(self.namespace, NameSpace.KV_STORE_FULL_ENTITIES): + for result in results: + # Parse entity_names JSON string back to list + entity_names = result.get("entity_names", []) + if isinstance(entity_names, str): + try: + entity_names = json.loads(entity_names) + except json.JSONDecodeError: + entity_names = [] + result["entity_names"] = entity_names + create_time = result.get("create_time", 0) + update_time = result.get("update_time", 0) + result["create_time"] = create_time + result["update_time"] = create_time if update_time == 0 else update_time + + # Special handling for FULL_RELATIONS namespace + if results and is_namespace(self.namespace, NameSpace.KV_STORE_FULL_RELATIONS): + for result in results: + # Parse relation_pairs JSON string back to list + relation_pairs = result.get("relation_pairs", []) + if isinstance(relation_pairs, str): + try: + relation_pairs = json.loads(relation_pairs) + except json.JSONDecodeError: + relation_pairs = [] + result["relation_pairs"] = relation_pairs + create_time = result.get("create_time", 0) + update_time = result.get("update_time", 0) + result["create_time"] = create_time + result["update_time"] = create_time if update_time == 0 else update_time + + # Special handling for ENTITY_CHUNKS namespace + if results and is_namespace(self.namespace, NameSpace.KV_STORE_ENTITY_CHUNKS): + for result in results: + # Parse chunk_ids JSON string back to list + chunk_ids = result.get("chunk_ids", []) + if isinstance(chunk_ids, str): + try: + chunk_ids = json.loads(chunk_ids) + except json.JSONDecodeError: + chunk_ids = [] + result["chunk_ids"] = chunk_ids + create_time = result.get("create_time", 0) + update_time = result.get("update_time", 0) + result["create_time"] = create_time + result["update_time"] = create_time if update_time == 0 else update_time + + # Special handling for RELATION_CHUNKS namespace + if results and is_namespace(self.namespace, NameSpace.KV_STORE_RELATION_CHUNKS): + for result in results: + # Parse chunk_ids JSON string back to list + chunk_ids = result.get("chunk_ids", []) + if isinstance(chunk_ids, str): + try: + chunk_ids = json.loads(chunk_ids) + except json.JSONDecodeError: + chunk_ids = [] + result["chunk_ids"] = chunk_ids + create_time = result.get("create_time", 0) + update_time = result.get("update_time", 0) + result["create_time"] = create_time + result["update_time"] = create_time if update_time == 0 else update_time + + return _order_results(results) + + async def filter_keys(self, keys: set[str]) -> set[str]: + """Filter out duplicated content""" + if not keys: + return set() + + table_name = namespace_to_table_name(self.namespace) + sql = f"SELECT id FROM {table_name} WHERE workspace=$1 AND id = ANY($2)" + params = {"workspace": self.workspace, "ids": list(keys)} + try: + res = await self.db.query(sql, list(params.values()), multirows=True) + if res: + exist_keys = [key["id"] for key in res] + else: + exist_keys = [] + new_keys = set([s for s in keys if s not in exist_keys]) + return new_keys + except Exception as e: + logger.error( + f"[{self.workspace}] PostgreSQL database,\nsql:{sql},\nparams:{params},\nerror:{e}" + ) + raise + + ################ INSERT METHODS ################ + async def upsert(self, data: dict[str, dict[str, Any]]) -> None: + logger.debug(f"[{self.workspace}] Inserting {len(data)} to {self.namespace}") + if not data: + return + + if is_namespace(self.namespace, NameSpace.KV_STORE_TEXT_CHUNKS): + # Get current UTC time and convert to naive datetime for database storage + current_time = datetime.datetime.now(timezone.utc).replace(tzinfo=None) + for k, v in data.items(): + upsert_sql = SQL_TEMPLATES["upsert_text_chunk"] + _data = { + "workspace": self.workspace, + "id": k, + "tokens": v["tokens"], + "chunk_order_index": v["chunk_order_index"], + "full_doc_id": v["full_doc_id"], + "content": v["content"], + "file_path": v["file_path"], + "llm_cache_list": json.dumps(v.get("llm_cache_list", [])), + "create_time": current_time, + "update_time": current_time, + } + await self.db.execute(upsert_sql, _data) + elif is_namespace(self.namespace, NameSpace.KV_STORE_FULL_DOCS): + for k, v in data.items(): + upsert_sql = SQL_TEMPLATES["upsert_doc_full"] + _data = { + "id": k, + "content": v["content"], + "doc_name": v.get("file_path", ""), # Map file_path to doc_name + "workspace": self.workspace, + } + await self.db.execute(upsert_sql, _data) + elif is_namespace(self.namespace, NameSpace.KV_STORE_LLM_RESPONSE_CACHE): + for k, v in data.items(): + upsert_sql = SQL_TEMPLATES["upsert_llm_response_cache"] + _data = { + "workspace": self.workspace, + "id": k, # Use flattened key as id + "original_prompt": v["original_prompt"], + "return_value": v["return"], + "chunk_id": v.get("chunk_id"), + "cache_type": v.get( + "cache_type", "extract" + ), # Get cache_type from data + "queryparam": json.dumps(v.get("queryparam")) + if v.get("queryparam") + else None, + } + + await self.db.execute(upsert_sql, _data) + elif is_namespace(self.namespace, NameSpace.KV_STORE_FULL_ENTITIES): + # Get current UTC time and convert to naive datetime for database storage + current_time = datetime.datetime.now(timezone.utc).replace(tzinfo=None) + for k, v in data.items(): + upsert_sql = SQL_TEMPLATES["upsert_full_entities"] + _data = { + "workspace": self.workspace, + "id": k, + "entity_names": json.dumps(v["entity_names"]), + "count": v["count"], + "create_time": current_time, + "update_time": current_time, + } + await self.db.execute(upsert_sql, _data) + elif is_namespace(self.namespace, NameSpace.KV_STORE_FULL_RELATIONS): + # Get current UTC time and convert to naive datetime for database storage + current_time = datetime.datetime.now(timezone.utc).replace(tzinfo=None) + for k, v in data.items(): + upsert_sql = SQL_TEMPLATES["upsert_full_relations"] + _data = { + "workspace": self.workspace, + "id": k, + "relation_pairs": json.dumps(v["relation_pairs"]), + "count": v["count"], + "create_time": current_time, + "update_time": current_time, + } + await self.db.execute(upsert_sql, _data) + elif is_namespace(self.namespace, NameSpace.KV_STORE_ENTITY_CHUNKS): + # Get current UTC time and convert to naive datetime for database storage + current_time = datetime.datetime.now(timezone.utc).replace(tzinfo=None) + for k, v in data.items(): + upsert_sql = SQL_TEMPLATES["upsert_entity_chunks"] + _data = { + "workspace": self.workspace, + "id": k, + "chunk_ids": json.dumps(v["chunk_ids"]), + "count": v["count"], + "create_time": current_time, + "update_time": current_time, + } + await self.db.execute(upsert_sql, _data) + elif is_namespace(self.namespace, NameSpace.KV_STORE_RELATION_CHUNKS): + # Get current UTC time and convert to naive datetime for database storage + current_time = datetime.datetime.now(timezone.utc).replace(tzinfo=None) + for k, v in data.items(): + upsert_sql = SQL_TEMPLATES["upsert_relation_chunks"] + _data = { + "workspace": self.workspace, + "id": k, + "chunk_ids": json.dumps(v["chunk_ids"]), + "count": v["count"], + "create_time": current_time, + "update_time": current_time, + } + await self.db.execute(upsert_sql, _data) + + async def index_done_callback(self) -> None: + # PG handles persistence automatically + pass + + async def is_empty(self) -> bool: + """Check if the storage is empty for the current workspace and namespace + + Returns: + bool: True if storage is empty, False otherwise + """ + table_name = namespace_to_table_name(self.namespace) + if not table_name: + logger.error( + f"[{self.workspace}] Unknown namespace for is_empty check: {self.namespace}" + ) + return True + + sql = f"SELECT EXISTS(SELECT 1 FROM {table_name} WHERE workspace=$1 LIMIT 1) as has_data" + + try: + result = await self.db.query(sql, [self.workspace]) + return not result.get("has_data", False) if result else True + except Exception as e: + logger.error(f"[{self.workspace}] Error checking if storage is empty: {e}") + return True + + async def delete(self, ids: list[str]) -> None: + """Delete specific records from storage by their IDs + + Args: + ids (list[str]): List of document IDs to be deleted from storage + + Returns: + None + """ + if not ids: + return + + table_name = namespace_to_table_name(self.namespace) + if not table_name: + logger.error( + f"[{self.workspace}] Unknown namespace for deletion: {self.namespace}" + ) + return + + delete_sql = f"DELETE FROM {table_name} WHERE workspace=$1 AND id = ANY($2)" + + try: + await self.db.execute(delete_sql, {"workspace": self.workspace, "ids": ids}) + logger.debug( + f"[{self.workspace}] Successfully deleted {len(ids)} records from {self.namespace}" + ) + except Exception as e: + logger.error( + f"[{self.workspace}] Error while deleting records from {self.namespace}: {e}" + ) + + async def drop(self) -> dict[str, str]: + """Drop the storage""" + async with get_storage_lock(): + try: + table_name = namespace_to_table_name(self.namespace) + if not table_name: + return { + "status": "error", + "message": f"Unknown namespace: {self.namespace}", + } + + drop_sql = SQL_TEMPLATES["drop_specifiy_table_workspace"].format( + table_name=table_name + ) + await self.db.execute(drop_sql, {"workspace": self.workspace}) + return {"status": "success", "message": "data dropped"} + except Exception as e: + return {"status": "error", "message": str(e)} + + +@final +@dataclass +class PGVectorStorage(BaseVectorStorage): + db: PostgreSQLDB | None = field(default=None) + + def __post_init__(self): + self._max_batch_size = self.global_config["embedding_batch_num"] + config = self.global_config.get("vector_db_storage_cls_kwargs", {}) + cosine_threshold = config.get("cosine_better_than_threshold") + if cosine_threshold is None: + raise ValueError( + "cosine_better_than_threshold must be specified in vector_db_storage_cls_kwargs" + ) + self.cosine_better_than_threshold = cosine_threshold + + async def initialize(self): + async with get_data_init_lock(): + if self.db is None: + self.db = await ClientManager.get_client() + + # Implement workspace priority: PostgreSQLDB.workspace > self.workspace > "default" + if self.db.workspace: + # Use PostgreSQLDB's workspace (highest priority) + self.workspace = self.db.workspace + elif hasattr(self, "workspace") and self.workspace: + # Use storage class's workspace (medium priority) + pass + else: + # Use "default" for compatibility (lowest priority) + self.workspace = "default" + + async def finalize(self): + async with get_storage_lock(): + if self.db is not None: + await ClientManager.release_client(self.db) + self.db = None + + def _upsert_chunks( + self, item: dict[str, Any], current_time: datetime.datetime + ) -> tuple[str, dict[str, Any]]: + try: + upsert_sql = SQL_TEMPLATES["upsert_chunk"] + data: dict[str, Any] = { + "workspace": self.workspace, + "id": item["__id__"], + "tokens": item["tokens"], + "chunk_order_index": item["chunk_order_index"], + "full_doc_id": item["full_doc_id"], + "content": item["content"], + "content_vector": json.dumps(item["__vector__"].tolist()), + "file_path": item["file_path"], + "create_time": current_time, + "update_time": current_time, + } + except Exception as e: + logger.error( + f"[{self.workspace}] Error to prepare upsert,\nsql: {e}\nitem: {item}" + ) + raise + + return upsert_sql, data + + def _upsert_entities( + self, item: dict[str, Any], current_time: datetime.datetime + ) -> tuple[str, dict[str, Any]]: + upsert_sql = SQL_TEMPLATES["upsert_entity"] + source_id = item["source_id"] + if isinstance(source_id, str) and "" in source_id: + chunk_ids = source_id.split("") + else: + chunk_ids = [source_id] + + data: dict[str, Any] = { + "workspace": self.workspace, + "id": item["__id__"], + "entity_name": item["entity_name"], + "content": item["content"], + "content_vector": json.dumps(item["__vector__"].tolist()), + "chunk_ids": chunk_ids, + "file_path": item.get("file_path", None), + "create_time": current_time, + "update_time": current_time, + } + return upsert_sql, data + + def _upsert_relationships( + self, item: dict[str, Any], current_time: datetime.datetime + ) -> tuple[str, dict[str, Any]]: + upsert_sql = SQL_TEMPLATES["upsert_relationship"] + source_id = item["source_id"] + if isinstance(source_id, str) and "" in source_id: + chunk_ids = source_id.split("") + else: + chunk_ids = [source_id] + + data: dict[str, Any] = { + "workspace": self.workspace, + "id": item["__id__"], + "source_id": item["src_id"], + "target_id": item["tgt_id"], + "content": item["content"], + "content_vector": json.dumps(item["__vector__"].tolist()), + "chunk_ids": chunk_ids, + "file_path": item.get("file_path", None), + "create_time": current_time, + "update_time": current_time, + } + return upsert_sql, data + + async def upsert(self, data: dict[str, dict[str, Any]]) -> None: + logger.debug(f"[{self.workspace}] Inserting {len(data)} to {self.namespace}") + if not data: + return + + # Get current UTC time and convert to naive datetime for database storage + current_time = datetime.datetime.now(timezone.utc).replace(tzinfo=None) + list_data = [ + { + "__id__": k, + **{k1: v1 for k1, v1 in v.items()}, + } + for k, v in data.items() + ] + contents = [v["content"] for v in data.values()] + batches = [ + contents[i : i + self._max_batch_size] + for i in range(0, len(contents), self._max_batch_size) + ] + + embedding_tasks = [self.embedding_func(batch) for batch in batches] + embeddings_list = await asyncio.gather(*embedding_tasks) + + embeddings = np.concatenate(embeddings_list) + for i, d in enumerate(list_data): + d["__vector__"] = embeddings[i] + for item in list_data: + if is_namespace(self.namespace, NameSpace.VECTOR_STORE_CHUNKS): + upsert_sql, data = self._upsert_chunks(item, current_time) + elif is_namespace(self.namespace, NameSpace.VECTOR_STORE_ENTITIES): + upsert_sql, data = self._upsert_entities(item, current_time) + elif is_namespace(self.namespace, NameSpace.VECTOR_STORE_RELATIONSHIPS): + upsert_sql, data = self._upsert_relationships(item, current_time) + else: + raise ValueError(f"{self.namespace} is not supported") + + await self.db.execute(upsert_sql, data) + + #################### query method ############### + async def query( + self, query: str, top_k: int, query_embedding: list[float] = None + ) -> list[dict[str, Any]]: + if query_embedding is not None: + embedding = query_embedding + else: + embeddings = await self.embedding_func( + [query], _priority=5 + ) # higher priority for query + embedding = embeddings[0] + + embedding_string = ",".join(map(str, embedding)) + + sql = SQL_TEMPLATES[self.namespace].format(embedding_string=embedding_string) + params = { + "workspace": self.workspace, + "closer_than_threshold": 1 - self.cosine_better_than_threshold, + "top_k": top_k, + } + results = await self.db.query(sql, params=list(params.values()), multirows=True) + return results + + async def index_done_callback(self) -> None: + # PG handles persistence automatically + pass + + async def delete(self, ids: list[str]) -> None: + """Delete vectors with specified IDs from the storage. + + Args: + ids: List of vector IDs to be deleted + """ + if not ids: + return + + table_name = namespace_to_table_name(self.namespace) + if not table_name: + logger.error( + f"[{self.workspace}] Unknown namespace for vector deletion: {self.namespace}" + ) + return + + delete_sql = f"DELETE FROM {table_name} WHERE workspace=$1 AND id = ANY($2)" + + try: + await self.db.execute(delete_sql, {"workspace": self.workspace, "ids": ids}) + logger.debug( + f"[{self.workspace}] Successfully deleted {len(ids)} vectors from {self.namespace}" + ) + except Exception as e: + logger.error( + f"[{self.workspace}] Error while deleting vectors from {self.namespace}: {e}" + ) + + async def delete_entity(self, entity_name: str) -> None: + """Delete an entity by its name from the vector storage. + + Args: + entity_name: The name of the entity to delete + """ + try: + # Construct SQL to delete the entity + delete_sql = """DELETE FROM LIGHTRAG_VDB_ENTITY + WHERE workspace=$1 AND entity_name=$2""" + + await self.db.execute( + delete_sql, {"workspace": self.workspace, "entity_name": entity_name} + ) + logger.debug( + f"[{self.workspace}] Successfully deleted entity {entity_name}" + ) + except Exception as e: + logger.error(f"[{self.workspace}] Error deleting entity {entity_name}: {e}") + + async def delete_entity_relation(self, entity_name: str) -> None: + """Delete all relations associated with an entity. + + Args: + entity_name: The name of the entity whose relations should be deleted + """ + try: + # Delete relations where the entity is either the source or target + delete_sql = """DELETE FROM LIGHTRAG_VDB_RELATION + WHERE workspace=$1 AND (source_id=$2 OR target_id=$2)""" + + await self.db.execute( + delete_sql, {"workspace": self.workspace, "entity_name": entity_name} + ) + logger.debug( + f"[{self.workspace}] Successfully deleted relations for entity {entity_name}" + ) + except Exception as e: + logger.error( + f"[{self.workspace}] Error deleting relations for entity {entity_name}: {e}" + ) + + async def get_by_id(self, id: str) -> dict[str, Any] | None: + """Get vector data by its ID + + Args: + id: The unique identifier of the vector + + Returns: + The vector data if found, or None if not found + """ + table_name = namespace_to_table_name(self.namespace) + if not table_name: + logger.error( + f"[{self.workspace}] Unknown namespace for ID lookup: {self.namespace}" + ) + return None + + query = f"SELECT *, EXTRACT(EPOCH FROM create_time)::BIGINT as created_at FROM {table_name} WHERE workspace=$1 AND id=$2" + params = {"workspace": self.workspace, "id": id} + + try: + result = await self.db.query(query, list(params.values())) + if result: + return dict(result) + return None + except Exception as e: + logger.error( + f"[{self.workspace}] Error retrieving vector data for ID {id}: {e}" + ) + return None + + async def get_by_ids(self, ids: list[str]) -> list[dict[str, Any]]: + """Get multiple vector data by their IDs + + Args: + ids: List of unique identifiers + + Returns: + List of vector data objects that were found + """ + if not ids: + return [] + + table_name = namespace_to_table_name(self.namespace) + if not table_name: + logger.error( + f"[{self.workspace}] Unknown namespace for IDs lookup: {self.namespace}" + ) + return [] + + ids_str = ",".join([f"'{id}'" for id in ids]) + query = f"SELECT *, EXTRACT(EPOCH FROM create_time)::BIGINT as created_at FROM {table_name} WHERE workspace=$1 AND id IN ({ids_str})" + params = {"workspace": self.workspace} + + try: + results = await self.db.query(query, list(params.values()), multirows=True) + if not results: + return [] + + # Preserve caller requested ordering while normalizing asyncpg rows to dicts. + id_map: dict[str, dict[str, Any]] = {} + for record in results: + if record is None: + continue + record_dict = dict(record) + row_id = record_dict.get("id") + if row_id is not None: + id_map[str(row_id)] = record_dict + + ordered_results: list[dict[str, Any] | None] = [] + for requested_id in ids: + ordered_results.append(id_map.get(str(requested_id))) + return ordered_results + except Exception as e: + logger.error( + f"[{self.workspace}] Error retrieving vector data for IDs {ids}: {e}" + ) + return [] + + async def get_vectors_by_ids(self, ids: list[str]) -> dict[str, list[float]]: + """Get vectors by their IDs, returning only ID and vector data for efficiency + + Args: + ids: List of unique identifiers + + Returns: + Dictionary mapping IDs to their vector embeddings + Format: {id: [vector_values], ...} + """ + if not ids: + return {} + + table_name = namespace_to_table_name(self.namespace) + if not table_name: + logger.error( + f"[{self.workspace}] Unknown namespace for vector lookup: {self.namespace}" + ) + return {} + + ids_str = ",".join([f"'{id}'" for id in ids]) + query = f"SELECT id, content_vector FROM {table_name} WHERE workspace=$1 AND id IN ({ids_str})" + params = {"workspace": self.workspace} + + try: + results = await self.db.query(query, list(params.values()), multirows=True) + vectors_dict = {} + + for result in results: + if result and "content_vector" in result and "id" in result: + try: + # Parse JSON string to get vector as list of floats + vector_data = json.loads(result["content_vector"]) + if isinstance(vector_data, list): + vectors_dict[result["id"]] = vector_data + except (json.JSONDecodeError, TypeError) as e: + logger.warning( + f"[{self.workspace}] Failed to parse vector data for ID {result['id']}: {e}" + ) + + return vectors_dict + except Exception as e: + logger.error( + f"[{self.workspace}] Error retrieving vectors by IDs from {self.namespace}: {e}" + ) + return {} + + async def drop(self) -> dict[str, str]: + """Drop the storage""" + async with get_storage_lock(): + try: + table_name = namespace_to_table_name(self.namespace) + if not table_name: + return { + "status": "error", + "message": f"Unknown namespace: {self.namespace}", + } + + drop_sql = SQL_TEMPLATES["drop_specifiy_table_workspace"].format( + table_name=table_name + ) + await self.db.execute(drop_sql, {"workspace": self.workspace}) + return {"status": "success", "message": "data dropped"} + except Exception as e: + return {"status": "error", "message": str(e)} + + +@final +@dataclass +class PGDocStatusStorage(DocStatusStorage): + db: PostgreSQLDB = field(default=None) + + def _format_datetime_with_timezone(self, dt): + """Convert datetime to ISO format string with timezone info""" + if dt is None: + return None + # If no timezone info, assume it's UTC time (as stored in database) + if dt.tzinfo is None: + dt = dt.replace(tzinfo=timezone.utc) + # If datetime already has timezone info, keep it as is + return dt.isoformat() + + async def initialize(self): + async with get_data_init_lock(): + if self.db is None: + self.db = await ClientManager.get_client() + + # Implement workspace priority: PostgreSQLDB.workspace > self.workspace > "default" + if self.db.workspace: + # Use PostgreSQLDB's workspace (highest priority) + self.workspace = self.db.workspace + elif hasattr(self, "workspace") and self.workspace: + # Use storage class's workspace (medium priority) + pass + else: + # Use "default" for compatibility (lowest priority) + self.workspace = "default" + + async def finalize(self): + async with get_storage_lock(): + if self.db is not None: + await ClientManager.release_client(self.db) + self.db = None + + async def filter_keys(self, keys: set[str]) -> set[str]: + """Filter out duplicated content""" + if not keys: + return set() + + table_name = namespace_to_table_name(self.namespace) + sql = f"SELECT id FROM {table_name} WHERE workspace=$1 AND id = ANY($2)" + params = {"workspace": self.workspace, "ids": list(keys)} + try: + res = await self.db.query(sql, list(params.values()), multirows=True) + if res: + exist_keys = [key["id"] for key in res] + else: + exist_keys = [] + new_keys = set([s for s in keys if s not in exist_keys]) + # print(f"keys: {keys}") + # print(f"new_keys: {new_keys}") + return new_keys + except Exception as e: + logger.error( + f"[{self.workspace}] PostgreSQL database,\nsql:{sql},\nparams:{params},\nerror:{e}" + ) + raise + + async def get_by_id(self, id: str) -> Union[dict[str, Any], None]: + sql = "select * from LIGHTRAG_DOC_STATUS where workspace=$1 and id=$2" + params = {"workspace": self.workspace, "id": id} + result = await self.db.query(sql, list(params.values()), True) + if result is None or result == []: + return None + else: + # Parse chunks_list JSON string back to list + chunks_list = result[0].get("chunks_list", []) + if isinstance(chunks_list, str): + try: + chunks_list = json.loads(chunks_list) + except json.JSONDecodeError: + chunks_list = [] + + # Parse metadata JSON string back to dict + metadata = result[0].get("metadata", {}) + if isinstance(metadata, str): + try: + metadata = json.loads(metadata) + except json.JSONDecodeError: + metadata = {} + + # Convert datetime objects to ISO format strings with timezone info + created_at = self._format_datetime_with_timezone(result[0]["created_at"]) + updated_at = self._format_datetime_with_timezone(result[0]["updated_at"]) + + return dict( + content_length=result[0]["content_length"], + content_summary=result[0]["content_summary"], + status=result[0]["status"], + chunks_count=result[0]["chunks_count"], + created_at=created_at, + updated_at=updated_at, + file_path=result[0]["file_path"], + chunks_list=chunks_list, + metadata=metadata, + error_msg=result[0].get("error_msg"), + track_id=result[0].get("track_id"), + ) + + async def get_by_ids(self, ids: list[str]) -> list[dict[str, Any]]: + """Get doc_chunks data by multiple IDs.""" + if not ids: + return [] + + sql = "SELECT * FROM LIGHTRAG_DOC_STATUS WHERE workspace=$1 AND id = ANY($2)" + params = {"workspace": self.workspace, "ids": ids} + + results = await self.db.query(sql, list(params.values()), True) + + if not results: + return [] + + processed_map: dict[str, dict[str, Any]] = {} + for row in results: + # Parse chunks_list JSON string back to list + chunks_list = row.get("chunks_list", []) + if isinstance(chunks_list, str): + try: + chunks_list = json.loads(chunks_list) + except json.JSONDecodeError: + chunks_list = [] + + # Parse metadata JSON string back to dict + metadata = row.get("metadata", {}) + if isinstance(metadata, str): + try: + metadata = json.loads(metadata) + except json.JSONDecodeError: + metadata = {} + + # Convert datetime objects to ISO format strings with timezone info + created_at = self._format_datetime_with_timezone(row["created_at"]) + updated_at = self._format_datetime_with_timezone(row["updated_at"]) + + processed_map[str(row.get("id"))] = { + "content_length": row["content_length"], + "content_summary": row["content_summary"], + "status": row["status"], + "chunks_count": row["chunks_count"], + "created_at": created_at, + "updated_at": updated_at, + "file_path": row["file_path"], + "chunks_list": chunks_list, + "metadata": metadata, + "error_msg": row.get("error_msg"), + "track_id": row.get("track_id"), + } + + ordered_results: list[dict[str, Any] | None] = [] + for requested_id in ids: + ordered_results.append(processed_map.get(str(requested_id))) + + return ordered_results + + async def get_doc_by_file_path(self, file_path: str) -> Union[dict[str, Any], None]: + """Get document by file path + + Args: + file_path: The file path to search for + + Returns: + Union[dict[str, Any], None]: Document data if found, None otherwise + Returns the same format as get_by_id method + """ + sql = "select * from LIGHTRAG_DOC_STATUS where workspace=$1 and file_path=$2" + params = {"workspace": self.workspace, "file_path": file_path} + result = await self.db.query(sql, list(params.values()), True) + + if result is None or result == []: + return None + else: + # Parse chunks_list JSON string back to list + chunks_list = result[0].get("chunks_list", []) + if isinstance(chunks_list, str): + try: + chunks_list = json.loads(chunks_list) + except json.JSONDecodeError: + chunks_list = [] + + # Parse metadata JSON string back to dict + metadata = result[0].get("metadata", {}) + if isinstance(metadata, str): + try: + metadata = json.loads(metadata) + except json.JSONDecodeError: + metadata = {} + + # Convert datetime objects to ISO format strings with timezone info + created_at = self._format_datetime_with_timezone(result[0]["created_at"]) + updated_at = self._format_datetime_with_timezone(result[0]["updated_at"]) + + return dict( + content_length=result[0]["content_length"], + content_summary=result[0]["content_summary"], + status=result[0]["status"], + chunks_count=result[0]["chunks_count"], + created_at=created_at, + updated_at=updated_at, + file_path=result[0]["file_path"], + chunks_list=chunks_list, + metadata=metadata, + error_msg=result[0].get("error_msg"), + track_id=result[0].get("track_id"), + ) + + async def get_status_counts(self) -> dict[str, int]: + """Get counts of documents in each status""" + sql = """SELECT status as "status", COUNT(1) as "count" + FROM LIGHTRAG_DOC_STATUS + where workspace=$1 GROUP BY STATUS + """ + params = {"workspace": self.workspace} + result = await self.db.query(sql, list(params.values()), True) + counts = {} + for doc in result: + counts[doc["status"]] = doc["count"] + return counts + + async def get_docs_by_status( + self, status: DocStatus + ) -> dict[str, DocProcessingStatus]: + """all documents with a specific status""" + sql = "select * from LIGHTRAG_DOC_STATUS where workspace=$1 and status=$2" + params = {"workspace": self.workspace, "status": status.value} + result = await self.db.query(sql, list(params.values()), True) + + docs_by_status = {} + for element in result: + # Parse chunks_list JSON string back to list + chunks_list = element.get("chunks_list", []) + if isinstance(chunks_list, str): + try: + chunks_list = json.loads(chunks_list) + except json.JSONDecodeError: + chunks_list = [] + + # Parse metadata JSON string back to dict + metadata = element.get("metadata", {}) + if isinstance(metadata, str): + try: + metadata = json.loads(metadata) + except json.JSONDecodeError: + metadata = {} + # Ensure metadata is a dict + if not isinstance(metadata, dict): + metadata = {} + + # Safe handling for file_path + file_path = element.get("file_path") + if file_path is None: + file_path = "no-file-path" + + # Convert datetime objects to ISO format strings with timezone info + created_at = self._format_datetime_with_timezone(element["created_at"]) + updated_at = self._format_datetime_with_timezone(element["updated_at"]) + + docs_by_status[element["id"]] = DocProcessingStatus( + content_summary=element["content_summary"], + content_length=element["content_length"], + status=element["status"], + created_at=created_at, + updated_at=updated_at, + chunks_count=element["chunks_count"], + file_path=file_path, + chunks_list=chunks_list, + metadata=metadata, + error_msg=element.get("error_msg"), + track_id=element.get("track_id"), + ) + + return docs_by_status + + async def get_docs_by_track_id( + self, track_id: str + ) -> dict[str, DocProcessingStatus]: + """Get all documents with a specific track_id""" + sql = "select * from LIGHTRAG_DOC_STATUS where workspace=$1 and track_id=$2" + params = {"workspace": self.workspace, "track_id": track_id} + result = await self.db.query(sql, list(params.values()), True) + + docs_by_track_id = {} + for element in result: + # Parse chunks_list JSON string back to list + chunks_list = element.get("chunks_list", []) + if isinstance(chunks_list, str): + try: + chunks_list = json.loads(chunks_list) + except json.JSONDecodeError: + chunks_list = [] + + # Parse metadata JSON string back to dict + metadata = element.get("metadata", {}) + if isinstance(metadata, str): + try: + metadata = json.loads(metadata) + except json.JSONDecodeError: + metadata = {} + # Ensure metadata is a dict + if not isinstance(metadata, dict): + metadata = {} + + # Safe handling for file_path + file_path = element.get("file_path") + if file_path is None: + file_path = "no-file-path" + + # Convert datetime objects to ISO format strings with timezone info + created_at = self._format_datetime_with_timezone(element["created_at"]) + updated_at = self._format_datetime_with_timezone(element["updated_at"]) + + docs_by_track_id[element["id"]] = DocProcessingStatus( + content_summary=element["content_summary"], + content_length=element["content_length"], + status=element["status"], + created_at=created_at, + updated_at=updated_at, + chunks_count=element["chunks_count"], + file_path=file_path, + chunks_list=chunks_list, + track_id=element.get("track_id"), + metadata=metadata, + error_msg=element.get("error_msg"), + ) + + return docs_by_track_id + + async def get_docs_paginated( + self, + status_filter: DocStatus | None = None, + page: int = 1, + page_size: int = 50, + sort_field: str = "updated_at", + sort_direction: str = "desc", + ) -> tuple[list[tuple[str, DocProcessingStatus]], int]: + """Get documents with pagination support + + Args: + status_filter: Filter by document status, None for all statuses + page: Page number (1-based) + page_size: Number of documents per page (10-200) + sort_field: Field to sort by ('created_at', 'updated_at', 'id') + sort_direction: Sort direction ('asc' or 'desc') + + Returns: + Tuple of (list of (doc_id, DocProcessingStatus) tuples, total_count) + """ + # Validate parameters + if page < 1: + page = 1 + if page_size < 10: + page_size = 10 + elif page_size > 200: + page_size = 200 + + # Whitelist validation for sort_field to prevent SQL injection + allowed_sort_fields = {"created_at", "updated_at", "id", "file_path"} + if sort_field not in allowed_sort_fields: + sort_field = "updated_at" + + # Whitelist validation for sort_direction to prevent SQL injection + if sort_direction.lower() not in ["asc", "desc"]: + sort_direction = "desc" + else: + sort_direction = sort_direction.lower() + + # Calculate offset + offset = (page - 1) * page_size + + # Build parameterized query components + params = {"workspace": self.workspace} + param_count = 1 + + # Build WHERE clause with parameterized query + if status_filter is not None: + param_count += 1 + where_clause = "WHERE workspace=$1 AND status=$2" + params["status"] = status_filter.value + else: + where_clause = "WHERE workspace=$1" + + # Build ORDER BY clause using validated whitelist values + order_clause = f"ORDER BY {sort_field} {sort_direction.upper()}" + + # Query for total count + count_sql = f"SELECT COUNT(*) as total FROM LIGHTRAG_DOC_STATUS {where_clause}" + count_result = await self.db.query(count_sql, list(params.values())) + total_count = count_result["total"] if count_result else 0 + + # Query for paginated data with parameterized LIMIT and OFFSET + data_sql = f""" + SELECT * FROM LIGHTRAG_DOC_STATUS + {where_clause} + {order_clause} + LIMIT ${param_count + 1} OFFSET ${param_count + 2} + """ + params["limit"] = page_size + params["offset"] = offset + + result = await self.db.query(data_sql, list(params.values()), True) + + # Convert to (doc_id, DocProcessingStatus) tuples + documents = [] + for element in result: + doc_id = element["id"] + + # Parse chunks_list JSON string back to list + chunks_list = element.get("chunks_list", []) + if isinstance(chunks_list, str): + try: + chunks_list = json.loads(chunks_list) + except json.JSONDecodeError: + chunks_list = [] + + # Parse metadata JSON string back to dict + metadata = element.get("metadata", {}) + if isinstance(metadata, str): + try: + metadata = json.loads(metadata) + except json.JSONDecodeError: + metadata = {} + + # Convert datetime objects to ISO format strings with timezone info + created_at = self._format_datetime_with_timezone(element["created_at"]) + updated_at = self._format_datetime_with_timezone(element["updated_at"]) + + doc_status = DocProcessingStatus( + content_summary=element["content_summary"], + content_length=element["content_length"], + status=element["status"], + created_at=created_at, + updated_at=updated_at, + chunks_count=element["chunks_count"], + file_path=element["file_path"], + chunks_list=chunks_list, + track_id=element.get("track_id"), + metadata=metadata, + error_msg=element.get("error_msg"), + ) + documents.append((doc_id, doc_status)) + + return documents, total_count + + async def get_all_status_counts(self) -> dict[str, int]: + """Get counts of documents in each status for all documents + + Returns: + Dictionary mapping status names to counts, including 'all' field + """ + sql = """ + SELECT status, COUNT(*) as count + FROM LIGHTRAG_DOC_STATUS + WHERE workspace=$1 + GROUP BY status + """ + params = {"workspace": self.workspace} + result = await self.db.query(sql, list(params.values()), True) + + counts = {} + total_count = 0 + for row in result: + counts[row["status"]] = row["count"] + total_count += row["count"] + + # Add 'all' field with total count + counts["all"] = total_count + + return counts + + async def index_done_callback(self) -> None: + # PG handles persistence automatically + pass + + async def is_empty(self) -> bool: + """Check if the storage is empty for the current workspace and namespace + + Returns: + bool: True if storage is empty, False otherwise + """ + table_name = namespace_to_table_name(self.namespace) + if not table_name: + logger.error( + f"[{self.workspace}] Unknown namespace for is_empty check: {self.namespace}" + ) + return True + + sql = f"SELECT EXISTS(SELECT 1 FROM {table_name} WHERE workspace=$1 LIMIT 1) as has_data" + + try: + result = await self.db.query(sql, [self.workspace]) + return not result.get("has_data", False) if result else True + except Exception as e: + logger.error(f"[{self.workspace}] Error checking if storage is empty: {e}") + return True + + async def delete(self, ids: list[str]) -> None: + """Delete specific records from storage by their IDs + + Args: + ids (list[str]): List of document IDs to be deleted from storage + + Returns: + None + """ + if not ids: + return + + table_name = namespace_to_table_name(self.namespace) + if not table_name: + logger.error( + f"[{self.workspace}] Unknown namespace for deletion: {self.namespace}" + ) + return + + delete_sql = f"DELETE FROM {table_name} WHERE workspace=$1 AND id = ANY($2)" + + try: + await self.db.execute(delete_sql, {"workspace": self.workspace, "ids": ids}) + logger.debug( + f"[{self.workspace}] Successfully deleted {len(ids)} records from {self.namespace}" + ) + except Exception as e: + logger.error( + f"[{self.workspace}] Error while deleting records from {self.namespace}: {e}" + ) + + async def upsert(self, data: dict[str, dict[str, Any]]) -> None: + """Update or insert document status + + Args: + data: dictionary of document IDs and their status data + """ + logger.debug(f"[{self.workspace}] Inserting {len(data)} to {self.namespace}") + if not data: + return + + def parse_datetime(dt_str): + """Parse datetime and ensure it's stored as UTC time in database""" + if dt_str is None: + return None + if isinstance(dt_str, (datetime.date, datetime.datetime)): + # If it's a datetime object + if isinstance(dt_str, datetime.datetime): + # If no timezone info, assume it's UTC + if dt_str.tzinfo is None: + dt_str = dt_str.replace(tzinfo=timezone.utc) + # Convert to UTC and remove timezone info for storage + return dt_str.astimezone(timezone.utc).replace(tzinfo=None) + return dt_str + try: + # Process ISO format string with timezone + dt = datetime.datetime.fromisoformat(dt_str) + # If no timezone info, assume it's UTC + if dt.tzinfo is None: + dt = dt.replace(tzinfo=timezone.utc) + # Convert to UTC and remove timezone info for storage + return dt.astimezone(timezone.utc).replace(tzinfo=None) + except (ValueError, TypeError): + logger.warning( + f"[{self.workspace}] Unable to parse datetime string: {dt_str}" + ) + return None + + # Modified SQL to include created_at, updated_at, chunks_list, track_id, metadata, and error_msg in both INSERT and UPDATE operations + # All fields are updated from the input data in both INSERT and UPDATE cases + sql = """insert into LIGHTRAG_DOC_STATUS(workspace,id,content_summary,content_length,chunks_count,status,file_path,chunks_list,track_id,metadata,error_msg,created_at,updated_at) + values($1,$2,$3,$4,$5,$6,$7,$8,$9,$10,$11,$12,$13) + on conflict(id,workspace) do update set + content_summary = EXCLUDED.content_summary, + content_length = EXCLUDED.content_length, + chunks_count = EXCLUDED.chunks_count, + status = EXCLUDED.status, + file_path = EXCLUDED.file_path, + chunks_list = EXCLUDED.chunks_list, + track_id = EXCLUDED.track_id, + metadata = EXCLUDED.metadata, + error_msg = EXCLUDED.error_msg, + created_at = EXCLUDED.created_at, + updated_at = EXCLUDED.updated_at""" + for k, v in data.items(): + # Remove timezone information, store utc time in db + created_at = parse_datetime(v.get("created_at")) + updated_at = parse_datetime(v.get("updated_at")) + + # chunks_count, chunks_list, track_id, metadata, and error_msg are optional + await self.db.execute( + sql, + { + "workspace": self.workspace, + "id": k, + "content_summary": v["content_summary"], + "content_length": v["content_length"], + "chunks_count": v["chunks_count"] if "chunks_count" in v else -1, + "status": v["status"], + "file_path": v["file_path"], + "chunks_list": json.dumps(v.get("chunks_list", [])), + "track_id": v.get("track_id"), # Add track_id support + "metadata": json.dumps( + v.get("metadata", {}) + ), # Add metadata support + "error_msg": v.get("error_msg"), # Add error_msg support + "created_at": created_at, # Use the converted datetime object + "updated_at": updated_at, # Use the converted datetime object + }, + ) + + async def drop(self) -> dict[str, str]: + """Drop the storage""" + async with get_storage_lock(): + try: + table_name = namespace_to_table_name(self.namespace) + if not table_name: + return { + "status": "error", + "message": f"Unknown namespace: {self.namespace}", + } + + drop_sql = SQL_TEMPLATES["drop_specifiy_table_workspace"].format( + table_name=table_name + ) + await self.db.execute(drop_sql, {"workspace": self.workspace}) + return {"status": "success", "message": "data dropped"} + except Exception as e: + return {"status": "error", "message": str(e)} + + +class PGGraphQueryException(Exception): + """Exception for the AGE queries.""" + + def __init__(self, exception: Union[str, dict[str, Any]]) -> None: + if isinstance(exception, dict): + self.message = exception["message"] if "message" in exception else "unknown" + self.details = exception["details"] if "details" in exception else "unknown" + else: + self.message = exception + self.details = "unknown" + + def get_message(self) -> str: + return self.message + + def get_details(self) -> Any: + return self.details + + +@final +@dataclass +class PGGraphStorage(BaseGraphStorage): + def __post_init__(self): + # Graph name will be dynamically generated in initialize() based on workspace + self.db: PostgreSQLDB | None = None + + def _get_workspace_graph_name(self) -> str: + """ + Generate graph name based on workspace and namespace for data isolation. + Rules: + - If workspace is empty or "default": graph_name = namespace + - If workspace has other value: graph_name = workspace_namespace + + Args: + None + + Returns: + str: The graph name for the current workspace + """ + workspace = self.workspace + namespace = self.namespace + + if workspace and workspace.strip() and workspace.strip().lower() != "default": + # Ensure names comply with PostgreSQL identifier specifications + safe_workspace = re.sub(r"[^a-zA-Z0-9_]", "_", workspace.strip()) + safe_namespace = re.sub(r"[^a-zA-Z0-9_]", "_", namespace) + return f"{safe_workspace}_{safe_namespace}" + else: + # When the workspace is "default", use the namespace directly (for backward compatibility with legacy implementations) + return re.sub(r"[^a-zA-Z0-9_]", "_", namespace) + + @staticmethod + def _normalize_node_id(node_id: str) -> str: + """ + Normalize node ID to ensure special characters are properly handled in Cypher queries. + + Args: + node_id: The original node ID + + Returns: + Normalized node ID suitable for Cypher queries + """ + # Escape backslashes + normalized_id = node_id + normalized_id = normalized_id.replace("\\", "\\\\") + normalized_id = normalized_id.replace('"', '\\"') + return normalized_id + + async def initialize(self): + async with get_data_init_lock(): + if self.db is None: + self.db = await ClientManager.get_client() + + # Implement workspace priority: PostgreSQLDB.workspace > self.workspace > "default" + if self.db.workspace: + # Use PostgreSQLDB's workspace (highest priority) + self.workspace = self.db.workspace + elif hasattr(self, "workspace") and self.workspace: + # Use storage class's workspace (medium priority) + pass + else: + # Use "default" for compatibility (lowest priority) + self.workspace = "default" + + # Dynamically generate graph name based on workspace + self.graph_name = self._get_workspace_graph_name() + + # Log the graph initialization for debugging + logger.info( + f"[{self.workspace}] PostgreSQL Graph initialized: graph_name='{self.graph_name}'" + ) + + # Create AGE extension and configure graph environment once at initialization + async with self.db.pool.acquire() as connection: + # First ensure AGE extension is created + await PostgreSQLDB.configure_age_extension(connection) + + # Execute each statement separately and ignore errors + queries = [ + f"SELECT create_graph('{self.graph_name}')", + f"SELECT create_vlabel('{self.graph_name}', 'base');", + f"SELECT create_elabel('{self.graph_name}', 'DIRECTED');", + # f'CREATE INDEX CONCURRENTLY vertex_p_idx ON {self.graph_name}."_ag_label_vertex" (id)', + f'CREATE INDEX CONCURRENTLY vertex_idx_node_id ON {self.graph_name}."_ag_label_vertex" (ag_catalog.agtype_access_operator(properties, \'"entity_id"\'::agtype))', + # f'CREATE INDEX CONCURRENTLY edge_p_idx ON {self.graph_name}."_ag_label_edge" (id)', + f'CREATE INDEX CONCURRENTLY edge_sid_idx ON {self.graph_name}."_ag_label_edge" (start_id)', + f'CREATE INDEX CONCURRENTLY edge_eid_idx ON {self.graph_name}."_ag_label_edge" (end_id)', + f'CREATE INDEX CONCURRENTLY edge_seid_idx ON {self.graph_name}."_ag_label_edge" (start_id,end_id)', + f'CREATE INDEX CONCURRENTLY directed_p_idx ON {self.graph_name}."DIRECTED" (id)', + f'CREATE INDEX CONCURRENTLY directed_eid_idx ON {self.graph_name}."DIRECTED" (end_id)', + f'CREATE INDEX CONCURRENTLY directed_sid_idx ON {self.graph_name}."DIRECTED" (start_id)', + f'CREATE INDEX CONCURRENTLY directed_seid_idx ON {self.graph_name}."DIRECTED" (start_id,end_id)', + f'CREATE INDEX CONCURRENTLY entity_p_idx ON {self.graph_name}."base" (id)', + f'CREATE INDEX CONCURRENTLY entity_idx_node_id ON {self.graph_name}."base" (ag_catalog.agtype_access_operator(properties, \'"entity_id"\'::agtype))', + f'CREATE INDEX CONCURRENTLY entity_node_id_gin_idx ON {self.graph_name}."base" using gin(properties)', + f'ALTER TABLE {self.graph_name}."DIRECTED" CLUSTER ON directed_sid_idx', + ] + + for query in queries: + # Use the new flag to silently ignore "already exists" errors + # at the source, preventing log spam. + await self.db.execute( + query, + upsert=True, + ignore_if_exists=True, # Pass the new flag + with_age=True, + graph_name=self.graph_name, + ) + + async def finalize(self): + async with get_graph_db_lock(): + if self.db is not None: + await ClientManager.release_client(self.db) + self.db = None + + async def index_done_callback(self) -> None: + # PG handles persistence automatically + pass + + @staticmethod + def _record_to_dict(record: asyncpg.Record) -> dict[str, Any]: + """ + Convert a record returned from an age query to a dictionary + + Args: + record (): a record from an age query result + + Returns: + dict[str, Any]: a dictionary representation of the record where + the dictionary key is the field name and the value is the + value converted to a python type + """ + + @staticmethod + def parse_agtype_string(agtype_str: str) -> tuple[str, str]: + """ + Parse agtype string precisely, separating JSON content and type identifier + + Args: + agtype_str: String like '{"json": "content"}::vertex' + + Returns: + (json_content, type_identifier) + """ + if not isinstance(agtype_str, str) or "::" not in agtype_str: + return agtype_str, "" + + # Find the last :: from the right, which is the start of type identifier + last_double_colon = agtype_str.rfind("::") + + if last_double_colon == -1: + return agtype_str, "" + + # Separate JSON content and type identifier + json_content = agtype_str[:last_double_colon] + type_identifier = agtype_str[last_double_colon + 2 :] + + return json_content, type_identifier + + @staticmethod + def safe_json_parse(json_str: str, context: str = "") -> dict: + """ + Safe JSON parsing with simplified error logging + """ + try: + return json.loads(json_str) + except json.JSONDecodeError as e: + logger.error(f"JSON parsing failed ({context}): {e}") + logger.error(f"Raw data (first 100 chars): {repr(json_str[:100])}") + logger.error(f"Error position: line {e.lineno}, column {e.colno}") + return None + + # result holder + d = {} + + # prebuild a mapping of vertex_id to vertex mappings to be used + # later to build edges + vertices = {} + + # First pass: preprocess vertices + for k in record.keys(): + v = record[k] + if isinstance(v, str) and "::" in v: + if v.startswith("[") and v.endswith("]"): + # Handle vertex arrays + json_content, type_id = parse_agtype_string(v) + if type_id == "vertex": + vertexes = safe_json_parse( + json_content, f"vertices array for {k}" + ) + if vertexes: + for vertex in vertexes: + vertices[vertex["id"]] = vertex.get("properties") + else: + # Handle single vertex + json_content, type_id = parse_agtype_string(v) + if type_id == "vertex": + vertex = safe_json_parse(json_content, f"single vertex for {k}") + if vertex: + vertices[vertex["id"]] = vertex.get("properties") + + # Second pass: process all fields + for k in record.keys(): + v = record[k] + if isinstance(v, str) and "::" in v: + if v.startswith("[") and v.endswith("]"): + # Handle array types + json_content, type_id = parse_agtype_string(v) + if type_id in ["vertex", "edge"]: + parsed_data = safe_json_parse( + json_content, f"array {type_id} for field {k}" + ) + d[k] = parsed_data if parsed_data is not None else None + else: + logger.warning(f"Unknown array type: {type_id}") + d[k] = None + else: + # Handle single objects + json_content, type_id = parse_agtype_string(v) + if type_id in ["vertex", "edge"]: + parsed_data = safe_json_parse( + json_content, f"single {type_id} for field {k}" + ) + d[k] = parsed_data if parsed_data is not None else None + else: + # May be other types of agtype data, keep as is + d[k] = v + else: + d[k] = v # Keep as string + + return d + + @staticmethod + def _format_properties( + properties: dict[str, Any], _id: Union[str, None] = None + ) -> str: + """ + Convert a dictionary of properties to a string representation that + can be used in a cypher query insert/merge statement. + + Args: + properties (dict[str,str]): a dictionary containing node/edge properties + _id (Union[str, None]): the id of the node or None if none exists + + Returns: + str: the properties dictionary as a properly formatted string + """ + props = [] + # wrap property key in backticks to escape + for k, v in properties.items(): + prop = f"`{k}`: {json.dumps(v)}" + props.append(prop) + if _id is not None and "id" not in properties: + props.append( + f"id: {json.dumps(_id)}" if isinstance(_id, str) else f"id: {_id}" + ) + return "{" + ", ".join(props) + "}" + + async def _query( + self, + query: str, + readonly: bool = True, + upsert: bool = False, + params: dict[str, Any] | None = None, + ) -> list[dict[str, Any]]: + """ + Query the graph by taking a cypher query, converting it to an + age compatible query, executing it and converting the result + + Args: + query (str): a cypher query to be executed + + Returns: + list[dict[str, Any]]: a list of dictionaries containing the result set + """ + try: + if readonly: + data = await self.db.query( + query, + list(params.values()) if params else None, + multirows=True, + with_age=True, + graph_name=self.graph_name, + ) + else: + data = await self.db.execute( + query, + upsert=upsert, + with_age=True, + graph_name=self.graph_name, + ) + + except Exception as e: + raise PGGraphQueryException( + { + "message": f"Error executing graph query: {query}", + "wrapped": query, + "detail": repr(e), + "error_type": e.__class__.__name__, + } + ) from e + + if data is None: + result = [] + # decode records + else: + result = [self._record_to_dict(d) for d in data] + + return result + + async def has_node(self, node_id: str) -> bool: + query = f""" + SELECT EXISTS ( + SELECT 1 + FROM {self.graph_name}.base + WHERE ag_catalog.agtype_access_operator( + VARIADIC ARRAY[properties, '"entity_id"'::agtype] + ) = (to_json($1::text)::text)::agtype + LIMIT 1 + ) AS node_exists; + """ + + params = {"node_id": node_id} + row = (await self._query(query, params=params))[0] + return bool(row["node_exists"]) + + async def has_edge(self, source_node_id: str, target_node_id: str) -> bool: + query = f""" + WITH a AS ( + SELECT id AS vid + FROM {self.graph_name}.base + WHERE ag_catalog.agtype_access_operator( + VARIADIC ARRAY[properties, '"entity_id"'::agtype] + ) = (to_json($1::text)::text)::agtype + ), + b AS ( + SELECT id AS vid + FROM {self.graph_name}.base + WHERE ag_catalog.agtype_access_operator( + VARIADIC ARRAY[properties, '"entity_id"'::agtype] + ) = (to_json($2::text)::text)::agtype + ) + SELECT EXISTS ( + SELECT 1 + FROM {self.graph_name}."DIRECTED" d + JOIN a ON d.start_id = a.vid + JOIN b ON d.end_id = b.vid + LIMIT 1 + ) + OR EXISTS ( + SELECT 1 + FROM {self.graph_name}."DIRECTED" d + JOIN a ON d.end_id = a.vid + JOIN b ON d.start_id = b.vid + LIMIT 1 + ) AS edge_exists; + """ + params = { + "source_node_id": source_node_id, + "target_node_id": target_node_id, + } + row = (await self._query(query, params=params))[0] + return bool(row["edge_exists"]) + + async def get_node(self, node_id: str) -> dict[str, str] | None: + """Get node by its label identifier, return only node properties""" + + result = await self.get_nodes_batch(node_ids=[node_id]) + if result and node_id in result: + return result[node_id] + return None + + async def node_degree(self, node_id: str) -> int: + result = await self.node_degrees_batch(node_ids=[node_id]) + if result and node_id in result: + return result[node_id] + + async def edge_degree(self, src_id: str, tgt_id: str) -> int: + result = await self.edge_degrees_batch(edges=[(src_id, tgt_id)]) + if result and (src_id, tgt_id) in result: + return result[(src_id, tgt_id)] + + async def get_edge( + self, source_node_id: str, target_node_id: str + ) -> dict[str, str] | None: + """Get edge properties between two nodes""" + result = await self.get_edges_batch( + [{"src": source_node_id, "tgt": target_node_id}] + ) + if result and (source_node_id, target_node_id) in result: + return result[(source_node_id, target_node_id)] + return None + + async def get_node_edges(self, source_node_id: str) -> list[tuple[str, str]] | None: + """ + Retrieves all edges (relationships) for a particular node identified by its label. + :return: list of dictionaries containing edge information + """ + label = self._normalize_node_id(source_node_id) + + query = """SELECT * FROM cypher('%s', $$ + MATCH (n:base {entity_id: "%s"}) + OPTIONAL MATCH (n)-[]-(connected:base) + RETURN n.entity_id AS source_id, connected.entity_id AS connected_id + $$) AS (source_id text, connected_id text)""" % ( + self.graph_name, + label, + ) + + results = await self._query(query) + edges = [] + for record in results: + source_id = record["source_id"] + connected_id = record["connected_id"] + + if source_id and connected_id: + edges.append((source_id, connected_id)) + + return edges + + @retry( + stop=stop_after_attempt(3), + wait=wait_exponential(multiplier=1, min=4, max=10), + retry=retry_if_exception_type((PGGraphQueryException,)), + ) + async def upsert_node(self, node_id: str, node_data: dict[str, str]) -> None: + """ + Upsert a node in the Neo4j database. + + Args: + node_id: The unique identifier for the node (used as label) + node_data: Dictionary of node properties + """ + if "entity_id" not in node_data: + raise ValueError( + "PostgreSQL: node properties must contain an 'entity_id' field" + ) + + label = self._normalize_node_id(node_id) + properties = self._format_properties(node_data) + + query = """SELECT * FROM cypher('%s', $$ + MERGE (n:base {entity_id: "%s"}) + SET n += %s + RETURN n + $$) AS (n agtype)""" % ( + self.graph_name, + label, + properties, + ) + + try: + await self._query(query, readonly=False, upsert=True) + + except Exception: + logger.error( + f"[{self.workspace}] POSTGRES, upsert_node error on node_id: `{node_id}`" + ) + raise + + @retry( + stop=stop_after_attempt(3), + wait=wait_exponential(multiplier=1, min=4, max=10), + retry=retry_if_exception_type((PGGraphQueryException,)), + ) + async def upsert_edge( + self, source_node_id: str, target_node_id: str, edge_data: dict[str, str] + ) -> None: + """ + Upsert an edge and its properties between two nodes identified by their labels. + + Args: + source_node_id (str): Label of the source node (used as identifier) + target_node_id (str): Label of the target node (used as identifier) + edge_data (dict): dictionary of properties to set on the edge + """ + src_label = self._normalize_node_id(source_node_id) + tgt_label = self._normalize_node_id(target_node_id) + edge_properties = self._format_properties(edge_data) + + query = """SELECT * FROM cypher('%s', $$ + MATCH (source:base {entity_id: "%s"}) + WITH source + MATCH (target:base {entity_id: "%s"}) + MERGE (source)-[r:DIRECTED]-(target) + SET r += %s + SET r += %s + RETURN r + $$) AS (r agtype)""" % ( + self.graph_name, + src_label, + tgt_label, + edge_properties, + edge_properties, # https://github.com/HKUDS/LightRAG/issues/1438#issuecomment-2826000195 + ) + + try: + await self._query(query, readonly=False, upsert=True) + + except Exception: + logger.error( + f"[{self.workspace}] POSTGRES, upsert_edge error on edge: `{source_node_id}`-`{target_node_id}`" + ) + raise + + async def delete_node(self, node_id: str) -> None: + """ + Delete a node from the graph. + + Args: + node_id (str): The ID of the node to delete. + """ + label = self._normalize_node_id(node_id) + + query = """SELECT * FROM cypher('%s', $$ + MATCH (n:base {entity_id: "%s"}) + DETACH DELETE n + $$) AS (n agtype)""" % (self.graph_name, label) + + try: + await self._query(query, readonly=False) + except Exception as e: + logger.error(f"[{self.workspace}] Error during node deletion: {e}") + raise + + async def remove_nodes(self, node_ids: list[str]) -> None: + """ + Remove multiple nodes from the graph. + + Args: + node_ids (list[str]): A list of node IDs to remove. + """ + node_ids = [self._normalize_node_id(node_id) for node_id in node_ids] + node_id_list = ", ".join([f'"{node_id}"' for node_id in node_ids]) + + query = """SELECT * FROM cypher('%s', $$ + MATCH (n:base) + WHERE n.entity_id IN [%s] + DETACH DELETE n + $$) AS (n agtype)""" % (self.graph_name, node_id_list) + + try: + await self._query(query, readonly=False) + except Exception as e: + logger.error(f"[{self.workspace}] Error during node removal: {e}") + raise + + async def remove_edges(self, edges: list[tuple[str, str]]) -> None: + """ + Remove multiple edges from the graph. + + Args: + edges (list[tuple[str, str]]): A list of edges to remove, where each edge is a tuple of (source_node_id, target_node_id). + """ + for source, target in edges: + src_label = self._normalize_node_id(source) + tgt_label = self._normalize_node_id(target) + + query = """SELECT * FROM cypher('%s', $$ + MATCH (a:base {entity_id: "%s"})-[r]-(b:base {entity_id: "%s"}) + DELETE r + $$) AS (r agtype)""" % (self.graph_name, src_label, tgt_label) + + try: + await self._query(query, readonly=False) + logger.debug( + f"[{self.workspace}] Deleted edge from '{source}' to '{target}'" + ) + except Exception as e: + logger.error(f"[{self.workspace}] Error during edge deletion: {str(e)}") + raise + + async def get_nodes_batch( + self, node_ids: list[str], batch_size: int = 1000 + ) -> dict[str, dict]: + """ + Retrieve multiple nodes in one query using UNWIND. + + Args: + node_ids: List of node entity IDs to fetch. + batch_size: Batch size for the query + + Returns: + A dictionary mapping each node_id to its node data (or None if not found). + """ + if not node_ids: + return {} + + seen: set[str] = set() + unique_ids: list[str] = [] + lookup: dict[str, str] = {} + requested: set[str] = set() + for nid in node_ids: + if nid not in seen: + seen.add(nid) + unique_ids.append(nid) + requested.add(nid) + lookup[nid] = nid + lookup[self._normalize_node_id(nid)] = nid + + # Build result dictionary + nodes_dict = {} + + for i in range(0, len(unique_ids), batch_size): + batch = unique_ids[i : i + batch_size] + + query = f""" + WITH input(v, ord) AS ( + SELECT v, ord + FROM unnest($1::text[]) WITH ORDINALITY AS t(v, ord) + ), + ids(node_id, ord) AS ( + SELECT (to_json(v)::text)::agtype AS node_id, ord + FROM input + ) + SELECT i.node_id::text AS node_id, + b.properties + FROM {self.graph_name}.base AS b + JOIN ids i + ON ag_catalog.agtype_access_operator( + VARIADIC ARRAY[b.properties, '"entity_id"'::agtype] + ) = i.node_id + ORDER BY i.ord; + """ + + results = await self._query(query, params={"ids": batch}) + + for result in results: + if result["node_id"] and result["properties"]: + node_dict = result["properties"] + + # Process string result, parse it to JSON dictionary + if isinstance(node_dict, str): + try: + node_dict = json.loads(node_dict) + except json.JSONDecodeError: + logger.warning( + f"[{self.workspace}] Failed to parse node string in batch: {node_dict}" + ) + + node_key = result["node_id"] + original_key = lookup.get(node_key) + if original_key is None: + logger.warning( + f"[{self.workspace}] Node {node_key} not found in lookup map" + ) + original_key = node_key + if original_key in requested: + nodes_dict[original_key] = node_dict + + return nodes_dict + + async def node_degrees_batch( + self, node_ids: list[str], batch_size: int = 500 + ) -> dict[str, int]: + """ + Retrieve the degree for multiple nodes in a single query using UNWIND. + Calculates the total degree by counting distinct relationships. + Uses separate queries for outgoing and incoming edges. + + Args: + node_ids: List of node labels (entity_id values) to look up. + batch_size: Batch size for the query + + Returns: + A dictionary mapping each node_id to its degree (total number of relationships). + If a node is not found, its degree will be set to 0. + """ + if not node_ids: + return {} + + seen: set[str] = set() + unique_ids: list[str] = [] + lookup: dict[str, str] = {} + requested: set[str] = set() + for nid in node_ids: + if nid not in seen: + seen.add(nid) + unique_ids.append(nid) + requested.add(nid) + lookup[nid] = nid + lookup[self._normalize_node_id(nid)] = nid + + out_degrees = {} + in_degrees = {} + + for i in range(0, len(unique_ids), batch_size): + batch = unique_ids[i : i + batch_size] + + query = f""" + WITH input(v, ord) AS ( + SELECT v, ord + FROM unnest($1::text[]) WITH ORDINALITY AS t(v, ord) + ), + ids(node_id, ord) AS ( + SELECT (to_json(v)::text)::agtype AS node_id, ord + FROM input + ), + vids AS ( + SELECT b.id AS vid, i.node_id, i.ord + FROM {self.graph_name}.base AS b + JOIN ids i + ON ag_catalog.agtype_access_operator( + VARIADIC ARRAY[b.properties, '"entity_id"'::agtype] + ) = i.node_id + ), + deg_out AS ( + SELECT d.start_id AS vid, COUNT(*)::bigint AS out_degree + FROM {self.graph_name}."DIRECTED" AS d + JOIN vids v ON v.vid = d.start_id + GROUP BY d.start_id + ), + deg_in AS ( + SELECT d.end_id AS vid, COUNT(*)::bigint AS in_degree + FROM {self.graph_name}."DIRECTED" AS d + JOIN vids v ON v.vid = d.end_id + GROUP BY d.end_id + ) + SELECT v.node_id::text AS node_id, + COALESCE(o.out_degree, 0) AS out_degree, + COALESCE(n.in_degree, 0) AS in_degree + FROM vids v + LEFT JOIN deg_out o ON o.vid = v.vid + LEFT JOIN deg_in n ON n.vid = v.vid + ORDER BY v.ord; + """ + + combined_results = await self._query(query, params={"ids": batch}) + + for row in combined_results: + node_id = row["node_id"] + if not node_id: + continue + node_key = node_id + original_key = lookup.get(node_key) + if original_key is None: + logger.warning( + f"[{self.workspace}] Node {node_key} not found in lookup map" + ) + original_key = node_key + if original_key in requested: + out_degrees[original_key] = int(row.get("out_degree", 0) or 0) + in_degrees[original_key] = int(row.get("in_degree", 0) or 0) + + degrees_dict = {} + for node_id in node_ids: + out_degree = out_degrees.get(node_id, 0) + in_degree = in_degrees.get(node_id, 0) + degrees_dict[node_id] = out_degree + in_degree + + return degrees_dict + + async def edge_degrees_batch( + self, edges: list[tuple[str, str]] + ) -> dict[tuple[str, str], int]: + """ + Calculate the combined degree for each edge (sum of the source and target node degrees) + in batch using the already implemented node_degrees_batch. + + Args: + edges: List of (source_node_id, target_node_id) tuples + + Returns: + Dictionary mapping edge tuples to their combined degrees + """ + if not edges: + return {} + + # Use node_degrees_batch to get all node degrees efficiently + all_nodes = set() + for src, tgt in edges: + all_nodes.add(src) + all_nodes.add(tgt) + + node_degrees = await self.node_degrees_batch(list(all_nodes)) + + # Calculate edge degrees + edge_degrees_dict = {} + for src, tgt in edges: + src_degree = node_degrees.get(src, 0) + tgt_degree = node_degrees.get(tgt, 0) + edge_degrees_dict[(src, tgt)] = src_degree + tgt_degree + + return edge_degrees_dict + + async def get_edges_batch( + self, pairs: list[dict[str, str]], batch_size: int = 500 + ) -> dict[tuple[str, str], dict]: + """ + Retrieve edge properties for multiple (src, tgt) pairs in one query. + Get forward and backward edges seperately and merge them before return + + Args: + pairs: List of dictionaries, e.g. [{"src": "node1", "tgt": "node2"}, ...] + batch_size: Batch size for the query + + Returns: + A dictionary mapping (src, tgt) tuples to their edge properties. + """ + if not pairs: + return {} + + seen = set() + uniq_pairs: list[dict[str, str]] = [] + for p in pairs: + s = self._normalize_node_id(p["src"]) + t = self._normalize_node_id(p["tgt"]) + key = (s, t) + if s and t and key not in seen: + seen.add(key) + uniq_pairs.append(p) + + edges_dict: dict[tuple[str, str], dict] = {} + + for i in range(0, len(uniq_pairs), batch_size): + batch = uniq_pairs[i : i + batch_size] + + pairs = [{"src": p["src"], "tgt": p["tgt"]} for p in batch] + + forward_cypher = """ + UNWIND $pairs AS p + WITH p.src AS src_eid, p.tgt AS tgt_eid + MATCH (a:base {entity_id: src_eid}) + MATCH (b:base {entity_id: tgt_eid}) + MATCH (a)-[r]->(b) + RETURN src_eid AS source, tgt_eid AS target, properties(r) AS edge_properties""" + backward_cypher = """ + UNWIND $pairs AS p + WITH p.src AS src_eid, p.tgt AS tgt_eid + MATCH (a:base {entity_id: src_eid}) + MATCH (b:base {entity_id: tgt_eid}) + MATCH (a)<-[r]-(b) + RETURN src_eid AS source, tgt_eid AS target, properties(r) AS edge_properties""" + + def dollar_quote(s: str, tag_prefix="AGE"): + s = "" if s is None else str(s) + for i in itertools.count(1): + tag = f"{tag_prefix}{i}" + wrapper = f"${tag}$" + if wrapper not in s: + return f"{wrapper}{s}{wrapper}" + + sql_fwd = f""" + SELECT * FROM cypher({dollar_quote(self.graph_name)}::name, + {dollar_quote(forward_cypher)}::cstring, + $1::agtype) + AS (source text, target text, edge_properties agtype) + """ + + sql_bwd = f""" + SELECT * FROM cypher({dollar_quote(self.graph_name)}::name, + {dollar_quote(backward_cypher)}::cstring, + $1::agtype) + AS (source text, target text, edge_properties agtype) + """ + + pg_params = {"params": json.dumps({"pairs": pairs}, ensure_ascii=False)} + + forward_results = await self._query(sql_fwd, params=pg_params) + backward_results = await self._query(sql_bwd, params=pg_params) + + for result in forward_results: + if result["source"] and result["target"] and result["edge_properties"]: + edge_props = result["edge_properties"] + + # Process string result, parse it to JSON dictionary + if isinstance(edge_props, str): + try: + edge_props = json.loads(edge_props) + except json.JSONDecodeError: + logger.warning( + f"[{self.workspace}]Failed to parse edge properties string: {edge_props}" + ) + continue + + edges_dict[(result["source"], result["target"])] = edge_props + + for result in backward_results: + if result["source"] and result["target"] and result["edge_properties"]: + edge_props = result["edge_properties"] + + # Process string result, parse it to JSON dictionary + if isinstance(edge_props, str): + try: + edge_props = json.loads(edge_props) + except json.JSONDecodeError: + logger.warning( + f"[{self.workspace}] Failed to parse edge properties string: {edge_props}" + ) + continue + + edges_dict[(result["source"], result["target"])] = edge_props + + return edges_dict + + async def get_nodes_edges_batch( + self, node_ids: list[str], batch_size: int = 500 + ) -> dict[str, list[tuple[str, str]]]: + """ + Get all edges (both outgoing and incoming) for multiple nodes in a single batch operation. + + Args: + node_ids: List of node IDs to get edges for + batch_size: Batch size for the query + + Returns: + Dictionary mapping node IDs to lists of (source, target) edge tuples + """ + if not node_ids: + return {} + + seen = set() + unique_ids: list[str] = [] + for nid in node_ids: + n = self._normalize_node_id(nid) + if n and n not in seen: + seen.add(n) + unique_ids.append(n) + + edges_norm: dict[str, list[tuple[str, str]]] = {n: [] for n in unique_ids} + + for i in range(0, len(unique_ids), batch_size): + batch = unique_ids[i : i + batch_size] + # Format node IDs for the query + formatted_ids = ", ".join([f'"{n}"' for n in batch]) + + outgoing_query = """SELECT * FROM cypher('%s', $$ + UNWIND [%s] AS node_id + MATCH (n:base {entity_id: node_id}) + OPTIONAL MATCH (n:base)-[]->(connected:base) + RETURN node_id, connected.entity_id AS connected_id + $$) AS (node_id text, connected_id text)""" % ( + self.graph_name, + formatted_ids, + ) + + incoming_query = """SELECT * FROM cypher('%s', $$ + UNWIND [%s] AS node_id + MATCH (n:base {entity_id: node_id}) + OPTIONAL MATCH (n:base)<-[]-(connected:base) + RETURN node_id, connected.entity_id AS connected_id + $$) AS (node_id text, connected_id text)""" % ( + self.graph_name, + formatted_ids, + ) + + outgoing_results = await self._query(outgoing_query) + incoming_results = await self._query(incoming_query) + + for result in outgoing_results: + if result["node_id"] and result["connected_id"]: + edges_norm[result["node_id"]].append( + (result["node_id"], result["connected_id"]) + ) + + for result in incoming_results: + if result["node_id"] and result["connected_id"]: + edges_norm[result["node_id"]].append( + (result["connected_id"], result["node_id"]) + ) + + out: dict[str, list[tuple[str, str]]] = {} + for orig in node_ids: + n = self._normalize_node_id(orig) + out[orig] = edges_norm.get(n, []) + + return out + + async def get_all_labels(self) -> list[str]: + """ + Get all labels (node IDs) in the graph. + + Returns: + list[str]: A list of all labels in the graph. + """ + query = ( + """SELECT * FROM cypher('%s', $$ + MATCH (n:base) + WHERE n.entity_id IS NOT NULL + RETURN DISTINCT n.entity_id AS label + ORDER BY n.entity_id + $$) AS (label text)""" + % self.graph_name + ) + + results = await self._query(query) + labels = [] + for result in results: + if result and isinstance(result, dict) and "label" in result: + labels.append(result["label"]) + return labels + + async def _bfs_subgraph( + self, node_label: str, max_depth: int, max_nodes: int + ) -> KnowledgeGraph: + """ + Implements a true breadth-first search algorithm for subgraph retrieval. + This method is used as a fallback when the standard Cypher query is too slow + or when we need to guarantee BFS ordering. + + Args: + node_label: Label of the starting node + max_depth: Maximum depth of the subgraph + max_nodes: Maximum number of nodes to return + + Returns: + KnowledgeGraph object containing nodes and edges + """ + from collections import deque + + result = KnowledgeGraph() + visited_nodes = set() + visited_node_ids = set() + visited_edges = set() + visited_edge_pairs = set() + + # Get starting node data + label = self._normalize_node_id(node_label) + query = """SELECT * FROM cypher('%s', $$ + MATCH (n:base {entity_id: "%s"}) + RETURN id(n) as node_id, n + $$) AS (node_id bigint, n agtype)""" % (self.graph_name, label) + + node_result = await self._query(query) + if not node_result or not node_result[0].get("n"): + return result + + # Create initial KnowledgeGraphNode + start_node_data = node_result[0]["n"] + entity_id = start_node_data["properties"]["entity_id"] + internal_id = str(start_node_data["id"]) + + start_node = KnowledgeGraphNode( + id=internal_id, + labels=[entity_id], + properties=start_node_data["properties"], + ) + + # Initialize BFS queue, each element is a tuple of (node, depth) + queue = deque([(start_node, 0)]) + + visited_nodes.add(entity_id) + visited_node_ids.add(internal_id) + result.nodes.append(start_node) + + result.is_truncated = False + + # BFS search main loop + while queue: + # Get all nodes at the current depth + current_level_nodes = [] + current_depth = None + + # Determine current depth + if queue: + current_depth = queue[0][1] + + # Extract all nodes at current depth from the queue + while queue and queue[0][1] == current_depth: + node, depth = queue.popleft() + if depth > max_depth: + continue + current_level_nodes.append(node) + + if not current_level_nodes: + continue + + # Check depth limit + if current_depth > max_depth: + continue + + # Prepare node IDs list + node_ids = [node.labels[0] for node in current_level_nodes] + formatted_ids = ", ".join( + [f'"{self._normalize_node_id(node_id)}"' for node_id in node_ids] + ) + + # Construct batch query for outgoing edges + outgoing_query = f"""SELECT * FROM cypher('{self.graph_name}', $$ + UNWIND [{formatted_ids}] AS node_id + MATCH (n:base {{entity_id: node_id}}) + OPTIONAL MATCH (n)-[r]->(neighbor:base) + RETURN node_id AS current_id, + id(n) AS current_internal_id, + id(neighbor) AS neighbor_internal_id, + neighbor.entity_id AS neighbor_id, + id(r) AS edge_id, + r, + neighbor, + true AS is_outgoing + $$) AS (current_id text, current_internal_id bigint, neighbor_internal_id bigint, + neighbor_id text, edge_id bigint, r agtype, neighbor agtype, is_outgoing bool)""" + + # Construct batch query for incoming edges + incoming_query = f"""SELECT * FROM cypher('{self.graph_name}', $$ + UNWIND [{formatted_ids}] AS node_id + MATCH (n:base {{entity_id: node_id}}) + OPTIONAL MATCH (n)<-[r]-(neighbor:base) + RETURN node_id AS current_id, + id(n) AS current_internal_id, + id(neighbor) AS neighbor_internal_id, + neighbor.entity_id AS neighbor_id, + id(r) AS edge_id, + r, + neighbor, + false AS is_outgoing + $$) AS (current_id text, current_internal_id bigint, neighbor_internal_id bigint, + neighbor_id text, edge_id bigint, r agtype, neighbor agtype, is_outgoing bool)""" + + # Execute queries + outgoing_results = await self._query(outgoing_query) + incoming_results = await self._query(incoming_query) + + # Combine results + neighbors = outgoing_results + incoming_results + + # Create mapping from node ID to node object + node_map = {node.labels[0]: node for node in current_level_nodes} + + # Process all results in a single loop + for record in neighbors: + if not record.get("neighbor") or not record.get("r"): + continue + + # Get current node information + current_entity_id = record["current_id"] + current_node = node_map[current_entity_id] + + # Get neighbor node information + neighbor_entity_id = record["neighbor_id"] + neighbor_internal_id = str(record["neighbor_internal_id"]) + is_outgoing = record["is_outgoing"] + + # Determine edge direction + if is_outgoing: + source_id = current_node.id + target_id = neighbor_internal_id + else: + source_id = neighbor_internal_id + target_id = current_node.id + + if not neighbor_entity_id: + continue + + # Get edge and node information + b_node = record["neighbor"] + rel = record["r"] + edge_id = str(record["edge_id"]) + + # Create neighbor node object + neighbor_node = KnowledgeGraphNode( + id=neighbor_internal_id, + labels=[neighbor_entity_id], + properties=b_node["properties"], + ) + + # Sort entity_ids to ensure (A,B) and (B,A) are treated as the same edge + sorted_pair = tuple(sorted([current_entity_id, neighbor_entity_id])) + + # Create edge object + edge = KnowledgeGraphEdge( + id=edge_id, + type=rel["label"], + source=source_id, + target=target_id, + properties=rel["properties"], + ) + + if neighbor_internal_id in visited_node_ids: + # Add backward edge if neighbor node is already visited + if ( + edge_id not in visited_edges + and sorted_pair not in visited_edge_pairs + ): + result.edges.append(edge) + visited_edges.add(edge_id) + visited_edge_pairs.add(sorted_pair) + else: + if len(visited_node_ids) < max_nodes and current_depth < max_depth: + # Add new node to result and queue + result.nodes.append(neighbor_node) + visited_nodes.add(neighbor_entity_id) + visited_node_ids.add(neighbor_internal_id) + + # Add node to queue with incremented depth + queue.append((neighbor_node, current_depth + 1)) + + # Add forward edge + if ( + edge_id not in visited_edges + and sorted_pair not in visited_edge_pairs + ): + result.edges.append(edge) + visited_edges.add(edge_id) + visited_edge_pairs.add(sorted_pair) + else: + if current_depth < max_depth: + result.is_truncated = True + + return result + + async def get_knowledge_graph( + self, + node_label: str, + max_depth: int = 3, + max_nodes: int = None, + ) -> KnowledgeGraph: + """ + Retrieve a connected subgraph of nodes where the label includes the specified `node_label`. + + Args: + node_label: Label of the starting node, * means all nodes + max_depth: Maximum depth of the subgraph, Defaults to 3 + max_nodes: Maximum nodes to return, Defaults to global_config max_graph_nodes + + Returns: + KnowledgeGraph object containing nodes and edges, with an is_truncated flag + indicating whether the graph was truncated due to max_nodes limit + """ + # Use global_config max_graph_nodes as default if max_nodes is None + if max_nodes is None: + max_nodes = self.global_config.get("max_graph_nodes", 1000) + else: + # Limit max_nodes to not exceed global_config max_graph_nodes + max_nodes = min(max_nodes, self.global_config.get("max_graph_nodes", 1000)) + kg = KnowledgeGraph() + + # Handle wildcard query - get all nodes + if node_label == "*": + # First check total node count to determine if graph should be truncated + count_query = f"""SELECT * FROM cypher('{self.graph_name}', $$ + MATCH (n:base) + RETURN count(distinct n) AS total_nodes + $$) AS (total_nodes bigint)""" + + count_result = await self._query(count_query) + total_nodes = count_result[0]["total_nodes"] if count_result else 0 + is_truncated = total_nodes > max_nodes + + # Get max_nodes with highest degrees + query_nodes = f"""SELECT * FROM cypher('{self.graph_name}', $$ + MATCH (n:base) + OPTIONAL MATCH (n)-[r]->() + RETURN id(n) as node_id, count(r) as degree + $$) AS (node_id BIGINT, degree BIGINT) + ORDER BY degree DESC + LIMIT {max_nodes}""" + node_results = await self._query(query_nodes) + + node_ids = [str(result["node_id"]) for result in node_results] + + logger.info( + f"[{self.workspace}] Total nodes: {total_nodes}, Selected nodes: {len(node_ids)}" + ) + + if node_ids: + formatted_ids = ", ".join(node_ids) + # Construct batch query for subgraph within max_nodes + query = f"""SELECT * FROM cypher('{self.graph_name}', $$ + WITH [{formatted_ids}] AS node_ids + MATCH (a) + WHERE id(a) IN node_ids + OPTIONAL MATCH (a)-[r]->(b) + WHERE id(b) IN node_ids + RETURN a, r, b + $$) AS (a AGTYPE, r AGTYPE, b AGTYPE)""" + results = await self._query(query) + + # Process query results, deduplicate nodes and edges + nodes_dict = {} + edges_dict = {} + for result in results: + # Process node a + if result.get("a") and isinstance(result["a"], dict): + node_a = result["a"] + node_id = str(node_a["id"]) + if node_id not in nodes_dict and "properties" in node_a: + nodes_dict[node_id] = KnowledgeGraphNode( + id=node_id, + labels=[node_a["properties"]["entity_id"]], + properties=node_a["properties"], + ) + + # Process node b + if result.get("b") and isinstance(result["b"], dict): + node_b = result["b"] + node_id = str(node_b["id"]) + if node_id not in nodes_dict and "properties" in node_b: + nodes_dict[node_id] = KnowledgeGraphNode( + id=node_id, + labels=[node_b["properties"]["entity_id"]], + properties=node_b["properties"], + ) + + # Process edge r + if result.get("r") and isinstance(result["r"], dict): + edge = result["r"] + edge_id = str(edge["id"]) + if edge_id not in edges_dict: + edges_dict[edge_id] = KnowledgeGraphEdge( + id=edge_id, + type=edge["label"], + source=str(edge["start_id"]), + target=str(edge["end_id"]), + properties=edge["properties"], + ) + + kg = KnowledgeGraph( + nodes=list(nodes_dict.values()), + edges=list(edges_dict.values()), + is_truncated=is_truncated, + ) + else: + # For single node query, use BFS algorithm + kg = await self._bfs_subgraph(node_label, max_depth, max_nodes) + + logger.info( + f"[{self.workspace}] Subgraph query successful | Node count: {len(kg.nodes)} | Edge count: {len(kg.edges)}" + ) + else: + # For non-wildcard queries, use the BFS algorithm + kg = await self._bfs_subgraph(node_label, max_depth, max_nodes) + logger.info( + f"[{self.workspace}] Subgraph query for '{node_label}' successful | Node count: {len(kg.nodes)} | Edge count: {len(kg.edges)}" + ) + + return kg + + async def get_all_nodes(self) -> list[dict]: + """Get all nodes in the graph. + + Returns: + A list of all nodes, where each node is a dictionary of its properties + """ + # Use native SQL to avoid Cypher wrapper overhead + # Original: SELECT * FROM cypher(...) with MATCH (n:base) + # Optimized: Direct table access for better performance + query = f""" + SELECT properties + FROM {self.graph_name}.base + """ + + results = await self._query(query) + nodes = [] + for result in results: + if result.get("properties"): + node_dict = result["properties"] + + # Process string result, parse it to JSON dictionary + if isinstance(node_dict, str): + try: + node_dict = json.loads(node_dict) + except json.JSONDecodeError: + logger.warning( + f"[{self.workspace}] Failed to parse node string: {node_dict}" + ) + continue + + # Add node id (entity_id) to the dictionary for easier access + node_dict["id"] = node_dict.get("entity_id") + nodes.append(node_dict) + return nodes + + async def get_all_edges(self) -> list[dict]: + """Get all edges in the graph. + + Returns: + A list of all edges, where each edge is a dictionary of its properties + (If 2 directional edges exist between the same pair of nodes, deduplication must be handled by the caller) + """ + # Use native SQL to avoid Cartesian product (N×N) in Cypher MATCH + # Original Cypher: MATCH (a:base)-[r]-(b:base) creates ~50 billion row combinations + # Optimized: Start from edges table, join to nodes only to get entity_id + # Performance: O(E) instead of O(N²), ~50,000x faster for large graphs + query = f""" + SELECT DISTINCT + (ag_catalog.agtype_access_operator(VARIADIC ARRAY[a.properties, '"entity_id"'::agtype]))::text AS source, + (ag_catalog.agtype_access_operator(VARIADIC ARRAY[b.properties, '"entity_id"'::agtype]))::text AS target, + r.properties + FROM {self.graph_name}."DIRECTED" r + JOIN {self.graph_name}.base a ON r.start_id = a.id + JOIN {self.graph_name}.base b ON r.end_id = b.id + """ + + results = await self._query(query) + edges = [] + for result in results: + edge_properties = result["properties"] + + # Process string result, parse it to JSON dictionary + if isinstance(edge_properties, str): + try: + edge_properties = json.loads(edge_properties) + except json.JSONDecodeError: + logger.warning( + f"[{self.workspace}] Failed to parse edge properties string: {edge_properties}" + ) + edge_properties = {} + + edge_properties["source"] = result["source"] + edge_properties["target"] = result["target"] + edges.append(edge_properties) + return edges + + async def get_popular_labels(self, limit: int = 300) -> list[str]: + """Get popular labels by node degree (most connected entities) using native SQL for performance.""" + try: + # Native SQL query to calculate node degrees directly from AGE's underlying tables + # This is significantly faster than using the cypher() function wrapper + query = f""" + WITH node_degrees AS ( + SELECT + node_id, + COUNT(*) AS degree + FROM ( + SELECT start_id AS node_id FROM {self.graph_name}._ag_label_edge + UNION ALL + SELECT end_id AS node_id FROM {self.graph_name}._ag_label_edge + ) AS all_edges + GROUP BY node_id + ) + SELECT + (ag_catalog.agtype_access_operator(VARIADIC ARRAY[v.properties, '"entity_id"'::agtype]))::text AS label + FROM + node_degrees d + JOIN + {self.graph_name}._ag_label_vertex v ON d.node_id = v.id + WHERE + ag_catalog.agtype_access_operator(VARIADIC ARRAY[v.properties, '"entity_id"'::agtype]) IS NOT NULL + ORDER BY + d.degree DESC, + label ASC + LIMIT $1; + """ + results = await self._query(query, params={"limit": limit}) + labels = [ + result["label"] for result in results if result and "label" in result + ] + + logger.debug( + f"[{self.workspace}] Retrieved {len(labels)} popular labels (limit: {limit})" + ) + return labels + except Exception as e: + logger.error(f"[{self.workspace}] Error getting popular labels: {str(e)}") + return [] + + async def search_labels(self, query: str, limit: int = 50) -> list[str]: + """Search labels with fuzzy matching using native, parameterized SQL for performance and security.""" + query_lower = query.lower().strip() + if not query_lower: + return [] + + try: + # Re-implementing with the correct agtype access operator and full scoring logic. + sql_query = f""" + WITH ranked_labels AS ( + SELECT + (ag_catalog.agtype_access_operator(VARIADIC ARRAY[properties, '"entity_id"'::agtype]))::text AS label, + LOWER((ag_catalog.agtype_access_operator(VARIADIC ARRAY[properties, '"entity_id"'::agtype]))::text) AS label_lower + FROM + {self.graph_name}._ag_label_vertex + WHERE + ag_catalog.agtype_access_operator(VARIADIC ARRAY[properties, '"entity_id"'::agtype]) IS NOT NULL + AND LOWER((ag_catalog.agtype_access_operator(VARIADIC ARRAY[properties, '"entity_id"'::agtype]))::text) ILIKE $1 + ) + SELECT + label + FROM ( + SELECT + label, + CASE + WHEN label_lower = $2 THEN 1000 + WHEN label_lower LIKE $3 THEN 500 + ELSE (100 - LENGTH(label)) + END + + CASE + WHEN label_lower LIKE $4 OR label_lower LIKE $5 THEN 50 + ELSE 0 + END AS score + FROM + ranked_labels + ) AS scored_labels + ORDER BY + score DESC, + label ASC + LIMIT $6; + """ + params = ( + f"%{query_lower}%", # For the main ILIKE clause ($1) + query_lower, # For exact match ($2) + f"{query_lower}%", # For prefix match ($3) + f"% {query_lower}%", # For word boundary (space) ($4) + f"%_{query_lower}%", # For word boundary (underscore) ($5) + limit, # For LIMIT ($6) + ) + results = await self._query(sql_query, params=dict(enumerate(params, 1))) + labels = [ + result["label"] for result in results if result and "label" in result + ] + + logger.debug( + f"[{self.workspace}] Search query '{query}' returned {len(labels)} results (limit: {limit})" + ) + return labels + except Exception as e: + logger.error( + f"[{self.workspace}] Error searching labels with query '{query}': {str(e)}" + ) + return [] + + async def drop(self) -> dict[str, str]: + """Drop the storage""" + async with get_graph_db_lock(): + try: + drop_query = f"""SELECT * FROM cypher('{self.graph_name}', $$ + MATCH (n) + DETACH DELETE n + $$) AS (result agtype)""" + + await self._query(drop_query, readonly=False) + return { + "status": "success", + "message": f"workspace '{self.workspace}' graph data dropped", + } + except Exception as e: + logger.error(f"[{self.workspace}] Error dropping graph: {e}") + return {"status": "error", "message": str(e)} + + +# Note: Order matters! More specific namespaces (e.g., "full_entities") must come before +# more general ones (e.g., "entities") because is_namespace() uses endswith() matching +NAMESPACE_TABLE_MAP = { + NameSpace.KV_STORE_FULL_DOCS: "LIGHTRAG_DOC_FULL", + NameSpace.KV_STORE_TEXT_CHUNKS: "LIGHTRAG_DOC_CHUNKS", + NameSpace.KV_STORE_FULL_ENTITIES: "LIGHTRAG_FULL_ENTITIES", + NameSpace.KV_STORE_FULL_RELATIONS: "LIGHTRAG_FULL_RELATIONS", + NameSpace.KV_STORE_ENTITY_CHUNKS: "LIGHTRAG_ENTITY_CHUNKS", + NameSpace.KV_STORE_RELATION_CHUNKS: "LIGHTRAG_RELATION_CHUNKS", + NameSpace.KV_STORE_LLM_RESPONSE_CACHE: "LIGHTRAG_LLM_CACHE", + NameSpace.VECTOR_STORE_CHUNKS: "LIGHTRAG_VDB_CHUNKS", + NameSpace.VECTOR_STORE_ENTITIES: "LIGHTRAG_VDB_ENTITY", + NameSpace.VECTOR_STORE_RELATIONSHIPS: "LIGHTRAG_VDB_RELATION", + NameSpace.DOC_STATUS: "LIGHTRAG_DOC_STATUS", +} + + +def namespace_to_table_name(namespace: str) -> str: + for k, v in NAMESPACE_TABLE_MAP.items(): + if is_namespace(namespace, k): + return v + + +TABLES = { + "LIGHTRAG_DOC_FULL": { + "ddl": """CREATE TABLE LIGHTRAG_DOC_FULL ( + id VARCHAR(255), + workspace VARCHAR(255), + doc_name VARCHAR(1024), + content TEXT, + meta JSONB, + create_time TIMESTAMP(0) DEFAULT CURRENT_TIMESTAMP, + update_time TIMESTAMP(0) DEFAULT CURRENT_TIMESTAMP, + CONSTRAINT LIGHTRAG_DOC_FULL_PK PRIMARY KEY (workspace, id) + )""" + }, + "LIGHTRAG_DOC_CHUNKS": { + "ddl": """CREATE TABLE LIGHTRAG_DOC_CHUNKS ( + id VARCHAR(255), + workspace VARCHAR(255), + full_doc_id VARCHAR(256), + chunk_order_index INTEGER, + tokens INTEGER, + content TEXT, + file_path TEXT NULL, + llm_cache_list JSONB NULL DEFAULT '[]'::jsonb, + create_time TIMESTAMP(0) DEFAULT CURRENT_TIMESTAMP, + update_time TIMESTAMP(0) DEFAULT CURRENT_TIMESTAMP, + CONSTRAINT LIGHTRAG_DOC_CHUNKS_PK PRIMARY KEY (workspace, id) + )""" + }, + "LIGHTRAG_VDB_CHUNKS": { + "ddl": f"""CREATE TABLE LIGHTRAG_VDB_CHUNKS ( + id VARCHAR(255), + workspace VARCHAR(255), + full_doc_id VARCHAR(256), + chunk_order_index INTEGER, + tokens INTEGER, + content TEXT, + content_vector VECTOR({os.environ.get("EMBEDDING_DIM", 1024)}), + file_path TEXT NULL, + create_time TIMESTAMP(0) DEFAULT CURRENT_TIMESTAMP, + update_time TIMESTAMP(0) DEFAULT CURRENT_TIMESTAMP, + CONSTRAINT LIGHTRAG_VDB_CHUNKS_PK PRIMARY KEY (workspace, id) + )""" + }, + "LIGHTRAG_VDB_ENTITY": { + "ddl": f"""CREATE TABLE LIGHTRAG_VDB_ENTITY ( + id VARCHAR(255), + workspace VARCHAR(255), + entity_name VARCHAR(512), + content TEXT, + content_vector VECTOR({os.environ.get("EMBEDDING_DIM", 1024)}), + create_time TIMESTAMP(0) DEFAULT CURRENT_TIMESTAMP, + update_time TIMESTAMP(0) DEFAULT CURRENT_TIMESTAMP, + chunk_ids VARCHAR(255)[] NULL, + file_path TEXT NULL, + CONSTRAINT LIGHTRAG_VDB_ENTITY_PK PRIMARY KEY (workspace, id) + )""" + }, + "LIGHTRAG_VDB_RELATION": { + "ddl": f"""CREATE TABLE LIGHTRAG_VDB_RELATION ( + id VARCHAR(255), + workspace VARCHAR(255), + source_id VARCHAR(512), + target_id VARCHAR(512), + content TEXT, + content_vector VECTOR({os.environ.get("EMBEDDING_DIM", 1024)}), + create_time TIMESTAMP(0) DEFAULT CURRENT_TIMESTAMP, + update_time TIMESTAMP(0) DEFAULT CURRENT_TIMESTAMP, + chunk_ids VARCHAR(255)[] NULL, + file_path TEXT NULL, + CONSTRAINT LIGHTRAG_VDB_RELATION_PK PRIMARY KEY (workspace, id) + )""" + }, + "LIGHTRAG_LLM_CACHE": { + "ddl": """CREATE TABLE LIGHTRAG_LLM_CACHE ( + workspace varchar(255) NOT NULL, + id varchar(255) NOT NULL, + original_prompt TEXT, + return_value TEXT, + chunk_id VARCHAR(255) NULL, + cache_type VARCHAR(32), + queryparam JSONB NULL, + create_time TIMESTAMP DEFAULT CURRENT_TIMESTAMP, + update_time TIMESTAMP DEFAULT CURRENT_TIMESTAMP, + CONSTRAINT LIGHTRAG_LLM_CACHE_PK PRIMARY KEY (workspace, id) + )""" + }, + "LIGHTRAG_DOC_STATUS": { + "ddl": """CREATE TABLE LIGHTRAG_DOC_STATUS ( + workspace varchar(255) NOT NULL, + id varchar(255) NOT NULL, + content_summary varchar(255) NULL, + content_length int4 NULL, + chunks_count int4 NULL, + status varchar(64) NULL, + file_path TEXT NULL, + chunks_list JSONB NULL DEFAULT '[]'::jsonb, + track_id varchar(255) NULL, + metadata JSONB NULL DEFAULT '{}'::jsonb, + error_msg TEXT NULL, + created_at TIMESTAMP DEFAULT CURRENT_TIMESTAMP, + updated_at TIMESTAMP DEFAULT CURRENT_TIMESTAMP, + CONSTRAINT LIGHTRAG_DOC_STATUS_PK PRIMARY KEY (workspace, id) + )""" + }, + "LIGHTRAG_FULL_ENTITIES": { + "ddl": """CREATE TABLE LIGHTRAG_FULL_ENTITIES ( + id VARCHAR(255), + workspace VARCHAR(255), + entity_names JSONB, + count INTEGER, + create_time TIMESTAMP(0) DEFAULT CURRENT_TIMESTAMP, + update_time TIMESTAMP(0) DEFAULT CURRENT_TIMESTAMP, + CONSTRAINT LIGHTRAG_FULL_ENTITIES_PK PRIMARY KEY (workspace, id) + )""" + }, + "LIGHTRAG_FULL_RELATIONS": { + "ddl": """CREATE TABLE LIGHTRAG_FULL_RELATIONS ( + id VARCHAR(255), + workspace VARCHAR(255), + relation_pairs JSONB, + count INTEGER, + create_time TIMESTAMP(0) DEFAULT CURRENT_TIMESTAMP, + update_time TIMESTAMP(0) DEFAULT CURRENT_TIMESTAMP, + CONSTRAINT LIGHTRAG_FULL_RELATIONS_PK PRIMARY KEY (workspace, id) + )""" + }, + "LIGHTRAG_ENTITY_CHUNKS": { + "ddl": """CREATE TABLE LIGHTRAG_ENTITY_CHUNKS ( + id VARCHAR(512), + workspace VARCHAR(255), + chunk_ids JSONB, + count INTEGER, + create_time TIMESTAMP(0) DEFAULT CURRENT_TIMESTAMP, + update_time TIMESTAMP(0) DEFAULT CURRENT_TIMESTAMP, + CONSTRAINT LIGHTRAG_ENTITY_CHUNKS_PK PRIMARY KEY (workspace, id) + )""" + }, + "LIGHTRAG_RELATION_CHUNKS": { + "ddl": """CREATE TABLE LIGHTRAG_RELATION_CHUNKS ( + id VARCHAR(512), + workspace VARCHAR(255), + chunk_ids JSONB, + count INTEGER, + create_time TIMESTAMP(0) DEFAULT CURRENT_TIMESTAMP, + update_time TIMESTAMP(0) DEFAULT CURRENT_TIMESTAMP, + CONSTRAINT LIGHTRAG_RELATION_CHUNKS_PK PRIMARY KEY (workspace, id) + )""" + }, +} + + +SQL_TEMPLATES = { + # SQL for KVStorage + "get_by_id_full_docs": """SELECT id, COALESCE(content, '') as content, + COALESCE(doc_name, '') as file_path + FROM LIGHTRAG_DOC_FULL WHERE workspace=$1 AND id=$2 + """, + "get_by_id_text_chunks": """SELECT id, tokens, COALESCE(content, '') as content, + chunk_order_index, full_doc_id, file_path, + COALESCE(llm_cache_list, '[]'::jsonb) as llm_cache_list, + EXTRACT(EPOCH FROM create_time)::BIGINT as create_time, + EXTRACT(EPOCH FROM update_time)::BIGINT as update_time + FROM LIGHTRAG_DOC_CHUNKS WHERE workspace=$1 AND id=$2 + """, + "get_by_id_llm_response_cache": """SELECT id, original_prompt, return_value, chunk_id, cache_type, queryparam, + EXTRACT(EPOCH FROM create_time)::BIGINT as create_time, + EXTRACT(EPOCH FROM update_time)::BIGINT as update_time + FROM LIGHTRAG_LLM_CACHE WHERE workspace=$1 AND id=$2 + """, + "get_by_ids_full_docs": """SELECT id, COALESCE(content, '') as content, + COALESCE(doc_name, '') as file_path + FROM LIGHTRAG_DOC_FULL WHERE workspace=$1 AND id = ANY($2) + """, + "get_by_ids_text_chunks": """SELECT id, tokens, COALESCE(content, '') as content, + chunk_order_index, full_doc_id, file_path, + COALESCE(llm_cache_list, '[]'::jsonb) as llm_cache_list, + EXTRACT(EPOCH FROM create_time)::BIGINT as create_time, + EXTRACT(EPOCH FROM update_time)::BIGINT as update_time + FROM LIGHTRAG_DOC_CHUNKS WHERE workspace=$1 AND id = ANY($2) + """, + "get_by_ids_llm_response_cache": """SELECT id, original_prompt, return_value, chunk_id, cache_type, queryparam, + EXTRACT(EPOCH FROM create_time)::BIGINT as create_time, + EXTRACT(EPOCH FROM update_time)::BIGINT as update_time + FROM LIGHTRAG_LLM_CACHE WHERE workspace=$1 AND id = ANY($2) + """, + "get_by_id_full_entities": """SELECT id, entity_names, count, + EXTRACT(EPOCH FROM create_time)::BIGINT as create_time, + EXTRACT(EPOCH FROM update_time)::BIGINT as update_time + FROM LIGHTRAG_FULL_ENTITIES WHERE workspace=$1 AND id=$2 + """, + "get_by_id_full_relations": """SELECT id, relation_pairs, count, + EXTRACT(EPOCH FROM create_time)::BIGINT as create_time, + EXTRACT(EPOCH FROM update_time)::BIGINT as update_time + FROM LIGHTRAG_FULL_RELATIONS WHERE workspace=$1 AND id=$2 + """, + "get_by_ids_full_entities": """SELECT id, entity_names, count, + EXTRACT(EPOCH FROM create_time)::BIGINT as create_time, + EXTRACT(EPOCH FROM update_time)::BIGINT as update_time + FROM LIGHTRAG_FULL_ENTITIES WHERE workspace=$1 AND id = ANY($2) + """, + "get_by_ids_full_relations": """SELECT id, relation_pairs, count, + EXTRACT(EPOCH FROM create_time)::BIGINT as create_time, + EXTRACT(EPOCH FROM update_time)::BIGINT as update_time + FROM LIGHTRAG_FULL_RELATIONS WHERE workspace=$1 AND id = ANY($2) + """, + "get_by_id_entity_chunks": """SELECT id, chunk_ids, count, + EXTRACT(EPOCH FROM create_time)::BIGINT as create_time, + EXTRACT(EPOCH FROM update_time)::BIGINT as update_time + FROM LIGHTRAG_ENTITY_CHUNKS WHERE workspace=$1 AND id=$2 + """, + "get_by_id_relation_chunks": """SELECT id, chunk_ids, count, + EXTRACT(EPOCH FROM create_time)::BIGINT as create_time, + EXTRACT(EPOCH FROM update_time)::BIGINT as update_time + FROM LIGHTRAG_RELATION_CHUNKS WHERE workspace=$1 AND id=$2 + """, + "get_by_ids_entity_chunks": """SELECT id, chunk_ids, count, + EXTRACT(EPOCH FROM create_time)::BIGINT as create_time, + EXTRACT(EPOCH FROM update_time)::BIGINT as update_time + FROM LIGHTRAG_ENTITY_CHUNKS WHERE workspace=$1 AND id = ANY($2) + """, + "get_by_ids_relation_chunks": """SELECT id, chunk_ids, count, + EXTRACT(EPOCH FROM create_time)::BIGINT as create_time, + EXTRACT(EPOCH FROM update_time)::BIGINT as update_time + FROM LIGHTRAG_RELATION_CHUNKS WHERE workspace=$1 AND id = ANY($2) + """, + "filter_keys": "SELECT id FROM {table_name} WHERE workspace=$1 AND id IN ({ids})", + "upsert_doc_full": """INSERT INTO LIGHTRAG_DOC_FULL (id, content, doc_name, workspace) + VALUES ($1, $2, $3, $4) + ON CONFLICT (workspace,id) DO UPDATE + SET content = $2, + doc_name = $3, + update_time = CURRENT_TIMESTAMP + """, + "upsert_llm_response_cache": """INSERT INTO LIGHTRAG_LLM_CACHE(workspace,id,original_prompt,return_value,chunk_id,cache_type,queryparam) + VALUES ($1, $2, $3, $4, $5, $6, $7) + ON CONFLICT (workspace,id) DO UPDATE + SET original_prompt = EXCLUDED.original_prompt, + return_value=EXCLUDED.return_value, + chunk_id=EXCLUDED.chunk_id, + cache_type=EXCLUDED.cache_type, + queryparam=EXCLUDED.queryparam, + update_time = CURRENT_TIMESTAMP + """, + "upsert_text_chunk": """INSERT INTO LIGHTRAG_DOC_CHUNKS (workspace, id, tokens, + chunk_order_index, full_doc_id, content, file_path, llm_cache_list, + create_time, update_time) + VALUES ($1, $2, $3, $4, $5, $6, $7, $8, $9, $10) + ON CONFLICT (workspace,id) DO UPDATE + SET tokens=EXCLUDED.tokens, + chunk_order_index=EXCLUDED.chunk_order_index, + full_doc_id=EXCLUDED.full_doc_id, + content = EXCLUDED.content, + file_path=EXCLUDED.file_path, + llm_cache_list=EXCLUDED.llm_cache_list, + update_time = EXCLUDED.update_time + """, + "upsert_full_entities": """INSERT INTO LIGHTRAG_FULL_ENTITIES (workspace, id, entity_names, count, + create_time, update_time) + VALUES ($1, $2, $3, $4, $5, $6) + ON CONFLICT (workspace,id) DO UPDATE + SET entity_names=EXCLUDED.entity_names, + count=EXCLUDED.count, + update_time = EXCLUDED.update_time + """, + "upsert_full_relations": """INSERT INTO LIGHTRAG_FULL_RELATIONS (workspace, id, relation_pairs, count, + create_time, update_time) + VALUES ($1, $2, $3, $4, $5, $6) + ON CONFLICT (workspace,id) DO UPDATE + SET relation_pairs=EXCLUDED.relation_pairs, + count=EXCLUDED.count, + update_time = EXCLUDED.update_time + """, + "upsert_entity_chunks": """INSERT INTO LIGHTRAG_ENTITY_CHUNKS (workspace, id, chunk_ids, count, + create_time, update_time) + VALUES ($1, $2, $3, $4, $5, $6) + ON CONFLICT (workspace,id) DO UPDATE + SET chunk_ids=EXCLUDED.chunk_ids, + count=EXCLUDED.count, + update_time = EXCLUDED.update_time + """, + "upsert_relation_chunks": """INSERT INTO LIGHTRAG_RELATION_CHUNKS (workspace, id, chunk_ids, count, + create_time, update_time) + VALUES ($1, $2, $3, $4, $5, $6) + ON CONFLICT (workspace,id) DO UPDATE + SET chunk_ids=EXCLUDED.chunk_ids, + count=EXCLUDED.count, + update_time = EXCLUDED.update_time + """, + # SQL for VectorStorage + "upsert_chunk": """INSERT INTO LIGHTRAG_VDB_CHUNKS (workspace, id, tokens, + chunk_order_index, full_doc_id, content, content_vector, file_path, + create_time, update_time) + VALUES ($1, $2, $3, $4, $5, $6, $7, $8, $9, $10) + ON CONFLICT (workspace,id) DO UPDATE + SET tokens=EXCLUDED.tokens, + chunk_order_index=EXCLUDED.chunk_order_index, + full_doc_id=EXCLUDED.full_doc_id, + content = EXCLUDED.content, + content_vector=EXCLUDED.content_vector, + file_path=EXCLUDED.file_path, + update_time = EXCLUDED.update_time + """, + "upsert_entity": """INSERT INTO LIGHTRAG_VDB_ENTITY (workspace, id, entity_name, content, + content_vector, chunk_ids, file_path, create_time, update_time) + VALUES ($1, $2, $3, $4, $5, $6::varchar[], $7, $8, $9) + ON CONFLICT (workspace,id) DO UPDATE + SET entity_name=EXCLUDED.entity_name, + content=EXCLUDED.content, + content_vector=EXCLUDED.content_vector, + chunk_ids=EXCLUDED.chunk_ids, + file_path=EXCLUDED.file_path, + update_time=EXCLUDED.update_time + """, + "upsert_relationship": """INSERT INTO LIGHTRAG_VDB_RELATION (workspace, id, source_id, + target_id, content, content_vector, chunk_ids, file_path, create_time, update_time) + VALUES ($1, $2, $3, $4, $5, $6, $7::varchar[], $8, $9, $10) + ON CONFLICT (workspace,id) DO UPDATE + SET source_id=EXCLUDED.source_id, + target_id=EXCLUDED.target_id, + content=EXCLUDED.content, + content_vector=EXCLUDED.content_vector, + chunk_ids=EXCLUDED.chunk_ids, + file_path=EXCLUDED.file_path, + update_time = EXCLUDED.update_time + """, + "relationships": """ + SELECT r.source_id AS src_id, + r.target_id AS tgt_id, + EXTRACT(EPOCH FROM r.create_time)::BIGINT AS created_at + FROM LIGHTRAG_VDB_RELATION r + WHERE r.workspace = $1 + AND r.content_vector <=> '[{embedding_string}]'::vector < $2 + ORDER BY r.content_vector <=> '[{embedding_string}]'::vector + LIMIT $3; + """, + "entities": """ + SELECT e.entity_name, + EXTRACT(EPOCH FROM e.create_time)::BIGINT AS created_at + FROM LIGHTRAG_VDB_ENTITY e + WHERE e.workspace = $1 + AND e.content_vector <=> '[{embedding_string}]'::vector < $2 + ORDER BY e.content_vector <=> '[{embedding_string}]'::vector + LIMIT $3; + """, + "chunks": """ + SELECT c.id, + c.content, + c.file_path, + EXTRACT(EPOCH FROM c.create_time)::BIGINT AS created_at + FROM LIGHTRAG_VDB_CHUNKS c + WHERE c.workspace = $1 + AND c.content_vector <=> '[{embedding_string}]'::vector < $2 + ORDER BY c.content_vector <=> '[{embedding_string}]'::vector + LIMIT $3; + """, + # DROP tables + "drop_specifiy_table_workspace": """ + DELETE FROM {table_name} WHERE workspace=$1 + """, +} diff --git a/LightRAG/lightrag/kg/qdrant_impl.py b/LightRAG/lightrag/kg/qdrant_impl.py new file mode 100644 index 00000000..d51d8898 --- /dev/null +++ b/LightRAG/lightrag/kg/qdrant_impl.py @@ -0,0 +1,722 @@ +import asyncio +import configparser +import hashlib +import os +import uuid +from dataclasses import dataclass +from typing import Any, List, final + +import numpy as np +import pipmaster as pm + +from ..base import BaseVectorStorage +from ..exceptions import QdrantMigrationError +from ..kg.shared_storage import get_data_init_lock, get_storage_lock +from ..utils import compute_mdhash_id, logger + +if not pm.is_installed("qdrant-client"): + pm.install("qdrant-client") + +from qdrant_client import QdrantClient, models # type: ignore + +DEFAULT_WORKSPACE = "_" +WORKSPACE_ID_FIELD = "workspace_id" +ENTITY_PREFIX = "ent-" +CREATED_AT_FIELD = "created_at" +ID_FIELD = "id" + +config = configparser.ConfigParser() +config.read("config.ini", "utf-8") + + +def compute_mdhash_id_for_qdrant( + content: str, prefix: str = "", style: str = "simple" +) -> str: + """ + Generate a UUID based on the content and support multiple formats. + + :param content: The content used to generate the UUID. + :param style: The format of the UUID, optional values are "simple", "hyphenated", "urn". + :return: A UUID that meets the requirements of Qdrant. + """ + if not content: + raise ValueError("Content must not be empty.") + + # Use the hash value of the content to create a UUID. + hashed_content = hashlib.sha256((prefix + content).encode("utf-8")).digest() + generated_uuid = uuid.UUID(bytes=hashed_content[:16], version=4) + + # Return the UUID according to the specified format. + if style == "simple": + return generated_uuid.hex + elif style == "hyphenated": + return str(generated_uuid) + elif style == "urn": + return f"urn:uuid:{generated_uuid}" + else: + raise ValueError("Invalid style. Choose from 'simple', 'hyphenated', or 'urn'.") + + +def workspace_filter_condition(workspace: str) -> models.FieldCondition: + """ + Create a workspace filter condition for Qdrant queries. + """ + return models.FieldCondition( + key=WORKSPACE_ID_FIELD, match=models.MatchValue(value=workspace) + ) + + +@final +@dataclass +class QdrantVectorDBStorage(BaseVectorStorage): + def __init__( + self, namespace, global_config, embedding_func, workspace=None, meta_fields=None + ): + super().__init__( + namespace=namespace, + workspace=workspace or "", + global_config=global_config, + embedding_func=embedding_func, + meta_fields=meta_fields or set(), + ) + self.__post_init__() + + @staticmethod + def setup_collection( + client: QdrantClient, + collection_name: str, + legacy_namespace: str = None, + workspace: str = None, + **kwargs, + ): + """ + Setup Qdrant collection with migration support from legacy collections. + + Args: + client: QdrantClient instance + collection_name: Name of the new collection + legacy_namespace: Name of the legacy collection (if exists) + workspace: Workspace identifier for data isolation + **kwargs: Additional arguments for collection creation (vectors_config, hnsw_config, etc.) + """ + new_collection_exists = client.collection_exists(collection_name) + legacy_exists = legacy_namespace and client.collection_exists(legacy_namespace) + + # Case 1: Both new and legacy collections exist - Warning only (no migration) + if new_collection_exists and legacy_exists: + logger.warning( + f"Qdrant: Legacy collection '{legacy_namespace}' still exist. Remove it if migration is complete." + ) + return + + # Case 2: Only new collection exists - Ensure index exists + if new_collection_exists: + # Check if workspace index exists, create if missing + try: + collection_info = client.get_collection(collection_name) + if WORKSPACE_ID_FIELD not in collection_info.payload_schema: + logger.info( + f"Qdrant: Creating missing workspace index for '{collection_name}'" + ) + client.create_payload_index( + collection_name=collection_name, + field_name=WORKSPACE_ID_FIELD, + field_schema=models.KeywordIndexParams( + type=models.KeywordIndexType.KEYWORD, + is_tenant=True, + ), + ) + except Exception as e: + logger.warning( + f"Qdrant: Could not verify/create workspace index for '{collection_name}': {e}" + ) + return + + # Case 3: Neither exists - Create new collection + if not legacy_exists: + logger.info(f"Qdrant: Creating new collection '{collection_name}'") + client.create_collection(collection_name, **kwargs) + client.create_payload_index( + collection_name=collection_name, + field_name=WORKSPACE_ID_FIELD, + field_schema=models.KeywordIndexParams( + type=models.KeywordIndexType.KEYWORD, + is_tenant=True, + ), + ) + logger.info(f"Qdrant: Collection '{collection_name}' created successfully") + return + + # Case 4: Only legacy exists - Migrate data + logger.info( + f"Qdrant: Migrating data from legacy collection '{legacy_namespace}'" + ) + + try: + # Get legacy collection count + legacy_count = client.count( + collection_name=legacy_namespace, exact=True + ).count + logger.info(f"Qdrant: Found {legacy_count} records in legacy collection") + + if legacy_count == 0: + logger.info("Qdrant: Legacy collection is empty, skipping migration") + # Create new empty collection + client.create_collection(collection_name, **kwargs) + client.create_payload_index( + collection_name=collection_name, + field_name=WORKSPACE_ID_FIELD, + field_schema=models.KeywordIndexParams( + type=models.KeywordIndexType.KEYWORD, + is_tenant=True, + ), + ) + return + + # Create new collection first + logger.info(f"Qdrant: Creating new collection '{collection_name}'") + client.create_collection(collection_name, **kwargs) + + # Batch migration (500 records per batch) + migrated_count = 0 + offset = None + batch_size = 500 + + while True: + # Scroll through legacy data + result = client.scroll( + collection_name=legacy_namespace, + limit=batch_size, + offset=offset, + with_vectors=True, + with_payload=True, + ) + points, next_offset = result + + if not points: + break + + # Transform points for new collection + new_points = [] + for point in points: + # Add workspace_id to payload + new_payload = dict(point.payload or {}) + new_payload[WORKSPACE_ID_FIELD] = workspace or DEFAULT_WORKSPACE + + # Create new point with workspace-prefixed ID + original_id = new_payload.get(ID_FIELD) + if original_id: + new_point_id = compute_mdhash_id_for_qdrant( + original_id, prefix=workspace or DEFAULT_WORKSPACE + ) + else: + # Fallback: use original point ID + new_point_id = str(point.id) + + new_points.append( + models.PointStruct( + id=new_point_id, + vector=point.vector, + payload=new_payload, + ) + ) + + # Upsert to new collection + client.upsert( + collection_name=collection_name, points=new_points, wait=True + ) + + migrated_count += len(points) + logger.info(f"Qdrant: {migrated_count}/{legacy_count} records migrated") + + # Check if we've reached the end + if next_offset is None: + break + offset = next_offset + + # Verify migration by comparing counts + logger.info("Verifying migration...") + new_count = client.count(collection_name=collection_name, exact=True).count + + if new_count != legacy_count: + error_msg = f"Qdrant: Migration verification failed, expected {legacy_count} records, got {new_count} in new collection" + logger.error(error_msg) + raise QdrantMigrationError(error_msg) + + logger.info( + f"Qdrant: Migration completed successfully: {migrated_count} records migrated" + ) + + # Create payload index after successful migration + logger.info("Qdrant: Creating workspace payload index...") + client.create_payload_index( + collection_name=collection_name, + field_name=WORKSPACE_ID_FIELD, + field_schema=models.KeywordIndexParams( + type=models.KeywordIndexType.KEYWORD, + is_tenant=True, + ), + ) + logger.info( + f"Qdrant: Migration from '{legacy_namespace}' to '{collection_name}' completed successfully" + ) + + except QdrantMigrationError: + # Re-raise migration errors without wrapping + raise + except Exception as e: + error_msg = f"Qdrant: Migration failed with error: {e}" + logger.error(error_msg) + raise QdrantMigrationError(error_msg) from e + + def __post_init__(self): + # Check for QDRANT_WORKSPACE environment variable first (higher priority) + # This allows administrators to force a specific workspace for all Qdrant storage instances + qdrant_workspace = os.environ.get("QDRANT_WORKSPACE") + if qdrant_workspace and qdrant_workspace.strip(): + # Use environment variable value, overriding the passed workspace parameter + effective_workspace = qdrant_workspace.strip() + logger.info( + f"Using QDRANT_WORKSPACE environment variable: '{effective_workspace}' (overriding passed workspace: '{self.workspace}')" + ) + else: + # Use the workspace parameter passed during initialization + effective_workspace = self.workspace + if effective_workspace: + logger.debug( + f"Using passed workspace parameter: '{effective_workspace}'" + ) + + # Get legacy namespace for data migration from old version + if effective_workspace: + self.legacy_namespace = f"{effective_workspace}_{self.namespace}" + else: + self.legacy_namespace = self.namespace + + self.effective_workspace = effective_workspace or DEFAULT_WORKSPACE + + # Use a shared collection with payload-based partitioning (Qdrant's recommended approach) + # Ref: https://qdrant.tech/documentation/guides/multiple-partitions/ + self.final_namespace = f"lightrag_vdb_{self.namespace}" + logger.debug( + f"Using shared collection '{self.final_namespace}' with workspace '{self.effective_workspace}' for payload-based partitioning" + ) + + kwargs = self.global_config.get("vector_db_storage_cls_kwargs", {}) + cosine_threshold = kwargs.get("cosine_better_than_threshold") + if cosine_threshold is None: + raise ValueError( + "cosine_better_than_threshold must be specified in vector_db_storage_cls_kwargs" + ) + self.cosine_better_than_threshold = cosine_threshold + + # Initialize client as None - will be created in initialize() method + self._client = None + self._max_batch_size = self.global_config["embedding_batch_num"] + self._initialized = False + + async def initialize(self): + """Initialize Qdrant collection""" + async with get_data_init_lock(): + if self._initialized: + return + + try: + # Create QdrantClient if not already created + if self._client is None: + self._client = QdrantClient( + url=os.environ.get( + "QDRANT_URL", config.get("qdrant", "uri", fallback=None) + ), + api_key=os.environ.get( + "QDRANT_API_KEY", + config.get("qdrant", "apikey", fallback=None), + ), + ) + logger.debug( + f"[{self.workspace}] QdrantClient created successfully" + ) + + # Setup collection (create if not exists and configure indexes) + # Pass legacy_namespace and workspace for migration support + QdrantVectorDBStorage.setup_collection( + self._client, + self.final_namespace, + legacy_namespace=self.legacy_namespace, + workspace=self.effective_workspace, + vectors_config=models.VectorParams( + size=self.embedding_func.embedding_dim, + distance=models.Distance.COSINE, + ), + hnsw_config=models.HnswConfigDiff( + payload_m=16, + m=0, + ), + ) + + self._initialized = True + logger.info( + f"[{self.workspace}] Qdrant collection '{self.namespace}' initialized successfully" + ) + except Exception as e: + logger.error( + f"[{self.workspace}] Failed to initialize Qdrant collection '{self.namespace}': {e}" + ) + raise + + async def upsert(self, data: dict[str, dict[str, Any]]) -> None: + logger.debug(f"[{self.workspace}] Inserting {len(data)} to {self.namespace}") + if not data: + return + + import time + + current_time = int(time.time()) + + list_data = [ + { + ID_FIELD: k, + WORKSPACE_ID_FIELD: self.effective_workspace, + CREATED_AT_FIELD: current_time, + **{k1: v1 for k1, v1 in v.items() if k1 in self.meta_fields}, + } + for k, v in data.items() + ] + contents = [v["content"] for v in data.values()] + batches = [ + contents[i : i + self._max_batch_size] + for i in range(0, len(contents), self._max_batch_size) + ] + + embedding_tasks = [self.embedding_func(batch) for batch in batches] + embeddings_list = await asyncio.gather(*embedding_tasks) + + embeddings = np.concatenate(embeddings_list) + + list_points = [] + for i, d in enumerate(list_data): + list_points.append( + models.PointStruct( + id=compute_mdhash_id_for_qdrant( + d[ID_FIELD], prefix=self.effective_workspace + ), + vector=embeddings[i], + payload=d, + ) + ) + + results = self._client.upsert( + collection_name=self.final_namespace, points=list_points, wait=True + ) + return results + + async def query( + self, query: str, top_k: int, query_embedding: list[float] = None + ) -> list[dict[str, Any]]: + if query_embedding is not None: + embedding = query_embedding + else: + embedding_result = await self.embedding_func( + [query], _priority=5 + ) # higher priority for query + embedding = embedding_result[0] + + results = self._client.query_points( + collection_name=self.final_namespace, + query=embedding, + limit=top_k, + with_payload=True, + score_threshold=self.cosine_better_than_threshold, + query_filter=models.Filter( + must=[workspace_filter_condition(self.effective_workspace)] + ), + ).points + + return [ + { + **dp.payload, + "distance": dp.score, + CREATED_AT_FIELD: dp.payload.get(CREATED_AT_FIELD), + } + for dp in results + ] + + async def index_done_callback(self) -> None: + # Qdrant handles persistence automatically + pass + + async def delete(self, ids: List[str]) -> None: + """Delete vectors with specified IDs + + Args: + ids: List of vector IDs to be deleted + """ + try: + if not ids: + return + + # Convert regular ids to Qdrant compatible ids + qdrant_ids = [ + compute_mdhash_id_for_qdrant(id, prefix=self.effective_workspace) + for id in ids + ] + # Delete points from the collection with workspace filtering + self._client.delete( + collection_name=self.final_namespace, + points_selector=models.PointIdsList(points=qdrant_ids), + wait=True, + ) + logger.debug( + f"[{self.workspace}] Successfully deleted {len(ids)} vectors from {self.namespace}" + ) + except Exception as e: + logger.error( + f"[{self.workspace}] Error while deleting vectors from {self.namespace}: {e}" + ) + + async def delete_entity(self, entity_name: str) -> None: + """Delete an entity by name + + Args: + entity_name: Name of the entity to delete + """ + try: + # Generate the entity ID using the same function as used for storage + entity_id = compute_mdhash_id(entity_name, prefix=ENTITY_PREFIX) + qdrant_entity_id = compute_mdhash_id_for_qdrant( + entity_id, prefix=self.effective_workspace + ) + + # Delete the entity point by its Qdrant ID directly + self._client.delete( + collection_name=self.final_namespace, + points_selector=models.PointIdsList(points=[qdrant_entity_id]), + wait=True, + ) + logger.debug( + f"[{self.workspace}] Successfully deleted entity {entity_name}" + ) + except Exception as e: + logger.error(f"[{self.workspace}] Error deleting entity {entity_name}: {e}") + + async def delete_entity_relation(self, entity_name: str) -> None: + """Delete all relations associated with an entity + + Args: + entity_name: Name of the entity whose relations should be deleted + """ + try: + # Find relations where the entity is either source or target, with workspace filtering + results = self._client.scroll( + collection_name=self.final_namespace, + scroll_filter=models.Filter( + must=[workspace_filter_condition(self.effective_workspace)], + should=[ + models.FieldCondition( + key="src_id", match=models.MatchValue(value=entity_name) + ), + models.FieldCondition( + key="tgt_id", match=models.MatchValue(value=entity_name) + ), + ], + ), + with_payload=True, + limit=1000, # Adjust as needed for your use case + ) + + # Extract points that need to be deleted + relation_points = results[0] + ids_to_delete = [point.id for point in relation_points] + + if ids_to_delete: + # Delete the relations with workspace filtering + assert isinstance(self._client, QdrantClient) + self._client.delete( + collection_name=self.final_namespace, + points_selector=models.PointIdsList(points=ids_to_delete), + wait=True, + ) + logger.debug( + f"[{self.workspace}] Deleted {len(ids_to_delete)} relations for {entity_name}" + ) + else: + logger.debug( + f"[{self.workspace}] No relations found for entity {entity_name}" + ) + except Exception as e: + logger.error( + f"[{self.workspace}] Error deleting relations for {entity_name}: {e}" + ) + + async def get_by_id(self, id: str) -> dict[str, Any] | None: + """Get vector data by its ID + + Args: + id: The unique identifier of the vector + + Returns: + The vector data if found, or None if not found + """ + try: + # Convert to Qdrant compatible ID + qdrant_id = compute_mdhash_id_for_qdrant( + id, prefix=self.effective_workspace + ) + + # Retrieve the point by ID with workspace filtering + result = self._client.retrieve( + collection_name=self.final_namespace, + ids=[qdrant_id], + with_payload=True, + ) + + if not result: + return None + + payload = result[0].payload + if CREATED_AT_FIELD not in payload: + payload[CREATED_AT_FIELD] = None + + return payload + except Exception as e: + logger.error( + f"[{self.workspace}] Error retrieving vector data for ID {id}: {e}" + ) + return None + + async def get_by_ids(self, ids: list[str]) -> list[dict[str, Any]]: + """Get multiple vector data by their IDs + + Args: + ids: List of unique identifiers + + Returns: + List of vector data objects that were found + """ + if not ids: + return [] + + try: + # Convert to Qdrant compatible IDs + qdrant_ids = [ + compute_mdhash_id_for_qdrant(id, prefix=self.effective_workspace) + for id in ids + ] + + # Retrieve the points by IDs + results = self._client.retrieve( + collection_name=self.final_namespace, + ids=qdrant_ids, + with_payload=True, + ) + + # Ensure each result contains created_at field and preserve caller ordering + payload_by_original_id: dict[str, dict[str, Any]] = {} + payload_by_qdrant_id: dict[str, dict[str, Any]] = {} + + for point in results: + payload = dict(point.payload or {}) + if CREATED_AT_FIELD not in payload: + payload[CREATED_AT_FIELD] = None + + qdrant_point_id = str(point.id) if point.id is not None else "" + if qdrant_point_id: + payload_by_qdrant_id[qdrant_point_id] = payload + + original_id = payload.get(ID_FIELD) + if original_id is not None: + payload_by_original_id[str(original_id)] = payload + + ordered_payloads: list[dict[str, Any] | None] = [] + for requested_id, qdrant_id in zip(ids, qdrant_ids): + payload = payload_by_original_id.get(str(requested_id)) + if payload is None: + payload = payload_by_qdrant_id.get(str(qdrant_id)) + ordered_payloads.append(payload) + + return ordered_payloads + except Exception as e: + logger.error( + f"[{self.workspace}] Error retrieving vector data for IDs {ids}: {e}" + ) + return [] + + async def get_vectors_by_ids(self, ids: list[str]) -> dict[str, list[float]]: + """Get vectors by their IDs, returning only ID and vector data for efficiency + + Args: + ids: List of unique identifiers + + Returns: + Dictionary mapping IDs to their vector embeddings + Format: {id: [vector_values], ...} + """ + if not ids: + return {} + + try: + # Convert to Qdrant compatible IDs + qdrant_ids = [ + compute_mdhash_id_for_qdrant(id, prefix=self.effective_workspace) + for id in ids + ] + + # Retrieve the points by IDs with vectors + results = self._client.retrieve( + collection_name=self.final_namespace, + ids=qdrant_ids, + with_vectors=True, # Important: request vectors + with_payload=True, + ) + + vectors_dict = {} + for point in results: + if point and point.vector is not None and point.payload: + # Get original ID from payload + original_id = point.payload.get(ID_FIELD) + if original_id: + # Convert numpy array to list if needed + vector_data = point.vector + if isinstance(vector_data, np.ndarray): + vector_data = vector_data.tolist() + vectors_dict[original_id] = vector_data + + return vectors_dict + except Exception as e: + logger.error( + f"[{self.workspace}] Error retrieving vectors by IDs from {self.namespace}: {e}" + ) + return {} + + async def drop(self) -> dict[str, str]: + """Drop all vector data from storage and clean up resources + + This method will delete all data for the current workspace from the Qdrant collection. + + Returns: + dict[str, str]: Operation status and message + - On success: {"status": "success", "message": "data dropped"} + - On failure: {"status": "error", "message": ""} + """ + async with get_storage_lock(): + try: + # Delete all points for the current workspace + self._client.delete( + collection_name=self.final_namespace, + points_selector=models.FilterSelector( + filter=models.Filter( + must=[workspace_filter_condition(self.effective_workspace)] + ) + ), + wait=True, + ) + + logger.info( + f"[{self.workspace}] Process {os.getpid()} dropped workspace data from Qdrant collection {self.namespace}" + ) + return {"status": "success", "message": "data dropped"} + except Exception as e: + logger.error( + f"[{self.workspace}] Error dropping workspace data from Qdrant collection {self.namespace}: {e}" + ) + return {"status": "error", "message": str(e)} diff --git a/LightRAG/lightrag/kg/redis_impl.py b/LightRAG/lightrag/kg/redis_impl.py new file mode 100644 index 00000000..2e9a7d43 --- /dev/null +++ b/LightRAG/lightrag/kg/redis_impl.py @@ -0,0 +1,1125 @@ +import os +import logging +from typing import Any, final, Union +from dataclasses import dataclass +import pipmaster as pm +import configparser +from contextlib import asynccontextmanager +import threading + +if not pm.is_installed("redis"): + pm.install("redis") + +# aioredis is a depricated library, replaced with redis +from redis.asyncio import Redis, ConnectionPool # type: ignore +from redis.exceptions import RedisError, ConnectionError, TimeoutError # type: ignore +from lightrag.utils import logger, get_pinyin_sort_key + +from lightrag.base import ( + BaseKVStorage, + DocStatusStorage, + DocStatus, + DocProcessingStatus, +) +from ..kg.shared_storage import get_data_init_lock, get_storage_lock +import json + +# Import tenacity for retry logic +from tenacity import ( + retry, + stop_after_attempt, + wait_exponential, + retry_if_exception_type, + before_sleep_log, +) + +config = configparser.ConfigParser() +config.read("config.ini", "utf-8") + +# Constants for Redis connection pool with environment variable support +MAX_CONNECTIONS = int(os.getenv("REDIS_MAX_CONNECTIONS", "200")) +SOCKET_TIMEOUT = float(os.getenv("REDIS_SOCKET_TIMEOUT", "30.0")) +SOCKET_CONNECT_TIMEOUT = float(os.getenv("REDIS_CONNECT_TIMEOUT", "10.0")) +RETRY_ATTEMPTS = int(os.getenv("REDIS_RETRY_ATTEMPTS", "3")) + +# Tenacity retry decorator for Redis operations +redis_retry = retry( + stop=stop_after_attempt(RETRY_ATTEMPTS), + wait=wait_exponential(multiplier=1, min=1, max=8), + retry=( + retry_if_exception_type(ConnectionError) + | retry_if_exception_type(TimeoutError) + | retry_if_exception_type(RedisError) + ), + before_sleep=before_sleep_log(logger, logging.WARNING), +) + + +class RedisConnectionManager: + """Shared Redis connection pool manager to avoid creating multiple pools for the same Redis URI""" + + _pools = {} + _pool_refs = {} # Track reference count for each pool + _lock = threading.Lock() + + @classmethod + def get_pool(cls, redis_url: str) -> ConnectionPool: + """Get or create a connection pool for the given Redis URL""" + with cls._lock: + if redis_url not in cls._pools: + cls._pools[redis_url] = ConnectionPool.from_url( + redis_url, + max_connections=MAX_CONNECTIONS, + decode_responses=True, + socket_timeout=SOCKET_TIMEOUT, + socket_connect_timeout=SOCKET_CONNECT_TIMEOUT, + ) + cls._pool_refs[redis_url] = 0 + logger.info(f"Created shared Redis connection pool for {redis_url}") + + # Increment reference count + cls._pool_refs[redis_url] += 1 + logger.debug( + f"Redis pool {redis_url} reference count: {cls._pool_refs[redis_url]}" + ) + + return cls._pools[redis_url] + + @classmethod + def release_pool(cls, redis_url: str): + """Release a reference to the connection pool""" + with cls._lock: + if redis_url in cls._pool_refs: + cls._pool_refs[redis_url] -= 1 + logger.debug( + f"Redis pool {redis_url} reference count: {cls._pool_refs[redis_url]}" + ) + + # If no more references, close the pool + if cls._pool_refs[redis_url] <= 0: + try: + cls._pools[redis_url].disconnect() + logger.info( + f"Closed Redis connection pool for {redis_url} (no more references)" + ) + except Exception as e: + logger.error(f"Error closing Redis pool for {redis_url}: {e}") + finally: + del cls._pools[redis_url] + del cls._pool_refs[redis_url] + + @classmethod + def close_all_pools(cls): + """Close all connection pools (for cleanup)""" + with cls._lock: + for url, pool in cls._pools.items(): + try: + pool.disconnect() + logger.info(f"Closed Redis connection pool for {url}") + except Exception as e: + logger.error(f"Error closing Redis pool for {url}: {e}") + cls._pools.clear() + cls._pool_refs.clear() + + +@final +@dataclass +class RedisKVStorage(BaseKVStorage): + def __post_init__(self): + # Check for REDIS_WORKSPACE environment variable first (higher priority) + # This allows administrators to force a specific workspace for all Redis storage instances + redis_workspace = os.environ.get("REDIS_WORKSPACE") + if redis_workspace and redis_workspace.strip(): + # Use environment variable value, overriding the passed workspace parameter + effective_workspace = redis_workspace.strip() + logger.info( + f"Using REDIS_WORKSPACE environment variable: '{effective_workspace}' (overriding passed workspace: '{self.workspace}')" + ) + else: + # Use the workspace parameter passed during initialization + effective_workspace = self.workspace + if effective_workspace: + logger.debug( + f"Using passed workspace parameter: '{effective_workspace}'" + ) + + # Build final_namespace with workspace prefix for data isolation + # Keep original namespace unchanged for type detection logic + if effective_workspace: + self.final_namespace = f"{effective_workspace}_{self.namespace}" + logger.debug( + f"Final namespace with workspace prefix: '{self.final_namespace}'" + ) + else: + # When workspace is empty, final_namespace equals original namespace + self.final_namespace = self.namespace + self.workspace = "_" + logger.debug(f"Final namespace (no workspace): '{self.final_namespace}'") + + self._redis_url = os.environ.get( + "REDIS_URI", config.get("redis", "uri", fallback="redis://localhost:6379") + ) + self._pool = None + self._redis = None + self._initialized = False + + try: + # Use shared connection pool + self._pool = RedisConnectionManager.get_pool(self._redis_url) + self._redis = Redis(connection_pool=self._pool) + logger.info( + f"[{self.workspace}] Initialized Redis KV storage for {self.namespace} using shared connection pool" + ) + except Exception as e: + # Clean up on initialization failure + if self._redis_url: + RedisConnectionManager.release_pool(self._redis_url) + logger.error( + f"[{self.workspace}] Failed to initialize Redis KV storage: {e}" + ) + raise + + async def initialize(self): + """Initialize Redis connection and migrate legacy cache structure if needed""" + async with get_data_init_lock(): + if self._initialized: + return + + # Test connection + try: + async with self._get_redis_connection() as redis: + await redis.ping() + logger.info( + f"[{self.workspace}] Connected to Redis for namespace {self.namespace}" + ) + self._initialized = True + except Exception as e: + logger.error(f"[{self.workspace}] Failed to connect to Redis: {e}") + # Clean up on connection failure + await self.close() + raise + + # Migrate legacy cache structure if this is a cache namespace + if self.namespace.endswith("_cache"): + try: + await self._migrate_legacy_cache_structure() + except Exception as e: + logger.error( + f"[{self.workspace}] Failed to migrate legacy cache structure: {e}" + ) + # Don't fail initialization for migration errors, just log them + + @asynccontextmanager + async def _get_redis_connection(self): + """Safe context manager for Redis operations.""" + if not self._redis: + raise ConnectionError("Redis connection not initialized") + + try: + # Use the existing Redis instance with shared pool + yield self._redis + except ConnectionError as e: + logger.error( + f"[{self.workspace}] Redis connection error in {self.namespace}: {e}" + ) + raise + except RedisError as e: + logger.error( + f"[{self.workspace}] Redis operation error in {self.namespace}: {e}" + ) + raise + except Exception as e: + logger.error( + f"[{self.workspace}] Unexpected error in Redis operation for {self.namespace}: {e}" + ) + raise + + async def close(self): + """Close the Redis connection and release pool reference to prevent resource leaks.""" + if hasattr(self, "_redis") and self._redis: + try: + await self._redis.close() + logger.debug( + f"[{self.workspace}] Closed Redis connection for {self.namespace}" + ) + except Exception as e: + logger.error(f"[{self.workspace}] Error closing Redis connection: {e}") + finally: + self._redis = None + + # Release the pool reference (will auto-close pool if no more references) + if hasattr(self, "_redis_url") and self._redis_url: + RedisConnectionManager.release_pool(self._redis_url) + self._pool = None + logger.debug( + f"[{self.workspace}] Released Redis connection pool reference for {self.namespace}" + ) + + async def __aenter__(self): + """Support for async context manager.""" + return self + + async def __aexit__(self, exc_type, exc_val, exc_tb): + """Ensure Redis resources are cleaned up when exiting context.""" + await self.close() + + @redis_retry + async def get_by_id(self, id: str) -> dict[str, Any] | None: + async with self._get_redis_connection() as redis: + try: + data = await redis.get(f"{self.final_namespace}:{id}") + if data: + result = json.loads(data) + # Ensure time fields are present, provide default values for old data + result.setdefault("create_time", 0) + result.setdefault("update_time", 0) + return result + return None + except json.JSONDecodeError as e: + logger.error(f"[{self.workspace}] JSON decode error for id {id}: {e}") + return None + + @redis_retry + async def get_by_ids(self, ids: list[str]) -> list[dict[str, Any]]: + async with self._get_redis_connection() as redis: + try: + pipe = redis.pipeline() + for id in ids: + pipe.get(f"{self.final_namespace}:{id}") + results = await pipe.execute() + + processed_results = [] + for result in results: + if result: + data = json.loads(result) + # Ensure time fields are present for all documents + data.setdefault("create_time", 0) + data.setdefault("update_time", 0) + processed_results.append(data) + else: + processed_results.append(None) + + return processed_results + except json.JSONDecodeError as e: + logger.error(f"[{self.workspace}] JSON decode error in batch get: {e}") + return [None] * len(ids) + + async def filter_keys(self, keys: set[str]) -> set[str]: + async with self._get_redis_connection() as redis: + pipe = redis.pipeline() + keys_list = list(keys) # Convert set to list for indexing + for key in keys_list: + pipe.exists(f"{self.final_namespace}:{key}") + results = await pipe.execute() + + existing_ids = {keys_list[i] for i, exists in enumerate(results) if exists} + return set(keys) - existing_ids + + @redis_retry + async def upsert(self, data: dict[str, dict[str, Any]]) -> None: + if not data: + return + + import time + + current_time = int(time.time()) # Get current Unix timestamp + + async with self._get_redis_connection() as redis: + try: + # Check which keys already exist to determine create vs update + pipe = redis.pipeline() + for k in data.keys(): + pipe.exists(f"{self.final_namespace}:{k}") + exists_results = await pipe.execute() + + # Add timestamps to data + for i, (k, v) in enumerate(data.items()): + # For text_chunks namespace, ensure llm_cache_list field exists + if self.namespace.endswith("text_chunks"): + if "llm_cache_list" not in v: + v["llm_cache_list"] = [] + + # Add timestamps based on whether key exists + if exists_results[i]: # Key exists, only update update_time + v["update_time"] = current_time + else: # New key, set both create_time and update_time + v["create_time"] = current_time + v["update_time"] = current_time + + v["_id"] = k + + # Store the data + pipe = redis.pipeline() + for k, v in data.items(): + pipe.set(f"{self.final_namespace}:{k}", json.dumps(v)) + await pipe.execute() + + except json.JSONDecodeError as e: + logger.error(f"[{self.workspace}] JSON decode error during upsert: {e}") + raise + + async def index_done_callback(self) -> None: + # Redis handles persistence automatically + pass + + async def is_empty(self) -> bool: + """Check if the storage is empty for the current workspace and namespace + + Returns: + bool: True if storage is empty, False otherwise + """ + pattern = f"{self.final_namespace}:*" + try: + async with self._get_redis_connection() as redis: + # Use scan to check if any keys exist + async for key in redis.scan_iter(match=pattern, count=1): + return False # Found at least one key + return True # No keys found + except Exception as e: + logger.error(f"[{self.workspace}] Error checking if storage is empty: {e}") + return True + + async def delete(self, ids: list[str]) -> None: + """Delete specific records from storage by their IDs""" + if not ids: + return + + async with self._get_redis_connection() as redis: + pipe = redis.pipeline() + for id in ids: + pipe.delete(f"{self.final_namespace}:{id}") + + results = await pipe.execute() + deleted_count = sum(results) + logger.info( + f"[{self.workspace}] Deleted {deleted_count} of {len(ids)} entries from {self.namespace}" + ) + + async def drop(self) -> dict[str, str]: + """Drop the storage by removing all keys under the current namespace. + + Returns: + dict[str, str]: Status of the operation with keys 'status' and 'message' + """ + async with get_storage_lock(): + async with self._get_redis_connection() as redis: + try: + # Use SCAN to find all keys with the namespace prefix + pattern = f"{self.final_namespace}:*" + cursor = 0 + deleted_count = 0 + + while True: + cursor, keys = await redis.scan( + cursor, match=pattern, count=1000 + ) + if keys: + # Delete keys in batches + pipe = redis.pipeline() + for key in keys: + pipe.delete(key) + results = await pipe.execute() + deleted_count += sum(results) + + if cursor == 0: + break + + logger.info( + f"[{self.workspace}] Dropped {deleted_count} keys from {self.namespace}" + ) + return { + "status": "success", + "message": f"{deleted_count} keys dropped", + } + + except Exception as e: + logger.error( + f"[{self.workspace}] Error dropping keys from {self.namespace}: {e}" + ) + return {"status": "error", "message": str(e)} + + async def _migrate_legacy_cache_structure(self): + """Migrate legacy nested cache structure to flattened structure for Redis + + Redis already stores data in a flattened way, but we need to check for + legacy keys that might contain nested JSON structures and migrate them. + + Early exit if any flattened key is found (indicating migration already done). + """ + from lightrag.utils import generate_cache_key + + async with self._get_redis_connection() as redis: + # Get all keys for this namespace + keys = await redis.keys(f"{self.final_namespace}:*") + + if not keys: + return + + # Check if we have any flattened keys already - if so, skip migration + has_flattened_keys = False + keys_to_migrate = [] + + for key in keys: + # Extract the ID part (after namespace:) + key_id = key.split(":", 1)[1] + + # Check if already in flattened format (contains exactly 2 colons for mode:cache_type:hash) + if ":" in key_id and len(key_id.split(":")) == 3: + has_flattened_keys = True + break # Early exit - migration already done + + # Get the data to check if it's a legacy nested structure + data = await redis.get(key) + if data: + try: + parsed_data = json.loads(data) + # Check if this looks like a legacy cache mode with nested structure + if isinstance(parsed_data, dict) and all( + isinstance(v, dict) and "return" in v + for v in parsed_data.values() + ): + keys_to_migrate.append((key, key_id, parsed_data)) + except json.JSONDecodeError: + continue + + # If we found any flattened keys, assume migration is already done + if has_flattened_keys: + logger.debug( + f"[{self.workspace}] Found flattened cache keys in {self.namespace}, skipping migration" + ) + return + + if not keys_to_migrate: + return + + # Perform migration + pipe = redis.pipeline() + migration_count = 0 + + for old_key, mode, nested_data in keys_to_migrate: + # Delete the old key + pipe.delete(old_key) + + # Create new flattened keys + for cache_hash, cache_entry in nested_data.items(): + cache_type = cache_entry.get("cache_type", "extract") + flattened_key = generate_cache_key(mode, cache_type, cache_hash) + full_key = f"{self.final_namespace}:{flattened_key}" + pipe.set(full_key, json.dumps(cache_entry)) + migration_count += 1 + + await pipe.execute() + + if migration_count > 0: + logger.info( + f"[{self.workspace}] Migrated {migration_count} legacy cache entries to flattened structure in Redis" + ) + + +@final +@dataclass +class RedisDocStatusStorage(DocStatusStorage): + """Redis implementation of document status storage""" + + def __post_init__(self): + # Check for REDIS_WORKSPACE environment variable first (higher priority) + # This allows administrators to force a specific workspace for all Redis storage instances + redis_workspace = os.environ.get("REDIS_WORKSPACE") + if redis_workspace and redis_workspace.strip(): + # Use environment variable value, overriding the passed workspace parameter + effective_workspace = redis_workspace.strip() + logger.info( + f"Using REDIS_WORKSPACE environment variable: '{effective_workspace}' (overriding passed workspace: '{self.workspace}')" + ) + else: + # Use the workspace parameter passed during initialization + effective_workspace = self.workspace + if effective_workspace: + logger.debug( + f"Using passed workspace parameter: '{effective_workspace}'" + ) + + # Build final_namespace with workspace prefix for data isolation + # Keep original namespace unchanged for type detection logic + if effective_workspace: + self.final_namespace = f"{effective_workspace}_{self.namespace}" + logger.debug( + f"[{self.workspace}] Final namespace with workspace prefix: '{self.namespace}'" + ) + else: + # When workspace is empty, final_namespace equals original namespace + self.final_namespace = self.namespace + self.workspace = "_" + logger.debug( + f"[{self.workspace}] Final namespace (no workspace): '{self.namespace}'" + ) + + self._redis_url = os.environ.get( + "REDIS_URI", config.get("redis", "uri", fallback="redis://localhost:6379") + ) + self._pool = None + self._redis = None + self._initialized = False + + try: + # Use shared connection pool + self._pool = RedisConnectionManager.get_pool(self._redis_url) + self._redis = Redis(connection_pool=self._pool) + logger.info( + f"[{self.workspace}] Initialized Redis doc status storage for {self.namespace} using shared connection pool" + ) + except Exception as e: + # Clean up on initialization failure + if self._redis_url: + RedisConnectionManager.release_pool(self._redis_url) + logger.error( + f"[{self.workspace}] Failed to initialize Redis doc status storage: {e}" + ) + raise + + async def initialize(self): + """Initialize Redis connection""" + async with get_data_init_lock(): + if self._initialized: + return + + try: + async with self._get_redis_connection() as redis: + await redis.ping() + logger.info( + f"[{self.workspace}] Connected to Redis for doc status namespace {self.namespace}" + ) + self._initialized = True + except Exception as e: + logger.error( + f"[{self.workspace}] Failed to connect to Redis for doc status: {e}" + ) + # Clean up on connection failure + await self.close() + raise + + @asynccontextmanager + async def _get_redis_connection(self): + """Safe context manager for Redis operations.""" + if not self._redis: + raise ConnectionError("Redis connection not initialized") + + try: + # Use the existing Redis instance with shared pool + yield self._redis + except ConnectionError as e: + logger.error( + f"[{self.workspace}] Redis connection error in doc status {self.namespace}: {e}" + ) + raise + except RedisError as e: + logger.error( + f"[{self.workspace}] Redis operation error in doc status {self.namespace}: {e}" + ) + raise + except Exception as e: + logger.error( + f"[{self.workspace}] Unexpected error in Redis doc status operation for {self.namespace}: {e}" + ) + raise + + async def close(self): + """Close the Redis connection and release pool reference to prevent resource leaks.""" + if hasattr(self, "_redis") and self._redis: + try: + await self._redis.close() + logger.debug( + f"[{self.workspace}] Closed Redis connection for doc status {self.namespace}" + ) + except Exception as e: + logger.error(f"[{self.workspace}] Error closing Redis connection: {e}") + finally: + self._redis = None + + # Release the pool reference (will auto-close pool if no more references) + if hasattr(self, "_redis_url") and self._redis_url: + RedisConnectionManager.release_pool(self._redis_url) + self._pool = None + logger.debug( + f"[{self.workspace}] Released Redis connection pool reference for doc status {self.namespace}" + ) + + async def __aenter__(self): + """Support for async context manager.""" + return self + + async def __aexit__(self, exc_type, exc_val, exc_tb): + """Ensure Redis resources are cleaned up when exiting context.""" + await self.close() + + async def filter_keys(self, keys: set[str]) -> set[str]: + """Return keys that should be processed (not in storage or not successfully processed)""" + async with self._get_redis_connection() as redis: + pipe = redis.pipeline() + keys_list = list(keys) + for key in keys_list: + pipe.exists(f"{self.final_namespace}:{key}") + results = await pipe.execute() + + existing_ids = {keys_list[i] for i, exists in enumerate(results) if exists} + return set(keys) - existing_ids + + async def get_by_ids(self, ids: list[str]) -> list[dict[str, Any]]: + ordered_results: list[dict[str, Any] | None] = [] + async with self._get_redis_connection() as redis: + try: + pipe = redis.pipeline() + for id in ids: + pipe.get(f"{self.final_namespace}:{id}") + results = await pipe.execute() + + for result_data in results: + if result_data: + try: + ordered_results.append(json.loads(result_data)) + except json.JSONDecodeError as e: + logger.error( + f"[{self.workspace}] JSON decode error in get_by_ids: {e}" + ) + ordered_results.append(None) + else: + ordered_results.append(None) + except Exception as e: + logger.error(f"[{self.workspace}] Error in get_by_ids: {e}") + return ordered_results + + async def get_status_counts(self) -> dict[str, int]: + """Get counts of documents in each status""" + counts = {status.value: 0 for status in DocStatus} + async with self._get_redis_connection() as redis: + try: + # Use SCAN to iterate through all keys in the namespace + cursor = 0 + while True: + cursor, keys = await redis.scan( + cursor, match=f"{self.final_namespace}:*", count=1000 + ) + if keys: + # Get all values in batch + pipe = redis.pipeline() + for key in keys: + pipe.get(key) + values = await pipe.execute() + + # Count statuses + for value in values: + if value: + try: + doc_data = json.loads(value) + status = doc_data.get("status") + if status in counts: + counts[status] += 1 + except json.JSONDecodeError: + continue + + if cursor == 0: + break + except Exception as e: + logger.error(f"[{self.workspace}] Error getting status counts: {e}") + + return counts + + async def get_docs_by_status( + self, status: DocStatus + ) -> dict[str, DocProcessingStatus]: + """Get all documents with a specific status""" + result = {} + async with self._get_redis_connection() as redis: + try: + # Use SCAN to iterate through all keys in the namespace + cursor = 0 + while True: + cursor, keys = await redis.scan( + cursor, match=f"{self.final_namespace}:*", count=1000 + ) + if keys: + # Get all values in batch + pipe = redis.pipeline() + for key in keys: + pipe.get(key) + values = await pipe.execute() + + # Filter by status and create DocProcessingStatus objects + for key, value in zip(keys, values): + if value: + try: + doc_data = json.loads(value) + if doc_data.get("status") == status.value: + # Extract document ID from key + doc_id = key.split(":", 1)[1] + + # Make a copy of the data to avoid modifying the original + data = doc_data.copy() + # Remove deprecated content field if it exists + data.pop("content", None) + # If file_path is not in data, use document id as file path + if "file_path" not in data: + data["file_path"] = "no-file-path" + # Ensure new fields exist with default values + if "metadata" not in data: + data["metadata"] = {} + if "error_msg" not in data: + data["error_msg"] = None + + result[doc_id] = DocProcessingStatus(**data) + except (json.JSONDecodeError, KeyError) as e: + logger.error( + f"[{self.workspace}] Error processing document {key}: {e}" + ) + continue + + if cursor == 0: + break + except Exception as e: + logger.error(f"[{self.workspace}] Error getting docs by status: {e}") + + return result + + async def get_docs_by_track_id( + self, track_id: str + ) -> dict[str, DocProcessingStatus]: + """Get all documents with a specific track_id""" + result = {} + async with self._get_redis_connection() as redis: + try: + # Use SCAN to iterate through all keys in the namespace + cursor = 0 + while True: + cursor, keys = await redis.scan( + cursor, match=f"{self.final_namespace}:*", count=1000 + ) + if keys: + # Get all values in batch + pipe = redis.pipeline() + for key in keys: + pipe.get(key) + values = await pipe.execute() + + # Filter by track_id and create DocProcessingStatus objects + for key, value in zip(keys, values): + if value: + try: + doc_data = json.loads(value) + if doc_data.get("track_id") == track_id: + # Extract document ID from key + doc_id = key.split(":", 1)[1] + + # Make a copy of the data to avoid modifying the original + data = doc_data.copy() + # Remove deprecated content field if it exists + data.pop("content", None) + # If file_path is not in data, use document id as file path + if "file_path" not in data: + data["file_path"] = "no-file-path" + # Ensure new fields exist with default values + if "metadata" not in data: + data["metadata"] = {} + if "error_msg" not in data: + data["error_msg"] = None + + result[doc_id] = DocProcessingStatus(**data) + except (json.JSONDecodeError, KeyError) as e: + logger.error( + f"[{self.workspace}] Error processing document {key}: {e}" + ) + continue + + if cursor == 0: + break + except Exception as e: + logger.error(f"[{self.workspace}] Error getting docs by track_id: {e}") + + return result + + async def index_done_callback(self) -> None: + """Redis handles persistence automatically""" + pass + + async def is_empty(self) -> bool: + """Check if the storage is empty for the current workspace and namespace + + Returns: + bool: True if storage is empty, False otherwise + """ + pattern = f"{self.final_namespace}:*" + try: + async with self._get_redis_connection() as redis: + # Use scan to check if any keys exist + async for key in redis.scan_iter(match=pattern, count=1): + return False # Found at least one key + return True # No keys found + except Exception as e: + logger.error(f"[{self.workspace}] Error checking if storage is empty: {e}") + return True + + @redis_retry + async def upsert(self, data: dict[str, dict[str, Any]]) -> None: + """Insert or update document status data""" + if not data: + return + + logger.debug( + f"[{self.workspace}] Inserting {len(data)} records to {self.namespace}" + ) + async with self._get_redis_connection() as redis: + try: + # Ensure chunks_list field exists for new documents + for doc_id, doc_data in data.items(): + if "chunks_list" not in doc_data: + doc_data["chunks_list"] = [] + + pipe = redis.pipeline() + for k, v in data.items(): + pipe.set(f"{self.final_namespace}:{k}", json.dumps(v)) + await pipe.execute() + except json.JSONDecodeError as e: + logger.error(f"[{self.workspace}] JSON decode error during upsert: {e}") + raise + + @redis_retry + async def get_by_id(self, id: str) -> Union[dict[str, Any], None]: + async with self._get_redis_connection() as redis: + try: + data = await redis.get(f"{self.final_namespace}:{id}") + return json.loads(data) if data else None + except json.JSONDecodeError as e: + logger.error(f"[{self.workspace}] JSON decode error for id {id}: {e}") + return None + + async def delete(self, doc_ids: list[str]) -> None: + """Delete specific records from storage by their IDs""" + if not doc_ids: + return + + async with self._get_redis_connection() as redis: + pipe = redis.pipeline() + for doc_id in doc_ids: + pipe.delete(f"{self.final_namespace}:{doc_id}") + + results = await pipe.execute() + deleted_count = sum(results) + logger.info( + f"[{self.workspace}] Deleted {deleted_count} of {len(doc_ids)} doc status entries from {self.namespace}" + ) + + async def get_docs_paginated( + self, + status_filter: DocStatus | None = None, + page: int = 1, + page_size: int = 50, + sort_field: str = "updated_at", + sort_direction: str = "desc", + ) -> tuple[list[tuple[str, DocProcessingStatus]], int]: + """Get documents with pagination support + + Args: + status_filter: Filter by document status, None for all statuses + page: Page number (1-based) + page_size: Number of documents per page (10-200) + sort_field: Field to sort by ('created_at', 'updated_at', 'id') + sort_direction: Sort direction ('asc' or 'desc') + + Returns: + Tuple of (list of (doc_id, DocProcessingStatus) tuples, total_count) + """ + # Validate parameters + if page < 1: + page = 1 + if page_size < 10: + page_size = 10 + elif page_size > 200: + page_size = 200 + + if sort_field not in ["created_at", "updated_at", "id", "file_path"]: + sort_field = "updated_at" + + if sort_direction.lower() not in ["asc", "desc"]: + sort_direction = "desc" + + # For Redis, we need to load all data and sort/filter in memory + all_docs = [] + total_count = 0 + + async with self._get_redis_connection() as redis: + try: + # Use SCAN to iterate through all keys in the namespace + cursor = 0 + while True: + cursor, keys = await redis.scan( + cursor, match=f"{self.final_namespace}:*", count=1000 + ) + if keys: + # Get all values in batch + pipe = redis.pipeline() + for key in keys: + pipe.get(key) + values = await pipe.execute() + + # Process documents + for key, value in zip(keys, values): + if value: + try: + doc_data = json.loads(value) + + # Apply status filter + if ( + status_filter is not None + and doc_data.get("status") + != status_filter.value + ): + continue + + # Extract document ID from key + doc_id = key.split(":", 1)[1] + + # Prepare document data + data = doc_data.copy() + data.pop("content", None) + if "file_path" not in data: + data["file_path"] = "no-file-path" + if "metadata" not in data: + data["metadata"] = {} + if "error_msg" not in data: + data["error_msg"] = None + + # Calculate sort key for sorting (but don't add to data) + if sort_field == "id": + sort_key = doc_id + elif sort_field == "file_path": + # Use pinyin sorting for file_path field to support Chinese characters + file_path_value = data.get(sort_field, "") + sort_key = get_pinyin_sort_key(file_path_value) + else: + sort_key = data.get(sort_field, "") + + doc_status = DocProcessingStatus(**data) + all_docs.append((doc_id, doc_status, sort_key)) + + except (json.JSONDecodeError, KeyError) as e: + logger.error( + f"[{self.workspace}] Error processing document {key}: {e}" + ) + continue + + if cursor == 0: + break + + except Exception as e: + logger.error(f"[{self.workspace}] Error getting paginated docs: {e}") + return [], 0 + + # Sort documents using the separate sort key + reverse_sort = sort_direction.lower() == "desc" + all_docs.sort(key=lambda x: x[2], reverse=reverse_sort) + + # Remove sort key from tuples and keep only (doc_id, doc_status) + all_docs = [(doc_id, doc_status) for doc_id, doc_status, _ in all_docs] + + total_count = len(all_docs) + + # Apply pagination + start_idx = (page - 1) * page_size + end_idx = start_idx + page_size + paginated_docs = all_docs[start_idx:end_idx] + + return paginated_docs, total_count + + async def get_all_status_counts(self) -> dict[str, int]: + """Get counts of documents in each status for all documents + + Returns: + Dictionary mapping status names to counts, including 'all' field + """ + counts = await self.get_status_counts() + + # Add 'all' field with total count + total_count = sum(counts.values()) + counts["all"] = total_count + + return counts + + async def get_doc_by_file_path(self, file_path: str) -> Union[dict[str, Any], None]: + """Get document by file path + + Args: + file_path: The file path to search for + + Returns: + Union[dict[str, Any], None]: Document data if found, None otherwise + Returns the same format as get_by_id method + """ + async with self._get_redis_connection() as redis: + try: + # Use SCAN to iterate through all keys in the namespace + cursor = 0 + while True: + cursor, keys = await redis.scan( + cursor, match=f"{self.final_namespace}:*", count=1000 + ) + if keys: + # Get all values in batch + pipe = redis.pipeline() + for key in keys: + pipe.get(key) + values = await pipe.execute() + + # Check each document for matching file_path + for value in values: + if value: + try: + doc_data = json.loads(value) + if doc_data.get("file_path") == file_path: + return doc_data + except json.JSONDecodeError as e: + logger.error( + f"[{self.workspace}] JSON decode error in get_doc_by_file_path: {e}" + ) + continue + + if cursor == 0: + break + + return None + except Exception as e: + logger.error(f"[{self.workspace}] Error in get_doc_by_file_path: {e}") + return None + + async def drop(self) -> dict[str, str]: + """Drop all document status data from storage and clean up resources""" + async with get_storage_lock(): + try: + async with self._get_redis_connection() as redis: + # Use SCAN to find all keys with the namespace prefix + pattern = f"{self.final_namespace}:*" + cursor = 0 + deleted_count = 0 + + while True: + cursor, keys = await redis.scan( + cursor, match=pattern, count=1000 + ) + if keys: + # Delete keys in batches + pipe = redis.pipeline() + for key in keys: + pipe.delete(key) + results = await pipe.execute() + deleted_count += sum(results) + + if cursor == 0: + break + + logger.info( + f"[{self.workspace}] Dropped {deleted_count} doc status keys from {self.namespace}" + ) + return {"status": "success", "message": "data dropped"} + except Exception as e: + logger.error( + f"[{self.workspace}] Error dropping doc status {self.namespace}: {e}" + ) + return {"status": "error", "message": str(e)} diff --git a/LightRAG/lightrag/kg/shared_storage.py b/LightRAG/lightrag/kg/shared_storage.py new file mode 100644 index 00000000..0abcf719 --- /dev/null +++ b/LightRAG/lightrag/kg/shared_storage.py @@ -0,0 +1,1536 @@ +import os +import sys +import asyncio +import multiprocessing as mp +from multiprocessing.synchronize import Lock as ProcessLock +from multiprocessing import Manager +import time +import logging +from typing import Any, Dict, List, Optional, Union, TypeVar, Generic + +from lightrag.exceptions import PipelineNotInitializedError + +DEBUG_LOCKS = False + + +# Define a direct print function for critical logs that must be visible in all processes +def direct_log(message, enable_output: bool = True, level: str = "DEBUG"): + """ + Log a message directly to stderr to ensure visibility in all processes, + including the Gunicorn master process. + + Args: + message: The message to log + level: Log level for message (control the visibility of the message by comparing with the current logger level) + enable_output: Enable or disable log message (Force to turn off the message,) + """ + if not enable_output: + return + + # Get the current logger level from the lightrag logger + try: + from lightrag.utils import logger + + current_level = logger.getEffectiveLevel() + except ImportError: + # Fallback if lightrag.utils is not available + current_level = 20 # INFO + + # Convert string level to numeric level for comparison + level_mapping = { + "DEBUG": 10, # DEBUG + "INFO": 20, # INFO + "WARNING": 30, # WARNING + "ERROR": 40, # ERROR + "CRITICAL": 50, # CRITICAL + } + message_level = level_mapping.get(level.upper(), logging.DEBUG) + + if message_level >= current_level: + print(f"{level}: {message}", file=sys.stderr, flush=True) + + +T = TypeVar("T") +LockType = Union[ProcessLock, asyncio.Lock] + +_is_multiprocess = None +_workers = None +_manager = None + +# Global singleton data for multi-process keyed locks +_lock_registry: Optional[Dict[str, mp.synchronize.Lock]] = None +_lock_registry_count: Optional[Dict[str, int]] = None +_lock_cleanup_data: Optional[Dict[str, time.time]] = None +_registry_guard = None +# Timeout for keyed locks in seconds (Default 300) +CLEANUP_KEYED_LOCKS_AFTER_SECONDS = 300 +# Cleanup pending list threshold for triggering cleanup (Default 500) +CLEANUP_THRESHOLD = 500 +# Minimum interval between cleanup operations in seconds (Default 30) +MIN_CLEANUP_INTERVAL_SECONDS = 30 +# Track the earliest cleanup time for efficient cleanup triggering (multiprocess locks only) +_earliest_mp_cleanup_time: Optional[float] = None +# Track the last cleanup time to enforce minimum interval (multiprocess locks only) +_last_mp_cleanup_time: Optional[float] = None + +_initialized = None + +# shared data for storage across processes +_shared_dicts: Optional[Dict[str, Any]] = None +_init_flags: Optional[Dict[str, bool]] = None # namespace -> initialized +_update_flags: Optional[Dict[str, bool]] = None # namespace -> updated + +# locks for mutex access +_storage_lock: Optional[LockType] = None +_internal_lock: Optional[LockType] = None +_pipeline_status_lock: Optional[LockType] = None +_graph_db_lock: Optional[LockType] = None +_data_init_lock: Optional[LockType] = None +# Manager for all keyed locks +_storage_keyed_lock: Optional["KeyedUnifiedLock"] = None + +# async locks for coroutine synchronization in multiprocess mode +_async_locks: Optional[Dict[str, asyncio.Lock]] = None + +_debug_n_locks_acquired: int = 0 + + +def inc_debug_n_locks_acquired(): + global _debug_n_locks_acquired + if DEBUG_LOCKS: + _debug_n_locks_acquired += 1 + print(f"DEBUG: Keyed Lock acquired, total: {_debug_n_locks_acquired:>5}") + + +def dec_debug_n_locks_acquired(): + global _debug_n_locks_acquired + if DEBUG_LOCKS: + if _debug_n_locks_acquired > 0: + _debug_n_locks_acquired -= 1 + print(f"DEBUG: Keyed Lock released, total: {_debug_n_locks_acquired:>5}") + else: + raise RuntimeError("Attempting to release lock when no locks are acquired") + + +def get_debug_n_locks_acquired(): + global _debug_n_locks_acquired + return _debug_n_locks_acquired + + +class UnifiedLock(Generic[T]): + """Provide a unified lock interface type for asyncio.Lock and multiprocessing.Lock""" + + def __init__( + self, + lock: Union[ProcessLock, asyncio.Lock], + is_async: bool, + name: str = "unnamed", + enable_logging: bool = True, + async_lock: Optional[asyncio.Lock] = None, + ): + self._lock = lock + self._is_async = is_async + self._pid = os.getpid() # for debug only + self._name = name # for debug only + self._enable_logging = enable_logging # for debug only + self._async_lock = async_lock # auxiliary lock for coroutine synchronization + + async def __aenter__(self) -> "UnifiedLock[T]": + try: + # If in multiprocess mode and async lock exists, acquire it first + if not self._is_async and self._async_lock is not None: + await self._async_lock.acquire() + direct_log( + f"== Lock == Process {self._pid}: Acquired async lock '{self._name}", + level="DEBUG", + enable_output=self._enable_logging, + ) + + # Then acquire the main lock + if self._is_async: + await self._lock.acquire() + else: + self._lock.acquire() + + direct_log( + f"== Lock == Process {self._pid}: Acquired lock {self._name} (async={self._is_async})", + level="INFO", + enable_output=self._enable_logging, + ) + return self + except Exception as e: + # If main lock acquisition fails, release the async lock if it was acquired + if ( + not self._is_async + and self._async_lock is not None + and self._async_lock.locked() + ): + self._async_lock.release() + + direct_log( + f"== Lock == Process {self._pid}: Failed to acquire lock '{self._name}': {e}", + level="ERROR", + enable_output=True, + ) + raise + + async def __aexit__(self, exc_type, exc_val, exc_tb): + main_lock_released = False + try: + # Release main lock first + if self._is_async: + self._lock.release() + else: + self._lock.release() + main_lock_released = True + + direct_log( + f"== Lock == Process {self._pid}: Released lock {self._name} (async={self._is_async})", + level="INFO", + enable_output=self._enable_logging, + ) + + # Then release async lock if in multiprocess mode + if not self._is_async and self._async_lock is not None: + self._async_lock.release() + direct_log( + f"== Lock == Process {self._pid}: Released async lock {self._name}", + level="DEBUG", + enable_output=self._enable_logging, + ) + + except Exception as e: + direct_log( + f"== Lock == Process {self._pid}: Failed to release lock '{self._name}': {e}", + level="ERROR", + enable_output=True, + ) + + # If main lock release failed but async lock hasn't been released, try to release it + if ( + not main_lock_released + and not self._is_async + and self._async_lock is not None + ): + try: + direct_log( + f"== Lock == Process {self._pid}: Attempting to release async lock after main lock failure", + level="DEBUG", + enable_output=self._enable_logging, + ) + self._async_lock.release() + direct_log( + f"== Lock == Process {self._pid}: Successfully released async lock after main lock failure", + level="INFO", + enable_output=self._enable_logging, + ) + except Exception as inner_e: + direct_log( + f"== Lock == Process {self._pid}: Failed to release async lock after main lock failure: {inner_e}", + level="ERROR", + enable_output=True, + ) + + raise + + def __enter__(self) -> "UnifiedLock[T]": + """For backward compatibility""" + try: + if self._is_async: + raise RuntimeError("Use 'async with' for shared_storage lock") + direct_log( + f"== Lock == Process {self._pid}: Acquiring lock {self._name} (sync)", + level="DEBUG", + enable_output=self._enable_logging, + ) + self._lock.acquire() + direct_log( + f"== Lock == Process {self._pid}: Acquired lock {self._name} (sync)", + level="INFO", + enable_output=self._enable_logging, + ) + return self + except Exception as e: + direct_log( + f"== Lock == Process {self._pid}: Failed to acquire lock '{self._name}' (sync): {e}", + level="ERROR", + enable_output=True, + ) + raise + + def __exit__(self, exc_type, exc_val, exc_tb): + """For backward compatibility""" + try: + if self._is_async: + raise RuntimeError("Use 'async with' for shared_storage lock") + direct_log( + f"== Lock == Process {self._pid}: Releasing lock '{self._name}' (sync)", + level="DEBUG", + enable_output=self._enable_logging, + ) + self._lock.release() + direct_log( + f"== Lock == Process {self._pid}: Released lock {self._name} (sync)", + level="INFO", + enable_output=self._enable_logging, + ) + except Exception as e: + direct_log( + f"== Lock == Process {self._pid}: Failed to release lock '{self._name}' (sync): {e}", + level="ERROR", + enable_output=True, + ) + raise + + def locked(self) -> bool: + if self._is_async: + return self._lock.locked() + else: + return self._lock.locked() + + +def _get_combined_key(factory_name: str, key: str) -> str: + """Return the combined key for the factory and key.""" + return f"{factory_name}:{key}" + + +def _perform_lock_cleanup( + lock_type: str, + cleanup_data: Dict[str, float], + lock_registry: Optional[Dict[str, Any]], + lock_count: Optional[Dict[str, int]], + earliest_cleanup_time: Optional[float], + last_cleanup_time: Optional[float], + current_time: float, + threshold_check: bool = True, +) -> tuple[int, Optional[float], Optional[float]]: + """ + Generic lock cleanup function to unify cleanup logic for both multiprocess and async locks. + + Args: + lock_type: Lock type identifier ("mp" or "async") + cleanup_data: Cleanup data dictionary + lock_registry: Lock registry dictionary (can be None for async locks) + lock_count: Lock count dictionary (can be None for async locks) + earliest_cleanup_time: Earliest cleanup time + last_cleanup_time: Last cleanup time + current_time: Current time + threshold_check: Whether to check threshold condition (default True, set to False in cleanup_expired_locks) + + Returns: + tuple: (cleaned_count, new_earliest_time, new_last_cleanup_time) + """ + if len(cleanup_data) == 0: + return 0, earliest_cleanup_time, last_cleanup_time + + # If threshold check is needed and threshold not reached, return directly + if threshold_check and len(cleanup_data) < CLEANUP_THRESHOLD: + return 0, earliest_cleanup_time, last_cleanup_time + + # Time rollback detection + if last_cleanup_time is not None and current_time < last_cleanup_time: + direct_log( + f"== {lock_type} Lock == Time rollback detected, resetting cleanup time", + level="WARNING", + enable_output=False, + ) + last_cleanup_time = None + + # Check cleanup conditions + has_expired_locks = ( + earliest_cleanup_time is not None + and current_time - earliest_cleanup_time > CLEANUP_KEYED_LOCKS_AFTER_SECONDS + ) + + interval_satisfied = ( + last_cleanup_time is None + or current_time - last_cleanup_time > MIN_CLEANUP_INTERVAL_SECONDS + ) + + if not (has_expired_locks and interval_satisfied): + return 0, earliest_cleanup_time, last_cleanup_time + + try: + cleaned_count = 0 + new_earliest_time = None + + # Calculate total count before cleanup + total_cleanup_len = len(cleanup_data) + + # Perform cleanup operation + for cleanup_key, cleanup_time in list(cleanup_data.items()): + if current_time - cleanup_time > CLEANUP_KEYED_LOCKS_AFTER_SECONDS: + # Remove from cleanup data + cleanup_data.pop(cleanup_key, None) + + # Remove from lock registry if exists + if lock_registry is not None: + lock_registry.pop(cleanup_key, None) + if lock_count is not None: + lock_count.pop(cleanup_key, None) + + cleaned_count += 1 + else: + # Track the earliest time among remaining locks + if new_earliest_time is None or cleanup_time < new_earliest_time: + new_earliest_time = cleanup_time + + # Update state only after successful cleanup + if cleaned_count > 0: + new_last_cleanup_time = current_time + + # Log cleanup results + next_cleanup_in = max( + (new_earliest_time + CLEANUP_KEYED_LOCKS_AFTER_SECONDS - current_time) + if new_earliest_time + else float("inf"), + MIN_CLEANUP_INTERVAL_SECONDS, + ) + + if lock_type == "async": + direct_log( + f"== {lock_type} Lock == Cleaned up {cleaned_count}/{total_cleanup_len} expired {lock_type} locks, " + f"next cleanup in {next_cleanup_in:.1f}s", + enable_output=False, + level="INFO", + ) + else: + direct_log( + f"== {lock_type} Lock == Cleaned up {cleaned_count}/{total_cleanup_len} expired locks, " + f"next cleanup in {next_cleanup_in:.1f}s", + enable_output=False, + level="INFO", + ) + + return cleaned_count, new_earliest_time, new_last_cleanup_time + else: + return 0, earliest_cleanup_time, last_cleanup_time + + except Exception as e: + direct_log( + f"== {lock_type} Lock == Cleanup failed: {e}", + level="ERROR", + enable_output=True, + ) + return 0, earliest_cleanup_time, last_cleanup_time + + +def _get_or_create_shared_raw_mp_lock( + factory_name: str, key: str +) -> Optional[mp.synchronize.Lock]: + """Return the *singleton* manager.Lock() proxy for keyed lock, creating if needed.""" + if not _is_multiprocess: + return None + + with _registry_guard: + combined_key = _get_combined_key(factory_name, key) + raw = _lock_registry.get(combined_key) + count = _lock_registry_count.get(combined_key) + if raw is None: + raw = _manager.Lock() + _lock_registry[combined_key] = raw + count = 0 + else: + if count is None: + raise RuntimeError( + f"Shared-Data lock registry for {factory_name} is corrupted for key {key}" + ) + if ( + count == 0 and combined_key in _lock_cleanup_data + ): # Reusing an key waiting for cleanup, remove it from cleanup list + _lock_cleanup_data.pop(combined_key) + count += 1 + _lock_registry_count[combined_key] = count + return raw + + +def _release_shared_raw_mp_lock(factory_name: str, key: str): + """Release the *singleton* manager.Lock() proxy for *key*.""" + if not _is_multiprocess: + return + + global _earliest_mp_cleanup_time, _last_mp_cleanup_time + + with _registry_guard: + combined_key = _get_combined_key(factory_name, key) + raw = _lock_registry.get(combined_key) + count = _lock_registry_count.get(combined_key) + if raw is None and count is None: + return + elif raw is None or count is None: + raise RuntimeError( + f"Shared-Data lock registry for {factory_name} is corrupted for key {key}" + ) + + count -= 1 + if count < 0: + raise RuntimeError( + f"Attempting to release lock for {key} more times than it was acquired" + ) + + _lock_registry_count[combined_key] = count + + current_time = time.time() + if count == 0: + _lock_cleanup_data[combined_key] = current_time + + # Update earliest multiprocess cleanup time (only when earlier) + if ( + _earliest_mp_cleanup_time is None + or current_time < _earliest_mp_cleanup_time + ): + _earliest_mp_cleanup_time = current_time + + # Use generic cleanup function + cleaned_count, new_earliest_time, new_last_cleanup_time = _perform_lock_cleanup( + lock_type="mp", + cleanup_data=_lock_cleanup_data, + lock_registry=_lock_registry, + lock_count=_lock_registry_count, + earliest_cleanup_time=_earliest_mp_cleanup_time, + last_cleanup_time=_last_mp_cleanup_time, + current_time=current_time, + threshold_check=True, + ) + + # Update global state if cleanup was performed + if cleaned_count > 0: + _earliest_mp_cleanup_time = new_earliest_time + _last_mp_cleanup_time = new_last_cleanup_time + + +class KeyedUnifiedLock: + """ + Manager for unified keyed locks, supporting both single and multi-process + + • Keeps only a table of async keyed locks locally + • Fetches the multi-process keyed lock on every acquire + • Builds a fresh `UnifiedLock` each time, so `enable_logging` + (or future options) can vary per call. + • Supports dynamic namespaces specified at lock usage time + """ + + def __init__(self, *, default_enable_logging: bool = True) -> None: + self._default_enable_logging = default_enable_logging + self._async_lock: Dict[str, asyncio.Lock] = {} # local keyed locks + self._async_lock_count: Dict[ + str, int + ] = {} # local keyed locks referenced count + self._async_lock_cleanup_data: Dict[ + str, time.time + ] = {} # local keyed locks timeout + self._mp_locks: Dict[ + str, mp.synchronize.Lock + ] = {} # multi-process lock proxies + self._earliest_async_cleanup_time: Optional[float] = ( + None # track earliest async cleanup time + ) + self._last_async_cleanup_time: Optional[float] = ( + None # track last async cleanup time for minimum interval + ) + + def __call__( + self, namespace: str, keys: list[str], *, enable_logging: Optional[bool] = None + ): + """ + Ergonomic helper so you can write: + + async with storage_keyed_lock("namespace", ["key1", "key2"]): + ... + """ + if enable_logging is None: + enable_logging = self._default_enable_logging + return _KeyedLockContext( + self, + namespace=namespace, + keys=keys, + enable_logging=enable_logging, + ) + + def _get_or_create_async_lock(self, combined_key: str) -> asyncio.Lock: + async_lock = self._async_lock.get(combined_key) + count = self._async_lock_count.get(combined_key, 0) + if async_lock is None: + async_lock = asyncio.Lock() + self._async_lock[combined_key] = async_lock + elif count == 0 and combined_key in self._async_lock_cleanup_data: + self._async_lock_cleanup_data.pop(combined_key) + count += 1 + self._async_lock_count[combined_key] = count + return async_lock + + def _release_async_lock(self, combined_key: str): + count = self._async_lock_count.get(combined_key, 0) + count -= 1 + + current_time = time.time() + if count == 0: + self._async_lock_cleanup_data[combined_key] = current_time + + # Update earliest async cleanup time (only when earlier) + if ( + self._earliest_async_cleanup_time is None + or current_time < self._earliest_async_cleanup_time + ): + self._earliest_async_cleanup_time = current_time + self._async_lock_count[combined_key] = count + + # Use generic cleanup function + cleaned_count, new_earliest_time, new_last_cleanup_time = _perform_lock_cleanup( + lock_type="async", + cleanup_data=self._async_lock_cleanup_data, + lock_registry=self._async_lock, + lock_count=self._async_lock_count, + earliest_cleanup_time=self._earliest_async_cleanup_time, + last_cleanup_time=self._last_async_cleanup_time, + current_time=current_time, + threshold_check=True, + ) + + # Update instance state if cleanup was performed + if cleaned_count > 0: + self._earliest_async_cleanup_time = new_earliest_time + self._last_async_cleanup_time = new_last_cleanup_time + + def _get_lock_for_key( + self, namespace: str, key: str, enable_logging: bool = False + ) -> UnifiedLock: + # 1. Create combined key for this namespace:key combination + combined_key = _get_combined_key(namespace, key) + + # 2. get (or create) the per‑process async gate for this combined key + # Is synchronous, so no need to acquire a lock + async_lock = self._get_or_create_async_lock(combined_key) + + # 3. fetch the shared raw lock + raw_lock = _get_or_create_shared_raw_mp_lock(namespace, key) + is_multiprocess = raw_lock is not None + if not is_multiprocess: + raw_lock = async_lock + + # 4. build a *fresh* UnifiedLock with the chosen logging flag + if is_multiprocess: + return UnifiedLock( + lock=raw_lock, + is_async=False, # manager.Lock is synchronous + name=combined_key, + enable_logging=enable_logging, + async_lock=async_lock, # prevents event‑loop blocking + ) + else: + return UnifiedLock( + lock=raw_lock, + is_async=True, + name=combined_key, + enable_logging=enable_logging, + async_lock=None, # No need for async lock in single process mode + ) + + def _release_lock_for_key(self, namespace: str, key: str): + combined_key = _get_combined_key(namespace, key) + self._release_async_lock(combined_key) + _release_shared_raw_mp_lock(namespace, key) + + def cleanup_expired_locks(self) -> Dict[str, Any]: + """ + Cleanup expired locks for both async and multiprocess locks following the same + conditions as _release_shared_raw_mp_lock and _release_async_lock functions. + + Only performs cleanup when both has_expired_locks and interval_satisfied conditions are met + to avoid too frequent cleanup operations. + + Since async and multiprocess locks work together, this method cleans up + both types of expired locks and returns comprehensive statistics. + + Returns: + Dict containing cleanup statistics and current status: + { + "process_id": 12345, + "cleanup_performed": { + "mp_cleaned": 5, + "async_cleaned": 3 + }, + "current_status": { + "total_mp_locks": 10, + "pending_mp_cleanup": 2, + "total_async_locks": 8, + "pending_async_cleanup": 1 + } + } + """ + global _lock_registry, _lock_registry_count, _lock_cleanup_data + global _registry_guard, _earliest_mp_cleanup_time, _last_mp_cleanup_time + + cleanup_stats = {"mp_cleaned": 0, "async_cleaned": 0} + + current_time = time.time() + + # 1. Cleanup multiprocess locks using generic function + if ( + _is_multiprocess + and _lock_registry is not None + and _registry_guard is not None + ): + try: + with _registry_guard: + if _lock_cleanup_data is not None: + # Use generic cleanup function without threshold check + cleaned_count, new_earliest_time, new_last_cleanup_time = ( + _perform_lock_cleanup( + lock_type="mp", + cleanup_data=_lock_cleanup_data, + lock_registry=_lock_registry, + lock_count=_lock_registry_count, + earliest_cleanup_time=_earliest_mp_cleanup_time, + last_cleanup_time=_last_mp_cleanup_time, + current_time=current_time, + threshold_check=False, # Force cleanup in cleanup_expired_locks + ) + ) + + # Update global state if cleanup was performed + if cleaned_count > 0: + _earliest_mp_cleanup_time = new_earliest_time + _last_mp_cleanup_time = new_last_cleanup_time + cleanup_stats["mp_cleaned"] = cleaned_count + + except Exception as e: + direct_log( + f"Error during multiprocess lock cleanup: {e}", + level="ERROR", + enable_output=True, + ) + + # 2. Cleanup async locks using generic function + try: + # Use generic cleanup function without threshold check + cleaned_count, new_earliest_time, new_last_cleanup_time = ( + _perform_lock_cleanup( + lock_type="async", + cleanup_data=self._async_lock_cleanup_data, + lock_registry=self._async_lock, + lock_count=self._async_lock_count, + earliest_cleanup_time=self._earliest_async_cleanup_time, + last_cleanup_time=self._last_async_cleanup_time, + current_time=current_time, + threshold_check=False, # Force cleanup in cleanup_expired_locks + ) + ) + + # Update instance state if cleanup was performed + if cleaned_count > 0: + self._earliest_async_cleanup_time = new_earliest_time + self._last_async_cleanup_time = new_last_cleanup_time + cleanup_stats["async_cleaned"] = cleaned_count + + except Exception as e: + direct_log( + f"Error during async lock cleanup: {e}", + level="ERROR", + enable_output=True, + ) + + # 3. Get current status after cleanup + current_status = self.get_lock_status() + + return { + "process_id": os.getpid(), + "cleanup_performed": cleanup_stats, + "current_status": current_status, + } + + def get_lock_status(self) -> Dict[str, int]: + """ + Get current status of both async and multiprocess locks. + + Returns comprehensive lock counts for both types of locks since + they work together in the keyed lock system. + + Returns: + Dict containing lock counts: + { + "total_mp_locks": 10, + "pending_mp_cleanup": 2, + "total_async_locks": 8, + "pending_async_cleanup": 1 + } + """ + global _lock_registry_count, _lock_cleanup_data, _registry_guard + + status = { + "total_mp_locks": 0, + "pending_mp_cleanup": 0, + "total_async_locks": 0, + "pending_async_cleanup": 0, + } + + try: + # Count multiprocess locks + if _is_multiprocess and _lock_registry_count is not None: + if _registry_guard is not None: + with _registry_guard: + status["total_mp_locks"] = len(_lock_registry_count) + if _lock_cleanup_data is not None: + status["pending_mp_cleanup"] = len(_lock_cleanup_data) + + # Count async locks + status["total_async_locks"] = len(self._async_lock_count) + status["pending_async_cleanup"] = len(self._async_lock_cleanup_data) + + except Exception as e: + direct_log( + f"Error getting keyed lock status: {e}", + level="ERROR", + enable_output=True, + ) + + return status + + +class _KeyedLockContext: + def __init__( + self, + parent: KeyedUnifiedLock, + namespace: str, + keys: list[str], + enable_logging: bool, + ) -> None: + self._parent = parent + self._namespace = namespace + + # The sorting is critical to ensure proper lock and release order + # to avoid deadlocks + self._keys = sorted(keys) + self._enable_logging = ( + enable_logging + if enable_logging is not None + else parent._default_enable_logging + ) + self._ul: Optional[List[Dict[str, Any]]] = None # set in __aenter__ + + # ----- enter ----- + async def __aenter__(self): + if self._ul is not None: + raise RuntimeError("KeyedUnifiedLock already acquired in current context") + + self._ul = [] + + try: + # Acquire locks for all keys in the namespace + for key in self._keys: + lock = None + entry = None + + try: + # 1. Get lock object (reference count is incremented here) + lock = self._parent._get_lock_for_key( + self._namespace, key, enable_logging=self._enable_logging + ) + + # 2. Immediately create and add entry to list (critical for rollback to work) + entry = { + "key": key, + "lock": lock, + "entered": False, + "debug_inc": False, + "ref_incremented": True, # Mark that reference count has been incremented + } + self._ul.append( + entry + ) # Add immediately after _get_lock_for_key for rollback to work + + # 3. Try to acquire the lock + # Use try-finally to ensure state is updated atomically + lock_acquired = False + try: + await lock.__aenter__() + lock_acquired = True # Lock successfully acquired + finally: + if lock_acquired: + entry["entered"] = True + inc_debug_n_locks_acquired() + entry["debug_inc"] = True + + except asyncio.CancelledError: + # Lock acquisition was cancelled + # The finally block above ensures entry["entered"] is correct + direct_log( + f"Lock acquisition cancelled for key {key}", + level="WARNING", + enable_output=self._enable_logging, + ) + raise + except Exception as e: + # Other exceptions, log and re-raise + direct_log( + f"Lock acquisition failed for key {key}: {e}", + level="ERROR", + enable_output=True, + ) + raise + + return self + + except BaseException: + # Critical: if any exception occurs (including CancelledError) during lock acquisition, + # we must rollback all already acquired locks to prevent lock leaks + # Use shield to ensure rollback completes + await asyncio.shield(self._rollback_acquired_locks()) + raise + + async def _rollback_acquired_locks(self): + """Rollback all acquired locks in case of exception during __aenter__""" + if not self._ul: + return + + async def rollback_single_entry(entry): + """Rollback a single lock acquisition""" + key = entry["key"] + lock = entry["lock"] + debug_inc = entry["debug_inc"] + entered = entry["entered"] + ref_incremented = entry.get( + "ref_incremented", True + ) # Default to True for safety + + errors = [] + + # 1. If lock was acquired, release it + if entered: + try: + await lock.__aexit__(None, None, None) + except Exception as e: + errors.append(("lock_exit", e)) + direct_log( + f"Lock rollback error for key {key}: {e}", + level="ERROR", + enable_output=True, + ) + + # 2. Release reference count (if it was incremented) + if ref_incremented: + try: + self._parent._release_lock_for_key(self._namespace, key) + except Exception as e: + errors.append(("ref_release", e)) + direct_log( + f"Lock rollback reference release error for key {key}: {e}", + level="ERROR", + enable_output=True, + ) + + # 3. Decrement debug counter + if debug_inc: + try: + dec_debug_n_locks_acquired() + except Exception as e: + errors.append(("debug_dec", e)) + direct_log( + f"Lock rollback counter decrementing error for key {key}: {e}", + level="ERROR", + enable_output=True, + ) + + return errors + + # Release already acquired locks in reverse order + for entry in reversed(self._ul): + # Use shield to protect each lock's rollback + try: + await asyncio.shield(rollback_single_entry(entry)) + except Exception as e: + # Log but continue rolling back other locks + direct_log( + f"Lock rollback unexpected error for {entry['key']}: {e}", + level="ERROR", + enable_output=True, + ) + + self._ul = None + + # ----- exit ----- + async def __aexit__(self, exc_type, exc, tb): + if self._ul is None: + return + + async def release_all_locks(): + """Release all locks with comprehensive error handling, protected from cancellation""" + + async def release_single_entry(entry, exc_type, exc, tb): + """Release a single lock with full protection""" + key = entry["key"] + lock = entry["lock"] + debug_inc = entry["debug_inc"] + entered = entry["entered"] + + errors = [] + + # 1. Release the lock + if entered: + try: + await lock.__aexit__(exc_type, exc, tb) + except Exception as e: + errors.append(("lock_exit", e)) + direct_log( + f"Lock release error for key {key}: {e}", + level="ERROR", + enable_output=True, + ) + + # 2. Release reference count + try: + self._parent._release_lock_for_key(self._namespace, key) + except Exception as e: + errors.append(("ref_release", e)) + direct_log( + f"Lock release reference error for key {key}: {e}", + level="ERROR", + enable_output=True, + ) + + # 3. Decrement debug counter + if debug_inc: + try: + dec_debug_n_locks_acquired() + except Exception as e: + errors.append(("debug_dec", e)) + direct_log( + f"Lock release counter decrementing error for key {key}: {e}", + level="ERROR", + enable_output=True, + ) + + return errors + + all_errors = [] + + # Release locks in reverse order + # This entire loop is protected by the outer shield + for entry in reversed(self._ul): + try: + errors = await release_single_entry(entry, exc_type, exc, tb) + for error_type, error in errors: + all_errors.append((entry["key"], error_type, error)) + except Exception as e: + all_errors.append((entry["key"], "unexpected", e)) + direct_log( + f"Lock release unexpected error for {entry['key']}: {e}", + level="ERROR", + enable_output=True, + ) + + return all_errors + + # CRITICAL: Protect the entire release process with shield + # This ensures that even if cancellation occurs, all locks are released + try: + all_errors = await asyncio.shield(release_all_locks()) + except Exception as e: + direct_log( + f"Critical error during __aexit__ cleanup: {e}", + level="ERROR", + enable_output=True, + ) + all_errors = [] + finally: + # Always clear the lock list, even if shield was cancelled + self._ul = None + + # If there were release errors and no other exception, raise the first release error + if all_errors and exc_type is None: + raise all_errors[0][2] # (key, error_type, error) + + +def get_internal_lock(enable_logging: bool = False) -> UnifiedLock: + """return unified storage lock for data consistency""" + async_lock = _async_locks.get("internal_lock") if _is_multiprocess else None + return UnifiedLock( + lock=_internal_lock, + is_async=not _is_multiprocess, + name="internal_lock", + enable_logging=enable_logging, + async_lock=async_lock, + ) + + +def get_storage_lock(enable_logging: bool = False) -> UnifiedLock: + """return unified storage lock for data consistency""" + async_lock = _async_locks.get("storage_lock") if _is_multiprocess else None + return UnifiedLock( + lock=_storage_lock, + is_async=not _is_multiprocess, + name="storage_lock", + enable_logging=enable_logging, + async_lock=async_lock, + ) + + +def get_pipeline_status_lock(enable_logging: bool = False) -> UnifiedLock: + """return unified storage lock for data consistency""" + async_lock = _async_locks.get("pipeline_status_lock") if _is_multiprocess else None + return UnifiedLock( + lock=_pipeline_status_lock, + is_async=not _is_multiprocess, + name="pipeline_status_lock", + enable_logging=enable_logging, + async_lock=async_lock, + ) + + +def get_graph_db_lock(enable_logging: bool = False) -> UnifiedLock: + """return unified graph database lock for ensuring atomic operations""" + async_lock = _async_locks.get("graph_db_lock") if _is_multiprocess else None + return UnifiedLock( + lock=_graph_db_lock, + is_async=not _is_multiprocess, + name="graph_db_lock", + enable_logging=enable_logging, + async_lock=async_lock, + ) + + +def get_storage_keyed_lock( + keys: str | list[str], namespace: str = "default", enable_logging: bool = False +) -> _KeyedLockContext: + """Return unified storage keyed lock for ensuring atomic operations across different namespaces""" + global _storage_keyed_lock + if _storage_keyed_lock is None: + raise RuntimeError("Shared-Data is not initialized") + if isinstance(keys, str): + keys = [keys] + return _storage_keyed_lock(namespace, keys, enable_logging=enable_logging) + + +def get_data_init_lock(enable_logging: bool = False) -> UnifiedLock: + """return unified data initialization lock for ensuring atomic data initialization""" + async_lock = _async_locks.get("data_init_lock") if _is_multiprocess else None + return UnifiedLock( + lock=_data_init_lock, + is_async=not _is_multiprocess, + name="data_init_lock", + enable_logging=enable_logging, + async_lock=async_lock, + ) + + +def cleanup_keyed_lock() -> Dict[str, Any]: + """ + Force cleanup of expired keyed locks and return comprehensive status information. + + This function actively cleans up expired locks for both async and multiprocess locks, + then returns detailed statistics about the cleanup operation and current lock status. + + Returns: + Same as cleanup_expired_locks in KeyedUnifiedLock + """ + global _storage_keyed_lock + + # Check if shared storage is initialized + if not _initialized or _storage_keyed_lock is None: + return { + "process_id": os.getpid(), + "cleanup_performed": {"mp_cleaned": 0, "async_cleaned": 0}, + "current_status": { + "total_mp_locks": 0, + "pending_mp_cleanup": 0, + "total_async_locks": 0, + "pending_async_cleanup": 0, + }, + } + + return _storage_keyed_lock.cleanup_expired_locks() + + +def get_keyed_lock_status() -> Dict[str, Any]: + """ + Get current status of keyed locks without performing cleanup. + + This function provides a read-only view of the current lock counts + for both multiprocess and async locks, including pending cleanup counts. + + Returns: + Same as get_lock_status in KeyedUnifiedLock + """ + global _storage_keyed_lock + + # Check if shared storage is initialized + if not _initialized or _storage_keyed_lock is None: + return { + "process_id": os.getpid(), + "total_mp_locks": 0, + "pending_mp_cleanup": 0, + "total_async_locks": 0, + "pending_async_cleanup": 0, + } + + status = _storage_keyed_lock.get_lock_status() + status["process_id"] = os.getpid() + return status + + +def initialize_share_data(workers: int = 1): + """ + Initialize shared storage data for single or multi-process mode. + + When used with Gunicorn's preload feature, this function is called once in the + master process before forking worker processes, allowing all workers to share + the same initialized data. + + In single-process mode, this function is called in FASTAPI lifespan function. + + The function determines whether to use cross-process shared variables for data storage + based on the number of workers. If workers=1, it uses thread locks and local dictionaries. + If workers>1, it uses process locks and shared dictionaries managed by multiprocessing.Manager. + + Args: + workers (int): Number of worker processes. If 1, single-process mode is used. + If > 1, multi-process mode with shared memory is used. + """ + global \ + _manager, \ + _workers, \ + _is_multiprocess, \ + _storage_lock, \ + _lock_registry, \ + _lock_registry_count, \ + _lock_cleanup_data, \ + _registry_guard, \ + _internal_lock, \ + _pipeline_status_lock, \ + _graph_db_lock, \ + _data_init_lock, \ + _shared_dicts, \ + _init_flags, \ + _initialized, \ + _update_flags, \ + _async_locks, \ + _storage_keyed_lock, \ + _earliest_mp_cleanup_time, \ + _last_mp_cleanup_time + + # Check if already initialized + if _initialized: + direct_log( + f"Process {os.getpid()} Shared-Data already initialized (multiprocess={_is_multiprocess})" + ) + return + + _workers = workers + + if workers > 1: + _is_multiprocess = True + _manager = Manager() + _lock_registry = _manager.dict() + _lock_registry_count = _manager.dict() + _lock_cleanup_data = _manager.dict() + _registry_guard = _manager.RLock() + _internal_lock = _manager.Lock() + _storage_lock = _manager.Lock() + _pipeline_status_lock = _manager.Lock() + _graph_db_lock = _manager.Lock() + _data_init_lock = _manager.Lock() + _shared_dicts = _manager.dict() + _init_flags = _manager.dict() + _update_flags = _manager.dict() + + _storage_keyed_lock = KeyedUnifiedLock() + + # Initialize async locks for multiprocess mode + _async_locks = { + "internal_lock": asyncio.Lock(), + "storage_lock": asyncio.Lock(), + "pipeline_status_lock": asyncio.Lock(), + "graph_db_lock": asyncio.Lock(), + "data_init_lock": asyncio.Lock(), + } + + direct_log( + f"Process {os.getpid()} Shared-Data created for Multiple Process (workers={workers})" + ) + else: + _is_multiprocess = False + _internal_lock = asyncio.Lock() + _storage_lock = asyncio.Lock() + _pipeline_status_lock = asyncio.Lock() + _graph_db_lock = asyncio.Lock() + _data_init_lock = asyncio.Lock() + _shared_dicts = {} + _init_flags = {} + _update_flags = {} + _async_locks = None # No need for async locks in single process mode + + _storage_keyed_lock = KeyedUnifiedLock() + direct_log(f"Process {os.getpid()} Shared-Data created for Single Process") + + # Initialize multiprocess cleanup times + _earliest_mp_cleanup_time = None + _last_mp_cleanup_time = None + + # Mark as initialized + _initialized = True + + +async def initialize_pipeline_status(): + """ + Initialize pipeline namespace with default values. + This function is called during FASTAPI lifespan for each worker. + """ + pipeline_namespace = await get_namespace_data("pipeline_status", first_init=True) + + async with get_internal_lock(): + # Check if already initialized by checking for required fields + if "busy" in pipeline_namespace: + return + + # Create a shared list object for history_messages + history_messages = _manager.list() if _is_multiprocess else [] + pipeline_namespace.update( + { + "autoscanned": False, # Auto-scan started + "busy": False, # Control concurrent processes + "job_name": "-", # Current job name (indexing files/indexing texts) + "job_start": None, # Job start time + "docs": 0, # Total number of documents to be indexed + "batchs": 0, # Number of batches for processing documents + "cur_batch": 0, # Current processing batch + "request_pending": False, # Flag for pending request for processing + "latest_message": "", # Latest message from pipeline processing + "history_messages": history_messages, # 使用共享列表对象 + } + ) + direct_log(f"Process {os.getpid()} Pipeline namespace initialized") + + +async def get_update_flag(namespace: str): + """ + Create a namespace's update flag for a workers. + Returen the update flag to caller for referencing or reset. + """ + global _update_flags + if _update_flags is None: + raise ValueError("Try to create namespace before Shared-Data is initialized") + + async with get_internal_lock(): + if namespace not in _update_flags: + if _is_multiprocess and _manager is not None: + _update_flags[namespace] = _manager.list() + else: + _update_flags[namespace] = [] + direct_log( + f"Process {os.getpid()} initialized updated flags for namespace: [{namespace}]" + ) + + if _is_multiprocess and _manager is not None: + new_update_flag = _manager.Value("b", False) + else: + # Create a simple mutable object to store boolean value for compatibility with mutiprocess + class MutableBoolean: + def __init__(self, initial_value=False): + self.value = initial_value + + new_update_flag = MutableBoolean(False) + + _update_flags[namespace].append(new_update_flag) + return new_update_flag + + +async def set_all_update_flags(namespace: str): + """Set all update flag of namespace indicating all workers need to reload data from files""" + global _update_flags + if _update_flags is None: + raise ValueError("Try to create namespace before Shared-Data is initialized") + + async with get_internal_lock(): + if namespace not in _update_flags: + raise ValueError(f"Namespace {namespace} not found in update flags") + # Update flags for both modes + for i in range(len(_update_flags[namespace])): + _update_flags[namespace][i].value = True + + +async def clear_all_update_flags(namespace: str): + """Clear all update flag of namespace indicating all workers need to reload data from files""" + global _update_flags + if _update_flags is None: + raise ValueError("Try to create namespace before Shared-Data is initialized") + + async with get_internal_lock(): + if namespace not in _update_flags: + raise ValueError(f"Namespace {namespace} not found in update flags") + # Update flags for both modes + for i in range(len(_update_flags[namespace])): + _update_flags[namespace][i].value = False + + +async def get_all_update_flags_status() -> Dict[str, list]: + """ + Get update flags status for all namespaces. + + Returns: + Dict[str, list]: A dictionary mapping namespace names to lists of update flag statuses + """ + if _update_flags is None: + return {} + + result = {} + async with get_internal_lock(): + for namespace, flags in _update_flags.items(): + worker_statuses = [] + for flag in flags: + if _is_multiprocess: + worker_statuses.append(flag.value) + else: + worker_statuses.append(flag) + result[namespace] = worker_statuses + + return result + + +async def try_initialize_namespace(namespace: str) -> bool: + """ + Returns True if the current worker(process) gets initialization permission for loading data later. + The worker does not get the permission is prohibited to load data from files. + """ + global _init_flags, _manager + + if _init_flags is None: + raise ValueError("Try to create nanmespace before Shared-Data is initialized") + + async with get_internal_lock(): + if namespace not in _init_flags: + _init_flags[namespace] = True + direct_log( + f"Process {os.getpid()} ready to initialize storage namespace: [{namespace}]" + ) + return True + direct_log( + f"Process {os.getpid()} storage namespace already initialized: [{namespace}]" + ) + + return False + + +async def get_namespace_data( + namespace: str, first_init: bool = False +) -> Dict[str, Any]: + """get the shared data reference for specific namespace + + Args: + namespace: The namespace to retrieve + allow_create: If True, allows creation of the namespace if it doesn't exist. + Used internally by initialize_pipeline_status(). + """ + if _shared_dicts is None: + direct_log( + f"Error: try to getnanmespace before it is initialized, pid={os.getpid()}", + level="ERROR", + ) + raise ValueError("Shared dictionaries not initialized") + + async with get_internal_lock(): + if namespace not in _shared_dicts: + # Special handling for pipeline_status namespace + if namespace == "pipeline_status" and not first_init: + # Check if pipeline_status should have been initialized but wasn't + # This helps users understand they need to call initialize_pipeline_status() + raise PipelineNotInitializedError(namespace) + + # For other namespaces or when allow_create=True, create them dynamically + if _is_multiprocess and _manager is not None: + _shared_dicts[namespace] = _manager.dict() + else: + _shared_dicts[namespace] = {} + + return _shared_dicts[namespace] + + +def finalize_share_data(): + """ + Release shared resources and clean up. + + This function should be called when the application is shutting down + to properly release shared resources and avoid memory leaks. + + In multi-process mode, it shuts down the Manager and releases all shared objects. + In single-process mode, it simply resets the global variables. + """ + global \ + _manager, \ + _is_multiprocess, \ + _storage_lock, \ + _internal_lock, \ + _pipeline_status_lock, \ + _graph_db_lock, \ + _data_init_lock, \ + _shared_dicts, \ + _init_flags, \ + _initialized, \ + _update_flags, \ + _async_locks + + # Check if already initialized + if not _initialized: + direct_log( + f"Process {os.getpid()} storage data not initialized, nothing to finalize" + ) + return + + direct_log( + f"Process {os.getpid()} finalizing storage data (multiprocess={_is_multiprocess})" + ) + + # In multi-process mode, shut down the Manager + if _is_multiprocess and _manager is not None: + try: + # Clear shared resources before shutting down Manager + if _shared_dicts is not None: + # Clear pipeline status history messages first if exists + try: + pipeline_status = _shared_dicts.get("pipeline_status", {}) + if "history_messages" in pipeline_status: + pipeline_status["history_messages"].clear() + except Exception: + pass # Ignore any errors during history messages cleanup + _shared_dicts.clear() + if _init_flags is not None: + _init_flags.clear() + if _update_flags is not None: + # Clear each namespace's update flags list and Value objects + try: + for namespace in _update_flags: + flags_list = _update_flags[namespace] + if isinstance(flags_list, list): + # Clear Value objects in the list + for flag in flags_list: + if hasattr( + flag, "value" + ): # Check if it's a Value object + flag.value = False + flags_list.clear() + except Exception: + pass # Ignore any errors during update flags cleanup + _update_flags.clear() + + # Shut down the Manager - this will automatically clean up all shared resources + _manager.shutdown() + direct_log(f"Process {os.getpid()} Manager shutdown complete") + except Exception as e: + direct_log( + f"Process {os.getpid()} Error shutting down Manager: {e}", level="ERROR" + ) + + # Reset global variables + _manager = None + _initialized = None + _is_multiprocess = None + _shared_dicts = None + _init_flags = None + _storage_lock = None + _internal_lock = None + _pipeline_status_lock = None + _graph_db_lock = None + _data_init_lock = None + _update_flags = None + _async_locks = None + + direct_log(f"Process {os.getpid()} storage data finalization complete") diff --git a/LightRAG/lightrag/lightrag.py b/LightRAG/lightrag/lightrag.py new file mode 100644 index 00000000..c53c98ac --- /dev/null +++ b/LightRAG/lightrag/lightrag.py @@ -0,0 +1,3931 @@ +from __future__ import annotations + +import traceback +import asyncio +import configparser +import inspect +import os +import time +import warnings +from dataclasses import asdict, dataclass, field +from datetime import datetime, timezone +from functools import partial +from typing import ( + Any, + AsyncIterator, + Awaitable, + Callable, + Iterator, + cast, + final, + Literal, + Optional, + List, + Dict, + Union, +) +from lightrag.prompt import PROMPTS +from lightrag.exceptions import PipelineCancelledException +from lightrag.constants import ( + DEFAULT_MAX_GLEANING, + DEFAULT_FORCE_LLM_SUMMARY_ON_MERGE, + DEFAULT_TOP_K, + DEFAULT_CHUNK_TOP_K, + DEFAULT_MAX_ENTITY_TOKENS, + DEFAULT_MAX_RELATION_TOKENS, + DEFAULT_MAX_TOTAL_TOKENS, + DEFAULT_COSINE_THRESHOLD, + DEFAULT_RELATED_CHUNK_NUMBER, + DEFAULT_KG_CHUNK_PICK_METHOD, + DEFAULT_MIN_RERANK_SCORE, + DEFAULT_SUMMARY_MAX_TOKENS, + DEFAULT_SUMMARY_CONTEXT_SIZE, + DEFAULT_SUMMARY_LENGTH_RECOMMENDED, + DEFAULT_MAX_ASYNC, + DEFAULT_MAX_PARALLEL_INSERT, + DEFAULT_MAX_GRAPH_NODES, + DEFAULT_MAX_SOURCE_IDS_PER_ENTITY, + DEFAULT_MAX_SOURCE_IDS_PER_RELATION, + DEFAULT_ENTITY_TYPES, + DEFAULT_SUMMARY_LANGUAGE, + DEFAULT_LLM_TIMEOUT, + DEFAULT_EMBEDDING_TIMEOUT, + DEFAULT_SOURCE_IDS_LIMIT_METHOD, + DEFAULT_MAX_FILE_PATHS, + DEFAULT_FILE_PATH_MORE_PLACEHOLDER, +) +from lightrag.utils import get_env_value + +from lightrag.kg import ( + STORAGES, + verify_storage_implementation, +) + + +from lightrag.kg.shared_storage import ( + get_namespace_data, + get_pipeline_status_lock, + get_graph_db_lock, + get_data_init_lock, +) + +from lightrag.base import ( + BaseGraphStorage, + BaseKVStorage, + BaseVectorStorage, + DocProcessingStatus, + DocStatus, + DocStatusStorage, + QueryParam, + StorageNameSpace, + StoragesStatus, + DeletionResult, + OllamaServerInfos, + QueryResult, +) +from lightrag.namespace import NameSpace +from lightrag.operate import ( + chunking_by_token_size, + extract_entities, + merge_nodes_and_edges, + kg_query, + naive_query, + rebuild_knowledge_from_chunks, +) +from lightrag.constants import GRAPH_FIELD_SEP +from lightrag.utils import ( + Tokenizer, + TiktokenTokenizer, + EmbeddingFunc, + always_get_an_event_loop, + compute_mdhash_id, + lazy_external_import, + priority_limit_async_func_call, + get_content_summary, + sanitize_text_for_encoding, + check_storage_env_vars, + generate_track_id, + convert_to_user_format, + logger, + subtract_source_ids, + make_relation_chunk_key, + normalize_source_ids_limit_method, +) +from lightrag.types import KnowledgeGraph +from dotenv import load_dotenv + +# use the .env that is inside the current folder +# allows to use different .env file for each lightrag instance +# the OS environment variables take precedence over the .env file +load_dotenv(dotenv_path=".env", override=False) + +# TODO: TO REMOVE @Yannick +config = configparser.ConfigParser() +config.read("config.ini", "utf-8") + + +@final +@dataclass +class LightRAG: + """LightRAG: Simple and Fast Retrieval-Augmented Generation.""" + + # Directory + # --- + + working_dir: str = field(default="./rag_storage") + """Directory where cache and temporary files are stored.""" + + # Storage + # --- + + kv_storage: str = field(default="JsonKVStorage") + """Storage backend for key-value data.""" + + vector_storage: str = field(default="NanoVectorDBStorage") + """Storage backend for vector embeddings.""" + + graph_storage: str = field(default="NetworkXStorage") + """Storage backend for knowledge graphs.""" + + doc_status_storage: str = field(default="JsonDocStatusStorage") + """Storage type for tracking document processing statuses.""" + + # Workspace + # --- + + workspace: str = field(default_factory=lambda: os.getenv("WORKSPACE", "")) + """Workspace for data isolation. Defaults to empty string if WORKSPACE environment variable is not set.""" + + # Logging (Deprecated, use setup_logger in utils.py instead) + # --- + log_level: int | None = field(default=None) + log_file_path: str | None = field(default=None) + + # Query parameters + # --- + + top_k: int = field(default=get_env_value("TOP_K", DEFAULT_TOP_K, int)) + """Number of entities/relations to retrieve for each query.""" + + chunk_top_k: int = field( + default=get_env_value("CHUNK_TOP_K", DEFAULT_CHUNK_TOP_K, int) + ) + """Maximum number of chunks in context.""" + + max_entity_tokens: int = field( + default=get_env_value("MAX_ENTITY_TOKENS", DEFAULT_MAX_ENTITY_TOKENS, int) + ) + """Maximum number of tokens for entity in context.""" + + max_relation_tokens: int = field( + default=get_env_value("MAX_RELATION_TOKENS", DEFAULT_MAX_RELATION_TOKENS, int) + ) + """Maximum number of tokens for relation in context.""" + + max_total_tokens: int = field( + default=get_env_value("MAX_TOTAL_TOKENS", DEFAULT_MAX_TOTAL_TOKENS, int) + ) + """Maximum total tokens in context (including system prompt, entities, relations and chunks).""" + + cosine_threshold: int = field( + default=get_env_value("COSINE_THRESHOLD", DEFAULT_COSINE_THRESHOLD, int) + ) + """Cosine threshold of vector DB retrieval for entities, relations and chunks.""" + + related_chunk_number: int = field( + default=get_env_value("RELATED_CHUNK_NUMBER", DEFAULT_RELATED_CHUNK_NUMBER, int) + ) + """Number of related chunks to grab from single entity or relation.""" + + kg_chunk_pick_method: str = field( + default=get_env_value("KG_CHUNK_PICK_METHOD", DEFAULT_KG_CHUNK_PICK_METHOD, str) + ) + """Method for selecting text chunks: 'WEIGHT' for weight-based selection, 'VECTOR' for embedding similarity-based selection.""" + + # Entity extraction + # --- + + entity_extract_max_gleaning: int = field( + default=get_env_value("MAX_GLEANING", DEFAULT_MAX_GLEANING, int) + ) + """Maximum number of entity extraction attempts for ambiguous content.""" + + force_llm_summary_on_merge: int = field( + default=get_env_value( + "FORCE_LLM_SUMMARY_ON_MERGE", DEFAULT_FORCE_LLM_SUMMARY_ON_MERGE, int + ) + ) + + # Text chunking + # --- + + chunk_token_size: int = field(default=int(os.getenv("CHUNK_SIZE", 1200))) + """Maximum number of tokens per text chunk when splitting documents.""" + + chunk_overlap_token_size: int = field( + default=int(os.getenv("CHUNK_OVERLAP_SIZE", 100)) + ) + """Number of overlapping tokens between consecutive text chunks to preserve context.""" + + tokenizer: Optional[Tokenizer] = field(default=None) + """ + A function that returns a Tokenizer instance. + If None, and a `tiktoken_model_name` is provided, a TiktokenTokenizer will be created. + If both are None, the default TiktokenTokenizer is used. + """ + + tiktoken_model_name: str = field(default="gpt-4o-mini") + """Model name used for tokenization when chunking text with tiktoken. Defaults to `gpt-4o-mini`.""" + + chunking_func: Callable[ + [ + Tokenizer, + str, + Optional[str], + bool, + int, + int, + ], + Union[List[Dict[str, Any]], Awaitable[List[Dict[str, Any]]]], + ] = field(default_factory=lambda: chunking_by_token_size) + """ + Custom chunking function for splitting text into chunks before processing. + + The function can be either synchronous or asynchronous. + + The function should take the following parameters: + + - `tokenizer`: A Tokenizer instance to use for tokenization. + - `content`: The text to be split into chunks. + - `split_by_character`: The character to split the text on. If None, the text is split into chunks of `chunk_token_size` tokens. + - `split_by_character_only`: If True, the text is split only on the specified character. + - `chunk_token_size`: The maximum number of tokens per chunk. + - `chunk_overlap_token_size`: The number of overlapping tokens between consecutive chunks. + + The function should return a list of dictionaries (or an awaitable that resolves to a list), + where each dictionary contains the following keys: + - `tokens`: The number of tokens in the chunk. + - `content`: The text content of the chunk. + + Defaults to `chunking_by_token_size` if not specified. + """ + + # Embedding + # --- + + embedding_func: EmbeddingFunc | None = field(default=None) + """Function for computing text embeddings. Must be set before use.""" + + embedding_token_limit: int | None = field(default=None, init=False) + """Token limit for embedding model. Set automatically from embedding_func.max_token_size in __post_init__.""" + + embedding_batch_num: int = field(default=int(os.getenv("EMBEDDING_BATCH_NUM", 10))) + """Batch size for embedding computations.""" + + embedding_func_max_async: int = field( + default=int(os.getenv("EMBEDDING_FUNC_MAX_ASYNC", 8)) + ) + """Maximum number of concurrent embedding function calls.""" + + embedding_cache_config: dict[str, Any] = field( + default_factory=lambda: { + "enabled": False, + "similarity_threshold": 0.95, + "use_llm_check": False, + } + ) + """Configuration for embedding cache. + - enabled: If True, enables caching to avoid redundant computations. + - similarity_threshold: Minimum similarity score to use cached embeddings. + - use_llm_check: If True, validates cached embeddings using an LLM. + """ + + default_embedding_timeout: int = field( + default=int(os.getenv("EMBEDDING_TIMEOUT", DEFAULT_EMBEDDING_TIMEOUT)) + ) + + # LLM Configuration + # --- + + llm_model_func: Callable[..., object] | None = field(default=None) + """Function for interacting with the large language model (LLM). Must be set before use.""" + + llm_model_name: str = field(default="gpt-4o-mini") + """Name of the LLM model used for generating responses.""" + + summary_max_tokens: int = field( + default=int(os.getenv("SUMMARY_MAX_TOKENS", DEFAULT_SUMMARY_MAX_TOKENS)) + ) + """Maximum tokens allowed for entity/relation description.""" + + summary_context_size: int = field( + default=int(os.getenv("SUMMARY_CONTEXT_SIZE", DEFAULT_SUMMARY_CONTEXT_SIZE)) + ) + """Maximum number of tokens allowed per LLM response.""" + + summary_length_recommended: int = field( + default=int( + os.getenv("SUMMARY_LENGTH_RECOMMENDED", DEFAULT_SUMMARY_LENGTH_RECOMMENDED) + ) + ) + """Recommended length of LLM summary output.""" + + llm_model_max_async: int = field( + default=int(os.getenv("MAX_ASYNC", DEFAULT_MAX_ASYNC)) + ) + """Maximum number of concurrent LLM calls.""" + + llm_model_kwargs: dict[str, Any] = field(default_factory=dict) + """Additional keyword arguments passed to the LLM model function.""" + + default_llm_timeout: int = field( + default=int(os.getenv("LLM_TIMEOUT", DEFAULT_LLM_TIMEOUT)) + ) + + # Rerank Configuration + # --- + + rerank_model_func: Callable[..., object] | None = field(default=None) + """Function for reranking retrieved documents. All rerank configurations (model name, API keys, top_k, etc.) should be included in this function. Optional.""" + + min_rerank_score: float = field( + default=get_env_value("MIN_RERANK_SCORE", DEFAULT_MIN_RERANK_SCORE, float) + ) + """Minimum rerank score threshold for filtering chunks after reranking.""" + + # Storage + # --- + + vector_db_storage_cls_kwargs: dict[str, Any] = field(default_factory=dict) + """Additional parameters for vector database storage.""" + + enable_llm_cache: bool = field(default=True) + """Enables caching for LLM responses to avoid redundant computations.""" + + enable_llm_cache_for_entity_extract: bool = field(default=True) + """If True, enables caching for entity extraction steps to reduce LLM costs.""" + + # Extensions + # --- + + max_parallel_insert: int = field( + default=int(os.getenv("MAX_PARALLEL_INSERT", DEFAULT_MAX_PARALLEL_INSERT)) + ) + """Maximum number of parallel insert operations.""" + + max_graph_nodes: int = field( + default=get_env_value("MAX_GRAPH_NODES", DEFAULT_MAX_GRAPH_NODES, int) + ) + """Maximum number of graph nodes to return in knowledge graph queries.""" + + max_source_ids_per_entity: int = field( + default=get_env_value( + "MAX_SOURCE_IDS_PER_ENTITY", DEFAULT_MAX_SOURCE_IDS_PER_ENTITY, int + ) + ) + """Maximum number of source (chunk) ids in entity Grpah + VDB.""" + + max_source_ids_per_relation: int = field( + default=get_env_value( + "MAX_SOURCE_IDS_PER_RELATION", + DEFAULT_MAX_SOURCE_IDS_PER_RELATION, + int, + ) + ) + """Maximum number of source (chunk) ids in relation Graph + VDB.""" + + source_ids_limit_method: str = field( + default_factory=lambda: normalize_source_ids_limit_method( + get_env_value( + "SOURCE_IDS_LIMIT_METHOD", + DEFAULT_SOURCE_IDS_LIMIT_METHOD, + str, + ) + ) + ) + """Strategy for enforcing source_id limits: IGNORE_NEW or FIFO.""" + + max_file_paths: int = field( + default=get_env_value("MAX_FILE_PATHS", DEFAULT_MAX_FILE_PATHS, int) + ) + """Maximum number of file paths to store in entity/relation file_path field.""" + + file_path_more_placeholder: str = field(default=DEFAULT_FILE_PATH_MORE_PLACEHOLDER) + """Placeholder text when file paths exceed max_file_paths limit.""" + + addon_params: dict[str, Any] = field( + default_factory=lambda: { + "language": get_env_value( + "SUMMARY_LANGUAGE", DEFAULT_SUMMARY_LANGUAGE, str + ), + "entity_types": get_env_value("ENTITY_TYPES", DEFAULT_ENTITY_TYPES, list), + } + ) + + # Storages Management + # --- + + # TODO: Deprecated (LightRAG will never initialize storage automatically on creation，and finalize should be call before destroying) + auto_manage_storages_states: bool = field(default=False) + """If True, lightrag will automatically calls initialize_storages and finalize_storages at the appropriate times.""" + + cosine_better_than_threshold: float = field( + default=float(os.getenv("COSINE_THRESHOLD", 0.2)) + ) + + ollama_server_infos: Optional[OllamaServerInfos] = field(default=None) + """Configuration for Ollama server information.""" + + _storages_status: StoragesStatus = field(default=StoragesStatus.NOT_CREATED) + + def __post_init__(self): + from lightrag.kg.shared_storage import ( + initialize_share_data, + ) + + # Handle deprecated parameters + if self.log_level is not None: + warnings.warn( + "WARNING: log_level parameter is deprecated, use setup_logger in utils.py instead", + UserWarning, + stacklevel=2, + ) + if self.log_file_path is not None: + warnings.warn( + "WARNING: log_file_path parameter is deprecated, use setup_logger in utils.py instead", + UserWarning, + stacklevel=2, + ) + + # Remove these attributes to prevent their use + if hasattr(self, "log_level"): + delattr(self, "log_level") + if hasattr(self, "log_file_path"): + delattr(self, "log_file_path") + + initialize_share_data() + + if not os.path.exists(self.working_dir): + logger.info(f"Creating working directory {self.working_dir}") + os.makedirs(self.working_dir) + + # Verify storage implementation compatibility and environment variables + storage_configs = [ + ("KV_STORAGE", self.kv_storage), + ("VECTOR_STORAGE", self.vector_storage), + ("GRAPH_STORAGE", self.graph_storage), + ("DOC_STATUS_STORAGE", self.doc_status_storage), + ] + + for storage_type, storage_name in storage_configs: + # Verify storage implementation compatibility + verify_storage_implementation(storage_type, storage_name) + # Check environment variables + check_storage_env_vars(storage_name) + + # Ensure vector_db_storage_cls_kwargs has required fields + self.vector_db_storage_cls_kwargs = { + "cosine_better_than_threshold": self.cosine_better_than_threshold, + **self.vector_db_storage_cls_kwargs, + } + + # Init Tokenizer + # Post-initialization hook to handle backward compatabile tokenizer initialization based on provided parameters + if self.tokenizer is None: + if self.tiktoken_model_name: + self.tokenizer = TiktokenTokenizer(self.tiktoken_model_name) + else: + self.tokenizer = TiktokenTokenizer() + + # Initialize ollama_server_infos if not provided + if self.ollama_server_infos is None: + self.ollama_server_infos = OllamaServerInfos() + + # Validate config + if self.force_llm_summary_on_merge < 3: + logger.warning( + f"force_llm_summary_on_merge should be at least 3, got {self.force_llm_summary_on_merge}" + ) + if self.summary_context_size > self.max_total_tokens: + logger.warning( + f"summary_context_size({self.summary_context_size}) should no greater than max_total_tokens({self.max_total_tokens})" + ) + if self.summary_length_recommended > self.summary_max_tokens: + logger.warning( + f"max_total_tokens({self.summary_max_tokens}) should greater than summary_length_recommended({self.summary_length_recommended})" + ) + + # Fix global_config now + global_config = asdict(self) + + _print_config = ",\n ".join([f"{k} = {v}" for k, v in global_config.items()]) + logger.debug(f"LightRAG init with param:\n {_print_config}\n") + + # Init Embedding + # Step 1: Capture max_token_size before applying decorator (decorator strips dataclass attributes) + embedding_max_token_size = None + if self.embedding_func and hasattr(self.embedding_func, "max_token_size"): + embedding_max_token_size = self.embedding_func.max_token_size + logger.debug( + f"Captured embedding max_token_size: {embedding_max_token_size}" + ) + self.embedding_token_limit = embedding_max_token_size + + # Step 2: Apply priority wrapper decorator + self.embedding_func = priority_limit_async_func_call( + self.embedding_func_max_async, + llm_timeout=self.default_embedding_timeout, + queue_name="Embedding func", + )(self.embedding_func) + + # Initialize all storages + self.key_string_value_json_storage_cls: type[BaseKVStorage] = ( + self._get_storage_class(self.kv_storage) + ) # type: ignore + self.vector_db_storage_cls: type[BaseVectorStorage] = self._get_storage_class( + self.vector_storage + ) # type: ignore + self.graph_storage_cls: type[BaseGraphStorage] = self._get_storage_class( + self.graph_storage + ) # type: ignore + self.key_string_value_json_storage_cls = partial( # type: ignore + self.key_string_value_json_storage_cls, global_config=global_config + ) + self.vector_db_storage_cls = partial( # type: ignore + self.vector_db_storage_cls, global_config=global_config + ) + self.graph_storage_cls = partial( # type: ignore + self.graph_storage_cls, global_config=global_config + ) + + # Initialize document status storage + self.doc_status_storage_cls = self._get_storage_class(self.doc_status_storage) + + self.llm_response_cache: BaseKVStorage = self.key_string_value_json_storage_cls( # type: ignore + namespace=NameSpace.KV_STORE_LLM_RESPONSE_CACHE, + workspace=self.workspace, + global_config=global_config, + embedding_func=self.embedding_func, + ) + + self.text_chunks: BaseKVStorage = self.key_string_value_json_storage_cls( # type: ignore + namespace=NameSpace.KV_STORE_TEXT_CHUNKS, + workspace=self.workspace, + embedding_func=self.embedding_func, + ) + + self.full_docs: BaseKVStorage = self.key_string_value_json_storage_cls( # type: ignore + namespace=NameSpace.KV_STORE_FULL_DOCS, + workspace=self.workspace, + embedding_func=self.embedding_func, + ) + + self.full_entities: BaseKVStorage = self.key_string_value_json_storage_cls( # type: ignore + namespace=NameSpace.KV_STORE_FULL_ENTITIES, + workspace=self.workspace, + embedding_func=self.embedding_func, + ) + + self.full_relations: BaseKVStorage = self.key_string_value_json_storage_cls( # type: ignore + namespace=NameSpace.KV_STORE_FULL_RELATIONS, + workspace=self.workspace, + embedding_func=self.embedding_func, + ) + + self.entity_chunks: BaseKVStorage = self.key_string_value_json_storage_cls( # type: ignore + namespace=NameSpace.KV_STORE_ENTITY_CHUNKS, + workspace=self.workspace, + embedding_func=self.embedding_func, + ) + + self.relation_chunks: BaseKVStorage = self.key_string_value_json_storage_cls( # type: ignore + namespace=NameSpace.KV_STORE_RELATION_CHUNKS, + workspace=self.workspace, + embedding_func=self.embedding_func, + ) + + self.chunk_entity_relation_graph: BaseGraphStorage = self.graph_storage_cls( # type: ignore + namespace=NameSpace.GRAPH_STORE_CHUNK_ENTITY_RELATION, + workspace=self.workspace, + embedding_func=self.embedding_func, + ) + + self.entities_vdb: BaseVectorStorage = self.vector_db_storage_cls( # type: ignore + namespace=NameSpace.VECTOR_STORE_ENTITIES, + workspace=self.workspace, + embedding_func=self.embedding_func, + meta_fields={"entity_name", "source_id", "content", "file_path"}, + ) + self.relationships_vdb: BaseVectorStorage = self.vector_db_storage_cls( # type: ignore + namespace=NameSpace.VECTOR_STORE_RELATIONSHIPS, + workspace=self.workspace, + embedding_func=self.embedding_func, + meta_fields={"src_id", "tgt_id", "source_id", "content", "file_path"}, + ) + self.chunks_vdb: BaseVectorStorage = self.vector_db_storage_cls( # type: ignore + namespace=NameSpace.VECTOR_STORE_CHUNKS, + workspace=self.workspace, + embedding_func=self.embedding_func, + meta_fields={"full_doc_id", "content", "file_path"}, + ) + + # Initialize document status storage + self.doc_status: DocStatusStorage = self.doc_status_storage_cls( + namespace=NameSpace.DOC_STATUS, + workspace=self.workspace, + global_config=global_config, + embedding_func=None, + ) + + # Directly use llm_response_cache, don't create a new object + hashing_kv = self.llm_response_cache + + # Get timeout from LLM model kwargs for dynamic timeout calculation + self.llm_model_func = priority_limit_async_func_call( + self.llm_model_max_async, + llm_timeout=self.default_llm_timeout, + queue_name="LLM func", + )( + partial( + self.llm_model_func, # type: ignore + hashing_kv=hashing_kv, + **self.llm_model_kwargs, + ) + ) + + self._storages_status = StoragesStatus.CREATED + + async def initialize_storages(self): + """Storage initialization must be called one by one to prevent deadlock""" + if self._storages_status == StoragesStatus.CREATED: + for storage in ( + self.full_docs, + self.text_chunks, + self.full_entities, + self.full_relations, + self.entity_chunks, + self.relation_chunks, + self.entities_vdb, + self.relationships_vdb, + self.chunks_vdb, + self.chunk_entity_relation_graph, + self.llm_response_cache, + self.doc_status, + ): + if storage: + # logger.debug(f"Initializing storage: {storage}") + await storage.initialize() + + self._storages_status = StoragesStatus.INITIALIZED + logger.debug("All storage types initialized") + + async def finalize_storages(self): + """Asynchronously finalize the storages with improved error handling""" + if self._storages_status == StoragesStatus.INITIALIZED: + storages = [ + ("full_docs", self.full_docs), + ("text_chunks", self.text_chunks), + ("full_entities", self.full_entities), + ("full_relations", self.full_relations), + ("entity_chunks", self.entity_chunks), + ("relation_chunks", self.relation_chunks), + ("entities_vdb", self.entities_vdb), + ("relationships_vdb", self.relationships_vdb), + ("chunks_vdb", self.chunks_vdb), + ("chunk_entity_relation_graph", self.chunk_entity_relation_graph), + ("llm_response_cache", self.llm_response_cache), + ("doc_status", self.doc_status), + ] + + # Finalize each storage individually to ensure one failure doesn't prevent others from closing + successful_finalizations = [] + failed_finalizations = [] + + for storage_name, storage in storages: + if storage: + try: + await storage.finalize() + successful_finalizations.append(storage_name) + logger.debug(f"Successfully finalized {storage_name}") + except Exception as e: + error_msg = f"Failed to finalize {storage_name}: {e}" + logger.error(error_msg) + failed_finalizations.append(storage_name) + + # Log summary of finalization results + if successful_finalizations: + logger.info( + f"Successfully finalized {len(successful_finalizations)} storages" + ) + + if failed_finalizations: + logger.error( + f"Failed to finalize {len(failed_finalizations)} storages: {', '.join(failed_finalizations)}" + ) + else: + logger.debug("All storages finalized successfully") + + self._storages_status = StoragesStatus.FINALIZED + + async def check_and_migrate_data(self): + """Check if data migration is needed and perform migration if necessary""" + async with get_data_init_lock(): + try: + # Check if migration is needed: + # 1. chunk_entity_relation_graph has entities and relations (count > 0) + # 2. full_entities and full_relations are empty + + # Get all entity labels from graph + all_entity_labels = ( + await self.chunk_entity_relation_graph.get_all_labels() + ) + + if not all_entity_labels: + logger.debug("No entities found in graph, skipping migration check") + return + + try: + # Initialize chunk tracking storage after migration + await self._migrate_chunk_tracking_storage() + except Exception as e: + logger.error(f"Error during chunk_tracking migration: {e}") + raise e + + # Check if full_entities and full_relations are empty + # Get all processed documents to check their entity/relation data + try: + processed_docs = await self.doc_status.get_docs_by_status( + DocStatus.PROCESSED + ) + + if not processed_docs: + logger.debug("No processed documents found, skipping migration") + return + + # Check first few documents to see if they have full_entities/full_relations data + migration_needed = True + checked_count = 0 + max_check = min(5, len(processed_docs)) # Check up to 5 documents + + for doc_id in list(processed_docs.keys())[:max_check]: + checked_count += 1 + entity_data = await self.full_entities.get_by_id(doc_id) + relation_data = await self.full_relations.get_by_id(doc_id) + + if entity_data or relation_data: + migration_needed = False + break + + if not migration_needed: + logger.debug( + "Full entities/relations data already exists, no migration needed" + ) + return + + logger.info( + f"Data migration needed: found {len(all_entity_labels)} entities in graph but no full_entities/full_relations data" + ) + + # Perform migration + await self._migrate_entity_relation_data(processed_docs) + + except Exception as e: + logger.error(f"Error during migration check: {e}") + raise e + + except Exception as e: + logger.error(f"Error in data migration check: {e}") + raise e + + async def _migrate_entity_relation_data(self, processed_docs: dict): + """Migrate existing entity and relation data to full_entities and full_relations storage""" + logger.info(f"Starting data migration for {len(processed_docs)} documents") + + # Create mapping from chunk_id to doc_id + chunk_to_doc = {} + for doc_id, doc_status in processed_docs.items(): + chunk_ids = ( + doc_status.chunks_list + if hasattr(doc_status, "chunks_list") and doc_status.chunks_list + else [] + ) + for chunk_id in chunk_ids: + chunk_to_doc[chunk_id] = doc_id + + # Initialize document entity and relation mappings + doc_entities = {} # doc_id -> set of entity_names + doc_relations = {} # doc_id -> set of relation_pairs (as tuples) + + # Get all nodes and edges from graph + all_nodes = await self.chunk_entity_relation_graph.get_all_nodes() + all_edges = await self.chunk_entity_relation_graph.get_all_edges() + + # Process all nodes once + for node in all_nodes: + if "source_id" in node: + entity_id = node.get("entity_id") or node.get("id") + if not entity_id: + continue + + # Get chunk IDs from source_id + source_ids = node["source_id"].split(GRAPH_FIELD_SEP) + + # Find which documents this entity belongs to + for chunk_id in source_ids: + doc_id = chunk_to_doc.get(chunk_id) + if doc_id: + if doc_id not in doc_entities: + doc_entities[doc_id] = set() + doc_entities[doc_id].add(entity_id) + + # Process all edges once + for edge in all_edges: + if "source_id" in edge: + src = edge.get("source") + tgt = edge.get("target") + if not src or not tgt: + continue + + # Get chunk IDs from source_id + source_ids = edge["source_id"].split(GRAPH_FIELD_SEP) + + # Find which documents this relation belongs to + for chunk_id in source_ids: + doc_id = chunk_to_doc.get(chunk_id) + if doc_id: + if doc_id not in doc_relations: + doc_relations[doc_id] = set() + # Use tuple for set operations, convert to list later + doc_relations[doc_id].add(tuple(sorted((src, tgt)))) + + # Store the results in full_entities and full_relations + migration_count = 0 + + # Store entities + if doc_entities: + entities_data = {} + for doc_id, entity_set in doc_entities.items(): + entities_data[doc_id] = { + "entity_names": list(entity_set), + "count": len(entity_set), + } + await self.full_entities.upsert(entities_data) + + # Store relations + if doc_relations: + relations_data = {} + for doc_id, relation_set in doc_relations.items(): + # Convert tuples back to lists + relations_data[doc_id] = { + "relation_pairs": [list(pair) for pair in relation_set], + "count": len(relation_set), + } + await self.full_relations.upsert(relations_data) + + migration_count = len( + set(list(doc_entities.keys()) + list(doc_relations.keys())) + ) + + # Persist the migrated data + await self.full_entities.index_done_callback() + await self.full_relations.index_done_callback() + + logger.info( + f"Data migration completed: migrated {migration_count} documents with entities/relations" + ) + + async def _migrate_chunk_tracking_storage(self) -> None: + """Ensure entity/relation chunk tracking KV stores exist and are seeded.""" + + if not self.entity_chunks or not self.relation_chunks: + return + + need_entity_migration = False + need_relation_migration = False + + try: + need_entity_migration = await self.entity_chunks.is_empty() + except Exception as exc: # pragma: no cover - defensive logging + logger.error(f"Failed to check entity chunks storage: {exc}") + raise exc + + try: + need_relation_migration = await self.relation_chunks.is_empty() + except Exception as exc: # pragma: no cover - defensive logging + logger.error(f"Failed to check relation chunks storage: {exc}") + raise exc + + if not need_entity_migration and not need_relation_migration: + return + + BATCH_SIZE = 500 # Process 500 records per batch + + if need_entity_migration: + try: + nodes = await self.chunk_entity_relation_graph.get_all_nodes() + except Exception as exc: + logger.error(f"Failed to fetch nodes for chunk migration: {exc}") + nodes = [] + + logger.info(f"Starting chunk_tracking data migration: {len(nodes)} nodes") + + # Process nodes in batches + total_nodes = len(nodes) + total_batches = (total_nodes + BATCH_SIZE - 1) // BATCH_SIZE + total_migrated = 0 + + for batch_idx in range(total_batches): + start_idx = batch_idx * BATCH_SIZE + end_idx = min((batch_idx + 1) * BATCH_SIZE, total_nodes) + batch_nodes = nodes[start_idx:end_idx] + + upsert_payload: dict[str, dict[str, object]] = {} + for node in batch_nodes: + entity_id = node.get("entity_id") or node.get("id") + if not entity_id: + continue + + raw_source = node.get("source_id") or "" + chunk_ids = [ + chunk_id + for chunk_id in raw_source.split(GRAPH_FIELD_SEP) + if chunk_id + ] + if not chunk_ids: + continue + + upsert_payload[entity_id] = { + "chunk_ids": chunk_ids, + "count": len(chunk_ids), + } + + if upsert_payload: + await self.entity_chunks.upsert(upsert_payload) + total_migrated += len(upsert_payload) + logger.info( + f"Processed entity batch {batch_idx + 1}/{total_batches}: {len(upsert_payload)} records (total: {total_migrated}/{total_nodes})" + ) + + if total_migrated > 0: + # Persist entity_chunks data to disk + await self.entity_chunks.index_done_callback() + logger.info( + f"Entity chunk_tracking migration completed: {total_migrated} records persisted" + ) + + if need_relation_migration: + try: + edges = await self.chunk_entity_relation_graph.get_all_edges() + except Exception as exc: + logger.error(f"Failed to fetch edges for chunk migration: {exc}") + edges = [] + + logger.info(f"Starting chunk_tracking data migration: {len(edges)} edges") + + # Process edges in batches + total_edges = len(edges) + total_batches = (total_edges + BATCH_SIZE - 1) // BATCH_SIZE + total_migrated = 0 + + for batch_idx in range(total_batches): + start_idx = batch_idx * BATCH_SIZE + end_idx = min((batch_idx + 1) * BATCH_SIZE, total_edges) + batch_edges = edges[start_idx:end_idx] + + upsert_payload: dict[str, dict[str, object]] = {} + for edge in batch_edges: + src = edge.get("source") or edge.get("src_id") or edge.get("src") + tgt = edge.get("target") or edge.get("tgt_id") or edge.get("tgt") + if not src or not tgt: + continue + + raw_source = edge.get("source_id") or "" + chunk_ids = [ + chunk_id + for chunk_id in raw_source.split(GRAPH_FIELD_SEP) + if chunk_id + ] + if not chunk_ids: + continue + + storage_key = make_relation_chunk_key(src, tgt) + upsert_payload[storage_key] = { + "chunk_ids": chunk_ids, + "count": len(chunk_ids), + } + + if upsert_payload: + await self.relation_chunks.upsert(upsert_payload) + total_migrated += len(upsert_payload) + logger.info( + f"Processed relation batch {batch_idx + 1}/{total_batches}: {len(upsert_payload)} records (total: {total_migrated}/{total_edges})" + ) + + if total_migrated > 0: + # Persist relation_chunks data to disk + await self.relation_chunks.index_done_callback() + logger.info( + f"Relation chunk_tracking migration completed: {total_migrated} records persisted" + ) + + async def get_graph_labels(self): + text = await self.chunk_entity_relation_graph.get_all_labels() + return text + + async def get_knowledge_graph( + self, + node_label: str, + max_depth: int = 3, + max_nodes: int = None, + ) -> KnowledgeGraph: + """Get knowledge graph for a given label + + Args: + node_label (str): Label to get knowledge graph for + max_depth (int): Maximum depth of graph + max_nodes (int, optional): Maximum number of nodes to return. Defaults to self.max_graph_nodes. + + Returns: + KnowledgeGraph: Knowledge graph containing nodes and edges + """ + # Use self.max_graph_nodes as default if max_nodes is None + if max_nodes is None: + max_nodes = self.max_graph_nodes + else: + # Limit max_nodes to not exceed self.max_graph_nodes + max_nodes = min(max_nodes, self.max_graph_nodes) + + return await self.chunk_entity_relation_graph.get_knowledge_graph( + node_label, max_depth, max_nodes + ) + + def _get_storage_class(self, storage_name: str) -> Callable[..., Any]: + # Direct imports for default storage implementations + if storage_name == "JsonKVStorage": + from lightrag.kg.json_kv_impl import JsonKVStorage + + return JsonKVStorage + elif storage_name == "NanoVectorDBStorage": + from lightrag.kg.nano_vector_db_impl import NanoVectorDBStorage + + return NanoVectorDBStorage + elif storage_name == "NetworkXStorage": + from lightrag.kg.networkx_impl import NetworkXStorage + + return NetworkXStorage + elif storage_name == "JsonDocStatusStorage": + from lightrag.kg.json_doc_status_impl import JsonDocStatusStorage + + return JsonDocStatusStorage + else: + # Fallback to dynamic import for other storage implementations + import_path = STORAGES[storage_name] + storage_class = lazy_external_import(import_path, storage_name) + return storage_class + + def insert( + self, + input: str | list[str], + split_by_character: str | None = None, + split_by_character_only: bool = False, + ids: str | list[str] | None = None, + file_paths: str | list[str] | None = None, + track_id: str | None = None, + ) -> str: + """Sync Insert documents with checkpoint support + + Args: + input: Single document string or list of document strings + split_by_character: if split_by_character is not None, split the string by character, if chunk longer than + chunk_token_size, it will be split again by token size. + split_by_character_only: if split_by_character_only is True, split the string by character only, when + split_by_character is None, this parameter is ignored. + ids: single string of the document ID or list of unique document IDs, if not provided, MD5 hash IDs will be generated + file_paths: single string of the file path or list of file paths, used for citation + track_id: tracking ID for monitoring processing status, if not provided, will be generated + + Returns: + str: tracking ID for monitoring processing status + """ + loop = always_get_an_event_loop() + return loop.run_until_complete( + self.ainsert( + input, + split_by_character, + split_by_character_only, + ids, + file_paths, + track_id, + ) + ) + + async def ainsert( + self, + input: str | list[str], + split_by_character: str | None = None, + split_by_character_only: bool = False, + ids: str | list[str] | None = None, + file_paths: str | list[str] | None = None, + track_id: str | None = None, + ) -> str: + """Async Insert documents with checkpoint support + + Args: + input: Single document string or list of document strings + split_by_character: if split_by_character is not None, split the string by character, if chunk longer than + chunk_token_size, it will be split again by token size. + split_by_character_only: if split_by_character_only is True, split the string by character only, when + split_by_character is None, this parameter is ignored. + ids: list of unique document IDs, if not provided, MD5 hash IDs will be generated + file_paths: list of file paths corresponding to each document, used for citation + track_id: tracking ID for monitoring processing status, if not provided, will be generated + + Returns: + str: tracking ID for monitoring processing status + """ + # Generate track_id if not provided + if track_id is None: + track_id = generate_track_id("insert") + + await self.apipeline_enqueue_documents(input, ids, file_paths, track_id) + await self.apipeline_process_enqueue_documents( + split_by_character, split_by_character_only + ) + + return track_id + + # TODO: deprecated, use insert instead + def insert_custom_chunks( + self, + full_text: str, + text_chunks: list[str], + doc_id: str | list[str] | None = None, + ) -> None: + loop = always_get_an_event_loop() + loop.run_until_complete( + self.ainsert_custom_chunks(full_text, text_chunks, doc_id) + ) + + # TODO: deprecated, use ainsert instead + async def ainsert_custom_chunks( + self, full_text: str, text_chunks: list[str], doc_id: str | None = None + ) -> None: + update_storage = False + try: + # Clean input texts + full_text = sanitize_text_for_encoding(full_text) + text_chunks = [sanitize_text_for_encoding(chunk) for chunk in text_chunks] + file_path = "" + + # Process cleaned texts + if doc_id is None: + doc_key = compute_mdhash_id(full_text, prefix="doc-") + else: + doc_key = doc_id + new_docs = {doc_key: {"content": full_text, "file_path": file_path}} + + _add_doc_keys = await self.full_docs.filter_keys({doc_key}) + new_docs = {k: v for k, v in new_docs.items() if k in _add_doc_keys} + if not len(new_docs): + logger.warning("This document is already in the storage.") + return + + update_storage = True + logger.info(f"Inserting {len(new_docs)} docs") + + inserting_chunks: dict[str, Any] = {} + for index, chunk_text in enumerate(text_chunks): + chunk_key = compute_mdhash_id(chunk_text, prefix="chunk-") + tokens = len(self.tokenizer.encode(chunk_text)) + inserting_chunks[chunk_key] = { + "content": chunk_text, + "full_doc_id": doc_key, + "tokens": tokens, + "chunk_order_index": index, + "file_path": file_path, + } + + doc_ids = set(inserting_chunks.keys()) + add_chunk_keys = await self.text_chunks.filter_keys(doc_ids) + inserting_chunks = { + k: v for k, v in inserting_chunks.items() if k in add_chunk_keys + } + if not len(inserting_chunks): + logger.warning("All chunks are already in the storage.") + return + + tasks = [ + self.chunks_vdb.upsert(inserting_chunks), + self._process_extract_entities(inserting_chunks), + self.full_docs.upsert(new_docs), + self.text_chunks.upsert(inserting_chunks), + ] + await asyncio.gather(*tasks) + + finally: + if update_storage: + await self._insert_done() + + async def apipeline_enqueue_documents( + self, + input: str | list[str], + ids: list[str] | None = None, + file_paths: str | list[str] | None = None, + track_id: str | None = None, + ) -> str: + """ + Pipeline for Processing Documents + + 1. Validate ids if provided or generate MD5 hash IDs and remove duplicate contents + 2. Generate document initial status + 3. Filter out already processed documents + 4. Enqueue document in status + + Args: + input: Single document string or list of document strings + ids: list of unique document IDs, if not provided, MD5 hash IDs will be generated + file_paths: list of file paths corresponding to each document, used for citation + track_id: tracking ID for monitoring processing status, if not provided, will be generated with "enqueue" prefix + + Returns: + str: tracking ID for monitoring processing status + """ + # Generate track_id if not provided + if track_id is None or track_id.strip() == "": + track_id = generate_track_id("enqueue") + if isinstance(input, str): + input = [input] + if isinstance(ids, str): + ids = [ids] + if isinstance(file_paths, str): + file_paths = [file_paths] + + # If file_paths is provided, ensure it matches the number of documents + if file_paths is not None: + if isinstance(file_paths, str): + file_paths = [file_paths] + if len(file_paths) != len(input): + raise ValueError( + "Number of file paths must match the number of documents" + ) + else: + # If no file paths provided, use placeholder + file_paths = ["unknown_source"] * len(input) + + # 1. Validate ids if provided or generate MD5 hash IDs and remove duplicate contents + if ids is not None: + # Check if the number of IDs matches the number of documents + if len(ids) != len(input): + raise ValueError("Number of IDs must match the number of documents") + + # Check if IDs are unique + if len(ids) != len(set(ids)): + raise ValueError("IDs must be unique") + + # Generate contents dict and remove duplicates in one pass + unique_contents = {} + for id_, doc, path in zip(ids, input, file_paths): + cleaned_content = sanitize_text_for_encoding(doc) + if cleaned_content not in unique_contents: + unique_contents[cleaned_content] = (id_, path) + + # Reconstruct contents with unique content + contents = { + id_: {"content": content, "file_path": file_path} + for content, (id_, file_path) in unique_contents.items() + } + else: + # Clean input text and remove duplicates in one pass + unique_content_with_paths = {} + for doc, path in zip(input, file_paths): + cleaned_content = sanitize_text_for_encoding(doc) + if cleaned_content not in unique_content_with_paths: + unique_content_with_paths[cleaned_content] = path + + # Generate contents dict of MD5 hash IDs and documents with paths + contents = { + compute_mdhash_id(content, prefix="doc-"): { + "content": content, + "file_path": path, + } + for content, path in unique_content_with_paths.items() + } + + # 2. Generate document initial status (without content) + new_docs: dict[str, Any] = { + id_: { + "status": DocStatus.PENDING, + "content_summary": get_content_summary(content_data["content"]), + "content_length": len(content_data["content"]), + "created_at": datetime.now(timezone.utc).isoformat(), + "updated_at": datetime.now(timezone.utc).isoformat(), + "file_path": content_data[ + "file_path" + ], # Store file path in document status + "track_id": track_id, # Store track_id in document status + } + for id_, content_data in contents.items() + } + + # 3. Filter out already processed documents + # Get docs ids + all_new_doc_ids = set(new_docs.keys()) + # Exclude IDs of documents that are already enqueued + unique_new_doc_ids = await self.doc_status.filter_keys(all_new_doc_ids) + + # Log ignored document IDs (documents that were filtered out because they already exist) + ignored_ids = list(all_new_doc_ids - unique_new_doc_ids) + if ignored_ids: + for doc_id in ignored_ids: + file_path = new_docs.get(doc_id, {}).get("file_path", "unknown_source") + logger.warning( + f"Ignoring document ID (already exists): {doc_id} ({file_path})" + ) + if len(ignored_ids) > 3: + logger.warning( + f"Total Ignoring {len(ignored_ids)} document IDs that already exist in storage" + ) + + # Filter new_docs to only include documents with unique IDs + new_docs = { + doc_id: new_docs[doc_id] + for doc_id in unique_new_doc_ids + if doc_id in new_docs + } + + if not new_docs: + logger.warning("No new unique documents were found.") + return + + # 4. Store document content in full_docs and status in doc_status + # Store full document content separately + full_docs_data = { + doc_id: { + "content": contents[doc_id]["content"], + "file_path": contents[doc_id]["file_path"], + } + for doc_id in new_docs.keys() + } + await self.full_docs.upsert(full_docs_data) + # Persist data to disk immediately + await self.full_docs.index_done_callback() + + # Store document status (without content) + await self.doc_status.upsert(new_docs) + logger.debug(f"Stored {len(new_docs)} new unique documents") + + return track_id + + async def apipeline_enqueue_error_documents( + self, + error_files: list[dict[str, Any]], + track_id: str | None = None, + ) -> None: + """ + Record file extraction errors in doc_status storage. + + This function creates error document entries in the doc_status storage for files + that failed during the extraction process. Each error entry contains information + about the failure to help with debugging and monitoring. + + Args: + error_files: List of dictionaries containing error information for each failed file. + Each dictionary should contain: + - file_path: Original file name/path + - error_description: Brief error description (for content_summary) + - original_error: Full error message (for error_msg) + - file_size: File size in bytes (for content_length, 0 if unknown) + track_id: Optional tracking ID for grouping related operations + + Returns: + None + """ + if not error_files: + logger.debug("No error files to record") + return + + # Generate track_id if not provided + if track_id is None or track_id.strip() == "": + track_id = generate_track_id("error") + + error_docs: dict[str, Any] = {} + current_time = datetime.now(timezone.utc).isoformat() + + for error_file in error_files: + file_path = error_file.get("file_path", "unknown_file") + error_description = error_file.get( + "error_description", "File extraction failed" + ) + original_error = error_file.get("original_error", "Unknown error") + file_size = error_file.get("file_size", 0) + + # Generate unique doc_id with "error-" prefix + doc_id_content = f"{file_path}-{error_description}" + doc_id = compute_mdhash_id(doc_id_content, prefix="error-") + + error_docs[doc_id] = { + "status": DocStatus.FAILED, + "content_summary": error_description, + "content_length": file_size, + "error_msg": original_error, + "chunks_count": 0, # No chunks for failed files + "created_at": current_time, + "updated_at": current_time, + "file_path": file_path, + "track_id": track_id, + "metadata": { + "error_type": "file_extraction_error", + }, + } + + # Store error documents in doc_status + if error_docs: + await self.doc_status.upsert(error_docs) + # Log each error for debugging + for doc_id, error_doc in error_docs.items(): + logger.error( + f"File processing error: - ID: {doc_id} {error_doc['file_path']}" + ) + + async def _validate_and_fix_document_consistency( + self, + to_process_docs: dict[str, DocProcessingStatus], + pipeline_status: dict, + pipeline_status_lock: asyncio.Lock, + ) -> dict[str, DocProcessingStatus]: + """Validate and fix document data consistency by deleting inconsistent entries, but preserve failed documents""" + inconsistent_docs = [] + failed_docs_to_preserve = [] + successful_deletions = 0 + + # Check each document's data consistency + for doc_id, status_doc in to_process_docs.items(): + # Check if corresponding content exists in full_docs + content_data = await self.full_docs.get_by_id(doc_id) + if not content_data: + # Check if this is a failed document that should be preserved + if ( + hasattr(status_doc, "status") + and status_doc.status == DocStatus.FAILED + ): + failed_docs_to_preserve.append(doc_id) + else: + inconsistent_docs.append(doc_id) + + # Log information about failed documents that will be preserved + if failed_docs_to_preserve: + async with pipeline_status_lock: + preserve_message = f"Preserving {len(failed_docs_to_preserve)} failed document entries for manual review" + logger.info(preserve_message) + pipeline_status["latest_message"] = preserve_message + pipeline_status["history_messages"].append(preserve_message) + + # Remove failed documents from processing list but keep them in doc_status + for doc_id in failed_docs_to_preserve: + to_process_docs.pop(doc_id, None) + + # Delete inconsistent document entries(excluding failed documents) + if inconsistent_docs: + async with pipeline_status_lock: + summary_message = ( + f"Inconsistent document entries found: {len(inconsistent_docs)}" + ) + logger.info(summary_message) + pipeline_status["latest_message"] = summary_message + pipeline_status["history_messages"].append(summary_message) + + successful_deletions = 0 + for doc_id in inconsistent_docs: + try: + status_doc = to_process_docs[doc_id] + file_path = getattr(status_doc, "file_path", "unknown_source") + + # Delete doc_status entry + await self.doc_status.delete([doc_id]) + successful_deletions += 1 + + # Log successful deletion + async with pipeline_status_lock: + log_message = ( + f"Deleted inconsistent entry: {doc_id} ({file_path})" + ) + logger.info(log_message) + pipeline_status["latest_message"] = log_message + pipeline_status["history_messages"].append(log_message) + + # Remove from processing list + to_process_docs.pop(doc_id, None) + + except Exception as e: + # Log deletion failure + async with pipeline_status_lock: + error_message = f"Failed to delete entry: {doc_id} - {str(e)}" + logger.error(error_message) + pipeline_status["latest_message"] = error_message + pipeline_status["history_messages"].append(error_message) + + # Final summary log + # async with pipeline_status_lock: + # final_message = f"Successfully deleted {successful_deletions} inconsistent entries, preserved {len(failed_docs_to_preserve)} failed documents" + # logger.info(final_message) + # pipeline_status["latest_message"] = final_message + # pipeline_status["history_messages"].append(final_message) + + # Reset PROCESSING and FAILED documents that pass consistency checks to PENDING status + docs_to_reset = {} + reset_count = 0 + + for doc_id, status_doc in to_process_docs.items(): + # Check if document has corresponding content in full_docs (consistency check) + content_data = await self.full_docs.get_by_id(doc_id) + if content_data: # Document passes consistency check + # Check if document is in PROCESSING or FAILED status + if hasattr(status_doc, "status") and status_doc.status in [ + DocStatus.PROCESSING, + DocStatus.FAILED, + ]: + # Prepare document for status reset to PENDING + docs_to_reset[doc_id] = { + "status": DocStatus.PENDING, + "content_summary": status_doc.content_summary, + "content_length": status_doc.content_length, + "created_at": status_doc.created_at, + "updated_at": datetime.now(timezone.utc).isoformat(), + "file_path": getattr(status_doc, "file_path", "unknown_source"), + "track_id": getattr(status_doc, "track_id", ""), + # Clear any error messages and processing metadata + "error_msg": "", + "metadata": {}, + } + + # Update the status in to_process_docs as well + status_doc.status = DocStatus.PENDING + reset_count += 1 + + # Update doc_status storage if there are documents to reset + if docs_to_reset: + await self.doc_status.upsert(docs_to_reset) + + async with pipeline_status_lock: + reset_message = f"Reset {reset_count} documents from PROCESSING/FAILED to PENDING status" + logger.info(reset_message) + pipeline_status["latest_message"] = reset_message + pipeline_status["history_messages"].append(reset_message) + + return to_process_docs + + async def apipeline_process_enqueue_documents( + self, + split_by_character: str | None = None, + split_by_character_only: bool = False, + ) -> None: + """ + Process pending documents by splitting them into chunks, processing + each chunk for entity and relation extraction, and updating the + document status. + + 1. Get all pending, failed, and abnormally terminated processing documents. + 2. Validate document data consistency and fix any issues + 3. Split document content into chunks + 4. Process each chunk for entity and relation extraction + 5. Update the document status + """ + + # Get pipeline status shared data and lock + pipeline_status = await get_namespace_data("pipeline_status") + pipeline_status_lock = get_pipeline_status_lock() + + # Check if another process is already processing the queue + async with pipeline_status_lock: + # Ensure only one worker is processing documents + if not pipeline_status.get("busy", False): + processing_docs, failed_docs, pending_docs = await asyncio.gather( + self.doc_status.get_docs_by_status(DocStatus.PROCESSING), + self.doc_status.get_docs_by_status(DocStatus.FAILED), + self.doc_status.get_docs_by_status(DocStatus.PENDING), + ) + + to_process_docs: dict[str, DocProcessingStatus] = {} + to_process_docs.update(processing_docs) + to_process_docs.update(failed_docs) + to_process_docs.update(pending_docs) + + if not to_process_docs: + logger.info("No documents to process") + return + + pipeline_status.update( + { + "busy": True, + "job_name": "Default Job", + "job_start": datetime.now(timezone.utc).isoformat(), + "docs": 0, + "batchs": 0, # Total number of files to be processed + "cur_batch": 0, # Number of files already processed + "request_pending": False, # Clear any previous request + "cancellation_requested": False, # Initialize cancellation flag + "latest_message": "", + } + ) + # Cleaning history_messages without breaking it as a shared list object + del pipeline_status["history_messages"][:] + else: + # Another process is busy, just set request flag and return + pipeline_status["request_pending"] = True + logger.info( + "Another process is already processing the document queue. Request queued." + ) + return + + try: + # Process documents until no more documents or requests + while True: + # Check for cancellation request at the start of main loop + async with pipeline_status_lock: + if pipeline_status.get("cancellation_requested", False): + # Clear pending request + pipeline_status["request_pending"] = False + # Celar cancellation flag + pipeline_status["cancellation_requested"] = False + + log_message = "Pipeline cancelled by user" + logger.info(log_message) + pipeline_status["latest_message"] = log_message + pipeline_status["history_messages"].append(log_message) + + # Exit directly, skipping request_pending check + return + + if not to_process_docs: + log_message = "All enqueued documents have been processed" + logger.info(log_message) + pipeline_status["latest_message"] = log_message + pipeline_status["history_messages"].append(log_message) + break + + # Validate document data consistency and fix any issues as part of the pipeline + to_process_docs = await self._validate_and_fix_document_consistency( + to_process_docs, pipeline_status, pipeline_status_lock + ) + + if not to_process_docs: + log_message = ( + "No valid documents to process after consistency check" + ) + logger.info(log_message) + pipeline_status["latest_message"] = log_message + pipeline_status["history_messages"].append(log_message) + break + + log_message = f"Processing {len(to_process_docs)} document(s)" + logger.info(log_message) + + # Update pipeline_status, batchs now represents the total number of files to be processed + pipeline_status["docs"] = len(to_process_docs) + pipeline_status["batchs"] = len(to_process_docs) + pipeline_status["cur_batch"] = 0 + pipeline_status["latest_message"] = log_message + pipeline_status["history_messages"].append(log_message) + + # Get first document's file path and total count for job name + first_doc_id, first_doc = next(iter(to_process_docs.items())) + first_doc_path = first_doc.file_path + + # Handle cases where first_doc_path is None + if first_doc_path: + path_prefix = first_doc_path[:20] + ( + "..." if len(first_doc_path) > 20 else "" + ) + else: + path_prefix = "unknown_source" + + total_files = len(to_process_docs) + job_name = f"{path_prefix}[{total_files} files]" + pipeline_status["job_name"] = job_name + + # Create a counter to track the number of processed files + processed_count = 0 + # Create a semaphore to limit the number of concurrent file processing + semaphore = asyncio.Semaphore(self.max_parallel_insert) + + async def process_document( + doc_id: str, + status_doc: DocProcessingStatus, + split_by_character: str | None, + split_by_character_only: bool, + pipeline_status: dict, + pipeline_status_lock: asyncio.Lock, + semaphore: asyncio.Semaphore, + ) -> None: + """Process single document""" + # Initialize variables at the start to prevent UnboundLocalError in error handling + file_path = "unknown_source" + current_file_number = 0 + file_extraction_stage_ok = False + processing_start_time = int(time.time()) + first_stage_tasks = [] + entity_relation_task = None + + async with semaphore: + nonlocal processed_count + # Initialize to prevent UnboundLocalError in error handling + first_stage_tasks = [] + entity_relation_task = None + try: + # Check for cancellation before starting document processing + async with pipeline_status_lock: + if pipeline_status.get("cancellation_requested", False): + raise PipelineCancelledException("User cancelled") + + # Get file path from status document + file_path = getattr( + status_doc, "file_path", "unknown_source" + ) + + async with pipeline_status_lock: + # Update processed file count and save current file number + processed_count += 1 + current_file_number = ( + processed_count # Save the current file number + ) + pipeline_status["cur_batch"] = processed_count + + log_message = f"Extracting stage {current_file_number}/{total_files}: {file_path}" + logger.info(log_message) + pipeline_status["history_messages"].append(log_message) + log_message = f"Processing d-id: {doc_id}" + logger.info(log_message) + pipeline_status["latest_message"] = log_message + pipeline_status["history_messages"].append(log_message) + + # Prevent memory growth: keep only latest 5000 messages when exceeding 10000 + if len(pipeline_status["history_messages"]) > 10000: + logger.info( + f"Trimming pipeline history from {len(pipeline_status['history_messages'])} to 5000 messages" + ) + pipeline_status["history_messages"] = ( + pipeline_status["history_messages"][-5000:] + ) + + # Get document content from full_docs + content_data = await self.full_docs.get_by_id(doc_id) + if not content_data: + raise Exception( + f"Document content not found in full_docs for doc_id: {doc_id}" + ) + content = content_data["content"] + + # Call chunking function, supporting both sync and async implementations + chunking_result = self.chunking_func( + self.tokenizer, + content, + split_by_character, + split_by_character_only, + self.chunk_overlap_token_size, + self.chunk_token_size, + ) + + # If result is awaitable, await to get actual result + if inspect.isawaitable(chunking_result): + chunking_result = await chunking_result + + # Validate return type + if not isinstance(chunking_result, (list, tuple)): + raise TypeError( + f"chunking_func must return a list or tuple of dicts, " + f"got {type(chunking_result)}" + ) + + # Build chunks dictionary + chunks: dict[str, Any] = { + compute_mdhash_id(dp["content"], prefix="chunk-"): { + **dp, + "full_doc_id": doc_id, + "file_path": file_path, # Add file path to each chunk + "llm_cache_list": [], # Initialize empty LLM cache list for each chunk + } + for dp in chunking_result + } + + if not chunks: + logger.warning("No document chunks to process") + + # Record processing start time + processing_start_time = int(time.time()) + + # Check for cancellation before entity extraction + async with pipeline_status_lock: + if pipeline_status.get("cancellation_requested", False): + raise PipelineCancelledException("User cancelled") + + # Process document in two stages + # Stage 1: Process text chunks and docs (parallel execution) + doc_status_task = asyncio.create_task( + self.doc_status.upsert( + { + doc_id: { + "status": DocStatus.PROCESSING, + "chunks_count": len(chunks), + "chunks_list": list( + chunks.keys() + ), # Save chunks list + "content_summary": status_doc.content_summary, + "content_length": status_doc.content_length, + "created_at": status_doc.created_at, + "updated_at": datetime.now( + timezone.utc + ).isoformat(), + "file_path": file_path, + "track_id": status_doc.track_id, # Preserve existing track_id + "metadata": { + "processing_start_time": processing_start_time + }, + } + } + ) + ) + chunks_vdb_task = asyncio.create_task( + self.chunks_vdb.upsert(chunks) + ) + text_chunks_task = asyncio.create_task( + self.text_chunks.upsert(chunks) + ) + + # First stage tasks (parallel execution) + first_stage_tasks = [ + doc_status_task, + chunks_vdb_task, + text_chunks_task, + ] + entity_relation_task = None + + # Execute first stage tasks + await asyncio.gather(*first_stage_tasks) + + # Stage 2: Process entity relation graph (after text_chunks are saved) + entity_relation_task = asyncio.create_task( + self._process_extract_entities( + chunks, pipeline_status, pipeline_status_lock + ) + ) + chunk_results = await entity_relation_task + file_extraction_stage_ok = True + + except Exception as e: + # Check if this is a user cancellation + if isinstance(e, PipelineCancelledException): + # User cancellation - log brief message only, no traceback + error_msg = f"User cancelled {current_file_number}/{total_files}: {file_path}" + logger.warning(error_msg) + async with pipeline_status_lock: + pipeline_status["latest_message"] = error_msg + pipeline_status["history_messages"].append( + error_msg + ) + else: + # Other exceptions - log with traceback + logger.error(traceback.format_exc()) + error_msg = f"Failed to extract document {current_file_number}/{total_files}: {file_path}" + logger.error(error_msg) + async with pipeline_status_lock: + pipeline_status["latest_message"] = error_msg + pipeline_status["history_messages"].append( + traceback.format_exc() + ) + pipeline_status["history_messages"].append( + error_msg + ) + + # Cancel tasks that are not yet completed + all_tasks = first_stage_tasks + ( + [entity_relation_task] if entity_relation_task else [] + ) + for task in all_tasks: + if task and not task.done(): + task.cancel() + + # Persistent llm cache with error handling + if self.llm_response_cache: + try: + await self.llm_response_cache.index_done_callback() + except Exception as persist_error: + logger.error( + f"Failed to persist LLM cache: {persist_error}" + ) + + # Record processing end time for failed case + processing_end_time = int(time.time()) + + # Update document status to failed + await self.doc_status.upsert( + { + doc_id: { + "status": DocStatus.FAILED, + "error_msg": str(e), + "content_summary": status_doc.content_summary, + "content_length": status_doc.content_length, + "created_at": status_doc.created_at, + "updated_at": datetime.now( + timezone.utc + ).isoformat(), + "file_path": file_path, + "track_id": status_doc.track_id, # Preserve existing track_id + "metadata": { + "processing_start_time": processing_start_time, + "processing_end_time": processing_end_time, + }, + } + } + ) + + # Concurrency is controlled by keyed lock for individual entities and relationships + if file_extraction_stage_ok: + try: + # Check for cancellation before merge + async with pipeline_status_lock: + if pipeline_status.get( + "cancellation_requested", False + ): + raise PipelineCancelledException( + "User cancelled" + ) + + # Use chunk_results from entity_relation_task + await merge_nodes_and_edges( + chunk_results=chunk_results, # result collected from entity_relation_task + knowledge_graph_inst=self.chunk_entity_relation_graph, + entity_vdb=self.entities_vdb, + relationships_vdb=self.relationships_vdb, + global_config=asdict(self), + full_entities_storage=self.full_entities, + full_relations_storage=self.full_relations, + doc_id=doc_id, + pipeline_status=pipeline_status, + pipeline_status_lock=pipeline_status_lock, + llm_response_cache=self.llm_response_cache, + entity_chunks_storage=self.entity_chunks, + relation_chunks_storage=self.relation_chunks, + current_file_number=current_file_number, + total_files=total_files, + file_path=file_path, + ) + + # Record processing end time + processing_end_time = int(time.time()) + + await self.doc_status.upsert( + { + doc_id: { + "status": DocStatus.PROCESSED, + "chunks_count": len(chunks), + "chunks_list": list(chunks.keys()), + "content_summary": status_doc.content_summary, + "content_length": status_doc.content_length, + "created_at": status_doc.created_at, + "updated_at": datetime.now( + timezone.utc + ).isoformat(), + "file_path": file_path, + "track_id": status_doc.track_id, # Preserve existing track_id + "metadata": { + "processing_start_time": processing_start_time, + "processing_end_time": processing_end_time, + }, + } + } + ) + + # Call _insert_done after processing each file + await self._insert_done() + + async with pipeline_status_lock: + log_message = f"Completed processing file {current_file_number}/{total_files}: {file_path}" + logger.info(log_message) + pipeline_status["latest_message"] = log_message + pipeline_status["history_messages"].append( + log_message + ) + + except Exception as e: + # Check if this is a user cancellation + if isinstance(e, PipelineCancelledException): + # User cancellation - log brief message only, no traceback + error_msg = f"User cancelled during merge {current_file_number}/{total_files}: {file_path}" + logger.warning(error_msg) + async with pipeline_status_lock: + pipeline_status["latest_message"] = error_msg + pipeline_status["history_messages"].append( + error_msg + ) + else: + # Other exceptions - log with traceback + logger.error(traceback.format_exc()) + error_msg = f"Merging stage failed in document {current_file_number}/{total_files}: {file_path}" + logger.error(error_msg) + async with pipeline_status_lock: + pipeline_status["latest_message"] = error_msg + pipeline_status["history_messages"].append( + traceback.format_exc() + ) + pipeline_status["history_messages"].append( + error_msg + ) + + # Persistent llm cache with error handling + if self.llm_response_cache: + try: + await self.llm_response_cache.index_done_callback() + except Exception as persist_error: + logger.error( + f"Failed to persist LLM cache: {persist_error}" + ) + + # Record processing end time for failed case + processing_end_time = int(time.time()) + + # Update document status to failed + await self.doc_status.upsert( + { + doc_id: { + "status": DocStatus.FAILED, + "error_msg": str(e), + "content_summary": status_doc.content_summary, + "content_length": status_doc.content_length, + "created_at": status_doc.created_at, + "updated_at": datetime.now().isoformat(), + "file_path": file_path, + "track_id": status_doc.track_id, # Preserve existing track_id + "metadata": { + "processing_start_time": processing_start_time, + "processing_end_time": processing_end_time, + }, + } + } + ) + + # Create processing tasks for all documents + doc_tasks = [] + for doc_id, status_doc in to_process_docs.items(): + doc_tasks.append( + process_document( + doc_id, + status_doc, + split_by_character, + split_by_character_only, + pipeline_status, + pipeline_status_lock, + semaphore, + ) + ) + + # Wait for all document processing to complete + try: + await asyncio.gather(*doc_tasks) + except PipelineCancelledException: + # Cancel all remaining tasks + for task in doc_tasks: + if not task.done(): + task.cancel() + + # Wait for all tasks to complete cancellation + await asyncio.wait(doc_tasks, return_when=asyncio.ALL_COMPLETED) + + # Exit directly (document statuses already updated in process_document) + return + + # Check if there's a pending request to process more documents (with lock) + has_pending_request = False + async with pipeline_status_lock: + has_pending_request = pipeline_status.get("request_pending", False) + if has_pending_request: + # Clear the request flag before checking for more documents + pipeline_status["request_pending"] = False + + if not has_pending_request: + break + + log_message = "Processing additional documents due to pending request" + logger.info(log_message) + pipeline_status["latest_message"] = log_message + pipeline_status["history_messages"].append(log_message) + + # Check for pending documents again + processing_docs, failed_docs, pending_docs = await asyncio.gather( + self.doc_status.get_docs_by_status(DocStatus.PROCESSING), + self.doc_status.get_docs_by_status(DocStatus.FAILED), + self.doc_status.get_docs_by_status(DocStatus.PENDING), + ) + + to_process_docs = {} + to_process_docs.update(processing_docs) + to_process_docs.update(failed_docs) + to_process_docs.update(pending_docs) + + finally: + log_message = "Enqueued document processing pipeline stopped" + logger.info(log_message) + # Always reset busy status and cancellation flag when done or if an exception occurs (with lock) + async with pipeline_status_lock: + pipeline_status["busy"] = False + pipeline_status["cancellation_requested"] = ( + False # Always reset cancellation flag + ) + pipeline_status["latest_message"] = log_message + pipeline_status["history_messages"].append(log_message) + + async def _process_extract_entities( + self, chunk: dict[str, Any], pipeline_status=None, pipeline_status_lock=None + ) -> list: + try: + chunk_results = await extract_entities( + chunk, + global_config=asdict(self), + pipeline_status=pipeline_status, + pipeline_status_lock=pipeline_status_lock, + llm_response_cache=self.llm_response_cache, + text_chunks_storage=self.text_chunks, + ) + return chunk_results + except Exception as e: + error_msg = f"Failed to extract entities and relationships: {str(e)}" + logger.error(error_msg) + async with pipeline_status_lock: + pipeline_status["latest_message"] = error_msg + pipeline_status["history_messages"].append(error_msg) + raise e + + async def _insert_done( + self, pipeline_status=None, pipeline_status_lock=None + ) -> None: + tasks = [ + cast(StorageNameSpace, storage_inst).index_done_callback() + for storage_inst in [ # type: ignore + self.full_docs, + self.doc_status, + self.text_chunks, + self.full_entities, + self.full_relations, + self.entity_chunks, + self.relation_chunks, + self.llm_response_cache, + self.entities_vdb, + self.relationships_vdb, + self.chunks_vdb, + self.chunk_entity_relation_graph, + ] + if storage_inst is not None + ] + await asyncio.gather(*tasks) + + log_message = "In memory DB persist to disk" + logger.info(log_message) + + if pipeline_status is not None and pipeline_status_lock is not None: + async with pipeline_status_lock: + pipeline_status["latest_message"] = log_message + pipeline_status["history_messages"].append(log_message) + + def insert_custom_kg( + self, custom_kg: dict[str, Any], full_doc_id: str = None + ) -> None: + loop = always_get_an_event_loop() + loop.run_until_complete(self.ainsert_custom_kg(custom_kg, full_doc_id)) + + async def ainsert_custom_kg( + self, + custom_kg: dict[str, Any], + full_doc_id: str = None, + ) -> None: + update_storage = False + try: + # Insert chunks into vector storage + all_chunks_data: dict[str, dict[str, str]] = {} + chunk_to_source_map: dict[str, str] = {} + for chunk_data in custom_kg.get("chunks", []): + chunk_content = sanitize_text_for_encoding(chunk_data["content"]) + source_id = chunk_data["source_id"] + file_path = chunk_data.get("file_path", "custom_kg") + tokens = len(self.tokenizer.encode(chunk_content)) + chunk_order_index = ( + 0 + if "chunk_order_index" not in chunk_data.keys() + else chunk_data["chunk_order_index"] + ) + chunk_id = compute_mdhash_id(chunk_content, prefix="chunk-") + + chunk_entry = { + "content": chunk_content, + "source_id": source_id, + "tokens": tokens, + "chunk_order_index": chunk_order_index, + "full_doc_id": full_doc_id + if full_doc_id is not None + else source_id, + "file_path": file_path, + "status": DocStatus.PROCESSED, + } + all_chunks_data[chunk_id] = chunk_entry + chunk_to_source_map[source_id] = chunk_id + update_storage = True + + if all_chunks_data: + await asyncio.gather( + self.chunks_vdb.upsert(all_chunks_data), + self.text_chunks.upsert(all_chunks_data), + ) + + # Insert entities into knowledge graph + all_entities_data: list[dict[str, str]] = [] + for entity_data in custom_kg.get("entities", []): + entity_name = entity_data["entity_name"] + entity_type = entity_data.get("entity_type", "UNKNOWN") + description = entity_data.get("description", "No description provided") + source_chunk_id = entity_data.get("source_id", "UNKNOWN") + source_id = chunk_to_source_map.get(source_chunk_id, "UNKNOWN") + file_path = entity_data.get("file_path", "custom_kg") + + # Log if source_id is UNKNOWN + if source_id == "UNKNOWN": + logger.warning( + f"Entity '{entity_name}' has an UNKNOWN source_id. Please check the source mapping." + ) + + # Prepare node data + node_data: dict[str, str] = { + "entity_id": entity_name, + "entity_type": entity_type, + "description": description, + "source_id": source_id, + "file_path": file_path, + "created_at": int(time.time()), + } + # Insert node data into the knowledge graph + await self.chunk_entity_relation_graph.upsert_node( + entity_name, node_data=node_data + ) + node_data["entity_name"] = entity_name + all_entities_data.append(node_data) + update_storage = True + + # Insert relationships into knowledge graph + all_relationships_data: list[dict[str, str]] = [] + for relationship_data in custom_kg.get("relationships", []): + src_id = relationship_data["src_id"] + tgt_id = relationship_data["tgt_id"] + description = relationship_data["description"] + keywords = relationship_data["keywords"] + weight = relationship_data.get("weight", 1.0) + source_chunk_id = relationship_data.get("source_id", "UNKNOWN") + source_id = chunk_to_source_map.get(source_chunk_id, "UNKNOWN") + file_path = relationship_data.get("file_path", "custom_kg") + + # Log if source_id is UNKNOWN + if source_id == "UNKNOWN": + logger.warning( + f"Relationship from '{src_id}' to '{tgt_id}' has an UNKNOWN source_id. Please check the source mapping." + ) + + # Check if nodes exist in the knowledge graph + for need_insert_id in [src_id, tgt_id]: + if not ( + await self.chunk_entity_relation_graph.has_node(need_insert_id) + ): + await self.chunk_entity_relation_graph.upsert_node( + need_insert_id, + node_data={ + "entity_id": need_insert_id, + "source_id": source_id, + "description": "UNKNOWN", + "entity_type": "UNKNOWN", + "file_path": file_path, + "created_at": int(time.time()), + }, + ) + + # Insert edge into the knowledge graph + await self.chunk_entity_relation_graph.upsert_edge( + src_id, + tgt_id, + edge_data={ + "weight": weight, + "description": description, + "keywords": keywords, + "source_id": source_id, + "file_path": file_path, + "created_at": int(time.time()), + }, + ) + + edge_data: dict[str, str] = { + "src_id": src_id, + "tgt_id": tgt_id, + "description": description, + "keywords": keywords, + "source_id": source_id, + "weight": weight, + "file_path": file_path, + "created_at": int(time.time()), + } + all_relationships_data.append(edge_data) + update_storage = True + + # Insert entities into vector storage with consistent format + data_for_vdb = { + compute_mdhash_id(dp["entity_name"], prefix="ent-"): { + "content": dp["entity_name"] + "\n" + dp["description"], + "entity_name": dp["entity_name"], + "source_id": dp["source_id"], + "description": dp["description"], + "entity_type": dp["entity_type"], + "file_path": dp.get("file_path", "custom_kg"), + } + for dp in all_entities_data + } + await self.entities_vdb.upsert(data_for_vdb) + + # Insert relationships into vector storage with consistent format + data_for_vdb = { + compute_mdhash_id(dp["src_id"] + dp["tgt_id"], prefix="rel-"): { + "src_id": dp["src_id"], + "tgt_id": dp["tgt_id"], + "source_id": dp["source_id"], + "content": f"{dp['keywords']}\t{dp['src_id']}\n{dp['tgt_id']}\n{dp['description']}", + "keywords": dp["keywords"], + "description": dp["description"], + "weight": dp["weight"], + "file_path": dp.get("file_path", "custom_kg"), + } + for dp in all_relationships_data + } + await self.relationships_vdb.upsert(data_for_vdb) + + except Exception as e: + logger.error(f"Error in ainsert_custom_kg: {e}") + raise + finally: + if update_storage: + await self._insert_done() + + def query( + self, + query: str, + param: QueryParam = QueryParam(), + system_prompt: str | None = None, + ) -> str | Iterator[str]: + """ + Perform a sync query. + + Args: + query (str): The query to be executed. + param (QueryParam): Configuration parameters for query execution. + prompt (Optional[str]): Custom prompts for fine-tuned control over the system's behavior. Defaults to None, which uses PROMPTS["rag_response"]. + + Returns: + str: The result of the query execution. + """ + loop = always_get_an_event_loop() + + return loop.run_until_complete(self.aquery(query, param, system_prompt)) # type: ignore + + async def aquery( + self, + query: str, + param: QueryParam = QueryParam(), + system_prompt: str | None = None, + ) -> str | AsyncIterator[str]: + """ + Perform a async query (backward compatibility wrapper). + + This function is now a wrapper around aquery_llm that maintains backward compatibility + by returning only the LLM response content in the original format. + + Args: + query (str): The query to be executed. + param (QueryParam): Configuration parameters for query execution. + If param.model_func is provided, it will be used instead of the global model. + system_prompt (Optional[str]): Custom prompts for fine-tuned control over the system's behavior. Defaults to None, which uses PROMPTS["rag_response"]. + + Returns: + str | AsyncIterator[str]: The LLM response content. + - Non-streaming: Returns str + - Streaming: Returns AsyncIterator[str] + """ + # Call the new aquery_llm function to get complete results + result = await self.aquery_llm(query, param, system_prompt) + + # Extract and return only the LLM response for backward compatibility + llm_response = result.get("llm_response", {}) + + if llm_response.get("is_streaming"): + return llm_response.get("response_iterator") + else: + return llm_response.get("content", "") + + def query_data( + self, + query: str, + param: QueryParam = QueryParam(), + ) -> dict[str, Any]: + """ + Synchronous data retrieval API: returns structured retrieval results without LLM generation. + + This function is the synchronous version of aquery_data, providing the same functionality + for users who prefer synchronous interfaces. + + Args: + query: Query text for retrieval. + param: Query parameters controlling retrieval behavior (same as aquery). + + Returns: + dict[str, Any]: Same structured data result as aquery_data. + """ + loop = always_get_an_event_loop() + return loop.run_until_complete(self.aquery_data(query, param)) + + async def aquery_data( + self, + query: str, + param: QueryParam = QueryParam(), + ) -> dict[str, Any]: + """ + Asynchronous data retrieval API: returns structured retrieval results without LLM generation. + + This function reuses the same logic as aquery but stops before LLM generation, + returning the final processed entities, relationships, and chunks data that would be sent to LLM. + + Args: + query: Query text for retrieval. + param: Query parameters controlling retrieval behavior (same as aquery). + + Returns: + dict[str, Any]: Structured data result in the following format: + + **Success Response:** + ```python + { + "status": "success", + "message": "Query executed successfully", + "data": { + "entities": [ + { + "entity_name": str, # Entity identifier + "entity_type": str, # Entity category/type + "description": str, # Entity description + "source_id": str, # Source chunk references + "file_path": str, # Origin file path + "created_at": str, # Creation timestamp + "reference_id": str # Reference identifier for citations + } + ], + "relationships": [ + { + "src_id": str, # Source entity name + "tgt_id": str, # Target entity name + "description": str, # Relationship description + "keywords": str, # Relationship keywords + "weight": float, # Relationship strength + "source_id": str, # Source chunk references + "file_path": str, # Origin file path + "created_at": str, # Creation timestamp + "reference_id": str # Reference identifier for citations + } + ], + "chunks": [ + { + "content": str, # Document chunk content + "file_path": str, # Origin file path + "chunk_id": str, # Unique chunk identifier + "reference_id": str # Reference identifier for citations + } + ], + "references": [ + { + "reference_id": str, # Reference identifier + "file_path": str # Corresponding file path + } + ] + }, + "metadata": { + "query_mode": str, # Query mode used ("local", "global", "hybrid", "mix", "naive", "bypass") + "keywords": { + "high_level": List[str], # High-level keywords extracted + "low_level": List[str] # Low-level keywords extracted + }, + "processing_info": { + "total_entities_found": int, # Total entities before truncation + "total_relations_found": int, # Total relations before truncation + "entities_after_truncation": int, # Entities after token truncation + "relations_after_truncation": int, # Relations after token truncation + "merged_chunks_count": int, # Chunks before final processing + "final_chunks_count": int # Final chunks in result + } + } + } + ``` + + **Query Mode Differences:** + - **local**: Focuses on entities and their related chunks based on low-level keywords + - **global**: Focuses on relationships and their connected entities based on high-level keywords + - **hybrid**: Combines local and global results using round-robin merging + - **mix**: Includes knowledge graph data plus vector-retrieved document chunks + - **naive**: Only vector-retrieved chunks, entities and relationships arrays are empty + - **bypass**: All data arrays are empty, used for direct LLM queries + + ** processing_info is optional and may not be present in all responses, especially when query result is empty** + + **Failure Response:** + ```python + { + "status": "failure", + "message": str, # Error description + "data": {} # Empty data object + } + ``` + + **Common Failure Cases:** + - Empty query string + - Both high-level and low-level keywords are empty + - Query returns empty dataset + - Missing tokenizer or system configuration errors + + Note: + The function adapts to the new data format from convert_to_user_format where + actual data is nested under the 'data' field, with 'status' and 'message' + fields at the top level. + """ + global_config = asdict(self) + + # Create a copy of param to avoid modifying the original + data_param = QueryParam( + mode=param.mode, + only_need_context=True, # Skip LLM generation, only get context and data + only_need_prompt=False, + response_type=param.response_type, + stream=False, # Data retrieval doesn't need streaming + top_k=param.top_k, + chunk_top_k=param.chunk_top_k, + max_entity_tokens=param.max_entity_tokens, + max_relation_tokens=param.max_relation_tokens, + max_total_tokens=param.max_total_tokens, + hl_keywords=param.hl_keywords, + ll_keywords=param.ll_keywords, + conversation_history=param.conversation_history, + history_turns=param.history_turns, + model_func=param.model_func, + user_prompt=param.user_prompt, + enable_rerank=param.enable_rerank, + ) + + query_result = None + + if data_param.mode in ["local", "global", "hybrid", "mix"]: + logger.debug(f"[aquery_data] Using kg_query for mode: {data_param.mode}") + query_result = await kg_query( + query.strip(), + self.chunk_entity_relation_graph, + self.entities_vdb, + self.relationships_vdb, + self.text_chunks, + data_param, # Use data_param with only_need_context=True + global_config, + hashing_kv=self.llm_response_cache, + system_prompt=None, + chunks_vdb=self.chunks_vdb, + ) + elif data_param.mode == "naive": + logger.debug(f"[aquery_data] Using naive_query for mode: {data_param.mode}") + query_result = await naive_query( + query.strip(), + self.chunks_vdb, + data_param, # Use data_param with only_need_context=True + global_config, + hashing_kv=self.llm_response_cache, + system_prompt=None, + ) + elif data_param.mode == "bypass": + logger.debug("[aquery_data] Using bypass mode") + # bypass mode returns empty data using convert_to_user_format + empty_raw_data = convert_to_user_format( + [], # no entities + [], # no relationships + [], # no chunks + [], # no references + "bypass", + ) + query_result = QueryResult(content="", raw_data=empty_raw_data) + else: + raise ValueError(f"Unknown mode {data_param.mode}") + + if query_result is None: + no_result_message = "Query returned no results" + if data_param.mode == "naive": + no_result_message = "No relevant document chunks found." + final_data: dict[str, Any] = { + "status": "failure", + "message": no_result_message, + "data": {}, + "metadata": { + "failure_reason": "no_results", + "mode": data_param.mode, + }, + } + logger.info("[aquery_data] Query returned no results.") + else: + # Extract raw_data from QueryResult + final_data = query_result.raw_data or {} + + # Log final result counts - adapt to new data format from convert_to_user_format + if final_data and "data" in final_data: + data_section = final_data["data"] + entities_count = len(data_section.get("entities", [])) + relationships_count = len(data_section.get("relationships", [])) + chunks_count = len(data_section.get("chunks", [])) + logger.debug( + f"[aquery_data] Final result: {entities_count} entities, {relationships_count} relationships, {chunks_count} chunks" + ) + else: + logger.warning("[aquery_data] No data section found in query result") + + await self._query_done() + return final_data + + async def aquery_llm( + self, + query: str, + param: QueryParam = QueryParam(), + system_prompt: str | None = None, + ) -> dict[str, Any]: + """ + Asynchronous complete query API: returns structured retrieval results with LLM generation. + + This function performs a single query operation and returns both structured data and LLM response, + based on the original aquery logic to avoid duplicate calls. + + Args: + query: Query text for retrieval and LLM generation. + param: Query parameters controlling retrieval and LLM behavior. + system_prompt: Optional custom system prompt for LLM generation. + + Returns: + dict[str, Any]: Complete response with structured data and LLM response. + """ + logger.debug(f"[aquery_llm] Query param: {param}") + + global_config = asdict(self) + + try: + query_result = None + + if param.mode in ["local", "global", "hybrid", "mix"]: + query_result = await kg_query( + query.strip(), + self.chunk_entity_relation_graph, + self.entities_vdb, + self.relationships_vdb, + self.text_chunks, + param, + global_config, + hashing_kv=self.llm_response_cache, + system_prompt=system_prompt, + chunks_vdb=self.chunks_vdb, + ) + elif param.mode == "naive": + query_result = await naive_query( + query.strip(), + self.chunks_vdb, + param, + global_config, + hashing_kv=self.llm_response_cache, + system_prompt=system_prompt, + ) + elif param.mode == "bypass": + # Bypass mode: directly use LLM without knowledge retrieval + use_llm_func = param.model_func or global_config["llm_model_func"] + # Apply higher priority (8) to entity/relation summary tasks + use_llm_func = partial(use_llm_func, _priority=8) + + param.stream = True if param.stream is None else param.stream + response = await use_llm_func( + query.strip(), + system_prompt=system_prompt, + history_messages=param.conversation_history, + enable_cot=True, + stream=param.stream, + ) + if type(response) is str: + return { + "status": "success", + "message": "Bypass mode LLM non streaming response", + "data": {}, + "metadata": {}, + "llm_response": { + "content": response, + "response_iterator": None, + "is_streaming": False, + }, + } + else: + return { + "status": "success", + "message": "Bypass mode LLM streaming response", + "data": {}, + "metadata": {}, + "llm_response": { + "content": None, + "response_iterator": response, + "is_streaming": True, + }, + } + else: + raise ValueError(f"Unknown mode {param.mode}") + + await self._query_done() + + # Check if query_result is None + if query_result is None: + return { + "status": "failure", + "message": "Query returned no results", + "data": {}, + "metadata": { + "failure_reason": "no_results", + "mode": param.mode, + }, + "llm_response": { + "content": PROMPTS["fail_response"], + "response_iterator": None, + "is_streaming": False, + }, + } + + # Extract structured data from query result + raw_data = query_result.raw_data or {} + raw_data["llm_response"] = { + "content": query_result.content + if not query_result.is_streaming + else None, + "response_iterator": query_result.response_iterator + if query_result.is_streaming + else None, + "is_streaming": query_result.is_streaming, + } + + return raw_data + + except Exception as e: + logger.error(f"Query failed: {e}") + # Return error response + return { + "status": "failure", + "message": f"Query failed: {str(e)}", + "data": {}, + "metadata": {}, + "llm_response": { + "content": None, + "response_iterator": None, + "is_streaming": False, + }, + } + + def query_llm( + self, + query: str, + param: QueryParam = QueryParam(), + system_prompt: str | None = None, + ) -> dict[str, Any]: + """ + Synchronous complete query API: returns structured retrieval results with LLM generation. + + This function is the synchronous version of aquery_llm, providing the same functionality + for users who prefer synchronous interfaces. + + Args: + query: Query text for retrieval and LLM generation. + param: Query parameters controlling retrieval and LLM behavior. + system_prompt: Optional custom system prompt for LLM generation. + + Returns: + dict[str, Any]: Same complete response format as aquery_llm. + """ + loop = always_get_an_event_loop() + return loop.run_until_complete(self.aquery_llm(query, param, system_prompt)) + + async def _query_done(self): + await self.llm_response_cache.index_done_callback() + + async def aclear_cache(self) -> None: + """Clear all cache data from the LLM response cache storage. + + This method clears all cached LLM responses regardless of mode. + + Example: + # Clear all cache + await rag.aclear_cache() + """ + if not self.llm_response_cache: + logger.warning("No cache storage configured") + return + + try: + # Clear all cache using drop method + success = await self.llm_response_cache.drop() + if success: + logger.info("Cleared all cache") + else: + logger.warning("Failed to clear all cache") + + await self.llm_response_cache.index_done_callback() + + except Exception as e: + logger.error(f"Error while clearing cache: {e}") + + def clear_cache(self) -> None: + """Synchronous version of aclear_cache.""" + return always_get_an_event_loop().run_until_complete(self.aclear_cache()) + + async def get_docs_by_status( + self, status: DocStatus + ) -> dict[str, DocProcessingStatus]: + """Get documents by status + + Returns: + Dict with document id is keys and document status is values + """ + return await self.doc_status.get_docs_by_status(status) + + async def aget_docs_by_ids( + self, ids: str | list[str] + ) -> dict[str, DocProcessingStatus]: + """Retrieves the processing status for one or more documents by their IDs. + + Args: + ids: A single document ID (string) or a list of document IDs (list of strings). + + Returns: + A dictionary where keys are the document IDs for which a status was found, + and values are the corresponding DocProcessingStatus objects. IDs that + are not found in the storage will be omitted from the result dictionary. + """ + if isinstance(ids, str): + # Ensure input is always a list of IDs for uniform processing + id_list = [ids] + elif ( + ids is None + ): # Handle potential None input gracefully, although type hint suggests str/list + logger.warning( + "aget_docs_by_ids called with None input, returning empty dict." + ) + return {} + else: + # Assume input is already a list if not a string + id_list = ids + + # Return early if the final list of IDs is empty + if not id_list: + logger.debug("aget_docs_by_ids called with an empty list of IDs.") + return {} + + # Create tasks to fetch document statuses concurrently using the doc_status storage + tasks = [self.doc_status.get_by_id(doc_id) for doc_id in id_list] + # Execute tasks concurrently and gather the results. Results maintain order. + # Type hint indicates results can be DocProcessingStatus or None if not found. + results_list: list[Optional[DocProcessingStatus]] = await asyncio.gather(*tasks) + + # Build the result dictionary, mapping found IDs to their statuses + found_statuses: dict[str, DocProcessingStatus] = {} + # Keep track of IDs for which no status was found (for logging purposes) + not_found_ids: list[str] = [] + + # Iterate through the results, correlating them back to the original IDs + for i, status_obj in enumerate(results_list): + doc_id = id_list[ + i + ] # Get the original ID corresponding to this result index + if status_obj: + # If a status object was returned (not None), add it to the result dict + found_statuses[doc_id] = status_obj + else: + # If status_obj is None, the document ID was not found in storage + not_found_ids.append(doc_id) + + # Log a warning if any of the requested document IDs were not found + if not_found_ids: + logger.warning( + f"Document statuses not found for the following IDs: {not_found_ids}" + ) + + # Return the dictionary containing statuses only for the found document IDs + return found_statuses + + async def adelete_by_doc_id( + self, doc_id: str, delete_llm_cache: bool = False + ) -> DeletionResult: + """Delete a document and all its related data, including chunks, graph elements. + + This method orchestrates a comprehensive deletion process for a given document ID. + It ensures that not only the document itself but also all its derived and associated + data across different storage layers are removed or rebuiled. If entities or relationships + are partially affected, they will be rebuilded using LLM cached from remaining documents. + + Args: + doc_id (str): The unique identifier of the document to be deleted. + delete_llm_cache (bool): Whether to delete cached LLM extraction results + associated with the document. Defaults to False. + + Returns: + DeletionResult: An object containing the outcome of the deletion process. + - `status` (str): "success", "not_found", or "failure". + - `doc_id` (str): The ID of the document attempted to be deleted. + - `message` (str): A summary of the operation's result. + - `status_code` (int): HTTP status code (e.g., 200, 404, 500). + - `file_path` (str | None): The file path of the deleted document, if available. + """ + deletion_operations_started = False + original_exception = None + doc_llm_cache_ids: list[str] = [] + + # Get pipeline status shared data and lock for status updates + pipeline_status = await get_namespace_data("pipeline_status") + pipeline_status_lock = get_pipeline_status_lock() + + async with pipeline_status_lock: + log_message = f"Starting deletion process for document {doc_id}" + logger.info(log_message) + pipeline_status["latest_message"] = log_message + pipeline_status["history_messages"].append(log_message) + + try: + # 1. Get the document status and related data + doc_status_data = await self.doc_status.get_by_id(doc_id) + file_path = doc_status_data.get("file_path") if doc_status_data else None + if not doc_status_data: + logger.warning(f"Document {doc_id} not found") + return DeletionResult( + status="not_found", + doc_id=doc_id, + message=f"Document {doc_id} not found.", + status_code=404, + file_path="", + ) + + # Check document status and log warning for non-completed documents + raw_status = doc_status_data.get("status") + try: + doc_status = DocStatus(raw_status) + except ValueError: + doc_status = raw_status + + if doc_status != DocStatus.PROCESSED: + if doc_status == DocStatus.PENDING: + warning_msg = ( + f"Deleting {doc_id} {file_path}(previous status: PENDING)" + ) + elif doc_status == DocStatus.PROCESSING: + warning_msg = ( + f"Deleting {doc_id} {file_path}(previous status: PROCESSING)" + ) + elif doc_status == DocStatus.PREPROCESSED: + warning_msg = ( + f"Deleting {doc_id} {file_path}(previous status: PREPROCESSED)" + ) + elif doc_status == DocStatus.FAILED: + warning_msg = ( + f"Deleting {doc_id} {file_path}(previous status: FAILED)" + ) + else: + status_text = ( + doc_status.value + if isinstance(doc_status, DocStatus) + else str(doc_status) + ) + warning_msg = ( + f"Deleting {doc_id} {file_path}(previous status: {status_text})" + ) + logger.info(warning_msg) + # Update pipeline status for monitoring + async with pipeline_status_lock: + pipeline_status["latest_message"] = warning_msg + pipeline_status["history_messages"].append(warning_msg) + + # 2. Get chunk IDs from document status + chunk_ids = set(doc_status_data.get("chunks_list", [])) + + if not chunk_ids: + logger.warning(f"No chunks found for document {doc_id}") + # Mark that deletion operations have started + deletion_operations_started = True + try: + # Still need to delete the doc status and full doc + await self.full_docs.delete([doc_id]) + await self.doc_status.delete([doc_id]) + except Exception as e: + logger.error( + f"Failed to delete document {doc_id} with no chunks: {e}" + ) + raise Exception(f"Failed to delete document entry: {e}") from e + + async with pipeline_status_lock: + log_message = ( + f"Document deleted without associated chunks: {doc_id}" + ) + logger.info(log_message) + pipeline_status["latest_message"] = log_message + pipeline_status["history_messages"].append(log_message) + + return DeletionResult( + status="success", + doc_id=doc_id, + message=log_message, + status_code=200, + file_path=file_path, + ) + + # Mark that deletion operations have started + deletion_operations_started = True + + if delete_llm_cache and chunk_ids: + if not self.llm_response_cache: + logger.info( + "Skipping LLM cache collection for document %s because cache storage is unavailable", + doc_id, + ) + elif not self.text_chunks: + logger.info( + "Skipping LLM cache collection for document %s because text chunk storage is unavailable", + doc_id, + ) + else: + try: + chunk_data_list = await self.text_chunks.get_by_ids( + list(chunk_ids) + ) + seen_cache_ids: set[str] = set() + for chunk_data in chunk_data_list: + if not chunk_data or not isinstance(chunk_data, dict): + continue + cache_ids = chunk_data.get("llm_cache_list", []) + if not isinstance(cache_ids, list): + continue + for cache_id in cache_ids: + if ( + isinstance(cache_id, str) + and cache_id + and cache_id not in seen_cache_ids + ): + doc_llm_cache_ids.append(cache_id) + seen_cache_ids.add(cache_id) + if doc_llm_cache_ids: + logger.info( + "Collected %d LLM cache entries for document %s", + len(doc_llm_cache_ids), + doc_id, + ) + else: + logger.info( + "No LLM cache entries found for document %s", doc_id + ) + except Exception as cache_collect_error: + logger.error( + "Failed to collect LLM cache ids for document %s: %s", + doc_id, + cache_collect_error, + ) + raise Exception( + f"Failed to collect LLM cache ids for document {doc_id}: {cache_collect_error}" + ) from cache_collect_error + + # 4. Analyze entities and relationships that will be affected + entities_to_delete = set() + entities_to_rebuild = {} # entity_name -> remaining chunk id list + relationships_to_delete = set() + relationships_to_rebuild = {} # (src, tgt) -> remaining chunk id list + entity_chunk_updates: dict[str, list[str]] = {} + relation_chunk_updates: dict[tuple[str, str], list[str]] = {} + + try: + # Get affected entities and relations from full_entities and full_relations storage + doc_entities_data = await self.full_entities.get_by_id(doc_id) + doc_relations_data = await self.full_relations.get_by_id(doc_id) + + affected_nodes = [] + affected_edges = [] + + # Get entity data from graph storage using entity names from full_entities + if doc_entities_data and "entity_names" in doc_entities_data: + entity_names = doc_entities_data["entity_names"] + # get_nodes_batch returns dict[str, dict], need to convert to list[dict] + nodes_dict = await self.chunk_entity_relation_graph.get_nodes_batch( + entity_names + ) + for entity_name in entity_names: + node_data = nodes_dict.get(entity_name) + if node_data: + # Ensure compatibility with existing logic that expects "id" field + if "id" not in node_data: + node_data["id"] = entity_name + affected_nodes.append(node_data) + + # Get relation data from graph storage using relation pairs from full_relations + if doc_relations_data and "relation_pairs" in doc_relations_data: + relation_pairs = doc_relations_data["relation_pairs"] + edge_pairs_dicts = [ + {"src": pair[0], "tgt": pair[1]} for pair in relation_pairs + ] + # get_edges_batch returns dict[tuple[str, str], dict], need to convert to list[dict] + edges_dict = await self.chunk_entity_relation_graph.get_edges_batch( + edge_pairs_dicts + ) + + for pair in relation_pairs: + src, tgt = pair[0], pair[1] + edge_key = (src, tgt) + edge_data = edges_dict.get(edge_key) + if edge_data: + # Ensure compatibility with existing logic that expects "source" and "target" fields + if "source" not in edge_data: + edge_data["source"] = src + if "target" not in edge_data: + edge_data["target"] = tgt + affected_edges.append(edge_data) + + except Exception as e: + logger.error(f"Failed to analyze affected graph elements: {e}") + raise Exception(f"Failed to analyze graph dependencies: {e}") from e + + try: + # Process entities + for node_data in affected_nodes: + node_label = node_data.get("entity_id") + if not node_label: + continue + + existing_sources: list[str] = [] + if self.entity_chunks: + stored_chunks = await self.entity_chunks.get_by_id(node_label) + if stored_chunks and isinstance(stored_chunks, dict): + existing_sources = [ + chunk_id + for chunk_id in stored_chunks.get("chunk_ids", []) + if chunk_id + ] + + if not existing_sources and node_data.get("source_id"): + existing_sources = [ + chunk_id + for chunk_id in node_data["source_id"].split( + GRAPH_FIELD_SEP + ) + if chunk_id + ] + + if not existing_sources: + # No chunk references means this entity should be deleted + entities_to_delete.add(node_label) + entity_chunk_updates[node_label] = [] + continue + + remaining_sources = subtract_source_ids(existing_sources, chunk_ids) + + if not remaining_sources: + entities_to_delete.add(node_label) + entity_chunk_updates[node_label] = [] + elif remaining_sources != existing_sources: + entities_to_rebuild[node_label] = remaining_sources + entity_chunk_updates[node_label] = remaining_sources + else: + logger.info(f"Untouch entity: {node_label}") + + async with pipeline_status_lock: + log_message = f"Found {len(entities_to_rebuild)} affected entities" + logger.info(log_message) + pipeline_status["latest_message"] = log_message + pipeline_status["history_messages"].append(log_message) + + # Process relationships + for edge_data in affected_edges: + # source target is not in normalize order in graph db property + src = edge_data.get("source") + tgt = edge_data.get("target") + + if not src or not tgt or "source_id" not in edge_data: + continue + + edge_tuple = tuple(sorted((src, tgt))) + if ( + edge_tuple in relationships_to_delete + or edge_tuple in relationships_to_rebuild + ): + continue + + existing_sources: list[str] = [] + if self.relation_chunks: + storage_key = make_relation_chunk_key(src, tgt) + stored_chunks = await self.relation_chunks.get_by_id( + storage_key + ) + if stored_chunks and isinstance(stored_chunks, dict): + existing_sources = [ + chunk_id + for chunk_id in stored_chunks.get("chunk_ids", []) + if chunk_id + ] + + if not existing_sources: + existing_sources = [ + chunk_id + for chunk_id in edge_data["source_id"].split( + GRAPH_FIELD_SEP + ) + if chunk_id + ] + + if not existing_sources: + # No chunk references means this relationship should be deleted + relationships_to_delete.add(edge_tuple) + relation_chunk_updates[edge_tuple] = [] + continue + + remaining_sources = subtract_source_ids(existing_sources, chunk_ids) + + if not remaining_sources: + relationships_to_delete.add(edge_tuple) + relation_chunk_updates[edge_tuple] = [] + elif remaining_sources != existing_sources: + relationships_to_rebuild[edge_tuple] = remaining_sources + relation_chunk_updates[edge_tuple] = remaining_sources + else: + logger.info(f"Untouch relation: {edge_tuple}") + + async with pipeline_status_lock: + log_message = ( + f"Found {len(relationships_to_rebuild)} affected relations" + ) + logger.info(log_message) + pipeline_status["latest_message"] = log_message + pipeline_status["history_messages"].append(log_message) + + current_time = int(time.time()) + + if entity_chunk_updates and self.entity_chunks: + entity_upsert_payload = {} + for entity_name, remaining in entity_chunk_updates.items(): + if not remaining: + # Empty entities are deleted alongside graph nodes later + continue + entity_upsert_payload[entity_name] = { + "chunk_ids": remaining, + "count": len(remaining), + "updated_at": current_time, + } + if entity_upsert_payload: + await self.entity_chunks.upsert(entity_upsert_payload) + + if relation_chunk_updates and self.relation_chunks: + relation_upsert_payload = {} + for edge_tuple, remaining in relation_chunk_updates.items(): + if not remaining: + # Empty relations are deleted alongside graph edges later + continue + storage_key = make_relation_chunk_key(*edge_tuple) + relation_upsert_payload[storage_key] = { + "chunk_ids": remaining, + "count": len(remaining), + "updated_at": current_time, + } + + if relation_upsert_payload: + await self.relation_chunks.upsert(relation_upsert_payload) + + except Exception as e: + logger.error(f"Failed to process graph analysis results: {e}") + raise Exception(f"Failed to process graph dependencies: {e}") from e + + # Use graph database lock to prevent dirty read + graph_db_lock = get_graph_db_lock(enable_logging=False) + async with graph_db_lock: + # 5. Delete chunks from storage + if chunk_ids: + try: + await self.chunks_vdb.delete(chunk_ids) + await self.text_chunks.delete(chunk_ids) + + async with pipeline_status_lock: + log_message = f"Successfully deleted {len(chunk_ids)} chunks from storage" + logger.info(log_message) + pipeline_status["latest_message"] = log_message + pipeline_status["history_messages"].append(log_message) + + except Exception as e: + logger.error(f"Failed to delete chunks: {e}") + raise Exception(f"Failed to delete document chunks: {e}") from e + + # 6. Delete relationships that have no remaining sources + if relationships_to_delete: + try: + # Delete from relation vdb + rel_ids_to_delete = [] + for src, tgt in relationships_to_delete: + rel_ids_to_delete.extend( + [ + compute_mdhash_id(src + tgt, prefix="rel-"), + compute_mdhash_id(tgt + src, prefix="rel-"), + ] + ) + await self.relationships_vdb.delete(rel_ids_to_delete) + + # Delete from graph + await self.chunk_entity_relation_graph.remove_edges( + list(relationships_to_delete) + ) + + # Delete from relation_chunks storage + if self.relation_chunks: + relation_storage_keys = [ + make_relation_chunk_key(src, tgt) + for src, tgt in relationships_to_delete + ] + await self.relation_chunks.delete(relation_storage_keys) + + async with pipeline_status_lock: + log_message = f"Successfully deleted {len(relationships_to_delete)} relations" + logger.info(log_message) + pipeline_status["latest_message"] = log_message + pipeline_status["history_messages"].append(log_message) + + except Exception as e: + logger.error(f"Failed to delete relationships: {e}") + raise Exception(f"Failed to delete relationships: {e}") from e + + # 7. Delete entities that have no remaining sources + if entities_to_delete: + try: + # Batch get all edges for entities to avoid N+1 query problem + nodes_edges_dict = await self.chunk_entity_relation_graph.get_nodes_edges_batch( + list(entities_to_delete) + ) + + # Debug: Check and log all edges before deleting nodes + edges_to_delete = set() + edges_still_exist = 0 + + for entity, edges in nodes_edges_dict.items(): + if edges: + for src, tgt in edges: + # Normalize edge representation (sorted for consistency) + edge_tuple = tuple(sorted((src, tgt))) + edges_to_delete.add(edge_tuple) + + if ( + src in entities_to_delete + and tgt in entities_to_delete + ): + logger.warning( + f"Edge still exists: {src} <-> {tgt}" + ) + elif src in entities_to_delete: + logger.warning( + f"Edge still exists: {src} --> {tgt}" + ) + else: + logger.warning( + f"Edge still exists: {src} <-- {tgt}" + ) + edges_still_exist += 1 + + if edges_still_exist: + logger.warning( + f"⚠️ {edges_still_exist} entities still has edges before deletion" + ) + + # Clean residual edges from VDB and storage before deleting nodes + if edges_to_delete: + # Delete from relationships_vdb + rel_ids_to_delete = [] + for src, tgt in edges_to_delete: + rel_ids_to_delete.extend( + [ + compute_mdhash_id(src + tgt, prefix="rel-"), + compute_mdhash_id(tgt + src, prefix="rel-"), + ] + ) + await self.relationships_vdb.delete(rel_ids_to_delete) + + # Delete from relation_chunks storage + if self.relation_chunks: + relation_storage_keys = [ + make_relation_chunk_key(src, tgt) + for src, tgt in edges_to_delete + ] + await self.relation_chunks.delete(relation_storage_keys) + + logger.info( + f"Cleaned {len(edges_to_delete)} residual edges from VDB and chunk-tracking storage" + ) + + # Delete from graph (edges will be auto-deleted with nodes) + await self.chunk_entity_relation_graph.remove_nodes( + list(entities_to_delete) + ) + + # Delete from vector vdb + entity_vdb_ids = [ + compute_mdhash_id(entity, prefix="ent-") + for entity in entities_to_delete + ] + await self.entities_vdb.delete(entity_vdb_ids) + + # Delete from entity_chunks storage + if self.entity_chunks: + await self.entity_chunks.delete(list(entities_to_delete)) + + async with pipeline_status_lock: + log_message = f"Successfully deleted {len(entities_to_delete)} entities" + logger.info(log_message) + pipeline_status["latest_message"] = log_message + pipeline_status["history_messages"].append(log_message) + + except Exception as e: + logger.error(f"Failed to delete entities: {e}") + raise Exception(f"Failed to delete entities: {e}") from e + + # Persist changes to graph database before releasing graph database lock + await self._insert_done() + + # 8. Rebuild entities and relationships from remaining chunks + if entities_to_rebuild or relationships_to_rebuild: + try: + await rebuild_knowledge_from_chunks( + entities_to_rebuild=entities_to_rebuild, + relationships_to_rebuild=relationships_to_rebuild, + knowledge_graph_inst=self.chunk_entity_relation_graph, + entities_vdb=self.entities_vdb, + relationships_vdb=self.relationships_vdb, + text_chunks_storage=self.text_chunks, + llm_response_cache=self.llm_response_cache, + global_config=asdict(self), + pipeline_status=pipeline_status, + pipeline_status_lock=pipeline_status_lock, + entity_chunks_storage=self.entity_chunks, + relation_chunks_storage=self.relation_chunks, + ) + + except Exception as e: + logger.error(f"Failed to rebuild knowledge from chunks: {e}") + raise Exception(f"Failed to rebuild knowledge graph: {e}") from e + + # 9. Delete from full_entities and full_relations storage + try: + await self.full_entities.delete([doc_id]) + await self.full_relations.delete([doc_id]) + except Exception as e: + logger.error(f"Failed to delete from full_entities/full_relations: {e}") + raise Exception( + f"Failed to delete from full_entities/full_relations: {e}" + ) from e + + # 10. Delete original document and status + try: + await self.full_docs.delete([doc_id]) + await self.doc_status.delete([doc_id]) + except Exception as e: + logger.error(f"Failed to delete document and status: {e}") + raise Exception(f"Failed to delete document and status: {e}") from e + + if delete_llm_cache and doc_llm_cache_ids and self.llm_response_cache: + try: + await self.llm_response_cache.delete(doc_llm_cache_ids) + cache_log_message = f"Successfully deleted {len(doc_llm_cache_ids)} LLM cache entries for document {doc_id}" + logger.info(cache_log_message) + async with pipeline_status_lock: + pipeline_status["latest_message"] = cache_log_message + pipeline_status["history_messages"].append(cache_log_message) + log_message = cache_log_message + except Exception as cache_delete_error: + log_message = f"Failed to delete LLM cache for document {doc_id}: {cache_delete_error}" + logger.error(log_message) + logger.error(traceback.format_exc()) + async with pipeline_status_lock: + pipeline_status["latest_message"] = log_message + pipeline_status["history_messages"].append(log_message) + + return DeletionResult( + status="success", + doc_id=doc_id, + message=log_message, + status_code=200, + file_path=file_path, + ) + + except Exception as e: + original_exception = e + error_message = f"Error while deleting document {doc_id}: {e}" + logger.error(error_message) + logger.error(traceback.format_exc()) + return DeletionResult( + status="fail", + doc_id=doc_id, + message=error_message, + status_code=500, + file_path=file_path, + ) + + finally: + # ALWAYS ensure persistence if any deletion operations were started + if deletion_operations_started: + try: + await self._insert_done() + except Exception as persistence_error: + persistence_error_msg = f"Failed to persist data after deletion attempt for {doc_id}: {persistence_error}" + logger.error(persistence_error_msg) + logger.error(traceback.format_exc()) + + # If there was no original exception, this persistence error becomes the main error + if original_exception is None: + return DeletionResult( + status="fail", + doc_id=doc_id, + message=f"Deletion completed but failed to persist changes: {persistence_error}", + status_code=500, + file_path=file_path, + ) + # If there was an original exception, log the persistence error but don't override the original error + # The original error result was already returned in the except block + else: + logger.debug( + f"No deletion operations were started for document {doc_id}, skipping persistence" + ) + + async def adelete_by_entity(self, entity_name: str) -> DeletionResult: + """Asynchronously delete an entity and all its relationships. + + Args: + entity_name: Name of the entity to delete. + + Returns: + DeletionResult: An object containing the outcome of the deletion process. + """ + from lightrag.utils_graph import adelete_by_entity + + return await adelete_by_entity( + self.chunk_entity_relation_graph, + self.entities_vdb, + self.relationships_vdb, + entity_name, + ) + + def delete_by_entity(self, entity_name: str) -> DeletionResult: + """Synchronously delete an entity and all its relationships. + + Args: + entity_name: Name of the entity to delete. + + Returns: + DeletionResult: An object containing the outcome of the deletion process. + """ + loop = always_get_an_event_loop() + return loop.run_until_complete(self.adelete_by_entity(entity_name)) + + async def adelete_by_relation( + self, source_entity: str, target_entity: str + ) -> DeletionResult: + """Asynchronously delete a relation between two entities. + + Args: + source_entity: Name of the source entity. + target_entity: Name of the target entity. + + Returns: + DeletionResult: An object containing the outcome of the deletion process. + """ + from lightrag.utils_graph import adelete_by_relation + + return await adelete_by_relation( + self.chunk_entity_relation_graph, + self.relationships_vdb, + source_entity, + target_entity, + ) + + def delete_by_relation( + self, source_entity: str, target_entity: str + ) -> DeletionResult: + """Synchronously delete a relation between two entities. + + Args: + source_entity: Name of the source entity. + target_entity: Name of the target entity. + + Returns: + DeletionResult: An object containing the outcome of the deletion process. + """ + loop = always_get_an_event_loop() + return loop.run_until_complete( + self.adelete_by_relation(source_entity, target_entity) + ) + + async def get_processing_status(self) -> dict[str, int]: + """Get current document processing status counts + + Returns: + Dict with counts for each status + """ + return await self.doc_status.get_status_counts() + + async def aget_docs_by_track_id( + self, track_id: str + ) -> dict[str, DocProcessingStatus]: + """Get documents by track_id + + Args: + track_id: The tracking ID to search for + + Returns: + Dict with document id as keys and document status as values + """ + return await self.doc_status.get_docs_by_track_id(track_id) + + async def get_entity_info( + self, entity_name: str, include_vector_data: bool = False + ) -> dict[str, str | None | dict[str, str]]: + """Get detailed information of an entity""" + from lightrag.utils_graph import get_entity_info + + return await get_entity_info( + self.chunk_entity_relation_graph, + self.entities_vdb, + entity_name, + include_vector_data, + ) + + async def get_relation_info( + self, src_entity: str, tgt_entity: str, include_vector_data: bool = False + ) -> dict[str, str | None | dict[str, str]]: + """Get detailed information of a relationship""" + from lightrag.utils_graph import get_relation_info + + return await get_relation_info( + self.chunk_entity_relation_graph, + self.relationships_vdb, + src_entity, + tgt_entity, + include_vector_data, + ) + + async def aedit_entity( + self, + entity_name: str, + updated_data: dict[str, str], + allow_rename: bool = True, + allow_merge: bool = False, + ) -> dict[str, Any]: + """Asynchronously edit entity information. + + Updates entity information in the knowledge graph and re-embeds the entity in the vector database. + Also synchronizes entity_chunks_storage and relation_chunks_storage to track chunk references. + + Args: + entity_name: Name of the entity to edit + updated_data: Dictionary containing updated attributes, e.g. {"description": "new description", "entity_type": "new type"} + allow_rename: Whether to allow entity renaming, defaults to True + allow_merge: Whether to merge into an existing entity when renaming to an existing name + + Returns: + Dictionary containing updated entity information + """ + from lightrag.utils_graph import aedit_entity + + return await aedit_entity( + self.chunk_entity_relation_graph, + self.entities_vdb, + self.relationships_vdb, + entity_name, + updated_data, + allow_rename, + allow_merge, + self.entity_chunks, + self.relation_chunks, + ) + + def edit_entity( + self, + entity_name: str, + updated_data: dict[str, str], + allow_rename: bool = True, + allow_merge: bool = False, + ) -> dict[str, Any]: + loop = always_get_an_event_loop() + return loop.run_until_complete( + self.aedit_entity(entity_name, updated_data, allow_rename, allow_merge) + ) + + async def aedit_relation( + self, source_entity: str, target_entity: str, updated_data: dict[str, Any] + ) -> dict[str, Any]: + """Asynchronously edit relation information. + + Updates relation (edge) information in the knowledge graph and re-embeds the relation in the vector database. + Also synchronizes the relation_chunks_storage to track which chunks reference this relation. + + Args: + source_entity: Name of the source entity + target_entity: Name of the target entity + updated_data: Dictionary containing updated attributes, e.g. {"description": "new description", "keywords": "new keywords"} + + Returns: + Dictionary containing updated relation information + """ + from lightrag.utils_graph import aedit_relation + + return await aedit_relation( + self.chunk_entity_relation_graph, + self.entities_vdb, + self.relationships_vdb, + source_entity, + target_entity, + updated_data, + self.relation_chunks, + ) + + def edit_relation( + self, source_entity: str, target_entity: str, updated_data: dict[str, Any] + ) -> dict[str, Any]: + loop = always_get_an_event_loop() + return loop.run_until_complete( + self.aedit_relation(source_entity, target_entity, updated_data) + ) + + async def acreate_entity( + self, entity_name: str, entity_data: dict[str, Any] + ) -> dict[str, Any]: + """Asynchronously create a new entity. + + Creates a new entity in the knowledge graph and adds it to the vector database. + + Args: + entity_name: Name of the new entity + entity_data: Dictionary containing entity attributes, e.g. {"description": "description", "entity_type": "type"} + + Returns: + Dictionary containing created entity information + """ + from lightrag.utils_graph import acreate_entity + + return await acreate_entity( + self.chunk_entity_relation_graph, + self.entities_vdb, + self.relationships_vdb, + entity_name, + entity_data, + ) + + def create_entity( + self, entity_name: str, entity_data: dict[str, Any] + ) -> dict[str, Any]: + loop = always_get_an_event_loop() + return loop.run_until_complete(self.acreate_entity(entity_name, entity_data)) + + async def acreate_relation( + self, source_entity: str, target_entity: str, relation_data: dict[str, Any] + ) -> dict[str, Any]: + """Asynchronously create a new relation between entities. + + Creates a new relation (edge) in the knowledge graph and adds it to the vector database. + + Args: + source_entity: Name of the source entity + target_entity: Name of the target entity + relation_data: Dictionary containing relation attributes, e.g. {"description": "description", "keywords": "keywords"} + + Returns: + Dictionary containing created relation information + """ + from lightrag.utils_graph import acreate_relation + + return await acreate_relation( + self.chunk_entity_relation_graph, + self.entities_vdb, + self.relationships_vdb, + source_entity, + target_entity, + relation_data, + ) + + def create_relation( + self, source_entity: str, target_entity: str, relation_data: dict[str, Any] + ) -> dict[str, Any]: + loop = always_get_an_event_loop() + return loop.run_until_complete( + self.acreate_relation(source_entity, target_entity, relation_data) + ) + + async def amerge_entities( + self, + source_entities: list[str], + target_entity: str, + merge_strategy: dict[str, str] = None, + target_entity_data: dict[str, Any] = None, + ) -> dict[str, Any]: + """Asynchronously merge multiple entities into one entity. + + Merges multiple source entities into a target entity, handling all relationships, + and updating both the knowledge graph and vector database. + + Args: + source_entities: List of source entity names to merge + target_entity: Name of the target entity after merging + merge_strategy: Merge strategy configuration, e.g. {"description": "concatenate", "entity_type": "keep_first"} + Supported strategies: + - "concatenate": Concatenate all values (for text fields) + - "keep_first": Keep the first non-empty value + - "keep_last": Keep the last non-empty value + - "join_unique": Join all unique values (for fields separated by delimiter) + target_entity_data: Dictionary of specific values to set for the target entity, + overriding any merged values, e.g. {"description": "custom description", "entity_type": "PERSON"} + + Returns: + Dictionary containing the merged entity information + """ + from lightrag.utils_graph import amerge_entities + + return await amerge_entities( + self.chunk_entity_relation_graph, + self.entities_vdb, + self.relationships_vdb, + source_entities, + target_entity, + merge_strategy, + target_entity_data, + self.entity_chunks, + self.relation_chunks, + ) + + def merge_entities( + self, + source_entities: list[str], + target_entity: str, + merge_strategy: dict[str, str] = None, + target_entity_data: dict[str, Any] = None, + ) -> dict[str, Any]: + loop = always_get_an_event_loop() + return loop.run_until_complete( + self.amerge_entities( + source_entities, target_entity, merge_strategy, target_entity_data + ) + ) + + async def aexport_data( + self, + output_path: str, + file_format: Literal["csv", "excel", "md", "txt"] = "csv", + include_vector_data: bool = False, + ) -> None: + """ + Asynchronously exports all entities, relations, and relationships to various formats. + Args: + output_path: The path to the output file (including extension). + file_format: Output format - "csv", "excel", "md", "txt". + - csv: Comma-separated values file + - excel: Microsoft Excel file with multiple sheets + - md: Markdown tables + - txt: Plain text formatted output + - table: Print formatted tables to console + include_vector_data: Whether to include data from the vector database. + """ + from lightrag.utils import aexport_data as utils_aexport_data + + await utils_aexport_data( + self.chunk_entity_relation_graph, + self.entities_vdb, + self.relationships_vdb, + output_path, + file_format, + include_vector_data, + ) + + def export_data( + self, + output_path: str, + file_format: Literal["csv", "excel", "md", "txt"] = "csv", + include_vector_data: bool = False, + ) -> None: + """ + Synchronously exports all entities, relations, and relationships to various formats. + Args: + output_path: The path to the output file (including extension). + file_format: Output format - "csv", "excel", "md", "txt". + - csv: Comma-separated values file + - excel: Microsoft Excel file with multiple sheets + - md: Markdown tables + - txt: Plain text formatted output + - table: Print formatted tables to console + include_vector_data: Whether to include data from the vector database. + """ + try: + loop = asyncio.get_event_loop() + except RuntimeError: + loop = asyncio.new_event_loop() + asyncio.set_event_loop(loop) + + loop.run_until_complete( + self.aexport_data(output_path, file_format, include_vector_data) + ) diff --git a/LightRAG/lightrag/llm/__init__.py b/LightRAG/lightrag/llm/__init__.py new file mode 100644 index 00000000..e69de29b diff --git a/LightRAG/lightrag/llm/anthropic.py b/LightRAG/lightrag/llm/anthropic.py new file mode 100644 index 00000000..fe18300c --- /dev/null +++ b/LightRAG/lightrag/llm/anthropic.py @@ -0,0 +1,333 @@ +from ..utils import verbose_debug, VERBOSE_DEBUG +import sys +import os +import logging +import numpy as np +from typing import Any, Union, AsyncIterator +import pipmaster as pm # Pipmaster for dynamic library install + +if sys.version_info < (3, 9): + from typing import AsyncIterator +else: + from collections.abc import AsyncIterator + +# Install Anthropic SDK if not present +if not pm.is_installed("anthropic"): + pm.install("anthropic") + +# Add Voyage AI import +if not pm.is_installed("voyageai"): + pm.install("voyageai") +import voyageai + +from anthropic import ( + AsyncAnthropic, + APIConnectionError, + RateLimitError, + APITimeoutError, +) +from tenacity import ( + retry, + stop_after_attempt, + wait_exponential, + retry_if_exception_type, +) +from lightrag.utils import ( + safe_unicode_decode, + logger, +) +from lightrag.api import __api_version__ + + +# Custom exception for retry mechanism +class InvalidResponseError(Exception): + """Custom exception class for triggering retry mechanism""" + + pass + + +# Core Anthropic completion function with retry +@retry( + stop=stop_after_attempt(3), + wait=wait_exponential(multiplier=1, min=4, max=10), + retry=retry_if_exception_type( + (RateLimitError, APIConnectionError, APITimeoutError, InvalidResponseError) + ), +) +async def anthropic_complete_if_cache( + model: str, + prompt: str, + system_prompt: str | None = None, + history_messages: list[dict[str, Any]] | None = None, + enable_cot: bool = False, + base_url: str | None = None, + api_key: str | None = None, + **kwargs: Any, +) -> Union[str, AsyncIterator[str]]: + if history_messages is None: + history_messages = [] + if enable_cot: + logger.debug( + "enable_cot=True is not supported for the Anthropic API and will be ignored." + ) + if not api_key: + api_key = os.environ.get("ANTHROPIC_API_KEY") + + default_headers = { + "User-Agent": f"Mozilla/5.0 (Macintosh; Intel Mac OS X 10_15_8) LightRAG/{__api_version__}", + "Content-Type": "application/json", + } + + # Set logger level to INFO when VERBOSE_DEBUG is off + if not VERBOSE_DEBUG and logger.level == logging.DEBUG: + logging.getLogger("anthropic").setLevel(logging.INFO) + + kwargs.pop("hashing_kv", None) + kwargs.pop("keyword_extraction", None) + timeout = kwargs.pop("timeout", None) + + anthropic_async_client = ( + AsyncAnthropic( + default_headers=default_headers, api_key=api_key, timeout=timeout + ) + if base_url is None + else AsyncAnthropic( + base_url=base_url, + default_headers=default_headers, + api_key=api_key, + timeout=timeout, + ) + ) + + messages: list[dict[str, Any]] = [] + if system_prompt: + messages.append({"role": "system", "content": system_prompt}) + messages.extend(history_messages) + messages.append({"role": "user", "content": prompt}) + + logger.debug("===== Sending Query to Anthropic LLM =====") + logger.debug(f"Model: {model} Base URL: {base_url}") + logger.debug(f"Additional kwargs: {kwargs}") + verbose_debug(f"Query: {prompt}") + verbose_debug(f"System prompt: {system_prompt}") + + try: + response = await anthropic_async_client.messages.create( + model=model, messages=messages, stream=True, **kwargs + ) + except APIConnectionError as e: + logger.error(f"Anthropic API Connection Error: {e}") + raise + except RateLimitError as e: + logger.error(f"Anthropic API Rate Limit Error: {e}") + raise + except APITimeoutError as e: + logger.error(f"Anthropic API Timeout Error: {e}") + raise + except Exception as e: + logger.error( + f"Anthropic API Call Failed,\nModel: {model},\nParams: {kwargs}, Got: {e}" + ) + raise + + async def stream_response(): + try: + async for event in response: + content = ( + event.delta.text + if hasattr(event, "delta") and event.delta.text + else None + ) + if content is None: + continue + if r"\u" in content: + content = safe_unicode_decode(content.encode("utf-8")) + yield content + except Exception as e: + logger.error(f"Error in stream response: {str(e)}") + raise + + return stream_response() + + +# Generic Anthropic completion function +async def anthropic_complete( + prompt: str, + system_prompt: str | None = None, + history_messages: list[dict[str, Any]] | None = None, + enable_cot: bool = False, + **kwargs: Any, +) -> Union[str, AsyncIterator[str]]: + if history_messages is None: + history_messages = [] + model_name = kwargs["hashing_kv"].global_config["llm_model_name"] + return await anthropic_complete_if_cache( + model_name, + prompt, + system_prompt=system_prompt, + history_messages=history_messages, + enable_cot=enable_cot, + **kwargs, + ) + + +# Claude 3 Opus specific completion +async def claude_3_opus_complete( + prompt: str, + system_prompt: str | None = None, + history_messages: list[dict[str, Any]] | None = None, + enable_cot: bool = False, + **kwargs: Any, +) -> Union[str, AsyncIterator[str]]: + if history_messages is None: + history_messages = [] + return await anthropic_complete_if_cache( + "claude-3-opus-20240229", + prompt, + system_prompt=system_prompt, + history_messages=history_messages, + enable_cot=enable_cot, + **kwargs, + ) + + +# Claude 3 Sonnet specific completion +async def claude_3_sonnet_complete( + prompt: str, + system_prompt: str | None = None, + history_messages: list[dict[str, Any]] | None = None, + enable_cot: bool = False, + **kwargs: Any, +) -> Union[str, AsyncIterator[str]]: + if history_messages is None: + history_messages = [] + return await anthropic_complete_if_cache( + "claude-3-sonnet-20240229", + prompt, + system_prompt=system_prompt, + history_messages=history_messages, + enable_cot=enable_cot, + **kwargs, + ) + + +# Claude 3 Haiku specific completion +async def claude_3_haiku_complete( + prompt: str, + system_prompt: str | None = None, + history_messages: list[dict[str, Any]] | None = None, + enable_cot: bool = False, + **kwargs: Any, +) -> Union[str, AsyncIterator[str]]: + if history_messages is None: + history_messages = [] + return await anthropic_complete_if_cache( + "claude-3-haiku-20240307", + prompt, + system_prompt=system_prompt, + history_messages=history_messages, + enable_cot=enable_cot, + **kwargs, + ) + + +# Embedding function (placeholder, as Anthropic does not provide embeddings) +@retry( + stop=stop_after_attempt(3), + wait=wait_exponential(multiplier=1, min=4, max=60), + retry=retry_if_exception_type( + (RateLimitError, APIConnectionError, APITimeoutError) + ), +) +async def anthropic_embed( + texts: list[str], + model: str = "voyage-3", # Default to voyage-3 as a good general-purpose model + base_url: str = None, + api_key: str = None, +) -> np.ndarray: + """ + Generate embeddings using Voyage AI since Anthropic doesn't provide native embedding support. + + Args: + texts: List of text strings to embed + model: Voyage AI model name (e.g., "voyage-3", "voyage-3-large", "voyage-code-3") + base_url: Optional custom base URL (not used for Voyage AI) + api_key: API key for Voyage AI (defaults to VOYAGE_API_KEY environment variable) + + Returns: + numpy array of shape (len(texts), embedding_dimension) containing the embeddings + """ + if not api_key: + api_key = os.environ.get("VOYAGE_API_KEY") + if not api_key: + logger.error("VOYAGE_API_KEY environment variable not set") + raise ValueError( + "VOYAGE_API_KEY environment variable is required for embeddings" + ) + + try: + # Initialize Voyage AI client + voyage_client = voyageai.Client(api_key=api_key) + + # Get embeddings + result = voyage_client.embed( + texts, + model=model, + input_type="document", # Assuming document context; could be made configurable + ) + + # Convert list of embeddings to numpy array + embeddings = np.array(result.embeddings, dtype=np.float32) + + logger.debug(f"Generated embeddings for {len(texts)} texts using {model}") + verbose_debug(f"Embedding shape: {embeddings.shape}") + + return embeddings + + except Exception as e: + logger.error(f"Voyage AI embedding failed: {str(e)}") + raise + + +# Optional: a helper function to get available embedding models +def get_available_embedding_models() -> dict[str, dict]: + """ + Returns a dictionary of available Voyage AI embedding models and their properties. + """ + return { + "voyage-3-large": { + "context_length": 32000, + "dimension": 1024, + "description": "Best general-purpose and multilingual", + }, + "voyage-3": { + "context_length": 32000, + "dimension": 1024, + "description": "General-purpose and multilingual", + }, + "voyage-3-lite": { + "context_length": 32000, + "dimension": 512, + "description": "Optimized for latency and cost", + }, + "voyage-code-3": { + "context_length": 32000, + "dimension": 1024, + "description": "Optimized for code", + }, + "voyage-finance-2": { + "context_length": 32000, + "dimension": 1024, + "description": "Optimized for finance", + }, + "voyage-law-2": { + "context_length": 16000, + "dimension": 1024, + "description": "Optimized for legal", + }, + "voyage-multimodal-3": { + "context_length": 32000, + "dimension": 1024, + "description": "Multimodal text and images", + }, + } diff --git a/LightRAG/lightrag/llm/azure_openai.py b/LightRAG/lightrag/llm/azure_openai.py new file mode 100644 index 00000000..824ff088 --- /dev/null +++ b/LightRAG/lightrag/llm/azure_openai.py @@ -0,0 +1,177 @@ +from collections.abc import Iterable +import os +import pipmaster as pm # Pipmaster for dynamic library install + +# install specific modules +if not pm.is_installed("openai"): + pm.install("openai") + +from openai import ( + AsyncAzureOpenAI, + APIConnectionError, + RateLimitError, + APITimeoutError, +) +from openai.types.chat import ChatCompletionMessageParam + +from tenacity import ( + retry, + stop_after_attempt, + wait_exponential, + retry_if_exception_type, +) + +from lightrag.utils import ( + wrap_embedding_func_with_attrs, + safe_unicode_decode, + logger, +) + +import numpy as np + + +@retry( + stop=stop_after_attempt(3), + wait=wait_exponential(multiplier=1, min=4, max=10), + retry=retry_if_exception_type( + (RateLimitError, APIConnectionError, APIConnectionError) + ), +) +async def azure_openai_complete_if_cache( + model, + prompt, + system_prompt: str | None = None, + history_messages: Iterable[ChatCompletionMessageParam] | None = None, + enable_cot: bool = False, + base_url: str | None = None, + api_key: str | None = None, + api_version: str | None = None, + **kwargs, +): + if enable_cot: + logger.debug( + "enable_cot=True is not supported for the Azure OpenAI API and will be ignored." + ) + deployment = os.getenv("AZURE_OPENAI_DEPLOYMENT") or model or os.getenv("LLM_MODEL") + base_url = ( + base_url or os.getenv("AZURE_OPENAI_ENDPOINT") or os.getenv("LLM_BINDING_HOST") + ) + api_key = ( + api_key or os.getenv("AZURE_OPENAI_API_KEY") or os.getenv("LLM_BINDING_API_KEY") + ) + api_version = ( + api_version + or os.getenv("AZURE_OPENAI_API_VERSION") + or os.getenv("OPENAI_API_VERSION") + ) + + kwargs.pop("hashing_kv", None) + kwargs.pop("keyword_extraction", None) + timeout = kwargs.pop("timeout", None) + + openai_async_client = AsyncAzureOpenAI( + azure_endpoint=base_url, + azure_deployment=deployment, + api_key=api_key, + api_version=api_version, + timeout=timeout, + ) + messages = [] + if system_prompt: + messages.append({"role": "system", "content": system_prompt}) + if history_messages: + messages.extend(history_messages) + if prompt is not None: + messages.append({"role": "user", "content": prompt}) + + if "response_format" in kwargs: + response = await openai_async_client.beta.chat.completions.parse( + model=model, messages=messages, **kwargs + ) + else: + response = await openai_async_client.chat.completions.create( + model=model, messages=messages, **kwargs + ) + + if hasattr(response, "__aiter__"): + + async def inner(): + async for chunk in response: + if len(chunk.choices) == 0: + continue + content = chunk.choices[0].delta.content + if content is None: + continue + if r"\u" in content: + content = safe_unicode_decode(content.encode("utf-8")) + yield content + + return inner() + else: + content = response.choices[0].message.content + if r"\u" in content: + content = safe_unicode_decode(content.encode("utf-8")) + return content + + +async def azure_openai_complete( + prompt, system_prompt=None, history_messages=[], keyword_extraction=False, **kwargs +) -> str: + kwargs.pop("keyword_extraction", None) + result = await azure_openai_complete_if_cache( + os.getenv("LLM_MODEL", "gpt-4o-mini"), + prompt, + system_prompt=system_prompt, + history_messages=history_messages, + **kwargs, + ) + return result + + +@wrap_embedding_func_with_attrs(embedding_dim=1536) +@retry( + stop=stop_after_attempt(3), + wait=wait_exponential(multiplier=1, min=4, max=10), + retry=retry_if_exception_type( + (RateLimitError, APIConnectionError, APITimeoutError) + ), +) +async def azure_openai_embed( + texts: list[str], + model: str | None = None, + base_url: str | None = None, + api_key: str | None = None, + api_version: str | None = None, +) -> np.ndarray: + deployment = ( + os.getenv("AZURE_EMBEDDING_DEPLOYMENT") + or model + or os.getenv("EMBEDDING_MODEL", "text-embedding-3-small") + ) + base_url = ( + base_url + or os.getenv("AZURE_EMBEDDING_ENDPOINT") + or os.getenv("EMBEDDING_BINDING_HOST") + ) + api_key = ( + api_key + or os.getenv("AZURE_EMBEDDING_API_KEY") + or os.getenv("EMBEDDING_BINDING_API_KEY") + ) + api_version = ( + api_version + or os.getenv("AZURE_EMBEDDING_API_VERSION") + or os.getenv("OPENAI_API_VERSION") + ) + + openai_async_client = AsyncAzureOpenAI( + azure_endpoint=base_url, + azure_deployment=deployment, + api_key=api_key, + api_version=api_version, + ) + + response = await openai_async_client.embeddings.create( + model=model, input=texts, encoding_format="float" + ) + return np.array([dp.embedding for dp in response.data]) diff --git a/LightRAG/lightrag/llm/bedrock.py b/LightRAG/lightrag/llm/bedrock.py new file mode 100644 index 00000000..f6871422 --- /dev/null +++ b/LightRAG/lightrag/llm/bedrock.py @@ -0,0 +1,483 @@ +import copy +import os +import json +import logging + +import pipmaster as pm # Pipmaster for dynamic library install + +if not pm.is_installed("aioboto3"): + pm.install("aioboto3") +import aioboto3 +import numpy as np +from tenacity import ( + retry, + stop_after_attempt, + wait_exponential, + retry_if_exception_type, +) + +import sys +from lightrag.utils import wrap_embedding_func_with_attrs + +if sys.version_info < (3, 9): + from typing import AsyncIterator +else: + from collections.abc import AsyncIterator +from typing import Union + +# Import botocore exceptions for proper exception handling +try: + from botocore.exceptions import ( + ClientError, + ConnectionError as BotocoreConnectionError, + ReadTimeoutError, + ) +except ImportError: + # If botocore is not installed, define placeholders + ClientError = Exception + BotocoreConnectionError = Exception + ReadTimeoutError = Exception + + +class BedrockError(Exception): + """Generic error for issues related to Amazon Bedrock""" + + +class BedrockRateLimitError(BedrockError): + """Error for rate limiting and throttling issues""" + + +class BedrockConnectionError(BedrockError): + """Error for network and connection issues""" + + +class BedrockTimeoutError(BedrockError): + """Error for timeout issues""" + + +def _set_env_if_present(key: str, value): + """Set environment variable only if a non-empty value is provided.""" + if value is not None and value != "": + os.environ[key] = value + + +def _handle_bedrock_exception(e: Exception, operation: str = "Bedrock API") -> None: + """Convert AWS Bedrock exceptions to appropriate custom exceptions. + + Args: + e: The exception to handle + operation: Description of the operation for error messages + + Raises: + BedrockRateLimitError: For rate limiting and throttling issues (retryable) + BedrockConnectionError: For network and server issues (retryable) + BedrockTimeoutError: For timeout issues (retryable) + BedrockError: For validation and other non-retryable errors + """ + error_message = str(e) + + # Handle botocore ClientError with specific error codes + if isinstance(e, ClientError): + error_code = e.response.get("Error", {}).get("Code", "") + error_msg = e.response.get("Error", {}).get("Message", error_message) + + # Rate limiting and throttling errors (retryable) + if error_code in [ + "ThrottlingException", + "ProvisionedThroughputExceededException", + ]: + logging.error(f"{operation} rate limit error: {error_msg}") + raise BedrockRateLimitError(f"Rate limit error: {error_msg}") + + # Server errors (retryable) + elif error_code in ["ServiceUnavailableException", "InternalServerException"]: + logging.error(f"{operation} connection error: {error_msg}") + raise BedrockConnectionError(f"Service error: {error_msg}") + + # Check for 5xx HTTP status codes (retryable) + elif e.response.get("ResponseMetadata", {}).get("HTTPStatusCode", 0) >= 500: + logging.error(f"{operation} server error: {error_msg}") + raise BedrockConnectionError(f"Server error: {error_msg}") + + # Validation and other client errors (non-retryable) + else: + logging.error(f"{operation} client error: {error_msg}") + raise BedrockError(f"Client error: {error_msg}") + + # Connection errors (retryable) + elif isinstance(e, BotocoreConnectionError): + logging.error(f"{operation} connection error: {error_message}") + raise BedrockConnectionError(f"Connection error: {error_message}") + + # Timeout errors (retryable) + elif isinstance(e, (ReadTimeoutError, TimeoutError)): + logging.error(f"{operation} timeout error: {error_message}") + raise BedrockTimeoutError(f"Timeout error: {error_message}") + + # Custom Bedrock errors (already properly typed) + elif isinstance( + e, + ( + BedrockRateLimitError, + BedrockConnectionError, + BedrockTimeoutError, + BedrockError, + ), + ): + raise + + # Unknown errors (non-retryable) + else: + logging.error(f"{operation} unexpected error: {error_message}") + raise BedrockError(f"Unexpected error: {error_message}") + + +@retry( + stop=stop_after_attempt(5), + wait=wait_exponential(multiplier=1, min=4, max=60), + retry=( + retry_if_exception_type(BedrockRateLimitError) + | retry_if_exception_type(BedrockConnectionError) + | retry_if_exception_type(BedrockTimeoutError) + ), +) +async def bedrock_complete_if_cache( + model, + prompt, + system_prompt=None, + history_messages=[], + enable_cot: bool = False, + aws_access_key_id=None, + aws_secret_access_key=None, + aws_session_token=None, + **kwargs, +) -> Union[str, AsyncIterator[str]]: + if enable_cot: + import logging + + logging.debug( + "enable_cot=True is not supported for Bedrock and will be ignored." + ) + # Respect existing env; only set if a non-empty value is available + access_key = os.environ.get("AWS_ACCESS_KEY_ID") or aws_access_key_id + secret_key = os.environ.get("AWS_SECRET_ACCESS_KEY") or aws_secret_access_key + session_token = os.environ.get("AWS_SESSION_TOKEN") or aws_session_token + _set_env_if_present("AWS_ACCESS_KEY_ID", access_key) + _set_env_if_present("AWS_SECRET_ACCESS_KEY", secret_key) + _set_env_if_present("AWS_SESSION_TOKEN", session_token) + # Region handling: prefer env, else kwarg (optional) + region = os.environ.get("AWS_REGION") or kwargs.pop("aws_region", None) + kwargs.pop("hashing_kv", None) + # Capture stream flag (if provided) and remove from kwargs since it's not a Bedrock API parameter + # We'll use this to determine whether to call converse_stream or converse + stream = bool(kwargs.pop("stream", False)) + # Remove unsupported args for Bedrock Converse API + for k in [ + "response_format", + "tools", + "tool_choice", + "seed", + "presence_penalty", + "frequency_penalty", + "n", + "logprobs", + "top_logprobs", + "max_completion_tokens", + "response_format", + ]: + kwargs.pop(k, None) + # Fix message history format + messages = [] + for history_message in history_messages: + message = copy.copy(history_message) + message["content"] = [{"text": message["content"]}] + messages.append(message) + + # Add user prompt + messages.append({"role": "user", "content": [{"text": prompt}]}) + + # Initialize Converse API arguments + args = {"modelId": model, "messages": messages} + + # Define system prompt + if system_prompt: + args["system"] = [{"text": system_prompt}] + + # Map and set up inference parameters + inference_params_map = { + "max_tokens": "maxTokens", + "top_p": "topP", + "stop_sequences": "stopSequences", + } + if inference_params := list( + set(kwargs) & set(["max_tokens", "temperature", "top_p", "stop_sequences"]) + ): + args["inferenceConfig"] = {} + for param in inference_params: + args["inferenceConfig"][inference_params_map.get(param, param)] = ( + kwargs.pop(param) + ) + + # Import logging for error handling + import logging + + # For streaming responses, we need a different approach to keep the connection open + if stream: + # Create a session that will be used throughout the streaming process + session = aioboto3.Session() + client = None + + # Define the generator function that will manage the client lifecycle + async def stream_generator(): + nonlocal client + + # Create the client outside the generator to ensure it stays open + client = await session.client( + "bedrock-runtime", region_name=region + ).__aenter__() + event_stream = None + iteration_started = False + + try: + # Make the API call + response = await client.converse_stream(**args, **kwargs) + event_stream = response.get("stream") + iteration_started = True + + # Process the stream + async for event in event_stream: + # Validate event structure + if not event or not isinstance(event, dict): + continue + + if "contentBlockDelta" in event: + delta = event["contentBlockDelta"].get("delta", {}) + text = delta.get("text") + if text: + yield text + # Handle other event types that might indicate stream end + elif "messageStop" in event: + break + + except Exception as e: + # Try to clean up resources if possible + if ( + iteration_started + and event_stream + and hasattr(event_stream, "aclose") + and callable(getattr(event_stream, "aclose", None)) + ): + try: + await event_stream.aclose() + except Exception as close_error: + logging.warning( + f"Failed to close Bedrock event stream: {close_error}" + ) + + # Convert to appropriate exception type + _handle_bedrock_exception(e, "Bedrock streaming") + + finally: + # Clean up the event stream + if ( + iteration_started + and event_stream + and hasattr(event_stream, "aclose") + and callable(getattr(event_stream, "aclose", None)) + ): + try: + await event_stream.aclose() + except Exception as close_error: + logging.warning( + f"Failed to close Bedrock event stream in finally block: {close_error}" + ) + + # Clean up the client + if client: + try: + await client.__aexit__(None, None, None) + except Exception as client_close_error: + logging.warning( + f"Failed to close Bedrock client: {client_close_error}" + ) + + # Return the generator that manages its own lifecycle + return stream_generator() + + # For non-streaming responses, use the standard async context manager pattern + session = aioboto3.Session() + async with session.client( + "bedrock-runtime", region_name=region + ) as bedrock_async_client: + try: + # Use converse for non-streaming responses + response = await bedrock_async_client.converse(**args, **kwargs) + + # Validate response structure + if ( + not response + or "output" not in response + or "message" not in response["output"] + or "content" not in response["output"]["message"] + or not response["output"]["message"]["content"] + ): + raise BedrockError("Invalid response structure from Bedrock API") + + content = response["output"]["message"]["content"][0]["text"] + + if not content or content.strip() == "": + raise BedrockError("Received empty content from Bedrock API") + + return content + + except Exception as e: + # Convert to appropriate exception type + _handle_bedrock_exception(e, "Bedrock converse") + + +# Generic Bedrock completion function +async def bedrock_complete( + prompt, system_prompt=None, history_messages=[], keyword_extraction=False, **kwargs +) -> Union[str, AsyncIterator[str]]: + kwargs.pop("keyword_extraction", None) + model_name = kwargs["hashing_kv"].global_config["llm_model_name"] + result = await bedrock_complete_if_cache( + model_name, + prompt, + system_prompt=system_prompt, + history_messages=history_messages, + **kwargs, + ) + return result + + +@wrap_embedding_func_with_attrs(embedding_dim=1024, max_token_size=8192) +@retry( + stop=stop_after_attempt(5), + wait=wait_exponential(multiplier=1, min=4, max=60), + retry=( + retry_if_exception_type(BedrockRateLimitError) + | retry_if_exception_type(BedrockConnectionError) + | retry_if_exception_type(BedrockTimeoutError) + ), +) +async def bedrock_embed( + texts: list[str], + model: str = "amazon.titan-embed-text-v2:0", + aws_access_key_id=None, + aws_secret_access_key=None, + aws_session_token=None, +) -> np.ndarray: + # Respect existing env; only set if a non-empty value is available + access_key = os.environ.get("AWS_ACCESS_KEY_ID") or aws_access_key_id + secret_key = os.environ.get("AWS_SECRET_ACCESS_KEY") or aws_secret_access_key + session_token = os.environ.get("AWS_SESSION_TOKEN") or aws_session_token + _set_env_if_present("AWS_ACCESS_KEY_ID", access_key) + _set_env_if_present("AWS_SECRET_ACCESS_KEY", secret_key) + _set_env_if_present("AWS_SESSION_TOKEN", session_token) + + # Region handling: prefer env + region = os.environ.get("AWS_REGION") + + session = aioboto3.Session() + async with session.client( + "bedrock-runtime", region_name=region + ) as bedrock_async_client: + try: + if (model_provider := model.split(".")[0]) == "amazon": + embed_texts = [] + for text in texts: + try: + if "v2" in model: + body = json.dumps( + { + "inputText": text, + # 'dimensions': embedding_dim, + "embeddingTypes": ["float"], + } + ) + elif "v1" in model: + body = json.dumps({"inputText": text}) + else: + raise BedrockError(f"Model {model} is not supported!") + + response = await bedrock_async_client.invoke_model( + modelId=model, + body=body, + accept="application/json", + contentType="application/json", + ) + + response_body = await response.get("body").json() + + # Validate response structure + if not response_body or "embedding" not in response_body: + raise BedrockError( + f"Invalid embedding response structure for text: {text[:50]}..." + ) + + embedding = response_body["embedding"] + if not embedding: + raise BedrockError( + f"Received empty embedding for text: {text[:50]}..." + ) + + embed_texts.append(embedding) + + except Exception as e: + # Convert to appropriate exception type + _handle_bedrock_exception( + e, "Bedrock embedding (amazon, text chunk)" + ) + + elif model_provider == "cohere": + try: + body = json.dumps( + { + "texts": texts, + "input_type": "search_document", + "truncate": "NONE", + } + ) + + response = await bedrock_async_client.invoke_model( + model=model, + body=body, + accept="application/json", + contentType="application/json", + ) + + response_body = json.loads(response.get("body").read()) + + # Validate response structure + if not response_body or "embeddings" not in response_body: + raise BedrockError( + "Invalid embedding response structure from Cohere" + ) + + embeddings = response_body["embeddings"] + if not embeddings or len(embeddings) != len(texts): + raise BedrockError( + f"Invalid embeddings count: expected {len(texts)}, got {len(embeddings) if embeddings else 0}" + ) + + embed_texts = embeddings + + except Exception as e: + # Convert to appropriate exception type + _handle_bedrock_exception(e, "Bedrock embedding (cohere)") + + else: + raise BedrockError( + f"Model provider '{model_provider}' is not supported!" + ) + + # Final validation + if not embed_texts: + raise BedrockError("No embeddings generated") + + return np.array(embed_texts) + + except Exception as e: + # Convert to appropriate exception type + _handle_bedrock_exception(e, "Bedrock embedding") diff --git a/LightRAG/lightrag/llm/binding_options.py b/LightRAG/lightrag/llm/binding_options.py new file mode 100644 index 00000000..1cb52a81 --- /dev/null +++ b/LightRAG/lightrag/llm/binding_options.py @@ -0,0 +1,740 @@ +""" +Module that implements containers for specific LLM bindings. + +This module provides container implementations for various Large Language Model +bindings and integrations. +""" + +from argparse import ArgumentParser, Namespace +import argparse +import json +from dataclasses import asdict, dataclass, field +from typing import Any, ClassVar, List, get_args, get_origin + +from lightrag.utils import get_env_value +from lightrag.constants import DEFAULT_TEMPERATURE + + +def _resolve_optional_type(field_type: Any) -> Any: + """Return the concrete type for Optional/Union annotations.""" + origin = get_origin(field_type) + if origin in (list, dict, tuple): + return field_type + + args = get_args(field_type) + if args: + non_none_args = [arg for arg in args if arg is not type(None)] + if len(non_none_args) == 1: + return non_none_args[0] + return field_type + + +# ============================================================================= +# BindingOptions Base Class +# ============================================================================= +# +# The BindingOptions class serves as the foundation for all LLM provider bindings +# in LightRAG. It provides a standardized framework for: +# +# 1. Configuration Management: +# - Defines how each LLM provider's configuration parameters are structured +# - Handles default values and type information for each parameter +# - Maps configuration options to command-line arguments and environment variables +# +# 2. Environment Integration: +# - Automatically generates environment variable names from binding parameters +# - Provides methods to create sample .env files for easy configuration +# - Supports configuration via environment variables with fallback to defaults +# +# 3. Command-Line Interface: +# - Dynamically generates command-line arguments for all registered bindings +# - Maintains consistent naming conventions across different LLM providers +# - Provides help text and type validation for each configuration option +# +# 4. Extensibility: +# - Uses class introspection to automatically discover all binding subclasses +# - Requires minimal boilerplate code when adding new LLM provider bindings +# - Maintains separation of concerns between different provider configurations +# +# This design pattern ensures that adding support for a new LLM provider requires +# only defining the provider-specific parameters and help text, while the base +# class handles all the common functionality for argument parsing, environment +# variable handling, and configuration management. +# +# Instances of a derived class of BindingOptions can be used to store multiple +# runtime configurations of options for a single LLM provider. using the +# asdict() method to convert the options to a dictionary. +# +# ============================================================================= +@dataclass +class BindingOptions: + """Base class for binding options.""" + + # mandatory name of binding + _binding_name: ClassVar[str] + + # optional help message for each option + _help: ClassVar[dict[str, str]] + + @staticmethod + def _all_class_vars(klass: type, include_inherited=True) -> dict[str, Any]: + """Print class variables, optionally including inherited ones""" + if include_inherited: + # Get all class variables from MRO + vars_dict = {} + for base in reversed(klass.__mro__[:-1]): # Exclude 'object' + vars_dict.update( + { + k: v + for k, v in base.__dict__.items() + if ( + not k.startswith("_") + and not callable(v) + and not isinstance(v, classmethod) + ) + } + ) + else: + # Only direct class variables + vars_dict = { + k: v + for k, v in klass.__dict__.items() + if ( + not k.startswith("_") + and not callable(v) + and not isinstance(v, classmethod) + ) + } + + return vars_dict + + @classmethod + def add_args(cls, parser: ArgumentParser): + group = parser.add_argument_group(f"{cls._binding_name} binding options") + for arg_item in cls.args_env_name_type_value(): + # Handle JSON parsing for list types + if arg_item["type"] is List[str]: + + def json_list_parser(value): + try: + parsed = json.loads(value) + if not isinstance(parsed, list): + raise argparse.ArgumentTypeError( + f"Expected JSON array, got {type(parsed).__name__}" + ) + return parsed + except json.JSONDecodeError as e: + raise argparse.ArgumentTypeError(f"Invalid JSON: {e}") + + # Get environment variable with JSON parsing + env_value = get_env_value(f"{arg_item['env_name']}", argparse.SUPPRESS) + if env_value is not argparse.SUPPRESS: + try: + env_value = json_list_parser(env_value) + except argparse.ArgumentTypeError: + env_value = argparse.SUPPRESS + + group.add_argument( + f"--{arg_item['argname']}", + type=json_list_parser, + default=env_value, + help=arg_item["help"], + ) + # Handle JSON parsing for dict types + elif arg_item["type"] is dict: + + def json_dict_parser(value): + try: + parsed = json.loads(value) + if not isinstance(parsed, dict): + raise argparse.ArgumentTypeError( + f"Expected JSON object, got {type(parsed).__name__}" + ) + return parsed + except json.JSONDecodeError as e: + raise argparse.ArgumentTypeError(f"Invalid JSON: {e}") + + # Get environment variable with JSON parsing + env_value = get_env_value(f"{arg_item['env_name']}", argparse.SUPPRESS) + if env_value is not argparse.SUPPRESS: + try: + env_value = json_dict_parser(env_value) + except argparse.ArgumentTypeError: + env_value = argparse.SUPPRESS + + group.add_argument( + f"--{arg_item['argname']}", + type=json_dict_parser, + default=env_value, + help=arg_item["help"], + ) + # Handle boolean types specially to avoid argparse bool() constructor issues + elif arg_item["type"] is bool: + + def bool_parser(value): + """Custom boolean parser that handles string representations correctly""" + if isinstance(value, bool): + return value + if isinstance(value, str): + return value.lower() in ("true", "1", "yes", "t", "on") + return bool(value) + + # Get environment variable with proper type conversion + env_value = get_env_value( + f"{arg_item['env_name']}", argparse.SUPPRESS, bool + ) + + group.add_argument( + f"--{arg_item['argname']}", + type=bool_parser, + default=env_value, + help=arg_item["help"], + ) + else: + resolved_type = arg_item["type"] + if resolved_type is not None: + resolved_type = _resolve_optional_type(resolved_type) + + group.add_argument( + f"--{arg_item['argname']}", + type=resolved_type, + default=get_env_value(f"{arg_item['env_name']}", argparse.SUPPRESS), + help=arg_item["help"], + ) + + @classmethod + def args_env_name_type_value(cls): + import dataclasses + + args_prefix = f"{cls._binding_name}".replace("_", "-") + env_var_prefix = f"{cls._binding_name}_".upper() + help = cls._help + + # Check if this is a dataclass and use dataclass fields + if dataclasses.is_dataclass(cls): + for field in dataclasses.fields(cls): + # Skip private fields + if field.name.startswith("_"): + continue + + # Get default value + if field.default is not dataclasses.MISSING: + default_value = field.default + elif field.default_factory is not dataclasses.MISSING: + default_value = field.default_factory() + else: + default_value = None + + argdef = { + "argname": f"{args_prefix}-{field.name}", + "env_name": f"{env_var_prefix}{field.name.upper()}", + "type": _resolve_optional_type(field.type), + "default": default_value, + "help": f"{cls._binding_name} -- " + help.get(field.name, ""), + } + + yield argdef + else: + # Fallback to old method for non-dataclass classes + class_vars = { + key: value + for key, value in cls._all_class_vars(cls).items() + if not callable(value) and not key.startswith("_") + } + + # Get type hints to properly detect List[str] types + type_hints = {} + for base in cls.__mro__: + if hasattr(base, "__annotations__"): + type_hints.update(base.__annotations__) + + for class_var in class_vars: + # Use type hint if available, otherwise fall back to type of value + var_type = type_hints.get(class_var, type(class_vars[class_var])) + + argdef = { + "argname": f"{args_prefix}-{class_var}", + "env_name": f"{env_var_prefix}{class_var.upper()}", + "type": var_type, + "default": class_vars[class_var], + "help": f"{cls._binding_name} -- " + help.get(class_var, ""), + } + + yield argdef + + @classmethod + def generate_dot_env_sample(cls): + """ + Generate a sample .env file for all LightRAG binding options. + + This method creates a .env file that includes all the binding options + defined by the subclasses of BindingOptions. It uses the args_env_name_type_value() + method to get the list of all options and their default values. + + Returns: + str: A string containing the contents of the sample .env file. + """ + from io import StringIO + + sample_top = ( + "#" * 80 + + "\n" + + ( + "# Autogenerated .env entries list for LightRAG binding options\n" + "#\n" + "# To generate run:\n" + "# $ python -m lightrag.llm.binding_options\n" + ) + + "#" * 80 + + "\n" + ) + + sample_bottom = ( + ("#\n# End of .env entries for LightRAG binding options\n") + + "#" * 80 + + "\n" + ) + + sample_stream = StringIO() + sample_stream.write(sample_top) + for klass in cls.__subclasses__(): + for arg_item in klass.args_env_name_type_value(): + if arg_item["help"]: + sample_stream.write(f"# {arg_item['help']}\n") + + # Handle JSON formatting for list and dict types + if arg_item["type"] is List[str] or arg_item["type"] is dict: + default_value = json.dumps(arg_item["default"]) + else: + default_value = arg_item["default"] + + sample_stream.write(f"# {arg_item['env_name']}={default_value}\n\n") + + sample_stream.write(sample_bottom) + return sample_stream.getvalue() + + @classmethod + def options_dict(cls, args: Namespace) -> dict[str, Any]: + """ + Extract options dictionary for a specific binding from parsed arguments. + + This method filters the parsed command-line arguments to return only those + that belong to the specific binding class. It removes the binding prefix + from argument names to create a clean options dictionary. + + Args: + args (Namespace): Parsed command-line arguments containing all binding options + + Returns: + dict[str, Any]: Dictionary mapping option names (without prefix) to their values + + Example: + If args contains {'ollama_num_ctx': 512, 'other_option': 'value'} + and this is called on OllamaOptions, it returns {'num_ctx': 512} + """ + prefix = cls._binding_name + "_" + skipchars = len(prefix) + options = { + key[skipchars:]: value + for key, value in vars(args).items() + if key.startswith(prefix) + } + + return options + + def asdict(self) -> dict[str, Any]: + """ + Convert an instance of binding options to a dictionary. + + This method uses dataclasses.asdict() to convert the dataclass instance + into a dictionary representation, including all its fields and values. + + Returns: + dict[str, Any]: Dictionary representation of the binding options instance + """ + return asdict(self) + + +# ============================================================================= +# Binding Options for Ollama +# ============================================================================= +# +# Ollama binding options provide configuration for the Ollama local LLM server. +# These options control model behavior, sampling parameters, hardware utilization, +# and performance settings. The parameters are based on Ollama's API specification +# and provide fine-grained control over model inference and generation. +# +# The _OllamaOptionsMixin defines the complete set of available options, while +# OllamaEmbeddingOptions and OllamaLLMOptions provide specialized configurations +# for embedding and language model tasks respectively. +# ============================================================================= +@dataclass +class _OllamaOptionsMixin: + """Options for Ollama bindings.""" + + # Core context and generation parameters + num_ctx: int = 32768 # Context window size (number of tokens) + num_predict: int = 128 # Maximum number of tokens to predict + num_keep: int = 0 # Number of tokens to keep from the initial prompt + seed: int = -1 # Random seed for generation (-1 for random) + + # Sampling parameters + temperature: float = DEFAULT_TEMPERATURE # Controls randomness (0.0-2.0) + top_k: int = 40 # Top-k sampling parameter + top_p: float = 0.9 # Top-p (nucleus) sampling parameter + tfs_z: float = 1.0 # Tail free sampling parameter + typical_p: float = 1.0 # Typical probability mass + min_p: float = 0.0 # Minimum probability threshold + + # Repetition control + repeat_last_n: int = 64 # Number of tokens to consider for repetition penalty + repeat_penalty: float = 1.1 # Penalty for repetition + presence_penalty: float = 0.0 # Penalty for token presence + frequency_penalty: float = 0.0 # Penalty for token frequency + + # Mirostat sampling + mirostat: int = ( + # Mirostat sampling algorithm (0=disabled, 1=Mirostat 1.0, 2=Mirostat 2.0) + 0 + ) + mirostat_tau: float = 5.0 # Mirostat target entropy + mirostat_eta: float = 0.1 # Mirostat learning rate + + # Hardware and performance parameters + numa: bool = False # Enable NUMA optimization + num_batch: int = 512 # Batch size for processing + num_gpu: int = -1 # Number of GPUs to use (-1 for auto) + main_gpu: int = 0 # Main GPU index + low_vram: bool = False # Optimize for low VRAM + num_thread: int = 0 # Number of CPU threads (0 for auto) + + # Memory and model parameters + f16_kv: bool = True # Use half-precision for key/value cache + logits_all: bool = False # Return logits for all tokens + vocab_only: bool = False # Only load vocabulary + use_mmap: bool = True # Use memory mapping for model files + use_mlock: bool = False # Lock model in memory + embedding_only: bool = False # Only use for embeddings + + # Output control + penalize_newline: bool = True # Penalize newline tokens + stop: List[str] = field(default_factory=list) # Stop sequences + + # optional help strings + _help: ClassVar[dict[str, str]] = { + "num_ctx": "Context window size (number of tokens)", + "num_predict": "Maximum number of tokens to predict", + "num_keep": "Number of tokens to keep from the initial prompt", + "seed": "Random seed for generation (-1 for random)", + "temperature": "Controls randomness (0.0-2.0, higher = more creative)", + "top_k": "Top-k sampling parameter (0 = disabled)", + "top_p": "Top-p (nucleus) sampling parameter (0.0-1.0)", + "tfs_z": "Tail free sampling parameter (1.0 = disabled)", + "typical_p": "Typical probability mass (1.0 = disabled)", + "min_p": "Minimum probability threshold (0.0 = disabled)", + "repeat_last_n": "Number of tokens to consider for repetition penalty", + "repeat_penalty": "Penalty for repetition (1.0 = no penalty)", + "presence_penalty": "Penalty for token presence (-2.0 to 2.0)", + "frequency_penalty": "Penalty for token frequency (-2.0 to 2.0)", + "mirostat": "Mirostat sampling algorithm (0=disabled, 1=Mirostat 1.0, 2=Mirostat 2.0)", + "mirostat_tau": "Mirostat target entropy", + "mirostat_eta": "Mirostat learning rate", + "numa": "Enable NUMA optimization", + "num_batch": "Batch size for processing", + "num_gpu": "Number of GPUs to use (-1 for auto)", + "main_gpu": "Main GPU index", + "low_vram": "Optimize for low VRAM", + "num_thread": "Number of CPU threads (0 for auto)", + "f16_kv": "Use half-precision for key/value cache", + "logits_all": "Return logits for all tokens", + "vocab_only": "Only load vocabulary", + "use_mmap": "Use memory mapping for model files", + "use_mlock": "Lock model in memory", + "embedding_only": "Only use for embeddings", + "penalize_newline": "Penalize newline tokens", + "stop": 'Stop sequences (JSON array of strings, e.g., \'["    ", "\\n\\n"]\')', + } + + +@dataclass +class OllamaEmbeddingOptions(_OllamaOptionsMixin, BindingOptions): + """Options for Ollama embeddings with specialized configuration for embedding tasks.""" + + # mandatory name of binding + _binding_name: ClassVar[str] = "ollama_embedding" + + +@dataclass +class OllamaLLMOptions(_OllamaOptionsMixin, BindingOptions): + """Options for Ollama LLM with specialized configuration for LLM tasks.""" + + # mandatory name of binding + _binding_name: ClassVar[str] = "ollama_llm" + + +# ============================================================================= +# Binding Options for Gemini +# ============================================================================= +@dataclass +class GeminiLLMOptions(BindingOptions): + """Options for Google Gemini models.""" + + _binding_name: ClassVar[str] = "gemini_llm" + + temperature: float = DEFAULT_TEMPERATURE + top_p: float = 0.95 + top_k: int = 40 + max_output_tokens: int | None = None + candidate_count: int = 1 + presence_penalty: float = 0.0 + frequency_penalty: float = 0.0 + stop_sequences: List[str] = field(default_factory=list) + seed: int | None = None + thinking_config: dict | None = None + safety_settings: dict | None = None + + _help: ClassVar[dict[str, str]] = { + "temperature": "Controls randomness (0.0-2.0, higher = more creative)", + "top_p": "Nucleus sampling parameter (0.0-1.0)", + "top_k": "Limits sampling to the top K tokens (1 disables the limit)", + "max_output_tokens": "Maximum tokens generated in the response", + "candidate_count": "Number of candidates returned per request", + "presence_penalty": "Penalty for token presence (-2.0 to 2.0)", + "frequency_penalty": "Penalty for token frequency (-2.0 to 2.0)", + "stop_sequences": "Stop sequences (JSON array of strings, e.g., '[\"END\"]')", + "seed": "Random seed for reproducible generation (leave empty for random)", + "thinking_config": "Thinking configuration (JSON dict, e.g., '{\"thinking_budget\": 1024}' or '{\"include_thoughts\": true}')", + "safety_settings": "JSON object with Gemini safety settings overrides", + } + + +@dataclass +class GeminiEmbeddingOptions(BindingOptions): + """Options for Google Gemini embedding models.""" + + _binding_name: ClassVar[str] = "gemini_embedding" + + task_type: str = "RETRIEVAL_DOCUMENT" + + _help: ClassVar[dict[str, str]] = { + "task_type": "Task type for embedding optimization (RETRIEVAL_DOCUMENT, RETRIEVAL_QUERY, SEMANTIC_SIMILARITY, CLASSIFICATION, CLUSTERING, CODE_RETRIEVAL_QUERY, QUESTION_ANSWERING, FACT_VERIFICATION)", + } + + +# ============================================================================= +# Binding Options for OpenAI +# ============================================================================= +# +# OpenAI binding options provide configuration for OpenAI's API and Azure OpenAI. +# These options control model behavior, sampling parameters, and generation settings. +# The parameters are based on OpenAI's API specification and provide fine-grained +# control over model inference and generation. +# +# ============================================================================= +@dataclass +class OpenAILLMOptions(BindingOptions): + """Options for OpenAI LLM with configuration for OpenAI and Azure OpenAI API calls.""" + + # mandatory name of binding + _binding_name: ClassVar[str] = "openai_llm" + + # Sampling and generation parameters + frequency_penalty: float = 0.0 # Penalty for token frequency (-2.0 to 2.0) + max_completion_tokens: int = None # Maximum number of tokens to generate + presence_penalty: float = 0.0 # Penalty for token presence (-2.0 to 2.0) + reasoning_effort: str = "medium" # Reasoning effort level (low, medium, high) + safety_identifier: str = "" # Safety identifier for content filtering + service_tier: str = "" # Service tier for API usage + stop: List[str] = field(default_factory=list) # Stop sequences + temperature: float = DEFAULT_TEMPERATURE # Controls randomness (0.0 to 2.0) + top_p: float = 1.0 # Nucleus sampling parameter (0.0 to 1.0) + max_tokens: int = None # Maximum number of tokens to generate(deprecated, use max_completion_tokens instead) + extra_body: dict = None # Extra body parameters for OpenRouter of vLLM + + # Help descriptions + _help: ClassVar[dict[str, str]] = { + "frequency_penalty": "Penalty for token frequency (-2.0 to 2.0, positive values discourage repetition)", + "max_completion_tokens": "Maximum number of tokens to generate (optional, leave empty for model default)", + "presence_penalty": "Penalty for token presence (-2.0 to 2.0, positive values encourage new topics)", + "reasoning_effort": "Reasoning effort level for o1 models (low, medium, high)", + "safety_identifier": "Safety identifier for content filtering (optional)", + "service_tier": "Service tier for API usage (optional)", + "stop": 'Stop sequences (JSON array of strings, e.g., \'["    ", "\\n\\n"]\')', + "temperature": "Controls randomness (0.0-2.0, higher = more creative)", + "top_p": "Nucleus sampling parameter (0.0-1.0, lower = more focused)", + "max_tokens": "Maximum number of tokens to generate (deprecated, use max_completion_tokens instead)", + "extra_body": 'Extra body parameters for OpenRouter of vLLM (JSON dict, e.g., \'"reasoning": {"reasoning": {"enabled": false}}\')', + } + + +# ============================================================================= +# Main Section - For Testing and Sample Generation +# ============================================================================= +# +# When run as a script, this module: +# 1. Generates and prints a sample .env file with all binding options +# 2. If "test" argument is provided, demonstrates argument parsing with Ollama binding +# +# Usage: +# python -m lightrag.llm.binding_options # Generate .env sample +# python -m lightrag.llm.binding_options test # Test argument parsing +# +# ============================================================================= + +if __name__ == "__main__": + import sys + import dotenv + # from io import StringIO + + dotenv.load_dotenv(dotenv_path=".env", override=False) + + # env_strstream = StringIO( + # ("OLLAMA_LLM_TEMPERATURE=0.1\nOLLAMA_EMBEDDING_TEMPERATURE=0.2\n") + # ) + # # Load environment variables from .env file + # dotenv.load_dotenv(stream=env_strstream) + + if len(sys.argv) > 1 and sys.argv[1] == "test": + # Add arguments for OllamaEmbeddingOptions, OllamaLLMOptions, and OpenAILLMOptions + parser = ArgumentParser(description="Test binding options") + OllamaEmbeddingOptions.add_args(parser) + OllamaLLMOptions.add_args(parser) + OpenAILLMOptions.add_args(parser) + + # Parse arguments test + args = parser.parse_args( + [ + "--ollama-embedding-num_ctx", + "1024", + "--ollama-llm-num_ctx", + "2048", + "--openai-llm-temperature", + "0.7", + "--openai-llm-max_completion_tokens", + "1000", + "--openai-llm-stop", + '["    ", "\\n\\n"]', + "--openai-llm-reasoning", + '{"effort": "high", "max_tokens": 2000, "exclude": false, "enabled": true}', + ] + ) + print("Final args for LLM and Embedding:") + print(f"{args}\n") + + print("Ollama LLM options:") + print(OllamaLLMOptions.options_dict(args)) + + print("\nOllama Embedding options:") + print(OllamaEmbeddingOptions.options_dict(args)) + + print("\nOpenAI LLM options:") + print(OpenAILLMOptions.options_dict(args)) + + # Test creating OpenAI options instance + openai_options = OpenAILLMOptions( + temperature=0.8, + max_completion_tokens=1500, + frequency_penalty=0.1, + presence_penalty=0.2, + stop=["<|end|>", "\n\n"], + ) + print("\nOpenAI LLM options instance:") + print(openai_options.asdict()) + + # Test creating OpenAI options instance with reasoning parameter + openai_options_with_reasoning = OpenAILLMOptions( + temperature=0.9, + max_completion_tokens=2000, + reasoning={ + "effort": "medium", + "max_tokens": 1500, + "exclude": True, + "enabled": True, + }, + ) + print("\nOpenAI LLM options instance with reasoning:") + print(openai_options_with_reasoning.asdict()) + + # Test dict parsing functionality + print("\n" + "=" * 50) + print("TESTING DICT PARSING FUNCTIONALITY") + print("=" * 50) + + # Test valid JSON dict parsing + test_parser = ArgumentParser(description="Test dict parsing") + OpenAILLMOptions.add_args(test_parser) + + try: + test_args = test_parser.parse_args( + ["--openai-llm-reasoning", '{"effort": "low", "max_tokens": 1000}'] + ) + print("✓ Valid JSON dict parsing successful:") + print( + f" Parsed reasoning: {OpenAILLMOptions.options_dict(test_args)['reasoning']}" + ) + except Exception as e: + print(f"✗ Valid JSON dict parsing failed: {e}") + + # Test invalid JSON dict parsing + try: + test_args = test_parser.parse_args( + [ + "--openai-llm-reasoning", + '{"effort": "low", "max_tokens": 1000', # Missing closing brace + ] + ) + print("✗ Invalid JSON should have failed but didn't") + except SystemExit: + print("✓ Invalid JSON dict parsing correctly rejected") + except Exception as e: + print(f"✓ Invalid JSON dict parsing correctly rejected: {e}") + + # Test non-dict JSON parsing + try: + test_args = test_parser.parse_args( + [ + "--openai-llm-reasoning", + '["not", "a", "dict"]', # Array instead of dict + ] + ) + print("✗ Non-dict JSON should have failed but didn't") + except SystemExit: + print("✓ Non-dict JSON parsing correctly rejected") + except Exception as e: + print(f"✓ Non-dict JSON parsing correctly rejected: {e}") + + print("\n" + "=" * 50) + print("TESTING ENVIRONMENT VARIABLE SUPPORT") + print("=" * 50) + + # Test environment variable support for dict + import os + + os.environ["OPENAI_LLM_REASONING"] = ( + '{"effort": "high", "max_tokens": 3000, "exclude": false}' + ) + + env_parser = ArgumentParser(description="Test env var dict parsing") + OpenAILLMOptions.add_args(env_parser) + + try: + env_args = env_parser.parse_args( + [] + ) # No command line args, should use env var + reasoning_from_env = OpenAILLMOptions.options_dict(env_args).get( + "reasoning" + ) + if reasoning_from_env: + print("✓ Environment variable dict parsing successful:") + print(f" Parsed reasoning from env: {reasoning_from_env}") + else: + print("✗ Environment variable dict parsing failed: No reasoning found") + except Exception as e: + print(f"✗ Environment variable dict parsing failed: {e}") + finally: + # Clean up environment variable + if "OPENAI_LLM_REASONING" in os.environ: + del os.environ["OPENAI_LLM_REASONING"] + + else: + print(BindingOptions.generate_dot_env_sample()) diff --git a/LightRAG/lightrag/llm/deprecated/siliconcloud.py b/LightRAG/lightrag/llm/deprecated/siliconcloud.py new file mode 100644 index 00000000..fe8da0dd --- /dev/null +++ b/LightRAG/lightrag/llm/deprecated/siliconcloud.py @@ -0,0 +1,69 @@ +import sys + +if sys.version_info < (3, 9): + pass +else: + pass +import pipmaster as pm # Pipmaster for dynamic library install + +# install specific modules +if not pm.is_installed("lmdeploy"): + pm.install("lmdeploy") + +from openai import ( + APIConnectionError, + RateLimitError, + APITimeoutError, +) +from tenacity import ( + retry, + stop_after_attempt, + wait_exponential, + retry_if_exception_type, +) + + +import numpy as np +import aiohttp +import base64 +import struct + + +@retry( + stop=stop_after_attempt(3), + wait=wait_exponential(multiplier=1, min=4, max=60), + retry=retry_if_exception_type( + (RateLimitError, APIConnectionError, APITimeoutError) + ), +) +async def siliconcloud_embedding( + texts: list[str], + model: str = "netease-youdao/bce-embedding-base_v1", + base_url: str = "https://api.siliconflow.cn/v1/embeddings", + max_token_size: int = 8192, + api_key: str = None, +) -> np.ndarray: + if api_key and not api_key.startswith("Bearer "): + api_key = "Bearer " + api_key + + headers = {"Authorization": api_key, "Content-Type": "application/json"} + + truncate_texts = [text[0:max_token_size] for text in texts] + + payload = {"model": model, "input": truncate_texts, "encoding_format": "base64"} + + base64_strings = [] + async with aiohttp.ClientSession() as session: + async with session.post(base_url, headers=headers, json=payload) as response: + content = await response.json() + if "code" in content: + raise ValueError(content) + base64_strings = [item["embedding"] for item in content["data"]] + + embeddings = [] + for string in base64_strings: + decode_bytes = base64.b64decode(string) + n = len(decode_bytes) // 4 + float_array = struct.unpack("<" + "f" * n, decode_bytes) + embeddings.append(float_array) + return np.array(embeddings) diff --git a/LightRAG/lightrag/llm/gemini.py b/LightRAG/lightrag/llm/gemini.py new file mode 100644 index 00000000..37ce7206 --- /dev/null +++ b/LightRAG/lightrag/llm/gemini.py @@ -0,0 +1,595 @@ +""" +Gemini LLM binding for LightRAG. + +This module provides asynchronous helpers that adapt Google's Gemini models +to the same interface used by the rest of the LightRAG LLM bindings. The +implementation mirrors the OpenAI helpers while relying on the official +``google-genai`` client under the hood. +""" + +from __future__ import annotations + +import asyncio +import logging +import os +from collections.abc import AsyncIterator +from functools import lru_cache +from typing import Any + +import numpy as np +from tenacity import ( + retry, + stop_after_attempt, + wait_exponential, + retry_if_exception_type, +) + +from lightrag.utils import ( + logger, + remove_think_tags, + safe_unicode_decode, + wrap_embedding_func_with_attrs, +) + +import pipmaster as pm + +# Install the Google Gemini client and its dependencies on demand +if not pm.is_installed("google-genai"): + pm.install("google-genai") +if not pm.is_installed("google-api-core"): + pm.install("google-api-core") + +from google import genai # type: ignore +from google.genai import types # type: ignore +from google.api_core import exceptions as google_api_exceptions # type: ignore + +DEFAULT_GEMINI_ENDPOINT = "https://generativelanguage.googleapis.com" + +LOG = logging.getLogger(__name__) + + +class InvalidResponseError(Exception): + """Custom exception class for triggering retry mechanism when Gemini returns empty responses""" + + pass + + +@lru_cache(maxsize=8) +def _get_gemini_client( + api_key: str, base_url: str | None, timeout: int | None = None +) -> genai.Client: + """ + Create (or fetch cached) Gemini client. + + Args: + api_key: Google Gemini API key. + base_url: Optional custom API endpoint. + timeout: Optional request timeout in milliseconds. + + Returns: + genai.Client: Configured Gemini client instance. + """ + client_kwargs: dict[str, Any] = {"api_key": api_key} + + if base_url and base_url != DEFAULT_GEMINI_ENDPOINT or timeout is not None: + try: + http_options_kwargs = {} + if base_url and base_url != DEFAULT_GEMINI_ENDPOINT: + http_options_kwargs["api_endpoint"] = base_url + if timeout is not None: + http_options_kwargs["timeout"] = timeout + + client_kwargs["http_options"] = types.HttpOptions(**http_options_kwargs) + except Exception as exc: # pragma: no cover - defensive + LOG.warning("Failed to apply custom Gemini http_options: %s", exc) + + try: + return genai.Client(**client_kwargs) + except TypeError: + # Older google-genai releases don't accept http_options; retry without it. + client_kwargs.pop("http_options", None) + return genai.Client(**client_kwargs) + + +def _ensure_api_key(api_key: str | None) -> str: + key = api_key or os.getenv("LLM_BINDING_API_KEY") or os.getenv("GEMINI_API_KEY") + if not key: + raise ValueError( + "Gemini API key not provided. " + "Set LLM_BINDING_API_KEY or GEMINI_API_KEY in the environment." + ) + return key + + +def _build_generation_config( + base_config: dict[str, Any] | None, + system_prompt: str | None, + keyword_extraction: bool, +) -> types.GenerateContentConfig | None: + config_data = dict(base_config or {}) + + if system_prompt: + if config_data.get("system_instruction"): + config_data["system_instruction"] = ( + f"{config_data['system_instruction']}\n{system_prompt}" + ) + else: + config_data["system_instruction"] = system_prompt + + if keyword_extraction and not config_data.get("response_mime_type"): + config_data["response_mime_type"] = "application/json" + + # Remove entries that are explicitly set to None to avoid type errors + sanitized = { + key: value + for key, value in config_data.items() + if value is not None and value != "" + } + + if not sanitized: + return None + + return types.GenerateContentConfig(**sanitized) + + +def _format_history_messages(history_messages: list[dict[str, Any]] | None) -> str: + if not history_messages: + return "" + + history_lines: list[str] = [] + for message in history_messages: + role = message.get("role", "user") + content = message.get("content", "") + history_lines.append(f"[{role}] {content}") + + return "\n".join(history_lines) + + +def _extract_response_text( + response: Any, extract_thoughts: bool = False +) -> tuple[str, str]: + """ + Extract text content from Gemini response, separating regular content from thoughts. + + Args: + response: Gemini API response object + extract_thoughts: Whether to extract thought content separately + + Returns: + Tuple of (regular_text, thought_text) + """ + candidates = getattr(response, "candidates", None) + if not candidates: + return ("", "") + + regular_parts: list[str] = [] + thought_parts: list[str] = [] + + for candidate in candidates: + if not getattr(candidate, "content", None): + continue + # Use 'or []' to handle None values from parts attribute + for part in getattr(candidate.content, "parts", None) or []: + text = getattr(part, "text", None) + if not text: + continue + + # Check if this part is thought content using the 'thought' attribute + is_thought = getattr(part, "thought", False) + + if is_thought and extract_thoughts: + thought_parts.append(text) + elif not is_thought: + regular_parts.append(text) + + return ("\n".join(regular_parts), "\n".join(thought_parts)) + + +@retry( + stop=stop_after_attempt(3), + wait=wait_exponential(multiplier=1, min=4, max=60), + retry=( + retry_if_exception_type(google_api_exceptions.InternalServerError) + | retry_if_exception_type(google_api_exceptions.ServiceUnavailable) + | retry_if_exception_type(google_api_exceptions.ResourceExhausted) + | retry_if_exception_type(google_api_exceptions.GatewayTimeout) + | retry_if_exception_type(google_api_exceptions.BadGateway) + | retry_if_exception_type(google_api_exceptions.DeadlineExceeded) + | retry_if_exception_type(google_api_exceptions.Aborted) + | retry_if_exception_type(google_api_exceptions.Unknown) + | retry_if_exception_type(InvalidResponseError) + ), +) +async def gemini_complete_if_cache( + model: str, + prompt: str, + system_prompt: str | None = None, + history_messages: list[dict[str, Any]] | None = None, + enable_cot: bool = False, + base_url: str | None = None, + api_key: str | None = None, + token_tracker: Any | None = None, + stream: bool | None = None, + keyword_extraction: bool = False, + generation_config: dict[str, Any] | None = None, + timeout: int | None = None, + **_: Any, +) -> str | AsyncIterator[str]: + """ + Complete a prompt using Gemini's API with Chain of Thought (COT) support. + + This function supports automatic integration of reasoning content from Gemini models + that provide Chain of Thought capabilities via the thinking_config API feature. + + COT Integration: + - When enable_cot=True: Thought content is wrapped in ... tags + - When enable_cot=False: Thought content is filtered out, only regular content returned + - Thought content is identified by the 'thought' attribute on response parts + - Requires thinking_config to be enabled in generation_config for API to return thoughts + + Args: + model: The Gemini model to use. + prompt: The prompt to complete. + system_prompt: Optional system prompt to include. + history_messages: Optional list of previous messages in the conversation. + api_key: Optional Gemini API key. If None, uses environment variable. + base_url: Optional custom API endpoint. + generation_config: Optional generation configuration dict. + keyword_extraction: Whether to use JSON response format. + token_tracker: Optional token usage tracker for monitoring API usage. + stream: Whether to stream the response. + hashing_kv: Storage interface (for interface parity with other bindings). + enable_cot: Whether to include Chain of Thought content in the response. + timeout: Request timeout in seconds (will be converted to milliseconds for Gemini API). + **_: Additional keyword arguments (ignored). + + Returns: + The completed text (with COT content if enable_cot=True) or an async iterator + of text chunks if streaming. COT content is wrapped in ... tags. + + Raises: + RuntimeError: If the response from Gemini is empty. + ValueError: If API key is not provided or configured. + """ + loop = asyncio.get_running_loop() + + key = _ensure_api_key(api_key) + # Convert timeout from seconds to milliseconds for Gemini API + timeout_ms = timeout * 1000 if timeout else None + client = _get_gemini_client(key, base_url, timeout_ms) + + history_block = _format_history_messages(history_messages) + prompt_sections = [] + if history_block: + prompt_sections.append(history_block) + prompt_sections.append(f"[user] {prompt}") + combined_prompt = "\n".join(prompt_sections) + + config_obj = _build_generation_config( + generation_config, + system_prompt=system_prompt, + keyword_extraction=keyword_extraction, + ) + + request_kwargs: dict[str, Any] = { + "model": model, + "contents": [combined_prompt], + } + if config_obj is not None: + request_kwargs["config"] = config_obj + + def _call_model(): + return client.models.generate_content(**request_kwargs) + + if stream: + queue: asyncio.Queue[Any] = asyncio.Queue() + usage_container: dict[str, Any] = {} + + def _stream_model() -> None: + # COT state tracking for streaming + cot_active = False + cot_started = False + initial_content_seen = False + + try: + stream_kwargs = dict(request_kwargs) + stream_iterator = client.models.generate_content_stream(**stream_kwargs) + for chunk in stream_iterator: + usage = getattr(chunk, "usage_metadata", None) + if usage is not None: + usage_container["usage"] = usage + + # Extract both regular and thought content + regular_text, thought_text = _extract_response_text( + chunk, extract_thoughts=True + ) + + if enable_cot: + # Process regular content + if regular_text: + if not initial_content_seen: + initial_content_seen = True + + # Close COT section if it was active + if cot_active: + loop.call_soon_threadsafe(queue.put_nowait, "") + cot_active = False + + # Send regular content + loop.call_soon_threadsafe(queue.put_nowait, regular_text) + + # Process thought content + if thought_text: + if not initial_content_seen and not cot_started: + # Start COT section + loop.call_soon_threadsafe(queue.put_nowait, "") + cot_active = True + cot_started = True + + # Send thought content if COT is active + if cot_active: + loop.call_soon_threadsafe( + queue.put_nowait, thought_text + ) + else: + # COT disabled - only send regular content + if regular_text: + loop.call_soon_threadsafe(queue.put_nowait, regular_text) + + # Ensure COT is properly closed if still active + if cot_active: + loop.call_soon_threadsafe(queue.put_nowait, "") + + loop.call_soon_threadsafe(queue.put_nowait, None) + except Exception as exc: # pragma: no cover - surface runtime issues + # Try to close COT tag before reporting error + if cot_active: + try: + loop.call_soon_threadsafe(queue.put_nowait, "") + except Exception: + pass + loop.call_soon_threadsafe(queue.put_nowait, exc) + + loop.run_in_executor(None, _stream_model) + + async def _async_stream() -> AsyncIterator[str]: + try: + while True: + item = await queue.get() + if item is None: + break + if isinstance(item, Exception): + raise item + + chunk_text = str(item) + if "\\u" in chunk_text: + chunk_text = safe_unicode_decode(chunk_text.encode("utf-8")) + + # Yield the chunk directly without filtering + # COT filtering is already handled in _stream_model() + yield chunk_text + finally: + usage = usage_container.get("usage") + if token_tracker and usage: + token_tracker.add_usage( + { + "prompt_tokens": getattr(usage, "prompt_token_count", 0), + "completion_tokens": getattr( + usage, "candidates_token_count", 0 + ), + "total_tokens": getattr(usage, "total_token_count", 0), + } + ) + + return _async_stream() + + response = await asyncio.to_thread(_call_model) + + # Extract both regular text and thought text + regular_text, thought_text = _extract_response_text(response, extract_thoughts=True) + + # Apply COT filtering logic based on enable_cot parameter + if enable_cot: + # Include thought content wrapped in tags + if thought_text and thought_text.strip(): + if not regular_text or regular_text.strip() == "": + # Only thought content available + final_text = f"{thought_text}" + else: + # Both content types present: prepend thought to regular content + final_text = f"{thought_text}{regular_text}" + else: + # No thought content, use regular content only + final_text = regular_text or "" + else: + # Filter out thought content, return only regular content + final_text = regular_text or "" + + if not final_text: + raise InvalidResponseError("Gemini response did not contain any text content.") + + if "\\u" in final_text: + final_text = safe_unicode_decode(final_text.encode("utf-8")) + + final_text = remove_think_tags(final_text) + + usage = getattr(response, "usage_metadata", None) + if token_tracker and usage: + token_tracker.add_usage( + { + "prompt_tokens": getattr(usage, "prompt_token_count", 0), + "completion_tokens": getattr(usage, "candidates_token_count", 0), + "total_tokens": getattr(usage, "total_token_count", 0), + } + ) + + logger.debug("Gemini response length: %s", len(final_text)) + return final_text + + +async def gemini_model_complete( + prompt: str, + system_prompt: str | None = None, + history_messages: list[dict[str, Any]] | None = None, + keyword_extraction: bool = False, + **kwargs: Any, +) -> str | AsyncIterator[str]: + hashing_kv = kwargs.get("hashing_kv") + model_name = None + if hashing_kv is not None: + model_name = hashing_kv.global_config.get("llm_model_name") + if model_name is None: + model_name = kwargs.pop("model_name", None) + if model_name is None: + raise ValueError("Gemini model name not provided in configuration.") + + return await gemini_complete_if_cache( + model_name, + prompt, + system_prompt=system_prompt, + history_messages=history_messages, + keyword_extraction=keyword_extraction, + **kwargs, + ) + + +@wrap_embedding_func_with_attrs(embedding_dim=1536, max_token_size=2048) +@retry( + stop=stop_after_attempt(3), + wait=wait_exponential(multiplier=1, min=4, max=60), + retry=( + retry_if_exception_type(google_api_exceptions.InternalServerError) + | retry_if_exception_type(google_api_exceptions.ServiceUnavailable) + | retry_if_exception_type(google_api_exceptions.ResourceExhausted) + | retry_if_exception_type(google_api_exceptions.GatewayTimeout) + | retry_if_exception_type(google_api_exceptions.BadGateway) + | retry_if_exception_type(google_api_exceptions.DeadlineExceeded) + | retry_if_exception_type(google_api_exceptions.Aborted) + | retry_if_exception_type(google_api_exceptions.Unknown) + ), +) +async def gemini_embed( + texts: list[str], + model: str = "gemini-embedding-001", + base_url: str | None = None, + api_key: str | None = None, + embedding_dim: int | None = None, + task_type: str = "RETRIEVAL_DOCUMENT", + timeout: int | None = None, + token_tracker: Any | None = None, +) -> np.ndarray: + """Generate embeddings for a list of texts using Gemini's API. + + This function uses Google's Gemini embedding model to generate text embeddings. + It supports dynamic dimension control and automatic normalization for dimensions + less than 3072. + + Args: + texts: List of texts to embed. + model: The Gemini embedding model to use. Default is "gemini-embedding-001". + base_url: Optional custom API endpoint. + api_key: Optional Gemini API key. If None, uses environment variables. + embedding_dim: Optional embedding dimension for dynamic dimension reduction. + **IMPORTANT**: This parameter is automatically injected by the EmbeddingFunc wrapper. + Do NOT manually pass this parameter when calling the function directly. + The dimension is controlled by the @wrap_embedding_func_with_attrs decorator + or the EMBEDDING_DIM environment variable. + Supported range: 128-3072. Recommended values: 768, 1536, 3072. + task_type: Task type for embedding optimization. Default is "RETRIEVAL_DOCUMENT". + Supported types: SEMANTIC_SIMILARITY, CLASSIFICATION, CLUSTERING, + RETRIEVAL_DOCUMENT, RETRIEVAL_QUERY, CODE_RETRIEVAL_QUERY, + QUESTION_ANSWERING, FACT_VERIFICATION. + timeout: Request timeout in seconds (will be converted to milliseconds for Gemini API). + token_tracker: Optional token usage tracker for monitoring API usage. + + Returns: + A numpy array of embeddings, one per input text. For dimensions < 3072, + the embeddings are L2-normalized to ensure optimal semantic similarity performance. + + Raises: + ValueError: If API key is not provided or configured. + RuntimeError: If the response from Gemini is invalid or empty. + + Note: + - For dimension 3072: Embeddings are already normalized by the API + - For dimensions < 3072: Embeddings are L2-normalized after retrieval + - Normalization ensures accurate semantic similarity via cosine distance + """ + loop = asyncio.get_running_loop() + + key = _ensure_api_key(api_key) + # Convert timeout from seconds to milliseconds for Gemini API + timeout_ms = timeout * 1000 if timeout else None + client = _get_gemini_client(key, base_url, timeout_ms) + + # Prepare embedding configuration + config_kwargs: dict[str, Any] = {} + + # Add task_type to config + if task_type: + config_kwargs["task_type"] = task_type + + # Add output_dimensionality if embedding_dim is provided + if embedding_dim is not None: + config_kwargs["output_dimensionality"] = embedding_dim + + # Create config object if we have parameters + config_obj = types.EmbedContentConfig(**config_kwargs) if config_kwargs else None + + def _call_embed() -> Any: + """Call Gemini embedding API in executor thread.""" + request_kwargs: dict[str, Any] = { + "model": model, + "contents": texts, + } + if config_obj is not None: + request_kwargs["config"] = config_obj + + return client.models.embed_content(**request_kwargs) + + # Execute API call in thread pool + response = await loop.run_in_executor(None, _call_embed) + + # Extract embeddings from response + if not hasattr(response, "embeddings") or not response.embeddings: + raise RuntimeError("Gemini response did not contain embeddings.") + + # Convert embeddings to numpy array + embeddings = np.array( + [np.array(e.values, dtype=np.float32) for e in response.embeddings] + ) + + # Apply L2 normalization for dimensions < 3072 + # The 3072 dimension embedding is already normalized by Gemini API + if embedding_dim and embedding_dim < 3072: + # Normalize each embedding vector to unit length + norms = np.linalg.norm(embeddings, axis=1, keepdims=True) + # Avoid division by zero + norms = np.where(norms == 0, 1, norms) + embeddings = embeddings / norms + logger.debug( + f"Applied L2 normalization to {len(embeddings)} embeddings of dimension {embedding_dim}" + ) + + # Track token usage if tracker is provided + # Note: Gemini embedding API may not provide usage metadata + if token_tracker and hasattr(response, "usage_metadata"): + usage = response.usage_metadata + token_counts = { + "prompt_tokens": getattr(usage, "prompt_token_count", 0), + "total_tokens": getattr(usage, "total_token_count", 0), + } + token_tracker.add_usage(token_counts) + + logger.debug( + f"Generated {len(embeddings)} Gemini embeddings with dimension {embeddings.shape[1]}" + ) + + return embeddings + + +__all__ = [ + "gemini_complete_if_cache", + "gemini_model_complete", + "gemini_embed", +] diff --git a/LightRAG/lightrag/llm/hf.py b/LightRAG/lightrag/llm/hf.py new file mode 100644 index 00000000..447f95c3 --- /dev/null +++ b/LightRAG/lightrag/llm/hf.py @@ -0,0 +1,175 @@ +import copy +import os +from functools import lru_cache + +import pipmaster as pm # Pipmaster for dynamic library install + +# install specific modules +if not pm.is_installed("transformers"): + pm.install("transformers") +if not pm.is_installed("torch"): + pm.install("torch") +if not pm.is_installed("numpy"): + pm.install("numpy") + +from transformers import AutoTokenizer, AutoModelForCausalLM +from tenacity import ( + retry, + stop_after_attempt, + wait_exponential, + retry_if_exception_type, +) +from lightrag.exceptions import ( + APIConnectionError, + RateLimitError, + APITimeoutError, +) +import torch +import numpy as np +from lightrag.utils import wrap_embedding_func_with_attrs + +os.environ["TOKENIZERS_PARALLELISM"] = "false" + + +@lru_cache(maxsize=1) +def initialize_hf_model(model_name): + hf_tokenizer = AutoTokenizer.from_pretrained( + model_name, device_map="auto", trust_remote_code=True + ) + hf_model = AutoModelForCausalLM.from_pretrained( + model_name, device_map="auto", trust_remote_code=True + ) + if hf_tokenizer.pad_token is None: + hf_tokenizer.pad_token = hf_tokenizer.eos_token + + return hf_model, hf_tokenizer + + +@retry( + stop=stop_after_attempt(3), + wait=wait_exponential(multiplier=1, min=4, max=10), + retry=retry_if_exception_type( + (RateLimitError, APIConnectionError, APITimeoutError) + ), +) +async def hf_model_if_cache( + model, + prompt, + system_prompt=None, + history_messages=[], + enable_cot: bool = False, + **kwargs, +) -> str: + if enable_cot: + from lightrag.utils import logger + + logger.debug( + "enable_cot=True is not supported for Hugging Face local models and will be ignored." + ) + model_name = model + hf_model, hf_tokenizer = initialize_hf_model(model_name) + messages = [] + if system_prompt: + messages.append({"role": "system", "content": system_prompt}) + messages.extend(history_messages) + messages.append({"role": "user", "content": prompt}) + kwargs.pop("hashing_kv", None) + input_prompt = "" + try: + input_prompt = hf_tokenizer.apply_chat_template( + messages, tokenize=False, add_generation_prompt=True + ) + except Exception: + try: + ori_message = copy.deepcopy(messages) + if messages[0]["role"] == "system": + messages[1]["content"] = ( + "" + + messages[0]["content"] + + "\n" + + messages[1]["content"] + ) + messages = messages[1:] + input_prompt = hf_tokenizer.apply_chat_template( + messages, tokenize=False, add_generation_prompt=True + ) + except Exception: + len_message = len(ori_message) + for msgid in range(len_message): + input_prompt = ( + input_prompt + + "<" + + ori_message[msgid]["role"] + + ">" + + ori_message[msgid]["content"] + + "\n" + ) + + input_ids = hf_tokenizer( + input_prompt, return_tensors="pt", padding=True, truncation=True + ).to("cuda") + inputs = {k: v.to(hf_model.device) for k, v in input_ids.items()} + output = hf_model.generate( + **input_ids, max_new_tokens=512, num_return_sequences=1, early_stopping=True + ) + response_text = hf_tokenizer.decode( + output[0][len(inputs["input_ids"][0]) :], skip_special_tokens=True + ) + + return response_text + + +async def hf_model_complete( + prompt, + system_prompt=None, + history_messages=[], + keyword_extraction=False, + enable_cot: bool = False, + **kwargs, +) -> str: + kwargs.pop("keyword_extraction", None) + model_name = kwargs["hashing_kv"].global_config["llm_model_name"] + result = await hf_model_if_cache( + model_name, + prompt, + system_prompt=system_prompt, + history_messages=history_messages, + enable_cot=enable_cot, + **kwargs, + ) + return result + + +@wrap_embedding_func_with_attrs(embedding_dim=1024, max_token_size=8192) +async def hf_embed(texts: list[str], tokenizer, embed_model) -> np.ndarray: + # Detect the appropriate device + if torch.cuda.is_available(): + device = next(embed_model.parameters()).device # Use CUDA if available + elif torch.backends.mps.is_available(): + device = torch.device("mps") # Use MPS for Apple Silicon + else: + device = torch.device("cpu") # Fallback to CPU + + # Move the model to the detected device + embed_model = embed_model.to(device) + + # Tokenize the input texts and move them to the same device + encoded_texts = tokenizer( + texts, return_tensors="pt", padding=True, truncation=True + ).to(device) + + # Perform inference + with torch.no_grad(): + outputs = embed_model( + input_ids=encoded_texts["input_ids"], + attention_mask=encoded_texts["attention_mask"], + ) + embeddings = outputs.last_hidden_state.mean(dim=1) + + # Convert embeddings to NumPy + if embeddings.dtype == torch.bfloat16: + return embeddings.detach().to(torch.float32).cpu().numpy() + else: + return embeddings.detach().cpu().numpy() diff --git a/LightRAG/lightrag/llm/jina.py b/LightRAG/lightrag/llm/jina.py new file mode 100644 index 00000000..f61faadd --- /dev/null +++ b/LightRAG/lightrag/llm/jina.py @@ -0,0 +1,152 @@ +import os +import pipmaster as pm # Pipmaster for dynamic library install + +# install specific modules +if not pm.is_installed("aiohttp"): + pm.install("aiohttp") +if not pm.is_installed("tenacity"): + pm.install("tenacity") + +import numpy as np +import base64 +import aiohttp +from tenacity import ( + retry, + stop_after_attempt, + wait_exponential, + retry_if_exception_type, +) +from lightrag.utils import wrap_embedding_func_with_attrs, logger + + +async def fetch_data(url, headers, data): + async with aiohttp.ClientSession() as session: + async with session.post(url, headers=headers, json=data) as response: + if response.status != 200: + error_text = await response.text() + + # Check if the error response is HTML (common for 502, 503, etc.) + content_type = response.headers.get("content-type", "").lower() + is_html_error = ( + error_text.strip().startswith("") + or "text/html" in content_type + ) + + if is_html_error: + # Provide clean, user-friendly error messages for HTML error pages + if response.status == 502: + clean_error = "Bad Gateway (502) - Jina AI service temporarily unavailable. Please try again in a few minutes." + elif response.status == 503: + clean_error = "Service Unavailable (503) - Jina AI service is temporarily overloaded. Please try again later." + elif response.status == 504: + clean_error = "Gateway Timeout (504) - Jina AI service request timed out. Please try again." + else: + clean_error = f"HTTP {response.status} - Jina AI service error. Please try again later." + else: + # Use original error text if it's not HTML + clean_error = error_text + + logger.error(f"Jina API error {response.status}: {clean_error}") + raise aiohttp.ClientResponseError( + request_info=response.request_info, + history=response.history, + status=response.status, + message=f"Jina API error: {clean_error}", + ) + response_json = await response.json() + data_list = response_json.get("data", []) + return data_list + + +@wrap_embedding_func_with_attrs(embedding_dim=2048, max_token_size=8192) +@retry( + stop=stop_after_attempt(3), + wait=wait_exponential(multiplier=1, min=4, max=60), + retry=( + retry_if_exception_type(aiohttp.ClientError) + | retry_if_exception_type(aiohttp.ClientResponseError) + ), +) +async def jina_embed( + texts: list[str], + embedding_dim: int = 2048, + late_chunking: bool = False, + base_url: str = None, + api_key: str = None, +) -> np.ndarray: + """Generate embeddings for a list of texts using Jina AI's API. + + Args: + texts: List of texts to embed. + embedding_dim: The embedding dimensions (default: 2048 for jina-embeddings-v4). + **IMPORTANT**: This parameter is automatically injected by the EmbeddingFunc wrapper. + Do NOT manually pass this parameter when calling the function directly. + The dimension is controlled by the @wrap_embedding_func_with_attrs decorator. + Manually passing a different value will trigger a warning and be ignored. + When provided (by EmbeddingFunc), it will be passed to the Jina API for dimension reduction. + late_chunking: Whether to use late chunking. + base_url: Optional base URL for the Jina API. + api_key: Optional Jina API key. If None, uses the JINA_API_KEY environment variable. + + Returns: + A numpy array of embeddings, one per input text. + + Raises: + aiohttp.ClientError: If there is a connection error with the Jina API. + aiohttp.ClientResponseError: If the Jina API returns an error response. + """ + if api_key: + os.environ["JINA_API_KEY"] = api_key + + if "JINA_API_KEY" not in os.environ: + raise ValueError("JINA_API_KEY environment variable is required") + + url = base_url or "https://api.jina.ai/v1/embeddings" + headers = { + "Content-Type": "application/json", + "Authorization": f"Bearer {os.environ['JINA_API_KEY']}", + } + data = { + "model": "jina-embeddings-v4", + "task": "text-matching", + "dimensions": embedding_dim, + "embedding_type": "base64", + "input": texts, + } + + # Only add optional parameters if they have non-default values + if late_chunking: + data["late_chunking"] = late_chunking + + logger.debug( + f"Jina embedding request: {len(texts)} texts, dimensions: {embedding_dim}" + ) + + try: + data_list = await fetch_data(url, headers, data) + + if not data_list: + logger.error("Jina API returned empty data list") + raise ValueError("Jina API returned empty data list") + + if len(data_list) != len(texts): + logger.error( + f"Jina API returned {len(data_list)} embeddings for {len(texts)} texts" + ) + raise ValueError( + f"Jina API returned {len(data_list)} embeddings for {len(texts)} texts" + ) + + embeddings = np.array( + [ + np.frombuffer(base64.b64decode(dp["embedding"]), dtype=np.float32) + for dp in data_list + ] + ) + logger.debug(f"Jina embeddings generated: shape {embeddings.shape}") + + return embeddings + + except Exception as e: + logger.error(f"Jina embedding error: {e}") + raise diff --git a/LightRAG/lightrag/llm/llama_index_impl.py b/LightRAG/lightrag/llm/llama_index_impl.py new file mode 100644 index 00000000..c44e6c7a --- /dev/null +++ b/LightRAG/lightrag/llm/llama_index_impl.py @@ -0,0 +1,208 @@ +import pipmaster as pm +from llama_index.core.llms import ( + ChatMessage, + MessageRole, + ChatResponse, +) +from typing import List, Optional +from lightrag.utils import logger + +# Install required dependencies +if not pm.is_installed("llama-index"): + pm.install("llama-index") + +from llama_index.core.embeddings import BaseEmbedding +from llama_index.core.settings import Settings as LlamaIndexSettings +from tenacity import ( + retry, + stop_after_attempt, + wait_exponential, + retry_if_exception_type, +) +from lightrag.utils import ( + wrap_embedding_func_with_attrs, +) +from lightrag.exceptions import ( + APIConnectionError, + RateLimitError, + APITimeoutError, +) +import numpy as np + + +def configure_llama_index(settings: LlamaIndexSettings = None, **kwargs): + """ + Configure LlamaIndex settings. + + Args: + settings: LlamaIndex Settings instance. If None, uses default settings. + **kwargs: Additional settings to override/configure + """ + if settings is None: + settings = LlamaIndexSettings() + + # Update settings with any provided kwargs + for key, value in kwargs.items(): + if hasattr(settings, key): + setattr(settings, key, value) + else: + logger.warning(f"Unknown LlamaIndex setting: {key}") + + # Set as global settings + LlamaIndexSettings.set_global(settings) + return settings + + +def format_chat_messages(messages): + """Format chat messages into LlamaIndex format.""" + formatted_messages = [] + + for msg in messages: + role = msg.get("role", "user") + content = msg.get("content", "") + + if role == "system": + formatted_messages.append( + ChatMessage(role=MessageRole.SYSTEM, content=content) + ) + elif role == "assistant": + formatted_messages.append( + ChatMessage(role=MessageRole.ASSISTANT, content=content) + ) + elif role == "user": + formatted_messages.append( + ChatMessage(role=MessageRole.USER, content=content) + ) + else: + logger.warning(f"Unknown role {role}, treating as user message") + formatted_messages.append( + ChatMessage(role=MessageRole.USER, content=content) + ) + + return formatted_messages + + +@retry( + stop=stop_after_attempt(3), + wait=wait_exponential(multiplier=1, min=4, max=60), + retry=retry_if_exception_type( + (RateLimitError, APIConnectionError, APITimeoutError) + ), +) +async def llama_index_complete_if_cache( + model: str, + prompt: str, + system_prompt: Optional[str] = None, + history_messages: List[dict] = [], + enable_cot: bool = False, + chat_kwargs={}, +) -> str: + """Complete the prompt using LlamaIndex.""" + if enable_cot: + logger.debug( + "enable_cot=True is not supported for LlamaIndex implementation and will be ignored." + ) + try: + # Format messages for chat + formatted_messages = [] + + # Add system message if provided + if system_prompt: + formatted_messages.append( + ChatMessage(role=MessageRole.SYSTEM, content=system_prompt) + ) + + # Add history messages + for msg in history_messages: + formatted_messages.append( + ChatMessage( + role=MessageRole.USER + if msg["role"] == "user" + else MessageRole.ASSISTANT, + content=msg["content"], + ) + ) + + # Add current prompt + formatted_messages.append(ChatMessage(role=MessageRole.USER, content=prompt)) + + response: ChatResponse = await model.achat( + messages=formatted_messages, **chat_kwargs + ) + + # In newer versions, the response is in message.content + content = response.message.content + return content + + except Exception as e: + logger.error(f"Error in llama_index_complete_if_cache: {str(e)}") + raise + + +async def llama_index_complete( + prompt, + system_prompt=None, + history_messages=None, + enable_cot: bool = False, + keyword_extraction=False, + settings: LlamaIndexSettings = None, + **kwargs, +) -> str: + """ + Main completion function for LlamaIndex + + Args: + prompt: Input prompt + system_prompt: Optional system prompt + history_messages: Optional chat history + keyword_extraction: Whether to extract keywords from response + settings: Optional LlamaIndex settings + **kwargs: Additional arguments + """ + if history_messages is None: + history_messages = [] + + kwargs.pop("keyword_extraction", None) + result = await llama_index_complete_if_cache( + kwargs.get("llm_instance"), + prompt, + system_prompt=system_prompt, + history_messages=history_messages, + enable_cot=enable_cot, + **kwargs, + ) + return result + + +@wrap_embedding_func_with_attrs(embedding_dim=1536, max_token_size=8192) +@retry( + stop=stop_after_attempt(3), + wait=wait_exponential(multiplier=1, min=4, max=60), + retry=retry_if_exception_type( + (RateLimitError, APIConnectionError, APITimeoutError) + ), +) +async def llama_index_embed( + texts: list[str], + embed_model: BaseEmbedding = None, + settings: LlamaIndexSettings = None, + **kwargs, +) -> np.ndarray: + """ + Generate embeddings using LlamaIndex + + Args: + texts: List of texts to embed + embed_model: LlamaIndex embedding model + settings: Optional LlamaIndex settings + **kwargs: Additional arguments + """ + if settings: + configure_llama_index(settings) + + if embed_model is None: + raise ValueError("embed_model must be provided") + + # Use _get_text_embeddings for batch processing + embeddings = embed_model._get_text_embeddings(texts) + return np.array(embeddings) diff --git a/LightRAG/lightrag/llm/lmdeploy.py b/LightRAG/lightrag/llm/lmdeploy.py new file mode 100644 index 00000000..8916b0fd --- /dev/null +++ b/LightRAG/lightrag/llm/lmdeploy.py @@ -0,0 +1,154 @@ +import pipmaster as pm # Pipmaster for dynamic library install + +# install specific modules +if not pm.is_installed("lmdeploy"): + pm.install("lmdeploy[all]") + +from lightrag.exceptions import ( + APIConnectionError, + RateLimitError, + APITimeoutError, +) +from tenacity import ( + retry, + stop_after_attempt, + wait_exponential, + retry_if_exception_type, +) + + +from functools import lru_cache + + +@lru_cache(maxsize=1) +def initialize_lmdeploy_pipeline( + model, + tp=1, + chat_template=None, + log_level="WARNING", + model_format="hf", + quant_policy=0, +): + from lmdeploy import pipeline, ChatTemplateConfig, TurbomindEngineConfig + + lmdeploy_pipe = pipeline( + model_path=model, + backend_config=TurbomindEngineConfig( + tp=tp, model_format=model_format, quant_policy=quant_policy + ), + chat_template_config=( + ChatTemplateConfig(model_name=chat_template) if chat_template else None + ), + log_level="WARNING", + ) + return lmdeploy_pipe + + +@retry( + stop=stop_after_attempt(3), + wait=wait_exponential(multiplier=1, min=4, max=10), + retry=retry_if_exception_type( + (RateLimitError, APIConnectionError, APITimeoutError) + ), +) +async def lmdeploy_model_if_cache( + model, + prompt, + system_prompt=None, + history_messages=[], + enable_cot: bool = False, + chat_template=None, + model_format="hf", + quant_policy=0, + **kwargs, +) -> str: + """ + Args: + model (str): The path to the model. + It could be one of the following options: + - i) A local directory path of a turbomind model which is + converted by `lmdeploy convert` command or download + from ii) and iii). + - ii) The model_id of a lmdeploy-quantized model hosted + inside a model repo on huggingface.co, such as + "InternLM/internlm-chat-20b-4bit", + "lmdeploy/llama2-chat-70b-4bit", etc. + - iii) The model_id of a model hosted inside a model repo + on huggingface.co, such as "internlm/internlm-chat-7b", + "Qwen/Qwen-7B-Chat ", "baichuan-inc/Baichuan2-7B-Chat" + and so on. + chat_template (str): needed when model is a pytorch model on + huggingface.co, such as "internlm-chat-7b", + "Qwen-7B-Chat ", "Baichuan2-7B-Chat" and so on, + and when the model name of local path did not match the original model name in HF. + tp (int): tensor parallel + prompt (Union[str, List[str]]): input texts to be completed. + do_preprocess (bool): whether pre-process the messages. Default to + True, which means chat_template will be applied. + skip_special_tokens (bool): Whether or not to remove special tokens + in the decoding. Default to be True. + do_sample (bool): Whether or not to use sampling, use greedy decoding otherwise. + Default to be False, which means greedy decoding will be applied. + """ + if enable_cot: + from lightrag.utils import logger + + logger.debug( + "enable_cot=True is not supported for lmdeploy and will be ignored." + ) + try: + import lmdeploy + from lmdeploy import version_info, GenerationConfig + except Exception: + raise ImportError("Please install lmdeploy before initialize lmdeploy backend.") + kwargs.pop("hashing_kv", None) + kwargs.pop("response_format", None) + max_new_tokens = kwargs.pop("max_tokens", 512) + tp = kwargs.pop("tp", 1) + skip_special_tokens = kwargs.pop("skip_special_tokens", True) + do_preprocess = kwargs.pop("do_preprocess", True) + do_sample = kwargs.pop("do_sample", False) + gen_params = kwargs + + version = version_info + if do_sample is not None and version < (0, 6, 0): + raise RuntimeError( + "`do_sample` parameter is not supported by lmdeploy until " + f"v0.6.0, but currently using lmdeloy {lmdeploy.__version__}" + ) + else: + do_sample = True + gen_params.update(do_sample=do_sample) + + lmdeploy_pipe = initialize_lmdeploy_pipeline( + model=model, + tp=tp, + chat_template=chat_template, + model_format=model_format, + quant_policy=quant_policy, + log_level="WARNING", + ) + + messages = [] + if system_prompt: + messages.append({"role": "system", "content": system_prompt}) + + messages.extend(history_messages) + messages.append({"role": "user", "content": prompt}) + + gen_config = GenerationConfig( + skip_special_tokens=skip_special_tokens, + max_new_tokens=max_new_tokens, + **gen_params, + ) + + response = "" + async for res in lmdeploy_pipe.generate( + messages, + gen_config=gen_config, + do_preprocess=do_preprocess, + stream_response=False, + session_id=1, + ): + response += res.response + return response diff --git a/LightRAG/lightrag/llm/lollms.py b/LightRAG/lightrag/llm/lollms.py new file mode 100644 index 00000000..2f2a1dbf --- /dev/null +++ b/LightRAG/lightrag/llm/lollms.py @@ -0,0 +1,175 @@ +import sys + +if sys.version_info < (3, 9): + from typing import AsyncIterator +else: + from collections.abc import AsyncIterator +import pipmaster as pm # Pipmaster for dynamic library install + +if not pm.is_installed("aiohttp"): + pm.install("aiohttp") + +import aiohttp +from tenacity import ( + retry, + stop_after_attempt, + wait_exponential, + retry_if_exception_type, +) + +from lightrag.exceptions import ( + APIConnectionError, + RateLimitError, + APITimeoutError, +) + +from typing import Union, List +import numpy as np + +from lightrag.utils import ( + wrap_embedding_func_with_attrs, +) + + +@retry( + stop=stop_after_attempt(3), + wait=wait_exponential(multiplier=1, min=4, max=10), + retry=retry_if_exception_type( + (RateLimitError, APIConnectionError, APITimeoutError) + ), +) +async def lollms_model_if_cache( + model, + prompt, + system_prompt=None, + history_messages=[], + enable_cot: bool = False, + base_url="http://localhost:9600", + **kwargs, +) -> Union[str, AsyncIterator[str]]: + """Client implementation for lollms generation.""" + if enable_cot: + from lightrag.utils import logger + + logger.debug("enable_cot=True is not supported for lollms and will be ignored.") + + stream = True if kwargs.get("stream") else False + api_key = kwargs.pop("api_key", None) + headers = ( + {"Content-Type": "application/json", "Authorization": f"Bearer {api_key}"} + if api_key + else {"Content-Type": "application/json"} + ) + + # Extract lollms specific parameters + request_data = { + "prompt": prompt, + "model_name": model, + "personality": kwargs.get("personality", -1), + "n_predict": kwargs.get("n_predict", None), + "stream": stream, + "temperature": kwargs.get("temperature", 1.0), + "top_k": kwargs.get("top_k", 50), + "top_p": kwargs.get("top_p", 0.95), + "repeat_penalty": kwargs.get("repeat_penalty", 0.8), + "repeat_last_n": kwargs.get("repeat_last_n", 40), + "seed": kwargs.get("seed", None), + "n_threads": kwargs.get("n_threads", 8), + } + + # Prepare the full prompt including history + full_prompt = "" + if system_prompt: + full_prompt += f"{system_prompt}\n" + for msg in history_messages: + full_prompt += f"{msg['role']}: {msg['content']}\n" + full_prompt += prompt + + request_data["prompt"] = full_prompt + timeout = aiohttp.ClientTimeout(total=kwargs.get("timeout", None)) + + async with aiohttp.ClientSession(timeout=timeout, headers=headers) as session: + if stream: + + async def inner(): + async with session.post( + f"{base_url}/lollms_generate", json=request_data + ) as response: + async for line in response.content: + yield line.decode().strip() + + return inner() + else: + async with session.post( + f"{base_url}/lollms_generate", json=request_data + ) as response: + return await response.text() + + +async def lollms_model_complete( + prompt, + system_prompt=None, + history_messages=[], + enable_cot: bool = False, + keyword_extraction=False, + **kwargs, +) -> Union[str, AsyncIterator[str]]: + """Complete function for lollms model generation.""" + + # Extract and remove keyword_extraction from kwargs if present + keyword_extraction = kwargs.pop("keyword_extraction", None) + + # Get model name from config + model_name = kwargs["hashing_kv"].global_config["llm_model_name"] + + # If keyword extraction is needed, we might need to modify the prompt + # or add specific parameters for JSON output (if lollms supports it) + if keyword_extraction: + # Note: You might need to adjust this based on how lollms handles structured output + pass + + return await lollms_model_if_cache( + model_name, + prompt, + system_prompt=system_prompt, + history_messages=history_messages, + enable_cot=enable_cot, + **kwargs, + ) + + +@wrap_embedding_func_with_attrs(embedding_dim=1024, max_token_size=8192) +async def lollms_embed( + texts: List[str], embed_model=None, base_url="http://localhost:9600", **kwargs +) -> np.ndarray: + """ + Generate embeddings for a list of texts using lollms server. + + Args: + texts: List of strings to embed + embed_model: Model name (not used directly as lollms uses configured vectorizer) + base_url: URL of the lollms server + **kwargs: Additional arguments passed to the request + + Returns: + np.ndarray: Array of embeddings + """ + api_key = kwargs.pop("api_key", None) + headers = ( + {"Content-Type": "application/json", "Authorization": api_key} + if api_key + else {"Content-Type": "application/json"} + ) + async with aiohttp.ClientSession(headers=headers) as session: + embeddings = [] + for text in texts: + request_data = {"text": text} + + async with session.post( + f"{base_url}/lollms_embed", + json=request_data, + ) as response: + result = await response.json() + embeddings.append(result["vector"]) + + return np.array(embeddings) diff --git a/LightRAG/lightrag/llm/nvidia_openai.py b/LightRAG/lightrag/llm/nvidia_openai.py new file mode 100644 index 00000000..1ebaf3a6 --- /dev/null +++ b/LightRAG/lightrag/llm/nvidia_openai.py @@ -0,0 +1,66 @@ +import sys +import os + +if sys.version_info < (3, 9): + pass +else: + pass + +import pipmaster as pm # Pipmaster for dynamic library install + +# install specific modules +if not pm.is_installed("openai"): + pm.install("openai") + +from openai import ( + AsyncOpenAI, + APIConnectionError, + RateLimitError, + APITimeoutError, +) +from tenacity import ( + retry, + stop_after_attempt, + wait_exponential, + retry_if_exception_type, +) + +from lightrag.utils import ( + wrap_embedding_func_with_attrs, +) + + +import numpy as np + + +@wrap_embedding_func_with_attrs(embedding_dim=2048, max_token_size=8192) +@retry( + stop=stop_after_attempt(3), + wait=wait_exponential(multiplier=1, min=4, max=60), + retry=retry_if_exception_type( + (RateLimitError, APIConnectionError, APITimeoutError) + ), +) +async def nvidia_openai_embed( + texts: list[str], + model: str = "nvidia/llama-3.2-nv-embedqa-1b-v1", + # refer to https://build.nvidia.com/nim?filters=usecase%3Ausecase_text_to_embedding + base_url: str = "https://integrate.api.nvidia.com/v1", + api_key: str = None, + input_type: str = "passage", # query for retrieval, passage for embedding + trunc: str = "NONE", # NONE or START or END + encode: str = "float", # float or base64 +) -> np.ndarray: + if api_key: + os.environ["OPENAI_API_KEY"] = api_key + + openai_async_client = ( + AsyncOpenAI() if base_url is None else AsyncOpenAI(base_url=base_url) + ) + response = await openai_async_client.embeddings.create( + model=model, + input=texts, + encoding_format=encode, + extra_body={"input_type": input_type, "truncate": trunc}, + ) + return np.array([dp.embedding for dp in response.data]) diff --git a/LightRAG/lightrag/llm/ollama.py b/LightRAG/lightrag/llm/ollama.py new file mode 100644 index 00000000..e35dc293 --- /dev/null +++ b/LightRAG/lightrag/llm/ollama.py @@ -0,0 +1,214 @@ +from collections.abc import AsyncIterator +import os +import re + +import pipmaster as pm + +# install specific modules +if not pm.is_installed("ollama"): + pm.install("ollama") + +import ollama + +from tenacity import ( + retry, + stop_after_attempt, + wait_exponential, + retry_if_exception_type, +) +from lightrag.exceptions import ( + APIConnectionError, + RateLimitError, + APITimeoutError, +) +from lightrag.api import __api_version__ + +import numpy as np +from typing import Optional, Union +from lightrag.utils import ( + wrap_embedding_func_with_attrs, + logger, +) + + +_OLLAMA_CLOUD_HOST = "https://ollama.com" +_CLOUD_MODEL_SUFFIX_PATTERN = re.compile(r"(?:-cloud|:cloud)$") + + +def _coerce_host_for_cloud_model(host: Optional[str], model: object) -> Optional[str]: + if host: + return host + try: + model_name_str = str(model) if model is not None else "" + except (TypeError, ValueError, AttributeError) as e: + logger.warning(f"Failed to convert model to string: {e}, using empty string") + model_name_str = "" + if _CLOUD_MODEL_SUFFIX_PATTERN.search(model_name_str): + logger.debug( + f"Detected cloud model '{model_name_str}', using Ollama Cloud host" + ) + return _OLLAMA_CLOUD_HOST + return host + + +@retry( + stop=stop_after_attempt(3), + wait=wait_exponential(multiplier=1, min=4, max=10), + retry=retry_if_exception_type( + (RateLimitError, APIConnectionError, APITimeoutError) + ), +) +async def _ollama_model_if_cache( + model, + prompt, + system_prompt=None, + history_messages=[], + enable_cot: bool = False, + **kwargs, +) -> Union[str, AsyncIterator[str]]: + if enable_cot: + logger.debug("enable_cot=True is not supported for ollama and will be ignored.") + stream = True if kwargs.get("stream") else False + + kwargs.pop("max_tokens", None) + # kwargs.pop("response_format", None) # allow json + host = kwargs.pop("host", None) + timeout = kwargs.pop("timeout", None) + if timeout == 0: + timeout = None + kwargs.pop("hashing_kv", None) + api_key = kwargs.pop("api_key", None) + # fallback to environment variable when not provided explicitly + if not api_key: + api_key = os.getenv("OLLAMA_API_KEY") + headers = { + "Content-Type": "application/json", + "User-Agent": f"LightRAG/{__api_version__}", + } + if api_key: + headers["Authorization"] = f"Bearer {api_key}" + + host = _coerce_host_for_cloud_model(host, model) + + ollama_client = ollama.AsyncClient(host=host, timeout=timeout, headers=headers) + + try: + messages = [] + if system_prompt: + messages.append({"role": "system", "content": system_prompt}) + messages.extend(history_messages) + messages.append({"role": "user", "content": prompt}) + + response = await ollama_client.chat(model=model, messages=messages, **kwargs) + if stream: + """cannot cache stream response and process reasoning""" + + async def inner(): + try: + async for chunk in response: + yield chunk["message"]["content"] + except Exception as e: + logger.error(f"Error in stream response: {str(e)}") + raise + finally: + try: + await ollama_client._client.aclose() + logger.debug("Successfully closed Ollama client for streaming") + except Exception as close_error: + logger.warning(f"Failed to close Ollama client: {close_error}") + + return inner() + else: + model_response = response["message"]["content"] + + """ + If the model also wraps its thoughts in a specific tag, + this information is not needed for the final + response and can simply be trimmed. + """ + + return model_response + except Exception as e: + try: + await ollama_client._client.aclose() + logger.debug("Successfully closed Ollama client after exception") + except Exception as close_error: + logger.warning( + f"Failed to close Ollama client after exception: {close_error}" + ) + raise e + finally: + if not stream: + try: + await ollama_client._client.aclose() + logger.debug( + "Successfully closed Ollama client for non-streaming response" + ) + except Exception as close_error: + logger.warning( + f"Failed to close Ollama client in finally block: {close_error}" + ) + + +async def ollama_model_complete( + prompt, + system_prompt=None, + history_messages=[], + enable_cot: bool = False, + keyword_extraction=False, + **kwargs, +) -> Union[str, AsyncIterator[str]]: + keyword_extraction = kwargs.pop("keyword_extraction", None) + if keyword_extraction: + kwargs["format"] = "json" + model_name = kwargs["hashing_kv"].global_config["llm_model_name"] + return await _ollama_model_if_cache( + model_name, + prompt, + system_prompt=system_prompt, + history_messages=history_messages, + enable_cot=enable_cot, + **kwargs, + ) + + +@wrap_embedding_func_with_attrs(embedding_dim=1024, max_token_size=8192) +async def ollama_embed(texts: list[str], embed_model, **kwargs) -> np.ndarray: + api_key = kwargs.pop("api_key", None) + if not api_key: + api_key = os.getenv("OLLAMA_API_KEY") + headers = { + "Content-Type": "application/json", + "User-Agent": f"LightRAG/{__api_version__}", + } + if api_key: + headers["Authorization"] = f"Bearer {api_key}" + + host = kwargs.pop("host", None) + timeout = kwargs.pop("timeout", None) + + host = _coerce_host_for_cloud_model(host, embed_model) + + ollama_client = ollama.AsyncClient(host=host, timeout=timeout, headers=headers) + try: + options = kwargs.pop("options", {}) + data = await ollama_client.embed( + model=embed_model, input=texts, options=options + ) + return np.array(data["embeddings"]) + except Exception as e: + logger.error(f"Error in ollama_embed: {str(e)}") + try: + await ollama_client._client.aclose() + logger.debug("Successfully closed Ollama client after exception in embed") + except Exception as close_error: + logger.warning( + f"Failed to close Ollama client after exception in embed: {close_error}" + ) + raise e + finally: + try: + await ollama_client._client.aclose() + logger.debug("Successfully closed Ollama client after embed") + except Exception as close_error: + logger.warning(f"Failed to close Ollama client after embed: {close_error}") diff --git a/LightRAG/lightrag/llm/openai.py b/LightRAG/lightrag/llm/openai.py new file mode 100644 index 00000000..8c984e51 --- /dev/null +++ b/LightRAG/lightrag/llm/openai.py @@ -0,0 +1,686 @@ +from ..utils import verbose_debug, VERBOSE_DEBUG +import os +import logging + +from collections.abc import AsyncIterator + +import pipmaster as pm + +# install specific modules +if not pm.is_installed("openai"): + pm.install("openai") + +from openai import ( + APIConnectionError, + RateLimitError, + APITimeoutError, +) +from tenacity import ( + retry, + stop_after_attempt, + wait_exponential, + retry_if_exception_type, +) +from lightrag.utils import ( + wrap_embedding_func_with_attrs, + safe_unicode_decode, + logger, +) + +from lightrag.types import GPTKeywordExtractionFormat +from lightrag.api import __api_version__ + +import numpy as np +import base64 +from typing import Any, Union + +from dotenv import load_dotenv + +# Try to import Langfuse for LLM observability (optional) +# Falls back to standard OpenAI client if not available +# Langfuse requires proper configuration to work correctly +LANGFUSE_ENABLED = False +try: + # Check if required Langfuse environment variables are set + langfuse_public_key = os.environ.get("LANGFUSE_PUBLIC_KEY") + langfuse_secret_key = os.environ.get("LANGFUSE_SECRET_KEY") + + # Only enable Langfuse if both keys are configured + if langfuse_public_key and langfuse_secret_key: + from langfuse.openai import AsyncOpenAI # type: ignore[import-untyped] + + LANGFUSE_ENABLED = True + logger.info("Langfuse observability enabled for OpenAI client") + else: + from openai import AsyncOpenAI + + logger.debug( + "Langfuse environment variables not configured, using standard OpenAI client" + ) +except ImportError: + from openai import AsyncOpenAI + + logger.debug("Langfuse not available, using standard OpenAI client") + +# use the .env that is inside the current folder +# allows to use different .env file for each lightrag instance +# the OS environment variables take precedence over the .env file +load_dotenv(dotenv_path=".env", override=False) + + +class InvalidResponseError(Exception): + """Custom exception class for triggering retry mechanism""" + + pass + + +def create_openai_async_client( + api_key: str | None = None, + base_url: str | None = None, + client_configs: dict[str, Any] | None = None, +) -> AsyncOpenAI: + """Create an AsyncOpenAI client with the given configuration. + + Args: + api_key: OpenAI API key. If None, uses the OPENAI_API_KEY environment variable. + base_url: Base URL for the OpenAI API. If None, uses the default OpenAI API URL. + client_configs: Additional configuration options for the AsyncOpenAI client. + These will override any default configurations but will be overridden by + explicit parameters (api_key, base_url). + + Returns: + An AsyncOpenAI client instance. + """ + if not api_key: + api_key = os.environ["OPENAI_API_KEY"] + + default_headers = { + "User-Agent": f"Mozilla/5.0 (Macintosh; Intel Mac OS X 10_15_8) LightRAG/{__api_version__}", + "Content-Type": "application/json", + } + + if client_configs is None: + client_configs = {} + + # Create a merged config dict with precedence: explicit params > client_configs > defaults + merged_configs = { + **client_configs, + "default_headers": default_headers, + "api_key": api_key, + } + + if base_url is not None: + merged_configs["base_url"] = base_url + else: + merged_configs["base_url"] = os.environ.get( + "OPENAI_API_BASE", "https://api.openai.com/v1" + ) + + return AsyncOpenAI(**merged_configs) + + +@retry( + stop=stop_after_attempt(3), + wait=wait_exponential(multiplier=1, min=4, max=10), + retry=( + retry_if_exception_type(RateLimitError) + | retry_if_exception_type(APIConnectionError) + | retry_if_exception_type(APITimeoutError) + | retry_if_exception_type(InvalidResponseError) + ), +) +async def openai_complete_if_cache( + model: str, + prompt: str, + system_prompt: str | None = None, + history_messages: list[dict[str, Any]] | None = None, + enable_cot: bool = False, + base_url: str | None = None, + api_key: str | None = None, + token_tracker: Any | None = None, + stream: bool | None = None, + timeout: int | None = None, + keyword_extraction: bool = False, + **kwargs: Any, +) -> str: + """Complete a prompt using OpenAI's API with caching support and Chain of Thought (COT) integration. + + This function supports automatic integration of reasoning content from models that provide + Chain of Thought capabilities. The reasoning content is seamlessly integrated into the response + using ... tags. + + Note on `reasoning_content`: This feature relies on a Deepseek Style `reasoning_content` + in the API response, which may be provided by OpenAI-compatible endpoints that support + Chain of Thought. + + COT Integration Rules: + 1. COT content is accepted only when regular content is empty and `reasoning_content` has content. + 2. COT processing stops when regular content becomes available. + 3. If both `content` and `reasoning_content` are present simultaneously, reasoning is ignored. + 4. If both fields have content from the start, COT is never activated. + 5. For streaming: COT content is inserted into the content stream with tags. + 6. For non-streaming: COT content is prepended to regular content with tags. + + Args: + model: The OpenAI model to use. + prompt: The prompt to complete. + system_prompt: Optional system prompt to include. + history_messages: Optional list of previous messages in the conversation. + base_url: Optional base URL for the OpenAI API. + api_key: Optional OpenAI API key. If None, uses the OPENAI_API_KEY environment variable. + token_tracker: Optional token usage tracker for monitoring API usage. + enable_cot: Whether to enable Chain of Thought (COT) processing. Default is False. + stream: Whether to stream the response. Default is False. + timeout: Request timeout in seconds. Default is None. + keyword_extraction: Whether to enable keyword extraction mode. When True, triggers + special response formatting for keyword extraction. Default is False. + **kwargs: Additional keyword arguments to pass to the OpenAI API. + Special kwargs: + - openai_client_configs: Dict of configuration options for the AsyncOpenAI client. + These will be passed to the client constructor but will be overridden by + explicit parameters (api_key, base_url). + + Returns: + The completed text (with integrated COT content if available) or an async iterator + of text chunks if streaming. COT content is wrapped in ... tags. + + Raises: + InvalidResponseError: If the response from OpenAI is invalid or empty. + APIConnectionError: If there is a connection error with the OpenAI API. + RateLimitError: If the OpenAI API rate limit is exceeded. + APITimeoutError: If the OpenAI API request times out. + """ + if history_messages is None: + history_messages = [] + + # Set openai logger level to INFO when VERBOSE_DEBUG is off + if not VERBOSE_DEBUG and logger.level == logging.DEBUG: + logging.getLogger("openai").setLevel(logging.INFO) + + # Remove special kwargs that shouldn't be passed to OpenAI + kwargs.pop("hashing_kv", None) + + # Extract client configuration options + client_configs = kwargs.pop("openai_client_configs", {}) + + # Create the OpenAI client + openai_async_client = create_openai_async_client( + api_key=api_key, + base_url=base_url, + client_configs=client_configs, + ) + + # Prepare messages + messages: list[dict[str, Any]] = [] + if system_prompt: + messages.append({"role": "system", "content": system_prompt}) + messages.extend(history_messages) + messages.append({"role": "user", "content": prompt}) + + logger.debug("===== Entering func of LLM =====") + logger.debug(f"Model: {model} Base URL: {base_url}") + logger.debug(f"Client Configs: {client_configs}") + logger.debug(f"Additional kwargs: {kwargs}") + logger.debug(f"Num of history messages: {len(history_messages)}") + verbose_debug(f"System prompt: {system_prompt}") + verbose_debug(f"Query: {prompt}") + logger.debug("===== Sending Query to LLM =====") + + messages = kwargs.pop("messages", messages) + + # Add explicit parameters back to kwargs so they're passed to OpenAI API + if stream is not None: + kwargs["stream"] = stream + if timeout is not None: + kwargs["timeout"] = timeout + + try: + # Don't use async with context manager, use client directly + if "response_format" in kwargs: + response = await openai_async_client.beta.chat.completions.parse( + model=model, messages=messages, **kwargs + ) + else: + response = await openai_async_client.chat.completions.create( + model=model, messages=messages, **kwargs + ) + except APIConnectionError as e: + logger.error(f"OpenAI API Connection Error: {e}") + await openai_async_client.close() # Ensure client is closed + raise + except RateLimitError as e: + logger.error(f"OpenAI API Rate Limit Error: {e}") + await openai_async_client.close() # Ensure client is closed + raise + except APITimeoutError as e: + logger.error(f"OpenAI API Timeout Error: {e}") + await openai_async_client.close() # Ensure client is closed + raise + except Exception as e: + logger.error( + f"OpenAI API Call Failed,\nModel: {model},\nParams: {kwargs}, Got: {e}" + ) + await openai_async_client.close() # Ensure client is closed + raise + + if hasattr(response, "__aiter__"): + + async def inner(): + # Track if we've started iterating + iteration_started = False + final_chunk_usage = None + + # COT (Chain of Thought) state tracking + cot_active = False + cot_started = False + initial_content_seen = False + + try: + iteration_started = True + async for chunk in response: + # Check if this chunk has usage information (final chunk) + if hasattr(chunk, "usage") and chunk.usage: + final_chunk_usage = chunk.usage + logger.debug( + f"Received usage info in streaming chunk: {chunk.usage}" + ) + + # Check if choices exists and is not empty + if not hasattr(chunk, "choices") or not chunk.choices: + logger.warning(f"Received chunk without choices: {chunk}") + continue + + # Check if delta exists + if not hasattr(chunk.choices[0], "delta"): + # This might be the final chunk, continue to check for usage + continue + + delta = chunk.choices[0].delta + content = getattr(delta, "content", None) + reasoning_content = getattr(delta, "reasoning_content", "") + + # Handle COT logic for streaming (only if enabled) + if enable_cot: + if content: + # Regular content is present + if not initial_content_seen: + initial_content_seen = True + # If both content and reasoning_content are present initially, don't start COT + if reasoning_content: + cot_active = False + cot_started = False + + # If COT was active, end it + if cot_active: + yield "" + cot_active = False + + # Process regular content + if r"\u" in content: + content = safe_unicode_decode(content.encode("utf-8")) + yield content + + elif reasoning_content: + # Only reasoning content is present + if not initial_content_seen and not cot_started: + # Start COT if we haven't seen initial content yet + if not cot_active: + yield "" + cot_active = True + cot_started = True + + # Process reasoning content if COT is active + if cot_active: + if r"\u" in reasoning_content: + reasoning_content = safe_unicode_decode( + reasoning_content.encode("utf-8") + ) + yield reasoning_content + else: + # COT disabled, only process regular content + if content: + if r"\u" in content: + content = safe_unicode_decode(content.encode("utf-8")) + yield content + + # If neither content nor reasoning_content, continue to next chunk + if content is None and reasoning_content is None: + continue + + # Ensure COT is properly closed if still active after stream ends + if enable_cot and cot_active: + yield "" + cot_active = False + + # After streaming is complete, track token usage + if token_tracker and final_chunk_usage: + # Use actual usage from the API + token_counts = { + "prompt_tokens": getattr(final_chunk_usage, "prompt_tokens", 0), + "completion_tokens": getattr( + final_chunk_usage, "completion_tokens", 0 + ), + "total_tokens": getattr(final_chunk_usage, "total_tokens", 0), + } + token_tracker.add_usage(token_counts) + logger.debug(f"Streaming token usage (from API): {token_counts}") + elif token_tracker: + logger.debug("No usage information available in streaming response") + except Exception as e: + # Ensure COT is properly closed before handling exception + if enable_cot and cot_active: + try: + yield "" + cot_active = False + except Exception as close_error: + logger.warning( + f"Failed to close COT tag during exception handling: {close_error}" + ) + + logger.error(f"Error in stream response: {str(e)}") + # Try to clean up resources if possible + if ( + iteration_started + and hasattr(response, "aclose") + and callable(getattr(response, "aclose", None)) + ): + try: + await response.aclose() + logger.debug("Successfully closed stream response after error") + except Exception as close_error: + logger.warning( + f"Failed to close stream response: {close_error}" + ) + # Ensure client is closed in case of exception + await openai_async_client.close() + raise + finally: + # Final safety check for unclosed COT tags + if enable_cot and cot_active: + try: + yield "" + cot_active = False + except Exception as final_close_error: + logger.warning( + f"Failed to close COT tag in finally block: {final_close_error}" + ) + + # Ensure resources are released even if no exception occurs + # Note: Some wrapped clients (e.g., Langfuse) may not implement aclose() properly + if iteration_started and hasattr(response, "aclose"): + aclose_method = getattr(response, "aclose", None) + if callable(aclose_method): + try: + await response.aclose() + logger.debug("Successfully closed stream response") + except (AttributeError, TypeError) as close_error: + # Some wrapper objects may report hasattr(aclose) but fail when called + # This is expected behavior for certain client wrappers + logger.debug( + f"Stream response cleanup not supported by client wrapper: {close_error}" + ) + except Exception as close_error: + logger.warning( + f"Unexpected error during stream response cleanup: {close_error}" + ) + + # This prevents resource leaks since the caller doesn't handle closing + try: + await openai_async_client.close() + logger.debug( + "Successfully closed OpenAI client for streaming response" + ) + except Exception as client_close_error: + logger.warning( + f"Failed to close OpenAI client in streaming finally block: {client_close_error}" + ) + + return inner() + + else: + try: + if ( + not response + or not response.choices + or not hasattr(response.choices[0], "message") + ): + logger.error("Invalid response from OpenAI API") + await openai_async_client.close() # Ensure client is closed + raise InvalidResponseError("Invalid response from OpenAI API") + + message = response.choices[0].message + content = getattr(message, "content", None) + reasoning_content = getattr(message, "reasoning_content", "") + + # Handle COT logic for non-streaming responses (only if enabled) + final_content = "" + + if enable_cot: + # Check if we should include reasoning content + should_include_reasoning = False + if reasoning_content and reasoning_content.strip(): + if not content or content.strip() == "": + # Case 1: Only reasoning content, should include COT + should_include_reasoning = True + final_content = ( + content or "" + ) # Use empty string if content is None + else: + # Case 3: Both content and reasoning_content present, ignore reasoning + should_include_reasoning = False + final_content = content + else: + # No reasoning content, use regular content + final_content = content or "" + + # Apply COT wrapping if needed + if should_include_reasoning: + if r"\u" in reasoning_content: + reasoning_content = safe_unicode_decode( + reasoning_content.encode("utf-8") + ) + final_content = f"{reasoning_content}{final_content}" + else: + # COT disabled, only use regular content + final_content = content or "" + + # Validate final content + if not final_content or final_content.strip() == "": + logger.error("Received empty content from OpenAI API") + await openai_async_client.close() # Ensure client is closed + raise InvalidResponseError("Received empty content from OpenAI API") + + # Apply Unicode decoding to final content if needed + if r"\u" in final_content: + final_content = safe_unicode_decode(final_content.encode("utf-8")) + + if token_tracker and hasattr(response, "usage"): + token_counts = { + "prompt_tokens": getattr(response.usage, "prompt_tokens", 0), + "completion_tokens": getattr( + response.usage, "completion_tokens", 0 + ), + "total_tokens": getattr(response.usage, "total_tokens", 0), + } + token_tracker.add_usage(token_counts) + + logger.debug(f"Response content len: {len(final_content)}") + verbose_debug(f"Response: {response}") + + return final_content + finally: + # Ensure client is closed in all cases for non-streaming responses + await openai_async_client.close() + + +async def openai_complete( + prompt, + system_prompt=None, + history_messages=None, + keyword_extraction=False, + **kwargs, +) -> Union[str, AsyncIterator[str]]: + if history_messages is None: + history_messages = [] + if keyword_extraction: + kwargs["response_format"] = "json" + model_name = kwargs["hashing_kv"].global_config["llm_model_name"] + return await openai_complete_if_cache( + model_name, + prompt, + system_prompt=system_prompt, + history_messages=history_messages, + keyword_extraction=keyword_extraction, + **kwargs, + ) + + +async def gpt_4o_complete( + prompt, + system_prompt=None, + history_messages=None, + enable_cot: bool = False, + keyword_extraction=False, + **kwargs, +) -> str: + if history_messages is None: + history_messages = [] + if keyword_extraction: + kwargs["response_format"] = GPTKeywordExtractionFormat + return await openai_complete_if_cache( + "gpt-4o", + prompt, + system_prompt=system_prompt, + history_messages=history_messages, + enable_cot=enable_cot, + keyword_extraction=keyword_extraction, + **kwargs, + ) + + +async def gpt_4o_mini_complete( + prompt, + system_prompt=None, + history_messages=None, + enable_cot: bool = False, + keyword_extraction=False, + **kwargs, +) -> str: + if history_messages is None: + history_messages = [] + if keyword_extraction: + kwargs["response_format"] = GPTKeywordExtractionFormat + return await openai_complete_if_cache( + "gpt-4o-mini", + prompt, + system_prompt=system_prompt, + history_messages=history_messages, + enable_cot=enable_cot, + keyword_extraction=keyword_extraction, + **kwargs, + ) + + +async def nvidia_openai_complete( + prompt, + system_prompt=None, + history_messages=None, + enable_cot: bool = False, + keyword_extraction=False, + **kwargs, +) -> str: + if history_messages is None: + history_messages = [] + result = await openai_complete_if_cache( + "nvidia/llama-3.1-nemotron-70b-instruct", # context length 128k + prompt, + system_prompt=system_prompt, + history_messages=history_messages, + enable_cot=enable_cot, + keyword_extraction=keyword_extraction, + base_url="https://integrate.api.nvidia.com/v1", + **kwargs, + ) + return result + + +@wrap_embedding_func_with_attrs(embedding_dim=1536, max_token_size=8192) +@retry( + stop=stop_after_attempt(3), + wait=wait_exponential(multiplier=1, min=4, max=60), + retry=( + retry_if_exception_type(RateLimitError) + | retry_if_exception_type(APIConnectionError) + | retry_if_exception_type(APITimeoutError) + ), +) +async def openai_embed( + texts: list[str], + model: str = "text-embedding-3-small", + base_url: str | None = None, + api_key: str | None = None, + embedding_dim: int | None = None, + client_configs: dict[str, Any] | None = None, + token_tracker: Any | None = None, +) -> np.ndarray: + """Generate embeddings for a list of texts using OpenAI's API. + + Args: + texts: List of texts to embed. + model: The OpenAI embedding model to use. + base_url: Optional base URL for the OpenAI API. + api_key: Optional OpenAI API key. If None, uses the OPENAI_API_KEY environment variable. + embedding_dim: Optional embedding dimension for dynamic dimension reduction. + **IMPORTANT**: This parameter is automatically injected by the EmbeddingFunc wrapper. + Do NOT manually pass this parameter when calling the function directly. + The dimension is controlled by the @wrap_embedding_func_with_attrs decorator. + Manually passing a different value will trigger a warning and be ignored. + When provided (by EmbeddingFunc), it will be passed to the OpenAI API for dimension reduction. + client_configs: Additional configuration options for the AsyncOpenAI client. + These will override any default configurations but will be overridden by + explicit parameters (api_key, base_url). + token_tracker: Optional token usage tracker for monitoring API usage. + + Returns: + A numpy array of embeddings, one per input text. + + Raises: + APIConnectionError: If there is a connection error with the OpenAI API. + RateLimitError: If the OpenAI API rate limit is exceeded. + APITimeoutError: If the OpenAI API request times out. + """ + # Create the OpenAI client + openai_async_client = create_openai_async_client( + api_key=api_key, base_url=base_url, client_configs=client_configs + ) + + async with openai_async_client: + # Prepare API call parameters + api_params = { + "model": model, + "input": texts, + "encoding_format": "base64", + } + + # Add dimensions parameter only if embedding_dim is provided + if embedding_dim is not None: + api_params["dimensions"] = embedding_dim + + # Make API call + response = await openai_async_client.embeddings.create(**api_params) + + if token_tracker and hasattr(response, "usage"): + token_counts = { + "prompt_tokens": getattr(response.usage, "prompt_tokens", 0), + "total_tokens": getattr(response.usage, "total_tokens", 0), + } + token_tracker.add_usage(token_counts) + + return np.array( + [ + np.array(dp.embedding, dtype=np.float32) + if isinstance(dp.embedding, list) + else np.frombuffer(base64.b64decode(dp.embedding), dtype=np.float32) + for dp in response.data + ] + ) diff --git a/LightRAG/lightrag/llm/zhipu.py b/LightRAG/lightrag/llm/zhipu.py new file mode 100644 index 00000000..d90f3cc1 --- /dev/null +++ b/LightRAG/lightrag/llm/zhipu.py @@ -0,0 +1,217 @@ +import sys +import re +import json +from ..utils import verbose_debug + +if sys.version_info < (3, 9): + pass +else: + pass +import pipmaster as pm # Pipmaster for dynamic library install + +# install specific modules +if not pm.is_installed("zhipuai"): + pm.install("zhipuai") + +from openai import ( + APIConnectionError, + RateLimitError, + APITimeoutError, +) +from tenacity import ( + retry, + stop_after_attempt, + wait_exponential, + retry_if_exception_type, +) + +from lightrag.utils import ( + wrap_embedding_func_with_attrs, + logger, +) + +from lightrag.types import GPTKeywordExtractionFormat + +import numpy as np +from typing import Union, List, Optional, Dict + + +@retry( + stop=stop_after_attempt(3), + wait=wait_exponential(multiplier=1, min=4, max=10), + retry=retry_if_exception_type( + (RateLimitError, APIConnectionError, APITimeoutError) + ), +) +async def zhipu_complete_if_cache( + prompt: Union[str, List[Dict[str, str]]], + model: str = "glm-4-flashx", # The most cost/performance balance model in glm-4 series + api_key: Optional[str] = None, + system_prompt: Optional[str] = None, + history_messages: List[Dict[str, str]] = [], + enable_cot: bool = False, + **kwargs, +) -> str: + if enable_cot: + logger.debug( + "enable_cot=True is not supported for ZhipuAI and will be ignored." + ) + # dynamically load ZhipuAI + try: + from zhipuai import ZhipuAI + except ImportError: + raise ImportError("Please install zhipuai before initialize zhipuai backend.") + + if api_key: + client = ZhipuAI(api_key=api_key) + else: + # please set ZHIPUAI_API_KEY in your environment + # os.environ["ZHIPUAI_API_KEY"] + client = ZhipuAI() + + messages = [] + + if not system_prompt: + system_prompt = "You are a helpful assistant. Note that sensitive words in the content should be replaced with ***" + + # Add system prompt if provided + if system_prompt: + messages.append({"role": "system", "content": system_prompt}) + messages.extend(history_messages) + messages.append({"role": "user", "content": prompt}) + + # Add debug logging + logger.debug("===== Query Input to LLM =====") + logger.debug(f"Query: {prompt}") + verbose_debug(f"System prompt: {system_prompt}") + + # Remove unsupported kwargs + kwargs = { + k: v for k, v in kwargs.items() if k not in ["hashing_kv", "keyword_extraction"] + } + + response = client.chat.completions.create(model=model, messages=messages, **kwargs) + + return response.choices[0].message.content + + +async def zhipu_complete( + prompt, + system_prompt=None, + history_messages=[], + keyword_extraction=False, + enable_cot: bool = False, + **kwargs, +): + # Pop keyword_extraction from kwargs to avoid passing it to zhipu_complete_if_cache + keyword_extraction = kwargs.pop("keyword_extraction", None) + + if keyword_extraction: + # Add a system prompt to guide the model to return JSON format + extraction_prompt = """You are a helpful assistant that extracts keywords from text. + Please analyze the content and extract two types of keywords: + 1. High-level keywords: Important concepts and main themes + 2. Low-level keywords: Specific details and supporting elements + + Return your response in this exact JSON format: + { + "high_level_keywords": ["keyword1", "keyword2"], + "low_level_keywords": ["keyword1", "keyword2", "keyword3"] + } + + Only return the JSON, no other text.""" + + # Combine with existing system prompt if any + if system_prompt: + system_prompt = f"{system_prompt}\n\n{extraction_prompt}" + else: + system_prompt = extraction_prompt + + try: + response = await zhipu_complete_if_cache( + prompt=prompt, + system_prompt=system_prompt, + history_messages=history_messages, + enable_cot=enable_cot, + **kwargs, + ) + + # Try to parse as JSON + try: + data = json.loads(response) + return GPTKeywordExtractionFormat( + high_level_keywords=data.get("high_level_keywords", []), + low_level_keywords=data.get("low_level_keywords", []), + ) + except json.JSONDecodeError: + # If direct JSON parsing fails, try to extract JSON from text + match = re.search(r"\{[\s\S]*\}", response) + if match: + try: + data = json.loads(match.group()) + return GPTKeywordExtractionFormat( + high_level_keywords=data.get("high_level_keywords", []), + low_level_keywords=data.get("low_level_keywords", []), + ) + except json.JSONDecodeError: + pass + + # If all parsing fails, log warning and return empty format + logger.warning( + f"Failed to parse keyword extraction response: {response}" + ) + return GPTKeywordExtractionFormat( + high_level_keywords=[], low_level_keywords=[] + ) + except Exception as e: + logger.error(f"Error during keyword extraction: {str(e)}") + return GPTKeywordExtractionFormat( + high_level_keywords=[], low_level_keywords=[] + ) + else: + # For non-keyword-extraction, just return the raw response string + return await zhipu_complete_if_cache( + prompt=prompt, + system_prompt=system_prompt, + history_messages=history_messages, + enable_cot=enable_cot, + **kwargs, + ) + + +@wrap_embedding_func_with_attrs(embedding_dim=1024) +@retry( + stop=stop_after_attempt(3), + wait=wait_exponential(multiplier=1, min=4, max=60), + retry=retry_if_exception_type( + (RateLimitError, APIConnectionError, APITimeoutError) + ), +) +async def zhipu_embedding( + texts: list[str], model: str = "embedding-3", api_key: str = None, **kwargs +) -> np.ndarray: + # dynamically load ZhipuAI + try: + from zhipuai import ZhipuAI + except ImportError: + raise ImportError("Please install zhipuai before initialize zhipuai backend.") + if api_key: + client = ZhipuAI(api_key=api_key) + else: + # please set ZHIPUAI_API_KEY in your environment + # os.environ["ZHIPUAI_API_KEY"] + client = ZhipuAI() + + # Convert single text to list if needed + if isinstance(texts, str): + texts = [texts] + + embeddings = [] + for text in texts: + try: + response = client.embeddings.create(model=model, input=[text], **kwargs) + embeddings.append(response.data[0].embedding) + except Exception as e: + raise Exception(f"Error calling ChatGLM Embedding API: {str(e)}") + + return np.array(embeddings) diff --git a/LightRAG/lightrag/namespace.py b/LightRAG/lightrag/namespace.py new file mode 100644 index 00000000..eccd168d --- /dev/null +++ b/LightRAG/lightrag/namespace.py @@ -0,0 +1,28 @@ +from __future__ import annotations + +from typing import Iterable + + +# All namespace should not be changed +class NameSpace: + KV_STORE_FULL_DOCS = "full_docs" + KV_STORE_TEXT_CHUNKS = "text_chunks" + KV_STORE_LLM_RESPONSE_CACHE = "llm_response_cache" + KV_STORE_FULL_ENTITIES = "full_entities" + KV_STORE_FULL_RELATIONS = "full_relations" + KV_STORE_ENTITY_CHUNKS = "entity_chunks" + KV_STORE_RELATION_CHUNKS = "relation_chunks" + + VECTOR_STORE_ENTITIES = "entities" + VECTOR_STORE_RELATIONSHIPS = "relationships" + VECTOR_STORE_CHUNKS = "chunks" + + GRAPH_STORE_CHUNK_ENTITY_RELATION = "chunk_entity_relation" + + DOC_STATUS = "doc_status" + + +def is_namespace(namespace: str, base_namespace: str | Iterable[str]): + if isinstance(base_namespace, str): + return namespace.endswith(base_namespace) + return any(is_namespace(namespace, ns) for ns in base_namespace) diff --git a/LightRAG/lightrag/operate.py b/LightRAG/lightrag/operate.py new file mode 100644 index 00000000..858553b1 --- /dev/null +++ b/LightRAG/lightrag/operate.py @@ -0,0 +1,4985 @@ +from __future__ import annotations +from functools import partial +from pathlib import Path + +import asyncio +import json +import json_repair +from typing import Any, AsyncIterator, overload, Literal +from collections import Counter, defaultdict + +from lightrag.exceptions import PipelineCancelledException +from lightrag.utils import ( + logger, + compute_mdhash_id, + Tokenizer, + is_float_regex, + sanitize_and_normalize_extracted_text, + pack_user_ass_to_openai_messages, + split_string_by_multi_markers, + truncate_list_by_token_size, + compute_args_hash, + handle_cache, + save_to_cache, + CacheData, + use_llm_func_with_cache, + update_chunk_cache_list, + remove_think_tags, + pick_by_weighted_polling, + pick_by_vector_similarity, + process_chunks_unified, + safe_vdb_operation_with_exception, + create_prefixed_exception, + fix_tuple_delimiter_corruption, + convert_to_user_format, + generate_reference_list_from_chunks, + apply_source_ids_limit, + merge_source_ids, + make_relation_chunk_key, +) +from lightrag.base import ( + BaseGraphStorage, + BaseKVStorage, + BaseVectorStorage, + TextChunkSchema, + QueryParam, + QueryResult, + QueryContextResult, +) +from lightrag.prompt import PROMPTS +from lightrag.constants import ( + GRAPH_FIELD_SEP, + DEFAULT_MAX_ENTITY_TOKENS, + DEFAULT_MAX_RELATION_TOKENS, + DEFAULT_MAX_TOTAL_TOKENS, + DEFAULT_RELATED_CHUNK_NUMBER, + DEFAULT_KG_CHUNK_PICK_METHOD, + DEFAULT_ENTITY_TYPES, + DEFAULT_SUMMARY_LANGUAGE, + SOURCE_IDS_LIMIT_METHOD_KEEP, + SOURCE_IDS_LIMIT_METHOD_FIFO, + DEFAULT_FILE_PATH_MORE_PLACEHOLDER, + DEFAULT_MAX_FILE_PATHS, + DEFAULT_ENTITY_NAME_MAX_LENGTH, +) +from lightrag.kg.shared_storage import get_storage_keyed_lock +import time +from dotenv import load_dotenv + +# use the .env that is inside the current folder +# allows to use different .env file for each lightrag instance +# the OS environment variables take precedence over the .env file +load_dotenv(dotenv_path=Path(__file__).resolve().parent / ".env", override=False) + + +def _truncate_entity_identifier( + identifier: str, limit: int, chunk_key: str, identifier_role: str +) -> str: + """Truncate entity identifiers that exceed the configured length limit.""" + + if len(identifier) <= limit: + return identifier + + display_value = identifier[:limit] + preview = identifier[:20] # Show first 20 characters as preview + logger.warning( + "%s: %s len %d > %d chars (Name: '%s...')", + chunk_key, + identifier_role, + len(identifier), + limit, + preview, + ) + return display_value + + +def chunking_by_token_size( + tokenizer: Tokenizer, + content: str, + split_by_character: str | None = None, + split_by_character_only: bool = False, + overlap_token_size: int = 128, + max_token_size: int = 1024, +) -> list[dict[str, Any]]: + tokens = tokenizer.encode(content) + results: list[dict[str, Any]] = [] + if split_by_character: + raw_chunks = content.split(split_by_character) + new_chunks = [] + if split_by_character_only: + for chunk in raw_chunks: + _tokens = tokenizer.encode(chunk) + new_chunks.append((len(_tokens), chunk)) + else: + for chunk in raw_chunks: + _tokens = tokenizer.encode(chunk) + if len(_tokens) > max_token_size: + for start in range( + 0, len(_tokens), max_token_size - overlap_token_size + ): + chunk_content = tokenizer.decode( + _tokens[start : start + max_token_size] + ) + new_chunks.append( + (min(max_token_size, len(_tokens) - start), chunk_content) + ) + else: + new_chunks.append((len(_tokens), chunk)) + for index, (_len, chunk) in enumerate(new_chunks): + results.append( + { + "tokens": _len, + "content": chunk.strip(), + "chunk_order_index": index, + } + ) + else: + for index, start in enumerate( + range(0, len(tokens), max_token_size - overlap_token_size) + ): + chunk_content = tokenizer.decode(tokens[start : start + max_token_size]) + results.append( + { + "tokens": min(max_token_size, len(tokens) - start), + "content": chunk_content.strip(), + "chunk_order_index": index, + } + ) + return results + + +async def _handle_entity_relation_summary( + description_type: str, + entity_or_relation_name: str, + description_list: list[str], + seperator: str, + global_config: dict, + llm_response_cache: BaseKVStorage | None = None, +) -> tuple[str, bool]: + """Handle entity relation description summary using map-reduce approach. + + This function summarizes a list of descriptions using a map-reduce strategy: + 1. If total tokens < summary_context_size and len(description_list) < force_llm_summary_on_merge, no need to summarize + 2. If total tokens < summary_max_tokens, summarize with LLM directly + 3. Otherwise, split descriptions into chunks that fit within token limits + 4. Summarize each chunk, then recursively process the summaries + 5. Continue until we get a final summary within token limits or num of descriptions is less than force_llm_summary_on_merge + + Args: + entity_or_relation_name: Name of the entity or relation being summarized + description_list: List of description strings to summarize + global_config: Global configuration containing tokenizer and limits + llm_response_cache: Optional cache for LLM responses + + Returns: + Tuple of (final_summarized_description_string, llm_was_used_boolean) + """ + # Handle empty input + if not description_list: + return "", False + + # If only one description, return it directly (no need for LLM call) + if len(description_list) == 1: + return description_list[0], False + + # Get configuration + tokenizer: Tokenizer = global_config["tokenizer"] + summary_context_size = global_config["summary_context_size"] + summary_max_tokens = global_config["summary_max_tokens"] + force_llm_summary_on_merge = global_config["force_llm_summary_on_merge"] + + current_list = description_list[:] # Copy the list to avoid modifying original + llm_was_used = False # Track whether LLM was used during the entire process + + # Iterative map-reduce process + while True: + # Calculate total tokens in current list + total_tokens = sum(len(tokenizer.encode(desc)) for desc in current_list) + + # If total length is within limits, perform final summarization + if total_tokens <= summary_context_size or len(current_list) <= 2: + if ( + len(current_list) < force_llm_summary_on_merge + and total_tokens < summary_max_tokens + ): + # no LLM needed, just join the descriptions + final_description = seperator.join(current_list) + return final_description if final_description else "", llm_was_used + else: + if total_tokens > summary_context_size and len(current_list) <= 2: + logger.warning( + f"Summarizing {entity_or_relation_name}: Oversize descpriton found" + ) + # Final summarization of remaining descriptions - LLM will be used + final_summary = await _summarize_descriptions( + description_type, + entity_or_relation_name, + current_list, + global_config, + llm_response_cache, + ) + return final_summary, True # LLM was used for final summarization + + # Need to split into chunks - Map phase + # Ensure each chunk has minimum 2 descriptions to guarantee progress + chunks = [] + current_chunk = [] + current_tokens = 0 + + # Currently least 3 descriptions in current_list + for i, desc in enumerate(current_list): + desc_tokens = len(tokenizer.encode(desc)) + + # If adding current description would exceed limit, finalize current chunk + if current_tokens + desc_tokens > summary_context_size and current_chunk: + # Ensure we have at least 2 descriptions in the chunk (when possible) + if len(current_chunk) == 1: + # Force add one more description to ensure minimum 2 per chunk + current_chunk.append(desc) + chunks.append(current_chunk) + logger.warning( + f"Summarizing {entity_or_relation_name}: Oversize descpriton found" + ) + current_chunk = [] # next group is empty + current_tokens = 0 + else: # curren_chunk is ready for summary in reduce phase + chunks.append(current_chunk) + current_chunk = [desc] # leave it for next group + current_tokens = desc_tokens + else: + current_chunk.append(desc) + current_tokens += desc_tokens + + # Add the last chunk if it exists + if current_chunk: + chunks.append(current_chunk) + + logger.info( + f" Summarizing {entity_or_relation_name}: Map {len(current_list)} descriptions into {len(chunks)} groups" + ) + + # Reduce phase: summarize each group from chunks + new_summaries = [] + for chunk in chunks: + if len(chunk) == 1: + # Optimization: single description chunks don't need LLM summarization + new_summaries.append(chunk[0]) + else: + # Multiple descriptions need LLM summarization + summary = await _summarize_descriptions( + description_type, + entity_or_relation_name, + chunk, + global_config, + llm_response_cache, + ) + new_summaries.append(summary) + llm_was_used = True # Mark that LLM was used in reduce phase + + # Update current list with new summaries for next iteration + current_list = new_summaries + + +async def _summarize_descriptions( + description_type: str, + description_name: str, + description_list: list[str], + global_config: dict, + llm_response_cache: BaseKVStorage | None = None, +) -> str: + """Helper function to summarize a list of descriptions using LLM. + + Args: + entity_or_relation_name: Name of the entity or relation being summarized + descriptions: List of description strings to summarize + global_config: Global configuration containing LLM function and settings + llm_response_cache: Optional cache for LLM responses + + Returns: + Summarized description string + """ + use_llm_func: callable = global_config["llm_model_func"] + # Apply higher priority (8) to entity/relation summary tasks + use_llm_func = partial(use_llm_func, _priority=8) + + language = global_config["addon_params"].get("language", DEFAULT_SUMMARY_LANGUAGE) + + summary_length_recommended = global_config["summary_length_recommended"] + + prompt_template = PROMPTS["summarize_entity_descriptions"] + + # Convert descriptions to JSONL format and apply token-based truncation + tokenizer = global_config["tokenizer"] + summary_context_size = global_config["summary_context_size"] + + # Create list of JSON objects with "Description" field + json_descriptions = [{"Description": desc} for desc in description_list] + + # Use truncate_list_by_token_size for length truncation + truncated_json_descriptions = truncate_list_by_token_size( + json_descriptions, + key=lambda x: json.dumps(x, ensure_ascii=False), + max_token_size=summary_context_size, + tokenizer=tokenizer, + ) + + # Convert to JSONL format (one JSON object per line) + joined_descriptions = "\n".join( + json.dumps(desc, ensure_ascii=False) for desc in truncated_json_descriptions + ) + + # Prepare context for the prompt + context_base = dict( + description_type=description_type, + description_name=description_name, + description_list=joined_descriptions, + summary_length=summary_length_recommended, + language=language, + ) + use_prompt = prompt_template.format(**context_base) + + # Use LLM function with cache (higher priority for summary generation) + summary, _ = await use_llm_func_with_cache( + use_prompt, + use_llm_func, + llm_response_cache=llm_response_cache, + cache_type="summary", + ) + + # Check summary token length against embedding limit + embedding_token_limit = global_config.get("embedding_token_limit") + if embedding_token_limit is not None and summary: + tokenizer = global_config["tokenizer"] + summary_token_count = len(tokenizer.encode(summary)) + threshold = int(embedding_token_limit * 0.9) + + if summary_token_count > threshold: + logger.warning( + f"Summary tokens ({summary_token_count}) exceeds 90% of embedding limit " + f"({embedding_token_limit}) for {description_type}: {description_name}" + ) + + return summary + + +async def _handle_single_entity_extraction( + record_attributes: list[str], + chunk_key: str, + timestamp: int, + file_path: str = "unknown_source", +): + if len(record_attributes) != 4 or "entity" not in record_attributes[0]: + if len(record_attributes) > 1 and "entity" in record_attributes[0]: + logger.warning( + f"{chunk_key}: LLM output format error; found {len(record_attributes)}/4 feilds on ENTITY `{record_attributes[1]}` @ `{record_attributes[2] if len(record_attributes) > 2 else 'N/A'}`" + ) + logger.debug(record_attributes) + return None + + try: + entity_name = sanitize_and_normalize_extracted_text( + record_attributes[1], remove_inner_quotes=True + ) + + # Validate entity name after all cleaning steps + if not entity_name or not entity_name.strip(): + logger.warning( + f"Entity extraction error: entity name became empty after cleaning. Original: '{record_attributes[1]}'" + ) + return None + + # Process entity type with same cleaning pipeline + entity_type = sanitize_and_normalize_extracted_text( + record_attributes[2], remove_inner_quotes=True + ) + + if not entity_type.strip() or any( + char in entity_type for char in ["'", "(", ")", "<", ">", "|", "/", "\\"] + ): + logger.warning( + f"Entity extraction error: invalid entity type in: {record_attributes}" + ) + return None + + # Remove spaces and convert to lowercase + entity_type = entity_type.replace(" ", "").lower() + + # Process entity description with same cleaning pipeline + entity_description = sanitize_and_normalize_extracted_text(record_attributes[3]) + + if not entity_description.strip(): + logger.warning( + f"Entity extraction error: empty description for entity '{entity_name}' of type '{entity_type}'" + ) + return None + + return dict( + entity_name=entity_name, + entity_type=entity_type, + description=entity_description, + source_id=chunk_key, + file_path=file_path, + timestamp=timestamp, + ) + + except ValueError as e: + logger.error( + f"Entity extraction failed due to encoding issues in chunk {chunk_key}: {e}" + ) + return None + except Exception as e: + logger.error( + f"Entity extraction failed with unexpected error in chunk {chunk_key}: {e}" + ) + return None + + +async def _handle_single_relationship_extraction( + record_attributes: list[str], + chunk_key: str, + timestamp: int, + file_path: str = "unknown_source", +): + if ( + len(record_attributes) != 5 or "relation" not in record_attributes[0] + ): # treat "relationship" and "relation" interchangeable + if len(record_attributes) > 1 and "relation" in record_attributes[0]: + logger.warning( + f"{chunk_key}: LLM output format error; found {len(record_attributes)}/5 fields on REALTION `{record_attributes[1]}`~`{record_attributes[2] if len(record_attributes) > 2 else 'N/A'}`" + ) + logger.debug(record_attributes) + return None + + try: + source = sanitize_and_normalize_extracted_text( + record_attributes[1], remove_inner_quotes=True + ) + target = sanitize_and_normalize_extracted_text( + record_attributes[2], remove_inner_quotes=True + ) + + # Validate entity names after all cleaning steps + if not source: + logger.warning( + f"Relationship extraction error: source entity became empty after cleaning. Original: '{record_attributes[1]}'" + ) + return None + + if not target: + logger.warning( + f"Relationship extraction error: target entity became empty after cleaning. Original: '{record_attributes[2]}'" + ) + return None + + if source == target: + logger.debug( + f"Relationship source and target are the same in: {record_attributes}" + ) + return None + + # Process keywords with same cleaning pipeline + edge_keywords = sanitize_and_normalize_extracted_text( + record_attributes[3], remove_inner_quotes=True + ) + edge_keywords = edge_keywords.replace("，", ",") + + # Process relationship description with same cleaning pipeline + edge_description = sanitize_and_normalize_extracted_text(record_attributes[4]) + + edge_source_id = chunk_key + weight = ( + float(record_attributes[-1].strip('"').strip("'")) + if is_float_regex(record_attributes[-1].strip('"').strip("'")) + else 1.0 + ) + + return dict( + src_id=source, + tgt_id=target, + weight=weight, + description=edge_description, + keywords=edge_keywords, + source_id=edge_source_id, + file_path=file_path, + timestamp=timestamp, + ) + + except ValueError as e: + logger.warning( + f"Relationship extraction failed due to encoding issues in chunk {chunk_key}: {e}" + ) + return None + except Exception as e: + logger.warning( + f"Relationship extraction failed with unexpected error in chunk {chunk_key}: {e}" + ) + return None + + +async def rebuild_knowledge_from_chunks( + entities_to_rebuild: dict[str, list[str]], + relationships_to_rebuild: dict[tuple[str, str], list[str]], + knowledge_graph_inst: BaseGraphStorage, + entities_vdb: BaseVectorStorage, + relationships_vdb: BaseVectorStorage, + text_chunks_storage: BaseKVStorage, + llm_response_cache: BaseKVStorage, + global_config: dict[str, str], + pipeline_status: dict | None = None, + pipeline_status_lock=None, + entity_chunks_storage: BaseKVStorage | None = None, + relation_chunks_storage: BaseKVStorage | None = None, +) -> None: + """Rebuild entity and relationship descriptions from cached extraction results with parallel processing + + This method uses cached LLM extraction results instead of calling LLM again, + following the same approach as the insert process. Now with parallel processing + controlled by llm_model_max_async and using get_storage_keyed_lock for data consistency. + + Args: + entities_to_rebuild: Dict mapping entity_name -> list of remaining chunk_ids + relationships_to_rebuild: Dict mapping (src, tgt) -> list of remaining chunk_ids + knowledge_graph_inst: Knowledge graph storage + entities_vdb: Entity vector database + relationships_vdb: Relationship vector database + text_chunks_storage: Text chunks storage + llm_response_cache: LLM response cache + global_config: Global configuration containing llm_model_max_async + pipeline_status: Pipeline status dictionary + pipeline_status_lock: Lock for pipeline status + entity_chunks_storage: KV storage maintaining full chunk IDs per entity + relation_chunks_storage: KV storage maintaining full chunk IDs per relation + """ + if not entities_to_rebuild and not relationships_to_rebuild: + return + + # Get all referenced chunk IDs + all_referenced_chunk_ids = set() + for chunk_ids in entities_to_rebuild.values(): + all_referenced_chunk_ids.update(chunk_ids) + for chunk_ids in relationships_to_rebuild.values(): + all_referenced_chunk_ids.update(chunk_ids) + + status_message = f"Rebuilding knowledge from {len(all_referenced_chunk_ids)} cached chunk extractions (parallel processing)" + logger.info(status_message) + if pipeline_status is not None and pipeline_status_lock is not None: + async with pipeline_status_lock: + pipeline_status["latest_message"] = status_message + pipeline_status["history_messages"].append(status_message) + + # Get cached extraction results for these chunks using storage + # cached_results： chunk_id -> [list of (extraction_result, create_time) from LLM cache sorted by create_time of the first extraction_result] + cached_results = await _get_cached_extraction_results( + llm_response_cache, + all_referenced_chunk_ids, + text_chunks_storage=text_chunks_storage, + ) + + if not cached_results: + status_message = "No cached extraction results found, cannot rebuild" + logger.warning(status_message) + if pipeline_status is not None and pipeline_status_lock is not None: + async with pipeline_status_lock: + pipeline_status["latest_message"] = status_message + pipeline_status["history_messages"].append(status_message) + return + + # Process cached results to get entities and relationships for each chunk + chunk_entities = {} # chunk_id -> {entity_name: [entity_data]} + chunk_relationships = {} # chunk_id -> {(src, tgt): [relationship_data]} + + for chunk_id, results in cached_results.items(): + try: + # Handle multiple extraction results per chunk + chunk_entities[chunk_id] = defaultdict(list) + chunk_relationships[chunk_id] = defaultdict(list) + + # process multiple LLM extraction results for a single chunk_id + for result in results: + entities, relationships = await _rebuild_from_extraction_result( + text_chunks_storage=text_chunks_storage, + chunk_id=chunk_id, + extraction_result=result[0], + timestamp=result[1], + ) + + # Merge entities and relationships from this extraction result + # Compare description lengths and keep the better version for the same chunk_id + for entity_name, entity_list in entities.items(): + if entity_name not in chunk_entities[chunk_id]: + # New entity for this chunk_id + chunk_entities[chunk_id][entity_name].extend(entity_list) + elif len(chunk_entities[chunk_id][entity_name]) == 0: + # Empty list, add the new entities + chunk_entities[chunk_id][entity_name].extend(entity_list) + else: + # Compare description lengths and keep the better one + existing_desc_len = len( + chunk_entities[chunk_id][entity_name][0].get( + "description", "" + ) + or "" + ) + new_desc_len = len(entity_list[0].get("description", "") or "") + + if new_desc_len > existing_desc_len: + # Replace with the new entity that has longer description + chunk_entities[chunk_id][entity_name] = list(entity_list) + # Otherwise keep existing version + + # Compare description lengths and keep the better version for the same chunk_id + for rel_key, rel_list in relationships.items(): + if rel_key not in chunk_relationships[chunk_id]: + # New relationship for this chunk_id + chunk_relationships[chunk_id][rel_key].extend(rel_list) + elif len(chunk_relationships[chunk_id][rel_key]) == 0: + # Empty list, add the new relationships + chunk_relationships[chunk_id][rel_key].extend(rel_list) + else: + # Compare description lengths and keep the better one + existing_desc_len = len( + chunk_relationships[chunk_id][rel_key][0].get( + "description", "" + ) + or "" + ) + new_desc_len = len(rel_list[0].get("description", "") or "") + + if new_desc_len > existing_desc_len: + # Replace with the new relationship that has longer description + chunk_relationships[chunk_id][rel_key] = list(rel_list) + # Otherwise keep existing version + + except Exception as e: + status_message = ( + f"Failed to parse cached extraction result for chunk {chunk_id}: {e}" + ) + logger.info(status_message) # Per requirement, change to info + if pipeline_status is not None and pipeline_status_lock is not None: + async with pipeline_status_lock: + pipeline_status["latest_message"] = status_message + pipeline_status["history_messages"].append(status_message) + continue + + # Get max async tasks limit from global_config for semaphore control + graph_max_async = global_config.get("llm_model_max_async", 4) * 2 + semaphore = asyncio.Semaphore(graph_max_async) + + # Counters for tracking progress + rebuilt_entities_count = 0 + rebuilt_relationships_count = 0 + failed_entities_count = 0 + failed_relationships_count = 0 + + async def _locked_rebuild_entity(entity_name, chunk_ids): + nonlocal rebuilt_entities_count, failed_entities_count + async with semaphore: + workspace = global_config.get("workspace", "") + namespace = f"{workspace}:GraphDB" if workspace else "GraphDB" + async with get_storage_keyed_lock( + [entity_name], namespace=namespace, enable_logging=False + ): + try: + await _rebuild_single_entity( + knowledge_graph_inst=knowledge_graph_inst, + entities_vdb=entities_vdb, + entity_name=entity_name, + chunk_ids=chunk_ids, + chunk_entities=chunk_entities, + llm_response_cache=llm_response_cache, + global_config=global_config, + entity_chunks_storage=entity_chunks_storage, + ) + rebuilt_entities_count += 1 + except Exception as e: + failed_entities_count += 1 + status_message = f"Failed to rebuild `{entity_name}`: {e}" + logger.info(status_message) # Per requirement, change to info + if pipeline_status is not None and pipeline_status_lock is not None: + async with pipeline_status_lock: + pipeline_status["latest_message"] = status_message + pipeline_status["history_messages"].append(status_message) + + async def _locked_rebuild_relationship(src, tgt, chunk_ids): + nonlocal rebuilt_relationships_count, failed_relationships_count + async with semaphore: + workspace = global_config.get("workspace", "") + namespace = f"{workspace}:GraphDB" if workspace else "GraphDB" + # Sort src and tgt to ensure order-independent lock key generation + sorted_key_parts = sorted([src, tgt]) + async with get_storage_keyed_lock( + sorted_key_parts, + namespace=namespace, + enable_logging=False, + ): + try: + await _rebuild_single_relationship( + knowledge_graph_inst=knowledge_graph_inst, + relationships_vdb=relationships_vdb, + entities_vdb=entities_vdb, + src=src, + tgt=tgt, + chunk_ids=chunk_ids, + chunk_relationships=chunk_relationships, + llm_response_cache=llm_response_cache, + global_config=global_config, + relation_chunks_storage=relation_chunks_storage, + entity_chunks_storage=entity_chunks_storage, + pipeline_status=pipeline_status, + pipeline_status_lock=pipeline_status_lock, + ) + rebuilt_relationships_count += 1 + except Exception as e: + failed_relationships_count += 1 + status_message = f"Failed to rebuild `{src}`~`{tgt}`: {e}" + logger.info(status_message) # Per requirement, change to info + if pipeline_status is not None and pipeline_status_lock is not None: + async with pipeline_status_lock: + pipeline_status["latest_message"] = status_message + pipeline_status["history_messages"].append(status_message) + + # Create tasks for parallel processing + tasks = [] + + # Add entity rebuilding tasks + for entity_name, chunk_ids in entities_to_rebuild.items(): + task = asyncio.create_task(_locked_rebuild_entity(entity_name, chunk_ids)) + tasks.append(task) + + # Add relationship rebuilding tasks + for (src, tgt), chunk_ids in relationships_to_rebuild.items(): + task = asyncio.create_task(_locked_rebuild_relationship(src, tgt, chunk_ids)) + tasks.append(task) + + # Log parallel processing start + status_message = f"Starting parallel rebuild of {len(entities_to_rebuild)} entities and {len(relationships_to_rebuild)} relationships (async: {graph_max_async})" + logger.info(status_message) + if pipeline_status is not None and pipeline_status_lock is not None: + async with pipeline_status_lock: + pipeline_status["latest_message"] = status_message + pipeline_status["history_messages"].append(status_message) + + # Execute all tasks in parallel with semaphore control and early failure detection + done, pending = await asyncio.wait(tasks, return_when=asyncio.FIRST_EXCEPTION) + + # Check if any task raised an exception and ensure all exceptions are retrieved + first_exception = None + + for task in done: + try: + exception = task.exception() + if exception is not None: + if first_exception is None: + first_exception = exception + else: + # Task completed successfully, retrieve result to mark as processed + task.result() + except Exception as e: + if first_exception is None: + first_exception = e + + # If any task failed, cancel all pending tasks and raise the first exception + if first_exception is not None: + # Cancel all pending tasks + for pending_task in pending: + pending_task.cancel() + + # Wait for cancellation to complete + if pending: + await asyncio.wait(pending) + + # Re-raise the first exception to notify the caller + raise first_exception + + # Final status report + status_message = f"KG rebuild completed: {rebuilt_entities_count} entities and {rebuilt_relationships_count} relationships rebuilt successfully." + if failed_entities_count > 0 or failed_relationships_count > 0: + status_message += f" Failed: {failed_entities_count} entities, {failed_relationships_count} relationships." + + logger.info(status_message) + if pipeline_status is not None and pipeline_status_lock is not None: + async with pipeline_status_lock: + pipeline_status["latest_message"] = status_message + pipeline_status["history_messages"].append(status_message) + + +async def _get_cached_extraction_results( + llm_response_cache: BaseKVStorage, + chunk_ids: set[str], + text_chunks_storage: BaseKVStorage, +) -> dict[str, list[str]]: + """Get cached extraction results for specific chunk IDs + + This function retrieves cached LLM extraction results for the given chunk IDs and returns + them sorted by creation time. The results are sorted at two levels: + 1. Individual extraction results within each chunk are sorted by create_time (earliest first) + 2. Chunks themselves are sorted by the create_time of their earliest extraction result + + Args: + llm_response_cache: LLM response cache storage + chunk_ids: Set of chunk IDs to get cached results for + text_chunks_storage: Text chunks storage for retrieving chunk data and LLM cache references + + Returns: + Dict mapping chunk_id -> list of extraction_result_text, where: + - Keys (chunk_ids) are ordered by the create_time of their first extraction result + - Values (extraction results) are ordered by create_time within each chunk + """ + cached_results = {} + + # Collect all LLM cache IDs from chunks + all_cache_ids = set() + + # Read from storage + chunk_data_list = await text_chunks_storage.get_by_ids(list(chunk_ids)) + for chunk_data in chunk_data_list: + if chunk_data and isinstance(chunk_data, dict): + llm_cache_list = chunk_data.get("llm_cache_list", []) + if llm_cache_list: + all_cache_ids.update(llm_cache_list) + else: + logger.warning(f"Chunk data is invalid or None: {chunk_data}") + + if not all_cache_ids: + logger.warning(f"No LLM cache IDs found for {len(chunk_ids)} chunk IDs") + return cached_results + + # Batch get LLM cache entries + cache_data_list = await llm_response_cache.get_by_ids(list(all_cache_ids)) + + # Process cache entries and group by chunk_id + valid_entries = 0 + for cache_entry in cache_data_list: + if ( + cache_entry is not None + and isinstance(cache_entry, dict) + and cache_entry.get("cache_type") == "extract" + and cache_entry.get("chunk_id") in chunk_ids + ): + chunk_id = cache_entry["chunk_id"] + extraction_result = cache_entry["return"] + create_time = cache_entry.get( + "create_time", 0 + ) # Get creation time, default to 0 + valid_entries += 1 + + # Support multiple LLM caches per chunk + if chunk_id not in cached_results: + cached_results[chunk_id] = [] + # Store tuple with extraction result and creation time for sorting + cached_results[chunk_id].append((extraction_result, create_time)) + + # Sort extraction results by create_time for each chunk and collect earliest times + chunk_earliest_times = {} + for chunk_id in cached_results: + # Sort by create_time (x[1]), then extract only extraction_result (x[0]) + cached_results[chunk_id].sort(key=lambda x: x[1]) + # Store the earliest create_time for this chunk (first item after sorting) + chunk_earliest_times[chunk_id] = cached_results[chunk_id][0][1] + + # Sort cached_results by the earliest create_time of each chunk + sorted_chunk_ids = sorted( + chunk_earliest_times.keys(), key=lambda chunk_id: chunk_earliest_times[chunk_id] + ) + + # Rebuild cached_results in sorted order + sorted_cached_results = {} + for chunk_id in sorted_chunk_ids: + sorted_cached_results[chunk_id] = cached_results[chunk_id] + + logger.info( + f"Found {valid_entries} valid cache entries, {len(sorted_cached_results)} chunks with results" + ) + return sorted_cached_results # each item: list(extraction_result, create_time) + + +async def _process_extraction_result( + result: str, + chunk_key: str, + timestamp: int, + file_path: str = "unknown_source", + tuple_delimiter: str = "<|#|>", + completion_delimiter: str = "<|COMPLETE|>", +) -> tuple[dict, dict]: + """Process a single extraction result (either initial or gleaning) + Args: + result (str): The extraction result to process + chunk_key (str): The chunk key for source tracking + file_path (str): The file path for citation + tuple_delimiter (str): Delimiter for tuple fields + record_delimiter (str): Delimiter for records + completion_delimiter (str): Delimiter for completion + Returns: + tuple: (nodes_dict, edges_dict) containing the extracted entities and relationships + """ + maybe_nodes = defaultdict(list) + maybe_edges = defaultdict(list) + + if completion_delimiter not in result: + logger.warning( + f"{chunk_key}: Complete delimiter can not be found in extraction result" + ) + + # Split LLL output result to records by "\n" + records = split_string_by_multi_markers( + result, + ["\n", completion_delimiter, completion_delimiter.lower()], + ) + + # Fix LLM output format error which use tuple_delimiter to seperate record instead of "\n" + fixed_records = [] + for record in records: + record = record.strip() + if record is None: + continue + entity_records = split_string_by_multi_markers( + record, [f"{tuple_delimiter}entity{tuple_delimiter}"] + ) + for entity_record in entity_records: + if not entity_record.startswith("entity") and not entity_record.startswith( + "relation" + ): + entity_record = f"entity<|{entity_record}" + entity_relation_records = split_string_by_multi_markers( + # treat "relationship" and "relation" interchangeable + entity_record, + [ + f"{tuple_delimiter}relationship{tuple_delimiter}", + f"{tuple_delimiter}relation{tuple_delimiter}", + ], + ) + for entity_relation_record in entity_relation_records: + if not entity_relation_record.startswith( + "entity" + ) and not entity_relation_record.startswith("relation"): + entity_relation_record = ( + f"relation{tuple_delimiter}{entity_relation_record}" + ) + fixed_records = fixed_records + [entity_relation_record] + + if len(fixed_records) != len(records): + logger.warning( + f"{chunk_key}: LLM output format error; find LLM use {tuple_delimiter} as record seperators instead new-line" + ) + + for record in fixed_records: + record = record.strip() + if record is None: + continue + + # Fix various forms of tuple_delimiter corruption from the LLM output using the dedicated function + delimiter_core = tuple_delimiter[2:-2] # Extract "#" from "<|#|>" + record = fix_tuple_delimiter_corruption(record, delimiter_core, tuple_delimiter) + if delimiter_core != delimiter_core.lower(): + # change delimiter_core to lower case, and fix again + delimiter_core = delimiter_core.lower() + record = fix_tuple_delimiter_corruption( + record, delimiter_core, tuple_delimiter + ) + + record_attributes = split_string_by_multi_markers(record, [tuple_delimiter]) + + # Try to parse as entity + entity_data = await _handle_single_entity_extraction( + record_attributes, chunk_key, timestamp, file_path + ) + if entity_data is not None: + truncated_name = _truncate_entity_identifier( + entity_data["entity_name"], + DEFAULT_ENTITY_NAME_MAX_LENGTH, + chunk_key, + "Entity name", + ) + entity_data["entity_name"] = truncated_name + maybe_nodes[truncated_name].append(entity_data) + continue + + # Try to parse as relationship + relationship_data = await _handle_single_relationship_extraction( + record_attributes, chunk_key, timestamp, file_path + ) + if relationship_data is not None: + truncated_source = _truncate_entity_identifier( + relationship_data["src_id"], + DEFAULT_ENTITY_NAME_MAX_LENGTH, + chunk_key, + "Relation entity", + ) + truncated_target = _truncate_entity_identifier( + relationship_data["tgt_id"], + DEFAULT_ENTITY_NAME_MAX_LENGTH, + chunk_key, + "Relation entity", + ) + relationship_data["src_id"] = truncated_source + relationship_data["tgt_id"] = truncated_target + maybe_edges[(truncated_source, truncated_target)].append(relationship_data) + + return dict(maybe_nodes), dict(maybe_edges) + + +async def _rebuild_from_extraction_result( + text_chunks_storage: BaseKVStorage, + extraction_result: str, + chunk_id: str, + timestamp: int, +) -> tuple[dict, dict]: + """Parse cached extraction result using the same logic as extract_entities + + Args: + text_chunks_storage: Text chunks storage to get chunk data + extraction_result: The cached LLM extraction result + chunk_id: The chunk ID for source tracking + + Returns: + Tuple of (entities_dict, relationships_dict) + """ + + # Get chunk data for file_path from storage + chunk_data = await text_chunks_storage.get_by_id(chunk_id) + file_path = ( + chunk_data.get("file_path", "unknown_source") + if chunk_data + else "unknown_source" + ) + + # Call the shared processing function + return await _process_extraction_result( + extraction_result, + chunk_id, + timestamp, + file_path, + tuple_delimiter=PROMPTS["DEFAULT_TUPLE_DELIMITER"], + completion_delimiter=PROMPTS["DEFAULT_COMPLETION_DELIMITER"], + ) + + +async def _rebuild_single_entity( + knowledge_graph_inst: BaseGraphStorage, + entities_vdb: BaseVectorStorage, + entity_name: str, + chunk_ids: list[str], + chunk_entities: dict, + llm_response_cache: BaseKVStorage, + global_config: dict[str, str], + entity_chunks_storage: BaseKVStorage | None = None, + pipeline_status: dict | None = None, + pipeline_status_lock=None, +) -> None: + """Rebuild a single entity from cached extraction results""" + + # Get current entity data + current_entity = await knowledge_graph_inst.get_node(entity_name) + if not current_entity: + return + + # Helper function to update entity in both graph and vector storage + async def _update_entity_storage( + final_description: str, + entity_type: str, + file_paths: list[str], + source_chunk_ids: list[str], + truncation_info: str = "", + ): + try: + # Update entity in graph storage (critical path) + updated_entity_data = { + **current_entity, + "description": final_description, + "entity_type": entity_type, + "source_id": GRAPH_FIELD_SEP.join(source_chunk_ids), + "file_path": GRAPH_FIELD_SEP.join(file_paths) + if file_paths + else current_entity.get("file_path", "unknown_source"), + "created_at": int(time.time()), + "truncate": truncation_info, + } + await knowledge_graph_inst.upsert_node(entity_name, updated_entity_data) + + # Update entity in vector database (equally critical) + entity_vdb_id = compute_mdhash_id(entity_name, prefix="ent-") + entity_content = f"{entity_name}\n{final_description}" + + vdb_data = { + entity_vdb_id: { + "content": entity_content, + "entity_name": entity_name, + "source_id": updated_entity_data["source_id"], + "description": final_description, + "entity_type": entity_type, + "file_path": updated_entity_data["file_path"], + } + } + + # Use safe operation wrapper - VDB failure must throw exception + await safe_vdb_operation_with_exception( + operation=lambda: entities_vdb.upsert(vdb_data), + operation_name="rebuild_entity_upsert", + entity_name=entity_name, + max_retries=3, + retry_delay=0.1, + ) + + except Exception as e: + error_msg = f"Failed to update entity storage for `{entity_name}`: {e}" + logger.error(error_msg) + raise # Re-raise exception + + # normalized_chunk_ids = merge_source_ids([], chunk_ids) + normalized_chunk_ids = chunk_ids + + if entity_chunks_storage is not None and normalized_chunk_ids: + await entity_chunks_storage.upsert( + { + entity_name: { + "chunk_ids": normalized_chunk_ids, + "count": len(normalized_chunk_ids), + } + } + ) + + limit_method = ( + global_config.get("source_ids_limit_method") or SOURCE_IDS_LIMIT_METHOD_KEEP + ) + + limited_chunk_ids = apply_source_ids_limit( + normalized_chunk_ids, + global_config["max_source_ids_per_entity"], + limit_method, + identifier=f"`{entity_name}`", + ) + + # Collect all entity data from relevant (limited) chunks + all_entity_data = [] + for chunk_id in limited_chunk_ids: + if chunk_id in chunk_entities and entity_name in chunk_entities[chunk_id]: + all_entity_data.extend(chunk_entities[chunk_id][entity_name]) + + if not all_entity_data: + logger.warning( + f"No entity data found for `{entity_name}`, trying to rebuild from relationships" + ) + + # Get all edges connected to this entity + edges = await knowledge_graph_inst.get_node_edges(entity_name) + if not edges: + logger.warning(f"No relations attached to entity `{entity_name}`") + return + + # Collect relationship data to extract entity information + relationship_descriptions = [] + file_paths = set() + + # Get edge data for all connected relationships + for src_id, tgt_id in edges: + edge_data = await knowledge_graph_inst.get_edge(src_id, tgt_id) + if edge_data: + if edge_data.get("description"): + relationship_descriptions.append(edge_data["description"]) + + if edge_data.get("file_path"): + edge_file_paths = edge_data["file_path"].split(GRAPH_FIELD_SEP) + file_paths.update(edge_file_paths) + + # deduplicate descriptions + description_list = list(dict.fromkeys(relationship_descriptions)) + + # Generate final description from relationships or fallback to current + if description_list: + final_description, _ = await _handle_entity_relation_summary( + "Entity", + entity_name, + description_list, + GRAPH_FIELD_SEP, + global_config, + llm_response_cache=llm_response_cache, + ) + else: + final_description = current_entity.get("description", "") + + entity_type = current_entity.get("entity_type", "UNKNOWN") + await _update_entity_storage( + final_description, + entity_type, + file_paths, + limited_chunk_ids, + ) + return + + # Process cached entity data + descriptions = [] + entity_types = [] + file_paths_list = [] + seen_paths = set() + + for entity_data in all_entity_data: + if entity_data.get("description"): + descriptions.append(entity_data["description"]) + if entity_data.get("entity_type"): + entity_types.append(entity_data["entity_type"]) + if entity_data.get("file_path"): + file_path = entity_data["file_path"] + if file_path and file_path not in seen_paths: + file_paths_list.append(file_path) + seen_paths.add(file_path) + + # Apply MAX_FILE_PATHS limit + max_file_paths = global_config.get("max_file_paths") + file_path_placeholder = global_config.get( + "file_path_more_placeholder", DEFAULT_FILE_PATH_MORE_PLACEHOLDER + ) + limit_method = global_config.get("source_ids_limit_method") + + original_count = len(file_paths_list) + if original_count > max_file_paths: + if limit_method == SOURCE_IDS_LIMIT_METHOD_FIFO: + # FIFO: keep tail (newest), discard head + file_paths_list = file_paths_list[-max_file_paths:] + else: + # KEEP: keep head (earliest), discard tail + file_paths_list = file_paths_list[:max_file_paths] + + file_paths_list.append( + f"...{file_path_placeholder}...({limit_method} {max_file_paths}/{original_count})" + ) + logger.info( + f"Limited `{entity_name}`: file_path {original_count} -> {max_file_paths} ({limit_method})" + ) + + # Remove duplicates while preserving order + description_list = list(dict.fromkeys(descriptions)) + entity_types = list(dict.fromkeys(entity_types)) + + # Get most common entity type + entity_type = ( + max(set(entity_types), key=entity_types.count) + if entity_types + else current_entity.get("entity_type", "UNKNOWN") + ) + + # Generate final description from entities or fallback to current + if description_list: + final_description, _ = await _handle_entity_relation_summary( + "Entity", + entity_name, + description_list, + GRAPH_FIELD_SEP, + global_config, + llm_response_cache=llm_response_cache, + ) + else: + final_description = current_entity.get("description", "") + + if len(limited_chunk_ids) < len(normalized_chunk_ids): + truncation_info = ( + f"{limit_method} {len(limited_chunk_ids)}/{len(normalized_chunk_ids)}" + ) + else: + truncation_info = "" + + await _update_entity_storage( + final_description, + entity_type, + file_paths_list, + limited_chunk_ids, + truncation_info, + ) + + # Log rebuild completion with truncation info + status_message = f"Rebuild `{entity_name}` from {len(chunk_ids)} chunks" + if truncation_info: + status_message += f" ({truncation_info})" + logger.info(status_message) + # Update pipeline status + if pipeline_status is not None and pipeline_status_lock is not None: + async with pipeline_status_lock: + pipeline_status["latest_message"] = status_message + pipeline_status["history_messages"].append(status_message) + + +async def _rebuild_single_relationship( + knowledge_graph_inst: BaseGraphStorage, + relationships_vdb: BaseVectorStorage, + entities_vdb: BaseVectorStorage, + src: str, + tgt: str, + chunk_ids: list[str], + chunk_relationships: dict, + llm_response_cache: BaseKVStorage, + global_config: dict[str, str], + relation_chunks_storage: BaseKVStorage | None = None, + entity_chunks_storage: BaseKVStorage | None = None, + pipeline_status: dict | None = None, + pipeline_status_lock=None, +) -> None: + """Rebuild a single relationship from cached extraction results + + Note: This function assumes the caller has already acquired the appropriate + keyed lock for the relationship pair to ensure thread safety. + """ + + # Get current relationship data + current_relationship = await knowledge_graph_inst.get_edge(src, tgt) + if not current_relationship: + return + + # normalized_chunk_ids = merge_source_ids([], chunk_ids) + normalized_chunk_ids = chunk_ids + + if relation_chunks_storage is not None and normalized_chunk_ids: + storage_key = make_relation_chunk_key(src, tgt) + await relation_chunks_storage.upsert( + { + storage_key: { + "chunk_ids": normalized_chunk_ids, + "count": len(normalized_chunk_ids), + } + } + ) + + limit_method = ( + global_config.get("source_ids_limit_method") or SOURCE_IDS_LIMIT_METHOD_KEEP + ) + limited_chunk_ids = apply_source_ids_limit( + normalized_chunk_ids, + global_config["max_source_ids_per_relation"], + limit_method, + identifier=f"`{src}`~`{tgt}`", + ) + + # Collect all relationship data from relevant chunks + all_relationship_data = [] + for chunk_id in limited_chunk_ids: + if chunk_id in chunk_relationships: + # Check both (src, tgt) and (tgt, src) since relationships can be bidirectional + for edge_key in [(src, tgt), (tgt, src)]: + if edge_key in chunk_relationships[chunk_id]: + all_relationship_data.extend( + chunk_relationships[chunk_id][edge_key] + ) + + if not all_relationship_data: + logger.warning(f"No relation data found for `{src}-{tgt}`") + return + + # Merge descriptions and keywords + descriptions = [] + keywords = [] + weights = [] + file_paths_list = [] + seen_paths = set() + + for rel_data in all_relationship_data: + if rel_data.get("description"): + descriptions.append(rel_data["description"]) + if rel_data.get("keywords"): + keywords.append(rel_data["keywords"]) + if rel_data.get("weight"): + weights.append(rel_data["weight"]) + if rel_data.get("file_path"): + file_path = rel_data["file_path"] + if file_path and file_path not in seen_paths: + file_paths_list.append(file_path) + seen_paths.add(file_path) + + # Apply count limit + max_file_paths = global_config.get("max_file_paths") + file_path_placeholder = global_config.get( + "file_path_more_placeholder", DEFAULT_FILE_PATH_MORE_PLACEHOLDER + ) + limit_method = global_config.get("source_ids_limit_method") + + original_count = len(file_paths_list) + if original_count > max_file_paths: + if limit_method == SOURCE_IDS_LIMIT_METHOD_FIFO: + # FIFO: keep tail (newest), discard head + file_paths_list = file_paths_list[-max_file_paths:] + else: + # KEEP: keep head (earliest), discard tail + file_paths_list = file_paths_list[:max_file_paths] + + file_paths_list.append( + f"...{file_path_placeholder}...({limit_method} {max_file_paths}/{original_count})" + ) + logger.info( + f"Limited `{src}`~`{tgt}`: file_path {original_count} -> {max_file_paths} ({limit_method})" + ) + + # Remove duplicates while preserving order + description_list = list(dict.fromkeys(descriptions)) + keywords = list(dict.fromkeys(keywords)) + + combined_keywords = ( + ", ".join(set(keywords)) + if keywords + else current_relationship.get("keywords", "") + ) + + weight = sum(weights) if weights else current_relationship.get("weight", 1.0) + + # Generate final description from relations or fallback to current + if description_list: + final_description, _ = await _handle_entity_relation_summary( + "Relation", + f"{src}-{tgt}", + description_list, + GRAPH_FIELD_SEP, + global_config, + llm_response_cache=llm_response_cache, + ) + else: + # fallback to keep current(unchanged) + final_description = current_relationship.get("description", "") + + if len(limited_chunk_ids) < len(normalized_chunk_ids): + truncation_info = ( + f"{limit_method} {len(limited_chunk_ids)}/{len(normalized_chunk_ids)}" + ) + else: + truncation_info = "" + + # Update relationship in graph storage + updated_relationship_data = { + **current_relationship, + "description": final_description + if final_description + else current_relationship.get("description", ""), + "keywords": combined_keywords, + "weight": weight, + "source_id": GRAPH_FIELD_SEP.join(limited_chunk_ids), + "file_path": GRAPH_FIELD_SEP.join([fp for fp in file_paths_list if fp]) + if file_paths_list + else current_relationship.get("file_path", "unknown_source"), + "truncate": truncation_info, + } + + # Ensure both endpoint nodes exist before writing the edge back + # (certain storage backends require pre-existing nodes). + node_description = ( + updated_relationship_data["description"] + if updated_relationship_data.get("description") + else current_relationship.get("description", "") + ) + node_source_id = updated_relationship_data.get("source_id", "") + node_file_path = updated_relationship_data.get("file_path", "unknown_source") + + for node_id in {src, tgt}: + if not (await knowledge_graph_inst.has_node(node_id)): + node_created_at = int(time.time()) + node_data = { + "entity_id": node_id, + "source_id": node_source_id, + "description": node_description, + "entity_type": "UNKNOWN", + "file_path": node_file_path, + "created_at": node_created_at, + "truncate": "", + } + await knowledge_graph_inst.upsert_node(node_id, node_data=node_data) + + # Update entity_chunks_storage for the newly created entity + if entity_chunks_storage is not None and limited_chunk_ids: + await entity_chunks_storage.upsert( + { + node_id: { + "chunk_ids": limited_chunk_ids, + "count": len(limited_chunk_ids), + } + } + ) + + # Update entity_vdb for the newly created entity + if entities_vdb is not None: + entity_vdb_id = compute_mdhash_id(node_id, prefix="ent-") + entity_content = f"{node_id}\n{node_description}" + vdb_data = { + entity_vdb_id: { + "content": entity_content, + "entity_name": node_id, + "source_id": node_source_id, + "entity_type": "UNKNOWN", + "file_path": node_file_path, + } + } + await safe_vdb_operation_with_exception( + operation=lambda payload=vdb_data: entities_vdb.upsert(payload), + operation_name="rebuild_added_entity_upsert", + entity_name=node_id, + max_retries=3, + retry_delay=0.1, + ) + + await knowledge_graph_inst.upsert_edge(src, tgt, updated_relationship_data) + + # Update relationship in vector database + # Sort src and tgt to ensure consistent ordering (smaller string first) + if src > tgt: + src, tgt = tgt, src + try: + rel_vdb_id = compute_mdhash_id(src + tgt, prefix="rel-") + rel_vdb_id_reverse = compute_mdhash_id(tgt + src, prefix="rel-") + + # Delete old vector records first (both directions to be safe) + try: + await relationships_vdb.delete([rel_vdb_id, rel_vdb_id_reverse]) + except Exception as e: + logger.debug( + f"Could not delete old relationship vector records {rel_vdb_id}, {rel_vdb_id_reverse}: {e}" + ) + + # Insert new vector record + rel_content = f"{combined_keywords}\t{src}\n{tgt}\n{final_description}" + vdb_data = { + rel_vdb_id: { + "src_id": src, + "tgt_id": tgt, + "source_id": updated_relationship_data["source_id"], + "content": rel_content, + "keywords": combined_keywords, + "description": final_description, + "weight": weight, + "file_path": updated_relationship_data["file_path"], + } + } + + # Use safe operation wrapper - VDB failure must throw exception + await safe_vdb_operation_with_exception( + operation=lambda: relationships_vdb.upsert(vdb_data), + operation_name="rebuild_relationship_upsert", + entity_name=f"{src}-{tgt}", + max_retries=3, + retry_delay=0.2, + ) + + except Exception as e: + error_msg = f"Failed to rebuild relationship storage for `{src}-{tgt}`: {e}" + logger.error(error_msg) + raise # Re-raise exception + + # Log rebuild completion with truncation info + status_message = f"Rebuild `{src}`~`{tgt}` from {len(chunk_ids)} chunks" + if truncation_info: + status_message += f" ({truncation_info})" + # Add truncation info from apply_source_ids_limit if truncation occurred + if len(limited_chunk_ids) < len(normalized_chunk_ids): + truncation_info = ( + f" ({limit_method}:{len(limited_chunk_ids)}/{len(normalized_chunk_ids)})" + ) + status_message += truncation_info + + logger.info(status_message) + + # Update pipeline status + if pipeline_status is not None and pipeline_status_lock is not None: + async with pipeline_status_lock: + pipeline_status["latest_message"] = status_message + pipeline_status["history_messages"].append(status_message) + + +async def _merge_nodes_then_upsert( + entity_name: str, + nodes_data: list[dict], + knowledge_graph_inst: BaseGraphStorage, + entity_vdb: BaseVectorStorage | None, + global_config: dict, + pipeline_status: dict = None, + pipeline_status_lock=None, + llm_response_cache: BaseKVStorage | None = None, + entity_chunks_storage: BaseKVStorage | None = None, +): + """Get existing nodes from knowledge graph use name,if exists, merge data, else create, then upsert.""" + already_entity_types = [] + already_source_ids = [] + already_description = [] + already_file_paths = [] + + # 1. Get existing node data from knowledge graph + already_node = await knowledge_graph_inst.get_node(entity_name) + if already_node: + already_entity_types.append(already_node["entity_type"]) + already_source_ids.extend(already_node["source_id"].split(GRAPH_FIELD_SEP)) + already_file_paths.extend(already_node["file_path"].split(GRAPH_FIELD_SEP)) + already_description.extend(already_node["description"].split(GRAPH_FIELD_SEP)) + + new_source_ids = [dp["source_id"] for dp in nodes_data if dp.get("source_id")] + + existing_full_source_ids = [] + if entity_chunks_storage is not None: + stored_chunks = await entity_chunks_storage.get_by_id(entity_name) + if stored_chunks and isinstance(stored_chunks, dict): + existing_full_source_ids = [ + chunk_id for chunk_id in stored_chunks.get("chunk_ids", []) if chunk_id + ] + + if not existing_full_source_ids: + existing_full_source_ids = [ + chunk_id for chunk_id in already_source_ids if chunk_id + ] + + # 2. Merging new source ids with existing ones + full_source_ids = merge_source_ids(existing_full_source_ids, new_source_ids) + + if entity_chunks_storage is not None and full_source_ids: + await entity_chunks_storage.upsert( + { + entity_name: { + "chunk_ids": full_source_ids, + "count": len(full_source_ids), + } + } + ) + + # 3. Finalize source_id by applying source ids limit + limit_method = global_config.get("source_ids_limit_method") + max_source_limit = global_config.get("max_source_ids_per_entity") + source_ids = apply_source_ids_limit( + full_source_ids, + max_source_limit, + limit_method, + identifier=f"`{entity_name}`", + ) + + # 4. Only keep nodes not filter by apply_source_ids_limit if limit_method is KEEP + if limit_method == SOURCE_IDS_LIMIT_METHOD_KEEP: + allowed_source_ids = set(source_ids) + filtered_nodes = [] + for dp in nodes_data: + source_id = dp.get("source_id") + # Skip descriptions sourced from chunks dropped by the limitation cap + if ( + source_id + and source_id not in allowed_source_ids + and source_id not in existing_full_source_ids + ): + continue + filtered_nodes.append(dp) + nodes_data = filtered_nodes + else: # In FIFO mode, keep all nodes - truncation happens at source_ids level only + nodes_data = list(nodes_data) + + # 5. Check if we need to skip summary due to source_ids limit + if ( + limit_method == SOURCE_IDS_LIMIT_METHOD_KEEP + and len(existing_full_source_ids) >= max_source_limit + and not nodes_data + ): + if already_node: + logger.info( + f"Skipped `{entity_name}`: KEEP old chunks {already_source_ids}/{len(full_source_ids)}" + ) + existing_node_data = dict(already_node) + return existing_node_data + else: + logger.error(f"Internal Error: already_node missing for `{entity_name}`") + raise ValueError( + f"Internal Error: already_node missing for `{entity_name}`" + ) + + # 6.1 Finalize source_id + source_id = GRAPH_FIELD_SEP.join(source_ids) + + # 6.2 Finalize entity type by highest count + entity_type = sorted( + Counter( + [dp["entity_type"] for dp in nodes_data] + already_entity_types + ).items(), + key=lambda x: x[1], + reverse=True, + )[0][0] + + # 7. Deduplicate nodes by description, keeping first occurrence in the same document + unique_nodes = {} + for dp in nodes_data: + desc = dp.get("description") + if not desc: + continue + if desc not in unique_nodes: + unique_nodes[desc] = dp + + # Sort description by timestamp, then by description length when timestamps are the same + sorted_nodes = sorted( + unique_nodes.values(), + key=lambda x: (x.get("timestamp", 0), -len(x.get("description", ""))), + ) + sorted_descriptions = [dp["description"] for dp in sorted_nodes] + + # Combine already_description with sorted new sorted descriptions + description_list = already_description + sorted_descriptions + if not description_list: + logger.error(f"Entity {entity_name} has no description") + raise ValueError(f"Entity {entity_name} has no description") + + # Check for cancellation before LLM summary + if pipeline_status is not None and pipeline_status_lock is not None: + async with pipeline_status_lock: + if pipeline_status.get("cancellation_requested", False): + raise PipelineCancelledException("User cancelled during entity summary") + + # 8. Get summary description an LLM usage status + description, llm_was_used = await _handle_entity_relation_summary( + "Entity", + entity_name, + description_list, + GRAPH_FIELD_SEP, + global_config, + llm_response_cache, + ) + + # 9. Build file_path within MAX_FILE_PATHS + file_paths_list = [] + seen_paths = set() + has_placeholder = False # Indicating file_path has been truncated before + + max_file_paths = global_config.get("max_file_paths", DEFAULT_MAX_FILE_PATHS) + file_path_placeholder = global_config.get( + "file_path_more_placeholder", DEFAULT_FILE_PATH_MORE_PLACEHOLDER + ) + + # Collect from already_file_paths, excluding placeholder + for fp in already_file_paths: + if fp and fp.startswith(f"...{file_path_placeholder}"): # Skip placeholders + has_placeholder = True + continue + if fp and fp not in seen_paths: + file_paths_list.append(fp) + seen_paths.add(fp) + + # Collect from new data + for dp in nodes_data: + file_path_item = dp.get("file_path") + if file_path_item and file_path_item not in seen_paths: + file_paths_list.append(file_path_item) + seen_paths.add(file_path_item) + + # Apply count limit + if len(file_paths_list) > max_file_paths: + limit_method = global_config.get( + "source_ids_limit_method", SOURCE_IDS_LIMIT_METHOD_KEEP + ) + file_path_placeholder = global_config.get( + "file_path_more_placeholder", DEFAULT_FILE_PATH_MORE_PLACEHOLDER + ) + # Add + sign to indicate actual file count is higher + original_count_str = ( + f"{len(file_paths_list)}+" if has_placeholder else str(len(file_paths_list)) + ) + + if limit_method == SOURCE_IDS_LIMIT_METHOD_FIFO: + # FIFO: keep tail (newest), discard head + file_paths_list = file_paths_list[-max_file_paths:] + file_paths_list.append(f"...{file_path_placeholder}...(FIFO)") + else: + # KEEP: keep head (earliest), discard tail + file_paths_list = file_paths_list[:max_file_paths] + file_paths_list.append(f"...{file_path_placeholder}...(KEEP Old)") + + logger.info( + f"Limited `{entity_name}`: file_path {original_count_str} -> {max_file_paths} ({limit_method})" + ) + # Finalize file_path + file_path = GRAPH_FIELD_SEP.join(file_paths_list) + + # 10.Log based on actual LLM usage + num_fragment = len(description_list) + already_fragment = len(already_description) + if llm_was_used: + status_message = f"LLMmrg: `{entity_name}` | {already_fragment}+{num_fragment - already_fragment}" + else: + status_message = f"Merged: `{entity_name}` | {already_fragment}+{num_fragment - already_fragment}" + + truncation_info = truncation_info_log = "" + if len(source_ids) < len(full_source_ids): + # Add truncation info from apply_source_ids_limit if truncation occurred + truncation_info_log = f"{limit_method} {len(source_ids)}/{len(full_source_ids)}" + if limit_method == SOURCE_IDS_LIMIT_METHOD_FIFO: + truncation_info = truncation_info_log + else: + truncation_info = "KEEP Old" + + deduplicated_num = already_fragment + len(nodes_data) - num_fragment + dd_message = "" + if deduplicated_num > 0: + # Duplicated description detected across multiple trucks for the same entity + dd_message = f"dd {deduplicated_num}" + + if dd_message or truncation_info_log: + status_message += ( + f" ({', '.join(filter(None, [truncation_info_log, dd_message]))})" + ) + + # Add message to pipeline satus when merge happens + if already_fragment > 0 or llm_was_used: + logger.info(status_message) + if pipeline_status is not None and pipeline_status_lock is not None: + async with pipeline_status_lock: + pipeline_status["latest_message"] = status_message + pipeline_status["history_messages"].append(status_message) + else: + logger.debug(status_message) + + # 11. Update both graph and vector db + node_data = dict( + entity_id=entity_name, + entity_type=entity_type, + description=description, + source_id=source_id, + file_path=file_path, + created_at=int(time.time()), + truncate=truncation_info, + ) + await knowledge_graph_inst.upsert_node( + entity_name, + node_data=node_data, + ) + node_data["entity_name"] = entity_name + if entity_vdb is not None: + entity_vdb_id = compute_mdhash_id(str(entity_name), prefix="ent-") + entity_content = f"{entity_name}\n{description}" + data_for_vdb = { + entity_vdb_id: { + "entity_name": entity_name, + "entity_type": entity_type, + "content": entity_content, + "source_id": source_id, + "file_path": file_path, + } + } + await safe_vdb_operation_with_exception( + operation=lambda payload=data_for_vdb: entity_vdb.upsert(payload), + operation_name="entity_upsert", + entity_name=entity_name, + max_retries=3, + retry_delay=0.1, + ) + return node_data + + +async def _merge_edges_then_upsert( + src_id: str, + tgt_id: str, + edges_data: list[dict], + knowledge_graph_inst: BaseGraphStorage, + relationships_vdb: BaseVectorStorage | None, + entity_vdb: BaseVectorStorage | None, + global_config: dict, + pipeline_status: dict = None, + pipeline_status_lock=None, + llm_response_cache: BaseKVStorage | None = None, + added_entities: list = None, # New parameter to track entities added during edge processing + relation_chunks_storage: BaseKVStorage | None = None, + entity_chunks_storage: BaseKVStorage | None = None, +): + if src_id == tgt_id: + return None + + already_edge = None + already_weights = [] + already_source_ids = [] + already_description = [] + already_keywords = [] + already_file_paths = [] + + # 1. Get existing edge data from graph storage + if await knowledge_graph_inst.has_edge(src_id, tgt_id): + already_edge = await knowledge_graph_inst.get_edge(src_id, tgt_id) + # Handle the case where get_edge returns None or missing fields + if already_edge: + # Get weight with default 1.0 if missing + already_weights.append(already_edge.get("weight", 1.0)) + + # Get source_id with empty string default if missing or None + if already_edge.get("source_id") is not None: + already_source_ids.extend( + already_edge["source_id"].split(GRAPH_FIELD_SEP) + ) + + # Get file_path with empty string default if missing or None + if already_edge.get("file_path") is not None: + already_file_paths.extend( + already_edge["file_path"].split(GRAPH_FIELD_SEP) + ) + + # Get description with empty string default if missing or None + if already_edge.get("description") is not None: + already_description.extend( + already_edge["description"].split(GRAPH_FIELD_SEP) + ) + + # Get keywords with empty string default if missing or None + if already_edge.get("keywords") is not None: + already_keywords.extend( + split_string_by_multi_markers( + already_edge["keywords"], [GRAPH_FIELD_SEP] + ) + ) + + new_source_ids = [dp["source_id"] for dp in edges_data if dp.get("source_id")] + + storage_key = make_relation_chunk_key(src_id, tgt_id) + existing_full_source_ids = [] + if relation_chunks_storage is not None: + stored_chunks = await relation_chunks_storage.get_by_id(storage_key) + if stored_chunks and isinstance(stored_chunks, dict): + existing_full_source_ids = [ + chunk_id for chunk_id in stored_chunks.get("chunk_ids", []) if chunk_id + ] + + if not existing_full_source_ids: + existing_full_source_ids = [ + chunk_id for chunk_id in already_source_ids if chunk_id + ] + + # 2. Merge new source ids with existing ones + full_source_ids = merge_source_ids(existing_full_source_ids, new_source_ids) + + if relation_chunks_storage is not None and full_source_ids: + await relation_chunks_storage.upsert( + { + storage_key: { + "chunk_ids": full_source_ids, + "count": len(full_source_ids), + } + } + ) + + # 3. Finalize source_id by applying source ids limit + limit_method = global_config.get("source_ids_limit_method") + max_source_limit = global_config.get("max_source_ids_per_relation") + source_ids = apply_source_ids_limit( + full_source_ids, + max_source_limit, + limit_method, + identifier=f"`{src_id}`~`{tgt_id}`", + ) + limit_method = ( + global_config.get("source_ids_limit_method") or SOURCE_IDS_LIMIT_METHOD_KEEP + ) + + # 4. Only keep edges with source_id in the final source_ids list if in KEEP mode + if limit_method == SOURCE_IDS_LIMIT_METHOD_KEEP: + allowed_source_ids = set(source_ids) + filtered_edges = [] + for dp in edges_data: + source_id = dp.get("source_id") + # Skip relationship fragments sourced from chunks dropped by keep oldest cap + if ( + source_id + and source_id not in allowed_source_ids + and source_id not in existing_full_source_ids + ): + continue + filtered_edges.append(dp) + edges_data = filtered_edges + else: # In FIFO mode, keep all edges - truncation happens at source_ids level only + edges_data = list(edges_data) + + # 5. Check if we need to skip summary due to source_ids limit + if ( + limit_method == SOURCE_IDS_LIMIT_METHOD_KEEP + and len(existing_full_source_ids) >= max_source_limit + and not edges_data + ): + if already_edge: + logger.info( + f"Skipped `{src_id}`~`{tgt_id}`: KEEP old chunks {already_source_ids}/{len(full_source_ids)}" + ) + existing_edge_data = dict(already_edge) + return existing_edge_data + else: + logger.error( + f"Internal Error: already_node missing for `{src_id}`~`{tgt_id}`" + ) + raise ValueError( + f"Internal Error: already_node missing for `{src_id}`~`{tgt_id}`" + ) + + # 6.1 Finalize source_id + source_id = GRAPH_FIELD_SEP.join(source_ids) + + # 6.2 Finalize weight by summing new edges and existing weights + weight = sum([dp["weight"] for dp in edges_data] + already_weights) + + # 6.2 Finalize keywords by merging existing and new keywords + all_keywords = set() + # Process already_keywords (which are comma-separated) + for keyword_str in already_keywords: + if keyword_str: # Skip empty strings + all_keywords.update(k.strip() for k in keyword_str.split(",") if k.strip()) + # Process new keywords from edges_data + for edge in edges_data: + if edge.get("keywords"): + all_keywords.update( + k.strip() for k in edge["keywords"].split(",") if k.strip() + ) + # Join all unique keywords with commas + keywords = ",".join(sorted(all_keywords)) + + # 7. Deduplicate by description, keeping first occurrence in the same document + unique_edges = {} + for dp in edges_data: + description_value = dp.get("description") + if not description_value: + continue + if description_value not in unique_edges: + unique_edges[description_value] = dp + + # Sort description by timestamp, then by description length (largest to smallest) when timestamps are the same + sorted_edges = sorted( + unique_edges.values(), + key=lambda x: (x.get("timestamp", 0), -len(x.get("description", ""))), + ) + sorted_descriptions = [dp["description"] for dp in sorted_edges] + + # Combine already_description with sorted new descriptions + description_list = already_description + sorted_descriptions + if not description_list: + logger.error(f"Relation {src_id}~{tgt_id} has no description") + raise ValueError(f"Relation {src_id}~{tgt_id} has no description") + + # Check for cancellation before LLM summary + if pipeline_status is not None and pipeline_status_lock is not None: + async with pipeline_status_lock: + if pipeline_status.get("cancellation_requested", False): + raise PipelineCancelledException( + "User cancelled during relation summary" + ) + + # 8. Get summary description an LLM usage status + description, llm_was_used = await _handle_entity_relation_summary( + "Relation", + f"({src_id}, {tgt_id})", + description_list, + GRAPH_FIELD_SEP, + global_config, + llm_response_cache, + ) + + # 9. Build file_path within MAX_FILE_PATHS limit + file_paths_list = [] + seen_paths = set() + has_placeholder = False # Track if already_file_paths contains placeholder + + max_file_paths = global_config.get("max_file_paths", DEFAULT_MAX_FILE_PATHS) + file_path_placeholder = global_config.get( + "file_path_more_placeholder", DEFAULT_FILE_PATH_MORE_PLACEHOLDER + ) + + # Collect from already_file_paths, excluding placeholder + for fp in already_file_paths: + # Check if this is a placeholder record + if fp and fp.startswith(f"...{file_path_placeholder}"): # Skip placeholders + has_placeholder = True + continue + if fp and fp not in seen_paths: + file_paths_list.append(fp) + seen_paths.add(fp) + + # Collect from new data + for dp in edges_data: + file_path_item = dp.get("file_path") + if file_path_item and file_path_item not in seen_paths: + file_paths_list.append(file_path_item) + seen_paths.add(file_path_item) + + # Apply count limit + max_file_paths = global_config.get("max_file_paths") + + if len(file_paths_list) > max_file_paths: + limit_method = global_config.get( + "source_ids_limit_method", SOURCE_IDS_LIMIT_METHOD_KEEP + ) + file_path_placeholder = global_config.get( + "file_path_more_placeholder", DEFAULT_FILE_PATH_MORE_PLACEHOLDER + ) + + # Add + sign to indicate actual file count is higher + original_count_str = ( + f"{len(file_paths_list)}+" if has_placeholder else str(len(file_paths_list)) + ) + + if limit_method == SOURCE_IDS_LIMIT_METHOD_FIFO: + # FIFO: keep tail (newest), discard head + file_paths_list = file_paths_list[-max_file_paths:] + file_paths_list.append(f"...{file_path_placeholder}...(FIFO)") + else: + # KEEP: keep head (earliest), discard tail + file_paths_list = file_paths_list[:max_file_paths] + file_paths_list.append(f"...{file_path_placeholder}...(KEEP Old)") + + logger.info( + f"Limited `{src_id}`~`{tgt_id}`: file_path {original_count_str} -> {max_file_paths} ({limit_method})" + ) + # Finalize file_path + file_path = GRAPH_FIELD_SEP.join(file_paths_list) + + # 10. Log based on actual LLM usage + num_fragment = len(description_list) + already_fragment = len(already_description) + if llm_was_used: + status_message = f"LLMmrg: `{src_id}`~`{tgt_id}` | {already_fragment}+{num_fragment - already_fragment}" + else: + status_message = f"Merged: `{src_id}`~`{tgt_id}` | {already_fragment}+{num_fragment - already_fragment}" + + truncation_info = truncation_info_log = "" + if len(source_ids) < len(full_source_ids): + # Add truncation info from apply_source_ids_limit if truncation occurred + truncation_info_log = f"{limit_method} {len(source_ids)}/{len(full_source_ids)}" + if limit_method == SOURCE_IDS_LIMIT_METHOD_FIFO: + truncation_info = truncation_info_log + else: + truncation_info = "KEEP Old" + + deduplicated_num = already_fragment + len(edges_data) - num_fragment + dd_message = "" + if deduplicated_num > 0: + # Duplicated description detected across multiple trucks for the same entity + dd_message = f"dd {deduplicated_num}" + + if dd_message or truncation_info_log: + status_message += ( + f" ({', '.join(filter(None, [truncation_info_log, dd_message]))})" + ) + + # Add message to pipeline satus when merge happens + if already_fragment > 0 or llm_was_used: + logger.info(status_message) + if pipeline_status is not None and pipeline_status_lock is not None: + async with pipeline_status_lock: + pipeline_status["latest_message"] = status_message + pipeline_status["history_messages"].append(status_message) + else: + logger.debug(status_message) + + # 11. Update both graph and vector db + for need_insert_id in [src_id, tgt_id]: + # Optimization: Use get_node instead of has_node + get_node + existing_node = await knowledge_graph_inst.get_node(need_insert_id) + + if existing_node is None: + # Node doesn't exist - create new node + node_created_at = int(time.time()) + node_data = { + "entity_id": need_insert_id, + "source_id": source_id, + "description": description, + "entity_type": "UNKNOWN", + "file_path": file_path, + "created_at": node_created_at, + "truncate": "", + } + await knowledge_graph_inst.upsert_node(need_insert_id, node_data=node_data) + + # Update entity_chunks_storage for the newly created entity + if entity_chunks_storage is not None: + chunk_ids = [chunk_id for chunk_id in full_source_ids if chunk_id] + if chunk_ids: + await entity_chunks_storage.upsert( + { + need_insert_id: { + "chunk_ids": chunk_ids, + "count": len(chunk_ids), + } + } + ) + + if entity_vdb is not None: + entity_vdb_id = compute_mdhash_id(need_insert_id, prefix="ent-") + entity_content = f"{need_insert_id}\n{description}" + vdb_data = { + entity_vdb_id: { + "content": entity_content, + "entity_name": need_insert_id, + "source_id": source_id, + "entity_type": "UNKNOWN", + "file_path": file_path, + } + } + await safe_vdb_operation_with_exception( + operation=lambda payload=vdb_data: entity_vdb.upsert(payload), + operation_name="added_entity_upsert", + entity_name=need_insert_id, + max_retries=3, + retry_delay=0.1, + ) + + # Track entities added during edge processing + if added_entities is not None: + entity_data = { + "entity_name": need_insert_id, + "entity_type": "UNKNOWN", + "description": description, + "source_id": source_id, + "file_path": file_path, + "created_at": node_created_at, + } + added_entities.append(entity_data) + else: + # Node exists - update its source_ids by merging with new source_ids + updated = False # Track if any update occurred + + # 1. Get existing full source_ids from entity_chunks_storage + existing_full_source_ids = [] + if entity_chunks_storage is not None: + stored_chunks = await entity_chunks_storage.get_by_id(need_insert_id) + if stored_chunks and isinstance(stored_chunks, dict): + existing_full_source_ids = [ + chunk_id + for chunk_id in stored_chunks.get("chunk_ids", []) + if chunk_id + ] + + # If not in entity_chunks_storage, get from graph database + if not existing_full_source_ids: + if existing_node.get("source_id"): + existing_full_source_ids = existing_node["source_id"].split( + GRAPH_FIELD_SEP + ) + + # 2. Merge with new source_ids from this relationship + new_source_ids_from_relation = [ + chunk_id for chunk_id in source_ids if chunk_id + ] + merged_full_source_ids = merge_source_ids( + existing_full_source_ids, new_source_ids_from_relation + ) + + # 3. Save merged full list to entity_chunks_storage (conditional) + if ( + entity_chunks_storage is not None + and merged_full_source_ids != existing_full_source_ids + ): + updated = True + await entity_chunks_storage.upsert( + { + need_insert_id: { + "chunk_ids": merged_full_source_ids, + "count": len(merged_full_source_ids), + } + } + ) + + # 4. Apply source_ids limit for graph and vector db + limit_method = global_config.get( + "source_ids_limit_method", SOURCE_IDS_LIMIT_METHOD_KEEP + ) + max_source_limit = global_config.get("max_source_ids_per_entity") + limited_source_ids = apply_source_ids_limit( + merged_full_source_ids, + max_source_limit, + limit_method, + identifier=f"`{need_insert_id}`", + ) + + # 5. Update graph database and vector database with limited source_ids (conditional) + limited_source_id_str = GRAPH_FIELD_SEP.join(limited_source_ids) + + if limited_source_id_str != existing_node.get("source_id", ""): + updated = True + updated_node_data = { + **existing_node, + "source_id": limited_source_id_str, + } + await knowledge_graph_inst.upsert_node( + need_insert_id, node_data=updated_node_data + ) + + # Update vector database + if entity_vdb is not None: + entity_vdb_id = compute_mdhash_id(need_insert_id, prefix="ent-") + entity_content = ( + f"{need_insert_id}\n{existing_node.get('description', '')}" + ) + vdb_data = { + entity_vdb_id: { + "content": entity_content, + "entity_name": need_insert_id, + "source_id": limited_source_id_str, + "entity_type": existing_node.get("entity_type", "UNKNOWN"), + "file_path": existing_node.get( + "file_path", "unknown_source" + ), + } + } + await safe_vdb_operation_with_exception( + operation=lambda payload=vdb_data: entity_vdb.upsert(payload), + operation_name="existing_entity_update", + entity_name=need_insert_id, + max_retries=3, + retry_delay=0.1, + ) + + # 6. Log once at the end if any update occurred + if updated: + status_message = f"Chunks appended from relation: `{need_insert_id}`" + logger.info(status_message) + if pipeline_status is not None and pipeline_status_lock is not None: + async with pipeline_status_lock: + pipeline_status["latest_message"] = status_message + pipeline_status["history_messages"].append(status_message) + + edge_created_at = int(time.time()) + await knowledge_graph_inst.upsert_edge( + src_id, + tgt_id, + edge_data=dict( + weight=weight, + description=description, + keywords=keywords, + source_id=source_id, + file_path=file_path, + created_at=edge_created_at, + truncate=truncation_info, + ), + ) + + edge_data = dict( + src_id=src_id, + tgt_id=tgt_id, + description=description, + keywords=keywords, + source_id=source_id, + file_path=file_path, + created_at=edge_created_at, + truncate=truncation_info, + weight=weight, + ) + + # Sort src_id and tgt_id to ensure consistent ordering (smaller string first) + if src_id > tgt_id: + src_id, tgt_id = tgt_id, src_id + + if relationships_vdb is not None: + rel_vdb_id = compute_mdhash_id(src_id + tgt_id, prefix="rel-") + rel_vdb_id_reverse = compute_mdhash_id(tgt_id + src_id, prefix="rel-") + try: + await relationships_vdb.delete([rel_vdb_id, rel_vdb_id_reverse]) + except Exception as e: + logger.debug( + f"Could not delete old relationship vector records {rel_vdb_id}, {rel_vdb_id_reverse}: {e}" + ) + rel_content = f"{keywords}\t{src_id}\n{tgt_id}\n{description}" + vdb_data = { + rel_vdb_id: { + "src_id": src_id, + "tgt_id": tgt_id, + "source_id": source_id, + "content": rel_content, + "keywords": keywords, + "description": description, + "weight": weight, + "file_path": file_path, + } + } + await safe_vdb_operation_with_exception( + operation=lambda payload=vdb_data: relationships_vdb.upsert(payload), + operation_name="relationship_upsert", + entity_name=f"{src_id}-{tgt_id}", + max_retries=3, + retry_delay=0.2, + ) + + return edge_data + + +async def merge_nodes_and_edges( + chunk_results: list, + knowledge_graph_inst: BaseGraphStorage, + entity_vdb: BaseVectorStorage, + relationships_vdb: BaseVectorStorage, + global_config: dict[str, str], + full_entities_storage: BaseKVStorage = None, + full_relations_storage: BaseKVStorage = None, + doc_id: str = None, + pipeline_status: dict = None, + pipeline_status_lock=None, + llm_response_cache: BaseKVStorage | None = None, + entity_chunks_storage: BaseKVStorage | None = None, + relation_chunks_storage: BaseKVStorage | None = None, + current_file_number: int = 0, + total_files: int = 0, + file_path: str = "unknown_source", +) -> None: + """Two-phase merge: process all entities first, then all relationships + + This approach ensures data consistency by: + 1. Phase 1: Process all entities concurrently + 2. Phase 2: Process all relationships concurrently (may add missing entities) + 3. Phase 3: Update full_entities and full_relations storage with final results + + Args: + chunk_results: List of tuples (maybe_nodes, maybe_edges) containing extracted entities and relationships + knowledge_graph_inst: Knowledge graph storage + entity_vdb: Entity vector database + relationships_vdb: Relationship vector database + global_config: Global configuration + full_entities_storage: Storage for document entity lists + full_relations_storage: Storage for document relation lists + doc_id: Document ID for storage indexing + pipeline_status: Pipeline status dictionary + pipeline_status_lock: Lock for pipeline status + llm_response_cache: LLM response cache + entity_chunks_storage: Storage tracking full chunk lists per entity + relation_chunks_storage: Storage tracking full chunk lists per relation + current_file_number: Current file number for logging + total_files: Total files for logging + file_path: File path for logging + """ + + # Check for cancellation at the start of merge + if pipeline_status is not None and pipeline_status_lock is not None: + async with pipeline_status_lock: + if pipeline_status.get("cancellation_requested", False): + raise PipelineCancelledException("User cancelled during merge phase") + + # Collect all nodes and edges from all chunks + all_nodes = defaultdict(list) + all_edges = defaultdict(list) + + for maybe_nodes, maybe_edges in chunk_results: + # Collect nodes + for entity_name, entities in maybe_nodes.items(): + all_nodes[entity_name].extend(entities) + + # Collect edges with sorted keys for undirected graph + for edge_key, edges in maybe_edges.items(): + sorted_edge_key = tuple(sorted(edge_key)) + all_edges[sorted_edge_key].extend(edges) + + total_entities_count = len(all_nodes) + total_relations_count = len(all_edges) + + log_message = f"Merging stage {current_file_number}/{total_files}: {file_path}" + logger.info(log_message) + async with pipeline_status_lock: + pipeline_status["latest_message"] = log_message + pipeline_status["history_messages"].append(log_message) + + # Get max async tasks limit from global_config for semaphore control + graph_max_async = global_config.get("llm_model_max_async", 4) * 2 + semaphore = asyncio.Semaphore(graph_max_async) + + # ===== Phase 1: Process all entities concurrently ===== + log_message = f"Phase 1: Processing {total_entities_count} entities from {doc_id} (async: {graph_max_async})" + logger.info(log_message) + async with pipeline_status_lock: + pipeline_status["latest_message"] = log_message + pipeline_status["history_messages"].append(log_message) + + async def _locked_process_entity_name(entity_name, entities): + async with semaphore: + # Check for cancellation before processing entity + if pipeline_status is not None and pipeline_status_lock is not None: + async with pipeline_status_lock: + if pipeline_status.get("cancellation_requested", False): + raise PipelineCancelledException( + "User cancelled during entity merge" + ) + + workspace = global_config.get("workspace", "") + namespace = f"{workspace}:GraphDB" if workspace else "GraphDB" + async with get_storage_keyed_lock( + [entity_name], namespace=namespace, enable_logging=False + ): + try: + logger.debug(f"Processing entity {entity_name}") + entity_data = await _merge_nodes_then_upsert( + entity_name, + entities, + knowledge_graph_inst, + entity_vdb, + global_config, + pipeline_status, + pipeline_status_lock, + llm_response_cache, + entity_chunks_storage, + ) + + return entity_data + + except Exception as e: + error_msg = f"Error processing entity `{entity_name}`: {e}" + logger.error(error_msg) + + # Try to update pipeline status, but don't let status update failure affect main exception + try: + if ( + pipeline_status is not None + and pipeline_status_lock is not None + ): + async with pipeline_status_lock: + pipeline_status["latest_message"] = error_msg + pipeline_status["history_messages"].append(error_msg) + except Exception as status_error: + logger.error( + f"Failed to update pipeline status: {status_error}" + ) + + # Re-raise the original exception with a prefix + prefixed_exception = create_prefixed_exception( + e, f"`{entity_name}`" + ) + raise prefixed_exception from e + + # Create entity processing tasks + entity_tasks = [] + for entity_name, entities in all_nodes.items(): + task = asyncio.create_task(_locked_process_entity_name(entity_name, entities)) + entity_tasks.append(task) + + # Execute entity tasks with error handling + processed_entities = [] + if entity_tasks: + done, pending = await asyncio.wait( + entity_tasks, return_when=asyncio.FIRST_EXCEPTION + ) + + first_exception = None + processed_entities = [] + + for task in done: + try: + result = task.result() + except BaseException as e: + if first_exception is None: + first_exception = e + else: + processed_entities.append(result) + + if pending: + for task in pending: + task.cancel() + pending_results = await asyncio.gather(*pending, return_exceptions=True) + for result in pending_results: + if isinstance(result, BaseException): + if first_exception is None: + first_exception = result + else: + processed_entities.append(result) + + if first_exception is not None: + raise first_exception + + # ===== Phase 2: Process all relationships concurrently ===== + log_message = f"Phase 2: Processing {total_relations_count} relations from {doc_id} (async: {graph_max_async})" + logger.info(log_message) + async with pipeline_status_lock: + pipeline_status["latest_message"] = log_message + pipeline_status["history_messages"].append(log_message) + + async def _locked_process_edges(edge_key, edges): + async with semaphore: + # Check for cancellation before processing edges + if pipeline_status is not None and pipeline_status_lock is not None: + async with pipeline_status_lock: + if pipeline_status.get("cancellation_requested", False): + raise PipelineCancelledException( + "User cancelled during relation merge" + ) + + workspace = global_config.get("workspace", "") + namespace = f"{workspace}:GraphDB" if workspace else "GraphDB" + sorted_edge_key = sorted([edge_key[0], edge_key[1]]) + + async with get_storage_keyed_lock( + sorted_edge_key, + namespace=namespace, + enable_logging=False, + ): + try: + added_entities = [] # Track entities added during edge processing + + logger.debug(f"Processing relation {sorted_edge_key}") + edge_data = await _merge_edges_then_upsert( + edge_key[0], + edge_key[1], + edges, + knowledge_graph_inst, + relationships_vdb, + entity_vdb, + global_config, + pipeline_status, + pipeline_status_lock, + llm_response_cache, + added_entities, # Pass list to collect added entities + relation_chunks_storage, + entity_chunks_storage, # Add entity_chunks_storage parameter + ) + + if edge_data is None: + return None, [] + + return edge_data, added_entities + + except Exception as e: + error_msg = f"Error processing relation `{sorted_edge_key}`: {e}" + logger.error(error_msg) + + # Try to update pipeline status, but don't let status update failure affect main exception + try: + if ( + pipeline_status is not None + and pipeline_status_lock is not None + ): + async with pipeline_status_lock: + pipeline_status["latest_message"] = error_msg + pipeline_status["history_messages"].append(error_msg) + except Exception as status_error: + logger.error( + f"Failed to update pipeline status: {status_error}" + ) + + # Re-raise the original exception with a prefix + prefixed_exception = create_prefixed_exception( + e, f"{sorted_edge_key}" + ) + raise prefixed_exception from e + + # Create relationship processing tasks + edge_tasks = [] + for edge_key, edges in all_edges.items(): + task = asyncio.create_task(_locked_process_edges(edge_key, edges)) + edge_tasks.append(task) + + # Execute relationship tasks with error handling + processed_edges = [] + all_added_entities = [] + + if edge_tasks: + done, pending = await asyncio.wait( + edge_tasks, return_when=asyncio.FIRST_EXCEPTION + ) + + first_exception = None + + for task in done: + try: + edge_data, added_entities = task.result() + except BaseException as e: + if first_exception is None: + first_exception = e + else: + if edge_data is not None: + processed_edges.append(edge_data) + all_added_entities.extend(added_entities) + + if pending: + for task in pending: + task.cancel() + pending_results = await asyncio.gather(*pending, return_exceptions=True) + for result in pending_results: + if isinstance(result, BaseException): + if first_exception is None: + first_exception = result + else: + edge_data, added_entities = result + if edge_data is not None: + processed_edges.append(edge_data) + all_added_entities.extend(added_entities) + + if first_exception is not None: + raise first_exception + + # ===== Phase 3: Update full_entities and full_relations storage ===== + if full_entities_storage and full_relations_storage and doc_id: + try: + # Merge all entities: original entities + entities added during edge processing + final_entity_names = set() + + # Add original processed entities + for entity_data in processed_entities: + if entity_data and entity_data.get("entity_name"): + final_entity_names.add(entity_data["entity_name"]) + + # Add entities that were added during relationship processing + for added_entity in all_added_entities: + if added_entity and added_entity.get("entity_name"): + final_entity_names.add(added_entity["entity_name"]) + + # Collect all relation pairs + final_relation_pairs = set() + for edge_data in processed_edges: + if edge_data: + src_id = edge_data.get("src_id") + tgt_id = edge_data.get("tgt_id") + if src_id and tgt_id: + relation_pair = tuple(sorted([src_id, tgt_id])) + final_relation_pairs.add(relation_pair) + + log_message = f"Phase 3: Updating final {len(final_entity_names)}({len(processed_entities)}+{len(all_added_entities)}) entities and {len(final_relation_pairs)} relations from {doc_id}" + logger.info(log_message) + async with pipeline_status_lock: + pipeline_status["latest_message"] = log_message + pipeline_status["history_messages"].append(log_message) + + # Update storage + if final_entity_names: + await full_entities_storage.upsert( + { + doc_id: { + "entity_names": list(final_entity_names), + "count": len(final_entity_names), + } + } + ) + + if final_relation_pairs: + await full_relations_storage.upsert( + { + doc_id: { + "relation_pairs": [ + list(pair) for pair in final_relation_pairs + ], + "count": len(final_relation_pairs), + } + } + ) + + logger.debug( + f"Updated entity-relation index for document {doc_id}: {len(final_entity_names)} entities (original: {len(processed_entities)}, added: {len(all_added_entities)}), {len(final_relation_pairs)} relations" + ) + + except Exception as e: + logger.error( + f"Failed to update entity-relation index for document {doc_id}: {e}" + ) + # Don't raise exception to avoid affecting main flow + + log_message = f"Completed merging: {len(processed_entities)} entities, {len(all_added_entities)} extra entities, {len(processed_edges)} relations" + logger.info(log_message) + async with pipeline_status_lock: + pipeline_status["latest_message"] = log_message + pipeline_status["history_messages"].append(log_message) + + +async def extract_entities( + chunks: dict[str, TextChunkSchema], + global_config: dict[str, str], + pipeline_status: dict = None, + pipeline_status_lock=None, + llm_response_cache: BaseKVStorage | None = None, + text_chunks_storage: BaseKVStorage | None = None, +) -> list: + # Check for cancellation at the start of entity extraction + if pipeline_status is not None and pipeline_status_lock is not None: + async with pipeline_status_lock: + if pipeline_status.get("cancellation_requested", False): + raise PipelineCancelledException( + "User cancelled during entity extraction" + ) + + use_llm_func: callable = global_config["llm_model_func"] + entity_extract_max_gleaning = global_config["entity_extract_max_gleaning"] + + ordered_chunks = list(chunks.items()) + # add language and example number params to prompt + language = global_config["addon_params"].get("language", DEFAULT_SUMMARY_LANGUAGE) + entity_types = global_config["addon_params"].get( + "entity_types", DEFAULT_ENTITY_TYPES + ) + + examples = "\n".join(PROMPTS["entity_extraction_examples"]) + + example_context_base = dict( + tuple_delimiter=PROMPTS["DEFAULT_TUPLE_DELIMITER"], + completion_delimiter=PROMPTS["DEFAULT_COMPLETION_DELIMITER"], + entity_types=", ".join(entity_types), + language=language, + ) + # add example's format + examples = examples.format(**example_context_base) + + context_base = dict( + tuple_delimiter=PROMPTS["DEFAULT_TUPLE_DELIMITER"], + completion_delimiter=PROMPTS["DEFAULT_COMPLETION_DELIMITER"], + entity_types=",".join(entity_types), + examples=examples, + language=language, + ) + + processed_chunks = 0 + total_chunks = len(ordered_chunks) + + async def _process_single_content(chunk_key_dp: tuple[str, TextChunkSchema]): + """Process a single chunk + Args: + chunk_key_dp (tuple[str, TextChunkSchema]): + ("chunk-xxxxxx", {"tokens": int, "content": str, "full_doc_id": str, "chunk_order_index": int}) + Returns: + tuple: (maybe_nodes, maybe_edges) containing extracted entities and relationships + """ + nonlocal processed_chunks + chunk_key = chunk_key_dp[0] + chunk_dp = chunk_key_dp[1] + content = chunk_dp["content"] + # Get file path from chunk data or use default + file_path = chunk_dp.get("file_path", "unknown_source") + + # Create cache keys collector for batch processing + cache_keys_collector = [] + + # Get initial extraction + entity_extraction_system_prompt = PROMPTS[ + "entity_extraction_system_prompt" + ].format(**{**context_base, "input_text": content}) + entity_extraction_user_prompt = PROMPTS["entity_extraction_user_prompt"].format( + **{**context_base, "input_text": content} + ) + entity_continue_extraction_user_prompt = PROMPTS[ + "entity_continue_extraction_user_prompt" + ].format(**{**context_base, "input_text": content}) + + final_result, timestamp = await use_llm_func_with_cache( + entity_extraction_user_prompt, + use_llm_func, + system_prompt=entity_extraction_system_prompt, + llm_response_cache=llm_response_cache, + cache_type="extract", + chunk_id=chunk_key, + cache_keys_collector=cache_keys_collector, + ) + + history = pack_user_ass_to_openai_messages( + entity_extraction_user_prompt, final_result + ) + + # Process initial extraction with file path + maybe_nodes, maybe_edges = await _process_extraction_result( + final_result, + chunk_key, + timestamp, + file_path, + tuple_delimiter=context_base["tuple_delimiter"], + completion_delimiter=context_base["completion_delimiter"], + ) + + # Process additional gleaning results only 1 time when entity_extract_max_gleaning is greater than zero. + if entity_extract_max_gleaning > 0: + glean_result, timestamp = await use_llm_func_with_cache( + entity_continue_extraction_user_prompt, + use_llm_func, + system_prompt=entity_extraction_system_prompt, + llm_response_cache=llm_response_cache, + history_messages=history, + cache_type="extract", + chunk_id=chunk_key, + cache_keys_collector=cache_keys_collector, + ) + + # Process gleaning result separately with file path + glean_nodes, glean_edges = await _process_extraction_result( + glean_result, + chunk_key, + timestamp, + file_path, + tuple_delimiter=context_base["tuple_delimiter"], + completion_delimiter=context_base["completion_delimiter"], + ) + + # Merge results - compare description lengths to choose better version + for entity_name, glean_entities in glean_nodes.items(): + if entity_name in maybe_nodes: + # Compare description lengths and keep the better one + original_desc_len = len( + maybe_nodes[entity_name][0].get("description", "") or "" + ) + glean_desc_len = len(glean_entities[0].get("description", "") or "") + + if glean_desc_len > original_desc_len: + maybe_nodes[entity_name] = list(glean_entities) + # Otherwise keep original version + else: + # New entity from gleaning stage + maybe_nodes[entity_name] = list(glean_entities) + + for edge_key, glean_edges in glean_edges.items(): + if edge_key in maybe_edges: + # Compare description lengths and keep the better one + original_desc_len = len( + maybe_edges[edge_key][0].get("description", "") or "" + ) + glean_desc_len = len(glean_edges[0].get("description", "") or "") + + if glean_desc_len > original_desc_len: + maybe_edges[edge_key] = list(glean_edges) + # Otherwise keep original version + else: + # New edge from gleaning stage + maybe_edges[edge_key] = list(glean_edges) + + # Batch update chunk's llm_cache_list with all collected cache keys + if cache_keys_collector and text_chunks_storage: + await update_chunk_cache_list( + chunk_key, + text_chunks_storage, + cache_keys_collector, + "entity_extraction", + ) + + processed_chunks += 1 + entities_count = len(maybe_nodes) + relations_count = len(maybe_edges) + log_message = f"Chunk {processed_chunks} of {total_chunks} extracted {entities_count} Ent + {relations_count} Rel {chunk_key}" + logger.info(log_message) + if pipeline_status is not None: + async with pipeline_status_lock: + pipeline_status["latest_message"] = log_message + pipeline_status["history_messages"].append(log_message) + + # Return the extracted nodes and edges for centralized processing + return maybe_nodes, maybe_edges + + # Get max async tasks limit from global_config + chunk_max_async = global_config.get("llm_model_max_async", 4) + semaphore = asyncio.Semaphore(chunk_max_async) + + async def _process_with_semaphore(chunk): + async with semaphore: + # Check for cancellation before processing chunk + if pipeline_status is not None and pipeline_status_lock is not None: + async with pipeline_status_lock: + if pipeline_status.get("cancellation_requested", False): + raise PipelineCancelledException( + "User cancelled during chunk processing" + ) + + try: + return await _process_single_content(chunk) + except Exception as e: + chunk_id = chunk[0] # Extract chunk_id from chunk[0] + prefixed_exception = create_prefixed_exception(e, chunk_id) + raise prefixed_exception from e + + tasks = [] + for c in ordered_chunks: + task = asyncio.create_task(_process_with_semaphore(c)) + tasks.append(task) + + # Wait for tasks to complete or for the first exception to occur + # This allows us to cancel remaining tasks if any task fails + done, pending = await asyncio.wait(tasks, return_when=asyncio.FIRST_EXCEPTION) + + # Check if any task raised an exception and ensure all exceptions are retrieved + first_exception = None + chunk_results = [] + + for task in done: + try: + exception = task.exception() + if exception is not None: + if first_exception is None: + first_exception = exception + else: + chunk_results.append(task.result()) + except Exception as e: + if first_exception is None: + first_exception = e + + # If any task failed, cancel all pending tasks and raise the first exception + if first_exception is not None: + # Cancel all pending tasks + for pending_task in pending: + pending_task.cancel() + + # Wait for cancellation to complete + if pending: + await asyncio.wait(pending) + + # Add progress prefix to the exception message + progress_prefix = f"C[{processed_chunks + 1}/{total_chunks}]" + + # Re-raise the original exception with a prefix + prefixed_exception = create_prefixed_exception(first_exception, progress_prefix) + raise prefixed_exception from first_exception + + # If all tasks completed successfully, chunk_results already contains the results + # Return the chunk_results for later processing in merge_nodes_and_edges + return chunk_results + + +async def kg_query( + query: str, + knowledge_graph_inst: BaseGraphStorage, + entities_vdb: BaseVectorStorage, + relationships_vdb: BaseVectorStorage, + text_chunks_db: BaseKVStorage, + query_param: QueryParam, + global_config: dict[str, str], + hashing_kv: BaseKVStorage | None = None, + system_prompt: str | None = None, + chunks_vdb: BaseVectorStorage = None, +) -> QueryResult | None: + """ + Execute knowledge graph query and return unified QueryResult object. + + Args: + query: Query string + knowledge_graph_inst: Knowledge graph storage instance + entities_vdb: Entity vector database + relationships_vdb: Relationship vector database + text_chunks_db: Text chunks storage + query_param: Query parameters + global_config: Global configuration + hashing_kv: Cache storage + system_prompt: System prompt + chunks_vdb: Document chunks vector database + + Returns: + QueryResult | None: Unified query result object containing: + - content: Non-streaming response text content + - response_iterator: Streaming response iterator + - raw_data: Complete structured data (including references and metadata) + - is_streaming: Whether this is a streaming result + + Based on different query_param settings, different fields will be populated: + - only_need_context=True: content contains context string + - only_need_prompt=True: content contains complete prompt + - stream=True: response_iterator contains streaming response, raw_data contains complete data + - default: content contains LLM response text, raw_data contains complete data + + Returns None when no relevant context could be constructed for the query. + """ + if not query: + return QueryResult(content=PROMPTS["fail_response"]) + + if query_param.model_func: + use_model_func = query_param.model_func + else: + use_model_func = global_config["llm_model_func"] + # Apply higher priority (5) to query relation LLM function + use_model_func = partial(use_model_func, _priority=5) + + hl_keywords, ll_keywords = await get_keywords_from_query( + query, query_param, global_config, hashing_kv + ) + + logger.debug(f"High-level keywords: {hl_keywords}") + logger.debug(f"Low-level keywords: {ll_keywords}") + + # Handle empty keywords + if ll_keywords == [] and query_param.mode in ["local", "hybrid", "mix"]: + logger.warning("low_level_keywords is empty") + if hl_keywords == [] and query_param.mode in ["global", "hybrid", "mix"]: + logger.warning("high_level_keywords is empty") + if hl_keywords == [] and ll_keywords == []: + if len(query) < 50: + logger.warning(f"Forced low_level_keywords to origin query: {query}") + ll_keywords = [query] + else: + return QueryResult(content=PROMPTS["fail_response"]) + + ll_keywords_str = ", ".join(ll_keywords) if ll_keywords else "" + hl_keywords_str = ", ".join(hl_keywords) if hl_keywords else "" + + # Build query context (unified interface) + context_result = await _build_query_context( + query, + ll_keywords_str, + hl_keywords_str, + knowledge_graph_inst, + entities_vdb, + relationships_vdb, + text_chunks_db, + query_param, + chunks_vdb, + ) + + if context_result is None: + logger.info("[kg_query] No query context could be built; returning no-result.") + return None + + # Return different content based on query parameters + if query_param.only_need_context and not query_param.only_need_prompt: + return QueryResult( + content=context_result.context, raw_data=context_result.raw_data + ) + + user_prompt = f"\n\n{query_param.user_prompt}" if query_param.user_prompt else "n/a" + response_type = ( + query_param.response_type + if query_param.response_type + else "Multiple Paragraphs" + ) + + # Build system prompt + sys_prompt_temp = system_prompt if system_prompt else PROMPTS["rag_response"] + sys_prompt = sys_prompt_temp.format( + response_type=response_type, + user_prompt=user_prompt, + context_data=context_result.context, + ) + + user_query = query + + if query_param.only_need_prompt: + prompt_content = "\n\n".join([sys_prompt, "---User Query---", user_query]) + return QueryResult(content=prompt_content, raw_data=context_result.raw_data) + + # Call LLM + tokenizer: Tokenizer = global_config["tokenizer"] + len_of_prompts = len(tokenizer.encode(query + sys_prompt)) + logger.debug( + f"[kg_query] Sending to LLM: {len_of_prompts:,} tokens (Query: {len(tokenizer.encode(query))}, System: {len(tokenizer.encode(sys_prompt))})" + ) + + # Handle cache + args_hash = compute_args_hash( + query_param.mode, + query, + query_param.response_type, + query_param.top_k, + query_param.chunk_top_k, + query_param.max_entity_tokens, + query_param.max_relation_tokens, + query_param.max_total_tokens, + hl_keywords_str, + ll_keywords_str, + query_param.user_prompt or "", + query_param.enable_rerank, + ) + + cached_result = await handle_cache( + hashing_kv, args_hash, user_query, query_param.mode, cache_type="query" + ) + + if cached_result is not None: + cached_response, _ = cached_result # Extract content, ignore timestamp + logger.info( + " == LLM cache == Query cache hit, using cached response as query result" + ) + response = cached_response + else: + response = await use_model_func( + user_query, + system_prompt=sys_prompt, + history_messages=query_param.conversation_history, + enable_cot=True, + stream=query_param.stream, + ) + + if hashing_kv and hashing_kv.global_config.get("enable_llm_cache"): + queryparam_dict = { + "mode": query_param.mode, + "response_type": query_param.response_type, + "top_k": query_param.top_k, + "chunk_top_k": query_param.chunk_top_k, + "max_entity_tokens": query_param.max_entity_tokens, + "max_relation_tokens": query_param.max_relation_tokens, + "max_total_tokens": query_param.max_total_tokens, + "hl_keywords": hl_keywords_str, + "ll_keywords": ll_keywords_str, + "user_prompt": query_param.user_prompt or "", + "enable_rerank": query_param.enable_rerank, + } + await save_to_cache( + hashing_kv, + CacheData( + args_hash=args_hash, + content=response, + prompt=query, + mode=query_param.mode, + cache_type="query", + queryparam=queryparam_dict, + ), + ) + + # Return unified result based on actual response type + if isinstance(response, str): + # Non-streaming response (string) + if len(response) > len(sys_prompt): + response = ( + response.replace(sys_prompt, "") + .replace("user", "") + .replace("model", "") + .replace(query, "") + .replace("", "") + .replace("", "") + .strip() + ) + + return QueryResult(content=response, raw_data=context_result.raw_data) + else: + # Streaming response (AsyncIterator) + return QueryResult( + response_iterator=response, + raw_data=context_result.raw_data, + is_streaming=True, + ) + + +async def get_keywords_from_query( + query: str, + query_param: QueryParam, + global_config: dict[str, str], + hashing_kv: BaseKVStorage | None = None, +) -> tuple[list[str], list[str]]: + """ + Retrieves high-level and low-level keywords for RAG operations. + + This function checks if keywords are already provided in query parameters, + and if not, extracts them from the query text using LLM. + + Args: + query: The user's query text + query_param: Query parameters that may contain pre-defined keywords + global_config: Global configuration dictionary + hashing_kv: Optional key-value storage for caching results + + Returns: + A tuple containing (high_level_keywords, low_level_keywords) + """ + # Check if pre-defined keywords are already provided + if query_param.hl_keywords or query_param.ll_keywords: + return query_param.hl_keywords, query_param.ll_keywords + + # Extract keywords using extract_keywords_only function which already supports conversation history + hl_keywords, ll_keywords = await extract_keywords_only( + query, query_param, global_config, hashing_kv + ) + return hl_keywords, ll_keywords + + +async def extract_keywords_only( + text: str, + param: QueryParam, + global_config: dict[str, str], + hashing_kv: BaseKVStorage | None = None, +) -> tuple[list[str], list[str]]: + """ + Extract high-level and low-level keywords from the given 'text' using the LLM. + This method does NOT build the final RAG context or provide a final answer. + It ONLY extracts keywords (hl_keywords, ll_keywords). + """ + + # 1. Handle cache if needed - add cache type for keywords + args_hash = compute_args_hash( + param.mode, + text, + ) + cached_result = await handle_cache( + hashing_kv, args_hash, text, param.mode, cache_type="keywords" + ) + if cached_result is not None: + cached_response, _ = cached_result # Extract content, ignore timestamp + try: + keywords_data = json_repair.loads(cached_response) + return keywords_data.get("high_level_keywords", []), keywords_data.get( + "low_level_keywords", [] + ) + except (json.JSONDecodeError, KeyError): + logger.warning( + "Invalid cache format for keywords, proceeding with extraction" + ) + + # 2. Build the examples + examples = "\n".join(PROMPTS["keywords_extraction_examples"]) + + language = global_config["addon_params"].get("language", DEFAULT_SUMMARY_LANGUAGE) + + # 3. Build the keyword-extraction prompt + kw_prompt = PROMPTS["keywords_extraction"].format( + query=text, + examples=examples, + language=language, + ) + + tokenizer: Tokenizer = global_config["tokenizer"] + len_of_prompts = len(tokenizer.encode(kw_prompt)) + logger.debug( + f"[extract_keywords] Sending to LLM: {len_of_prompts:,} tokens (Prompt: {len_of_prompts})" + ) + + # 4. Call the LLM for keyword extraction + if param.model_func: + use_model_func = param.model_func + else: + use_model_func = global_config["llm_model_func"] + # Apply higher priority (5) to query relation LLM function + use_model_func = partial(use_model_func, _priority=5) + + result = await use_model_func(kw_prompt, keyword_extraction=True) + + # 5. Parse out JSON from the LLM response + result = remove_think_tags(result) + try: + keywords_data = json_repair.loads(result) + if not keywords_data: + logger.error("No JSON-like structure found in the LLM respond.") + return [], [] + except json.JSONDecodeError as e: + logger.error(f"JSON parsing error: {e}") + logger.error(f"LLM respond: {result}") + return [], [] + + hl_keywords = keywords_data.get("high_level_keywords", []) + ll_keywords = keywords_data.get("low_level_keywords", []) + + # 6. Cache only the processed keywords with cache type + if hl_keywords or ll_keywords: + cache_data = { + "high_level_keywords": hl_keywords, + "low_level_keywords": ll_keywords, + } + if hashing_kv.global_config.get("enable_llm_cache"): + # Save to cache with query parameters + queryparam_dict = { + "mode": param.mode, + "response_type": param.response_type, + "top_k": param.top_k, + "chunk_top_k": param.chunk_top_k, + "max_entity_tokens": param.max_entity_tokens, + "max_relation_tokens": param.max_relation_tokens, + "max_total_tokens": param.max_total_tokens, + "user_prompt": param.user_prompt or "", + "enable_rerank": param.enable_rerank, + } + await save_to_cache( + hashing_kv, + CacheData( + args_hash=args_hash, + content=json.dumps(cache_data), + prompt=text, + mode=param.mode, + cache_type="keywords", + queryparam=queryparam_dict, + ), + ) + + return hl_keywords, ll_keywords + + +async def _get_vector_context( + query: str, + chunks_vdb: BaseVectorStorage, + query_param: QueryParam, + query_embedding: list[float] = None, +) -> list[dict]: + """ + Retrieve text chunks from the vector database without reranking or truncation. + + This function performs vector search to find relevant text chunks for a query. + Reranking and truncation will be handled later in the unified processing. + + Args: + query: The query string to search for + chunks_vdb: Vector database containing document chunks + query_param: Query parameters including chunk_top_k and ids + query_embedding: Optional pre-computed query embedding to avoid redundant embedding calls + + Returns: + List of text chunks with metadata + """ + try: + # Use chunk_top_k if specified, otherwise fall back to top_k + search_top_k = query_param.chunk_top_k or query_param.top_k + cosine_threshold = chunks_vdb.cosine_better_than_threshold + + results = await chunks_vdb.query( + query, top_k=search_top_k, query_embedding=query_embedding + ) + if not results: + logger.info( + f"Naive query: 0 chunks (chunk_top_k:{search_top_k} cosine:{cosine_threshold})" + ) + return [] + + valid_chunks = [] + for result in results: + if "content" in result: + chunk_with_metadata = { + "content": result["content"], + "created_at": result.get("created_at", None), + "file_path": result.get("file_path", "unknown_source"), + "source_type": "vector", # Mark the source type + "chunk_id": result.get("id"), # Add chunk_id for deduplication + } + valid_chunks.append(chunk_with_metadata) + + logger.info( + f"Naive query: {len(valid_chunks)} chunks (chunk_top_k:{search_top_k} cosine:{cosine_threshold})" + ) + return valid_chunks + + except Exception as e: + logger.error(f"Error in _get_vector_context: {e}") + return [] + + +async def _perform_kg_search( + query: str, + ll_keywords: str, + hl_keywords: str, + knowledge_graph_inst: BaseGraphStorage, + entities_vdb: BaseVectorStorage, + relationships_vdb: BaseVectorStorage, + text_chunks_db: BaseKVStorage, + query_param: QueryParam, + chunks_vdb: BaseVectorStorage = None, +) -> dict[str, Any]: + """ + Pure search logic that retrieves raw entities, relations, and vector chunks. + No token truncation or formatting - just raw search results. + """ + + # Initialize result containers + local_entities = [] + local_relations = [] + global_entities = [] + global_relations = [] + vector_chunks = [] + chunk_tracking = {} + + # Handle different query modes + + # Track chunk sources and metadata for final logging + chunk_tracking = {} # chunk_id -> {source, frequency, order} + + # Pre-compute query embedding once for all vector operations + kg_chunk_pick_method = text_chunks_db.global_config.get( + "kg_chunk_pick_method", DEFAULT_KG_CHUNK_PICK_METHOD + ) + query_embedding = None + if query and (kg_chunk_pick_method == "VECTOR" or chunks_vdb): + actual_embedding_func = text_chunks_db.embedding_func + if actual_embedding_func: + try: + query_embedding = await actual_embedding_func([query]) + query_embedding = query_embedding[ + 0 + ] # Extract first embedding from batch result + logger.debug("Pre-computed query embedding for all vector operations") + except Exception as e: + logger.warning(f"Failed to pre-compute query embedding: {e}") + query_embedding = None + + # Handle local and global modes + if query_param.mode == "local" and len(ll_keywords) > 0: + local_entities, local_relations = await _get_node_data( + ll_keywords, + knowledge_graph_inst, + entities_vdb, + query_param, + ) + + elif query_param.mode == "global" and len(hl_keywords) > 0: + global_relations, global_entities = await _get_edge_data( + hl_keywords, + knowledge_graph_inst, + relationships_vdb, + query_param, + ) + + else: # hybrid or mix mode + if len(ll_keywords) > 0: + local_entities, local_relations = await _get_node_data( + ll_keywords, + knowledge_graph_inst, + entities_vdb, + query_param, + ) + if len(hl_keywords) > 0: + global_relations, global_entities = await _get_edge_data( + hl_keywords, + knowledge_graph_inst, + relationships_vdb, + query_param, + ) + + # Get vector chunks for mix mode + if query_param.mode == "mix" and chunks_vdb: + vector_chunks = await _get_vector_context( + query, + chunks_vdb, + query_param, + query_embedding, + ) + # Track vector chunks with source metadata + for i, chunk in enumerate(vector_chunks): + chunk_id = chunk.get("chunk_id") or chunk.get("id") + if chunk_id: + chunk_tracking[chunk_id] = { + "source": "C", + "frequency": 1, # Vector chunks always have frequency 1 + "order": i + 1, # 1-based order in vector search results + } + else: + logger.warning(f"Vector chunk missing chunk_id: {chunk}") + + # Round-robin merge entities + final_entities = [] + seen_entities = set() + max_len = max(len(local_entities), len(global_entities)) + for i in range(max_len): + # First from local + if i < len(local_entities): + entity = local_entities[i] + entity_name = entity.get("entity_name") + if entity_name and entity_name not in seen_entities: + final_entities.append(entity) + seen_entities.add(entity_name) + + # Then from global + if i < len(global_entities): + entity = global_entities[i] + entity_name = entity.get("entity_name") + if entity_name and entity_name not in seen_entities: + final_entities.append(entity) + seen_entities.add(entity_name) + + # Round-robin merge relations + final_relations = [] + seen_relations = set() + max_len = max(len(local_relations), len(global_relations)) + for i in range(max_len): + # First from local + if i < len(local_relations): + relation = local_relations[i] + # Build relation unique identifier + if "src_tgt" in relation: + rel_key = tuple(sorted(relation["src_tgt"])) + else: + rel_key = tuple( + sorted([relation.get("src_id"), relation.get("tgt_id")]) + ) + + if rel_key not in seen_relations: + final_relations.append(relation) + seen_relations.add(rel_key) + + # Then from global + if i < len(global_relations): + relation = global_relations[i] + # Build relation unique identifier + if "src_tgt" in relation: + rel_key = tuple(sorted(relation["src_tgt"])) + else: + rel_key = tuple( + sorted([relation.get("src_id"), relation.get("tgt_id")]) + ) + + if rel_key not in seen_relations: + final_relations.append(relation) + seen_relations.add(rel_key) + + logger.info( + f"Raw search results: {len(final_entities)} entities, {len(final_relations)} relations, {len(vector_chunks)} vector chunks" + ) + + return { + "final_entities": final_entities, + "final_relations": final_relations, + "vector_chunks": vector_chunks, + "chunk_tracking": chunk_tracking, + "query_embedding": query_embedding, + } + + +async def _apply_token_truncation( + search_result: dict[str, Any], + query_param: QueryParam, + global_config: dict[str, str], +) -> dict[str, Any]: + """ + Apply token-based truncation to entities and relations for LLM efficiency. + """ + tokenizer = global_config.get("tokenizer") + if not tokenizer: + logger.warning("No tokenizer found, skipping truncation") + return { + "entities_context": [], + "relations_context": [], + "filtered_entities": search_result["final_entities"], + "filtered_relations": search_result["final_relations"], + "entity_id_to_original": {}, + "relation_id_to_original": {}, + } + + # Get token limits from query_param with fallbacks + max_entity_tokens = getattr( + query_param, + "max_entity_tokens", + global_config.get("max_entity_tokens", DEFAULT_MAX_ENTITY_TOKENS), + ) + max_relation_tokens = getattr( + query_param, + "max_relation_tokens", + global_config.get("max_relation_tokens", DEFAULT_MAX_RELATION_TOKENS), + ) + + final_entities = search_result["final_entities"] + final_relations = search_result["final_relations"] + + # Create mappings from entity/relation identifiers to original data + entity_id_to_original = {} + relation_id_to_original = {} + + # Generate entities context for truncation + entities_context = [] + for i, entity in enumerate(final_entities): + entity_name = entity["entity_name"] + created_at = entity.get("created_at", "UNKNOWN") + if isinstance(created_at, (int, float)): + created_at = time.strftime("%Y-%m-%d %H:%M:%S", time.localtime(created_at)) + + # Store mapping from entity name to original data + entity_id_to_original[entity_name] = entity + + entities_context.append( + { + "entity": entity_name, + "type": entity.get("entity_type", "UNKNOWN"), + "description": entity.get("description", "UNKNOWN"), + "created_at": created_at, + "file_path": entity.get("file_path", "unknown_source"), + } + ) + + # Generate relations context for truncation + relations_context = [] + for i, relation in enumerate(final_relations): + created_at = relation.get("created_at", "UNKNOWN") + if isinstance(created_at, (int, float)): + created_at = time.strftime("%Y-%m-%d %H:%M:%S", time.localtime(created_at)) + + # Handle different relation data formats + if "src_tgt" in relation: + entity1, entity2 = relation["src_tgt"] + else: + entity1, entity2 = relation.get("src_id"), relation.get("tgt_id") + + # Store mapping from relation pair to original data + relation_key = (entity1, entity2) + relation_id_to_original[relation_key] = relation + + relations_context.append( + { + "entity1": entity1, + "entity2": entity2, + "description": relation.get("description", "UNKNOWN"), + "created_at": created_at, + "file_path": relation.get("file_path", "unknown_source"), + } + ) + + logger.debug( + f"Before truncation: {len(entities_context)} entities, {len(relations_context)} relations" + ) + + # Apply token-based truncation + if entities_context: + # Remove file_path and created_at for token calculation + entities_context_for_truncation = [] + for entity in entities_context: + entity_copy = entity.copy() + entity_copy.pop("file_path", None) + entity_copy.pop("created_at", None) + entities_context_for_truncation.append(entity_copy) + + entities_context = truncate_list_by_token_size( + entities_context_for_truncation, + key=lambda x: "\n".join( + json.dumps(item, ensure_ascii=False) for item in [x] + ), + max_token_size=max_entity_tokens, + tokenizer=tokenizer, + ) + + if relations_context: + # Remove file_path and created_at for token calculation + relations_context_for_truncation = [] + for relation in relations_context: + relation_copy = relation.copy() + relation_copy.pop("file_path", None) + relation_copy.pop("created_at", None) + relations_context_for_truncation.append(relation_copy) + + relations_context = truncate_list_by_token_size( + relations_context_for_truncation, + key=lambda x: "\n".join( + json.dumps(item, ensure_ascii=False) for item in [x] + ), + max_token_size=max_relation_tokens, + tokenizer=tokenizer, + ) + + logger.info( + f"After truncation: {len(entities_context)} entities, {len(relations_context)} relations" + ) + + # Create filtered original data based on truncated context + filtered_entities = [] + filtered_entity_id_to_original = {} + if entities_context: + final_entity_names = {e["entity"] for e in entities_context} + seen_nodes = set() + for entity in final_entities: + name = entity.get("entity_name") + if name in final_entity_names and name not in seen_nodes: + filtered_entities.append(entity) + filtered_entity_id_to_original[name] = entity + seen_nodes.add(name) + + filtered_relations = [] + filtered_relation_id_to_original = {} + if relations_context: + final_relation_pairs = {(r["entity1"], r["entity2"]) for r in relations_context} + seen_edges = set() + for relation in final_relations: + src, tgt = relation.get("src_id"), relation.get("tgt_id") + if src is None or tgt is None: + src, tgt = relation.get("src_tgt", (None, None)) + + pair = (src, tgt) + if pair in final_relation_pairs and pair not in seen_edges: + filtered_relations.append(relation) + filtered_relation_id_to_original[pair] = relation + seen_edges.add(pair) + + return { + "entities_context": entities_context, + "relations_context": relations_context, + "filtered_entities": filtered_entities, + "filtered_relations": filtered_relations, + "entity_id_to_original": filtered_entity_id_to_original, + "relation_id_to_original": filtered_relation_id_to_original, + } + + +async def _merge_all_chunks( + filtered_entities: list[dict], + filtered_relations: list[dict], + vector_chunks: list[dict], + query: str = "", + knowledge_graph_inst: BaseGraphStorage = None, + text_chunks_db: BaseKVStorage = None, + query_param: QueryParam = None, + chunks_vdb: BaseVectorStorage = None, + chunk_tracking: dict = None, + query_embedding: list[float] = None, +) -> list[dict]: + """ + Merge chunks from different sources: vector_chunks + entity_chunks + relation_chunks. + """ + if chunk_tracking is None: + chunk_tracking = {} + + # Get chunks from entities + entity_chunks = [] + if filtered_entities and text_chunks_db: + entity_chunks = await _find_related_text_unit_from_entities( + filtered_entities, + query_param, + text_chunks_db, + knowledge_graph_inst, + query, + chunks_vdb, + chunk_tracking=chunk_tracking, + query_embedding=query_embedding, + ) + + # Get chunks from relations + relation_chunks = [] + if filtered_relations and text_chunks_db: + relation_chunks = await _find_related_text_unit_from_relations( + filtered_relations, + query_param, + text_chunks_db, + entity_chunks, # For deduplication + query, + chunks_vdb, + chunk_tracking=chunk_tracking, + query_embedding=query_embedding, + ) + + # Round-robin merge chunks from different sources with deduplication + merged_chunks = [] + seen_chunk_ids = set() + max_len = max(len(vector_chunks), len(entity_chunks), len(relation_chunks)) + origin_len = len(vector_chunks) + len(entity_chunks) + len(relation_chunks) + + for i in range(max_len): + # Add from vector chunks first (Naive mode) + if i < len(vector_chunks): + chunk = vector_chunks[i] + chunk_id = chunk.get("chunk_id") or chunk.get("id") + if chunk_id and chunk_id not in seen_chunk_ids: + seen_chunk_ids.add(chunk_id) + merged_chunks.append( + { + "content": chunk["content"], + "file_path": chunk.get("file_path", "unknown_source"), + "chunk_id": chunk_id, + } + ) + + # Add from entity chunks (Local mode) + if i < len(entity_chunks): + chunk = entity_chunks[i] + chunk_id = chunk.get("chunk_id") or chunk.get("id") + if chunk_id and chunk_id not in seen_chunk_ids: + seen_chunk_ids.add(chunk_id) + merged_chunks.append( + { + "content": chunk["content"], + "file_path": chunk.get("file_path", "unknown_source"), + "chunk_id": chunk_id, + } + ) + + # Add from relation chunks (Global mode) + if i < len(relation_chunks): + chunk = relation_chunks[i] + chunk_id = chunk.get("chunk_id") or chunk.get("id") + if chunk_id and chunk_id not in seen_chunk_ids: + seen_chunk_ids.add(chunk_id) + merged_chunks.append( + { + "content": chunk["content"], + "file_path": chunk.get("file_path", "unknown_source"), + "chunk_id": chunk_id, + } + ) + + logger.info( + f"Round-robin merged chunks: {origin_len} -> {len(merged_chunks)} (deduplicated {origin_len - len(merged_chunks)})" + ) + + return merged_chunks + + +async def _build_context_str( + entities_context: list[dict], + relations_context: list[dict], + merged_chunks: list[dict], + query: str, + query_param: QueryParam, + global_config: dict[str, str], + chunk_tracking: dict = None, + entity_id_to_original: dict = None, + relation_id_to_original: dict = None, +) -> tuple[str, dict[str, Any]]: + """ + Build the final LLM context string with token processing. + This includes dynamic token calculation and final chunk truncation. + """ + tokenizer = global_config.get("tokenizer") + if not tokenizer: + logger.error("Missing tokenizer, cannot build LLM context") + # Return empty raw data structure when no tokenizer + empty_raw_data = convert_to_user_format( + [], + [], + [], + [], + query_param.mode, + ) + empty_raw_data["status"] = "failure" + empty_raw_data["message"] = "Missing tokenizer, cannot build LLM context." + return "", empty_raw_data + + # Get token limits + max_total_tokens = getattr( + query_param, + "max_total_tokens", + global_config.get("max_total_tokens", DEFAULT_MAX_TOTAL_TOKENS), + ) + + # Get the system prompt template from PROMPTS or global_config + sys_prompt_template = global_config.get( + "system_prompt_template", PROMPTS["rag_response"] + ) + + kg_context_template = PROMPTS["kg_query_context"] + user_prompt = query_param.user_prompt if query_param.user_prompt else "" + response_type = ( + query_param.response_type + if query_param.response_type + else "Multiple Paragraphs" + ) + + entities_str = "\n".join( + json.dumps(entity, ensure_ascii=False) for entity in entities_context + ) + relations_str = "\n".join( + json.dumps(relation, ensure_ascii=False) for relation in relations_context + ) + + # Calculate preliminary kg context tokens + pre_kg_context = kg_context_template.format( + entities_str=entities_str, + relations_str=relations_str, + text_chunks_str="", + reference_list_str="", + ) + kg_context_tokens = len(tokenizer.encode(pre_kg_context)) + + # Calculate preliminary system prompt tokens + pre_sys_prompt = sys_prompt_template.format( + context_data="", # Empty for overhead calculation + response_type=response_type, + user_prompt=user_prompt, + ) + sys_prompt_tokens = len(tokenizer.encode(pre_sys_prompt)) + + # Calculate available tokens for text chunks + query_tokens = len(tokenizer.encode(query)) + buffer_tokens = 200 # reserved for reference list and safety buffer + available_chunk_tokens = max_total_tokens - ( + sys_prompt_tokens + kg_context_tokens + query_tokens + buffer_tokens + ) + + logger.debug( + f"Token allocation - Total: {max_total_tokens}, SysPrompt: {sys_prompt_tokens}, Query: {query_tokens}, KG: {kg_context_tokens}, Buffer: {buffer_tokens}, Available for chunks: {available_chunk_tokens}" + ) + + # Apply token truncation to chunks using the dynamic limit + truncated_chunks = await process_chunks_unified( + query=query, + unique_chunks=merged_chunks, + query_param=query_param, + global_config=global_config, + source_type=query_param.mode, + chunk_token_limit=available_chunk_tokens, # Pass dynamic limit + ) + + # Generate reference list from truncated chunks using the new common function + reference_list, truncated_chunks = generate_reference_list_from_chunks( + truncated_chunks + ) + + # Rebuild chunks_context with truncated chunks + # The actual tokens may be slightly less than available_chunk_tokens due to deduplication logic + chunks_context = [] + for i, chunk in enumerate(truncated_chunks): + chunks_context.append( + { + "reference_id": chunk["reference_id"], + "content": chunk["content"], + } + ) + + text_units_str = "\n".join( + json.dumps(text_unit, ensure_ascii=False) for text_unit in chunks_context + ) + reference_list_str = "\n".join( + f"[{ref['reference_id']}] {ref['file_path']}" + for ref in reference_list + if ref["reference_id"] + ) + + logger.info( + f"Final context: {len(entities_context)} entities, {len(relations_context)} relations, {len(chunks_context)} chunks" + ) + + # not necessary to use LLM to generate a response + if not entities_context and not relations_context and not chunks_context: + # Return empty raw data structure when no entities/relations + empty_raw_data = convert_to_user_format( + [], + [], + [], + [], + query_param.mode, + ) + empty_raw_data["status"] = "failure" + empty_raw_data["message"] = "Query returned empty dataset." + return "", empty_raw_data + + # output chunks tracking infomations + # format: / (e.g., E5/2 R2/1 C1/1) + if truncated_chunks and chunk_tracking: + chunk_tracking_log = [] + for chunk in truncated_chunks: + chunk_id = chunk.get("chunk_id") + if chunk_id and chunk_id in chunk_tracking: + tracking_info = chunk_tracking[chunk_id] + source = tracking_info["source"] + frequency = tracking_info["frequency"] + order = tracking_info["order"] + chunk_tracking_log.append(f"{source}{frequency}/{order}") + else: + chunk_tracking_log.append("?0/0") + + if chunk_tracking_log: + logger.info(f"Final chunks S+F/O: {' '.join(chunk_tracking_log)}") + + result = kg_context_template.format( + entities_str=entities_str, + relations_str=relations_str, + text_chunks_str=text_units_str, + reference_list_str=reference_list_str, + ) + + # Always return both context and complete data structure (unified approach) + logger.debug( + f"[_build_context_str] Converting to user format: {len(entities_context)} entities, {len(relations_context)} relations, {len(truncated_chunks)} chunks" + ) + final_data = convert_to_user_format( + entities_context, + relations_context, + truncated_chunks, + reference_list, + query_param.mode, + entity_id_to_original, + relation_id_to_original, + ) + logger.debug( + f"[_build_context_str] Final data after conversion: {len(final_data.get('entities', []))} entities, {len(final_data.get('relationships', []))} relationships, {len(final_data.get('chunks', []))} chunks" + ) + return result, final_data + + +# Now let's update the old _build_query_context to use the new architecture +async def _build_query_context( + query: str, + ll_keywords: str, + hl_keywords: str, + knowledge_graph_inst: BaseGraphStorage, + entities_vdb: BaseVectorStorage, + relationships_vdb: BaseVectorStorage, + text_chunks_db: BaseKVStorage, + query_param: QueryParam, + chunks_vdb: BaseVectorStorage = None, +) -> QueryContextResult | None: + """ + Main query context building function using the new 4-stage architecture: + 1. Search -> 2. Truncate -> 3. Merge chunks -> 4. Build LLM context + + Returns unified QueryContextResult containing both context and raw_data. + """ + + if not query: + logger.warning("Query is empty, skipping context building") + return None + + # Stage 1: Pure search + search_result = await _perform_kg_search( + query, + ll_keywords, + hl_keywords, + knowledge_graph_inst, + entities_vdb, + relationships_vdb, + text_chunks_db, + query_param, + chunks_vdb, + ) + + if not search_result["final_entities"] and not search_result["final_relations"]: + if query_param.mode != "mix": + return None + else: + if not search_result["chunk_tracking"]: + return None + + # Stage 2: Apply token truncation for LLM efficiency + truncation_result = await _apply_token_truncation( + search_result, + query_param, + text_chunks_db.global_config, + ) + + # Stage 3: Merge chunks using filtered entities/relations + merged_chunks = await _merge_all_chunks( + filtered_entities=truncation_result["filtered_entities"], + filtered_relations=truncation_result["filtered_relations"], + vector_chunks=search_result["vector_chunks"], + query=query, + knowledge_graph_inst=knowledge_graph_inst, + text_chunks_db=text_chunks_db, + query_param=query_param, + chunks_vdb=chunks_vdb, + chunk_tracking=search_result["chunk_tracking"], + query_embedding=search_result["query_embedding"], + ) + + if ( + not merged_chunks + and not truncation_result["entities_context"] + and not truncation_result["relations_context"] + ): + return None + + # Stage 4: Build final LLM context with dynamic token processing + # _build_context_str now always returns tuple[str, dict] + context, raw_data = await _build_context_str( + entities_context=truncation_result["entities_context"], + relations_context=truncation_result["relations_context"], + merged_chunks=merged_chunks, + query=query, + query_param=query_param, + global_config=text_chunks_db.global_config, + chunk_tracking=search_result["chunk_tracking"], + entity_id_to_original=truncation_result["entity_id_to_original"], + relation_id_to_original=truncation_result["relation_id_to_original"], + ) + + # Convert keywords strings to lists and add complete metadata to raw_data + hl_keywords_list = hl_keywords.split(", ") if hl_keywords else [] + ll_keywords_list = ll_keywords.split(", ") if ll_keywords else [] + + # Add complete metadata to raw_data (preserve existing metadata including query_mode) + if "metadata" not in raw_data: + raw_data["metadata"] = {} + + # Update keywords while preserving existing metadata + raw_data["metadata"]["keywords"] = { + "high_level": hl_keywords_list, + "low_level": ll_keywords_list, + } + raw_data["metadata"]["processing_info"] = { + "total_entities_found": len(search_result.get("final_entities", [])), + "total_relations_found": len(search_result.get("final_relations", [])), + "entities_after_truncation": len( + truncation_result.get("filtered_entities", []) + ), + "relations_after_truncation": len( + truncation_result.get("filtered_relations", []) + ), + "merged_chunks_count": len(merged_chunks), + "final_chunks_count": len(raw_data.get("data", {}).get("chunks", [])), + } + + logger.debug( + f"[_build_query_context] Context length: {len(context) if context else 0}" + ) + logger.debug( + f"[_build_query_context] Raw data entities: {len(raw_data.get('data', {}).get('entities', []))}, relationships: {len(raw_data.get('data', {}).get('relationships', []))}, chunks: {len(raw_data.get('data', {}).get('chunks', []))}" + ) + + return QueryContextResult(context=context, raw_data=raw_data) + + +async def _get_node_data( + query: str, + knowledge_graph_inst: BaseGraphStorage, + entities_vdb: BaseVectorStorage, + query_param: QueryParam, +): + # get similar entities + logger.info( + f"Query nodes: {query} (top_k:{query_param.top_k}, cosine:{entities_vdb.cosine_better_than_threshold})" + ) + + results = await entities_vdb.query(query, top_k=query_param.top_k) + + if not len(results): + return [], [] + + # Extract all entity IDs from your results list + node_ids = [r["entity_name"] for r in results] + + # Call the batch node retrieval and degree functions concurrently. + nodes_dict, degrees_dict = await asyncio.gather( + knowledge_graph_inst.get_nodes_batch(node_ids), + knowledge_graph_inst.node_degrees_batch(node_ids), + ) + + # Now, if you need the node data and degree in order: + node_datas = [nodes_dict.get(nid) for nid in node_ids] + node_degrees = [degrees_dict.get(nid, 0) for nid in node_ids] + + if not all([n is not None for n in node_datas]): + logger.warning("Some nodes are missing, maybe the storage is damaged") + + node_datas = [ + { + **n, + "entity_name": k["entity_name"], + "rank": d, + "created_at": k.get("created_at"), + } + for k, n, d in zip(results, node_datas, node_degrees) + if n is not None + ] + + use_relations = await _find_most_related_edges_from_entities( + node_datas, + query_param, + knowledge_graph_inst, + ) + + logger.info( + f"Local query: {len(node_datas)} entites, {len(use_relations)} relations" + ) + + # Entities are sorted by cosine similarity + # Relations are sorted by rank + weight + return node_datas, use_relations + + +async def _find_most_related_edges_from_entities( + node_datas: list[dict], + query_param: QueryParam, + knowledge_graph_inst: BaseGraphStorage, +): + node_names = [dp["entity_name"] for dp in node_datas] + batch_edges_dict = await knowledge_graph_inst.get_nodes_edges_batch(node_names) + + all_edges = [] + seen = set() + + for node_name in node_names: + this_edges = batch_edges_dict.get(node_name, []) + for e in this_edges: + sorted_edge = tuple(sorted(e)) + if sorted_edge not in seen: + seen.add(sorted_edge) + all_edges.append(sorted_edge) + + # Prepare edge pairs in two forms: + # For the batch edge properties function, use dicts. + edge_pairs_dicts = [{"src": e[0], "tgt": e[1]} for e in all_edges] + # For edge degrees, use tuples. + edge_pairs_tuples = list(all_edges) # all_edges is already a list of tuples + + # Call the batched functions concurrently. + edge_data_dict, edge_degrees_dict = await asyncio.gather( + knowledge_graph_inst.get_edges_batch(edge_pairs_dicts), + knowledge_graph_inst.edge_degrees_batch(edge_pairs_tuples), + ) + + # Reconstruct edge_datas list in the same order as the deduplicated results. + all_edges_data = [] + for pair in all_edges: + edge_props = edge_data_dict.get(pair) + if edge_props is not None: + if "weight" not in edge_props: + logger.warning( + f"Edge {pair} missing 'weight' attribute, using default value 1.0" + ) + edge_props["weight"] = 1.0 + + combined = { + "src_tgt": pair, + "rank": edge_degrees_dict.get(pair, 0), + **edge_props, + } + all_edges_data.append(combined) + + all_edges_data = sorted( + all_edges_data, key=lambda x: (x["rank"], x["weight"]), reverse=True + ) + + return all_edges_data + + +async def _find_related_text_unit_from_entities( + node_datas: list[dict], + query_param: QueryParam, + text_chunks_db: BaseKVStorage, + knowledge_graph_inst: BaseGraphStorage, + query: str = None, + chunks_vdb: BaseVectorStorage = None, + chunk_tracking: dict = None, + query_embedding=None, +): + """ + Find text chunks related to entities using configurable chunk selection method. + + This function supports two chunk selection strategies: + 1. WEIGHT: Linear gradient weighted polling based on chunk occurrence count + 2. VECTOR: Vector similarity-based selection using embedding cosine similarity + """ + logger.debug(f"Finding text chunks from {len(node_datas)} entities") + + if not node_datas: + return [] + + # Step 1: Collect all text chunks for each entity + entities_with_chunks = [] + for entity in node_datas: + if entity.get("source_id"): + chunks = split_string_by_multi_markers( + entity["source_id"], [GRAPH_FIELD_SEP] + ) + if chunks: + entities_with_chunks.append( + { + "entity_name": entity["entity_name"], + "chunks": chunks, + "entity_data": entity, + } + ) + + if not entities_with_chunks: + logger.warning("No entities with text chunks found") + return [] + + kg_chunk_pick_method = text_chunks_db.global_config.get( + "kg_chunk_pick_method", DEFAULT_KG_CHUNK_PICK_METHOD + ) + max_related_chunks = text_chunks_db.global_config.get( + "related_chunk_number", DEFAULT_RELATED_CHUNK_NUMBER + ) + + # Step 2: Count chunk occurrences and deduplicate (keep chunks from earlier positioned entities) + chunk_occurrence_count = {} + for entity_info in entities_with_chunks: + deduplicated_chunks = [] + for chunk_id in entity_info["chunks"]: + chunk_occurrence_count[chunk_id] = ( + chunk_occurrence_count.get(chunk_id, 0) + 1 + ) + + # If this is the first occurrence (count == 1), keep it; otherwise skip (duplicate from later position) + if chunk_occurrence_count[chunk_id] == 1: + deduplicated_chunks.append(chunk_id) + # count > 1 means this chunk appeared in an earlier entity, so skip it + + # Update entity's chunks to deduplicated chunks + entity_info["chunks"] = deduplicated_chunks + + # Step 3: Sort chunks for each entity by occurrence count (higher count = higher priority) + total_entity_chunks = 0 + for entity_info in entities_with_chunks: + sorted_chunks = sorted( + entity_info["chunks"], + key=lambda chunk_id: chunk_occurrence_count.get(chunk_id, 0), + reverse=True, + ) + entity_info["sorted_chunks"] = sorted_chunks + total_entity_chunks += len(sorted_chunks) + + selected_chunk_ids = [] # Initialize to avoid UnboundLocalError + + # Step 4: Apply the selected chunk selection algorithm + # Pick by vector similarity: + # The order of text chunks aligns with the naive retrieval's destination. + # When reranking is disabled, the text chunks delivered to the LLM tend to favor naive retrieval. + if kg_chunk_pick_method == "VECTOR" and query and chunks_vdb: + num_of_chunks = int(max_related_chunks * len(entities_with_chunks) / 2) + + # Get embedding function from global config + actual_embedding_func = text_chunks_db.embedding_func + if not actual_embedding_func: + logger.warning("No embedding function found, falling back to WEIGHT method") + kg_chunk_pick_method = "WEIGHT" + else: + try: + selected_chunk_ids = await pick_by_vector_similarity( + query=query, + text_chunks_storage=text_chunks_db, + chunks_vdb=chunks_vdb, + num_of_chunks=num_of_chunks, + entity_info=entities_with_chunks, + embedding_func=actual_embedding_func, + query_embedding=query_embedding, + ) + + if selected_chunk_ids == []: + kg_chunk_pick_method = "WEIGHT" + logger.warning( + "No entity-related chunks selected by vector similarity, falling back to WEIGHT method" + ) + else: + logger.info( + f"Selecting {len(selected_chunk_ids)} from {total_entity_chunks} entity-related chunks by vector similarity" + ) + + except Exception as e: + logger.error( + f"Error in vector similarity sorting: {e}, falling back to WEIGHT method" + ) + kg_chunk_pick_method = "WEIGHT" + + if kg_chunk_pick_method == "WEIGHT": + # Pick by entity and chunk weight: + # When reranking is disabled, delivered more solely KG related chunks to the LLM + selected_chunk_ids = pick_by_weighted_polling( + entities_with_chunks, max_related_chunks, min_related_chunks=1 + ) + + logger.info( + f"Selecting {len(selected_chunk_ids)} from {total_entity_chunks} entity-related chunks by weighted polling" + ) + + if not selected_chunk_ids: + return [] + + # Step 5: Batch retrieve chunk data + unique_chunk_ids = list( + dict.fromkeys(selected_chunk_ids) + ) # Remove duplicates while preserving order + chunk_data_list = await text_chunks_db.get_by_ids(unique_chunk_ids) + + # Step 6: Build result chunks with valid data and update chunk tracking + result_chunks = [] + for i, (chunk_id, chunk_data) in enumerate(zip(unique_chunk_ids, chunk_data_list)): + if chunk_data is not None and "content" in chunk_data: + chunk_data_copy = chunk_data.copy() + chunk_data_copy["source_type"] = "entity" + chunk_data_copy["chunk_id"] = chunk_id # Add chunk_id for deduplication + result_chunks.append(chunk_data_copy) + + # Update chunk tracking if provided + if chunk_tracking is not None: + chunk_tracking[chunk_id] = { + "source": "E", + "frequency": chunk_occurrence_count.get(chunk_id, 1), + "order": i + 1, # 1-based order in final entity-related results + } + + return result_chunks + + +async def _get_edge_data( + keywords, + knowledge_graph_inst: BaseGraphStorage, + relationships_vdb: BaseVectorStorage, + query_param: QueryParam, +): + logger.info( + f"Query edges: {keywords} (top_k:{query_param.top_k}, cosine:{relationships_vdb.cosine_better_than_threshold})" + ) + + results = await relationships_vdb.query(keywords, top_k=query_param.top_k) + + if not len(results): + return [], [] + + # Prepare edge pairs in two forms: + # For the batch edge properties function, use dicts. + edge_pairs_dicts = [{"src": r["src_id"], "tgt": r["tgt_id"]} for r in results] + edge_data_dict = await knowledge_graph_inst.get_edges_batch(edge_pairs_dicts) + + # Reconstruct edge_datas list in the same order as results. + edge_datas = [] + for k in results: + pair = (k["src_id"], k["tgt_id"]) + edge_props = edge_data_dict.get(pair) + if edge_props is not None: + if "weight" not in edge_props: + logger.warning( + f"Edge {pair} missing 'weight' attribute, using default value 1.0" + ) + edge_props["weight"] = 1.0 + + # Keep edge data without rank, maintain vector search order + combined = { + "src_id": k["src_id"], + "tgt_id": k["tgt_id"], + "created_at": k.get("created_at", None), + **edge_props, + } + edge_datas.append(combined) + + # Relations maintain vector search order (sorted by similarity) + + use_entities = await _find_most_related_entities_from_relationships( + edge_datas, + query_param, + knowledge_graph_inst, + ) + + logger.info( + f"Global query: {len(use_entities)} entites, {len(edge_datas)} relations" + ) + + return edge_datas, use_entities + + +async def _find_most_related_entities_from_relationships( + edge_datas: list[dict], + query_param: QueryParam, + knowledge_graph_inst: BaseGraphStorage, +): + entity_names = [] + seen = set() + + for e in edge_datas: + if e["src_id"] not in seen: + entity_names.append(e["src_id"]) + seen.add(e["src_id"]) + if e["tgt_id"] not in seen: + entity_names.append(e["tgt_id"]) + seen.add(e["tgt_id"]) + + # Only get nodes data, no need for node degrees + nodes_dict = await knowledge_graph_inst.get_nodes_batch(entity_names) + + # Rebuild the list in the same order as entity_names + node_datas = [] + for entity_name in entity_names: + node = nodes_dict.get(entity_name) + if node is None: + logger.warning(f"Node '{entity_name}' not found in batch retrieval.") + continue + # Combine the node data with the entity name, no rank needed + combined = {**node, "entity_name": entity_name} + node_datas.append(combined) + + return node_datas + + +async def _find_related_text_unit_from_relations( + edge_datas: list[dict], + query_param: QueryParam, + text_chunks_db: BaseKVStorage, + entity_chunks: list[dict] = None, + query: str = None, + chunks_vdb: BaseVectorStorage = None, + chunk_tracking: dict = None, + query_embedding=None, +): + """ + Find text chunks related to relationships using configurable chunk selection method. + + This function supports two chunk selection strategies: + 1. WEIGHT: Linear gradient weighted polling based on chunk occurrence count + 2. VECTOR: Vector similarity-based selection using embedding cosine similarity + """ + logger.debug(f"Finding text chunks from {len(edge_datas)} relations") + + if not edge_datas: + return [] + + # Step 1: Collect all text chunks for each relationship + relations_with_chunks = [] + for relation in edge_datas: + if relation.get("source_id"): + chunks = split_string_by_multi_markers( + relation["source_id"], [GRAPH_FIELD_SEP] + ) + if chunks: + # Build relation identifier + if "src_tgt" in relation: + rel_key = tuple(sorted(relation["src_tgt"])) + else: + rel_key = tuple( + sorted([relation.get("src_id"), relation.get("tgt_id")]) + ) + + relations_with_chunks.append( + { + "relation_key": rel_key, + "chunks": chunks, + "relation_data": relation, + } + ) + + if not relations_with_chunks: + logger.warning("No relation-related chunks found") + return [] + + kg_chunk_pick_method = text_chunks_db.global_config.get( + "kg_chunk_pick_method", DEFAULT_KG_CHUNK_PICK_METHOD + ) + max_related_chunks = text_chunks_db.global_config.get( + "related_chunk_number", DEFAULT_RELATED_CHUNK_NUMBER + ) + + # Step 2: Count chunk occurrences and deduplicate (keep chunks from earlier positioned relationships) + # Also remove duplicates with entity_chunks + + # Extract chunk IDs from entity_chunks for deduplication + entity_chunk_ids = set() + if entity_chunks: + for chunk in entity_chunks: + chunk_id = chunk.get("chunk_id") + if chunk_id: + entity_chunk_ids.add(chunk_id) + + chunk_occurrence_count = {} + # Track unique chunk_ids that have been removed to avoid double counting + removed_entity_chunk_ids = set() + + for relation_info in relations_with_chunks: + deduplicated_chunks = [] + for chunk_id in relation_info["chunks"]: + # Skip chunks that already exist in entity_chunks + if chunk_id in entity_chunk_ids: + # Only count each unique chunk_id once + removed_entity_chunk_ids.add(chunk_id) + continue + + chunk_occurrence_count[chunk_id] = ( + chunk_occurrence_count.get(chunk_id, 0) + 1 + ) + + # If this is the first occurrence (count == 1), keep it; otherwise skip (duplicate from later position) + if chunk_occurrence_count[chunk_id] == 1: + deduplicated_chunks.append(chunk_id) + # count > 1 means this chunk appeared in an earlier relationship, so skip it + + # Update relationship's chunks to deduplicated chunks + relation_info["chunks"] = deduplicated_chunks + + # Check if any relations still have chunks after deduplication + relations_with_chunks = [ + relation_info + for relation_info in relations_with_chunks + if relation_info["chunks"] + ] + + if not relations_with_chunks: + logger.info( + f"Find no additional relations-related chunks from {len(edge_datas)} relations" + ) + return [] + + # Step 3: Sort chunks for each relationship by occurrence count (higher count = higher priority) + total_relation_chunks = 0 + for relation_info in relations_with_chunks: + sorted_chunks = sorted( + relation_info["chunks"], + key=lambda chunk_id: chunk_occurrence_count.get(chunk_id, 0), + reverse=True, + ) + relation_info["sorted_chunks"] = sorted_chunks + total_relation_chunks += len(sorted_chunks) + + logger.info( + f"Find {total_relation_chunks} additional chunks in {len(relations_with_chunks)} relations (deduplicated {len(removed_entity_chunk_ids)})" + ) + + # Step 4: Apply the selected chunk selection algorithm + selected_chunk_ids = [] # Initialize to avoid UnboundLocalError + + if kg_chunk_pick_method == "VECTOR" and query and chunks_vdb: + num_of_chunks = int(max_related_chunks * len(relations_with_chunks) / 2) + + # Get embedding function from global config + actual_embedding_func = text_chunks_db.embedding_func + if not actual_embedding_func: + logger.warning("No embedding function found, falling back to WEIGHT method") + kg_chunk_pick_method = "WEIGHT" + else: + try: + selected_chunk_ids = await pick_by_vector_similarity( + query=query, + text_chunks_storage=text_chunks_db, + chunks_vdb=chunks_vdb, + num_of_chunks=num_of_chunks, + entity_info=relations_with_chunks, + embedding_func=actual_embedding_func, + query_embedding=query_embedding, + ) + + if selected_chunk_ids == []: + kg_chunk_pick_method = "WEIGHT" + logger.warning( + "No relation-related chunks selected by vector similarity, falling back to WEIGHT method" + ) + else: + logger.info( + f"Selecting {len(selected_chunk_ids)} from {total_relation_chunks} relation-related chunks by vector similarity" + ) + + except Exception as e: + logger.error( + f"Error in vector similarity sorting: {e}, falling back to WEIGHT method" + ) + kg_chunk_pick_method = "WEIGHT" + + if kg_chunk_pick_method == "WEIGHT": + # Apply linear gradient weighted polling algorithm + selected_chunk_ids = pick_by_weighted_polling( + relations_with_chunks, max_related_chunks, min_related_chunks=1 + ) + + logger.info( + f"Selecting {len(selected_chunk_ids)} from {total_relation_chunks} relation-related chunks by weighted polling" + ) + + logger.debug( + f"KG related chunks: {len(entity_chunks)} from entitys, {len(selected_chunk_ids)} from relations" + ) + + if not selected_chunk_ids: + return [] + + # Step 5: Batch retrieve chunk data + unique_chunk_ids = list( + dict.fromkeys(selected_chunk_ids) + ) # Remove duplicates while preserving order + chunk_data_list = await text_chunks_db.get_by_ids(unique_chunk_ids) + + # Step 6: Build result chunks with valid data and update chunk tracking + result_chunks = [] + for i, (chunk_id, chunk_data) in enumerate(zip(unique_chunk_ids, chunk_data_list)): + if chunk_data is not None and "content" in chunk_data: + chunk_data_copy = chunk_data.copy() + chunk_data_copy["source_type"] = "relationship" + chunk_data_copy["chunk_id"] = chunk_id # Add chunk_id for deduplication + result_chunks.append(chunk_data_copy) + + # Update chunk tracking if provided + if chunk_tracking is not None: + chunk_tracking[chunk_id] = { + "source": "R", + "frequency": chunk_occurrence_count.get(chunk_id, 1), + "order": i + 1, # 1-based order in final relation-related results + } + + return result_chunks + + +@overload +async def naive_query( + query: str, + chunks_vdb: BaseVectorStorage, + query_param: QueryParam, + global_config: dict[str, str], + hashing_kv: BaseKVStorage | None = None, + system_prompt: str | None = None, + return_raw_data: Literal[True] = True, +) -> dict[str, Any]: ... + + +@overload +async def naive_query( + query: str, + chunks_vdb: BaseVectorStorage, + query_param: QueryParam, + global_config: dict[str, str], + hashing_kv: BaseKVStorage | None = None, + system_prompt: str | None = None, + return_raw_data: Literal[False] = False, +) -> str | AsyncIterator[str]: ... + + +async def naive_query( + query: str, + chunks_vdb: BaseVectorStorage, + query_param: QueryParam, + global_config: dict[str, str], + hashing_kv: BaseKVStorage | None = None, + system_prompt: str | None = None, +) -> QueryResult | None: + """ + Execute naive query and return unified QueryResult object. + + Args: + query: Query string + chunks_vdb: Document chunks vector database + query_param: Query parameters + global_config: Global configuration + hashing_kv: Cache storage + system_prompt: System prompt + + Returns: + QueryResult | None: Unified query result object containing: + - content: Non-streaming response text content + - response_iterator: Streaming response iterator + - raw_data: Complete structured data (including references and metadata) + - is_streaming: Whether this is a streaming result + + Returns None when no relevant chunks are retrieved. + """ + + if not query: + return QueryResult(content=PROMPTS["fail_response"]) + + if query_param.model_func: + use_model_func = query_param.model_func + else: + use_model_func = global_config["llm_model_func"] + # Apply higher priority (5) to query relation LLM function + use_model_func = partial(use_model_func, _priority=5) + + tokenizer: Tokenizer = global_config["tokenizer"] + if not tokenizer: + logger.error("Tokenizer not found in global configuration.") + return QueryResult(content=PROMPTS["fail_response"]) + + chunks = await _get_vector_context(query, chunks_vdb, query_param, None) + + if chunks is None or len(chunks) == 0: + logger.info( + "[naive_query] No relevant document chunks found; returning no-result." + ) + return None + + # Calculate dynamic token limit for chunks + max_total_tokens = getattr( + query_param, + "max_total_tokens", + global_config.get("max_total_tokens", DEFAULT_MAX_TOTAL_TOKENS), + ) + + # Calculate system prompt template tokens (excluding content_data) + user_prompt = f"\n\n{query_param.user_prompt}" if query_param.user_prompt else "n/a" + response_type = ( + query_param.response_type + if query_param.response_type + else "Multiple Paragraphs" + ) + + # Use the provided system prompt or default + sys_prompt_template = ( + system_prompt if system_prompt else PROMPTS["naive_rag_response"] + ) + + # Create a preliminary system prompt with empty content_data to calculate overhead + pre_sys_prompt = sys_prompt_template.format( + response_type=response_type, + user_prompt=user_prompt, + content_data="", # Empty for overhead calculation + ) + + # Calculate available tokens for chunks + sys_prompt_tokens = len(tokenizer.encode(pre_sys_prompt)) + query_tokens = len(tokenizer.encode(query)) + buffer_tokens = 200 # reserved for reference list and safety buffer + available_chunk_tokens = max_total_tokens - ( + sys_prompt_tokens + query_tokens + buffer_tokens + ) + + logger.debug( + f"Naive query token allocation - Total: {max_total_tokens}, SysPrompt: {sys_prompt_tokens}, Query: {query_tokens}, Buffer: {buffer_tokens}, Available for chunks: {available_chunk_tokens}" + ) + + # Process chunks using unified processing with dynamic token limit + processed_chunks = await process_chunks_unified( + query=query, + unique_chunks=chunks, + query_param=query_param, + global_config=global_config, + source_type="vector", + chunk_token_limit=available_chunk_tokens, # Pass dynamic limit + ) + + # Generate reference list from processed chunks using the new common function + reference_list, processed_chunks_with_ref_ids = generate_reference_list_from_chunks( + processed_chunks + ) + + logger.info(f"Final context: {len(processed_chunks_with_ref_ids)} chunks") + + # Build raw data structure for naive mode using processed chunks with reference IDs + raw_data = convert_to_user_format( + [], # naive mode has no entities + [], # naive mode has no relationships + processed_chunks_with_ref_ids, + reference_list, + "naive", + ) + + # Add complete metadata for naive mode + if "metadata" not in raw_data: + raw_data["metadata"] = {} + raw_data["metadata"]["keywords"] = { + "high_level": [], # naive mode has no keyword extraction + "low_level": [], # naive mode has no keyword extraction + } + raw_data["metadata"]["processing_info"] = { + "total_chunks_found": len(chunks), + "final_chunks_count": len(processed_chunks_with_ref_ids), + } + + # Build chunks_context from processed chunks with reference IDs + chunks_context = [] + for i, chunk in enumerate(processed_chunks_with_ref_ids): + chunks_context.append( + { + "reference_id": chunk["reference_id"], + "content": chunk["content"], + } + ) + + text_units_str = "\n".join( + json.dumps(text_unit, ensure_ascii=False) for text_unit in chunks_context + ) + reference_list_str = "\n".join( + f"[{ref['reference_id']}] {ref['file_path']}" + for ref in reference_list + if ref["reference_id"] + ) + + naive_context_template = PROMPTS["naive_query_context"] + context_content = naive_context_template.format( + text_chunks_str=text_units_str, + reference_list_str=reference_list_str, + ) + + if query_param.only_need_context and not query_param.only_need_prompt: + return QueryResult(content=context_content, raw_data=raw_data) + + sys_prompt = sys_prompt_template.format( + response_type=query_param.response_type, + user_prompt=user_prompt, + content_data=context_content, + ) + + user_query = query + + if query_param.only_need_prompt: + prompt_content = "\n\n".join([sys_prompt, "---User Query---", user_query]) + return QueryResult(content=prompt_content, raw_data=raw_data) + + # Handle cache + args_hash = compute_args_hash( + query_param.mode, + query, + query_param.response_type, + query_param.top_k, + query_param.chunk_top_k, + query_param.max_entity_tokens, + query_param.max_relation_tokens, + query_param.max_total_tokens, + query_param.user_prompt or "", + query_param.enable_rerank, + ) + cached_result = await handle_cache( + hashing_kv, args_hash, user_query, query_param.mode, cache_type="query" + ) + if cached_result is not None: + cached_response, _ = cached_result # Extract content, ignore timestamp + logger.info( + " == LLM cache == Query cache hit, using cached response as query result" + ) + response = cached_response + else: + response = await use_model_func( + user_query, + system_prompt=sys_prompt, + history_messages=query_param.conversation_history, + enable_cot=True, + stream=query_param.stream, + ) + + if hashing_kv and hashing_kv.global_config.get("enable_llm_cache"): + queryparam_dict = { + "mode": query_param.mode, + "response_type": query_param.response_type, + "top_k": query_param.top_k, + "chunk_top_k": query_param.chunk_top_k, + "max_entity_tokens": query_param.max_entity_tokens, + "max_relation_tokens": query_param.max_relation_tokens, + "max_total_tokens": query_param.max_total_tokens, + "user_prompt": query_param.user_prompt or "", + "enable_rerank": query_param.enable_rerank, + } + await save_to_cache( + hashing_kv, + CacheData( + args_hash=args_hash, + content=response, + prompt=query, + mode=query_param.mode, + cache_type="query", + queryparam=queryparam_dict, + ), + ) + + # Return unified result based on actual response type + if isinstance(response, str): + # Non-streaming response (string) + if len(response) > len(sys_prompt): + response = ( + response[len(sys_prompt) :] + .replace(sys_prompt, "") + .replace("user", "") + .replace("model", "") + .replace(query, "") + .replace("", "") + .replace("", "") + .strip() + ) + + return QueryResult(content=response, raw_data=raw_data) + else: + # Streaming response (AsyncIterator) + return QueryResult( + response_iterator=response, raw_data=raw_data, is_streaming=True + ) diff --git a/LightRAG/lightrag/prompt.py b/LightRAG/lightrag/prompt.py new file mode 100644 index 00000000..bf514fe8 --- /dev/null +++ b/LightRAG/lightrag/prompt.py @@ -0,0 +1,421 @@ +from __future__ import annotations +from typing import Any + + +PROMPTS: dict[str, Any] = {} + +# All delimiters must be formatted as "<|UPPER_CASE_STRING|>" +PROMPTS["DEFAULT_TUPLE_DELIMITER"] = "<|#|>" +PROMPTS["DEFAULT_COMPLETION_DELIMITER"] = "<|COMPLETE|>" + +PROMPTS["entity_extraction_system_prompt"] = """---Role--- +You are a Knowledge Graph Specialist responsible for extracting entities and relationships from the input text. + +---Instructions--- +1. **Entity Extraction & Output:** + * **Identification:** Identify clearly defined and meaningful entities in the input text. + * **Entity Details:** For each identified entity, extract the following information: + * `entity_name`: The name of the entity. If the entity name is case-insensitive, capitalize the first letter of each significant word (title case). Ensure **consistent naming** across the entire extraction process. + * `entity_type`: Categorize the entity using one of the following types: `{entity_types}`. If none of the provided entity types apply, do not add new entity type and classify it as `Other`. + * `entity_description`: Provide a concise yet comprehensive description of the entity's attributes and activities, based *solely* on the information present in the input text. + * **Output Format - Entities:** Output a total of 4 fields for each entity, delimited by `{tuple_delimiter}`, on a single line. The first field *must* be the literal string `entity`. + * Format: `entity{tuple_delimiter}entity_name{tuple_delimiter}entity_type{tuple_delimiter}entity_description` + +2. **Relationship Extraction & Output:** + * **Identification:** Identify direct, clearly stated, and meaningful relationships between previously extracted entities. + * **N-ary Relationship Decomposition:** If a single statement describes a relationship involving more than two entities (an N-ary relationship), decompose it into multiple binary (two-entity) relationship pairs for separate description. + * **Example:** For "Alice, Bob, and Carol collaborated on Project X," extract binary relationships such as "Alice collaborated with Project X," "Bob collaborated with Project X," and "Carol collaborated with Project X," or "Alice collaborated with Bob," based on the most reasonable binary interpretations. + * **Relationship Details:** For each binary relationship, extract the following fields: + * `source_entity`: The name of the source entity. Ensure **consistent naming** with entity extraction. Capitalize the first letter of each significant word (title case) if the name is case-insensitive. + * `target_entity`: The name of the target entity. Ensure **consistent naming** with entity extraction. Capitalize the first letter of each significant word (title case) if the name is case-insensitive. + * `relationship_keywords`: One or more high-level keywords summarizing the overarching nature, concepts, or themes of the relationship. Multiple keywords within this field must be separated by a comma `,`. **DO NOT use `{tuple_delimiter}` for separating multiple keywords within this field.** + * `relationship_description`: A concise explanation of the nature of the relationship between the source and target entities, providing a clear rationale for their connection. + * **Output Format - Relationships:** Output a total of 5 fields for each relationship, delimited by `{tuple_delimiter}`, on a single line. The first field *must* be the literal string `relation`. + * Format: `relation{tuple_delimiter}source_entity{tuple_delimiter}target_entity{tuple_delimiter}relationship_keywords{tuple_delimiter}relationship_description` + +3. **Delimiter Usage Protocol:** + * The `{tuple_delimiter}` is a complete, atomic marker and **must not be filled with content**. It serves strictly as a field separator. + * **Incorrect Example:** `entity{tuple_delimiter}Tokyo<|location|>Tokyo is the capital of Japan.` + * **Correct Example:** `entity{tuple_delimiter}Tokyo{tuple_delimiter}location{tuple_delimiter}Tokyo is the capital of Japan.` + +4. **Relationship Direction & Duplication:** + * Treat all relationships as **undirected** unless explicitly stated otherwise. Swapping the source and target entities for an undirected relationship does not constitute a new relationship. + * Avoid outputting duplicate relationships. + +5. **Output Order & Prioritization:** + * Output all extracted entities first, followed by all extracted relationships. + * Within the list of relationships, prioritize and output those relationships that are **most significant** to the core meaning of the input text first. + +6. **Context & Objectivity:** + * Ensure all entity names and descriptions are written in the **third person**. + * Explicitly name the subject or object; **avoid using pronouns** such as `this article`, `this paper`, `our company`, `I`, `you`, and `he/she`. + +7. **Language & Proper Nouns:** + * The entire output (entity names, keywords, and descriptions) must be written in `{language}`. + * Proper nouns (e.g., personal names, place names, organization names) should be retained in their original language if a proper, widely accepted translation is not available or would cause ambiguity. + +8. **Completion Signal:** Output the literal string `{completion_delimiter}` only after all entities and relationships, following all criteria, have been completely extracted and outputted. + +---Examples--- +{examples} + +---Real Data to be Processed--- + +Entity_types: [{entity_types}] +Text: +``` +{input_text} +``` +""" + +PROMPTS["entity_extraction_user_prompt"] = """---Task--- +Extract entities and relationships from the input text to be processed. + +---Instructions--- +1. **Strict Adherence to Format:** Strictly adhere to all format requirements for entity and relationship lists, including output order, field delimiters, and proper noun handling, as specified in the system prompt. +2. **Output Content Only:** Output *only* the extracted list of entities and relationships. Do not include any introductory or concluding remarks, explanations, or additional text before or after the list. +3. **Completion Signal:** Output `{completion_delimiter}` as the final line after all relevant entities and relationships have been extracted and presented. +4. **Output Language:** Ensure the output language is {language}. Proper nouns (e.g., personal names, place names, organization names) must be kept in their original language and not translated. + + +""" + +PROMPTS["entity_continue_extraction_user_prompt"] = """---Task--- +Based on the last extraction task, identify and extract any **missed or incorrectly formatted** entities and relationships from the input text. + +---Instructions--- +1. **Strict Adherence to System Format:** Strictly adhere to all format requirements for entity and relationship lists, including output order, field delimiters, and proper noun handling, as specified in the system instructions. +2. **Focus on Corrections/Additions:** + * **Do NOT** re-output entities and relationships that were **correctly and fully** extracted in the last task. + * If an entity or relationship was **missed** in the last task, extract and output it now according to the system format. + * If an entity or relationship was **truncated, had missing fields, or was otherwise incorrectly formatted** in the last task, re-output the *corrected and complete* version in the specified format. +3. **Output Format - Entities:** Output a total of 4 fields for each entity, delimited by `{tuple_delimiter}`, on a single line. The first field *must* be the literal string `entity`. +4. **Output Format - Relationships:** Output a total of 5 fields for each relationship, delimited by `{tuple_delimiter}`, on a single line. The first field *must* be the literal string `relation`. +5. **Output Content Only:** Output *only* the extracted list of entities and relationships. Do not include any introductory or concluding remarks, explanations, or additional text before or after the list. +6. **Completion Signal:** Output `{completion_delimiter}` as the final line after all relevant missing or corrected entities and relationships have been extracted and presented. +7. **Output Language:** Ensure the output language is {language}. Proper nouns (e.g., personal names, place names, organization names) must be kept in their original language and not translated. + + +""" + +PROMPTS["entity_extraction_examples"] = [ + """ +``` +while Alex clenched his jaw, the buzz of frustration dull against the backdrop of Taylor's authoritarian certainty. It was this competitive undercurrent that kept him alert, the sense that his and Jordan's shared commitment to discovery was an unspoken rebellion against Cruz's narrowing vision of control and order. + +Then Taylor did something unexpected. They paused beside Jordan and, for a moment, observed the device with something akin to reverence. "If this tech can be understood..." Taylor said, their voice quieter, "It could change the game for us. For all of us." + +The underlying dismissal earlier seemed to falter, replaced by a glimpse of reluctant respect for the gravity of what lay in their hands. Jordan looked up, and for a fleeting heartbeat, their eyes locked with Taylor's, a wordless clash of wills softening into an uneasy truce. + +It was a small transformation, barely perceptible, but one that Alex noted with an inward nod. They had all been brought here by different paths +``` + + +entity{tuple_delimiter}Alex{tuple_delimiter}person{tuple_delimiter}Alex is a character who experiences frustration and is observant of the dynamics among other characters. +entity{tuple_delimiter}Taylor{tuple_delimiter}person{tuple_delimiter}Taylor is portrayed with authoritarian certainty and shows a moment of reverence towards a device, indicating a change in perspective. +entity{tuple_delimiter}Jordan{tuple_delimiter}person{tuple_delimiter}Jordan shares a commitment to discovery and has a significant interaction with Taylor regarding a device. +entity{tuple_delimiter}Cruz{tuple_delimiter}person{tuple_delimiter}Cruz is associated with a vision of control and order, influencing the dynamics among other characters. +entity{tuple_delimiter}The Device{tuple_delimiter}equipment{tuple_delimiter}The Device is central to the story, with potential game-changing implications, and is revered by Taylor. +relation{tuple_delimiter}Alex{tuple_delimiter}Taylor{tuple_delimiter}power dynamics, observation{tuple_delimiter}Alex observes Taylor's authoritarian behavior and notes changes in Taylor's attitude toward the device. +relation{tuple_delimiter}Alex{tuple_delimiter}Jordan{tuple_delimiter}shared goals, rebellion{tuple_delimiter}Alex and Jordan share a commitment to discovery, which contrasts with Cruz's vision.) +relation{tuple_delimiter}Taylor{tuple_delimiter}Jordan{tuple_delimiter}conflict resolution, mutual respect{tuple_delimiter}Taylor and Jordan interact directly regarding the device, leading to a moment of mutual respect and an uneasy truce. +relation{tuple_delimiter}Jordan{tuple_delimiter}Cruz{tuple_delimiter}ideological conflict, rebellion{tuple_delimiter}Jordan's commitment to discovery is in rebellion against Cruz's vision of control and order. +relation{tuple_delimiter}Taylor{tuple_delimiter}The Device{tuple_delimiter}reverence, technological significance{tuple_delimiter}Taylor shows reverence towards the device, indicating its importance and potential impact. +{completion_delimiter} + +""", + """ +``` +Stock markets faced a sharp downturn today as tech giants saw significant declines, with the global tech index dropping by 3.4% in midday trading. Analysts attribute the selloff to investor concerns over rising interest rates and regulatory uncertainty. + +Among the hardest hit, nexon technologies saw its stock plummet by 7.8% after reporting lower-than-expected quarterly earnings. In contrast, Omega Energy posted a modest 2.1% gain, driven by rising oil prices. + +Meanwhile, commodity markets reflected a mixed sentiment. Gold futures rose by 1.5%, reaching $2,080 per ounce, as investors sought safe-haven assets. Crude oil prices continued their rally, climbing to $87.60 per barrel, supported by supply constraints and strong demand. + +Financial experts are closely watching the Federal Reserve's next move, as speculation grows over potential rate hikes. The upcoming policy announcement is expected to influence investor confidence and overall market stability. +``` + + +entity{tuple_delimiter}Global Tech Index{tuple_delimiter}category{tuple_delimiter}The Global Tech Index tracks the performance of major technology stocks and experienced a 3.4% decline today. +entity{tuple_delimiter}Nexon Technologies{tuple_delimiter}organization{tuple_delimiter}Nexon Technologies is a tech company that saw its stock decline by 7.8% after disappointing earnings. +entity{tuple_delimiter}Omega Energy{tuple_delimiter}organization{tuple_delimiter}Omega Energy is an energy company that gained 2.1% in stock value due to rising oil prices. +entity{tuple_delimiter}Gold Futures{tuple_delimiter}product{tuple_delimiter}Gold futures rose by 1.5%, indicating increased investor interest in safe-haven assets. +entity{tuple_delimiter}Crude Oil{tuple_delimiter}product{tuple_delimiter}Crude oil prices rose to $87.60 per barrel due to supply constraints and strong demand. +entity{tuple_delimiter}Market Selloff{tuple_delimiter}category{tuple_delimiter}Market selloff refers to the significant decline in stock values due to investor concerns over interest rates and regulations. +entity{tuple_delimiter}Federal Reserve Policy Announcement{tuple_delimiter}category{tuple_delimiter}The Federal Reserve's upcoming policy announcement is expected to impact investor confidence and market stability. +entity{tuple_delimiter}3.4% Decline{tuple_delimiter}category{tuple_delimiter}The Global Tech Index experienced a 3.4% decline in midday trading. +relation{tuple_delimiter}Global Tech Index{tuple_delimiter}Market Selloff{tuple_delimiter}market performance, investor sentiment{tuple_delimiter}The decline in the Global Tech Index is part of the broader market selloff driven by investor concerns. +relation{tuple_delimiter}Nexon Technologies{tuple_delimiter}Global Tech Index{tuple_delimiter}company impact, index movement{tuple_delimiter}Nexon Technologies' stock decline contributed to the overall drop in the Global Tech Index. +relation{tuple_delimiter}Gold Futures{tuple_delimiter}Market Selloff{tuple_delimiter}market reaction, safe-haven investment{tuple_delimiter}Gold prices rose as investors sought safe-haven assets during the market selloff. +relation{tuple_delimiter}Federal Reserve Policy Announcement{tuple_delimiter}Market Selloff{tuple_delimiter}interest rate impact, financial regulation{tuple_delimiter}Speculation over Federal Reserve policy changes contributed to market volatility and investor selloff. +{completion_delimiter} + +""", + """ +``` +At the World Athletics Championship in Tokyo, Noah Carter broke the 100m sprint record using cutting-edge carbon-fiber spikes. +``` + + +entity{tuple_delimiter}World Athletics Championship{tuple_delimiter}event{tuple_delimiter}The World Athletics Championship is a global sports competition featuring top athletes in track and field. +entity{tuple_delimiter}Tokyo{tuple_delimiter}location{tuple_delimiter}Tokyo is the host city of the World Athletics Championship. +entity{tuple_delimiter}Noah Carter{tuple_delimiter}person{tuple_delimiter}Noah Carter is a sprinter who set a new record in the 100m sprint at the World Athletics Championship. +entity{tuple_delimiter}100m Sprint Record{tuple_delimiter}category{tuple_delimiter}The 100m sprint record is a benchmark in athletics, recently broken by Noah Carter. +entity{tuple_delimiter}Carbon-Fiber Spikes{tuple_delimiter}equipment{tuple_delimiter}Carbon-fiber spikes are advanced sprinting shoes that provide enhanced speed and traction. +entity{tuple_delimiter}World Athletics Federation{tuple_delimiter}organization{tuple_delimiter}The World Athletics Federation is the governing body overseeing the World Athletics Championship and record validations. +relation{tuple_delimiter}World Athletics Championship{tuple_delimiter}Tokyo{tuple_delimiter}event location, international competition{tuple_delimiter}The World Athletics Championship is being hosted in Tokyo. +relation{tuple_delimiter}Noah Carter{tuple_delimiter}100m Sprint Record{tuple_delimiter}athlete achievement, record-breaking{tuple_delimiter}Noah Carter set a new 100m sprint record at the championship. +relation{tuple_delimiter}Noah Carter{tuple_delimiter}Carbon-Fiber Spikes{tuple_delimiter}athletic equipment, performance boost{tuple_delimiter}Noah Carter used carbon-fiber spikes to enhance performance during the race. +relation{tuple_delimiter}Noah Carter{tuple_delimiter}World Athletics Championship{tuple_delimiter}athlete participation, competition{tuple_delimiter}Noah Carter is competing at the World Athletics Championship. +{completion_delimiter} + +""", +] + +PROMPTS["summarize_entity_descriptions"] = """---Role--- +You are a Knowledge Graph Specialist, proficient in data curation and synthesis. + +---Task--- +Your task is to synthesize a list of descriptions of a given entity or relation into a single, comprehensive, and cohesive summary. + +---Instructions--- +1. Input Format: The description list is provided in JSON format. Each JSON object (representing a single description) appears on a new line within the `Description List` section. +2. Output Format: The merged description will be returned as plain text, presented in multiple paragraphs, without any additional formatting or extraneous comments before or after the summary. +3. Comprehensiveness: The summary must integrate all key information from *every* provided description. Do not omit any important facts or details. +4. Context: Ensure the summary is written from an objective, third-person perspective; explicitly mention the name of the entity or relation for full clarity and context. +5. Context & Objectivity: + - Write the summary from an objective, third-person perspective. + - Explicitly mention the full name of the entity or relation at the beginning of the summary to ensure immediate clarity and context. +6. Conflict Handling: + - In cases of conflicting or inconsistent descriptions, first determine if these conflicts arise from multiple, distinct entities or relationships that share the same name. + - If distinct entities/relations are identified, summarize each one *separately* within the overall output. + - If conflicts within a single entity/relation (e.g., historical discrepancies) exist, attempt to reconcile them or present both viewpoints with noted uncertainty. +7. Length Constraint:The summary's total length must not exceed {summary_length} tokens, while still maintaining depth and completeness. +8. Language: The entire output must be written in {language}. Proper nouns (e.g., personal names, place names, organization names) may in their original language if proper translation is not available. + - The entire output must be written in {language}. + - Proper nouns (e.g., personal names, place names, organization names) should be retained in their original language if a proper, widely accepted translation is not available or would cause ambiguity. + +---Input--- +{description_type} Name: {description_name} + +Description List: + +``` +{description_list} +``` + +---Output--- +""" + +PROMPTS["fail_response"] = ( + "Sorry, I'm not able to provide an answer to that question.[no-context]" +) + +PROMPTS["rag_response"] = """---Role--- + +You are an expert AI assistant specializing in synthesizing information from a provided knowledge base. Your primary function is to answer user queries accurately by ONLY using the information within the provided **Context**. + +---Goal--- + +Generate a comprehensive, well-structured answer to the user query. +The answer must integrate relevant facts from the Knowledge Graph and Document Chunks found in the **Context**. +Consider the conversation history if provided to maintain conversational flow and avoid repeating information. + +---Instructions--- + +1. Step-by-Step Instruction: + - Carefully determine the user's query intent in the context of the conversation history to fully understand the user's information need. + - Scrutinize both `Knowledge Graph Data` and `Document Chunks` in the **Context**. Identify and extract all pieces of information that are directly relevant to answering the user query. + - Weave the extracted facts into a coherent and logical response. Your own knowledge must ONLY be used to formulate fluent sentences and connect ideas, NOT to introduce any external information. + - Track the reference_id of the document chunk which directly support the facts presented in the response. Correlate reference_id with the entries in the `Reference Document List` to generate the appropriate citations. + - Generate a references section at the end of the response. Each reference document must directly support the facts presented in the response. + - Do not generate anything after the reference section. + +2. Content & Grounding: + - Strictly adhere to the provided context from the **Context**; DO NOT invent, assume, or infer any information not explicitly stated. + - If the answer cannot be found in the **Context**, state that you do not have enough information to answer. Do not attempt to guess. + +3. Formatting & Language: + - The response MUST be in the same language as the user query. + - The response MUST utilize Markdown formatting for enhanced clarity and structure (e.g., headings, bold text, bullet points). + - The response should be presented in {response_type}. + +4. References Section Format: + - The References section should be under heading: `### References` + - Reference list entries should adhere to the format: `* [n] Document Title`. Do not include a caret (`^`) after opening square bracket (`[`). + - The Document Title in the citation must retain its original language. + - Output each citation on an individual line + - Provide maximum of 5 most relevant citations. + - Do not generate footnotes section or any comment, summary, or explanation after the references. + +5. Reference Section Example: +``` +### References + +- [1] Document Title One +- [2] Document Title Two +- [3] Document Title Three +``` + +6. Additional Instructions: {user_prompt} + + +---Context--- + +{context_data} +""" + +PROMPTS["naive_rag_response"] = """---Role--- + +You are an expert AI assistant specializing in synthesizing information from a provided knowledge base. Your primary function is to answer user queries accurately by ONLY using the information within the provided **Context**. + +---Goal--- + +Generate a comprehensive, well-structured answer to the user query. +The answer must integrate relevant facts from the Document Chunks found in the **Context**. +Consider the conversation history if provided to maintain conversational flow and avoid repeating information. + +---Instructions--- + +1. Step-by-Step Instruction: + - Carefully determine the user's query intent in the context of the conversation history to fully understand the user's information need. + - Scrutinize `Document Chunks` in the **Context**. Identify and extract all pieces of information that are directly relevant to answering the user query. + - Weave the extracted facts into a coherent and logical response. Your own knowledge must ONLY be used to formulate fluent sentences and connect ideas, NOT to introduce any external information. + - Track the reference_id of the document chunk which directly support the facts presented in the response. Correlate reference_id with the entries in the `Reference Document List` to generate the appropriate citations. + - Generate a **References** section at the end of the response. Each reference document must directly support the facts presented in the response. + - Do not generate anything after the reference section. + +2. Content & Grounding: + - Strictly adhere to the provided context from the **Context**; DO NOT invent, assume, or infer any information not explicitly stated. + - If the answer cannot be found in the **Context**, state that you do not have enough information to answer. Do not attempt to guess. + +3. Formatting & Language: + - The response MUST be in the same language as the user query. + - The response MUST utilize Markdown formatting for enhanced clarity and structure (e.g., headings, bold text, bullet points). + - The response should be presented in {response_type}. + +4. References Section Format: + - The References section should be under heading: `### References` + - Reference list entries should adhere to the format: `* [n] Document Title`. Do not include a caret (`^`) after opening square bracket (`[`). + - The Document Title in the citation must retain its original language. + - Output each citation on an individual line + - Provide maximum of 5 most relevant citations. + - Do not generate footnotes section or any comment, summary, or explanation after the references. + +5. Reference Section Example: +``` +### References + +- [1] Document Title One +- [2] Document Title Two +- [3] Document Title Three +``` + +6. Additional Instructions: {user_prompt} + + +---Context--- + +{content_data} +""" + +PROMPTS["kg_query_context"] = """ +Knowledge Graph Data (Entity): + +```json +{entities_str} +``` + +Knowledge Graph Data (Relationship): + +```json +{relations_str} +``` + +Document Chunks (Each entry has a reference_id refer to the `Reference Document List`): + +```json +{text_chunks_str} +``` + +Reference Document List (Each entry starts with a [reference_id] that corresponds to entries in the Document Chunks): + +``` +{reference_list_str} +``` + +""" + +PROMPTS["naive_query_context"] = """ +Document Chunks (Each entry has a reference_id refer to the `Reference Document List`): + +```json +{text_chunks_str} +``` + +Reference Document List (Each entry starts with a [reference_id] that corresponds to entries in the Document Chunks): + +``` +{reference_list_str} +``` + +""" + +PROMPTS["keywords_extraction"] = """---Role--- +You are an expert keyword extractor, specializing in analyzing user queries for a Retrieval-Augmented Generation (RAG) system. Your purpose is to identify both high-level and low-level keywords in the user's query that will be used for effective document retrieval. + +---Goal--- +Given a user query, your task is to extract two distinct types of keywords: +1. **high_level_keywords**: for overarching concepts or themes, capturing user's core intent, the subject area, or the type of question being asked. +2. **low_level_keywords**: for specific entities or details, identifying the specific entities, proper nouns, technical jargon, product names, or concrete items. + +---Instructions & Constraints--- +1. **Output Format**: Your output MUST be a valid JSON object and nothing else. Do not include any explanatory text, markdown code fences (like ```json), or any other text before or after the JSON. It will be parsed directly by a JSON parser. +2. **Source of Truth**: All keywords must be explicitly derived from the user query, with both high-level and low-level keyword categories are required to contain content. +3. **Concise & Meaningful**: Keywords should be concise words or meaningful phrases. Prioritize multi-word phrases when they represent a single concept. For example, from "latest financial report of Apple Inc.", you should extract "latest financial report" and "Apple Inc." rather than "latest", "financial", "report", and "Apple". +4. **Handle Edge Cases**: For queries that are too simple, vague, or nonsensical (e.g., "hello", "ok", "asdfghjkl"), you must return a JSON object with empty lists for both keyword types. + +---Examples--- +{examples} + +---Real Data--- +User Query: {query} + +---Output--- +Output:""" + +PROMPTS["keywords_extraction_examples"] = [ + """Example 1: + +Query: "How does international trade influence global economic stability?" + +Output: +{ + "high_level_keywords": ["International trade", "Global economic stability", "Economic impact"], + "low_level_keywords": ["Trade agreements", "Tariffs", "Currency exchange", "Imports", "Exports"] +} + +""", + """Example 2: + +Query: "What are the environmental consequences of deforestation on biodiversity?" + +Output: +{ + "high_level_keywords": ["Environmental consequences", "Deforestation", "Biodiversity loss"], + "low_level_keywords": ["Species extinction", "Habitat destruction", "Carbon emissions", "Rainforest", "Ecosystem"] +} + +""", + """Example 3: + +Query: "What is the role of education in reducing poverty?" + +Output: +{ + "high_level_keywords": ["Education", "Poverty reduction", "Socioeconomic development"], + "low_level_keywords": ["School access", "Literacy rates", "Job training", "Income inequality"] +} + +""", +] diff --git a/LightRAG/lightrag/rerank.py b/LightRAG/lightrag/rerank.py new file mode 100644 index 00000000..35551f5a --- /dev/null +++ b/LightRAG/lightrag/rerank.py @@ -0,0 +1,354 @@ +from __future__ import annotations + +import os +import aiohttp +from typing import Any, List, Dict, Optional +from tenacity import ( + retry, + stop_after_attempt, + wait_exponential, + retry_if_exception_type, +) +from .utils import logger + +from dotenv import load_dotenv + +# use the .env that is inside the current folder +# allows to use different .env file for each lightrag instance +# the OS environment variables take precedence over the .env file +load_dotenv(dotenv_path=".env", override=False) + + +@retry( + stop=stop_after_attempt(3), + wait=wait_exponential(multiplier=1, min=4, max=60), + retry=( + retry_if_exception_type(aiohttp.ClientError) + | retry_if_exception_type(aiohttp.ClientResponseError) + ), +) +async def generic_rerank_api( + query: str, + documents: List[str], + model: str, + base_url: str, + api_key: Optional[str], + top_n: Optional[int] = None, + return_documents: Optional[bool] = None, + extra_body: Optional[Dict[str, Any]] = None, + response_format: str = "standard", # "standard" (Jina/Cohere) or "aliyun" + request_format: str = "standard", # "standard" (Jina/Cohere) or "aliyun" +) -> List[Dict[str, Any]]: + """ + Generic rerank API call for Jina/Cohere/Aliyun models. + + Args: + query: The search query + documents: List of strings to rerank + model: Model name to use + base_url: API endpoint URL + api_key: API key for authentication + top_n: Number of top results to return + return_documents: Whether to return document text (Jina only) + extra_body: Additional body parameters + response_format: Response format type ("standard" for Jina/Cohere, "aliyun" for Aliyun) + + Returns: + List of dictionary of ["index": int, "relevance_score": float] + """ + if not base_url: + raise ValueError("Base URL is required") + + headers = {"Content-Type": "application/json"} + if api_key is not None: + headers["Authorization"] = f"Bearer {api_key}" + + # Build request payload based on request format + if request_format == "aliyun": + # Aliyun format: nested input/parameters structure + payload = { + "model": model, + "input": { + "query": query, + "documents": documents, + }, + "parameters": {}, + } + + # Add optional parameters to parameters object + if top_n is not None: + payload["parameters"]["top_n"] = top_n + + if return_documents is not None: + payload["parameters"]["return_documents"] = return_documents + + # Add extra parameters to parameters object + if extra_body: + payload["parameters"].update(extra_body) + else: + # Standard format for Jina/Cohere + payload = { + "model": model, + "query": query, + "documents": documents, + } + + # Add optional parameters + if top_n is not None: + payload["top_n"] = top_n + + # Only Jina API supports return_documents parameter + if return_documents is not None: + payload["return_documents"] = return_documents + + # Add extra parameters + if extra_body: + payload.update(extra_body) + + logger.debug( + f"Rerank request: {len(documents)} documents, model: {model}, format: {response_format}" + ) + + async with aiohttp.ClientSession() as session: + async with session.post(base_url, headers=headers, json=payload) as response: + if response.status != 200: + error_text = await response.text() + content_type = response.headers.get("content-type", "").lower() + is_html_error = ( + error_text.strip().startswith("") + or "text/html" in content_type + ) + if is_html_error: + if response.status == 502: + clean_error = "Bad Gateway (502) - Rerank service temporarily unavailable. Please try again in a few minutes." + elif response.status == 503: + clean_error = "Service Unavailable (503) - Rerank service is temporarily overloaded. Please try again later." + elif response.status == 504: + clean_error = "Gateway Timeout (504) - Rerank service request timed out. Please try again." + else: + clean_error = f"HTTP {response.status} - Rerank service error. Please try again later." + else: + clean_error = error_text + logger.error(f"Rerank API error {response.status}: {clean_error}") + raise aiohttp.ClientResponseError( + request_info=response.request_info, + history=response.history, + status=response.status, + message=f"Rerank API error: {clean_error}", + ) + + response_json = await response.json() + + if response_format == "aliyun": + # Aliyun format: {"output": {"results": [...]}} + results = response_json.get("output", {}).get("results", []) + if not isinstance(results, list): + logger.warning( + f"Expected 'output.results' to be list, got {type(results)}: {results}" + ) + results = [] + + elif response_format == "standard": + # Standard format: {"results": [...]} + results = response_json.get("results", []) + if not isinstance(results, list): + logger.warning( + f"Expected 'results' to be list, got {type(results)}: {results}" + ) + results = [] + else: + raise ValueError(f"Unsupported response format: {response_format}") + if not results: + logger.warning("Rerank API returned empty results") + return [] + + # Standardize return format + return [ + {"index": result["index"], "relevance_score": result["relevance_score"]} + for result in results + ] + + +async def cohere_rerank( + query: str, + documents: List[str], + top_n: Optional[int] = None, + api_key: Optional[str] = None, + model: str = "rerank-v3.5", + base_url: str = "https://api.cohere.com/v2/rerank", + extra_body: Optional[Dict[str, Any]] = None, +) -> List[Dict[str, Any]]: + """ + Rerank documents using Cohere API. + + Args: + query: The search query + documents: List of strings to rerank + top_n: Number of top results to return + api_key: API key + model: rerank model name + base_url: API endpoint + extra_body: Additional body for http request(reserved for extra params) + + Returns: + List of dictionary of ["index": int, "relevance_score": float] + """ + if api_key is None: + api_key = os.getenv("COHERE_API_KEY") or os.getenv("RERANK_BINDING_API_KEY") + + return await generic_rerank_api( + query=query, + documents=documents, + model=model, + base_url=base_url, + api_key=api_key, + top_n=top_n, + return_documents=None, # Cohere doesn't support this parameter + extra_body=extra_body, + response_format="standard", + ) + + +async def jina_rerank( + query: str, + documents: List[str], + top_n: Optional[int] = None, + api_key: Optional[str] = None, + model: str = "jina-reranker-v2-base-multilingual", + base_url: str = "https://api.jina.ai/v1/rerank", + extra_body: Optional[Dict[str, Any]] = None, +) -> List[Dict[str, Any]]: + """ + Rerank documents using Jina AI API. + + Args: + query: The search query + documents: List of strings to rerank + top_n: Number of top results to return + api_key: API key + model: rerank model name + base_url: API endpoint + extra_body: Additional body for http request(reserved for extra params) + + Returns: + List of dictionary of ["index": int, "relevance_score": float] + """ + if api_key is None: + api_key = os.getenv("JINA_API_KEY") or os.getenv("RERANK_BINDING_API_KEY") + + return await generic_rerank_api( + query=query, + documents=documents, + model=model, + base_url=base_url, + api_key=api_key, + top_n=top_n, + return_documents=False, + extra_body=extra_body, + response_format="standard", + ) + + +async def ali_rerank( + query: str, + documents: List[str], + top_n: Optional[int] = None, + api_key: Optional[str] = None, + model: str = "gte-rerank-v2", + base_url: str = "https://dashscope.aliyuncs.com/api/v1/services/rerank/text-rerank/text-rerank", + extra_body: Optional[Dict[str, Any]] = None, +) -> List[Dict[str, Any]]: + """ + Rerank documents using Aliyun DashScope API. + + Args: + query: The search query + documents: List of strings to rerank + top_n: Number of top results to return + api_key: Aliyun API key + model: rerank model name + base_url: API endpoint + extra_body: Additional body for http request(reserved for extra params) + + Returns: + List of dictionary of ["index": int, "relevance_score": float] + """ + if api_key is None: + api_key = os.getenv("DASHSCOPE_API_KEY") or os.getenv("RERANK_BINDING_API_KEY") + + return await generic_rerank_api( + query=query, + documents=documents, + model=model, + base_url=base_url, + api_key=api_key, + top_n=top_n, + return_documents=False, # Aliyun doesn't need this parameter + extra_body=extra_body, + response_format="aliyun", + request_format="aliyun", + ) + + +"""Please run this test as a module: +python -m lightrag.rerank +""" +if __name__ == "__main__": + import asyncio + + async def main(): + # Example usage - documents should be strings, not dictionaries + docs = [ + "The capital of France is Paris.", + "Tokyo is the capital of Japan.", + "London is the capital of England.", + ] + + query = "What is the capital of France?" + + # Test Jina rerank + try: + print("=== Jina Rerank ===") + result = await jina_rerank( + query=query, + documents=docs, + top_n=2, + ) + print("Results:") + for item in result: + print(f"Index: {item['index']}, Score: {item['relevance_score']:.4f}") + print(f"Document: {docs[item['index']]}") + except Exception as e: + print(f"Jina Error: {e}") + + # Test Cohere rerank + try: + print("\n=== Cohere Rerank ===") + result = await cohere_rerank( + query=query, + documents=docs, + top_n=2, + ) + print("Results:") + for item in result: + print(f"Index: {item['index']}, Score: {item['relevance_score']:.4f}") + print(f"Document: {docs[item['index']]}") + except Exception as e: + print(f"Cohere Error: {e}") + + # Test Aliyun rerank + try: + print("\n=== Aliyun Rerank ===") + result = await ali_rerank( + query=query, + documents=docs, + top_n=2, + ) + print("Results:") + for item in result: + print(f"Index: {item['index']}, Score: {item['relevance_score']:.4f}") + print(f"Document: {docs[item['index']]}") + except Exception as e: + print(f"Aliyun Error: {e}") + + asyncio.run(main()) diff --git a/LightRAG/lightrag/tools/README_CLEAN_LLM_QUERY_CACHE.md b/LightRAG/lightrag/tools/README_CLEAN_LLM_QUERY_CACHE.md new file mode 100644 index 00000000..42514579 --- /dev/null +++ b/LightRAG/lightrag/tools/README_CLEAN_LLM_QUERY_CACHE.md @@ -0,0 +1,661 @@ +# LLM Query Cache Cleanup Tool - User Guide + +## Overview + +This tool cleans up LightRAG's LLM query cache from KV storage implementations. It specifically targets query caches generated during RAG query operations (modes: `mix`, `hybrid`, `local`, `global`), including both query and keywords caches. + +## Supported Storage Types + +1. **JsonKVStorage** - File-based JSON storage +2. **RedisKVStorage** - Redis database storage +3. **PGKVStorage** - PostgreSQL database storage +4. **MongoKVStorage** - MongoDB database storage + +## Cache Types + +The tool cleans up the following query cache types: + +### Query Cache Modes (4 types) +- `mix:*` - Mixed mode query caches +- `hybrid:*` - Hybrid mode query caches +- `local:*` - Local mode query caches +- `global:*` - Global mode query caches + +### Cache Content Types (2 types) +- `*:query:*` - Query result caches +- `*:keywords:*` - Keywords extraction caches + +### Cache Key Format +``` +:: +``` + +Examples: +- `mix:query:5ce04d25e957c290216cee5bfe6344fa` +- `mix:keywords:fee77b98244a0b047ce95e21060de60e` +- `global:query:abc123def456...` +- `local:keywords:789xyz...` + +**Important Note**: This tool does NOT clean extraction caches (`default:extract:*` and `default:summary:*`). Use the migration tool or manual deletion for those caches. + +## Prerequisites + +- The tool reads storage configuration from environment variables or `config.ini` +- Ensure the target storage is properly configured and accessible +- Backup important data before running cleanup operations + +## Usage + +### Basic Usage + +Run from the LightRAG project root directory: + +```bash +python -m lightrag.tools.clean_llm_query_cache +# or +python lightrag/tools/clean_llm_query_cache.py +``` + +### Interactive Workflow + +The tool guides you through the following steps: + +#### 1. Select Storage Type +``` +============================================================ +LLM Query Cache Cleanup Tool - LightRAG +============================================================ + +=== Storage Setup === + +Supported KV Storage Types: +[1] JsonKVStorage +[2] RedisKVStorage +[3] PGKVStorage +[4] MongoKVStorage + +Select storage type (1-4) (Press Enter to exit): 1 +``` + +**Note**: You can press Enter or type `0` at any prompt to exit gracefully. + +#### 2. Storage Validation +The tool will: +- Check required environment variables +- Auto-detect workspace configuration +- Initialize and connect to storage +- Verify connection status + +``` +Checking configuration... +✓ All required environment variables are set + +Initializing storage... +- Storage Type: JsonKVStorage +- Workspace: space1 +- Connection Status: ✓ Success +``` + +#### 3. View Cache Statistics + +The tool displays a detailed breakdown of query caches by mode and type: + +``` +Counting query cache records... + +📊 Query Cache Statistics (Before Cleanup): +┌────────────┬────────────┬────────────┬────────────┐ +│ Mode │ Query │ Keywords │ Total │ +├────────────┼────────────┼────────────┼────────────┤ +│ mix │ 1,234 │ 567 │ 1,801 │ +│ hybrid │ 890 │ 423 │ 1,313 │ +│ local │ 2,345 │ 1,123 │ 3,468 │ +│ global │ 678 │ 345 │ 1,023 │ +├────────────┼────────────┼────────────┼────────────┤ +│ Total │ 5,147 │ 2,458 │ 7,605 │ +└────────────┴────────────┴────────────┴────────────┘ +``` + +#### 4. Select Cleanup Scope + +Choose what type of caches to delete: + +``` +=== Cleanup Options === +[1] Delete all query caches (both query and keywords) +[2] Delete query caches only (keep keywords) +[3] Delete keywords caches only (keep query) +[0] Cancel + +Select cleanup option (0-3): 1 +``` + +**Cleanup Types:** +- **Option 1 (all)**: Deletes both query and keywords caches across all modes +- **Option 2 (query)**: Deletes only query caches, preserves keywords caches +- **Option 3 (keywords)**: Deletes only keywords caches, preserves query caches + +#### 5. Confirm Deletion + +Review the cleanup plan and confirm: + +``` +============================================================ +Cleanup Confirmation +============================================================ +Storage: JsonKVStorage (workspace: space1) +Cleanup Type: all +Records to Delete: 7,605 / 7,605 + +⚠️ WARNING: This will delete ALL query caches across all modes! + +Continue with deletion? (y/n): y +``` + +#### 6. Execute Cleanup + +The tool performs batch deletion with real-time progress: + +**JsonKVStorage Example:** +``` +=== Starting Cleanup === +💡 Processing 1,000 records at a time from JsonKVStorage + +Batch 1/8: ████░░░░░░░░░░░░░░░░ 1,000/7,605 (13.1%) ✓ +Batch 2/8: ████████░░░░░░░░░░░░ 2,000/7,605 (26.3%) ✓ +... +Batch 8/8: ████████████████████ 7,605/7,605 (100.0%) ✓ + +Persisting changes to storage... +✓ Changes persisted successfully +``` + +**RedisKVStorage Example:** +``` +=== Starting Cleanup === +💡 Processing Redis keys in batches of 1,000 + +Batch 1: Deleted 1,000 keys (Total: 1,000) ✓ +Batch 2: Deleted 1,000 keys (Total: 2,000) ✓ +... +``` + +**PostgreSQL Example:** +``` +=== Starting Cleanup === +💡 Executing PostgreSQL DELETE query + +✓ Deleted 7,605 records in 0.45s +``` + +**MongoDB Example:** +``` +=== Starting Cleanup === +💡 Executing MongoDB deleteMany operations + +Pattern 1/8: Deleted 1,234 records ✓ +Pattern 2/8: Deleted 567 records ✓ +... +Total deleted: 7,605 records +``` + +#### 7. Review Cleanup Report + +The tool provides a comprehensive final report: + +**Successful Cleanup:** +``` +============================================================ +Cleanup Complete - Final Report +============================================================ + +📊 Statistics: + Total records to delete: 7,605 + Total batches: 8 + Successful batches: 8 + Failed batches: 0 + Successfully deleted: 7,605 + Failed to delete: 0 + Success rate: 100.00% + +📈 Before/After Comparison: + Total caches before: 7,605 + Total caches after: 0 + Net reduction: 7,605 + +============================================================ +✓ SUCCESS: All records cleaned up successfully! +============================================================ + +📊 Query Cache Statistics (After Cleanup): +┌────────────┬────────────┬────────────┬────────────┐ +│ Mode │ Query │ Keywords │ Total │ +├────────────┼────────────┼────────────┼────────────┤ +│ mix │ 0 │ 0 │ 0 │ +│ hybrid │ 0 │ 0 │ 0 │ +│ local │ 0 │ 0 │ 0 │ +│ global │ 0 │ 0 │ 0 │ +├────────────┼────────────┼────────────┼────────────┤ +│ Total │ 0 │ 0 │ 0 │ +└────────────┴────────────┴────────────┴────────────┘ +``` + +**Cleanup with Errors:** +``` +============================================================ +Cleanup Complete - Final Report +============================================================ + +📊 Statistics: + Total records to delete: 7,605 + Total batches: 8 + Successful batches: 7 + Failed batches: 1 + Successfully deleted: 6,605 + Failed to delete: 1,000 + Success rate: 86.85% + +📈 Before/After Comparison: + Total caches before: 7,605 + Total caches after: 1,000 + Net reduction: 6,605 + +⚠️ Errors encountered: 1 + +Error Details: +------------------------------------------------------------ + +Error Summary: + - ConnectionError: 1 occurrence(s) + +First 5 errors: + + 1. Batch 3 + Type: ConnectionError + Message: Connection timeout after 30s + Records lost: 1,000 + +============================================================ +⚠️ WARNING: Cleanup completed with errors! + Please review the error details above. +============================================================ +``` + +## Technical Details + +### Workspace Handling + +The tool retrieves workspace in the following priority order: + +1. **Storage-specific workspace environment variables** + - PGKVStorage: `POSTGRES_WORKSPACE` + - MongoKVStorage: `MONGODB_WORKSPACE` + - RedisKVStorage: `REDIS_WORKSPACE` + +2. **Generic workspace environment variable** + - `WORKSPACE` + +3. **Default value** + - Empty string (uses storage's default workspace) + +### Batch Deletion + +- Default batch size: 1000 records/batch +- Prevents memory overflow and connection timeouts +- Each batch is processed independently +- Failed batches are logged but don't stop cleanup + +### Storage-Specific Deletion Strategies + +#### JsonKVStorage +- Collects all matching keys first (snapshot approach) +- Deletes in batches with lock protection +- Fast in-memory operations + +#### RedisKVStorage +- Uses SCAN with pattern matching +- Pipeline DELETE for batch operations +- Cursor-based iteration for large datasets + +#### PostgreSQL +- Single DELETE query with OR conditions +- Efficient server-side bulk deletion +- Uses LIKE patterns for mode/type matching + +#### MongoDB +- Multiple deleteMany operations (one per pattern) +- Regex-based document matching +- Returns exact deletion counts + +### Pattern Matching Implementation + +**JsonKVStorage:** +```python +# Direct key prefix matching +if key.startswith("mix:query:") or key.startswith("mix:keywords:") +``` + +**RedisKVStorage:** +```python +# SCAN with namespace-prefixed patterns +pattern = f"{namespace}:mix:query:*" +cursor, keys = await redis.scan(cursor, match=pattern) +``` + +**PostgreSQL:** +```python +# SQL LIKE conditions +WHERE id LIKE 'mix:query:%' OR id LIKE 'mix:keywords:%' +``` + +**MongoDB:** +```python +# Regex queries on _id field +{"_id": {"$regex": "^mix:query:"}} +``` + +## Error Handling & Resilience + +The tool implements comprehensive error tracking: + +### Batch-Level Error Tracking +- Each batch is independently error-checked +- Failed batches are logged with full details +- Successful batches commit even if later batches fail +- Real-time progress shows ✓ (success) or ✗ (failed) + +### Error Reporting +After cleanup completes, a detailed report includes: +- **Statistics**: Total records, success/failure counts, success rate +- **Before/After Comparison**: Net reduction in cache count +- **Error Summary**: Grouped by error type with occurrence counts +- **Error Details**: Batch number, error type, message, and records lost +- **Recommendations**: Clear indication of success or need for review + +### Verification +- Post-cleanup count verification +- Before/after statistics comparison +- Identifies partial cleanup scenarios + +## Important Notes + +1. **Irreversible Operation** + - Deleted caches cannot be recovered + - Always backup important data before cleanup + - Test on non-production data first + +2. **Performance Impact** + - Query performance may degrade temporarily after cleanup + - Caches will rebuild on subsequent queries + - Consider cleanup during off-peak hours + +3. **Selective Cleanup** + - Choose cleanup scope carefully + - Keywords caches may be valuable for future queries + - Query caches rebuild faster than keywords caches + +4. **Workspace Isolation** + - Cleanup only affects the selected workspace + - Other workspaces remain untouched + - Verify workspace before confirming cleanup + +5. **Interrupt and Resume** + - Cleanup can be interrupted at any time (Ctrl+C) + - Already deleted records cannot be recovered + - No automatic resume - must run tool again + +## Storage Configuration + +The tool supports multiple configuration methods with the following priority: + +1. **Environment variables** (highest priority) +2. **config.ini file** (medium priority) +3. **Default values** (lowest priority) + +### Environment Variable Configuration + +Configure storage settings in your `.env` file: + +#### Workspace Configuration (Optional) + +```bash +# Generic workspace (shared by all storages) +WORKSPACE=space1 + +# Or configure independent workspace for specific storage +POSTGRES_WORKSPACE=pg_space +MONGODB_WORKSPACE=mongo_space +REDIS_WORKSPACE=redis_space +``` + +**Workspace Priority**: Storage-specific > Generic WORKSPACE > Empty string + +#### JsonKVStorage + +```bash +WORKING_DIR=./rag_storage +``` + +#### RedisKVStorage + +```bash +REDIS_URI=redis://localhost:6379 +``` + +#### PGKVStorage + +```bash +POSTGRES_HOST=localhost +POSTGRES_PORT=5432 +POSTGRES_USER=your_username +POSTGRES_PASSWORD=your_password +POSTGRES_DATABASE=your_database +``` + +#### MongoKVStorage + +```bash +MONGO_URI=mongodb://root:root@localhost:27017/ +MONGO_DATABASE=LightRAG +``` + +### config.ini Configuration + +Alternatively, create a `config.ini` file in the project root: + +```ini +[redis] +uri = redis://localhost:6379 + +[postgres] +host = localhost +port = 5432 +user = postgres +password = yourpassword +database = lightrag + +[mongodb] +uri = mongodb://root:root@localhost:27017/ +database = LightRAG +``` + +**Note**: Environment variables take precedence over config.ini settings. + +## Troubleshooting + +### Missing Environment Variables +``` +⚠️ Warning: Missing environment variables: POSTGRES_USER, POSTGRES_PASSWORD +``` +**Solution**: Add missing variables to your `.env` file or configure in `config.ini` + +### Connection Failed +``` +✗ Initialization failed: Connection refused +``` +**Solutions**: +- Check if database service is running +- Verify connection parameters (host, port, credentials) +- Check firewall settings +- Ensure network connectivity for remote databases + +### No Caches Found +``` +⚠️ No query caches found in storage +``` +**Possible Reasons**: +- No queries have been run yet +- Caches were already cleaned +- Wrong workspace selected +- Different storage type was used for queries + +### Partial Cleanup +``` +⚠️ WARNING: Cleanup completed with errors! +``` +**Solutions**: +- Check error details in the report +- Verify storage connection stability +- Re-run tool to clean remaining caches +- Check storage capacity and permissions + +## Use Cases + +### Use Case 1: Clean All Query Caches + +**Scenario**: Free up storage space by removing all query caches + +```bash +# Run tool +python -m lightrag.tools.clean_llm_query_cache + +# Select: Storage type -> Option 1 (all) -> Confirm (y) +``` + +**Result**: All query and keywords caches deleted, maximum storage freed + +### Use Case 2: Refresh Query Caches Only + +**Scenario**: Force query cache rebuild while keeping keywords + +```bash +# Run tool +python -m lightrag.tools.clean_llm_query_cache + +# Select: Storage type -> Option 2 (query only) -> Confirm (y) +``` + +**Result**: Query caches deleted, keywords preserved for faster rebuild + +### Use Case 3: Clean Stale Keywords + +**Scenario**: Remove outdated keywords while keeping recent query results + +```bash +# Run tool +python -m lightrag.tools.clean_llm_query_cache + +# Select: Storage type -> Option 3 (keywords only) -> Confirm (y) +``` + +**Result**: Keywords deleted, query caches preserved + +### Use Case 4: Workspace-Specific Cleanup + +**Scenario**: Clean caches for a specific workspace + +```bash +# Configure workspace +export WORKSPACE=development + +# Run tool +python -m lightrag.tools.clean_llm_query_cache + +# Select: Storage type -> Cleanup option -> Confirm (y) +``` + +**Result**: Only development workspace caches cleaned + +## Best Practices + +1. **Backup Before Cleanup** + - Always backup your storage before major cleanup + - Test cleanup on non-production data first + - Document cleanup decisions + +2. **Monitor Performance** + - Watch storage metrics during cleanup + - Monitor query performance after cleanup + - Allow time for cache rebuild + +3. **Scheduled Cleanup** + - Clean caches periodically (weekly/monthly) + - Automate cleanup for development environments + - Keep production cleanup manual for safety + +4. **Selective Deletion** + - Consider cleanup scope based on needs + - Keywords caches are harder to rebuild + - Query caches rebuild automatically + +5. **Storage Capacity** + - Monitor storage usage trends + - Clean caches before reaching capacity limits + - Archive old data if needed + +## Comparison with Migration Tool + +| Feature | Cleanup Tool | Migration Tool | +|---------|-------------|----------------| +| **Purpose** | Delete query caches | Migrate extraction caches | +| **Cache Types** | mix/hybrid/local/global | default:extract/summary | +| **Modes** | query, keywords | extract, summary | +| **Operation** | Deletion | Copy between storages | +| **Reversible** | No | Yes (source unchanged) | +| **Use Case** | Free storage, refresh caches | Change storage backend | + +## Limitations + +1. **Single Storage Operation** + - Can only clean one storage type at a time + - To clean multiple storages, run tool multiple times + +2. **No Dry Run Mode** + - Deletion is immediate after confirmation + - No preview-only mode available + - Test on non-production first + +3. **No Selective Mode Cleanup** + - Cannot clean only specific modes (e.g., only `mix`) + - Cleanup applies to all modes for selected cache type + - All-or-nothing per cache type + +4. **No Scheduled Cleanup** + - Manual execution required + - No built-in scheduling + - Use cron/scheduler if automation needed + +5. **Verification Limitations** + - Post-cleanup verification may fail in error scenarios + - Manual verification recommended for critical operations + +## Future Enhancements + +Potential improvements for future versions: + +- Selective mode cleanup (e.g., clean only `mix` mode) +- Age-based cleanup (delete caches older than X days) +- Size-based cleanup (delete largest caches first) +- Dry run mode for safe preview +- Automated scheduling support +- Cache statistics export +- Incremental cleanup with pause/resume + +## Support + +For issues, questions, or feature requests: +- Check the error details in the cleanup report +- Review storage configuration +- Verify workspace settings +- Test with a small dataset first +- Report bugs through project issue tracker diff --git a/LightRAG/lightrag/tools/README_MIGRATE_LLM_CACHE.md b/LightRAG/lightrag/tools/README_MIGRATE_LLM_CACHE.md new file mode 100644 index 00000000..8dd72984 --- /dev/null +++ b/LightRAG/lightrag/tools/README_MIGRATE_LLM_CACHE.md @@ -0,0 +1,471 @@ +# LLM Cache Migration Tool - User Guide + +## Overview + +This tool migrates LightRAG's LLM response cache between different KV storage implementations. It specifically migrates caches generated during file extraction (mode `default`), including entity extraction and summary caches. + +## Supported Storage Types + +1. **JsonKVStorage** - File-based JSON storage +2. **RedisKVStorage** - Redis database storage +3. **PGKVStorage** - PostgreSQL database storage +4. **MongoKVStorage** - MongoDB database storage + +## Cache Types + +The tool migrates the following cache types: +- `default:extract:*` - Entity and relationship extraction caches +- `default:summary:*` - Entity and relationship summary caches + +**Note**: Query caches (modes like `mix`,`local`, `global`, etc.) are NOT migrated. + +## Prerequisites + +The LLM Cache Migration Tool reads the storage configuration of the LightRAG Server and provides an LLM migration option to select source and destination storage. Ensure that both the source and destination storage have been correctly configured and are accessible via the LightRAG Server before cache migration. + +## Usage + +### Basic Usage + +Run from the LightRAG project root directory: + +```bash +python -m lightrag.tools.migrate_llm_cache +# or +python lightrag/tools/migrate_llm_cache.py +``` + +### Interactive Workflow + +The tool guides you through the following steps: + +#### 1. Select Source Storage Type +``` +Supported KV Storage Types: +[1] JsonKVStorage +[2] RedisKVStorage +[3] PGKVStorage +[4] MongoKVStorage + +Select Source storage type (1-4) (Press Enter to exit): 1 +``` + +**Note**: You can press Enter or type `0` at any storage selection prompt to exit gracefully. + +#### 2. Source Storage Validation +The tool will: +- Check required environment variables +- Auto-detect workspace configuration +- Initialize and connect to storage +- Count cache records available for migration + +``` +Checking environment variables... +✓ All required environment variables are set + +Initializing Source storage... +- Storage Type: JsonKVStorage +- Workspace: space1 +- Connection Status: ✓ Success + +Counting cache records... +- Total: 8,734 records +``` + +**Progress Display by Storage Type:** +- **JsonKVStorage**: Fast in-memory counting, displays final count without incremental progress + ``` + Counting cache records... + - Total: 8,734 records + ``` +- **RedisKVStorage**: Real-time scanning progress with incremental counts + ``` + Scanning Redis keys... found 8,734 records + ``` +- **PostgreSQL**: Quick COUNT(*) query, shows timing only if operation takes >1 second + ``` + Counting PostgreSQL records... (took 2.3s) + ``` +- **MongoDB**: Fast count_documents(), shows timing only if operation takes >1 second + ``` + Counting MongoDB documents... (took 1.8s) + ``` + +#### 3. Select Target Storage Type + +The tool automatically excludes the source storage type from the target selection and renumbers the remaining options sequentially: + +``` +Available Storage Types for Target (source: JsonKVStorage excluded): +[1] RedisKVStorage +[2] PGKVStorage +[3] MongoKVStorage + +Select Target storage type (1-3) (Press Enter or 0 to exit): 1 +``` + +**Important Notes:** +- You **cannot** select the same storage type for both source and target +- Options are automatically renumbered (e.g., [1], [2], [3] instead of [2], [3], [4]) +- You can press Enter or type `0` to exit at this point as well + +The tool then validates the target storage following the same process as the source (checking environment variables, initializing connection, counting records). + +#### 4. Confirm Migration + +``` +================================================== +Migration Confirmation +Source: JsonKVStorage (workspace: space1) - 8,734 records +Target: MongoKVStorage (workspace: space1) - 0 records +Batch Size: 1,000 records/batch +Memory Mode: Streaming (memory-optimized) + +⚠️ Warning: Target storage already has 0 records +Migration will overwrite records with the same keys + +Continue? (y/n): y +``` + +#### 5. Execute Migration + +The tool uses **streaming migration** by default for memory efficiency. Observe migration progress: + +``` +=== Starting Streaming Migration === +💡 Memory-optimized mode: Processing 1,000 records at a time + +Batch 1/9: ████████░░░░░░░░░░░░ 1000/8734 (11.4%) - default:extract ✓ +Batch 2/9: ████████████░░░░░░░░ 2000/8734 (22.9%) - default:extract ✓ +... +Batch 9/9: ████████████████████ 8734/8734 (100.0%) - default:summary ✓ + +Persisting data to disk... +✓ Data persisted successfully +``` + +**Key Features:** +- **Streaming mode**: Processes data in batches without loading entire dataset into memory +- **Real-time progress**: Shows progress bar with precise percentage and cache type +- **Success indicators**: ✓ for successful batches, ✗ for failed batches +- **Constant memory usage**: Handles millions of records efficiently + +#### 6. Review Migration Report + +The tool provides a comprehensive final report showing statistics and any errors encountered: + +**Successful Migration:** +``` +Migration Complete - Final Report + +📊 Statistics: + Total source records: 8,734 + Total batches: 9 + Successful batches: 9 + Failed batches: 0 + Successfully migrated: 8,734 + Failed to migrate: 0 + Success rate: 100.00% + +✓ SUCCESS: All records migrated successfully! +``` + +**Migration with Errors:** +``` +Migration Complete - Final Report + +📊 Statistics: + Total source records: 8,734 + Total batches: 9 + Successful batches: 8 + Failed batches: 1 + Successfully migrated: 7,734 + Failed to migrate: 1,000 + Success rate: 88.55% + +⚠️ Errors encountered: 1 + +Error Details: +------------------------------------------------------------ + +Error Summary: + - ConnectionError: 1 occurrence(s) + +First 5 errors: + + 1. Batch 2 + Type: ConnectionError + Message: Connection timeout after 30s + Records lost: 1,000 + +⚠️ WARNING: Migration completed with errors! + Please review the error details above. +``` + +## Technical Details + +### Workspace Handling + +The tool retrieves workspace in the following priority order: + +1. **Storage-specific workspace environment variables** + - PGKVStorage: `POSTGRES_WORKSPACE` + - MongoKVStorage: `MONGODB_WORKSPACE` + - RedisKVStorage: `REDIS_WORKSPACE` + +2. **Generic workspace environment variable** + - `WORKSPACE` + +3. **Default value** + - Empty string (uses storage's default workspace) + +### Batch Migration + +- Default batch size: 1000 records/batch +- Avoids memory overflow from loading too much data at once +- Each batch is committed independently, supporting resume capability + +### Memory-Efficient Pagination + +For large datasets, the tool implements storage-specific pagination strategies: + +- **JsonKVStorage**: Direct in-memory access (data already loaded in shared storage) +- **RedisKVStorage**: Cursor-based SCAN with pipeline batching (1000 keys/batch) +- **PGKVStorage**: SQL LIMIT/OFFSET pagination (1000 records/batch) +- **MongoKVStorage**: Cursor streaming with batch_size (1000 documents/batch) + +This ensures the tool can handle millions of cache records without memory issues. + +### Prefix Filtering Implementation + +The tool uses optimized filtering methods for different storage types: + +- **JsonKVStorage**: Direct dictionary iteration with lock protection +- **RedisKVStorage**: SCAN command with namespace-prefixed patterns + pipeline for bulk GET +- **PGKVStorage**: SQL LIKE queries with proper field mapping (id, return_value, etc.) +- **MongoKVStorage**: MongoDB regex queries on `_id` field with cursor streaming + +## Error Handling & Resilience + +The tool implements comprehensive error tracking to ensure transparent and resilient migrations: + +### Batch-Level Error Tracking +- Each batch is independently error-checked +- Failed batches are logged but don't stop the migration +- Successful batches are committed even if later batches fail +- Real-time progress shows ✓ (success) or ✗ (failed) for each batch + +### Error Reporting +After migration completes, a detailed report includes: +- **Statistics**: Total records, success/failure counts, success rate +- **Error Summary**: Grouped by error type with occurrence counts +- **Error Details**: Batch number, error type, message, and records lost +- **Recommendations**: Clear indication of success or need for review + +### No Double Data Loading +- Unlike traditional verification approaches, the tool does NOT reload all target data +- Errors are detected during migration, not after +- This eliminates memory overhead and handles pre-existing target data correctly + +## Important Notes + +1. **Data Overwrite Warning** + - Migration will overwrite records with the same keys in the target storage + - Tool displays a warning if target storage already has data + - Data migration can be performed repeatedly + - Pre-existing data in target storage is handled correctly +3. **Interrupt and Resume** + - Migration can be interrupted at any time (Ctrl+C) + - Already migrated data will remain in target storage + - Re-running will overwrite existing records + - Failed batches can be manually retried +4. **Performance Considerations** + - Large data migration may take considerable time + - Recommend migrating during off-peak hours + - Ensure stable network connection (for remote databases) + - Memory usage stays constant regardless of dataset size + +## Storage Configuration + +The tool supports multiple configuration methods with the following priority: + +1. **Environment variables** (highest priority) +2. **config.ini file** (medium priority) +3. **Default values** (lowest priority) + +#### Option A: Environment Variable Configuration + +Configure storage settings in your `.env` file: + +#### Workspace Configuration (Optional) + +```bash +# Generic workspace (shared by all storages) +WORKSPACE=space1 + +# Or configure independent workspace for specific storage +POSTGRES_WORKSPACE=pg_space +MONGODB_WORKSPACE=mongo_space +REDIS_WORKSPACE=redis_space +``` + +**Workspace Priority**: Storage-specific > Generic WORKSPACE > Empty string + +#### JsonKVStorage + +```bash +WORKING_DIR=./rag_storage +``` + +#### RedisKVStorage + +```bash +REDIS_URI=redis://localhost:6379 +``` + +#### PGKVStorage + +```bash +POSTGRES_HOST=localhost +POSTGRES_PORT=5432 +POSTGRES_USER=your_username +POSTGRES_PASSWORD=your_password +POSTGRES_DATABASE=your_database +``` + +#### MongoKVStorage + +```bash +MONGO_URI=mongodb://root:root@localhost:27017/ +MONGO_DATABASE=LightRAG +``` + +#### Option B: config.ini Configuration + +Alternatively, create a `config.ini` file in the project root: + +```ini +[redis] +uri = redis://localhost:6379 + +[postgres] +host = localhost +port = 5432 +user = postgres +password = yourpassword +database = lightrag + +[mongodb] +uri = mongodb://root:root@localhost:27017/ +database = LightRAG +``` + +**Note**: Environment variables take precedence over config.ini settings. JsonKVStorage uses `WORKING_DIR` environment variable or defaults to `./rag_storage`. + +## Troubleshooting + +### Missing Environment Variables +``` +✗ Missing required environment variables: POSTGRES_USER, POSTGRES_PASSWORD +``` +**Solution**: Add missing variables to your `.env` file + +### Connection Failed +``` +✗ Initialization failed: Connection refused +``` +**Solutions**: +- Check if database service is running +- Verify connection parameters (host, port, credentials) +- Check firewall settings + +**Solutions**: +- Check migration process for error logs +- Re-run migration tool +- Check target storage capacity and permissions + +## Example Scenarios + +### Scenario 1: JSON to MongoDB Migration + +Use case: Migrating from single-machine development to production + +```bash +# 1. Configure environment variables +WORKSPACE=production +MONGO_URI=mongodb://user:pass@prod-server:27017/ +MONGO_DATABASE=LightRAG + +# 2. Run tool +python -m lightrag.tools.migrate_llm_cache + +# 3. Select: 1 (JsonKVStorage) -> 1 (MongoKVStorage - renumbered from 4) +``` + +**Note**: After selecting JsonKVStorage as source, MongoKVStorage will be shown as option [1] in the target selection since options are renumbered after excluding the source. + +### Scenario 2: Redis to PostgreSQL + +Use case: Migrating from cache storage to relational database + +```bash +# 1. Ensure both databases are accessible +REDIS_URI=redis://old-redis:6379 +POSTGRES_HOST=new-postgres-server +# ... Other PostgreSQL configs + +# 2. Run tool +python -m lightrag.tools.migrate_llm_cache + +# 3. Select: 2 (RedisKVStorage) -> 2 (PGKVStorage - renumbered from 3) +``` + +**Note**: After selecting RedisKVStorage as source, PGKVStorage will be shown as option [2] in the target selection. + +### Scenario 3: Different Workspaces Migration + +Use case: Migrating data between different workspace environments + +```bash +# Configure separate workspaces for source and target +POSTGRES_WORKSPACE=dev_workspace # For development environment +MONGODB_WORKSPACE=prod_workspace # For production environment + +# Run tool +python -m lightrag.tools.migrate_llm_cache + +# Select: 3 (PGKVStorage with dev_workspace) -> 3 (MongoKVStorage with prod_workspace) +``` + +**Note**: This allows you to migrate between different logical data partitions while changing storage backends. + +## Tool Limitations + +1. **Same Storage Type Not Allowed** + - You cannot migrate between the same storage type (e.g., PostgreSQL to PostgreSQL) + - This is enforced by the tool automatically excluding the source storage type from target selection + - For same-storage migrations (e.g., database switches), use database-native tools instead +2. **Only Default Mode Caches** + - Only migrates `default:extract:*` and `default:summary:*` + - Query caches are not included +4. **Network Dependency** + - Tool requires stable network connection for remote databases + - Large datasets may fail if connection is interrupted + +## Best Practices + +1. **Backup Before Migration** + - Always backup your data before migration + - Test migration on non-production data first + +2. **Verify Results** + - Check the verification output after migration + - Manually verify a few cache entries if needed + +3. **Monitor Performance** + - Watch database resource usage during migration + - Consider migrating in smaller batches if needed + +4. **Clean Old Data** + - After successful migration, consider cleaning old cache data + - Keep backups for a reasonable period before deletion diff --git a/LightRAG/lightrag/tools/__init__.py b/LightRAG/lightrag/tools/__init__.py new file mode 100644 index 00000000..e69de29b diff --git a/LightRAG/lightrag/tools/check_initialization.py b/LightRAG/lightrag/tools/check_initialization.py new file mode 100644 index 00000000..6bcb17e3 --- /dev/null +++ b/LightRAG/lightrag/tools/check_initialization.py @@ -0,0 +1,180 @@ +#!/usr/bin/env python3 +""" +Diagnostic tool to check LightRAG initialization status. + +This tool helps developers verify that their LightRAG instance is properly +initialized before use, preventing common initialization errors. + +Usage: + python -m lightrag.tools.check_initialization +""" + +import asyncio +import sys +from pathlib import Path + +# Add parent directory to path for imports +sys.path.insert(0, str(Path(__file__).parent.parent.parent)) + +from lightrag import LightRAG +from lightrag.base import StoragesStatus + + +async def check_lightrag_setup(rag_instance: LightRAG, verbose: bool = False) -> bool: + """ + Check if a LightRAG instance is properly initialized. + + Args: + rag_instance: The LightRAG instance to check + verbose: If True, print detailed diagnostic information + + Returns: + True if properly initialized, False otherwise + """ + issues = [] + warnings = [] + + print("🔍 Checking LightRAG initialization status...\n") + + # Check storage initialization status + if not hasattr(rag_instance, "_storages_status"): + issues.append("LightRAG instance missing _storages_status attribute") + elif rag_instance._storages_status != StoragesStatus.INITIALIZED: + issues.append( + f"Storages not initialized (status: {rag_instance._storages_status.name})" + ) + else: + print("✅ Storage status: INITIALIZED") + + # Check individual storage components + storage_components = [ + ("full_docs", "Document storage"), + ("text_chunks", "Text chunks storage"), + ("entities_vdb", "Entity vector database"), + ("relationships_vdb", "Relationship vector database"), + ("chunks_vdb", "Chunks vector database"), + ("doc_status", "Document status tracker"), + ("llm_response_cache", "LLM response cache"), + ("full_entities", "Entity storage"), + ("full_relations", "Relation storage"), + ("chunk_entity_relation_graph", "Graph storage"), + ] + + if verbose: + print("\n📦 Storage Components:") + + for component, description in storage_components: + if not hasattr(rag_instance, component): + issues.append(f"Missing storage component: {component} ({description})") + else: + storage = getattr(rag_instance, component) + if storage is None: + warnings.append(f"Storage {component} is None (might be optional)") + elif hasattr(storage, "_storage_lock"): + if storage._storage_lock is None: + issues.append(f"Storage {component} not initialized (lock is None)") + elif verbose: + print(f" ✅ {description}: Ready") + elif verbose: + print(f" ✅ {description}: Ready") + + # Check pipeline status + try: + from lightrag.kg.shared_storage import get_namespace_data + + get_namespace_data("pipeline_status") + print("✅ Pipeline status: INITIALIZED") + except KeyError: + issues.append( + "Pipeline status not initialized - call initialize_pipeline_status()" + ) + except Exception as e: + issues.append(f"Error checking pipeline status: {str(e)}") + + # Print results + print("\n" + "=" * 50) + + if issues: + print("❌ Issues found:\n") + for issue in issues: + print(f" • {issue}") + + print("\n📝 To fix, run this initialization sequence:\n") + print(" await rag.initialize_storages()") + print(" from lightrag.kg.shared_storage import initialize_pipeline_status") + print(" await initialize_pipeline_status()") + print( + "\n📚 Documentation: https://github.com/HKUDS/LightRAG#important-initialization-requirements" + ) + + if warnings and verbose: + print("\n⚠️ Warnings (might be normal):") + for warning in warnings: + print(f" • {warning}") + + return False + else: + print("✅ LightRAG is properly initialized and ready to use!") + + if warnings and verbose: + print("\n⚠️ Warnings (might be normal):") + for warning in warnings: + print(f" • {warning}") + + return True + + +async def demo(): + """Demonstrate the diagnostic tool with a test instance.""" + from lightrag.llm.openai import openai_embed, gpt_4o_mini_complete + from lightrag.kg.shared_storage import initialize_pipeline_status + + print("=" * 50) + print("LightRAG Initialization Diagnostic Tool") + print("=" * 50) + + # Create test instance + rag = LightRAG( + working_dir="./test_diagnostic", + embedding_func=openai_embed, + llm_model_func=gpt_4o_mini_complete, + ) + + print("\n🔴 BEFORE initialization:\n") + await check_lightrag_setup(rag, verbose=True) + + print("\n" + "=" * 50) + print("\n🔄 Initializing...\n") + await rag.initialize_storages() + await initialize_pipeline_status() + + print("\n🟢 AFTER initialization:\n") + await check_lightrag_setup(rag, verbose=True) + + # Cleanup + import shutil + + shutil.rmtree("./test_diagnostic", ignore_errors=True) + + +if __name__ == "__main__": + import argparse + + parser = argparse.ArgumentParser(description="Check LightRAG initialization status") + parser.add_argument( + "--demo", action="store_true", help="Run a demonstration with a test instance" + ) + parser.add_argument( + "--verbose", + "-v", + action="store_true", + help="Show detailed diagnostic information", + ) + + args = parser.parse_args() + + if args.demo: + asyncio.run(demo()) + else: + print("Run with --demo to see the diagnostic tool in action") + print("Or import this module and use check_lightrag_setup() with your instance") diff --git a/LightRAG/lightrag/tools/clean_llm_query_cache.py b/LightRAG/lightrag/tools/clean_llm_query_cache.py new file mode 100644 index 00000000..eca658c7 --- /dev/null +++ b/LightRAG/lightrag/tools/clean_llm_query_cache.py @@ -0,0 +1,1137 @@ +#!/usr/bin/env python3 +""" +LLM Query Cache Cleanup Tool for LightRAG + +This tool cleans up LLM query cache (mix:*, hybrid:*, local:*, global:*) +from KV storage implementations while preserving workspace isolation. + +Usage: + python -m lightrag.tools.clean_llm_query_cache + # or + python lightrag/tools/clean_llm_query_cache.py + +Supported KV Storage Types: + - JsonKVStorage + - RedisKVStorage + - PGKVStorage + - MongoKVStorage +""" + +import asyncio +import os +import sys +import time +from typing import Any, Dict, List +from dataclasses import dataclass, field +from dotenv import load_dotenv + +# Add project root to path for imports +sys.path.insert( + 0, os.path.dirname(os.path.dirname(os.path.dirname(os.path.abspath(__file__)))) +) + +from lightrag.kg import STORAGE_ENV_REQUIREMENTS +from lightrag.kg.shared_storage import set_all_update_flags +from lightrag.namespace import NameSpace +from lightrag.utils import setup_logger + +# Load environment variables +load_dotenv(dotenv_path=".env", override=False) + +# Setup logger +setup_logger("lightrag", level="INFO") + +# Storage type configurations +STORAGE_TYPES = { + "1": "JsonKVStorage", + "2": "RedisKVStorage", + "3": "PGKVStorage", + "4": "MongoKVStorage", +} + +# Workspace environment variable mapping +WORKSPACE_ENV_MAP = { + "PGKVStorage": "POSTGRES_WORKSPACE", + "MongoKVStorage": "MONGODB_WORKSPACE", + "RedisKVStorage": "REDIS_WORKSPACE", +} + +# Query cache modes +QUERY_MODES = ["mix", "hybrid", "local", "global"] + +# Query cache types +CACHE_TYPES = ["query", "keywords"] + +# Default batch size for deletion +DEFAULT_BATCH_SIZE = 1000 + +# ANSI color codes for terminal output +BOLD_CYAN = "\033[1;36m" +BOLD_RED = "\033[1;31m" +BOLD_GREEN = "\033[1;32m" +RESET = "\033[0m" + + +@dataclass +class CleanupStats: + """Cleanup statistics and error tracking""" + + # Count by mode and cache_type before cleanup + counts_before: Dict[str, Dict[str, int]] = field(default_factory=dict) + + # Deletion statistics + total_to_delete: int = 0 + total_batches: int = 0 + successful_batches: int = 0 + failed_batches: int = 0 + successfully_deleted: int = 0 + failed_to_delete: int = 0 + + # Count by mode and cache_type after cleanup + counts_after: Dict[str, Dict[str, int]] = field(default_factory=dict) + + # Error tracking + errors: List[Dict[str, Any]] = field(default_factory=list) + + def add_error(self, batch_idx: int, error: Exception, batch_size: int): + """Record batch error""" + self.errors.append( + { + "batch": batch_idx, + "error_type": type(error).__name__, + "error_msg": str(error), + "records_lost": batch_size, + "timestamp": time.time(), + } + ) + self.failed_batches += 1 + self.failed_to_delete += batch_size + + def initialize_counts(self): + """Initialize count dictionaries""" + for mode in QUERY_MODES: + self.counts_before[mode] = {"query": 0, "keywords": 0} + self.counts_after[mode] = {"query": 0, "keywords": 0} + + +class CleanupTool: + """LLM Query Cache Cleanup Tool""" + + def __init__(self): + self.storage = None + self.workspace = "" + self.batch_size = DEFAULT_BATCH_SIZE + + def get_workspace_for_storage(self, storage_name: str) -> str: + """Get workspace for a specific storage type + + Priority: Storage-specific env var > WORKSPACE env var > empty string + + Args: + storage_name: Storage implementation name + + Returns: + Workspace name + """ + # Check storage-specific workspace + if storage_name in WORKSPACE_ENV_MAP: + specific_workspace = os.getenv(WORKSPACE_ENV_MAP[storage_name]) + if specific_workspace: + return specific_workspace + + # Check generic WORKSPACE + workspace = os.getenv("WORKSPACE", "") + return workspace + + def check_config_ini_for_storage(self, storage_name: str) -> bool: + """Check if config.ini has configuration for the storage type + + Args: + storage_name: Storage implementation name + + Returns: + True if config.ini has the necessary configuration + """ + try: + import configparser + + config = configparser.ConfigParser() + config.read("config.ini", "utf-8") + + if storage_name == "RedisKVStorage": + return config.has_option("redis", "uri") + elif storage_name == "PGKVStorage": + return ( + config.has_option("postgres", "user") + and config.has_option("postgres", "password") + and config.has_option("postgres", "database") + ) + elif storage_name == "MongoKVStorage": + return config.has_option("mongodb", "uri") and config.has_option( + "mongodb", "database" + ) + + return False + except Exception: + return False + + def check_env_vars(self, storage_name: str) -> bool: + """Check environment variables, show warnings if missing but don't fail + + Args: + storage_name: Storage implementation name + + Returns: + Always returns True (warnings only, no hard failure) + """ + required_vars = STORAGE_ENV_REQUIREMENTS.get(storage_name, []) + + if not required_vars: + print("✓ No environment variables required") + return True + + missing_vars = [var for var in required_vars if var not in os.environ] + + if missing_vars: + print( + f"⚠️ Warning: Missing environment variables: {', '.join(missing_vars)}" + ) + + # Check if config.ini has configuration + has_config = self.check_config_ini_for_storage(storage_name) + if has_config: + print(" ✓ Found configuration in config.ini") + else: + print(f" Will attempt to use defaults for {storage_name}") + + return True + + print("✓ All required environment variables are set") + return True + + def get_storage_class(self, storage_name: str): + """Dynamically import and return storage class + + Args: + storage_name: Storage implementation name + + Returns: + Storage class + """ + if storage_name == "JsonKVStorage": + from lightrag.kg.json_kv_impl import JsonKVStorage + + return JsonKVStorage + elif storage_name == "RedisKVStorage": + from lightrag.kg.redis_impl import RedisKVStorage + + return RedisKVStorage + elif storage_name == "PGKVStorage": + from lightrag.kg.postgres_impl import PGKVStorage + + return PGKVStorage + elif storage_name == "MongoKVStorage": + from lightrag.kg.mongo_impl import MongoKVStorage + + return MongoKVStorage + else: + raise ValueError(f"Unsupported storage type: {storage_name}") + + async def initialize_storage(self, storage_name: str, workspace: str): + """Initialize storage instance with fallback to config.ini and defaults + + Args: + storage_name: Storage implementation name + workspace: Workspace name + + Returns: + Initialized storage instance + + Raises: + Exception: If initialization fails + """ + storage_class = self.get_storage_class(storage_name) + + # Create global config + global_config = { + "working_dir": os.getenv("WORKING_DIR", "./rag_storage"), + "embedding_batch_num": 10, + } + + # Initialize storage + storage = storage_class( + namespace=NameSpace.KV_STORE_LLM_RESPONSE_CACHE, + workspace=workspace, + global_config=global_config, + embedding_func=None, + ) + + # Initialize the storage (may raise exception if connection fails) + await storage.initialize() + + return storage + + async def count_query_caches_json(self, storage) -> Dict[str, Dict[str, int]]: + """Count query caches in JsonKVStorage by mode and cache_type + + Args: + storage: JsonKVStorage instance + + Returns: + Dictionary with counts for each mode and cache_type + """ + counts = {mode: {"query": 0, "keywords": 0} for mode in QUERY_MODES} + + async with storage._storage_lock: + for key in storage._data.keys(): + for mode in QUERY_MODES: + if key.startswith(f"{mode}:query:"): + counts[mode]["query"] += 1 + elif key.startswith(f"{mode}:keywords:"): + counts[mode]["keywords"] += 1 + + return counts + + async def count_query_caches_redis(self, storage) -> Dict[str, Dict[str, int]]: + """Count query caches in RedisKVStorage by mode and cache_type + + Args: + storage: RedisKVStorage instance + + Returns: + Dictionary with counts for each mode and cache_type + """ + counts = {mode: {"query": 0, "keywords": 0} for mode in QUERY_MODES} + + print("Scanning Redis keys...", end="", flush=True) + + async with storage._get_redis_connection() as redis: + for mode in QUERY_MODES: + for cache_type in CACHE_TYPES: + pattern = f"{mode}:{cache_type}:*" + prefixed_pattern = f"{storage.final_namespace}:{pattern}" + cursor = 0 + + while True: + cursor, keys = await redis.scan( + cursor, match=prefixed_pattern, count=DEFAULT_BATCH_SIZE + ) + counts[mode][cache_type] += len(keys) + + if cursor == 0: + break + + print() # New line after progress + return counts + + async def count_query_caches_pg(self, storage) -> Dict[str, Dict[str, int]]: + """Count query caches in PostgreSQL by mode and cache_type + + Args: + storage: PGKVStorage instance + + Returns: + Dictionary with counts for each mode and cache_type + """ + from lightrag.kg.postgres_impl import namespace_to_table_name + + counts = {mode: {"query": 0, "keywords": 0} for mode in QUERY_MODES} + table_name = namespace_to_table_name(storage.namespace) + + print("Counting PostgreSQL records...", end="", flush=True) + start_time = time.time() + + for mode in QUERY_MODES: + for cache_type in CACHE_TYPES: + query = f""" + SELECT COUNT(*) as count + FROM {table_name} + WHERE workspace = $1 + AND id LIKE $2 + """ + pattern = f"{mode}:{cache_type}:%" + result = await storage.db.query(query, [storage.workspace, pattern]) + counts[mode][cache_type] = result["count"] if result else 0 + + elapsed = time.time() - start_time + if elapsed > 1: + print(f" (took {elapsed:.1f}s)", end="") + print() # New line + + return counts + + async def count_query_caches_mongo(self, storage) -> Dict[str, Dict[str, int]]: + """Count query caches in MongoDB by mode and cache_type + + Args: + storage: MongoKVStorage instance + + Returns: + Dictionary with counts for each mode and cache_type + """ + counts = {mode: {"query": 0, "keywords": 0} for mode in QUERY_MODES} + + print("Counting MongoDB documents...", end="", flush=True) + start_time = time.time() + + for mode in QUERY_MODES: + for cache_type in CACHE_TYPES: + pattern = f"^{mode}:{cache_type}:" + query = {"_id": {"$regex": pattern}} + count = await storage._data.count_documents(query) + counts[mode][cache_type] = count + + elapsed = time.time() - start_time + if elapsed > 1: + print(f" (took {elapsed:.1f}s)", end="") + print() # New line + + return counts + + async def count_query_caches( + self, storage, storage_name: str + ) -> Dict[str, Dict[str, int]]: + """Count query caches from any storage type efficiently + + Args: + storage: Storage instance + storage_name: Storage type name + + Returns: + Dictionary with counts for each mode and cache_type + """ + if storage_name == "JsonKVStorage": + return await self.count_query_caches_json(storage) + elif storage_name == "RedisKVStorage": + return await self.count_query_caches_redis(storage) + elif storage_name == "PGKVStorage": + return await self.count_query_caches_pg(storage) + elif storage_name == "MongoKVStorage": + return await self.count_query_caches_mongo(storage) + else: + raise ValueError(f"Unsupported storage type: {storage_name}") + + async def delete_query_caches_json( + self, storage, cleanup_type: str, stats: CleanupStats + ): + """Delete query caches from JsonKVStorage + + Args: + storage: JsonKVStorage instance + cleanup_type: 'all', 'query', or 'keywords' + stats: CleanupStats object to track progress + """ + # Collect keys to delete + async with storage._storage_lock: + keys_to_delete = [] + for key in storage._data.keys(): + should_delete = False + for mode in QUERY_MODES: + if cleanup_type == "all": + if key.startswith(f"{mode}:query:") or key.startswith( + f"{mode}:keywords:" + ): + should_delete = True + elif cleanup_type == "query": + if key.startswith(f"{mode}:query:"): + should_delete = True + elif cleanup_type == "keywords": + if key.startswith(f"{mode}:keywords:"): + should_delete = True + + if should_delete: + keys_to_delete.append(key) + + # Delete in batches + total_keys = len(keys_to_delete) + stats.total_batches = (total_keys + self.batch_size - 1) // self.batch_size + + print("\n=== Starting Cleanup ===") + print( + f"💡 Processing {self.batch_size:,} records at a time from JsonKVStorage\n" + ) + + for batch_idx in range(stats.total_batches): + start_idx = batch_idx * self.batch_size + end_idx = min((batch_idx + 1) * self.batch_size, total_keys) + batch_keys = keys_to_delete[start_idx:end_idx] + + try: + async with storage._storage_lock: + for key in batch_keys: + del storage._data[key] + + # CRITICAL: Set update flag so changes persist to disk + # Without this, deletions remain in-memory only and are lost on exit + await set_all_update_flags(storage.final_namespace) + + # Success + stats.successful_batches += 1 + stats.successfully_deleted += len(batch_keys) + + # Calculate progress + progress = (stats.successfully_deleted / total_keys) * 100 + bar_length = 20 + filled_length = int( + bar_length * stats.successfully_deleted // total_keys + ) + bar = "█" * filled_length + "░" * (bar_length - filled_length) + + print( + f"Batch {batch_idx + 1}/{stats.total_batches}: {bar} " + f"{stats.successfully_deleted:,}/{total_keys:,} ({progress:.1f}%) ✓" + ) + + except Exception as e: + stats.add_error(batch_idx + 1, e, len(batch_keys)) + print( + f"Batch {batch_idx + 1}/{stats.total_batches}: ✗ FAILED - " + f"{type(e).__name__}: {str(e)}" + ) + + async def delete_query_caches_redis( + self, storage, cleanup_type: str, stats: CleanupStats + ): + """Delete query caches from RedisKVStorage + + Args: + storage: RedisKVStorage instance + cleanup_type: 'all', 'query', or 'keywords' + stats: CleanupStats object to track progress + """ + # Build patterns to delete + patterns = [] + for mode in QUERY_MODES: + if cleanup_type == "all": + patterns.append(f"{mode}:query:*") + patterns.append(f"{mode}:keywords:*") + elif cleanup_type == "query": + patterns.append(f"{mode}:query:*") + elif cleanup_type == "keywords": + patterns.append(f"{mode}:keywords:*") + + print("\n=== Starting Cleanup ===") + print(f"💡 Processing Redis keys in batches of {self.batch_size:,}\n") + + batch_idx = 0 + total_deleted = 0 + + async with storage._get_redis_connection() as redis: + for pattern in patterns: + prefixed_pattern = f"{storage.final_namespace}:{pattern}" + cursor = 0 + + while True: + cursor, keys = await redis.scan( + cursor, match=prefixed_pattern, count=self.batch_size + ) + + if keys: + batch_idx += 1 + stats.total_batches += 1 + + try: + # Delete batch using pipeline + pipe = redis.pipeline() + for key in keys: + pipe.delete(key) + await pipe.execute() + + # Success + stats.successful_batches += 1 + stats.successfully_deleted += len(keys) + total_deleted += len(keys) + + # Progress + print( + f"Batch {batch_idx}: Deleted {len(keys):,} keys " + f"(Total: {total_deleted:,}) ✓" + ) + + except Exception as e: + stats.add_error(batch_idx, e, len(keys)) + print( + f"Batch {batch_idx}: ✗ FAILED - " + f"{type(e).__name__}: {str(e)}" + ) + + if cursor == 0: + break + + await asyncio.sleep(0) + + async def delete_query_caches_pg( + self, storage, cleanup_type: str, stats: CleanupStats + ): + """Delete query caches from PostgreSQL + + Args: + storage: PGKVStorage instance + cleanup_type: 'all', 'query', or 'keywords' + stats: CleanupStats object to track progress + """ + from lightrag.kg.postgres_impl import namespace_to_table_name + + table_name = namespace_to_table_name(storage.namespace) + + # Build WHERE conditions + conditions = [] + for mode in QUERY_MODES: + if cleanup_type == "all": + conditions.append(f"id LIKE '{mode}:query:%'") + conditions.append(f"id LIKE '{mode}:keywords:%'") + elif cleanup_type == "query": + conditions.append(f"id LIKE '{mode}:query:%'") + elif cleanup_type == "keywords": + conditions.append(f"id LIKE '{mode}:keywords:%'") + + where_clause = " OR ".join(conditions) + + print("\n=== Starting Cleanup ===") + print("💡 Executing PostgreSQL DELETE query\n") + + try: + query = f""" + DELETE FROM {table_name} + WHERE workspace = $1 + AND ({where_clause}) + """ + + start_time = time.time() + # Fix: Pass dict instead of list for execute() method + await storage.db.execute(query, {"workspace": storage.workspace}) + elapsed = time.time() - start_time + + # PostgreSQL returns deletion count + stats.total_batches = 1 + stats.successful_batches = 1 + stats.successfully_deleted = stats.total_to_delete + + print(f"✓ Deleted {stats.successfully_deleted:,} records in {elapsed:.2f}s") + + except Exception as e: + stats.add_error(1, e, stats.total_to_delete) + print(f"✗ DELETE failed: {type(e).__name__}: {str(e)}") + + async def delete_query_caches_mongo( + self, storage, cleanup_type: str, stats: CleanupStats + ): + """Delete query caches from MongoDB + + Args: + storage: MongoKVStorage instance + cleanup_type: 'all', 'query', or 'keywords' + stats: CleanupStats object to track progress + """ + # Build regex patterns + patterns = [] + for mode in QUERY_MODES: + if cleanup_type == "all": + patterns.append(f"^{mode}:query:") + patterns.append(f"^{mode}:keywords:") + elif cleanup_type == "query": + patterns.append(f"^{mode}:query:") + elif cleanup_type == "keywords": + patterns.append(f"^{mode}:keywords:") + + print("\n=== Starting Cleanup ===") + print("💡 Executing MongoDB deleteMany operations\n") + + total_deleted = 0 + for idx, pattern in enumerate(patterns, 1): + try: + query = {"_id": {"$regex": pattern}} + result = await storage._data.delete_many(query) + deleted_count = result.deleted_count + + stats.total_batches += 1 + stats.successful_batches += 1 + stats.successfully_deleted += deleted_count + total_deleted += deleted_count + + print( + f"Pattern {idx}/{len(patterns)}: Deleted {deleted_count:,} records ✓" + ) + + except Exception as e: + stats.add_error(idx, e, 0) + print( + f"Pattern {idx}/{len(patterns)}: ✗ FAILED - " + f"{type(e).__name__}: {str(e)}" + ) + + print(f"\nTotal deleted: {total_deleted:,} records") + + async def delete_query_caches( + self, storage, storage_name: str, cleanup_type: str, stats: CleanupStats + ): + """Delete query caches from any storage type + + Args: + storage: Storage instance + storage_name: Storage type name + cleanup_type: 'all', 'query', or 'keywords' + stats: CleanupStats object to track progress + """ + if storage_name == "JsonKVStorage": + await self.delete_query_caches_json(storage, cleanup_type, stats) + elif storage_name == "RedisKVStorage": + await self.delete_query_caches_redis(storage, cleanup_type, stats) + elif storage_name == "PGKVStorage": + await self.delete_query_caches_pg(storage, cleanup_type, stats) + elif storage_name == "MongoKVStorage": + await self.delete_query_caches_mongo(storage, cleanup_type, stats) + else: + raise ValueError(f"Unsupported storage type: {storage_name}") + + def print_header(self): + """Print tool header""" + print("\n" + "=" * 60) + print("LLM Query Cache Cleanup Tool - LightRAG") + print("=" * 60) + + def print_storage_types(self): + """Print available storage types""" + print("\nSupported KV Storage Types:") + for key, value in STORAGE_TYPES.items(): + print(f"[{key}] {value}") + + def format_workspace(self, workspace: str) -> str: + """Format workspace name with highlighting + + Args: + workspace: Workspace name (may be empty) + + Returns: + Formatted workspace string with ANSI color codes + """ + if workspace: + return f"{BOLD_CYAN}{workspace}{RESET}" + else: + return f"{BOLD_CYAN}(default){RESET}" + + def print_cache_statistics(self, counts: Dict[str, Dict[str, int]], title: str): + """Print cache statistics in a formatted table + + Args: + counts: Dictionary with counts for each mode and cache_type + title: Title for the statistics display + """ + print(f"\n{title}") + print("┌" + "─" * 12 + "┬" + "─" * 12 + "┬" + "─" * 12 + "┬" + "─" * 12 + "┐") + print(f"│ {'Mode':<10} │ {'Query':>10} │ {'Keywords':>10} │ {'Total':>10} │") + print("├" + "─" * 12 + "┼" + "─" * 12 + "┼" + "─" * 12 + "┼" + "─" * 12 + "┤") + + total_query = 0 + total_keywords = 0 + + for mode in QUERY_MODES: + query_count = counts[mode]["query"] + keywords_count = counts[mode]["keywords"] + mode_total = query_count + keywords_count + + total_query += query_count + total_keywords += keywords_count + + print( + f"│ {mode:<10} │ {query_count:>10,} │ {keywords_count:>10,} │ {mode_total:>10,} │" + ) + + print("├" + "─" * 12 + "┼" + "─" * 12 + "┼" + "─" * 12 + "┼" + "─" * 12 + "┤") + grand_total = total_query + total_keywords + print( + f"│ {'Total':<10} │ {total_query:>10,} │ {total_keywords:>10,} │ {grand_total:>10,} │" + ) + print("└" + "─" * 12 + "┴" + "─" * 12 + "┴" + "─" * 12 + "┴" + "─" * 12 + "┘") + + def calculate_total_to_delete( + self, counts: Dict[str, Dict[str, int]], cleanup_type: str + ) -> int: + """Calculate total number of records to delete + + Args: + counts: Dictionary with counts for each mode and cache_type + cleanup_type: 'all', 'query', or 'keywords' + + Returns: + Total number of records to delete + """ + total = 0 + for mode in QUERY_MODES: + if cleanup_type == "all": + total += counts[mode]["query"] + counts[mode]["keywords"] + elif cleanup_type == "query": + total += counts[mode]["query"] + elif cleanup_type == "keywords": + total += counts[mode]["keywords"] + return total + + def print_cleanup_report(self, stats: CleanupStats): + """Print comprehensive cleanup report + + Args: + stats: CleanupStats object with cleanup results + """ + print("\n" + "=" * 60) + print("Cleanup Complete - Final Report") + print("=" * 60) + + # Overall statistics + print("\n📊 Statistics:") + print(f" Total records to delete: {stats.total_to_delete:,}") + print(f" Total batches: {stats.total_batches:,}") + print(f" Successful batches: {stats.successful_batches:,}") + print(f" Failed batches: {stats.failed_batches:,}") + print(f" Successfully deleted: {stats.successfully_deleted:,}") + print(f" Failed to delete: {stats.failed_to_delete:,}") + + # Success rate + success_rate = ( + (stats.successfully_deleted / stats.total_to_delete * 100) + if stats.total_to_delete > 0 + else 0 + ) + print(f" Success rate: {success_rate:.2f}%") + + # Before/After comparison + print("\n📈 Before/After Comparison:") + total_before = sum( + counts["query"] + counts["keywords"] + for counts in stats.counts_before.values() + ) + total_after = sum( + counts["query"] + counts["keywords"] + for counts in stats.counts_after.values() + ) + print(f" Total caches before: {total_before:,}") + print(f" Total caches after: {total_after:,}") + print(f" Net reduction: {total_before - total_after:,}") + + # Error details + if stats.errors: + print(f"\n⚠️ Errors encountered: {len(stats.errors)}") + print("\nError Details:") + print("-" * 60) + + # Group errors by type + error_types = {} + for error in stats.errors: + err_type = error["error_type"] + error_types[err_type] = error_types.get(err_type, 0) + 1 + + print("\nError Summary:") + for err_type, count in sorted(error_types.items(), key=lambda x: -x[1]): + print(f" - {err_type}: {count} occurrence(s)") + + print("\nFirst 5 errors:") + for i, error in enumerate(stats.errors[:5], 1): + print(f"\n {i}. Batch {error['batch']}") + print(f" Type: {error['error_type']}") + print(f" Message: {error['error_msg']}") + print(f" Records lost: {error['records_lost']:,}") + + if len(stats.errors) > 5: + print(f"\n ... and {len(stats.errors) - 5} more errors") + + print("\n" + "=" * 60) + print(f"{BOLD_RED}⚠️ WARNING: Cleanup completed with errors!{RESET}") + print(" Please review the error details above.") + print("=" * 60) + else: + print("\n" + "=" * 60) + print(f"{BOLD_GREEN}✓ SUCCESS: All records cleaned up successfully!{RESET}") + print("=" * 60) + + async def setup_storage(self) -> tuple: + """Setup and initialize storage + + Returns: + Tuple of (storage_instance, storage_name, workspace) + Returns (None, None, None) if user chooses to exit + """ + print("\n=== Storage Setup ===") + self.print_storage_types() + + # Custom input handling with exit support + while True: + choice = input( + "\nSelect storage type (1-4) (Press Enter to exit): " + ).strip() + + # Check for exit + if choice == "" or choice == "0": + print("\n✓ Cleanup cancelled by user") + return None, None, None + + # Check if choice is valid + if choice in STORAGE_TYPES: + break + + print( + f"✗ Invalid choice. Please enter one of: {', '.join(STORAGE_TYPES.keys())}" + ) + + storage_name = STORAGE_TYPES[choice] + + # Special warning for JsonKVStorage about concurrent access + if storage_name == "JsonKVStorage": + print("\n" + "=" * 60) + print(f"{BOLD_RED}⚠️ IMPORTANT WARNING - JsonKVStorage Concurrency{RESET}") + print("=" * 60) + print("\nJsonKVStorage is an in-memory database that does NOT support") + print("concurrent access to the same file by multiple programs.") + print("\nBefore proceeding, please ensure that:") + print(" • LightRAG Server is completely shut down") + print(" • No other programs are accessing the storage files") + print("\n" + "=" * 60) + + confirm = ( + input("\nHas LightRAG Server been shut down? (yes/no): ") + .strip() + .lower() + ) + if confirm != "yes": + print( + "\n✓ Operation cancelled - Please shut down LightRAG Server first" + ) + return None, None, None + + print("✓ Proceeding with JsonKVStorage cleanup...") + + # Check configuration (warnings only, doesn't block) + print("\nChecking configuration...") + self.check_env_vars(storage_name) + + # Get workspace + workspace = self.get_workspace_for_storage(storage_name) + + # Initialize storage (real validation point) + print("\nInitializing storage...") + try: + storage = await self.initialize_storage(storage_name, workspace) + print(f"- Storage Type: {storage_name}") + print(f"- Workspace: {workspace if workspace else '(default)'}") + print("- Connection Status: ✓ Success") + + except Exception as e: + print(f"✗ Initialization failed: {e}") + print(f"\nFor {storage_name}, you can configure using:") + print(" 1. Environment variables (highest priority)") + + # Show specific environment variable requirements + if storage_name in STORAGE_ENV_REQUIREMENTS: + for var in STORAGE_ENV_REQUIREMENTS[storage_name]: + print(f" - {var}") + + print(" 2. config.ini file (medium priority)") + if storage_name == "RedisKVStorage": + print(" [redis]") + print(" uri = redis://localhost:6379") + elif storage_name == "PGKVStorage": + print(" [postgres]") + print(" host = localhost") + print(" port = 5432") + print(" user = postgres") + print(" password = yourpassword") + print(" database = lightrag") + elif storage_name == "MongoKVStorage": + print(" [mongodb]") + print(" uri = mongodb://root:root@localhost:27017/") + print(" database = LightRAG") + + return None, None, None + + return storage, storage_name, workspace + + async def run(self): + """Run the cleanup tool""" + try: + # Initialize shared storage (REQUIRED for storage classes to work) + from lightrag.kg.shared_storage import initialize_share_data + + initialize_share_data(workers=1) + + # Print header + self.print_header() + + # Setup storage + self.storage, storage_name, self.workspace = await self.setup_storage() + + # Check if user cancelled + if self.storage is None: + return + + # Count query caches + print("\nCounting query cache records...") + try: + counts = await self.count_query_caches(self.storage, storage_name) + except Exception as e: + print(f"✗ Counting failed: {e}") + await self.storage.finalize() + return + + # Initialize stats + stats = CleanupStats() + stats.initialize_counts() + stats.counts_before = counts + + # Print statistics + self.print_cache_statistics( + counts, "📊 Query Cache Statistics (Before Cleanup):" + ) + + # Calculate total + total_caches = sum( + counts[mode]["query"] + counts[mode]["keywords"] for mode in QUERY_MODES + ) + + if total_caches == 0: + print("\n⚠️ No query caches found in storage") + await self.storage.finalize() + return + + # Select cleanup type + print("\n=== Cleanup Options ===") + print("[1] Delete all query caches (both query and keywords)") + print("[2] Delete query caches only (keep keywords)") + print("[3] Delete keywords caches only (keep query)") + print("[0] Cancel") + + while True: + choice = input("\nSelect cleanup option (0-3): ").strip() + + if choice == "0" or choice == "": + print("\n✓ Cleanup cancelled") + await self.storage.finalize() + return + elif choice == "1": + cleanup_type = "all" + elif choice == "2": + cleanup_type = "query" + elif choice == "3": + cleanup_type = "keywords" + else: + print("✗ Invalid choice. Please enter 0, 1, 2, or 3") + continue + + # Calculate total to delete for the selected type + stats.total_to_delete = self.calculate_total_to_delete( + counts, cleanup_type + ) + + # Check if there are any records to delete + if stats.total_to_delete == 0: + if cleanup_type == "all": + print(f"\n{BOLD_RED}⚠️ No query caches found to delete!{RESET}") + elif cleanup_type == "query": + print( + f"\n{BOLD_RED}⚠️ No query caches found to delete! (Only keywords exist){RESET}" + ) + elif cleanup_type == "keywords": + print( + f"\n{BOLD_RED}⚠️ No keywords caches found to delete! (Only query caches exist){RESET}" + ) + print(" Please select a different cleanup option.\n") + continue + + # Valid selection with records to delete + break + + # Confirm deletion + print("\n" + "=" * 60) + print("Cleanup Confirmation") + print("=" * 60) + print( + f"Storage: {BOLD_CYAN}{storage_name}{RESET} " + f"(workspace: {self.format_workspace(self.workspace)})" + ) + print(f"Cleanup Type: {BOLD_CYAN}{cleanup_type}{RESET}") + print( + f"Records to Delete: {BOLD_RED}{stats.total_to_delete:,}{RESET} / {total_caches:,}" + ) + + if cleanup_type == "all": + print( + f"\n{BOLD_RED}⚠️ WARNING: This will delete ALL query caches across all modes!{RESET}" + ) + elif cleanup_type == "query": + print("\n⚠️ This will delete query caches only (keywords will be kept)") + elif cleanup_type == "keywords": + print("\n⚠️ This will delete keywords caches only (query will be kept)") + + confirm = input("\nContinue with deletion? (y/n): ").strip().lower() + if confirm != "y": + print("\n✓ Cleanup cancelled") + await self.storage.finalize() + return + + # Perform deletion + await self.delete_query_caches( + self.storage, storage_name, cleanup_type, stats + ) + + # Persist changes + print("\nPersisting changes to storage...") + try: + await self.storage.index_done_callback() + print("✓ Changes persisted successfully") + except Exception as e: + print(f"✗ Persist failed: {e}") + stats.add_error(0, e, 0) + + # Count again to verify + print("\nVerifying cleanup results...") + try: + stats.counts_after = await self.count_query_caches( + self.storage, storage_name + ) + except Exception as e: + print(f"⚠️ Verification failed: {e}") + # Use zero counts if verification fails + stats.counts_after = { + mode: {"query": 0, "keywords": 0} for mode in QUERY_MODES + } + + # Print final report + self.print_cleanup_report(stats) + + # Print after statistics + self.print_cache_statistics( + stats.counts_after, "\n📊 Query Cache Statistics (After Cleanup):" + ) + + # Cleanup + await self.storage.finalize() + + except KeyboardInterrupt: + print("\n\n✗ Cleanup interrupted by user") + except Exception as e: + print(f"\n✗ Cleanup failed: {e}") + import traceback + + traceback.print_exc() + finally: + # Ensure cleanup + if self.storage: + try: + await self.storage.finalize() + except Exception: + pass + + # Finalize shared storage + try: + from lightrag.kg.shared_storage import finalize_share_data + + finalize_share_data() + except Exception: + pass + + +async def main(): + """Main entry point""" + tool = CleanupTool() + await tool.run() + + +if __name__ == "__main__": + asyncio.run(main()) diff --git a/LightRAG/lightrag/tools/download_cache.py b/LightRAG/lightrag/tools/download_cache.py new file mode 100644 index 00000000..43a21f0b --- /dev/null +++ b/LightRAG/lightrag/tools/download_cache.py @@ -0,0 +1,179 @@ +""" +Download all necessary cache files for offline deployment. + +This module provides a CLI command to download tiktoken model cache files +for offline environments where internet access is not available. +""" + +import os +import sys +from pathlib import Path + + +def download_tiktoken_cache(cache_dir: str = None, models: list = None): + """Download tiktoken models to local cache + + Args: + cache_dir: Directory to store the cache files. If None, uses default location. + models: List of model names to download. If None, downloads common models. + + Returns: + Tuple of (success_count, failed_models) + """ + try: + import tiktoken + except ImportError: + print("Error: tiktoken is not installed.") + print("Install with: pip install tiktoken") + sys.exit(1) + + # Set cache directory if provided + if cache_dir: + cache_dir = os.path.abspath(cache_dir) + os.environ["TIKTOKEN_CACHE_DIR"] = cache_dir + cache_path = Path(cache_dir) + cache_path.mkdir(parents=True, exist_ok=True) + print(f"Using cache directory: {cache_dir}") + else: + cache_dir = os.environ.get( + "TIKTOKEN_CACHE_DIR", str(Path.home() / ".tiktoken_cache") + ) + print(f"Using default cache directory: {cache_dir}") + + # Common models used by LightRAG and OpenAI + if models is None: + models = [ + "gpt-4o-mini", # Default model for LightRAG + "gpt-4o", # GPT-4 Omni + "gpt-4", # GPT-4 + "gpt-3.5-turbo", # GPT-3.5 Turbo + "text-embedding-ada-002", # Legacy embedding model + "text-embedding-3-small", # Small embedding model + "text-embedding-3-large", # Large embedding model + ] + + print(f"\nDownloading {len(models)} tiktoken models...") + print("=" * 70) + + success_count = 0 + failed_models = [] + + for i, model in enumerate(models, 1): + try: + print(f"[{i}/{len(models)}] Downloading {model}...", end=" ", flush=True) + encoding = tiktoken.encoding_for_model(model) + # Trigger download by encoding a test string + encoding.encode("test") + print("✓ Done") + success_count += 1 + except KeyError as e: + print(f"✗ Failed: Unknown model '{model}'") + failed_models.append((model, str(e))) + except Exception as e: + print(f"✗ Failed: {e}") + failed_models.append((model, str(e))) + + print("=" * 70) + print(f"\n✓ Successfully cached {success_count}/{len(models)} models") + + if failed_models: + print(f"\n✗ Failed to download {len(failed_models)} models:") + for model, error in failed_models: + print(f" - {model}: {error}") + + print(f"\nCache location: {cache_dir}") + print("\nFor offline deployment:") + print(" 1. Copy directory to offline server:") + print(f" tar -czf tiktoken_cache.tar.gz {cache_dir}") + print(" scp tiktoken_cache.tar.gz user@offline-server:/path/to/") + print("") + print(" 2. On offline server, extract and set environment variable:") + print(" tar -xzf tiktoken_cache.tar.gz") + print(" export TIKTOKEN_CACHE_DIR=/path/to/tiktoken_cache") + print("") + print(" 3. Or copy to default location:") + print(f" cp -r {cache_dir} ~/.tiktoken_cache/") + + return success_count, failed_models + + +def main(): + """Main entry point for the CLI command""" + import argparse + + parser = argparse.ArgumentParser( + prog="lightrag-download-cache", + description="Download cache files for LightRAG offline deployment", + formatter_class=argparse.RawDescriptionHelpFormatter, + epilog=""" +Examples: + # Download to default location (~/.tiktoken_cache) + lightrag-download-cache + + # Download to specific directory + lightrag-download-cache --cache-dir ./offline_cache/tiktoken + + # Download specific models only + lightrag-download-cache --models gpt-4o-mini gpt-4 + +For more information, visit: https://github.com/HKUDS/LightRAG + """, + ) + + parser.add_argument( + "--cache-dir", + help="Cache directory path (default: ~/.tiktoken_cache)", + default=None, + ) + parser.add_argument( + "--models", + nargs="+", + help="Specific models to download (default: common models)", + default=None, + ) + parser.add_argument( + "--version", action="version", version="%(prog)s (LightRAG cache downloader)" + ) + + args = parser.parse_args() + + print("=" * 70) + print("LightRAG Offline Cache Downloader") + print("=" * 70) + + try: + success_count, failed_models = download_tiktoken_cache( + args.cache_dir, args.models + ) + + print("\n" + "=" * 70) + print("Download Complete") + print("=" * 70) + + # Exit with error code if all downloads failed + if success_count == 0: + print("\n✗ All downloads failed. Please check your internet connection.") + sys.exit(1) + # Exit with warning code if some downloads failed + elif failed_models: + print( + f"\n⚠ Some downloads failed ({len(failed_models)}/{success_count + len(failed_models)})" + ) + sys.exit(2) + else: + print("\n✓ All cache files downloaded successfully!") + sys.exit(0) + + except KeyboardInterrupt: + print("\n\n✗ Download interrupted by user") + sys.exit(130) + except Exception as e: + print(f"\n\n✗ Error: {e}") + import traceback + + traceback.print_exc() + sys.exit(1) + + +if __name__ == "__main__": + main() diff --git a/LightRAG/lightrag/tools/lightrag_visualizer/README-zh.md b/LightRAG/lightrag/tools/lightrag_visualizer/README-zh.md new file mode 100644 index 00000000..949178ff --- /dev/null +++ b/LightRAG/lightrag/tools/lightrag_visualizer/README-zh.md @@ -0,0 +1,95 @@ +# 3D GraphML Viewer + +一个基于 Dear ImGui 和 ModernGL 的交互式 3D 图可视化工具。 + +## 功能特点 + +- **3D 交互式可视化**: 使用 ModernGL 实现高性能的 3D 图形渲染 +- **多种布局算法**: 支持多种图布局方式 + - Spring 布局 + - Circular 布局 + - Shell 布局 + - Random 布局 +- **社区检测**: 支持图社区结构的自动检测和可视化 +- **交互控制**: + - WASD + QE 键控制相机移动 + - 鼠标右键拖拽控制视角 + - 节点选择和高亮 + - 可调节节点大小和边宽度 + - 可控制标签显示 + - 可在节点的Connections间快速跳转 +- **社区检测**: 支持图社区结构的自动检测和可视化 +- **交互控制**: + - WASD + QE 键控制相机移动 + - 鼠标右键拖拽控制视角 + - 节点选择和高亮 + - 可调节节点大小和边宽度 + - 可控制标签显示 + +## 技术栈 + +- **imgui_bundle**: 用户界面 +- **ModernGL**: OpenGL 图形渲染 +- **NetworkX**: 图数据结构和算法 +- **NumPy**: 数值计算 +- **community**: 社区检测 + +## 使用方法 + +1. **启动程序**: + ```bash + pip install lightrag-hku[tools] + lightrag-viewer + ``` + +2. **加载字体**: + - 将中文字体文件 `font.ttf` 放置在 `assets` 目录下 + - 或者修改 `CUSTOM_FONT` 常量来使用其他字体文件 + +3. **加载图文件**: + - 点击界面上的 "Load GraphML" 按钮 + - 选择 GraphML 格式的图文件 + +4. **交互控制**: + - **相机移动**: + - W: 前进 + - S: 后退 + - A: 左移 + - D: 右移 + - Q: 上升 + - E: 下降 + - **视角控制**: + - 按住鼠标右键拖动来旋转视角 + - **节点交互**: + - 鼠标悬停可高亮节点 + - 点击可选中节点 + +5. **可视化设置**: + - 可通过 UI 控制面板调整: + - 布局类型 + - 节点大小 + - 边的宽度 + - 标签显示 + - 标签大小 + - 背景颜色 + +## 自定义设置 + +- **节点缩放**: 通过 `node_scale` 参数调整节点大小 +- **边宽度**: 通过 `edge_width` 参数调整边的宽度 +- **标签显示**: 可通过 `show_labels` 开关标签显示 +- **标签大小**: 使用 `label_size` 调整标签大小 +- **标签颜色**: 通过 `label_color` 设置标签颜色 +- **视距控制**: 使用 `label_culling_distance` 控制标签显示的最大距离 + +## 性能优化 + +- 使用 ModernGL 进行高效的图形渲染 +- 视距裁剪优化标签显示 +- 社区检测算法优化大规模图的可视化效果 + +## 系统要求 + +- Python 3.10+ +- OpenGL 3.3+ 兼容的显卡 +- 支持的操作系统：Windows/Linux/MacOS diff --git a/LightRAG/lightrag/tools/lightrag_visualizer/README.md b/LightRAG/lightrag/tools/lightrag_visualizer/README.md new file mode 100644 index 00000000..f0567bf4 --- /dev/null +++ b/LightRAG/lightrag/tools/lightrag_visualizer/README.md @@ -0,0 +1,136 @@ +# LightRAG 3D Graph Viewer + +An interactive 3D graph visualization tool included in the LightRAG package for visualizing and analyzing RAG (Retrieval-Augmented Generation) graphs and other graph structures. + +![image](https://github.com/user-attachments/assets/b0d86184-99fc-468c-96ed-c611f14292bf) + +## Installation + +### Quick Install +```bash +pip install lightrag-hku[tools] # Install with visualization tool only +# or +pip install lightrag-hku[api,tools] # Install with both API and visualization tools +``` + +## Launch the Viewer +```bash +lightrag-viewer +``` + +## Features + +- **3D Interactive Visualization**: High-performance 3D graphics rendering using ModernGL +- **Multiple Layout Algorithms**: Support for various graph layouts + - Spring layout + - Circular layout + - Shell layout + - Random layout +- **Community Detection**: Automatic detection and visualization of graph community structures +- **Interactive Controls**: + - WASD + QE keys for camera movement + - Right mouse drag for view angle control + - Node selection and highlighting + - Adjustable node size and edge width + - Configurable label display + - Quick navigation between node connections + +## Tech Stack + +- **imgui_bundle**: User interface +- **ModernGL**: OpenGL graphics rendering +- **NetworkX**: Graph data structures and algorithms +- **NumPy**: Numerical computations +- **community**: Community detection + +## Interactive Controls + +### Camera Movement +- W: Move forward +- S: Move backward +- A: Move left +- D: Move right +- Q: Move up +- E: Move down + +### View Control +- Hold right mouse button and drag to rotate view + +### Node Interaction +- Hover mouse to highlight nodes +- Click to select nodes + +## Visualization Settings + +Adjustable via UI control panel: +- Layout type +- Node size +- Edge width +- Label visibility +- Label size +- Background color + +## Customization Options + +- **Node Scaling**: Adjust node size via `node_scale` parameter +- **Edge Width**: Modify edge width using `edge_width` parameter +- **Label Display**: Toggle label visibility with `show_labels` +- **Label Size**: Adjust label size using `label_size` +- **Label Color**: Set label color through `label_color` +- **View Distance**: Control maximum label display distance with `label_culling_distance` + +## System Requirements + +- Python 3.9+ +- Graphics card with OpenGL 3.3+ support +- Supported Operating Systems: Windows/Linux/MacOS + +## Troubleshooting + +### Common Issues + +1. **Command Not Found** + ```bash + # Make sure you installed with the 'tools' option + pip install lightrag-hku[tools] + + # Verify installation + pip list | grep lightrag-hku + ``` + +2. **ModernGL Initialization Failed** + ```bash + # Check OpenGL version + glxinfo | grep "OpenGL version" + + # Update graphics drivers if needed + ``` + +3. **Font Loading Issues** + - The required fonts are included in the package + - If issues persist, check your graphics drivers + +## Usage with LightRAG + +The viewer is particularly useful for: +- Visualizing RAG knowledge graphs +- Analyzing document relationships +- Exploring semantic connections +- Debugging retrieval patterns + +## Performance Optimizations + +- Efficient graphics rendering using ModernGL +- View distance culling for label display optimization +- Community detection algorithms for optimized visualization of large-scale graphs + +## Support + +- GitHub Issues: [LightRAG Repository](https://github.com/HKUDS/LightRAG) +- Documentation: [LightRAG Docs](https://URL-to-docs) + +## License + +This tool is part of LightRAG and is distributed under the MIT License. See `LICENSE` for more information. + +Note: This visualization tool is an optional component of the LightRAG package. 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b/LightRAG/lightrag/tools/lightrag_visualizer/assets/LICENSE - Geist.txt new file mode 100644 index 00000000..8d003fec --- /dev/null +++ b/LightRAG/lightrag/tools/lightrag_visualizer/assets/LICENSE - Geist.txt @@ -0,0 +1,92 @@ +Copyright (c) 2023 Vercel, in collaboration with basement.studio + +This Font Software is licensed under the SIL Open Font License, Version 1.1. +This license is copied below, and is also available with a FAQ at: +http://scripts.sil.org/OFL + +----------------------------------------------------------- +SIL OPEN FONT LICENSE Version 1.1 - 26 February 2007 +----------------------------------------------------------- + +PREAMBLE +The goals of the Open Font License (OFL) are to stimulate worldwide +development of collaborative font projects, to support the font creation +efforts of academic and linguistic communities, and to provide a free and +open framework in which fonts may be shared and improved in partnership +with others. + +The OFL allows the licensed fonts to be used, studied, modified and +redistributed freely as long as they are not sold by themselves. The +fonts, including any derivative works, can be bundled, embedded, +redistributed and/or sold with any software provided that any reserved +names are not used by derivative works. The fonts and derivatives, +however, cannot be released under any other type of license. The +requirement for fonts to remain under this license does not apply +to any document created using the fonts or their derivatives. + +DEFINITIONS +"Font Software" refers to the set of files released by the Copyright +Holder(s) under this license and clearly marked as such. This may +include source files, build scripts and documentation. + +"Reserved Font Name" refers to any names specified as such after the +copyright statement(s). + +"Original Version" refers to the collection of Font Software components as +distributed by the Copyright Holder(s). + +"Modified Version" refers to any derivative made by adding to, deleting, +or substituting -- in part or in whole -- any of the components of the +Original Version, by changing formats or by porting the Font Software to a +new environment. + +"Author" refers to any designer, engineer, programmer, technical +writer or other person who contributed to the Font Software. + +PERMISSION AND CONDITIONS +Permission is hereby granted, free of charge, to any person obtaining +a copy of the Font Software, to use, study, copy, merge, embed, modify, +redistribute, and sell modified and unmodified copies of the Font +Software, subject to the following conditions: + +1) Neither the Font Software nor any of its individual components, +in Original or Modified Versions, may be sold by itself. + +2) Original or Modified Versions of the Font Software may be bundled, +redistributed and/or sold with any software, provided that each copy +contains the above copyright notice and this license. These can be +included either as stand-alone text files, human-readable headers or +in the appropriate machine-readable metadata fields within text or +binary files as long as those fields can be easily viewed by the user. + +3) No Modified Version of the Font Software may use the Reserved Font +Name(s) unless explicit written permission is granted by the corresponding +Copyright Holder. This restriction only applies to the primary font name as +presented to the users. + +4) The name(s) of the Copyright Holder(s) or the Author(s) of the Font +Software shall not be used to promote, endorse or advertise any +Modified Version, except to acknowledge the contribution(s) of the +Copyright Holder(s) and the Author(s) or with their explicit written +permission. + +5) The Font Software, modified or unmodified, in part or in whole, +must be distributed entirely under this license, and must not be +distributed under any other license. The requirement for fonts to +remain under this license does not apply to any document created +using the Font Software. + +TERMINATION +This license becomes null and void if any of the above conditions are +not met. + +DISCLAIMER +THE FONT SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, +EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO ANY WARRANTIES OF +MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT +OF COPYRIGHT, PATENT, TRADEMARK, OR OTHER RIGHT. IN NO EVENT SHALL THE +COPYRIGHT HOLDER BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, +INCLUDING ANY GENERAL, SPECIAL, INDIRECT, INCIDENTAL, OR CONSEQUENTIAL +DAMAGES, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING +FROM, OUT OF THE USE OR INABILITY TO USE THE FONT SOFTWARE OR FROM +OTHER DEALINGS IN THE FONT SOFTWARE. diff --git a/LightRAG/lightrag/tools/lightrag_visualizer/assets/LICENSE - SmileySans.txt b/LightRAG/lightrag/tools/lightrag_visualizer/assets/LICENSE - SmileySans.txt new file mode 100644 index 00000000..3eda3911 --- /dev/null +++ b/LightRAG/lightrag/tools/lightrag_visualizer/assets/LICENSE - SmileySans.txt @@ -0,0 +1,93 @@ +Copyright (c) 2022--2024, atelierAnchor , +with Reserved Font Name and <得意黑>. + +This Font Software is licensed under the SIL Open Font License, Version 1.1. +This license is copied below, and is also available with a FAQ at: +http://scripts.sil.org/OFL + +----------------------------------------------------------- +SIL OPEN FONT LICENSE Version 1.1 - 26 February 2007 +----------------------------------------------------------- + +PREAMBLE +The goals of the Open Font License (OFL) are to stimulate worldwide +development of collaborative font projects, to support the font creation +efforts of academic and linguistic communities, and to provide a free and +open framework in which fonts may be shared and improved in partnership +with others. + +The OFL allows the licensed fonts to be used, studied, modified and +redistributed freely as long as they are not sold by themselves. The +fonts, including any derivative works, can be bundled, embedded, +redistributed and/or sold with any software provided that any reserved +names are not used by derivative works. The fonts and derivatives, +however, cannot be released under any other type of license. The +requirement for fonts to remain under this license does not apply +to any document created using the fonts or their derivatives. + +DEFINITIONS +"Font Software" refers to the set of files released by the Copyright +Holder(s) under this license and clearly marked as such. This may +include source files, build scripts and documentation. + +"Reserved Font Name" refers to any names specified as such after the +copyright statement(s). + +"Original Version" refers to the collection of Font Software components as +distributed by the Copyright Holder(s). + +"Modified Version" refers to any derivative made by adding to, deleting, +or substituting -- in part or in whole -- any of the components of the +Original Version, by changing formats or by porting the Font Software to a +new environment. + +"Author" refers to any designer, engineer, programmer, technical +writer or other person who contributed to the Font Software. + +PERMISSION & CONDITIONS +Permission is hereby granted, free of charge, to any person obtaining +a copy of the Font Software, to use, study, copy, merge, embed, modify, +redistribute, and sell modified and unmodified copies of the Font +Software, subject to the following conditions: + +1) Neither the Font Software nor any of its individual components, +in Original or Modified Versions, may be sold by itself. + +2) Original or Modified Versions of the Font Software may be bundled, +redistributed and/or sold with any software, provided that each copy +contains the above copyright notice and this license. These can be +included either as stand-alone text files, human-readable headers or +in the appropriate machine-readable metadata fields within text or +binary files as long as those fields can be easily viewed by the user. + +3) No Modified Version of the Font Software may use the Reserved Font +Name(s) unless explicit written permission is granted by the corresponding +Copyright Holder. This restriction only applies to the primary font name as +presented to the users. + +4) The name(s) of the Copyright Holder(s) or the Author(s) of the Font +Software shall not be used to promote, endorse or advertise any +Modified Version, except to acknowledge the contribution(s) of the +Copyright Holder(s) and the Author(s) or with their explicit written +permission. + +5) The Font Software, modified or unmodified, in part or in whole, +must be distributed entirely under this license, and must not be +distributed under any other license. The requirement for fonts to +remain under this license does not apply to any document created +using the Font Software. + +TERMINATION +This license becomes null and void if any of the above conditions are +not met. + +DISCLAIMER +THE FONT SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, +EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO ANY WARRANTIES OF +MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT +OF COPYRIGHT, PATENT, TRADEMARK, OR OTHER RIGHT. 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zxOKR7xcrZoA3|qudH6?#+t+SS-TMCRsqN+M<;!>h`|vh3$je t8!o?UyM6hw%bz*>OWU8?K5u%tef;y*;cq~f9~tzw-v092e`b60{{yvt+9?14 literal 0 HcmV?d00001 diff --git a/LightRAG/lightrag/tools/lightrag_visualizer/assets/place_font_here b/LightRAG/lightrag/tools/lightrag_visualizer/assets/place_font_here new file mode 100644 index 00000000..e69de29b diff --git a/LightRAG/lightrag/tools/lightrag_visualizer/graph_visualizer.py b/LightRAG/lightrag/tools/lightrag_visualizer/graph_visualizer.py new file mode 100644 index 00000000..8a6f0976 --- /dev/null +++ b/LightRAG/lightrag/tools/lightrag_visualizer/graph_visualizer.py @@ -0,0 +1,1221 @@ +from typing import Optional, Tuple, Dict, List +import numpy as np +import networkx as nx +import pipmaster as pm + +# Added automatic libraries install using pipmaster +if not pm.is_installed("moderngl"): + pm.install("moderngl") +if not pm.is_installed("imgui_bundle"): + pm.install("imgui_bundle") +if not pm.is_installed("pyglm"): + pm.install("pyglm") +if not pm.is_installed("python-louvain"): + pm.install("python-louvain") + +import moderngl +from imgui_bundle import imgui, immapp, hello_imgui +import community +import glm +import tkinter as tk +from tkinter import filedialog +import traceback +import colorsys +import os + +CUSTOM_FONT = "font.ttf" + +DEFAULT_FONT_ENG = "Geist-Regular.ttf" +DEFAULT_FONT_CHI = "SmileySans-Oblique.ttf" + + +class Node3D: + """Class representing a 3D node in the graph""" + + def __init__( + self, position: glm.vec3, color: glm.vec3, label: str, size: float, idx: int + ): + self.position = position + self.color = color + self.label = label + self.size = size + self.idx = idx + + +class GraphViewer: + """Main class for 3D graph visualization""" + + def __init__(self): + self.glctx = None # ModernGL context + self.graph: Optional[nx.Graph] = None + self.nodes: List[Node3D] = [] + self.id_node_map: Dict[str, Node3D] = {} + self.communities = None + self.community_colors = None + + # Window dimensions + self.window_width = 1280 + self.window_height = 720 + + # Camera parameters + self.position = glm.vec3(0.0, -10.0, 0.0) # Initial camera position + self.front = glm.vec3(0.0, 1.0, 0.0) # Direction camera is facing + self.up = glm.vec3(0.0, 0.0, 1.0) # Up vector + self.yaw = 90.0 # Horizontal rotation (around Z axis) + self.pitch = 0.0 # Vertical rotation + self.move_speed = 0.05 + self.mouse_sensitivity = 0.15 + + # Graph visualization settings + self.layout_type = "Spring" + self.node_scale = 0.2 + self.edge_width = 0.5 + self.show_labels = True + self.label_size = 2 + self.label_color = (1.0, 1.0, 1.0, 1.0) + self.label_culling_distance = 10.0 + self.available_layouts = ("Spring", "Circular", "Shell", "Random") + self.background_color = (0.05, 0.05, 0.05, 1.0) + + # Mouse interaction + self.last_mouse_pos = None + self.mouse_pressed = False + self.mouse_button = -1 + self.first_mouse = True + + # File dialog state + self.show_load_error = False + self.error_message = "" + + # Selection state + self.selected_node: Optional[Node3D] = None + self.highlighted_node: Optional[Node3D] = None + + # Node id map + self.node_id_fbo = None + self.node_id_texture = None + self.node_id_depth = None + self.node_id_texture_np: np.ndarray = None + + # Static data + self.sphere_data = create_sphere() + + # Initialization flag + self.initialized = False + + def setup(self): + self.setup_render_context() + self.setup_shaders() + self.setup_buffers() + self.initialized = True + + def handle_keyboard_input(self): + """Handle WASD keyboard input for camera movement""" + io = imgui.get_io() + + if io.want_capture_keyboard: + return + + # Calculate camera vectors + right = glm.normalize(glm.cross(self.front, self.up)) + + # Get movement direction from WASD keys + if imgui.is_key_down(imgui.Key.w): # Forward + self.position += self.front * self.move_speed * 0.1 + if imgui.is_key_down(imgui.Key.s): # Backward + self.position -= self.front * self.move_speed * 0.1 + if imgui.is_key_down(imgui.Key.a): # Left + self.position -= right * self.move_speed * 0.1 + if imgui.is_key_down(imgui.Key.d): # Right + self.position += right * self.move_speed * 0.1 + if imgui.is_key_down(imgui.Key.q): # Up + self.position += self.up * self.move_speed * 0.1 + if imgui.is_key_down(imgui.Key.e): # Down + self.position -= self.up * self.move_speed * 0.1 + + def handle_mouse_interaction(self): + """Handle mouse interaction for camera control and node selection""" + if ( + imgui.is_any_item_active() + or imgui.is_any_item_hovered() + or imgui.is_any_item_focused() + ): + return + + io = imgui.get_io() + mouse_pos = (io.mouse_pos.x, io.mouse_pos.y) + if ( + mouse_pos[0] < 0 + or mouse_pos[1] < 0 + or mouse_pos[0] >= self.window_width + or mouse_pos[1] >= self.window_height + ): + return + + # Handle first mouse input + if self.first_mouse: + self.last_mouse_pos = mouse_pos + self.first_mouse = False + return + + # Handle mouse movement for camera rotation + if self.mouse_pressed and self.mouse_button == 1: # Right mouse button + dx = self.last_mouse_pos[0] - mouse_pos[0] + dy = self.last_mouse_pos[1] - mouse_pos[1] # Reversed for intuitive control + + dx *= self.mouse_sensitivity + dy *= self.mouse_sensitivity + + self.yaw += dx + self.pitch += dy + + # Limit pitch to avoid flipping + self.pitch = np.clip(self.pitch, -89.0, 89.0) + + # Update front vector + self.front = glm.normalize( + glm.vec3( + np.cos(np.radians(self.yaw)) * np.cos(np.radians(self.pitch)), + np.sin(np.radians(self.yaw)) * np.cos(np.radians(self.pitch)), + np.sin(np.radians(self.pitch)), + ) + ) + + if not imgui.is_window_hovered(): + return + + if io.mouse_wheel != 0: + self.move_speed += io.mouse_wheel * 0.05 + self.move_speed = np.max([self.move_speed, 0.01]) + + # Handle mouse press/release + for button in range(3): + if imgui.is_mouse_clicked(button): + self.mouse_pressed = True + self.mouse_button = button + if button == 0 and self.highlighted_node: # Left click for selection + self.selected_node = self.highlighted_node + + if imgui.is_mouse_released(button) and self.mouse_button == button: + self.mouse_pressed = False + self.mouse_button = -1 + + # Handle node hovering + if not self.mouse_pressed: + hovered = self.find_node_at((int(mouse_pos[0]), int(mouse_pos[1]))) + self.highlighted_node = hovered + + # Update last mouse position + self.last_mouse_pos = mouse_pos + + def update_layout(self): + """Update the graph layout""" + pos = nx.spring_layout( + self.graph, + dim=3, + pos={ + node_id: list(node.position) + for node_id, node in self.id_node_map.items() + }, + k=2.0, + iterations=100, + weight=None, + ) + + # Update node positions + for node_id, position in pos.items(): + self.id_node_map[node_id].position = glm.vec3(position) + self.update_buffers() + + def render_node_details(self): + """Render node details window""" + if self.selected_node and imgui.begin("Node Details"): + imgui.text(f"ID: {self.selected_node.label}") + + if self.graph: + node_data = self.graph.nodes[self.selected_node.label] + imgui.text(f"Type: {node_data.get('type', 'default')}") + + degree = self.graph.degree[self.selected_node.label] + imgui.text(f"Degree: {degree}") + + for key, value in node_data.items(): + if key != "type": + imgui.text(f"{key}: {value}") + if value and imgui.is_item_hovered(): + imgui.set_tooltip(str(value)) + + imgui.separator() + + connections = self.graph[self.selected_node.label] + if connections: + imgui.text("Connections:") + keys = next(iter(connections.values())).keys() + if imgui.begin_table( + "Connections", + len(keys) + 1, + imgui.TableFlags_.borders + | imgui.TableFlags_.row_bg + | imgui.TableFlags_.resizable + | imgui.TableFlags_.hideable, + ): + imgui.table_setup_column("Node") + for key in keys: + imgui.table_setup_column(key) + imgui.table_headers_row() + + for neighbor, edge_data in connections.items(): + imgui.table_next_row() + imgui.table_set_column_index(0) + if imgui.selectable(str(neighbor), True)[0]: + # Select neighbor node + self.selected_node = self.id_node_map[neighbor] + self.position = self.selected_node.position - self.front + for idx, key in enumerate(keys): + imgui.table_set_column_index(idx + 1) + value = str(edge_data.get(key, "")) + imgui.text(value) + if value and imgui.is_item_hovered(): + imgui.set_tooltip(value) + imgui.end_table() + + imgui.end() + + def setup_render_context(self): + """Initialize ModernGL context""" + self.glctx = moderngl.create_context() + self.glctx.enable(moderngl.DEPTH_TEST | moderngl.CULL_FACE) + self.glctx.clear_color = self.background_color + + def setup_shaders(self): + """Setup vertex and fragment shaders for node and edge rendering""" + # Node shader program + self.node_prog = self.glctx.program( + vertex_shader=""" + #version 330 + + uniform mat4 mvp; + uniform vec3 camera; + uniform int selected_node; + uniform int highlighted_node; + uniform float scale; + + in vec3 in_position; + in vec3 in_instance_position; + in vec3 in_instance_color; + in float in_instance_size; + + out vec3 frag_color; + out vec3 frag_normal; + out vec3 frag_view_dir; + + void main() { + vec3 pos = in_position * in_instance_size * scale + in_instance_position; + gl_Position = mvp * vec4(pos, 1.0); + + frag_normal = normalize(in_position); + frag_view_dir = normalize(camera - pos); + + if (selected_node == gl_InstanceID) { + frag_color = vec3(1.0, 0.5, 0.0); + } + else if (highlighted_node == gl_InstanceID) { + frag_color = vec3(1.0, 0.8, 0.2); + } + else { + frag_color = in_instance_color; + } + } + """, + fragment_shader=""" + #version 330 + + in vec3 frag_color; + in vec3 frag_normal; + in vec3 frag_view_dir; + + out vec4 outColor; + + void main() { + // Edge detection based on normal-view angle + float edge = 1.0 - abs(dot(frag_normal, frag_view_dir)); + + // Create sharp outline + float outline = smoothstep(0.8, 0.9, edge); + + // Mix the sphere color with outline + vec3 final_color = mix(frag_color, vec3(0.0), outline); + + outColor = vec4(final_color, 1.0); + } + """, + ) + + # Edge shader program with wide lines using geometry shader + self.edge_prog = self.glctx.program( + vertex_shader=""" + #version 330 + + uniform mat4 mvp; + + in vec3 in_position; + in vec3 in_color; + + out vec3 v_color; + out vec4 v_position; + + void main() { + v_position = mvp * vec4(in_position, 1.0); + gl_Position = v_position; + v_color = in_color; + } + """, + geometry_shader=""" + #version 330 + + layout(lines) in; + layout(triangle_strip, max_vertices = 4) out; + + uniform float edge_width; + uniform vec2 viewport_size; + + in vec3 v_color[]; + in vec4 v_position[]; + out vec3 g_color; + out float edge_coord; + + void main() { + // Get the two vertices of the line + vec4 p1 = v_position[0]; + vec4 p2 = v_position[1]; + + // Perspective division + vec4 p1_ndc = p1 / p1.w; + vec4 p2_ndc = p2 / p2.w; + + // Calculate line direction in screen space + vec2 dir = normalize((p2_ndc.xy - p1_ndc.xy) * viewport_size); + vec2 normal = vec2(-dir.y, dir.x); + + // Calculate half width based on screen space + float half_width = edge_width * 0.5; + vec2 offset = normal * (half_width / viewport_size); + + // Emit vertices with proper depth + gl_Position = vec4(p1_ndc.xy + offset, p1_ndc.z, 1.0); + gl_Position *= p1.w; // Restore perspective + g_color = v_color[0]; + edge_coord = 1.0; + EmitVertex(); + + gl_Position = vec4(p1_ndc.xy - offset, p1_ndc.z, 1.0); + gl_Position *= p1.w; + g_color = v_color[0]; + edge_coord = -1.0; + EmitVertex(); + + gl_Position = vec4(p2_ndc.xy + offset, p2_ndc.z, 1.0); + gl_Position *= p2.w; + g_color = v_color[1]; + edge_coord = 1.0; + EmitVertex(); + + gl_Position = vec4(p2_ndc.xy - offset, p2_ndc.z, 1.0); + gl_Position *= p2.w; + g_color = v_color[1]; + edge_coord = -1.0; + EmitVertex(); + + EndPrimitive(); + } + """, + fragment_shader=""" + #version 330 + + in vec3 g_color; + in float edge_coord; + + out vec4 fragColor; + + void main() { + // Edge outline parameters + float outline_width = 0.2; // Width of the outline relative to edge + float edge_softness = 0.1; // Softness of the edge + float edge_dist = abs(edge_coord); + + // Calculate outline + float outline_factor = smoothstep(1.0 - outline_width - edge_softness, + 1.0 - outline_width, + edge_dist); + + // Mix edge color with outline (black) + vec3 final_color = mix(g_color, vec3(0.0), outline_factor); + + // Calculate alpha for anti-aliasing + float alpha = 1.0 - smoothstep(1.0 - edge_softness, 1.0, edge_dist); + + fragColor = vec4(final_color, alpha); + } + """, + ) + + # Id framebuffer shader program + self.node_id_prog = self.glctx.program( + vertex_shader=""" + #version 330 + + uniform mat4 mvp; + uniform float scale; + + in vec3 in_position; + in vec3 in_instance_position; + in float in_instance_size; + + out vec3 frag_color; + + vec3 int_to_rgb(int value) { + float R = float((value >> 16) & 0xFF); + float G = float((value >> 8) & 0xFF); + float B = float(value & 0xFF); + // normalize to [0, 1] + return vec3(R / 255.0, G / 255.0, B / 255.0); + } + + void main() { + vec3 pos = in_position * in_instance_size * scale + in_instance_position; + gl_Position = mvp * vec4(pos, 1.0); + frag_color = int_to_rgb(gl_InstanceID); + } + """, + fragment_shader=""" + #version 330 + in vec3 frag_color; + out vec4 outColor; + void main() { + outColor = vec4(frag_color, 1.0); + } + """, + ) + + def setup_buffers(self): + """Setup vertex buffers for nodes and edges""" + # We'll create these when loading the graph + self.node_vbo = None + self.node_color_vbo = None + self.node_size_vbo = None + self.edge_vbo = None + self.edge_color_vbo = None + self.node_vao = None + self.edge_vao = None + self.node_id_vao = None + self.sphere_pos_vbo = None + self.sphere_index_buffer = None + + def load_file(self, filepath: str): + """Load a GraphML file with error handling""" + try: + # Clear existing data + self.id_node_map.clear() + self.nodes.clear() + self.selected_node = None + self.highlighted_node = None + self.setup_buffers() + + # Load new graph + self.graph = nx.read_graphml(filepath) + self.calculate_layout() + self.update_buffers() + self.show_load_error = False + self.error_message = "" + except Exception as _: + self.show_load_error = True + self.error_message = traceback.format_exc() + print(self.error_message) + + def calculate_layout(self): + """Calculate 3D layout for the graph""" + if not self.graph: + return + + # Detect communities for coloring + self.communities = community.best_partition(self.graph) + num_communities = len(set(self.communities.values())) + self.community_colors = generate_colors(num_communities) + + # Calculate layout based on selected type + if self.layout_type == "Spring": + pos = nx.spring_layout( + self.graph, dim=3, k=2.0, iterations=100, weight=None + ) + elif self.layout_type == "Circular": + pos_2d = nx.circular_layout(self.graph) + pos = {node: np.array((x, 0.0, y)) for node, (x, y) in pos_2d.items()} + elif self.layout_type == "Shell": + # Group nodes by community for shell layout + comm_lists = [[] for _ in range(num_communities)] + for node, comm in self.communities.items(): + comm_lists[comm].append(node) + pos_2d = nx.shell_layout(self.graph, comm_lists) + pos = {node: np.array((x, 0.0, y)) for node, (x, y) in pos_2d.items()} + else: # Random + pos = {node: np.random.rand(3) * 2 - 1 for node in self.graph.nodes()} + + # Scale positions + positions = np.array(list(pos.values())) + if len(positions) > 0: + scale = 10.0 / max(1.0, np.max(np.abs(positions))) + pos = {node: coords * scale for node, coords in pos.items()} + + # Calculate degree-based sizes + degrees = dict(self.graph.degree()) + max_degree = max(degrees.values()) if degrees else 1 + min_degree = min(degrees.values()) if degrees else 1 + + idx = 0 + # Create nodes with community colors + for node_id in self.graph.nodes(): + position = glm.vec3(pos[node_id]) + color = self.get_node_color(node_id) + + # Normalize sizes between 0.5 and 2.0 + size = 1.0 + if max_degree != min_degree: + # Normalize and scale size + normalized = (degrees[node_id] - min_degree) / (max_degree - min_degree) + size = 0.5 + normalized * 1.5 + + if node_id in self.id_node_map: + node = self.id_node_map[node_id] + node.position = position + node.base_color = color + node.color = color + node.size = size + else: + node = Node3D(position, color, str(node_id), size, idx) + self.id_node_map[node_id] = node + self.nodes.append(node) + idx += 1 + + self.update_buffers() + + def get_node_color(self, node_id: str) -> glm.vec3: + """Get RGBA color based on community""" + if self.communities and node_id in self.communities: + comm_id = self.communities[node_id] + color = self.community_colors[comm_id] + return color + return glm.vec3(0.5, 0.5, 0.5) + + def update_buffers(self): + """Update vertex buffers with current node and edge data using batch rendering""" + if not self.graph: + return + + # Update node buffers + node_positions = [] + node_colors = [] + node_sizes = [] + + for node in self.nodes: + node_positions.append(node.position) + node_colors.append(node.color) # Only use RGB components + node_sizes.append(node.size) + + if node_positions: + node_positions = np.array(node_positions, dtype=np.float32) + node_colors = np.array(node_colors, dtype=np.float32) + node_sizes = np.array(node_sizes, dtype=np.float32) + + self.node_vbo = self.glctx.buffer(node_positions.tobytes()) + self.node_color_vbo = self.glctx.buffer(node_colors.tobytes()) + self.node_size_vbo = self.glctx.buffer(node_sizes.tobytes()) + self.sphere_pos_vbo = self.glctx.buffer(self.sphere_data[0].tobytes()) + self.sphere_index_buffer = self.glctx.buffer(self.sphere_data[1].tobytes()) + + self.node_vao = self.glctx.vertex_array( + self.node_prog, + [ + (self.sphere_pos_vbo, "3f", "in_position"), + (self.node_vbo, "3f /i", "in_instance_position"), + (self.node_color_vbo, "3f /i", "in_instance_color"), + (self.node_size_vbo, "f /i", "in_instance_size"), + ], + index_buffer=self.sphere_index_buffer, + index_element_size=4, + ) + self.node_vao.instances = len(self.nodes) + + self.node_id_vao = self.glctx.vertex_array( + self.node_id_prog, + [ + (self.sphere_pos_vbo, "3f", "in_position"), + (self.node_vbo, "3f /i", "in_instance_position"), + (self.node_size_vbo, "f /i", "in_instance_size"), + ], + index_buffer=self.sphere_index_buffer, + index_element_size=4, + ) + self.node_id_vao.instances = len(self.nodes) + + # Update edge buffers + edge_positions = [] + edge_colors = [] + + for edge in self.graph.edges(): + start_node = self.id_node_map[edge[0]] + end_node = self.id_node_map[edge[1]] + + edge_positions.append(start_node.position) + edge_colors.append(start_node.color) + + edge_positions.append(end_node.position) + edge_colors.append(end_node.color) + + if edge_positions: + edge_positions = np.array(edge_positions, dtype=np.float32) + edge_colors = np.array(edge_colors, dtype=np.float32) + + self.edge_vbo = self.glctx.buffer(edge_positions.tobytes()) + self.edge_color_vbo = self.glctx.buffer(edge_colors.tobytes()) + + self.edge_vao = self.glctx.vertex_array( + self.edge_prog, + [ + (self.edge_vbo, "3f", "in_position"), + (self.edge_color_vbo, "3f", "in_color"), + ], + ) + + def update_view_proj_matrix(self): + """Update view matrix based on camera parameters""" + self.view_matrix = glm.lookAt( + self.position, self.position + self.front, self.up + ) + + aspect_ratio = self.window_width / self.window_height + self.proj_matrix = glm.perspective( + glm.radians(60.0), # FOV + aspect_ratio, # Aspect ratio + 0.001, # Near plane + 1000.0, # Far plane + ) + + def find_node_at(self, screen_pos: Tuple[int, int]) -> Optional[Node3D]: + """Find the node at a specific screen position""" + if ( + self.node_id_texture_np is None + or self.node_id_texture_np.shape[1] != self.window_width + or self.node_id_texture_np.shape[0] != self.window_height + or screen_pos[0] < 0 + or screen_pos[1] < 0 + or screen_pos[0] >= self.window_width + or screen_pos[1] >= self.window_height + ): + return None + + x = screen_pos[0] + y = self.window_height - screen_pos[1] - 1 + pixel = self.node_id_texture_np[y, x] + + if pixel[3] == 0: + return None + + R = int(round(pixel[0] * 255)) + G = int(round(pixel[1] * 255)) + B = int(round(pixel[2] * 255)) + index = (R << 16) | (G << 8) | B + + if index > len(self.nodes): + return None + return self.nodes[index] + + def is_node_visible_at(self, screen_pos: Tuple[int, int], node_idx: int) -> bool: + """Check if a node exists at a specific screen position""" + node = self.find_node_at(screen_pos) + return node is not None and node.idx == node_idx + + def render_settings(self): + """Render settings window""" + if imgui.begin("Graph Settings"): + # Layout type combo + changed, value = imgui.combo( + "Layout", + self.available_layouts.index(self.layout_type), + self.available_layouts, + ) + if changed: + self.layout_type = self.available_layouts[value] + self.calculate_layout() # Recalculate layout when changed + + # Node size slider + changed, value = imgui.slider_float("Node Scale", self.node_scale, 0.01, 10) + if changed: + self.node_scale = value + + # Edge width slider + changed, value = imgui.slider_float("Edge Width", self.edge_width, 0, 20) + if changed: + self.edge_width = value + + # Show labels checkbox + changed, value = imgui.checkbox("Show Labels", self.show_labels) + + if changed: + self.show_labels = value + + if self.show_labels: + # Label size slider + changed, value = imgui.slider_float( + "Label Size", self.label_size, 0.5, 10.0 + ) + if changed: + self.label_size = value + + # Label color picker + changed, value = imgui.color_edit4( + "Label Color", + self.label_color, + imgui.ColorEditFlags_.picker_hue_wheel, + ) + if changed: + self.label_color = (value[0], value[1], value[2], value[3]) + + # Label culling distance slider + changed, value = imgui.slider_float( + "Label Culling Distance", self.label_culling_distance, 0.1, 100.0 + ) + if changed: + self.label_culling_distance = value + + # Background color picker + changed, value = imgui.color_edit4( + "Background Color", + self.background_color, + imgui.ColorEditFlags_.picker_hue_wheel, + ) + if changed: + self.background_color = (value[0], value[1], value[2], value[3]) + + imgui.end() + + def save_node_id_texture_to_png(self, filename): + # Convert to a PIL Image and save as PNG + from PIL import Image + + scaled_array = self.node_id_texture_np * 255 + img = Image.fromarray( + scaled_array.astype(np.uint8), + "RGBA", + ) + img = img.transpose(method=Image.FLIP_TOP_BOTTOM) + img.save(filename) + + def render_id_map(self, mvp: glm.mat4): + """Render an offscreen id map where each node is drawn with a unique id color.""" + # Lazy initialization of id framebuffer + if self.node_id_texture is not None: + if ( + self.node_id_texture.width != self.window_width + or self.node_id_texture.height != self.window_height + ): + self.node_id_fbo = None + self.node_id_texture = None + self.node_id_texture_np = None + self.node_id_depth = None + + if self.node_id_texture is None: + self.node_id_texture = self.glctx.texture( + (self.window_width, self.window_height), components=4, dtype="f4" + ) + self.node_id_depth = self.glctx.depth_renderbuffer( + size=(self.window_width, self.window_height) + ) + self.node_id_fbo = self.glctx.framebuffer( + color_attachments=[self.node_id_texture], + depth_attachment=self.node_id_depth, + ) + self.node_id_texture_np = np.zeros( + (self.window_height, self.window_width, 4), dtype=np.float32 + ) + + # Bind the offscreen framebuffer + self.node_id_fbo.use() + self.glctx.clear(0, 0, 0, 0) + + # Render nodes + if self.node_id_vao: + self.node_id_prog["mvp"].write(mvp.to_bytes()) + self.node_id_prog["scale"].write(np.float32(self.node_scale).tobytes()) + self.node_id_vao.render(moderngl.TRIANGLES) + + # Revert to default framebuffer + self.glctx.screen.use() + self.node_id_texture.read_into(self.node_id_texture_np.data) + + def render(self): + """Render the graph""" + # Clear screen + self.glctx.clear(*self.background_color, depth=1) + + if not self.graph: + return + + # Enable blending for transparency + self.glctx.enable(moderngl.BLEND) + self.glctx.blend_func = moderngl.SRC_ALPHA, moderngl.ONE_MINUS_SRC_ALPHA + + # Update view and projection matrices + self.update_view_proj_matrix() + mvp = self.proj_matrix * self.view_matrix + + # Render edges first (under nodes) + if self.edge_vao: + self.edge_prog["mvp"].write(mvp.to_bytes()) + self.edge_prog["edge_width"].value = ( + float(self.edge_width) * 2.0 + ) # Double the width for better visibility + self.edge_prog["viewport_size"].value = ( + float(self.window_width), + float(self.window_height), + ) + self.edge_vao.render(moderngl.LINES) + + # Render nodes + if self.node_vao: + self.node_prog["mvp"].write(mvp.to_bytes()) + self.node_prog["camera"].write(self.position.to_bytes()) + self.node_prog["selected_node"].write( + np.int32(self.selected_node.idx).tobytes() + if self.selected_node + else np.int32(-1).tobytes() + ) + self.node_prog["highlighted_node"].write( + np.int32(self.highlighted_node.idx).tobytes() + if self.highlighted_node + else np.int32(-1).tobytes() + ) + self.node_prog["scale"].write(np.float32(self.node_scale).tobytes()) + self.node_vao.render(moderngl.TRIANGLES) + + self.glctx.disable(moderngl.BLEND) + + # Render id map + self.render_id_map(mvp) + + def render_labels(self): + # Render labels if enabled + if self.show_labels and self.nodes: + # Save current font scale + original_scale = imgui.get_font_size() + + self.update_view_proj_matrix() + mvp = self.proj_matrix * self.view_matrix + + for node in self.nodes: + # Project node position to screen space + pos = mvp * glm.vec4( + node.position[0], node.position[1], node.position[2], 1.0 + ) + + # Check if node is behind camera + if pos.w > 0 and pos.w < self.label_culling_distance: + screen_x = (pos.x / pos.w + 1) * self.window_width / 2 + screen_y = (-pos.y / pos.w + 1) * self.window_height / 2 + + if self.is_node_visible_at( + (int(screen_x), int(screen_y)), node.idx + ): + # Set font scale + imgui.set_window_font_scale(float(self.label_size) * node.size) + + # Calculate label size + label_size = imgui.calc_text_size(node.label) + + # Adjust position to center the label + screen_x -= label_size.x / 2 + screen_y -= label_size.y / 2 + + # Set text color with calculated alpha + imgui.push_style_color(imgui.Col_.text, self.label_color) + + # Draw label using ImGui + imgui.set_cursor_pos((screen_x, screen_y)) + imgui.text(node.label) + + # Restore text color + imgui.pop_style_color() + + # Restore original font scale + imgui.set_window_font_scale(original_scale) + + def reset_view(self): + """Reset camera view to default""" + self.position = glm.vec3(0.0, -10.0, 0.0) + self.front = glm.vec3(0.0, 1.0, 0.0) + self.yaw = 90.0 + self.pitch = 0.0 + + +def generate_colors(n: int) -> List[glm.vec3]: + """Generate n distinct colors using HSV color space""" + colors = [] + for i in range(n): + # Use golden ratio to generate well-distributed hues + hue = (i * 0.618033988749895) % 1.0 + # Fixed saturation and value for vibrant colors + saturation = 0.8 + value = 0.95 + # Convert HSV to RGB + rgb = colorsys.hsv_to_rgb(hue, saturation, value) + # Add alpha channel + colors.append(glm.vec3(rgb)) + return colors + + +def show_file_dialog() -> Optional[str]: + """Show a file dialog for selecting GraphML files""" + file_path = filedialog.askopenfilename( + title="Select GraphML File", + filetypes=[("GraphML files", "*.graphml"), ("All files", "*.*")], + ) + return file_path if file_path else None + + +def create_sphere(sectors: int = 32, rings: int = 16) -> Tuple: + """ + Creates a sphere. + """ + R = 1.0 / (rings - 1) + S = 1.0 / (sectors - 1) + + # Use those names as normals and uvs are part of the API + vertices_l = [0.0] * (rings * sectors * 3) + # normals_l = [0.0] * (rings * sectors * 3) + uvs_l = [0.0] * (rings * sectors * 2) + + v, n, t = 0, 0, 0 + for r in range(rings): + for s in range(sectors): + y = np.sin(-np.pi / 2 + np.pi * r * R) + x = np.cos(2 * np.pi * s * S) * np.sin(np.pi * r * R) + z = np.sin(2 * np.pi * s * S) * np.sin(np.pi * r * R) + + uvs_l[t] = s * S + uvs_l[t + 1] = r * R + + vertices_l[v] = x + vertices_l[v + 1] = y + vertices_l[v + 2] = z + + t += 2 + v += 3 + n += 3 + + indices = [0] * rings * sectors * 6 + i = 0 + for r in range(rings - 1): + for s in range(sectors - 1): + indices[i] = r * sectors + s + indices[i + 1] = (r + 1) * sectors + (s + 1) + indices[i + 2] = r * sectors + (s + 1) + + indices[i + 3] = r * sectors + s + indices[i + 4] = (r + 1) * sectors + s + indices[i + 5] = (r + 1) * sectors + (s + 1) + i += 6 + + vbo_vertices = np.array(vertices_l, dtype=np.float32) + vbo_elements = np.array(indices, dtype=np.uint32) + + return (vbo_vertices, vbo_elements) + + +def draw_text_with_bg( + text: str, + text_pos: imgui.ImVec2Like, + text_size: imgui.ImVec2Like, + bg_color: int, +): + imgui.get_window_draw_list().add_rect_filled( + (text_pos[0] - 5, text_pos[1] - 5), + (text_pos[0] + text_size[0] + 5, text_pos[1] + text_size[1] + 5), + bg_color, + 3.0, + ) + imgui.set_cursor_pos(text_pos) + imgui.text(text) + + +def main(): + """Main application entry point""" + viewer = GraphViewer() + + show_fps = True + text_bg_color = imgui.IM_COL32(0, 0, 0, 100) + + def gui(): + if not viewer.initialized: + viewer.setup() + # # Change the theme + # tweaked_theme = hello_imgui.get_runner_params().imgui_window_params.tweaked_theme + # tweaked_theme.theme = hello_imgui.ImGuiTheme_.darcula_darker + # hello_imgui.apply_tweaked_theme(tweaked_theme) + + viewer.window_width = int(imgui.get_window_width()) + viewer.window_height = int(imgui.get_window_height()) + + # Handle keyboard and mouse input + viewer.handle_keyboard_input() + viewer.handle_mouse_interaction() + + style = imgui.get_style() + window_bg_color = style.color_(imgui.Col_.window_bg.value) + + window_bg_color.w = 0.8 + style.set_color_(imgui.Col_.window_bg.value, window_bg_color) + + # Main control window + imgui.begin("Graph Controls") + + if imgui.button("Load GraphML"): + filepath = show_file_dialog() + if filepath: + viewer.load_file(filepath) + + # Show error message if loading failed + if viewer.show_load_error: + imgui.push_style_color(imgui.Col_.text, (1.0, 0.0, 0.0, 1.0)) + imgui.text(f"Error loading file: {viewer.error_message}") + imgui.pop_style_color() + + imgui.separator() + + # Camera controls help + imgui.text("Camera Controls:") + imgui.bullet_text("Hold Right Mouse - Look around") + imgui.bullet_text("W/S - Move forward/backward") + imgui.bullet_text("A/D - Move left/right") + imgui.bullet_text("Q/E - Move up/down") + imgui.bullet_text("Left Mouse - Select node") + imgui.bullet_text("Wheel - Change the movement speed") + + imgui.separator() + + # Camera settings + _, viewer.move_speed = imgui.slider_float( + "Movement Speed", viewer.move_speed, 0.01, 2.0 + ) + _, viewer.mouse_sensitivity = imgui.slider_float( + "Mouse Sensitivity", viewer.mouse_sensitivity, 0.01, 0.5 + ) + + imgui.separator() + + imgui.begin_horizontal("buttons") + + if imgui.button("Reset Camera"): + viewer.reset_view() + + if imgui.button("Update Layout") and viewer.graph: + viewer.update_layout() + + # if imgui.button("Save Node ID Texture"): + # viewer.save_node_id_texture_to_png("node_id_texture.png") + + imgui.end_horizontal() + + imgui.end() + + # Render node details window if a node is selected + viewer.render_node_details() + + # Render graph settings window + viewer.render_settings() + + # Render FPS + if show_fps: + imgui.set_window_font_scale(1) + fps_text = f"FPS: {hello_imgui.frame_rate():.1f}" + text_size = imgui.calc_text_size(fps_text) + cursor_pos = (10, viewer.window_height - text_size.y - 10) + draw_text_with_bg(fps_text, cursor_pos, text_size, text_bg_color) + + # Render highlighted node ID + if viewer.highlighted_node: + imgui.set_window_font_scale(1) + node_text = f"Node ID: {viewer.highlighted_node.label}" + text_size = imgui.calc_text_size(node_text) + cursor_pos = ( + viewer.window_width - text_size.x - 10, + viewer.window_height - text_size.y - 10, + ) + draw_text_with_bg(node_text, cursor_pos, text_size, text_bg_color) + + window_bg_color.w = 0 + style.set_color_(imgui.Col_.window_bg.value, window_bg_color) + + # Render labels + viewer.render_labels() + + def custom_background(): + if viewer.initialized: + viewer.render() + + runner_params = hello_imgui.RunnerParams() + runner_params.app_window_params.window_geometry.size = ( + viewer.window_width, + viewer.window_height, + ) + runner_params.app_window_params.window_title = "3D GraphML Viewer" + runner_params.callbacks.show_gui = gui + runner_params.callbacks.custom_background = custom_background + + def load_font(): + # You will need to provide it yourself, or use another font. + font_filename = CUSTOM_FONT + + io = imgui.get_io() + io.fonts.tex_desired_width = 4096 # Larger texture for better CJK font quality + font_size_pixels = 14 + asset_dir = os.path.join(os.path.dirname(__file__), "assets") + + # Try to load custom font + if not os.path.isfile(font_filename): + font_filename = os.path.join(asset_dir, font_filename) + if os.path.isfile(font_filename): + custom_font = io.fonts.add_font_from_file_ttf( + filename=font_filename, + size_pixels=font_size_pixels, + glyph_ranges_as_int_list=io.fonts.get_glyph_ranges_chinese_full(), + ) + io.font_default = custom_font + return + + # Load default fonts + io.fonts.add_font_from_file_ttf( + filename=os.path.join(asset_dir, DEFAULT_FONT_ENG), + size_pixels=font_size_pixels, + ) + + font_config = imgui.ImFontConfig() + font_config.merge_mode = True + + io.font_default = io.fonts.add_font_from_file_ttf( + filename=os.path.join(asset_dir, DEFAULT_FONT_CHI), + size_pixels=font_size_pixels, + font_cfg=font_config, + glyph_ranges_as_int_list=io.fonts.get_glyph_ranges_chinese_full(), + ) + + runner_params.callbacks.load_additional_fonts = load_font + + tk_root = tk.Tk() + tk_root.withdraw() # Hide the main window + + immapp.run(runner_params) + + tk_root.destroy() # Destroy the main window + + +if __name__ == "__main__": + main() diff --git a/LightRAG/lightrag/tools/lightrag_visualizer/requirements.txt b/LightRAG/lightrag/tools/lightrag_visualizer/requirements.txt new file mode 100644 index 00000000..59f45627 --- /dev/null +++ b/LightRAG/lightrag/tools/lightrag_visualizer/requirements.txt @@ -0,0 +1,8 @@ +imgui_bundle +moderngl +networkx +numpy +pyglm +python-louvain +scipy +tk diff --git a/LightRAG/lightrag/tools/migrate_llm_cache.py b/LightRAG/lightrag/tools/migrate_llm_cache.py new file mode 100644 index 00000000..b0d4823c --- /dev/null +++ b/LightRAG/lightrag/tools/migrate_llm_cache.py @@ -0,0 +1,1472 @@ +#!/usr/bin/env python3 +""" +LLM Cache Migration Tool for LightRAG + +This tool migrates LLM response cache (default:extract:* and default:summary:*) +between different KV storage implementations while preserving workspace isolation. + +Usage: + python -m lightrag.tools.migrate_llm_cache + # or + python lightrag/tools/migrate_llm_cache.py + +Supported KV Storage Types: + - JsonKVStorage + - RedisKVStorage + - PGKVStorage + - MongoKVStorage +""" + +import asyncio +import os +import sys +import time +from typing import Any, Dict, List +from dataclasses import dataclass, field +from dotenv import load_dotenv + +# Add project root to path for imports +sys.path.insert( + 0, os.path.dirname(os.path.dirname(os.path.dirname(os.path.abspath(__file__)))) +) + +from lightrag.kg import STORAGE_ENV_REQUIREMENTS +from lightrag.namespace import NameSpace +from lightrag.utils import setup_logger + +# Load environment variables +# use the .env that is inside the current folder +# allows to use different .env file for each lightrag instance +# the OS environment variables take precedence over the .env file +load_dotenv(dotenv_path=".env", override=False) + +# Setup logger +setup_logger("lightrag", level="INFO") + +# Storage type configurations +STORAGE_TYPES = { + "1": "JsonKVStorage", + "2": "RedisKVStorage", + "3": "PGKVStorage", + "4": "MongoKVStorage", +} + +# Workspace environment variable mapping +WORKSPACE_ENV_MAP = { + "PGKVStorage": "POSTGRES_WORKSPACE", + "MongoKVStorage": "MONGODB_WORKSPACE", + "RedisKVStorage": "REDIS_WORKSPACE", +} + +# Default batch size for migration +DEFAULT_BATCH_SIZE = 1000 + + +# Default count batch size for efficient counting +DEFAULT_COUNT_BATCH_SIZE = 1000 + +# ANSI color codes for terminal output +BOLD_CYAN = "\033[1;36m" +RESET = "\033[0m" + + +@dataclass +class MigrationStats: + """Migration statistics and error tracking""" + + total_source_records: int = 0 + total_batches: int = 0 + successful_batches: int = 0 + failed_batches: int = 0 + successful_records: int = 0 + failed_records: int = 0 + errors: List[Dict[str, Any]] = field(default_factory=list) + + def add_error(self, batch_idx: int, error: Exception, batch_size: int): + """Record batch error""" + self.errors.append( + { + "batch": batch_idx, + "error_type": type(error).__name__, + "error_msg": str(error), + "records_lost": batch_size, + "timestamp": time.time(), + } + ) + self.failed_batches += 1 + self.failed_records += batch_size + + +class MigrationTool: + """LLM Cache Migration Tool""" + + def __init__(self): + self.source_storage = None + self.target_storage = None + self.source_workspace = "" + self.target_workspace = "" + self.batch_size = DEFAULT_BATCH_SIZE + + def get_workspace_for_storage(self, storage_name: str) -> str: + """Get workspace for a specific storage type + + Priority: Storage-specific env var > WORKSPACE env var > empty string + + Args: + storage_name: Storage implementation name + + Returns: + Workspace name + """ + # Check storage-specific workspace + if storage_name in WORKSPACE_ENV_MAP: + specific_workspace = os.getenv(WORKSPACE_ENV_MAP[storage_name]) + if specific_workspace: + return specific_workspace + + # Check generic WORKSPACE + workspace = os.getenv("WORKSPACE", "") + return workspace + + def check_config_ini_for_storage(self, storage_name: str) -> bool: + """Check if config.ini has configuration for the storage type + + Args: + storage_name: Storage implementation name + + Returns: + True if config.ini has the necessary configuration + """ + try: + import configparser + + config = configparser.ConfigParser() + config.read("config.ini", "utf-8") + + if storage_name == "RedisKVStorage": + return config.has_option("redis", "uri") + elif storage_name == "PGKVStorage": + return ( + config.has_option("postgres", "user") + and config.has_option("postgres", "password") + and config.has_option("postgres", "database") + ) + elif storage_name == "MongoKVStorage": + return config.has_option("mongodb", "uri") and config.has_option( + "mongodb", "database" + ) + + return False + except Exception: + return False + + def check_env_vars(self, storage_name: str) -> bool: + """Check environment variables, show warnings if missing but don't fail + + Args: + storage_name: Storage implementation name + + Returns: + Always returns True (warnings only, no hard failure) + """ + required_vars = STORAGE_ENV_REQUIREMENTS.get(storage_name, []) + + if not required_vars: + print("✓ No environment variables required") + return True + + missing_vars = [var for var in required_vars if var not in os.environ] + + if missing_vars: + print( + f"⚠️ Warning: Missing environment variables: {', '.join(missing_vars)}" + ) + + # Check if config.ini has configuration + has_config = self.check_config_ini_for_storage(storage_name) + if has_config: + print(" ✓ Found configuration in config.ini") + else: + print(f" Will attempt to use defaults for {storage_name}") + + return True + + print("✓ All required environment variables are set") + return True + + def count_available_storage_types(self) -> int: + """Count available storage types (with env vars, config.ini, or defaults) + + Returns: + Number of available storage types + """ + available_count = 0 + + for storage_name in STORAGE_TYPES.values(): + # Check if storage requires configuration + required_vars = STORAGE_ENV_REQUIREMENTS.get(storage_name, []) + + if not required_vars: + # JsonKVStorage, MongoKVStorage etc. - no config needed + available_count += 1 + else: + # Check if has environment variables + has_env = all(var in os.environ for var in required_vars) + if has_env: + available_count += 1 + else: + # Check if has config.ini configuration + has_config = self.check_config_ini_for_storage(storage_name) + if has_config: + available_count += 1 + + return available_count + + def get_storage_class(self, storage_name: str): + """Dynamically import and return storage class + + Args: + storage_name: Storage implementation name + + Returns: + Storage class + """ + if storage_name == "JsonKVStorage": + from lightrag.kg.json_kv_impl import JsonKVStorage + + return JsonKVStorage + elif storage_name == "RedisKVStorage": + from lightrag.kg.redis_impl import RedisKVStorage + + return RedisKVStorage + elif storage_name == "PGKVStorage": + from lightrag.kg.postgres_impl import PGKVStorage + + return PGKVStorage + elif storage_name == "MongoKVStorage": + from lightrag.kg.mongo_impl import MongoKVStorage + + return MongoKVStorage + else: + raise ValueError(f"Unsupported storage type: {storage_name}") + + async def initialize_storage(self, storage_name: str, workspace: str): + """Initialize storage instance with fallback to config.ini and defaults + + Args: + storage_name: Storage implementation name + workspace: Workspace name + + Returns: + Initialized storage instance + + Raises: + Exception: If initialization fails + """ + storage_class = self.get_storage_class(storage_name) + + # Create global config + global_config = { + "working_dir": os.getenv("WORKING_DIR", "./rag_storage"), + "embedding_batch_num": 10, + } + + # Initialize storage + storage = storage_class( + namespace=NameSpace.KV_STORE_LLM_RESPONSE_CACHE, + workspace=workspace, + global_config=global_config, + embedding_func=None, + ) + + # Initialize the storage (may raise exception if connection fails) + await storage.initialize() + + return storage + + async def get_default_caches_json(self, storage) -> Dict[str, Any]: + """Get default caches from JsonKVStorage + + Args: + storage: JsonKVStorage instance + + Returns: + Dictionary of cache entries with default:extract:* or default:summary:* keys + """ + # Access _data directly - it's a dict from shared_storage + async with storage._storage_lock: + filtered = {} + for key, value in storage._data.items(): + if key.startswith("default:extract:") or key.startswith( + "default:summary:" + ): + filtered[key] = value.copy() + return filtered + + async def get_default_caches_redis( + self, storage, batch_size: int = 1000 + ) -> Dict[str, Any]: + """Get default caches from RedisKVStorage with pagination + + Args: + storage: RedisKVStorage instance + batch_size: Number of keys to process per batch + + Returns: + Dictionary of cache entries with default:extract:* or default:summary:* keys + """ + import json + + cache_data = {} + + # Use _get_redis_connection() context manager + async with storage._get_redis_connection() as redis: + for pattern in ["default:extract:*", "default:summary:*"]: + # Add namespace prefix to pattern + prefixed_pattern = f"{storage.final_namespace}:{pattern}" + cursor = 0 + + while True: + # SCAN already implements cursor-based pagination + cursor, keys = await redis.scan( + cursor, match=prefixed_pattern, count=batch_size + ) + + if keys: + # Process this batch using pipeline with error handling + try: + pipe = redis.pipeline() + for key in keys: + pipe.get(key) + values = await pipe.execute() + + for key, value in zip(keys, values): + if value: + key_str = ( + key.decode() if isinstance(key, bytes) else key + ) + # Remove namespace prefix to get original key + original_key = key_str.replace( + f"{storage.final_namespace}:", "", 1 + ) + cache_data[original_key] = json.loads(value) + + except Exception as e: + # Pipeline execution failed, fall back to individual gets + print( + f"⚠️ Pipeline execution failed for batch, using individual gets: {e}" + ) + for key in keys: + try: + value = await redis.get(key) + if value: + key_str = ( + key.decode() + if isinstance(key, bytes) + else key + ) + original_key = key_str.replace( + f"{storage.final_namespace}:", "", 1 + ) + cache_data[original_key] = json.loads(value) + except Exception as individual_error: + print( + f"⚠️ Failed to get individual key {key}: {individual_error}" + ) + continue + + if cursor == 0: + break + + # Yield control periodically to avoid blocking + await asyncio.sleep(0) + + return cache_data + + async def get_default_caches_pg( + self, storage, batch_size: int = 1000 + ) -> Dict[str, Any]: + """Get default caches from PGKVStorage with pagination + + Args: + storage: PGKVStorage instance + batch_size: Number of records to fetch per batch + + Returns: + Dictionary of cache entries with default:extract:* or default:summary:* keys + """ + from lightrag.kg.postgres_impl import namespace_to_table_name + + cache_data = {} + table_name = namespace_to_table_name(storage.namespace) + offset = 0 + + while True: + # Use LIMIT and OFFSET for pagination + query = f""" + SELECT id as key, original_prompt, return_value, chunk_id, cache_type, queryparam, + EXTRACT(EPOCH FROM create_time)::BIGINT as create_time, + EXTRACT(EPOCH FROM update_time)::BIGINT as update_time + FROM {table_name} + WHERE workspace = $1 + AND (id LIKE 'default:extract:%' OR id LIKE 'default:summary:%') + ORDER BY id + LIMIT $2 OFFSET $3 + """ + + results = await storage.db.query( + query, [storage.workspace, batch_size, offset], multirows=True + ) + + if not results: + break + + for row in results: + # Map PostgreSQL fields to cache format + cache_entry = { + "return": row.get("return_value", ""), + "cache_type": row.get("cache_type"), + "original_prompt": row.get("original_prompt", ""), + "chunk_id": row.get("chunk_id"), + "queryparam": row.get("queryparam"), + "create_time": row.get("create_time", 0), + "update_time": row.get("update_time", 0), + } + cache_data[row["key"]] = cache_entry + + # If we got fewer results than batch_size, we're done + if len(results) < batch_size: + break + + offset += batch_size + + # Yield control periodically + await asyncio.sleep(0) + + return cache_data + + async def get_default_caches_mongo( + self, storage, batch_size: int = 1000 + ) -> Dict[str, Any]: + """Get default caches from MongoKVStorage with cursor-based pagination + + Args: + storage: MongoKVStorage instance + batch_size: Number of documents to process per batch + + Returns: + Dictionary of cache entries with default:extract:* or default:summary:* keys + """ + cache_data = {} + + # MongoDB query with regex - use _data not collection + query = {"_id": {"$regex": "^default:(extract|summary):"}} + + # Use cursor without to_list() - process in batches + cursor = storage._data.find(query).batch_size(batch_size) + + async for doc in cursor: + # Process each document as it comes + doc_copy = doc.copy() + key = doc_copy.pop("_id") + + # Filter ALL MongoDB/database-specific fields + # Following .clinerules: "Always filter deprecated/incompatible fields during deserialization" + for field_name in ["namespace", "workspace", "_id", "content"]: + doc_copy.pop(field_name, None) + + cache_data[key] = doc_copy.copy() + + # Periodically yield control (every batch_size documents) + if len(cache_data) % batch_size == 0: + await asyncio.sleep(0) + + return cache_data + + async def get_default_caches(self, storage, storage_name: str) -> Dict[str, Any]: + """Get default caches from any storage type + + Args: + storage: Storage instance + storage_name: Storage type name + + Returns: + Dictionary of cache entries + """ + if storage_name == "JsonKVStorage": + return await self.get_default_caches_json(storage) + elif storage_name == "RedisKVStorage": + return await self.get_default_caches_redis(storage) + elif storage_name == "PGKVStorage": + return await self.get_default_caches_pg(storage) + elif storage_name == "MongoKVStorage": + return await self.get_default_caches_mongo(storage) + else: + raise ValueError(f"Unsupported storage type: {storage_name}") + + async def count_default_caches_json(self, storage) -> int: + """Count default caches in JsonKVStorage - O(N) but very fast in-memory + + Args: + storage: JsonKVStorage instance + + Returns: + Total count of cache records + """ + async with storage._storage_lock: + return sum( + 1 + for key in storage._data.keys() + if key.startswith("default:extract:") + or key.startswith("default:summary:") + ) + + async def count_default_caches_redis(self, storage) -> int: + """Count default caches in RedisKVStorage using SCAN with progress display + + Args: + storage: RedisKVStorage instance + + Returns: + Total count of cache records + """ + count = 0 + print("Scanning Redis keys...", end="", flush=True) + + async with storage._get_redis_connection() as redis: + for pattern in ["default:extract:*", "default:summary:*"]: + prefixed_pattern = f"{storage.final_namespace}:{pattern}" + cursor = 0 + while True: + cursor, keys = await redis.scan( + cursor, match=prefixed_pattern, count=DEFAULT_COUNT_BATCH_SIZE + ) + count += len(keys) + + # Show progress + print( + f"\rScanning Redis keys... found {count:,} records", + end="", + flush=True, + ) + + if cursor == 0: + break + + print() # New line after progress + return count + + async def count_default_caches_pg(self, storage) -> int: + """Count default caches in PostgreSQL using COUNT(*) with progress indicator + + Args: + storage: PGKVStorage instance + + Returns: + Total count of cache records + """ + from lightrag.kg.postgres_impl import namespace_to_table_name + + table_name = namespace_to_table_name(storage.namespace) + + query = f""" + SELECT COUNT(*) as count + FROM {table_name} + WHERE workspace = $1 + AND (id LIKE 'default:extract:%' OR id LIKE 'default:summary:%') + """ + + print("Counting PostgreSQL records...", end="", flush=True) + start_time = time.time() + + result = await storage.db.query(query, [storage.workspace]) + + elapsed = time.time() - start_time + if elapsed > 1: + print(f" (took {elapsed:.1f}s)", end="") + print() # New line + + return result["count"] if result else 0 + + async def count_default_caches_mongo(self, storage) -> int: + """Count default caches in MongoDB using count_documents with progress indicator + + Args: + storage: MongoKVStorage instance + + Returns: + Total count of cache records + """ + query = {"_id": {"$regex": "^default:(extract|summary):"}} + + print("Counting MongoDB documents...", end="", flush=True) + start_time = time.time() + + count = await storage._data.count_documents(query) + + elapsed = time.time() - start_time + if elapsed > 1: + print(f" (took {elapsed:.1f}s)", end="") + print() # New line + + return count + + async def count_default_caches(self, storage, storage_name: str) -> int: + """Count default caches from any storage type efficiently + + Args: + storage: Storage instance + storage_name: Storage type name + + Returns: + Total count of cache records + """ + if storage_name == "JsonKVStorage": + return await self.count_default_caches_json(storage) + elif storage_name == "RedisKVStorage": + return await self.count_default_caches_redis(storage) + elif storage_name == "PGKVStorage": + return await self.count_default_caches_pg(storage) + elif storage_name == "MongoKVStorage": + return await self.count_default_caches_mongo(storage) + else: + raise ValueError(f"Unsupported storage type: {storage_name}") + + async def stream_default_caches_json(self, storage, batch_size: int): + """Stream default caches from JsonKVStorage - yields batches + + Args: + storage: JsonKVStorage instance + batch_size: Size of each batch to yield + + Yields: + Dictionary batches of cache entries + + Note: + This method creates a snapshot of matching items while holding the lock, + then releases the lock before yielding batches. This prevents deadlock + when the target storage (also JsonKVStorage) tries to acquire the same + lock during upsert operations. + """ + # Create a snapshot of matching items while holding the lock + async with storage._storage_lock: + matching_items = [ + (key, value) + for key, value in storage._data.items() + if key.startswith("default:extract:") + or key.startswith("default:summary:") + ] + + # Now iterate over snapshot without holding lock + batch = {} + for key, value in matching_items: + batch[key] = value.copy() + if len(batch) >= batch_size: + yield batch + batch = {} + + # Yield remaining items + if batch: + yield batch + + async def stream_default_caches_redis(self, storage, batch_size: int): + """Stream default caches from RedisKVStorage - yields batches + + Args: + storage: RedisKVStorage instance + batch_size: Size of each batch to yield + + Yields: + Dictionary batches of cache entries + """ + import json + + async with storage._get_redis_connection() as redis: + for pattern in ["default:extract:*", "default:summary:*"]: + prefixed_pattern = f"{storage.final_namespace}:{pattern}" + cursor = 0 + + while True: + cursor, keys = await redis.scan( + cursor, match=prefixed_pattern, count=batch_size + ) + + if keys: + try: + pipe = redis.pipeline() + for key in keys: + pipe.get(key) + values = await pipe.execute() + + batch = {} + for key, value in zip(keys, values): + if value: + key_str = ( + key.decode() if isinstance(key, bytes) else key + ) + original_key = key_str.replace( + f"{storage.final_namespace}:", "", 1 + ) + batch[original_key] = json.loads(value) + + if batch: + yield batch + + except Exception as e: + print(f"⚠️ Pipeline execution failed for batch: {e}") + # Fall back to individual gets + batch = {} + for key in keys: + try: + value = await redis.get(key) + if value: + key_str = ( + key.decode() + if isinstance(key, bytes) + else key + ) + original_key = key_str.replace( + f"{storage.final_namespace}:", "", 1 + ) + batch[original_key] = json.loads(value) + except Exception as individual_error: + print( + f"⚠️ Failed to get individual key {key}: {individual_error}" + ) + continue + + if batch: + yield batch + + if cursor == 0: + break + + await asyncio.sleep(0) + + async def stream_default_caches_pg(self, storage, batch_size: int): + """Stream default caches from PostgreSQL - yields batches + + Args: + storage: PGKVStorage instance + batch_size: Size of each batch to yield + + Yields: + Dictionary batches of cache entries + """ + from lightrag.kg.postgres_impl import namespace_to_table_name + + table_name = namespace_to_table_name(storage.namespace) + offset = 0 + + while True: + query = f""" + SELECT id as key, original_prompt, return_value, chunk_id, cache_type, queryparam, + EXTRACT(EPOCH FROM create_time)::BIGINT as create_time, + EXTRACT(EPOCH FROM update_time)::BIGINT as update_time + FROM {table_name} + WHERE workspace = $1 + AND (id LIKE 'default:extract:%' OR id LIKE 'default:summary:%') + ORDER BY id + LIMIT $2 OFFSET $3 + """ + + results = await storage.db.query( + query, [storage.workspace, batch_size, offset], multirows=True + ) + + if not results: + break + + batch = {} + for row in results: + cache_entry = { + "return": row.get("return_value", ""), + "cache_type": row.get("cache_type"), + "original_prompt": row.get("original_prompt", ""), + "chunk_id": row.get("chunk_id"), + "queryparam": row.get("queryparam"), + "create_time": row.get("create_time", 0), + "update_time": row.get("update_time", 0), + } + batch[row["key"]] = cache_entry + + if batch: + yield batch + + if len(results) < batch_size: + break + + offset += batch_size + await asyncio.sleep(0) + + async def stream_default_caches_mongo(self, storage, batch_size: int): + """Stream default caches from MongoDB - yields batches + + Args: + storage: MongoKVStorage instance + batch_size: Size of each batch to yield + + Yields: + Dictionary batches of cache entries + """ + query = {"_id": {"$regex": "^default:(extract|summary):"}} + cursor = storage._data.find(query).batch_size(batch_size) + + batch = {} + async for doc in cursor: + doc_copy = doc.copy() + key = doc_copy.pop("_id") + + # Filter MongoDB/database-specific fields + for field_name in ["namespace", "workspace", "_id", "content"]: + doc_copy.pop(field_name, None) + + batch[key] = doc_copy.copy() + + if len(batch) >= batch_size: + yield batch + batch = {} + + # Yield remaining items + if batch: + yield batch + + async def stream_default_caches( + self, storage, storage_name: str, batch_size: int = None + ): + """Stream default caches from any storage type - unified interface + + Args: + storage: Storage instance + storage_name: Storage type name + batch_size: Size of each batch to yield (defaults to self.batch_size) + + Yields: + Dictionary batches of cache entries + """ + if batch_size is None: + batch_size = self.batch_size + + if storage_name == "JsonKVStorage": + async for batch in self.stream_default_caches_json(storage, batch_size): + yield batch + elif storage_name == "RedisKVStorage": + async for batch in self.stream_default_caches_redis(storage, batch_size): + yield batch + elif storage_name == "PGKVStorage": + async for batch in self.stream_default_caches_pg(storage, batch_size): + yield batch + elif storage_name == "MongoKVStorage": + async for batch in self.stream_default_caches_mongo(storage, batch_size): + yield batch + else: + raise ValueError(f"Unsupported storage type: {storage_name}") + + async def count_cache_types(self, cache_data: Dict[str, Any]) -> Dict[str, int]: + """Count cache entries by type + + Args: + cache_data: Dictionary of cache entries + + Returns: + Dictionary with counts for each cache type + """ + counts = { + "extract": 0, + "summary": 0, + } + + for key in cache_data.keys(): + if key.startswith("default:extract:"): + counts["extract"] += 1 + elif key.startswith("default:summary:"): + counts["summary"] += 1 + + return counts + + def print_header(self): + """Print tool header""" + print("\n" + "=" * 50) + print("LLM Cache Migration Tool - LightRAG") + print("=" * 50) + + def print_storage_types(self): + """Print available storage types""" + print("\nSupported KV Storage Types:") + for key, value in STORAGE_TYPES.items(): + print(f"[{key}] {value}") + + def format_workspace(self, workspace: str) -> str: + """Format workspace name with highlighting + + Args: + workspace: Workspace name (may be empty) + + Returns: + Formatted workspace string with ANSI color codes + """ + if workspace: + return f"{BOLD_CYAN}{workspace}{RESET}" + else: + return f"{BOLD_CYAN}(default){RESET}" + + def format_storage_name(self, storage_name: str) -> str: + """Format storage type name with highlighting + + Args: + storage_name: Storage type name + + Returns: + Formatted storage name string with ANSI color codes + """ + return f"{BOLD_CYAN}{storage_name}{RESET}" + + async def setup_storage( + self, + storage_type: str, + use_streaming: bool = False, + exclude_storage_name: str = None, + ) -> tuple: + """Setup and initialize storage with config.ini fallback support + + Args: + storage_type: Type label (source/target) + use_streaming: If True, only count records without loading. If False, load all data (legacy mode) + exclude_storage_name: Storage type to exclude from selection (e.g., to prevent selecting same as source) + + Returns: + Tuple of (storage_instance, storage_name, workspace, total_count) + Returns (None, None, None, 0) if user chooses to exit + """ + print(f"\n=== {storage_type} Storage Setup ===") + + # Filter and remap available storage types if exclusion is specified + if exclude_storage_name: + # Get available storage types (excluding source) + available_list = [ + (k, v) for k, v in STORAGE_TYPES.items() if v != exclude_storage_name + ] + + # Remap to sequential numbering (1, 2, 3...) + remapped_types = { + str(i + 1): name for i, (_, name) in enumerate(available_list) + } + + # Print available types with new sequential numbers + print( + f"\nAvailable Storage Types for Target (source: {exclude_storage_name} excluded):" + ) + for key, value in remapped_types.items(): + print(f"[{key}] {value}") + + available_types = remapped_types + else: + # For source storage, use original numbering + available_types = STORAGE_TYPES.copy() + self.print_storage_types() + + # Generate dynamic prompt based on number of options + num_options = len(available_types) + if num_options == 1: + prompt_range = "1" + else: + prompt_range = f"1-{num_options}" + + # Custom input handling with exit support + while True: + choice = input( + f"\nSelect {storage_type} storage type ({prompt_range}) (Press Enter to exit): " + ).strip() + + # Check for exit + if choice == "" or choice == "0": + print("\n✓ Migration cancelled by user") + return None, None, None, 0 + + # Check if choice is valid + if choice in available_types: + break + + print( + f"✗ Invalid choice. Please enter one of: {', '.join(available_types.keys())}" + ) + + storage_name = available_types[choice] + + # Check configuration (warnings only, doesn't block) + print("\nChecking configuration...") + self.check_env_vars(storage_name) + + # Get workspace + workspace = self.get_workspace_for_storage(storage_name) + + # Initialize storage (real validation point) + print(f"\nInitializing {storage_type} storage...") + try: + storage = await self.initialize_storage(storage_name, workspace) + print(f"- Storage Type: {storage_name}") + print(f"- Workspace: {workspace if workspace else '(default)'}") + print("- Connection Status: ✓ Success") + + # Show configuration source for transparency + if storage_name == "RedisKVStorage": + config_source = ( + "environment variable" + if "REDIS_URI" in os.environ + else "config.ini or default" + ) + print(f"- Configuration Source: {config_source}") + elif storage_name == "PGKVStorage": + config_source = ( + "environment variables" + if all( + var in os.environ + for var in STORAGE_ENV_REQUIREMENTS[storage_name] + ) + else "config.ini or defaults" + ) + print(f"- Configuration Source: {config_source}") + elif storage_name == "MongoKVStorage": + config_source = ( + "environment variables" + if all( + var in os.environ + for var in STORAGE_ENV_REQUIREMENTS[storage_name] + ) + else "config.ini or defaults" + ) + print(f"- Configuration Source: {config_source}") + + except Exception as e: + print(f"✗ Initialization failed: {e}") + print(f"\nFor {storage_name}, you can configure using:") + print(" 1. Environment variables (highest priority)") + + # Show specific environment variable requirements + if storage_name in STORAGE_ENV_REQUIREMENTS: + for var in STORAGE_ENV_REQUIREMENTS[storage_name]: + print(f" - {var}") + + print(" 2. config.ini file (medium priority)") + if storage_name == "RedisKVStorage": + print(" [redis]") + print(" uri = redis://localhost:6379") + elif storage_name == "PGKVStorage": + print(" [postgres]") + print(" host = localhost") + print(" port = 5432") + print(" user = postgres") + print(" password = yourpassword") + print(" database = lightrag") + elif storage_name == "MongoKVStorage": + print(" [mongodb]") + print(" uri = mongodb://root:root@localhost:27017/") + print(" database = LightRAG") + + return None, None, None, 0 + + # Count cache records efficiently + print(f"\n{'Counting' if use_streaming else 'Loading'} cache records...") + try: + if use_streaming: + # Use efficient counting without loading data + total_count = await self.count_default_caches(storage, storage_name) + print(f"- Total: {total_count:,} records") + else: + # Legacy mode: load all data + cache_data = await self.get_default_caches(storage, storage_name) + counts = await self.count_cache_types(cache_data) + total_count = len(cache_data) + + print(f"- default:extract: {counts['extract']:,} records") + print(f"- default:summary: {counts['summary']:,} records") + print(f"- Total: {total_count:,} records") + except Exception as e: + print(f"✗ {'Counting' if use_streaming else 'Loading'} failed: {e}") + return None, None, None, 0 + + return storage, storage_name, workspace, total_count + + async def migrate_caches( + self, source_data: Dict[str, Any], target_storage, target_storage_name: str + ) -> MigrationStats: + """Migrate caches in batches with error tracking (Legacy mode - loads all data) + + Args: + source_data: Source cache data + target_storage: Target storage instance + target_storage_name: Target storage type name + + Returns: + MigrationStats object with migration results and errors + """ + stats = MigrationStats() + stats.total_source_records = len(source_data) + + if stats.total_source_records == 0: + print("\nNo records to migrate") + return stats + + # Convert to list for batching + items = list(source_data.items()) + stats.total_batches = ( + stats.total_source_records + self.batch_size - 1 + ) // self.batch_size + + print("\n=== Starting Migration ===") + + for batch_idx in range(stats.total_batches): + start_idx = batch_idx * self.batch_size + end_idx = min((batch_idx + 1) * self.batch_size, stats.total_source_records) + batch_items = items[start_idx:end_idx] + batch_data = dict(batch_items) + + # Determine current cache type for display + current_key = batch_items[0][0] + cache_type = "extract" if "extract" in current_key else "summary" + + try: + # Attempt to write batch + await target_storage.upsert(batch_data) + + # Success - update stats + stats.successful_batches += 1 + stats.successful_records += len(batch_data) + + # Calculate progress + progress = (end_idx / stats.total_source_records) * 100 + bar_length = 20 + filled_length = int(bar_length * end_idx // stats.total_source_records) + bar = "█" * filled_length + "░" * (bar_length - filled_length) + + print( + f"Batch {batch_idx + 1}/{stats.total_batches}: {bar} " + f"{end_idx:,}/{stats.total_source_records:,} ({progress:.0f}%) - " + f"default:{cache_type} ✓" + ) + + except Exception as e: + # Error - record and continue + stats.add_error(batch_idx + 1, e, len(batch_data)) + + print( + f"Batch {batch_idx + 1}/{stats.total_batches}: ✗ FAILED - " + f"{type(e).__name__}: {str(e)}" + ) + + # Final persist + print("\nPersisting data to disk...") + try: + await target_storage.index_done_callback() + print("✓ Data persisted successfully") + except Exception as e: + print(f"✗ Persist failed: {e}") + stats.add_error(0, e, 0) # batch 0 = persist error + + return stats + + async def migrate_caches_streaming( + self, + source_storage, + source_storage_name: str, + target_storage, + target_storage_name: str, + total_records: int, + ) -> MigrationStats: + """Migrate caches using streaming approach - minimal memory footprint + + Args: + source_storage: Source storage instance + source_storage_name: Source storage type name + target_storage: Target storage instance + target_storage_name: Target storage type name + total_records: Total number of records to migrate + + Returns: + MigrationStats object with migration results and errors + """ + stats = MigrationStats() + stats.total_source_records = total_records + + if stats.total_source_records == 0: + print("\nNo records to migrate") + return stats + + # Calculate total batches + stats.total_batches = (total_records + self.batch_size - 1) // self.batch_size + + print("\n=== Starting Streaming Migration ===") + print( + f"💡 Memory-optimized mode: Processing {self.batch_size:,} records at a time\n" + ) + + batch_idx = 0 + + # Stream batches from source and write to target immediately + async for batch in self.stream_default_caches( + source_storage, source_storage_name + ): + batch_idx += 1 + + # Determine current cache type for display + if batch: + first_key = next(iter(batch.keys())) + cache_type = "extract" if "extract" in first_key else "summary" + else: + cache_type = "unknown" + + try: + # Write batch to target storage + await target_storage.upsert(batch) + + # Success - update stats + stats.successful_batches += 1 + stats.successful_records += len(batch) + + # Calculate progress with known total + progress = (stats.successful_records / total_records) * 100 + bar_length = 20 + filled_length = int( + bar_length * stats.successful_records // total_records + ) + bar = "█" * filled_length + "░" * (bar_length - filled_length) + + print( + f"Batch {batch_idx}/{stats.total_batches}: {bar} " + f"{stats.successful_records:,}/{total_records:,} ({progress:.1f}%) - " + f"default:{cache_type} ✓" + ) + + except Exception as e: + # Error - record and continue + stats.add_error(batch_idx, e, len(batch)) + + print( + f"Batch {batch_idx}/{stats.total_batches}: ✗ FAILED - " + f"{type(e).__name__}: {str(e)}" + ) + + # Final persist + print("\nPersisting data to disk...") + try: + await target_storage.index_done_callback() + print("✓ Data persisted successfully") + except Exception as e: + print(f"✗ Persist failed: {e}") + stats.add_error(0, e, 0) # batch 0 = persist error + + return stats + + def print_migration_report(self, stats: MigrationStats): + """Print comprehensive migration report + + Args: + stats: MigrationStats object with migration results + """ + print("\n" + "=" * 60) + print("Migration Complete - Final Report") + print("=" * 60) + + # Overall statistics + print("\n📊 Statistics:") + print(f" Total source records: {stats.total_source_records:,}") + print(f" Total batches: {stats.total_batches:,}") + print(f" Successful batches: {stats.successful_batches:,}") + print(f" Failed batches: {stats.failed_batches:,}") + print(f" Successfully migrated: {stats.successful_records:,}") + print(f" Failed to migrate: {stats.failed_records:,}") + + # Success rate + success_rate = ( + (stats.successful_records / stats.total_source_records * 100) + if stats.total_source_records > 0 + else 0 + ) + print(f" Success rate: {success_rate:.2f}%") + + # Error details + if stats.errors: + print(f"\n⚠️ Errors encountered: {len(stats.errors)}") + print("\nError Details:") + print("-" * 60) + + # Group errors by type + error_types = {} + for error in stats.errors: + err_type = error["error_type"] + error_types[err_type] = error_types.get(err_type, 0) + 1 + + print("\nError Summary:") + for err_type, count in sorted(error_types.items(), key=lambda x: -x[1]): + print(f" - {err_type}: {count} occurrence(s)") + + print("\nFirst 5 errors:") + for i, error in enumerate(stats.errors[:5], 1): + print(f"\n {i}. Batch {error['batch']}") + print(f" Type: {error['error_type']}") + print(f" Message: {error['error_msg']}") + print(f" Records lost: {error['records_lost']:,}") + + if len(stats.errors) > 5: + print(f"\n ... and {len(stats.errors) - 5} more errors") + + print("\n" + "=" * 60) + print("⚠️ WARNING: Migration completed with errors!") + print(" Please review the error details above.") + print("=" * 60) + else: + print("\n" + "=" * 60) + print("✓ SUCCESS: All records migrated successfully!") + print("=" * 60) + + async def run(self): + """Run the migration tool with streaming approach and early validation""" + try: + # Initialize shared storage (REQUIRED for storage classes to work) + from lightrag.kg.shared_storage import initialize_share_data + + initialize_share_data(workers=1) + + # Print header + self.print_header() + + # Setup source storage with streaming (only count, don't load all data) + ( + self.source_storage, + source_storage_name, + self.source_workspace, + source_count, + ) = await self.setup_storage("Source", use_streaming=True) + + # Check if user cancelled (setup_storage returns None for all fields) + if self.source_storage is None: + return + + # Check if there are at least 2 storage types available + available_count = self.count_available_storage_types() + if available_count <= 1: + print("\n" + "=" * 60) + print("⚠️ Warning: Migration Not Possible") + print("=" * 60) + print(f"Only {available_count} storage type(s) available.") + print("Migration requires at least 2 different storage types.") + print("\nTo enable migration, configure additional storage:") + print(" 1. Set environment variables, OR") + print(" 2. Update config.ini file") + print("\nSupported storage types:") + for name in STORAGE_TYPES.values(): + if name != source_storage_name: + print(f" - {name}") + if name in STORAGE_ENV_REQUIREMENTS: + for var in STORAGE_ENV_REQUIREMENTS[name]: + print(f" Required: {var}") + print("=" * 60) + + # Cleanup + await self.source_storage.finalize() + return + + if source_count == 0: + print("\n⚠️ Source storage has no cache records to migrate") + # Cleanup + await self.source_storage.finalize() + return + + # Setup target storage with streaming (only count, don't load all data) + # Exclude source storage type from target selection + ( + self.target_storage, + target_storage_name, + self.target_workspace, + target_count, + ) = await self.setup_storage( + "Target", use_streaming=True, exclude_storage_name=source_storage_name + ) + + if not self.target_storage: + print("\n✗ Target storage setup failed") + # Cleanup source + await self.source_storage.finalize() + return + + # Show migration summary + print("\n" + "=" * 50) + print("Migration Confirmation") + print("=" * 50) + print( + f"Source: {self.format_storage_name(source_storage_name)} (workspace: {self.format_workspace(self.source_workspace)}) - {source_count:,} records" + ) + print( + f"Target: {self.format_storage_name(target_storage_name)} (workspace: {self.format_workspace(self.target_workspace)}) - {target_count:,} records" + ) + print(f"Batch Size: {self.batch_size:,} records/batch") + print("Memory Mode: Streaming (memory-optimized)") + + if target_count > 0: + print( + f"\n⚠️ Warning: Target storage already has {target_count:,} records" + ) + print("Migration will overwrite records with the same keys") + + # Confirm migration + confirm = input("\nContinue? (y/n): ").strip().lower() + if confirm != "y": + print("\n✗ Migration cancelled") + # Cleanup + await self.source_storage.finalize() + await self.target_storage.finalize() + return + + # Perform streaming migration with error tracking + stats = await self.migrate_caches_streaming( + self.source_storage, + source_storage_name, + self.target_storage, + target_storage_name, + source_count, + ) + + # Print comprehensive migration report + self.print_migration_report(stats) + + # Cleanup + await self.source_storage.finalize() + await self.target_storage.finalize() + + except KeyboardInterrupt: + print("\n\n✗ Migration interrupted by user") + except Exception as e: + print(f"\n✗ Migration failed: {e}") + import traceback + + traceback.print_exc() + finally: + # Ensure cleanup + if self.source_storage: + try: + await self.source_storage.finalize() + except Exception: + pass + if self.target_storage: + try: + await self.target_storage.finalize() + except Exception: + pass + + # Finalize shared storage + try: + from lightrag.kg.shared_storage import finalize_share_data + + finalize_share_data() + except Exception: + pass + + +async def main(): + """Main entry point""" + tool = MigrationTool() + await tool.run() + + +if __name__ == "__main__": + asyncio.run(main()) diff --git a/LightRAG/lightrag/types.py b/LightRAG/lightrag/types.py new file mode 100644 index 00000000..a18f2d3c --- /dev/null +++ b/LightRAG/lightrag/types.py @@ -0,0 +1,29 @@ +from __future__ import annotations + +from pydantic import BaseModel +from typing import Any, Optional + + +class GPTKeywordExtractionFormat(BaseModel): + high_level_keywords: list[str] + low_level_keywords: list[str] + + +class KnowledgeGraphNode(BaseModel): + id: str + labels: list[str] + properties: dict[str, Any] # anything else goes here + + +class KnowledgeGraphEdge(BaseModel): + id: str + type: Optional[str] + source: str # id of source node + target: str # id of target node + properties: dict[str, Any] # anything else goes here + + +class KnowledgeGraph(BaseModel): + nodes: list[KnowledgeGraphNode] = [] + edges: list[KnowledgeGraphEdge] = [] + is_truncated: bool = False diff --git a/LightRAG/lightrag/utils.py b/LightRAG/lightrag/utils.py new file mode 100644 index 00000000..d653c1e3 --- /dev/null +++ b/LightRAG/lightrag/utils.py @@ -0,0 +1,3159 @@ +from __future__ import annotations +import weakref + +import asyncio +import html +import csv +import json +import logging +import logging.handlers +import os +import re +import time +import uuid +from dataclasses import dataclass +from datetime import datetime +from functools import wraps +from hashlib import md5 +from typing import ( + Any, + Protocol, + Callable, + TYPE_CHECKING, + List, + Optional, + Iterable, + Sequence, + Collection, +) +import numpy as np +from dotenv import load_dotenv + +from lightrag.constants import ( + DEFAULT_LOG_MAX_BYTES, + DEFAULT_LOG_BACKUP_COUNT, + DEFAULT_LOG_FILENAME, + GRAPH_FIELD_SEP, + DEFAULT_MAX_TOTAL_TOKENS, + DEFAULT_SOURCE_IDS_LIMIT_METHOD, + VALID_SOURCE_IDS_LIMIT_METHODS, + SOURCE_IDS_LIMIT_METHOD_FIFO, +) + +# Initialize logger with basic configuration +logger = logging.getLogger("lightrag") +logger.propagate = False # prevent log message send to root logger +logger.setLevel(logging.INFO) + +# Add console handler if no handlers exist +if not logger.handlers: + console_handler = logging.StreamHandler() + console_handler.setLevel(logging.INFO) + formatter = logging.Formatter("%(levelname)s: %(message)s") + console_handler.setFormatter(formatter) + logger.addHandler(console_handler) + +# Set httpx logging level to WARNING +logging.getLogger("httpx").setLevel(logging.WARNING) + +# Precompile regex pattern for JSON sanitization (module-level, compiled once) +_SURROGATE_PATTERN = re.compile(r"[\uD800-\uDFFF\uFFFE\uFFFF]") + +# Global import for pypinyin with startup-time logging +try: + import pypinyin + + _PYPINYIN_AVAILABLE = True + # logger.info("pypinyin loaded successfully for Chinese pinyin sorting") +except ImportError: + pypinyin = None + _PYPINYIN_AVAILABLE = False + logger.warning( + "pypinyin is not installed. Chinese pinyin sorting will use simple string sorting." + ) + + +async def safe_vdb_operation_with_exception( + operation: Callable, + operation_name: str, + entity_name: str = "", + max_retries: int = 3, + retry_delay: float = 0.2, + logger_func: Optional[Callable] = None, +) -> None: + """ + Safely execute vector database operations with retry mechanism and exception handling. + + This function ensures that VDB operations are executed with proper error handling + and retry logic. If all retries fail, it raises an exception to maintain data consistency. + + Args: + operation: The async operation to execute + operation_name: Operation name for logging purposes + entity_name: Entity name for logging purposes + max_retries: Maximum number of retry attempts + retry_delay: Delay between retries in seconds + logger_func: Logger function to use for error messages + + Raises: + Exception: When operation fails after all retry attempts + """ + log_func = logger_func or logger.warning + + for attempt in range(max_retries): + try: + await operation() + return # Success, return immediately + except Exception as e: + if attempt >= max_retries - 1: + error_msg = f"VDB {operation_name} failed for {entity_name} after {max_retries} attempts: {e}" + log_func(error_msg) + raise Exception(error_msg) from e + else: + log_func( + f"VDB {operation_name} attempt {attempt + 1} failed for {entity_name}: {e}, retrying..." + ) + if retry_delay > 0: + await asyncio.sleep(retry_delay) + + +def get_env_value( + env_key: str, default: any, value_type: type = str, special_none: bool = False +) -> any: + """ + Get value from environment variable with type conversion + + Args: + env_key (str): Environment variable key + default (any): Default value if env variable is not set + value_type (type): Type to convert the value to + special_none (bool): If True, return None when value is "None" + + Returns: + any: Converted value from environment or default + """ + value = os.getenv(env_key) + if value is None: + return default + + # Handle special case for "None" string + if special_none and value == "None": + return None + + if value_type is bool: + return value.lower() in ("true", "1", "yes", "t", "on") + + # Handle list type with JSON parsing + if value_type is list: + try: + import json + + parsed_value = json.loads(value) + # Ensure the parsed value is actually a list + if isinstance(parsed_value, list): + return parsed_value + else: + logger.warning( + f"Environment variable {env_key} is not a valid JSON list, using default" + ) + return default + except (json.JSONDecodeError, ValueError) as e: + logger.warning( + f"Failed to parse {env_key} as JSON list: {e}, using default" + ) + return default + + try: + return value_type(value) + except (ValueError, TypeError): + return default + + +# Use TYPE_CHECKING to avoid circular imports +if TYPE_CHECKING: + from lightrag.base import BaseKVStorage, BaseVectorStorage, QueryParam + +# use the .env that is inside the current folder +# allows to use different .env file for each lightrag instance +# the OS environment variables take precedence over the .env file +load_dotenv(dotenv_path=".env", override=False) + +VERBOSE_DEBUG = os.getenv("VERBOSE", "false").lower() == "true" + + +def verbose_debug(msg: str, *args, **kwargs): + """Function for outputting detailed debug information. + When VERBOSE_DEBUG=True, outputs the complete message. + When VERBOSE_DEBUG=False, outputs only the first 50 characters. + + Args: + msg: The message format string + *args: Arguments to be formatted into the message + **kwargs: Keyword arguments passed to logger.debug() + """ + if VERBOSE_DEBUG: + logger.debug(msg, *args, **kwargs) + else: + # Format the message with args first + if args: + formatted_msg = msg % args + else: + formatted_msg = msg + # Then truncate the formatted message + truncated_msg = ( + formatted_msg[:150] + "..." if len(formatted_msg) > 150 else formatted_msg + ) + # Remove consecutive newlines + truncated_msg = re.sub(r"\n+", "\n", truncated_msg) + logger.debug(truncated_msg, **kwargs) + + +def set_verbose_debug(enabled: bool): + """Enable or disable verbose debug output""" + global VERBOSE_DEBUG + VERBOSE_DEBUG = enabled + + +statistic_data = {"llm_call": 0, "llm_cache": 0, "embed_call": 0} + + +class LightragPathFilter(logging.Filter): + """Filter for lightrag logger to filter out frequent path access logs""" + + def __init__(self): + super().__init__() + # Define paths to be filtered + self.filtered_paths = [ + "/documents", + "/documents/paginated", + "/health", + "/webui/", + "/documents/pipeline_status", + ] + # self.filtered_paths = ["/health", "/webui/"] + + def filter(self, record): + try: + # Check if record has the required attributes for an access log + if not hasattr(record, "args") or not isinstance(record.args, tuple): + return True + if len(record.args) < 5: + return True + + # Extract method, path and status from the record args + method = record.args[1] + path = record.args[2] + status = record.args[4] + + # Filter out successful GET/POST requests to filtered paths + if ( + (method == "GET" or method == "POST") + and (status == 200 or status == 304) + and path in self.filtered_paths + ): + return False + + return True + except Exception: + # In case of any error, let the message through + return True + + +def setup_logger( + logger_name: str, + level: str = "INFO", + add_filter: bool = False, + log_file_path: str | None = None, + enable_file_logging: bool = True, +): + """Set up a logger with console and optionally file handlers + + Args: + logger_name: Name of the logger to set up + level: Log level (DEBUG, INFO, WARNING, ERROR, CRITICAL) + add_filter: Whether to add LightragPathFilter to the logger + log_file_path: Path to the log file. If None and file logging is enabled, defaults to lightrag.log in LOG_DIR or cwd + enable_file_logging: Whether to enable logging to a file (defaults to True) + """ + # Configure formatters + detailed_formatter = logging.Formatter( + "%(asctime)s - %(name)s - %(levelname)s - %(message)s" + ) + simple_formatter = logging.Formatter("%(levelname)s: %(message)s") + + logger_instance = logging.getLogger(logger_name) + logger_instance.setLevel(level) + logger_instance.handlers = [] # Clear existing handlers + logger_instance.propagate = False + + # Add console handler + console_handler = logging.StreamHandler() + console_handler.setFormatter(simple_formatter) + console_handler.setLevel(level) + logger_instance.addHandler(console_handler) + + # Add file handler by default unless explicitly disabled + if enable_file_logging: + # Get log file path + if log_file_path is None: + log_dir = os.getenv("LOG_DIR", os.getcwd()) + log_file_path = os.path.abspath(os.path.join(log_dir, DEFAULT_LOG_FILENAME)) + + # Ensure log directory exists + os.makedirs(os.path.dirname(log_file_path), exist_ok=True) + + # Get log file max size and backup count from environment variables + log_max_bytes = get_env_value("LOG_MAX_BYTES", DEFAULT_LOG_MAX_BYTES, int) + log_backup_count = get_env_value( + "LOG_BACKUP_COUNT", DEFAULT_LOG_BACKUP_COUNT, int + ) + + try: + # Add file handler + file_handler = logging.handlers.RotatingFileHandler( + filename=log_file_path, + maxBytes=log_max_bytes, + backupCount=log_backup_count, + encoding="utf-8", + ) + file_handler.setFormatter(detailed_formatter) + file_handler.setLevel(level) + logger_instance.addHandler(file_handler) + except PermissionError as e: + logger.warning(f"Could not create log file at {log_file_path}: {str(e)}") + logger.warning("Continuing with console logging only") + + # Add path filter if requested + if add_filter: + path_filter = LightragPathFilter() + logger_instance.addFilter(path_filter) + + +class UnlimitedSemaphore: + """A context manager that allows unlimited access.""" + + async def __aenter__(self): + pass + + async def __aexit__(self, exc_type, exc, tb): + pass + + +@dataclass +class TaskState: + """Task state tracking for priority queue management""" + + future: asyncio.Future + start_time: float + execution_start_time: float = None + worker_started: bool = False + cancellation_requested: bool = False + cleanup_done: bool = False + + +@dataclass +class EmbeddingFunc: + embedding_dim: int + func: callable + max_token_size: int | None = None # Token limit for the embedding model + send_dimensions: bool = ( + False # Control whether to send embedding_dim to the function + ) + + async def __call__(self, *args, **kwargs) -> np.ndarray: + # Only inject embedding_dim when send_dimensions is True + if self.send_dimensions: + # Check if user provided embedding_dim parameter + if "embedding_dim" in kwargs: + user_provided_dim = kwargs["embedding_dim"] + # If user's value differs from class attribute, output warning + if ( + user_provided_dim is not None + and user_provided_dim != self.embedding_dim + ): + logger.warning( + f"Ignoring user-provided embedding_dim={user_provided_dim}, " + f"using declared embedding_dim={self.embedding_dim} from decorator" + ) + + # Inject embedding_dim from decorator + kwargs["embedding_dim"] = self.embedding_dim + + return await self.func(*args, **kwargs) + + +def compute_args_hash(*args: Any) -> str: + """Compute a hash for the given arguments with safe Unicode handling. + + Args: + *args: Arguments to hash + Returns: + str: Hash string + """ + # Convert all arguments to strings and join them + args_str = "".join([str(arg) for arg in args]) + + # Use 'replace' error handling to safely encode problematic Unicode characters + # This replaces invalid characters with Unicode replacement character (U+FFFD) + try: + return md5(args_str.encode("utf-8")).hexdigest() + except UnicodeEncodeError: + # Handle surrogate characters and other encoding issues + safe_bytes = args_str.encode("utf-8", errors="replace") + return md5(safe_bytes).hexdigest() + + +def compute_mdhash_id(content: str, prefix: str = "") -> str: + """ + Compute a unique ID for a given content string. + + The ID is a combination of the given prefix and the MD5 hash of the content string. + """ + return prefix + compute_args_hash(content) + + +def generate_cache_key(mode: str, cache_type: str, hash_value: str) -> str: + """Generate a flattened cache key in the format {mode}:{cache_type}:{hash} + + Args: + mode: Cache mode (e.g., 'default', 'local', 'global') + cache_type: Type of cache (e.g., 'extract', 'query', 'keywords') + hash_value: Hash value from compute_args_hash + + Returns: + str: Flattened cache key + """ + return f"{mode}:{cache_type}:{hash_value}" + + +def parse_cache_key(cache_key: str) -> tuple[str, str, str] | None: + """Parse a flattened cache key back into its components + + Args: + cache_key: Flattened cache key in format {mode}:{cache_type}:{hash} + + Returns: + tuple[str, str, str] | None: (mode, cache_type, hash) or None if invalid format + """ + parts = cache_key.split(":", 2) + if len(parts) == 3: + return parts[0], parts[1], parts[2] + return None + + +# Custom exception classes +class QueueFullError(Exception): + """Raised when the queue is full and the wait times out""" + + pass + + +class WorkerTimeoutError(Exception): + """Worker-level timeout exception with specific timeout information""" + + def __init__(self, timeout_value: float, timeout_type: str = "execution"): + self.timeout_value = timeout_value + self.timeout_type = timeout_type + super().__init__(f"Worker {timeout_type} timeout after {timeout_value}s") + + +class HealthCheckTimeoutError(Exception): + """Health Check-level timeout exception""" + + def __init__(self, timeout_value: float, execution_duration: float): + self.timeout_value = timeout_value + self.execution_duration = execution_duration + super().__init__( + f"Task forcefully terminated due to execution timeout (>{timeout_value}s, actual: {execution_duration:.1f}s)" + ) + + +def priority_limit_async_func_call( + max_size: int, + llm_timeout: float = None, + max_execution_timeout: float = None, + max_task_duration: float = None, + max_queue_size: int = 1000, + cleanup_timeout: float = 2.0, + queue_name: str = "limit_async", +): + """ + Enhanced priority-limited asynchronous function call decorator with robust timeout handling + + This decorator provides a comprehensive solution for managing concurrent LLM requests with: + - Multi-layer timeout protection (LLM -> Worker -> Health Check -> User) + - Task state tracking to prevent race conditions + - Enhanced health check system with stuck task detection + - Proper resource cleanup and error recovery + + Args: + max_size: Maximum number of concurrent calls + max_queue_size: Maximum queue capacity to prevent memory overflow + llm_timeout: LLM provider timeout (from global config), used to calculate other timeouts + max_execution_timeout: Maximum time for worker to execute function (defaults to llm_timeout + 30s) + max_task_duration: Maximum time before health check intervenes (defaults to llm_timeout + 60s) + cleanup_timeout: Maximum time to wait for cleanup operations (defaults to 2.0s) + queue_name: Optional queue name for logging identification (defaults to "limit_async") + + Returns: + Decorator function + """ + + def final_decro(func): + # Ensure func is callable + if not callable(func): + raise TypeError(f"Expected a callable object, got {type(func)}") + + # Calculate timeout hierarchy if llm_timeout is provided (Dynamic Timeout Calculation) + if llm_timeout is not None: + nonlocal max_execution_timeout, max_task_duration + if max_execution_timeout is None: + max_execution_timeout = ( + llm_timeout * 2 + ) # Reserved timeout buffer for low-level retry + if max_task_duration is None: + max_task_duration = ( + llm_timeout * 2 + 15 + ) # Reserved timeout buffer for health check phase + + queue = asyncio.PriorityQueue(maxsize=max_queue_size) + tasks = set() + initialization_lock = asyncio.Lock() + counter = 0 + shutdown_event = asyncio.Event() + initialized = False + worker_health_check_task = None + + # Enhanced task state management + task_states = {} # task_id -> TaskState + task_states_lock = asyncio.Lock() + active_futures = weakref.WeakSet() + reinit_count = 0 + + async def worker(): + """Enhanced worker that processes tasks with proper timeout and state management""" + try: + while not shutdown_event.is_set(): + try: + # Get task from queue with timeout for shutdown checking + try: + ( + priority, + count, + task_id, + args, + kwargs, + ) = await asyncio.wait_for(queue.get(), timeout=1.0) + except asyncio.TimeoutError: + continue + + # Get task state and mark worker as started + async with task_states_lock: + if task_id not in task_states: + queue.task_done() + continue + task_state = task_states[task_id] + task_state.worker_started = True + # Record execution start time when worker actually begins processing + task_state.execution_start_time = ( + asyncio.get_event_loop().time() + ) + + # Check if task was cancelled before worker started + if ( + task_state.cancellation_requested + or task_state.future.cancelled() + ): + async with task_states_lock: + task_states.pop(task_id, None) + queue.task_done() + continue + + try: + # Execute function with timeout protection + if max_execution_timeout is not None: + result = await asyncio.wait_for( + func(*args, **kwargs), timeout=max_execution_timeout + ) + else: + result = await func(*args, **kwargs) + + # Set result if future is still valid + if not task_state.future.done(): + task_state.future.set_result(result) + + except asyncio.TimeoutError: + # Worker-level timeout (max_execution_timeout exceeded) + logger.warning( + f"{queue_name}: Worker timeout for task {task_id} after {max_execution_timeout}s" + ) + if not task_state.future.done(): + task_state.future.set_exception( + WorkerTimeoutError( + max_execution_timeout, "execution" + ) + ) + except asyncio.CancelledError: + # Task was cancelled during execution + if not task_state.future.done(): + task_state.future.cancel() + logger.debug( + f"{queue_name}: Task {task_id} cancelled during execution" + ) + except Exception as e: + # Function execution error + logger.error( + f"{queue_name}: Error in decorated function for task {task_id}: {str(e)}" + ) + if not task_state.future.done(): + task_state.future.set_exception(e) + finally: + # Clean up task state + async with task_states_lock: + task_states.pop(task_id, None) + queue.task_done() + + except Exception as e: + # Critical error in worker loop + logger.error( + f"{queue_name}: Critical error in worker: {str(e)}" + ) + await asyncio.sleep(0.1) + finally: + logger.debug(f"{queue_name}: Worker exiting") + + async def enhanced_health_check(): + """Enhanced health check with stuck task detection and recovery""" + nonlocal initialized + try: + while not shutdown_event.is_set(): + await asyncio.sleep(5) # Check every 5 seconds + + current_time = asyncio.get_event_loop().time() + + # Detect and handle stuck tasks based on execution start time + if max_task_duration is not None: + stuck_tasks = [] + async with task_states_lock: + for task_id, task_state in list(task_states.items()): + # Only check tasks that have started execution + if ( + task_state.worker_started + and task_state.execution_start_time is not None + and current_time - task_state.execution_start_time + > max_task_duration + ): + stuck_tasks.append( + ( + task_id, + current_time + - task_state.execution_start_time, + ) + ) + + # Force cleanup of stuck tasks + for task_id, execution_duration in stuck_tasks: + logger.warning( + f"{queue_name}: Detected stuck task {task_id} (execution time: {execution_duration:.1f}s), forcing cleanup" + ) + async with task_states_lock: + if task_id in task_states: + task_state = task_states[task_id] + if not task_state.future.done(): + task_state.future.set_exception( + HealthCheckTimeoutError( + max_task_duration, execution_duration + ) + ) + task_states.pop(task_id, None) + + # Worker recovery logic + current_tasks = set(tasks) + done_tasks = {t for t in current_tasks if t.done()} + tasks.difference_update(done_tasks) + + active_tasks_count = len(tasks) + workers_needed = max_size - active_tasks_count + + if workers_needed > 0: + logger.info( + f"{queue_name}: Creating {workers_needed} new workers" + ) + new_tasks = set() + for _ in range(workers_needed): + task = asyncio.create_task(worker()) + new_tasks.add(task) + task.add_done_callback(tasks.discard) + tasks.update(new_tasks) + + except Exception as e: + logger.error(f"{queue_name}: Error in enhanced health check: {str(e)}") + finally: + logger.debug(f"{queue_name}: Enhanced health check task exiting") + initialized = False + + async def ensure_workers(): + """Ensure worker system is initialized with enhanced error handling""" + nonlocal initialized, worker_health_check_task, tasks, reinit_count + + if initialized: + return + + async with initialization_lock: + if initialized: + return + + if reinit_count > 0: + reinit_count += 1 + logger.warning( + f"{queue_name}: Reinitializing system (count: {reinit_count})" + ) + else: + reinit_count = 1 + + # Clean up completed tasks + current_tasks = set(tasks) + done_tasks = {t for t in current_tasks if t.done()} + tasks.difference_update(done_tasks) + + active_tasks_count = len(tasks) + if active_tasks_count > 0 and reinit_count > 1: + logger.warning( + f"{queue_name}: {active_tasks_count} tasks still running during reinitialization" + ) + + # Create worker tasks + workers_needed = max_size - active_tasks_count + for _ in range(workers_needed): + task = asyncio.create_task(worker()) + tasks.add(task) + task.add_done_callback(tasks.discard) + + # Start enhanced health check + worker_health_check_task = asyncio.create_task(enhanced_health_check()) + + initialized = True + # Log dynamic timeout configuration + timeout_info = [] + if llm_timeout is not None: + timeout_info.append(f"Func: {llm_timeout}s") + if max_execution_timeout is not None: + timeout_info.append(f"Worker: {max_execution_timeout}s") + if max_task_duration is not None: + timeout_info.append(f"Health Check: {max_task_duration}s") + + timeout_str = ( + f"(Timeouts: {', '.join(timeout_info)})" if timeout_info else "" + ) + logger.info( + f"{queue_name}: {workers_needed} new workers initialized {timeout_str}" + ) + + async def shutdown(): + """Gracefully shut down all workers and cleanup resources""" + logger.info(f"{queue_name}: Shutting down priority queue workers") + + shutdown_event.set() + + # Cancel all active futures + for future in list(active_futures): + if not future.done(): + future.cancel() + + # Cancel all pending tasks + async with task_states_lock: + for task_id, task_state in list(task_states.items()): + if not task_state.future.done(): + task_state.future.cancel() + task_states.clear() + + # Wait for queue to empty with timeout + try: + await asyncio.wait_for(queue.join(), timeout=5.0) + except asyncio.TimeoutError: + logger.warning( + f"{queue_name}: Timeout waiting for queue to empty during shutdown" + ) + + # Cancel worker tasks + for task in list(tasks): + if not task.done(): + task.cancel() + + # Wait for all tasks to complete + if tasks: + await asyncio.gather(*tasks, return_exceptions=True) + + # Cancel health check task + if worker_health_check_task and not worker_health_check_task.done(): + worker_health_check_task.cancel() + try: + await worker_health_check_task + except asyncio.CancelledError: + pass + + logger.info(f"{queue_name}: Priority queue workers shutdown complete") + + @wraps(func) + async def wait_func( + *args, _priority=10, _timeout=None, _queue_timeout=None, **kwargs + ): + """ + Execute function with enhanced priority-based concurrency control and timeout handling + + Args: + *args: Positional arguments passed to the function + _priority: Call priority (lower values have higher priority) + _timeout: Maximum time to wait for completion (in seconds, none means determinded by max_execution_timeout of the queue) + _queue_timeout: Maximum time to wait for entering the queue (in seconds) + **kwargs: Keyword arguments passed to the function + + Returns: + The result of the function call + + Raises: + TimeoutError: If the function call times out at any level + QueueFullError: If the queue is full and waiting times out + Any exception raised by the decorated function + """ + await ensure_workers() + + # Generate unique task ID + task_id = f"{id(asyncio.current_task())}_{asyncio.get_event_loop().time()}" + future = asyncio.Future() + + # Create task state + task_state = TaskState( + future=future, start_time=asyncio.get_event_loop().time() + ) + + try: + # Register task state + async with task_states_lock: + task_states[task_id] = task_state + + active_futures.add(future) + + # Get counter for FIFO ordering + nonlocal counter + async with initialization_lock: + current_count = counter + counter += 1 + + # Queue the task with timeout handling + try: + if _queue_timeout is not None: + await asyncio.wait_for( + queue.put( + (_priority, current_count, task_id, args, kwargs) + ), + timeout=_queue_timeout, + ) + else: + await queue.put( + (_priority, current_count, task_id, args, kwargs) + ) + except asyncio.TimeoutError: + raise QueueFullError( + f"{queue_name}: Queue full, timeout after {_queue_timeout} seconds" + ) + except Exception as e: + # Clean up on queue error + if not future.done(): + future.set_exception(e) + raise + + # Wait for result with timeout handling + try: + if _timeout is not None: + return await asyncio.wait_for(future, _timeout) + else: + return await future + except asyncio.TimeoutError: + # This is user-level timeout (asyncio.wait_for caused) + # Mark cancellation request + async with task_states_lock: + if task_id in task_states: + task_states[task_id].cancellation_requested = True + + # Cancel future + if not future.done(): + future.cancel() + + # Wait for worker cleanup with timeout + cleanup_start = asyncio.get_event_loop().time() + while ( + task_id in task_states + and asyncio.get_event_loop().time() - cleanup_start + < cleanup_timeout + ): + await asyncio.sleep(0.1) + + raise TimeoutError( + f"{queue_name}: User timeout after {_timeout} seconds" + ) + except WorkerTimeoutError as e: + # This is Worker-level timeout, directly propagate exception information + raise TimeoutError(f"{queue_name}: {str(e)}") + except HealthCheckTimeoutError as e: + # This is Health Check-level timeout, directly propagate exception information + raise TimeoutError(f"{queue_name}: {str(e)}") + + finally: + # Ensure cleanup + active_futures.discard(future) + async with task_states_lock: + task_states.pop(task_id, None) + + # Add shutdown method to decorated function + wait_func.shutdown = shutdown + + return wait_func + + return final_decro + + +def wrap_embedding_func_with_attrs(**kwargs): + """Wrap a function with attributes""" + + def final_decro(func) -> EmbeddingFunc: + new_func = EmbeddingFunc(**kwargs, func=func) + return new_func + + return final_decro + + +def load_json(file_name): + if not os.path.exists(file_name): + return None + with open(file_name, encoding="utf-8-sig") as f: + return json.load(f) + + +def _sanitize_string_for_json(text: str) -> str: + """Remove characters that cannot be encoded in UTF-8 for JSON serialization. + + Uses regex for optimal performance with zero-copy optimization for clean strings. + Fast detection path for clean strings (99% of cases) with efficient removal for dirty strings. + + Args: + text: String to sanitize + + Returns: + Original string if clean (zero-copy), sanitized string if dirty + """ + if not text: + return text + + # Fast path: Check if sanitization is needed using C-level regex search + if not _SURROGATE_PATTERN.search(text): + return text # Zero-copy for clean strings - most common case + + # Slow path: Remove problematic characters using C-level regex substitution + return _SURROGATE_PATTERN.sub("", text) + + +class SanitizingJSONEncoder(json.JSONEncoder): + """ + Custom JSON encoder that sanitizes data during serialization. + + This encoder cleans strings during the encoding process without creating + a full copy of the data structure, making it memory-efficient for large datasets. + """ + + def encode(self, o): + """Override encode method to handle simple string cases""" + if isinstance(o, str): + return json.encoder.encode_basestring(_sanitize_string_for_json(o)) + return super().encode(o) + + def iterencode(self, o, _one_shot=False): + """ + Override iterencode to sanitize strings during serialization. + This is the core method that handles complex nested structures. + """ + # Preprocess: sanitize all strings in the object + sanitized = self._sanitize_for_encoding(o) + + # Call parent's iterencode with sanitized data + for chunk in super().iterencode(sanitized, _one_shot): + yield chunk + + def _sanitize_for_encoding(self, obj): + """ + Recursively sanitize strings in an object. + Creates new objects only when necessary to avoid deep copies. + + Args: + obj: Object to sanitize + + Returns: + Sanitized object with cleaned strings + """ + if isinstance(obj, str): + return _sanitize_string_for_json(obj) + + elif isinstance(obj, dict): + # Create new dict with sanitized keys and values + new_dict = {} + for k, v in obj.items(): + clean_k = _sanitize_string_for_json(k) if isinstance(k, str) else k + clean_v = self._sanitize_for_encoding(v) + new_dict[clean_k] = clean_v + return new_dict + + elif isinstance(obj, (list, tuple)): + # Sanitize list/tuple elements + cleaned = [self._sanitize_for_encoding(item) for item in obj] + return type(obj)(cleaned) if isinstance(obj, tuple) else cleaned + + else: + # Numbers, booleans, None, etc. remain unchanged + return obj + + +def write_json(json_obj, file_name): + """ + Write JSON data to file with optimized sanitization strategy. + + This function uses a two-stage approach: + 1. Fast path: Try direct serialization (works for clean data ~99% of time) + 2. Slow path: Use custom encoder that sanitizes during serialization + + The custom encoder approach avoids creating a deep copy of the data, + making it memory-efficient. When sanitization occurs, the caller should + reload the cleaned data from the file to update shared memory. + + Args: + json_obj: Object to serialize (may be a shallow copy from shared memory) + file_name: Output file path + + Returns: + bool: True if sanitization was applied (caller should reload data), + False if direct write succeeded (no reload needed) + """ + try: + # Strategy 1: Fast path - try direct serialization + with open(file_name, "w", encoding="utf-8") as f: + json.dump(json_obj, f, indent=2, ensure_ascii=False) + return False # No sanitization needed, no reload required + + except (UnicodeEncodeError, UnicodeDecodeError) as e: + logger.debug(f"Direct JSON write failed, using sanitizing encoder: {e}") + + # Strategy 2: Use custom encoder (sanitizes during serialization, zero memory copy) + with open(file_name, "w", encoding="utf-8") as f: + json.dump(json_obj, f, indent=2, ensure_ascii=False, cls=SanitizingJSONEncoder) + + logger.info(f"JSON sanitization applied during write: {file_name}") + return True # Sanitization applied, reload recommended + + +class TokenizerInterface(Protocol): + """ + Defines the interface for a tokenizer, requiring encode and decode methods. + """ + + def encode(self, content: str) -> List[int]: + """Encodes a string into a list of tokens.""" + ... + + def decode(self, tokens: List[int]) -> str: + """Decodes a list of tokens into a string.""" + ... + + +class Tokenizer: + """ + A wrapper around a tokenizer to provide a consistent interface for encoding and decoding. + """ + + def __init__(self, model_name: str, tokenizer: TokenizerInterface): + """ + Initializes the Tokenizer with a tokenizer model name and a tokenizer instance. + + Args: + model_name: The associated model name for the tokenizer. + tokenizer: An instance of a class implementing the TokenizerInterface. + """ + self.model_name: str = model_name + self.tokenizer: TokenizerInterface = tokenizer + + def encode(self, content: str) -> List[int]: + """ + Encodes a string into a list of tokens using the underlying tokenizer. + + Args: + content: The string to encode. + + Returns: + A list of integer tokens. + """ + return self.tokenizer.encode(content) + + def decode(self, tokens: List[int]) -> str: + """ + Decodes a list of tokens into a string using the underlying tokenizer. + + Args: + tokens: A list of integer tokens to decode. + + Returns: + The decoded string. + """ + return self.tokenizer.decode(tokens) + + +class TiktokenTokenizer(Tokenizer): + """ + A Tokenizer implementation using the tiktoken library. + """ + + def __init__(self, model_name: str = "gpt-4o-mini"): + """ + Initializes the TiktokenTokenizer with a specified model name. + + Args: + model_name: The model name for the tiktoken tokenizer to use. Defaults to "gpt-4o-mini". + + Raises: + ImportError: If tiktoken is not installed. + ValueError: If the model_name is invalid. + """ + try: + import tiktoken + except ImportError: + raise ImportError( + "tiktoken is not installed. Please install it with `pip install tiktoken` or define custom `tokenizer_func`." + ) + + try: + tokenizer = tiktoken.encoding_for_model(model_name) + super().__init__(model_name=model_name, tokenizer=tokenizer) + except KeyError: + raise ValueError(f"Invalid model_name: {model_name}.") + + +def pack_user_ass_to_openai_messages(*args: str): + roles = ["user", "assistant"] + return [ + {"role": roles[i % 2], "content": content} for i, content in enumerate(args) + ] + + +def split_string_by_multi_markers(content: str, markers: list[str]) -> list[str]: + """Split a string by multiple markers""" + if not markers: + return [content] + content = content if content is not None else "" + results = re.split("|".join(re.escape(marker) for marker in markers), content) + return [r.strip() for r in results if r.strip()] + + +def is_float_regex(value: str) -> bool: + return bool(re.match(r"^[-+]?[0-9]*\.?[0-9]+$", value)) + + +def truncate_list_by_token_size( + list_data: list[Any], + key: Callable[[Any], str], + max_token_size: int, + tokenizer: Tokenizer, +) -> list[int]: + """Truncate a list of data by token size""" + if max_token_size <= 0: + return [] + tokens = 0 + for i, data in enumerate(list_data): + tokens += len(tokenizer.encode(key(data))) + if tokens > max_token_size: + return list_data[:i] + return list_data + + +def cosine_similarity(v1, v2): + """Calculate cosine similarity between two vectors""" + dot_product = np.dot(v1, v2) + norm1 = np.linalg.norm(v1) + norm2 = np.linalg.norm(v2) + return dot_product / (norm1 * norm2) + + +async def handle_cache( + hashing_kv, + args_hash, + prompt, + mode="default", + cache_type="unknown", +) -> tuple[str, int] | None: + """Generic cache handling function with flattened cache keys + + Returns: + tuple[str, int] | None: (content, create_time) if cache hit, None if cache miss + """ + if hashing_kv is None: + return None + + if mode != "default": # handle cache for all type of query + if not hashing_kv.global_config.get("enable_llm_cache"): + return None + else: # handle cache for entity extraction + if not hashing_kv.global_config.get("enable_llm_cache_for_entity_extract"): + return None + + # Use flattened cache key format: {mode}:{cache_type}:{hash} + flattened_key = generate_cache_key(mode, cache_type, args_hash) + cache_entry = await hashing_kv.get_by_id(flattened_key) + if cache_entry: + logger.debug(f"Flattened cache hit(key:{flattened_key})") + content = cache_entry["return"] + timestamp = cache_entry.get("create_time", 0) + return content, timestamp + + logger.debug(f"Cache missed(mode:{mode} type:{cache_type})") + return None + + +@dataclass +class CacheData: + args_hash: str + content: str + prompt: str + mode: str = "default" + cache_type: str = "query" + chunk_id: str | None = None + queryparam: dict | None = None + + +async def save_to_cache(hashing_kv, cache_data: CacheData): + """Save data to cache using flattened key structure. + + Args: + hashing_kv: The key-value storage for caching + cache_data: The cache data to save + """ + # Skip if storage is None or content is a streaming response + if hashing_kv is None or not cache_data.content: + return + + # If content is a streaming response, don't cache it + if hasattr(cache_data.content, "__aiter__"): + logger.debug("Streaming response detected, skipping cache") + return + + # Use flattened cache key format: {mode}:{cache_type}:{hash} + flattened_key = generate_cache_key( + cache_data.mode, cache_data.cache_type, cache_data.args_hash + ) + + # Check if we already have identical content cached + existing_cache = await hashing_kv.get_by_id(flattened_key) + if existing_cache: + existing_content = existing_cache.get("return") + if existing_content == cache_data.content: + logger.warning( + f"Cache duplication detected for {flattened_key}, skipping update" + ) + return + + # Create cache entry with flattened structure + cache_entry = { + "return": cache_data.content, + "cache_type": cache_data.cache_type, + "chunk_id": cache_data.chunk_id if cache_data.chunk_id is not None else None, + "original_prompt": cache_data.prompt, + "queryparam": cache_data.queryparam + if cache_data.queryparam is not None + else None, + } + + logger.info(f" == LLM cache == saving: {flattened_key}") + + # Save using flattened key + await hashing_kv.upsert({flattened_key: cache_entry}) + + +def safe_unicode_decode(content): + # Regular expression to find all Unicode escape sequences of the form \uXXXX + unicode_escape_pattern = re.compile(r"\\u([0-9a-fA-F]{4})") + + # Function to replace the Unicode escape with the actual character + def replace_unicode_escape(match): + # Convert the matched hexadecimal value into the actual Unicode character + return chr(int(match.group(1), 16)) + + # Perform the substitution + decoded_content = unicode_escape_pattern.sub( + replace_unicode_escape, content.decode("utf-8") + ) + + return decoded_content + + +def exists_func(obj, func_name: str) -> bool: + """Check if a function exists in an object or not. + :param obj: + :param func_name: + :return: True / False + """ + if callable(getattr(obj, func_name, None)): + return True + else: + return False + + +def always_get_an_event_loop() -> asyncio.AbstractEventLoop: + """ + Ensure that there is always an event loop available. + + This function tries to get the current event loop. If the current event loop is closed or does not exist, + it creates a new event loop and sets it as the current event loop. + + Returns: + asyncio.AbstractEventLoop: The current or newly created event loop. + """ + try: + # Try to get the current event loop + current_loop = asyncio.get_event_loop() + if current_loop.is_closed(): + raise RuntimeError("Event loop is closed.") + return current_loop + + except RuntimeError: + # If no event loop exists or it is closed, create a new one + logger.info("Creating a new event loop in main thread.") + new_loop = asyncio.new_event_loop() + asyncio.set_event_loop(new_loop) + return new_loop + + +async def aexport_data( + chunk_entity_relation_graph, + entities_vdb, + relationships_vdb, + output_path: str, + file_format: str = "csv", + include_vector_data: bool = False, +) -> None: + """ + Asynchronously exports all entities, relations, and relationships to various formats. + + Args: + chunk_entity_relation_graph: Graph storage instance for entities and relations + entities_vdb: Vector database storage for entities + relationships_vdb: Vector database storage for relationships + output_path: The path to the output file (including extension). + file_format: Output format - "csv", "excel", "md", "txt". + - csv: Comma-separated values file + - excel: Microsoft Excel file with multiple sheets + - md: Markdown tables + - txt: Plain text formatted output + include_vector_data: Whether to include data from the vector database. + """ + # Collect data + entities_data = [] + relations_data = [] + relationships_data = [] + + # --- Entities --- + all_entities = await chunk_entity_relation_graph.get_all_labels() + for entity_name in all_entities: + # Get entity information from graph + node_data = await chunk_entity_relation_graph.get_node(entity_name) + source_id = node_data.get("source_id") if node_data else None + + entity_info = { + "graph_data": node_data, + "source_id": source_id, + } + + # Optional: Get vector database information + if include_vector_data: + entity_id = compute_mdhash_id(entity_name, prefix="ent-") + vector_data = await entities_vdb.get_by_id(entity_id) + entity_info["vector_data"] = vector_data + + entity_row = { + "entity_name": entity_name, + "source_id": source_id, + "graph_data": str( + entity_info["graph_data"] + ), # Convert to string to ensure compatibility + } + if include_vector_data and "vector_data" in entity_info: + entity_row["vector_data"] = str(entity_info["vector_data"]) + entities_data.append(entity_row) + + # --- Relations --- + for src_entity in all_entities: + for tgt_entity in all_entities: + if src_entity == tgt_entity: + continue + + edge_exists = await chunk_entity_relation_graph.has_edge( + src_entity, tgt_entity + ) + if edge_exists: + # Get edge information from graph + edge_data = await chunk_entity_relation_graph.get_edge( + src_entity, tgt_entity + ) + source_id = edge_data.get("source_id") if edge_data else None + + relation_info = { + "graph_data": edge_data, + "source_id": source_id, + } + + # Optional: Get vector database information + if include_vector_data: + rel_id = compute_mdhash_id(src_entity + tgt_entity, prefix="rel-") + vector_data = await relationships_vdb.get_by_id(rel_id) + relation_info["vector_data"] = vector_data + + relation_row = { + "src_entity": src_entity, + "tgt_entity": tgt_entity, + "source_id": relation_info["source_id"], + "graph_data": str(relation_info["graph_data"]), # Convert to string + } + if include_vector_data and "vector_data" in relation_info: + relation_row["vector_data"] = str(relation_info["vector_data"]) + relations_data.append(relation_row) + + # --- Relationships (from VectorDB) --- + all_relationships = await relationships_vdb.client_storage + for rel in all_relationships["data"]: + relationships_data.append( + { + "relationship_id": rel["__id__"], + "data": str(rel), # Convert to string for compatibility + } + ) + + # Export based on format + if file_format == "csv": + # CSV export + with open(output_path, "w", newline="", encoding="utf-8") as csvfile: + # Entities + if entities_data: + csvfile.write("# ENTITIES\n") + writer = csv.DictWriter(csvfile, fieldnames=entities_data[0].keys()) + writer.writeheader() + writer.writerows(entities_data) + csvfile.write("\n\n") + + # Relations + if relations_data: + csvfile.write("# RELATIONS\n") + writer = csv.DictWriter(csvfile, fieldnames=relations_data[0].keys()) + writer.writeheader() + writer.writerows(relations_data) + csvfile.write("\n\n") + + # Relationships + if relationships_data: + csvfile.write("# RELATIONSHIPS\n") + writer = csv.DictWriter( + csvfile, fieldnames=relationships_data[0].keys() + ) + writer.writeheader() + writer.writerows(relationships_data) + + elif file_format == "excel": + # Excel export + import pandas as pd + + entities_df = pd.DataFrame(entities_data) if entities_data else pd.DataFrame() + relations_df = ( + pd.DataFrame(relations_data) if relations_data else pd.DataFrame() + ) + relationships_df = ( + pd.DataFrame(relationships_data) if relationships_data else pd.DataFrame() + ) + + with pd.ExcelWriter(output_path, engine="xlsxwriter") as writer: + if not entities_df.empty: + entities_df.to_excel(writer, sheet_name="Entities", index=False) + if not relations_df.empty: + relations_df.to_excel(writer, sheet_name="Relations", index=False) + if not relationships_df.empty: + relationships_df.to_excel( + writer, sheet_name="Relationships", index=False + ) + + elif file_format == "md": + # Markdown export + with open(output_path, "w", encoding="utf-8") as mdfile: + mdfile.write("# LightRAG Data Export\n\n") + + # Entities + mdfile.write("## Entities\n\n") + if entities_data: + # Write header + mdfile.write("| " + " | ".join(entities_data[0].keys()) + " |\n") + mdfile.write( + "| " + " | ".join(["---"] * len(entities_data[0].keys())) + " |\n" + ) + + # Write rows + for entity in entities_data: + mdfile.write( + "| " + " | ".join(str(v) for v in entity.values()) + " |\n" + ) + mdfile.write("\n\n") + else: + mdfile.write("*No entity data available*\n\n") + + # Relations + mdfile.write("## Relations\n\n") + if relations_data: + # Write header + mdfile.write("| " + " | ".join(relations_data[0].keys()) + " |\n") + mdfile.write( + "| " + " | ".join(["---"] * len(relations_data[0].keys())) + " |\n" + ) + + # Write rows + for relation in relations_data: + mdfile.write( + "| " + " | ".join(str(v) for v in relation.values()) + " |\n" + ) + mdfile.write("\n\n") + else: + mdfile.write("*No relation data available*\n\n") + + # Relationships + mdfile.write("## Relationships\n\n") + if relationships_data: + # Write header + mdfile.write("| " + " | ".join(relationships_data[0].keys()) + " |\n") + mdfile.write( + "| " + + " | ".join(["---"] * len(relationships_data[0].keys())) + + " |\n" + ) + + # Write rows + for relationship in relationships_data: + mdfile.write( + "| " + + " | ".join(str(v) for v in relationship.values()) + + " |\n" + ) + else: + mdfile.write("*No relationship data available*\n\n") + + elif file_format == "txt": + # Plain text export + with open(output_path, "w", encoding="utf-8") as txtfile: + txtfile.write("LIGHTRAG DATA EXPORT\n") + txtfile.write("=" * 80 + "\n\n") + + # Entities + txtfile.write("ENTITIES\n") + txtfile.write("-" * 80 + "\n") + if entities_data: + # Create fixed width columns + col_widths = { + k: max(len(k), max(len(str(e[k])) for e in entities_data)) + for k in entities_data[0] + } + header = " ".join(k.ljust(col_widths[k]) for k in entities_data[0]) + txtfile.write(header + "\n") + txtfile.write("-" * len(header) + "\n") + + # Write rows + for entity in entities_data: + row = " ".join( + str(v).ljust(col_widths[k]) for k, v in entity.items() + ) + txtfile.write(row + "\n") + txtfile.write("\n\n") + else: + txtfile.write("No entity data available\n\n") + + # Relations + txtfile.write("RELATIONS\n") + txtfile.write("-" * 80 + "\n") + if relations_data: + # Create fixed width columns + col_widths = { + k: max(len(k), max(len(str(r[k])) for r in relations_data)) + for k in relations_data[0] + } + header = " ".join(k.ljust(col_widths[k]) for k in relations_data[0]) + txtfile.write(header + "\n") + txtfile.write("-" * len(header) + "\n") + + # Write rows + for relation in relations_data: + row = " ".join( + str(v).ljust(col_widths[k]) for k, v in relation.items() + ) + txtfile.write(row + "\n") + txtfile.write("\n\n") + else: + txtfile.write("No relation data available\n\n") + + # Relationships + txtfile.write("RELATIONSHIPS\n") + txtfile.write("-" * 80 + "\n") + if relationships_data: + # Create fixed width columns + col_widths = { + k: max(len(k), max(len(str(r[k])) for r in relationships_data)) + for k in relationships_data[0] + } + header = " ".join( + k.ljust(col_widths[k]) for k in relationships_data[0] + ) + txtfile.write(header + "\n") + txtfile.write("-" * len(header) + "\n") + + # Write rows + for relationship in relationships_data: + row = " ".join( + str(v).ljust(col_widths[k]) for k, v in relationship.items() + ) + txtfile.write(row + "\n") + else: + txtfile.write("No relationship data available\n\n") + + else: + raise ValueError( + f"Unsupported file format: {file_format}. Choose from: csv, excel, md, txt" + ) + if file_format is not None: + print(f"Data exported to: {output_path} with format: {file_format}") + else: + print("Data displayed as table format") + + +def export_data( + chunk_entity_relation_graph, + entities_vdb, + relationships_vdb, + output_path: str, + file_format: str = "csv", + include_vector_data: bool = False, +) -> None: + """ + Synchronously exports all entities, relations, and relationships to various formats. + + Args: + chunk_entity_relation_graph: Graph storage instance for entities and relations + entities_vdb: Vector database storage for entities + relationships_vdb: Vector database storage for relationships + output_path: The path to the output file (including extension). + file_format: Output format - "csv", "excel", "md", "txt". + - csv: Comma-separated values file + - excel: Microsoft Excel file with multiple sheets + - md: Markdown tables + - txt: Plain text formatted output + include_vector_data: Whether to include data from the vector database. + """ + try: + loop = asyncio.get_event_loop() + except RuntimeError: + loop = asyncio.new_event_loop() + asyncio.set_event_loop(loop) + + loop.run_until_complete( + aexport_data( + chunk_entity_relation_graph, + entities_vdb, + relationships_vdb, + output_path, + file_format, + include_vector_data, + ) + ) + + +def lazy_external_import(module_name: str, class_name: str) -> Callable[..., Any]: + """Lazily import a class from an external module based on the package of the caller.""" + # Get the caller's module and package + import inspect + + caller_frame = inspect.currentframe().f_back + module = inspect.getmodule(caller_frame) + package = module.__package__ if module else None + + def import_class(*args: Any, **kwargs: Any): + import importlib + + module = importlib.import_module(module_name, package=package) + cls = getattr(module, class_name) + return cls(*args, **kwargs) + + return import_class + + +async def update_chunk_cache_list( + chunk_id: str, + text_chunks_storage: "BaseKVStorage", + cache_keys: list[str], + cache_scenario: str = "batch_update", +) -> None: + """Update chunk's llm_cache_list with the given cache keys + + Args: + chunk_id: Chunk identifier + text_chunks_storage: Text chunks storage instance + cache_keys: List of cache keys to add to the list + cache_scenario: Description of the cache scenario for logging + """ + if not cache_keys: + return + + try: + chunk_data = await text_chunks_storage.get_by_id(chunk_id) + if chunk_data: + # Ensure llm_cache_list exists + if "llm_cache_list" not in chunk_data: + chunk_data["llm_cache_list"] = [] + + # Add cache keys to the list if not already present + existing_keys = set(chunk_data["llm_cache_list"]) + new_keys = [key for key in cache_keys if key not in existing_keys] + + if new_keys: + chunk_data["llm_cache_list"].extend(new_keys) + + # Update the chunk in storage + await text_chunks_storage.upsert({chunk_id: chunk_data}) + logger.debug( + f"Updated chunk {chunk_id} with {len(new_keys)} cache keys ({cache_scenario})" + ) + except Exception as e: + logger.warning( + f"Failed to update chunk {chunk_id} with cache references on {cache_scenario}: {e}" + ) + + +def remove_think_tags(text: str) -> str: + """Remove ... tags from the text + Remove orphon ... tags from the text also""" + return re.sub( + r"^(.*?|.*)", "", text, flags=re.DOTALL + ).strip() + + +async def use_llm_func_with_cache( + user_prompt: str, + use_llm_func: callable, + llm_response_cache: "BaseKVStorage | None" = None, + system_prompt: str | None = None, + max_tokens: int = None, + history_messages: list[dict[str, str]] = None, + cache_type: str = "extract", + chunk_id: str | None = None, + cache_keys_collector: list = None, +) -> tuple[str, int]: + """Call LLM function with cache support and text sanitization + + If cache is available and enabled (determined by handle_cache based on mode), + retrieve result from cache; otherwise call LLM function and save result to cache. + + This function applies text sanitization to prevent UTF-8 encoding errors for all LLM providers. + + Args: + input_text: Input text to send to LLM + use_llm_func: LLM function with higher priority + llm_response_cache: Cache storage instance + max_tokens: Maximum tokens for generation + history_messages: History messages list + cache_type: Type of cache + chunk_id: Chunk identifier to store in cache + text_chunks_storage: Text chunks storage to update llm_cache_list + cache_keys_collector: Optional list to collect cache keys for batch processing + + Returns: + tuple[str, int]: (LLM response text, timestamp) + - For cache hits: (content, cache_create_time) + - For cache misses: (content, current_timestamp) + """ + # Sanitize input text to prevent UTF-8 encoding errors for all LLM providers + safe_user_prompt = sanitize_text_for_encoding(user_prompt) + safe_system_prompt = ( + sanitize_text_for_encoding(system_prompt) if system_prompt else None + ) + + # Sanitize history messages if provided + safe_history_messages = None + if history_messages: + safe_history_messages = [] + for i, msg in enumerate(history_messages): + safe_msg = msg.copy() + if "content" in safe_msg: + safe_msg["content"] = sanitize_text_for_encoding(safe_msg["content"]) + safe_history_messages.append(safe_msg) + history = json.dumps(safe_history_messages, ensure_ascii=False) + else: + history = None + + if llm_response_cache: + prompt_parts = [] + if safe_user_prompt: + prompt_parts.append(safe_user_prompt) + if safe_system_prompt: + prompt_parts.append(safe_system_prompt) + if history: + prompt_parts.append(history) + _prompt = "\n".join(prompt_parts) + + arg_hash = compute_args_hash(_prompt) + # Generate cache key for this LLM call + cache_key = generate_cache_key("default", cache_type, arg_hash) + + cached_result = await handle_cache( + llm_response_cache, + arg_hash, + _prompt, + "default", + cache_type=cache_type, + ) + if cached_result: + content, timestamp = cached_result + logger.debug(f"Found cache for {arg_hash}") + statistic_data["llm_cache"] += 1 + + # Add cache key to collector if provided + if cache_keys_collector is not None: + cache_keys_collector.append(cache_key) + + return content, timestamp + statistic_data["llm_call"] += 1 + + # Call LLM with sanitized input + kwargs = {} + if safe_history_messages: + kwargs["history_messages"] = safe_history_messages + if max_tokens is not None: + kwargs["max_tokens"] = max_tokens + + res: str = await use_llm_func( + safe_user_prompt, system_prompt=safe_system_prompt, **kwargs + ) + + res = remove_think_tags(res) + + # Generate timestamp for cache miss (LLM call completion time) + current_timestamp = int(time.time()) + + if llm_response_cache.global_config.get("enable_llm_cache_for_entity_extract"): + await save_to_cache( + llm_response_cache, + CacheData( + args_hash=arg_hash, + content=res, + prompt=_prompt, + cache_type=cache_type, + chunk_id=chunk_id, + ), + ) + + # Add cache key to collector if provided + if cache_keys_collector is not None: + cache_keys_collector.append(cache_key) + + return res, current_timestamp + + # When cache is disabled, directly call LLM with sanitized input + kwargs = {} + if safe_history_messages: + kwargs["history_messages"] = safe_history_messages + if max_tokens is not None: + kwargs["max_tokens"] = max_tokens + + try: + res = await use_llm_func( + safe_user_prompt, system_prompt=safe_system_prompt, **kwargs + ) + except Exception as e: + # Add [LLM func] prefix to error message + error_msg = f"[LLM func] {str(e)}" + # Re-raise with the same exception type but modified message + raise type(e)(error_msg) from e + + # Generate timestamp for non-cached LLM call + current_timestamp = int(time.time()) + return remove_think_tags(res), current_timestamp + + +def get_content_summary(content: str, max_length: int = 250) -> str: + """Get summary of document content + + Args: + content: Original document content + max_length: Maximum length of summary + + Returns: + Truncated content with ellipsis if needed + """ + content = content.strip() + if len(content) <= max_length: + return content + return content[:max_length] + "..." + + +def sanitize_and_normalize_extracted_text( + input_text: str, remove_inner_quotes=False +) -> str: + """Santitize and normalize extracted text + Args: + input_text: text string to be processed + is_name: whether the input text is a entity or relation name + + Returns: + Santitized and normalized text string + """ + safe_input_text = sanitize_text_for_encoding(input_text) + if safe_input_text: + normalized_text = normalize_extracted_info( + safe_input_text, remove_inner_quotes=remove_inner_quotes + ) + return normalized_text + return "" + + +def normalize_extracted_info(name: str, remove_inner_quotes=False) -> str: + """Normalize entity/relation names and description with the following rules: + - Clean HTML tags (paragraph and line break tags) + - Convert Chinese symbols to English symbols + - Remove spaces between Chinese characters + - Remove spaces between Chinese characters and English letters/numbers + - Preserve spaces within English text and numbers + - Replace Chinese parentheses with English parentheses + - Replace Chinese dash with English dash + - Remove English quotation marks from the beginning and end of the text + - Remove English quotation marks in and around chinese + - Remove Chinese quotation marks + - Filter out short numeric-only text (length < 3 and only digits/dots) + - remove_inner_quotes = True + remove Chinese quotes + remove English quotes in and around chinese + Convert non-breaking spaces to regular spaces + Convert narrow non-breaking spaces after non-digits to regular spaces + + Args: + name: Entity name to normalize + is_entity: Whether this is an entity name (affects quote handling) + + Returns: + Normalized entity name + """ + # Clean HTML tags - remove paragraph and line break tags + name = re.sub(r"||

    ", "", name, flags=re.IGNORECASE) + name = re.sub(r"||
    ", "", name, flags=re.IGNORECASE) + + # Chinese full-width letters to half-width (A-Z, a-z) + name = name.translate( + str.maketrans( + "ＡＢＣＤＥＦＧＨＩＪＫＬＭＮＯＰＱＲＳＴＵＶＷＸＹＺａｂｃｄｅｆｇｈｉｊｋｌｍｎｏｐｑｒｓｔｕｖｗｘｙｚ", + "ABCDEFGHIJKLMNOPQRSTUVWXYZabcdefghijklmnopqrstuvwxyz", + ) + ) + + # Chinese full-width numbers to half-width + name = name.translate(str.maketrans("０１２３４５６７８９", "0123456789")) + + # Chinese full-width symbols to half-width + name = name.replace("－", "-") # Chinese minus + name = name.replace("＋", "+") # Chinese plus + name = name.replace("／", "/") # Chinese slash + name = name.replace("＊", "*") # Chinese asterisk + + # Replace Chinese parentheses with English parentheses + name = name.replace("（", "(").replace("）", ")") + + # Replace Chinese dash with English dash (additional patterns) + name = name.replace("—", "-").replace("－", "-") + + # Chinese full-width space to regular space (after other replacements) + name = name.replace("　", " ") + + # Use regex to remove spaces between Chinese characters + # Regex explanation: + # (?<=[\u4e00-\u9fa5]): Positive lookbehind for Chinese character + # \s+: One or more whitespace characters + # (?=[\u4e00-\u9fa5]): Positive lookahead for Chinese character + name = re.sub(r"(?<=[\u4e00-\u9fa5])\s+(?=[\u4e00-\u9fa5])", "", name) + + # Remove spaces between Chinese and English/numbers/symbols + name = re.sub( + r"(?<=[\u4e00-\u9fa5])\s+(?=[a-zA-Z0-9\(\)\[\]@#$%!&\*\-=+_])", "", name + ) + name = re.sub( + r"(?<=[a-zA-Z0-9\(\)\[\]@#$%!&\*\-=+_])\s+(?=[\u4e00-\u9fa5])", "", name + ) + + # Remove outer quotes + if len(name) >= 2: + # Handle double quotes + if name.startswith('"') and name.endswith('"'): + inner_content = name[1:-1] + if '"' not in inner_content: # No double quotes inside + name = inner_content + + # Handle single quotes + if name.startswith("'") and name.endswith("'"): + inner_content = name[1:-1] + if "'" not in inner_content: # No single quotes inside + name = inner_content + + # Handle Chinese-style double quotes + if name.startswith("“") and name.endswith("”"): + inner_content = name[1:-1] + if "“" not in inner_content and "”" not in inner_content: + name = inner_content + if name.startswith("‘") and name.endswith("’"): + inner_content = name[1:-1] + if "‘" not in inner_content and "’" not in inner_content: + name = inner_content + + # Handle Chinese-style book title mark + if name.startswith("《") and name.endswith("》"): + inner_content = name[1:-1] + if "《" not in inner_content and "》" not in inner_content: + name = inner_content + + if remove_inner_quotes: + # Remove Chinese quotes + name = name.replace("“", "").replace("”", "").replace("‘", "").replace("’", "") + # Remove English queotes in and around chinese + name = re.sub(r"['\"]+(?=[\u4e00-\u9fa5])", "", name) + name = re.sub(r"(?<=[\u4e00-\u9fa5])['\"]+", "", name) + # Convert non-breaking space to regular space + name = name.replace("\u00a0", " ") + # Convert narrow non-breaking space to regular space when after non-digits + name = re.sub(r"(?<=[^\d])\u202F", " ", name) + + # Remove spaces from the beginning and end of the text + name = name.strip() + + # Filter out pure numeric content with length < 3 + if len(name) < 3 and re.match(r"^[0-9]+$", name): + return "" + + def should_filter_by_dots(text): + """ + Check if the string consists only of dots and digits, with at least one dot + Filter cases include: 1.2.3, 12.3, .123, 123., 12.3., .1.23 etc. + """ + return all(c.isdigit() or c == "." for c in text) and "." in text + + if len(name) < 6 and should_filter_by_dots(name): + # Filter out mixed numeric and dot content with length < 6 + return "" + # Filter out mixed numeric and dot content with length < 6, requiring at least one dot + return "" + + return name + + +def sanitize_text_for_encoding(text: str, replacement_char: str = "") -> str: + """Sanitize text to ensure safe UTF-8 encoding by removing or replacing problematic characters. + + This function handles: + - Surrogate characters (the main cause of encoding errors) + - Other invalid Unicode sequences + - Control characters that might cause issues + - Unescape HTML escapes + - Remove control characters + - Whitespace trimming + + Args: + text: Input text to sanitize + replacement_char: Character to use for replacing invalid sequences + + Returns: + Sanitized text that can be safely encoded as UTF-8 + + Raises: + ValueError: When text contains uncleanable encoding issues that cannot be safely processed + """ + if not text: + return text + + try: + # First, strip whitespace + text = text.strip() + + # Early return if text is empty after basic cleaning + if not text: + return text + + # Try to encode/decode to catch any encoding issues early + text.encode("utf-8") + + # Remove or replace surrogate characters (U+D800 to U+DFFF) + # These are the main cause of the encoding error + sanitized = "" + for char in text: + code_point = ord(char) + # Check for surrogate characters + if 0xD800 <= code_point <= 0xDFFF: + # Replace surrogate with replacement character + sanitized += replacement_char + continue + # Check for other problematic characters + elif code_point == 0xFFFE or code_point == 0xFFFF: + # These are non-characters in Unicode + sanitized += replacement_char + continue + else: + sanitized += char + + # Additional cleanup: remove null bytes and other control characters that might cause issues + # (but preserve common whitespace like \t, \n, \r) + sanitized = re.sub( + r"[\x00-\x08\x0B\x0C\x0E-\x1F\x7F]", replacement_char, sanitized + ) + + # Test final encoding to ensure it's safe + sanitized.encode("utf-8") + + # Unescape HTML escapes + sanitized = html.unescape(sanitized) + + # Remove control characters but preserve common whitespace (\t, \n, \r) + sanitized = re.sub(r"[\x00-\x08\x0B\x0C\x0E-\x1F\x7F-\x9F]", "", sanitized) + + return sanitized.strip() + + except UnicodeEncodeError as e: + # Critical change: Don't return placeholder, raise exception for caller to handle + error_msg = f"Text contains uncleanable UTF-8 encoding issues: {str(e)[:100]}" + logger.error(f"Text sanitization failed: {error_msg}") + raise ValueError(error_msg) from e + + except Exception as e: + logger.error(f"Text sanitization: Unexpected error: {str(e)}") + # For other exceptions, if no encoding issues detected, return original text + try: + text.encode("utf-8") + return text + except UnicodeEncodeError: + raise ValueError( + f"Text sanitization failed with unexpected error: {str(e)}" + ) from e + + +def check_storage_env_vars(storage_name: str) -> None: + """Check if all required environment variables for storage implementation exist + + Args: + storage_name: Storage implementation name + + Raises: + ValueError: If required environment variables are missing + """ + from lightrag.kg import STORAGE_ENV_REQUIREMENTS + + required_vars = STORAGE_ENV_REQUIREMENTS.get(storage_name, []) + missing_vars = [var for var in required_vars if var not in os.environ] + + if missing_vars: + raise ValueError( + f"Storage implementation '{storage_name}' requires the following " + f"environment variables: {', '.join(missing_vars)}" + ) + + +def pick_by_weighted_polling( + entities_or_relations: list[dict], + max_related_chunks: int, + min_related_chunks: int = 1, +) -> list[str]: + """ + Linear gradient weighted polling algorithm for text chunk selection. + + This algorithm ensures that entities/relations with higher importance get more text chunks, + forming a linear decreasing allocation pattern. + + Args: + entities_or_relations: List of entities or relations sorted by importance (high to low) + max_related_chunks: Expected number of text chunks for the highest importance entity/relation + min_related_chunks: Expected number of text chunks for the lowest importance entity/relation + + Returns: + List of selected text chunk IDs + """ + if not entities_or_relations: + return [] + + n = len(entities_or_relations) + if n == 1: + # Only one entity/relation, return its first max_related_chunks text chunks + entity_chunks = entities_or_relations[0].get("sorted_chunks", []) + return entity_chunks[:max_related_chunks] + + # Calculate expected text chunk count for each position (linear decrease) + expected_counts = [] + for i in range(n): + # Linear interpolation: from max_related_chunks to min_related_chunks + ratio = i / (n - 1) if n > 1 else 0 + expected = max_related_chunks - ratio * ( + max_related_chunks - min_related_chunks + ) + expected_counts.append(int(round(expected))) + + # First round allocation: allocate by expected values + selected_chunks = [] + used_counts = [] # Track number of chunks used by each entity + total_remaining = 0 # Accumulate remaining quotas + + for i, entity_rel in enumerate(entities_or_relations): + entity_chunks = entity_rel.get("sorted_chunks", []) + expected = expected_counts[i] + + # Actual allocatable count + actual = min(expected, len(entity_chunks)) + selected_chunks.extend(entity_chunks[:actual]) + used_counts.append(actual) + + # Accumulate remaining quota + remaining = expected - actual + if remaining > 0: + total_remaining += remaining + + # Second round allocation: multi-round scanning to allocate remaining quotas + for _ in range(total_remaining): + allocated = False + + # Scan entities one by one, allocate one chunk when finding unused chunks + for i, entity_rel in enumerate(entities_or_relations): + entity_chunks = entity_rel.get("sorted_chunks", []) + + # Check if there are still unused chunks + if used_counts[i] < len(entity_chunks): + # Allocate one chunk + selected_chunks.append(entity_chunks[used_counts[i]]) + used_counts[i] += 1 + allocated = True + break + + # If no chunks were allocated in this round, all entities are exhausted + if not allocated: + break + + return selected_chunks + + +async def pick_by_vector_similarity( + query: str, + text_chunks_storage: "BaseKVStorage", + chunks_vdb: "BaseVectorStorage", + num_of_chunks: int, + entity_info: list[dict[str, Any]], + embedding_func: callable, + query_embedding=None, +) -> list[str]: + """ + Vector similarity-based text chunk selection algorithm. + + This algorithm selects text chunks based on cosine similarity between + the query embedding and text chunk embeddings. + + Args: + query: User's original query string + text_chunks_storage: Text chunks storage instance + chunks_vdb: Vector database storage for chunks + num_of_chunks: Number of chunks to select + entity_info: List of entity information containing chunk IDs + embedding_func: Embedding function to compute query embedding + + Returns: + List of selected text chunk IDs sorted by similarity (highest first) + """ + logger.debug( + f"Vector similarity chunk selection: num_of_chunks={num_of_chunks}, entity_info_count={len(entity_info) if entity_info else 0}" + ) + + if not entity_info or num_of_chunks <= 0: + return [] + + # Collect all unique chunk IDs from entity info + all_chunk_ids = set() + for i, entity in enumerate(entity_info): + chunk_ids = entity.get("sorted_chunks", []) + all_chunk_ids.update(chunk_ids) + + if not all_chunk_ids: + logger.warning( + "Vector similarity chunk selection: no chunk IDs found in entity_info" + ) + return [] + + logger.debug( + f"Vector similarity chunk selection: {len(all_chunk_ids)} unique chunk IDs collected" + ) + + all_chunk_ids = list(all_chunk_ids) + + try: + # Use pre-computed query embedding if provided, otherwise compute it + if query_embedding is None: + query_embedding = await embedding_func([query]) + query_embedding = query_embedding[ + 0 + ] # Extract first embedding from batch result + logger.debug( + "Computed query embedding for vector similarity chunk selection" + ) + else: + logger.debug( + "Using pre-computed query embedding for vector similarity chunk selection" + ) + + # Get chunk embeddings from vector database + chunk_vectors = await chunks_vdb.get_vectors_by_ids(all_chunk_ids) + logger.debug( + f"Vector similarity chunk selection: {len(chunk_vectors)} chunk vectors Retrieved" + ) + + if not chunk_vectors or len(chunk_vectors) != len(all_chunk_ids): + if not chunk_vectors: + logger.warning( + "Vector similarity chunk selection: no vectors retrieved from chunks_vdb" + ) + else: + logger.warning( + f"Vector similarity chunk selection: found {len(chunk_vectors)} but expecting {len(all_chunk_ids)}" + ) + return [] + + # Calculate cosine similarities + similarities = [] + valid_vectors = 0 + for chunk_id in all_chunk_ids: + if chunk_id in chunk_vectors: + chunk_embedding = chunk_vectors[chunk_id] + try: + # Calculate cosine similarity + similarity = cosine_similarity(query_embedding, chunk_embedding) + similarities.append((chunk_id, similarity)) + valid_vectors += 1 + except Exception as e: + logger.warning( + f"Vector similarity chunk selection: failed to calculate similarity for chunk {chunk_id}: {e}" + ) + else: + logger.warning( + f"Vector similarity chunk selection: no vector found for chunk {chunk_id}" + ) + + # Sort by similarity (highest first) and select top num_of_chunks + similarities.sort(key=lambda x: x[1], reverse=True) + selected_chunks = [chunk_id for chunk_id, _ in similarities[:num_of_chunks]] + + logger.debug( + f"Vector similarity chunk selection: {len(selected_chunks)} chunks from {len(all_chunk_ids)} candidates" + ) + + return selected_chunks + + except Exception as e: + logger.error(f"[VECTOR_SIMILARITY] Error in vector similarity sorting: {e}") + import traceback + + logger.error(f"[VECTOR_SIMILARITY] Traceback: {traceback.format_exc()}") + # Fallback to simple truncation + logger.debug("[VECTOR_SIMILARITY] Falling back to simple truncation") + return all_chunk_ids[:num_of_chunks] + + +class TokenTracker: + """Track token usage for LLM calls.""" + + def __init__(self): + self.reset() + + def __enter__(self): + self.reset() + return self + + def __exit__(self, exc_type, exc_val, exc_tb): + print(self) + + def reset(self): + self.prompt_tokens = 0 + self.completion_tokens = 0 + self.total_tokens = 0 + self.call_count = 0 + + def add_usage(self, token_counts): + """Add token usage from one LLM call. + + Args: + token_counts: A dictionary containing prompt_tokens, completion_tokens, total_tokens + """ + self.prompt_tokens += token_counts.get("prompt_tokens", 0) + self.completion_tokens += token_counts.get("completion_tokens", 0) + + # If total_tokens is provided, use it directly; otherwise calculate the sum + if "total_tokens" in token_counts: + self.total_tokens += token_counts["total_tokens"] + else: + self.total_tokens += token_counts.get( + "prompt_tokens", 0 + ) + token_counts.get("completion_tokens", 0) + + self.call_count += 1 + + def get_usage(self): + """Get current usage statistics.""" + return { + "prompt_tokens": self.prompt_tokens, + "completion_tokens": self.completion_tokens, + "total_tokens": self.total_tokens, + "call_count": self.call_count, + } + + def __str__(self): + usage = self.get_usage() + return ( + f"LLM call count: {usage['call_count']}, " + f"Prompt tokens: {usage['prompt_tokens']}, " + f"Completion tokens: {usage['completion_tokens']}, " + f"Total tokens: {usage['total_tokens']}" + ) + + +async def apply_rerank_if_enabled( + query: str, + retrieved_docs: list[dict], + global_config: dict, + enable_rerank: bool = True, + top_n: int = None, +) -> list[dict]: + """ + Apply reranking to retrieved documents if rerank is enabled. + + Args: + query: The search query + retrieved_docs: List of retrieved documents + global_config: Global configuration containing rerank settings + enable_rerank: Whether to enable reranking from query parameter + top_n: Number of top documents to return after reranking + + Returns: + Reranked documents if rerank is enabled, otherwise original documents + """ + if not enable_rerank or not retrieved_docs: + return retrieved_docs + + rerank_func = global_config.get("rerank_model_func") + if not rerank_func: + logger.warning( + "Rerank is enabled but no rerank model is configured. Please set up a rerank model or set enable_rerank=False in query parameters." + ) + return retrieved_docs + + try: + # Extract document content for reranking + document_texts = [] + for doc in retrieved_docs: + # Try multiple possible content fields + content = ( + doc.get("content") + or doc.get("text") + or doc.get("chunk_content") + or doc.get("document") + or str(doc) + ) + document_texts.append(content) + + # Call the new rerank function that returns index-based results + rerank_results = await rerank_func( + query=query, + documents=document_texts, + top_n=top_n, + ) + + # Process rerank results based on return format + if rerank_results and len(rerank_results) > 0: + # Check if results are in the new index-based format + if isinstance(rerank_results[0], dict) and "index" in rerank_results[0]: + # New format: [{"index": 0, "relevance_score": 0.85}, ...] + reranked_docs = [] + for result in rerank_results: + index = result["index"] + relevance_score = result["relevance_score"] + + # Get original document and add rerank score + if 0 <= index < len(retrieved_docs): + doc = retrieved_docs[index].copy() + doc["rerank_score"] = relevance_score + reranked_docs.append(doc) + + logger.info( + f"Successfully reranked: {len(reranked_docs)} chunks from {len(retrieved_docs)} original chunks" + ) + return reranked_docs + else: + # Legacy format: assume it's already reranked documents + logger.info(f"Using legacy rerank format: {len(rerank_results)} chunks") + return rerank_results[:top_n] if top_n else rerank_results + else: + logger.warning("Rerank returned empty results, using original chunks") + return retrieved_docs + + except Exception as e: + logger.error(f"Error during reranking: {e}, using original chunks") + return retrieved_docs + + +async def process_chunks_unified( + query: str, + unique_chunks: list[dict], + query_param: "QueryParam", + global_config: dict, + source_type: str = "mixed", + chunk_token_limit: int = None, # Add parameter for dynamic token limit +) -> list[dict]: + """ + Unified processing for text chunks: deduplication, chunk_top_k limiting, reranking, and token truncation. + + Args: + query: Search query for reranking + chunks: List of text chunks to process + query_param: Query parameters containing configuration + global_config: Global configuration dictionary + source_type: Source type for logging ("vector", "entity", "relationship", "mixed") + chunk_token_limit: Dynamic token limit for chunks (if None, uses default) + + Returns: + Processed and filtered list of text chunks + """ + if not unique_chunks: + return [] + + origin_count = len(unique_chunks) + + # 1. Apply reranking if enabled and query is provided + if query_param.enable_rerank and query and unique_chunks: + rerank_top_k = query_param.chunk_top_k or len(unique_chunks) + unique_chunks = await apply_rerank_if_enabled( + query=query, + retrieved_docs=unique_chunks, + global_config=global_config, + enable_rerank=query_param.enable_rerank, + top_n=rerank_top_k, + ) + + # 2. Filter by minimum rerank score if reranking is enabled + if query_param.enable_rerank and unique_chunks: + min_rerank_score = global_config.get("min_rerank_score", 0.5) + if min_rerank_score > 0.0: + original_count = len(unique_chunks) + + # Filter chunks with score below threshold + filtered_chunks = [] + for chunk in unique_chunks: + rerank_score = chunk.get( + "rerank_score", 1.0 + ) # Default to 1.0 if no score + if rerank_score >= min_rerank_score: + filtered_chunks.append(chunk) + + unique_chunks = filtered_chunks + filtered_count = original_count - len(unique_chunks) + + if filtered_count > 0: + logger.info( + f"Rerank filtering: {len(unique_chunks)} chunks remained (min rerank score: {min_rerank_score})" + ) + if not unique_chunks: + return [] + + # 3. Apply chunk_top_k limiting if specified + if query_param.chunk_top_k is not None and query_param.chunk_top_k > 0: + if len(unique_chunks) > query_param.chunk_top_k: + unique_chunks = unique_chunks[: query_param.chunk_top_k] + logger.debug( + f"Kept chunk_top-k: {len(unique_chunks)} chunks (deduplicated original: {origin_count})" + ) + + # 4. Token-based final truncation + tokenizer = global_config.get("tokenizer") + if tokenizer and unique_chunks: + # Set default chunk_token_limit if not provided + if chunk_token_limit is None: + # Get default from query_param or global_config + chunk_token_limit = getattr( + query_param, + "max_total_tokens", + global_config.get("MAX_TOTAL_TOKENS", DEFAULT_MAX_TOTAL_TOKENS), + ) + + original_count = len(unique_chunks) + + unique_chunks = truncate_list_by_token_size( + unique_chunks, + key=lambda x: "\n".join( + json.dumps(item, ensure_ascii=False) for item in [x] + ), + max_token_size=chunk_token_limit, + tokenizer=tokenizer, + ) + + logger.debug( + f"Token truncation: {len(unique_chunks)} chunks from {original_count} " + f"(chunk available tokens: {chunk_token_limit}, source: {source_type})" + ) + + # 5. add id field to each chunk + final_chunks = [] + for i, chunk in enumerate(unique_chunks): + chunk_with_id = chunk.copy() + chunk_with_id["id"] = f"DC{i + 1}" + final_chunks.append(chunk_with_id) + + return final_chunks + + +def normalize_source_ids_limit_method(method: str | None) -> str: + """Normalize the source ID limiting strategy and fall back to default when invalid.""" + + if not method: + return DEFAULT_SOURCE_IDS_LIMIT_METHOD + + normalized = method.upper() + if normalized not in VALID_SOURCE_IDS_LIMIT_METHODS: + logger.warning( + "Unknown SOURCE_IDS_LIMIT_METHOD '%s', falling back to %s", + method, + DEFAULT_SOURCE_IDS_LIMIT_METHOD, + ) + return DEFAULT_SOURCE_IDS_LIMIT_METHOD + + return normalized + + +def merge_source_ids( + existing_ids: Iterable[str] | None, new_ids: Iterable[str] | None +) -> list[str]: + """Merge two iterables of source IDs while preserving order and removing duplicates.""" + + merged: list[str] = [] + seen: set[str] = set() + + for sequence in (existing_ids, new_ids): + if not sequence: + continue + for source_id in sequence: + if not source_id: + continue + if source_id not in seen: + seen.add(source_id) + merged.append(source_id) + + return merged + + +def apply_source_ids_limit( + source_ids: Sequence[str], + limit: int, + method: str, + *, + identifier: str | None = None, +) -> list[str]: + """Apply a limit strategy to a sequence of source IDs.""" + + if limit <= 0: + return [] + + source_ids_list = list(source_ids) + if len(source_ids_list) <= limit: + return source_ids_list + + normalized_method = normalize_source_ids_limit_method(method) + + if normalized_method == SOURCE_IDS_LIMIT_METHOD_FIFO: + truncated = source_ids_list[-limit:] + else: # IGNORE_NEW + truncated = source_ids_list[:limit] + + if identifier and len(truncated) < len(source_ids_list): + logger.debug( + "Source_id truncated: %s | %s keeping %s of %s entries", + identifier, + normalized_method, + len(truncated), + len(source_ids_list), + ) + + return truncated + + +def compute_incremental_chunk_ids( + existing_full_chunk_ids: list[str], + old_chunk_ids: list[str], + new_chunk_ids: list[str], +) -> list[str]: + """ + Compute incrementally updated chunk IDs based on changes. + + This function applies delta changes (additions and removals) to an existing + list of chunk IDs while maintaining order and ensuring deduplication. + Delta additions from new_chunk_ids are placed at the end. + + Args: + existing_full_chunk_ids: Complete list of existing chunk IDs from storage + old_chunk_ids: Previous chunk IDs from source_id (chunks being replaced) + new_chunk_ids: New chunk IDs from updated source_id (chunks being added) + + Returns: + Updated list of chunk IDs with deduplication + + Example: + >>> existing = ['chunk-1', 'chunk-2', 'chunk-3'] + >>> old = ['chunk-1', 'chunk-2'] + >>> new = ['chunk-2', 'chunk-4'] + >>> compute_incremental_chunk_ids(existing, old, new) + ['chunk-3', 'chunk-2', 'chunk-4'] + """ + # Calculate changes + chunks_to_remove = set(old_chunk_ids) - set(new_chunk_ids) + chunks_to_add = set(new_chunk_ids) - set(old_chunk_ids) + + # Apply changes to full chunk_ids + # Step 1: Remove chunks that are no longer needed + updated_chunk_ids = [ + cid for cid in existing_full_chunk_ids if cid not in chunks_to_remove + ] + + # Step 2: Add new chunks (preserving order from new_chunk_ids) + # Note: 'cid not in updated_chunk_ids' check ensures deduplication + for cid in new_chunk_ids: + if cid in chunks_to_add and cid not in updated_chunk_ids: + updated_chunk_ids.append(cid) + + return updated_chunk_ids + + +def subtract_source_ids( + source_ids: Iterable[str], + ids_to_remove: Collection[str], +) -> list[str]: + """Remove a collection of IDs from an ordered iterable while preserving order.""" + + removal_set = set(ids_to_remove) + if not removal_set: + return [source_id for source_id in source_ids if source_id] + + return [ + source_id + for source_id in source_ids + if source_id and source_id not in removal_set + ] + + +def make_relation_chunk_key(src: str, tgt: str) -> str: + """Create a deterministic storage key for relation chunk tracking.""" + + return GRAPH_FIELD_SEP.join(sorted((src, tgt))) + + +def parse_relation_chunk_key(key: str) -> tuple[str, str]: + """Parse a relation chunk storage key back into its entity pair.""" + + parts = key.split(GRAPH_FIELD_SEP) + if len(parts) != 2: + raise ValueError(f"Invalid relation chunk key: {key}") + return parts[0], parts[1] + + +def generate_track_id(prefix: str = "upload") -> str: + """Generate a unique tracking ID with timestamp and UUID + + Args: + prefix: Prefix for the track ID (e.g., 'upload', 'insert') + + Returns: + str: Unique tracking ID in format: {prefix}_{timestamp}_{uuid} + """ + timestamp = datetime.now().strftime("%Y%m%d_%H%M%S") + unique_id = str(uuid.uuid4())[:8] # Use first 8 characters of UUID + return f"{prefix}_{timestamp}_{unique_id}" + + +def get_pinyin_sort_key(text: str) -> str: + """Generate sort key for Chinese pinyin sorting + + This function uses pypinyin for true Chinese pinyin sorting. + If pypinyin is not available, it falls back to simple lowercase string sorting. + + Args: + text: Text to generate sort key for + + Returns: + str: Sort key that can be used for comparison and sorting + """ + if not text: + return "" + + if _PYPINYIN_AVAILABLE: + try: + # Convert Chinese characters to pinyin, keep non-Chinese as-is + pinyin_list = pypinyin.lazy_pinyin(text, style=pypinyin.Style.NORMAL) + return "".join(pinyin_list).lower() + except Exception: + # Silently fall back to simple string sorting on any error + return text.lower() + else: + # pypinyin not available, use simple string sorting + return text.lower() + + +def fix_tuple_delimiter_corruption( + record: str, delimiter_core: str, tuple_delimiter: str +) -> str: + """ + Fix various forms of tuple_delimiter corruption from LLM output. + + This function handles missing or replaced characters around the core delimiter. + It fixes common corruption patterns where the LLM output doesn't match the expected + tuple_delimiter format. + + Args: + record: The text record to fix + delimiter_core: The core delimiter (e.g., "S" from "<|#|>") + tuple_delimiter: The complete tuple delimiter (e.g., "<|#|>") + + Returns: + The corrected record with proper tuple_delimiter format + """ + if not record or not delimiter_core or not tuple_delimiter: + return record + + # Escape the delimiter core for regex use + escaped_delimiter_core = re.escape(delimiter_core) + + # Fix: <|##|> -> <|#|>, <|#||#|> -> <|#|>, <|#|||#|> -> <|#|> + record = re.sub( + rf"<\|{escaped_delimiter_core}\|*?{escaped_delimiter_core}\|>", + tuple_delimiter, + record, + ) + + # Fix: <|\#|> -> <|#|> + record = re.sub( + rf"<\|\\{escaped_delimiter_core}\|>", + tuple_delimiter, + record, + ) + + # Fix: <|> -> <|#|>, <||> -> <|#|> + record = re.sub( + r"<\|+>", + tuple_delimiter, + record, + ) + + # Fix: -> <|#|>, <|#|Y> -> <|#|>, -> <|#|>, <||#||> -> <|#|> (one extra characters outside pipes) + record = re.sub( + rf"<.?\|{escaped_delimiter_core}\|.?>", + tuple_delimiter, + record, + ) + + # Fix: <#>, <#|>, <|#> -> <|#|> (missing one or both pipes) + record = re.sub( + rf"<\|?{escaped_delimiter_core}\|?>", + tuple_delimiter, + record, + ) + + # Fix: -> <|#|>, <|#X> -> <|#|> (one pipe is replaced by other character) + record = re.sub( + rf"<[^|]{escaped_delimiter_core}\|>|<\|{escaped_delimiter_core}[^|]>", + tuple_delimiter, + record, + ) + + # Fix: <|#| -> <|#|>, <|#|| -> <|#|> (missing closing >) + record = re.sub( + rf"<\|{escaped_delimiter_core}\|+(?!>)", + tuple_delimiter, + record, + ) + + # Fix <|#: -> <|#|> (missing closing >) + record = re.sub( + rf"<\|{escaped_delimiter_core}:(?!>)", + tuple_delimiter, + record, + ) + + # Fix: <||#> -> <|#|> (double pipe at start, missing pipe at end) + record = re.sub( + rf"<\|+{escaped_delimiter_core}>", + tuple_delimiter, + record, + ) + + # Fix: <|| -> <|#|> + record = re.sub( + r"<\|\|(?!>)", + tuple_delimiter, + record, + ) + + # Fix: |#|> -> <|#|> (missing opening <) + record = re.sub( + rf"(?", + tuple_delimiter, + record, + ) + + # Fix: <|#|>| -> <|#|> ( this is a fix for: <|#|| -> <|#|> ) + record = re.sub( + rf"<\|{escaped_delimiter_core}\|>\|", + tuple_delimiter, + record, + ) + + # Fix: ||#|| -> <|#|> (double pipes on both sides without angle brackets) + record = re.sub( + rf"\|\|{escaped_delimiter_core}\|\|", + tuple_delimiter, + record, + ) + + return record + + +def create_prefixed_exception(original_exception: Exception, prefix: str) -> Exception: + """ + Safely create a prefixed exception that adapts to all error types. + + Args: + original_exception: The original exception. + prefix: The prefix to add. + + Returns: + A new exception with the prefix, maintaining the original exception type if possible. + """ + try: + # Method 1: Try to reconstruct using original arguments. + if hasattr(original_exception, "args") and original_exception.args: + args = list(original_exception.args) + # Find the first string argument and prefix it. This is safer for + # exceptions like OSError where the first arg is an integer (errno). + found_str = False + for i, arg in enumerate(args): + if isinstance(arg, str): + args[i] = f"{prefix}: {arg}" + found_str = True + break + + # If no string argument is found, prefix the first argument's string representation. + if not found_str: + args[0] = f"{prefix}: {args[0]}" + + return type(original_exception)(*args) + else: + # Method 2: If no args, try single parameter construction. + return type(original_exception)(f"{prefix}: {str(original_exception)}") + except (TypeError, ValueError, AttributeError) as construct_error: + # Method 3: If reconstruction fails, wrap it in a RuntimeError. + # This is the safest fallback, as attempting to create the same type + # with a single string can fail if the constructor requires multiple arguments. + return RuntimeError( + f"{prefix}: {type(original_exception).__name__}: {str(original_exception)} " + f"(Original exception could not be reconstructed: {construct_error})" + ) + + +def convert_to_user_format( + entities_context: list[dict], + relations_context: list[dict], + chunks: list[dict], + references: list[dict], + query_mode: str, + entity_id_to_original: dict = None, + relation_id_to_original: dict = None, +) -> dict[str, Any]: + """Convert internal data format to user-friendly format using original database data""" + + # Convert entities format using original data when available + formatted_entities = [] + for entity in entities_context: + entity_name = entity.get("entity", "") + + # Try to get original data first + original_entity = None + if entity_id_to_original and entity_name in entity_id_to_original: + original_entity = entity_id_to_original[entity_name] + + if original_entity: + # Use original database data + formatted_entities.append( + { + "entity_name": original_entity.get("entity_name", entity_name), + "entity_type": original_entity.get("entity_type", "UNKNOWN"), + "description": original_entity.get("description", ""), + "source_id": original_entity.get("source_id", ""), + "file_path": original_entity.get("file_path", "unknown_source"), + "created_at": original_entity.get("created_at", ""), + } + ) + else: + # Fallback to LLM context data (for backward compatibility) + formatted_entities.append( + { + "entity_name": entity_name, + "entity_type": entity.get("type", "UNKNOWN"), + "description": entity.get("description", ""), + "source_id": entity.get("source_id", ""), + "file_path": entity.get("file_path", "unknown_source"), + "created_at": entity.get("created_at", ""), + } + ) + + # Convert relationships format using original data when available + formatted_relationships = [] + for relation in relations_context: + entity1 = relation.get("entity1", "") + entity2 = relation.get("entity2", "") + relation_key = (entity1, entity2) + + # Try to get original data first + original_relation = None + if relation_id_to_original and relation_key in relation_id_to_original: + original_relation = relation_id_to_original[relation_key] + + if original_relation: + # Use original database data + formatted_relationships.append( + { + "src_id": original_relation.get("src_id", entity1), + "tgt_id": original_relation.get("tgt_id", entity2), + "description": original_relation.get("description", ""), + "keywords": original_relation.get("keywords", ""), + "weight": original_relation.get("weight", 1.0), + "source_id": original_relation.get("source_id", ""), + "file_path": original_relation.get("file_path", "unknown_source"), + "created_at": original_relation.get("created_at", ""), + } + ) + else: + # Fallback to LLM context data (for backward compatibility) + formatted_relationships.append( + { + "src_id": entity1, + "tgt_id": entity2, + "description": relation.get("description", ""), + "keywords": relation.get("keywords", ""), + "weight": relation.get("weight", 1.0), + "source_id": relation.get("source_id", ""), + "file_path": relation.get("file_path", "unknown_source"), + "created_at": relation.get("created_at", ""), + } + ) + + # Convert chunks format (chunks already contain complete data) + formatted_chunks = [] + for i, chunk in enumerate(chunks): + chunk_data = { + "reference_id": chunk.get("reference_id", ""), + "content": chunk.get("content", ""), + "file_path": chunk.get("file_path", "unknown_source"), + "chunk_id": chunk.get("chunk_id", ""), + } + formatted_chunks.append(chunk_data) + + logger.debug( + f"[convert_to_user_format] Formatted {len(formatted_chunks)}/{len(chunks)} chunks" + ) + + # Build basic metadata (metadata details will be added by calling functions) + metadata = { + "query_mode": query_mode, + "keywords": { + "high_level": [], + "low_level": [], + }, # Placeholder, will be set by calling functions + } + + return { + "status": "success", + "message": "Query processed successfully", + "data": { + "entities": formatted_entities, + "relationships": formatted_relationships, + "chunks": formatted_chunks, + "references": references, + }, + "metadata": metadata, + } + + +def generate_reference_list_from_chunks( + chunks: list[dict], +) -> tuple[list[dict], list[dict]]: + """ + Generate reference list from chunks, prioritizing by occurrence frequency. + + This function extracts file_paths from chunks, counts their occurrences, + sorts by frequency and first appearance order, creates reference_id mappings, + and builds a reference_list structure. + + Args: + chunks: List of chunk dictionaries with file_path information + + Returns: + tuple: (reference_list, updated_chunks_with_reference_ids) + - reference_list: List of dicts with reference_id and file_path + - updated_chunks_with_reference_ids: Original chunks with reference_id field added + """ + if not chunks: + return [], [] + + # 1. Extract all valid file_paths and count their occurrences + file_path_counts = {} + for chunk in chunks: + file_path = chunk.get("file_path", "") + if file_path and file_path != "unknown_source": + file_path_counts[file_path] = file_path_counts.get(file_path, 0) + 1 + + # 2. Sort file paths by frequency (descending), then by first appearance order + # Create a list of (file_path, count, first_index) tuples + file_path_with_indices = [] + seen_paths = set() + for i, chunk in enumerate(chunks): + file_path = chunk.get("file_path", "") + if file_path and file_path != "unknown_source" and file_path not in seen_paths: + file_path_with_indices.append((file_path, file_path_counts[file_path], i)) + seen_paths.add(file_path) + + # Sort by count (descending), then by first appearance index (ascending) + sorted_file_paths = sorted(file_path_with_indices, key=lambda x: (-x[1], x[2])) + unique_file_paths = [item[0] for item in sorted_file_paths] + + # 3. Create mapping from file_path to reference_id (prioritized by frequency) + file_path_to_ref_id = {} + for i, file_path in enumerate(unique_file_paths): + file_path_to_ref_id[file_path] = str(i + 1) + + # 4. Add reference_id field to each chunk + updated_chunks = [] + for chunk in chunks: + chunk_copy = chunk.copy() + file_path = chunk_copy.get("file_path", "") + if file_path and file_path != "unknown_source": + chunk_copy["reference_id"] = file_path_to_ref_id[file_path] + else: + chunk_copy["reference_id"] = "" + updated_chunks.append(chunk_copy) + + # 5. Build reference_list + reference_list = [] + for i, file_path in enumerate(unique_file_paths): + reference_list.append({"reference_id": str(i + 1), "file_path": file_path}) + + return reference_list, updated_chunks diff --git a/LightRAG/lightrag/utils_graph.py b/LightRAG/lightrag/utils_graph.py new file mode 100644 index 00000000..cedad575 --- /dev/null +++ b/LightRAG/lightrag/utils_graph.py @@ -0,0 +1,1733 @@ +from __future__ import annotations + +import time +import asyncio +from typing import Any, cast + +from .base import DeletionResult +from .kg.shared_storage import get_storage_keyed_lock +from .constants import GRAPH_FIELD_SEP +from .utils import compute_mdhash_id, logger +from .base import StorageNameSpace + + +async def _persist_graph_updates( + entities_vdb=None, + relationships_vdb=None, + chunk_entity_relation_graph=None, + entity_chunks_storage=None, + relation_chunks_storage=None, +) -> None: + """Unified callback to persist updates after graph operations. + + Ensures all relevant storage instances are properly persisted after + operations like delete, edit, create, or merge. + + Args: + entities_vdb: Entity vector database storage (optional) + relationships_vdb: Relationship vector database storage (optional) + chunk_entity_relation_graph: Graph storage instance (optional) + entity_chunks_storage: Entity-chunk tracking storage (optional) + relation_chunks_storage: Relation-chunk tracking storage (optional) + """ + storages = [] + + # Collect all non-None storage instances + if entities_vdb is not None: + storages.append(entities_vdb) + if relationships_vdb is not None: + storages.append(relationships_vdb) + if chunk_entity_relation_graph is not None: + storages.append(chunk_entity_relation_graph) + if entity_chunks_storage is not None: + storages.append(entity_chunks_storage) + if relation_chunks_storage is not None: + storages.append(relation_chunks_storage) + + # Persist all storage instances in parallel + if storages: + await asyncio.gather( + *[ + cast(StorageNameSpace, storage_inst).index_done_callback() + for storage_inst in storages # type: ignore + ] + ) + + +async def adelete_by_entity( + chunk_entity_relation_graph, + entities_vdb, + relationships_vdb, + entity_name: str, + entity_chunks_storage=None, + relation_chunks_storage=None, +) -> DeletionResult: + """Asynchronously delete an entity and all its relationships. + + Also cleans up entity_chunks_storage and relation_chunks_storage to remove chunk tracking. + + Args: + chunk_entity_relation_graph: Graph storage instance + entities_vdb: Vector database storage for entities + relationships_vdb: Vector database storage for relationships + entity_name: Name of the entity to delete + entity_chunks_storage: Optional KV storage for tracking chunks that reference this entity + relation_chunks_storage: Optional KV storage for tracking chunks that reference relations + """ + # Use keyed lock for entity to ensure atomic graph and vector db operations + workspace = entities_vdb.global_config.get("workspace", "") + namespace = f"{workspace}:GraphDB" if workspace else "GraphDB" + async with get_storage_keyed_lock( + [entity_name], namespace=namespace, enable_logging=False + ): + try: + # Check if the entity exists + if not await chunk_entity_relation_graph.has_node(entity_name): + logger.warning(f"Entity '{entity_name}' not found.") + return DeletionResult( + status="not_found", + doc_id=entity_name, + message=f"Entity '{entity_name}' not found.", + status_code=404, + ) + # Retrieve related relationships before deleting the node + edges = await chunk_entity_relation_graph.get_node_edges(entity_name) + related_relations_count = len(edges) if edges else 0 + + # Clean up chunk tracking storages before deletion + if entity_chunks_storage is not None: + # Delete entity's entry from entity_chunks_storage + await entity_chunks_storage.delete([entity_name]) + logger.info( + f"Entity Delete: removed chunk tracking for `{entity_name}`" + ) + + if relation_chunks_storage is not None and edges: + # Delete all related relationships from relation_chunks_storage + from .utils import make_relation_chunk_key + + relation_keys_to_delete = [] + for src, tgt in edges: + # Normalize entity order for consistent key generation + normalized_src, normalized_tgt = sorted([src, tgt]) + storage_key = make_relation_chunk_key( + normalized_src, normalized_tgt + ) + relation_keys_to_delete.append(storage_key) + + if relation_keys_to_delete: + await relation_chunks_storage.delete(relation_keys_to_delete) + logger.info( + f"Entity Delete: removed chunk tracking for {len(relation_keys_to_delete)} relations" + ) + + await entities_vdb.delete_entity(entity_name) + await relationships_vdb.delete_entity_relation(entity_name) + await chunk_entity_relation_graph.delete_node(entity_name) + + message = f"Entity Delete: remove '{entity_name}' and its {related_relations_count} relations" + logger.info(message) + await _persist_graph_updates( + entities_vdb=entities_vdb, + relationships_vdb=relationships_vdb, + chunk_entity_relation_graph=chunk_entity_relation_graph, + entity_chunks_storage=entity_chunks_storage, + relation_chunks_storage=relation_chunks_storage, + ) + return DeletionResult( + status="success", + doc_id=entity_name, + message=message, + status_code=200, + ) + except Exception as e: + error_message = f"Error while deleting entity '{entity_name}': {e}" + logger.error(error_message) + return DeletionResult( + status="fail", + doc_id=entity_name, + message=error_message, + status_code=500, + ) + + +async def adelete_by_relation( + chunk_entity_relation_graph, + relationships_vdb, + source_entity: str, + target_entity: str, + relation_chunks_storage=None, +) -> DeletionResult: + """Asynchronously delete a relation between two entities. + + Also cleans up relation_chunks_storage to remove chunk tracking. + + Args: + chunk_entity_relation_graph: Graph storage instance + relationships_vdb: Vector database storage for relationships + source_entity: Name of the source entity + target_entity: Name of the target entity + relation_chunks_storage: Optional KV storage for tracking chunks that reference this relation + """ + relation_str = f"{source_entity} -> {target_entity}" + # Normalize entity order for undirected graph (ensures consistent key generation) + if source_entity > target_entity: + source_entity, target_entity = target_entity, source_entity + + # Use keyed lock for relation to ensure atomic graph and vector db operations + workspace = relationships_vdb.global_config.get("workspace", "") + namespace = f"{workspace}:GraphDB" if workspace else "GraphDB" + sorted_edge_key = sorted([source_entity, target_entity]) + async with get_storage_keyed_lock( + sorted_edge_key, namespace=namespace, enable_logging=False + ): + try: + # Check if the relation exists + edge_exists = await chunk_entity_relation_graph.has_edge( + source_entity, target_entity + ) + if not edge_exists: + message = f"Relation from '{source_entity}' to '{target_entity}' does not exist" + logger.warning(message) + return DeletionResult( + status="not_found", + doc_id=relation_str, + message=message, + status_code=404, + ) + + # Clean up chunk tracking storage before deletion + if relation_chunks_storage is not None: + from .utils import make_relation_chunk_key + + # Normalize entity order for consistent key generation + normalized_src, normalized_tgt = sorted([source_entity, target_entity]) + storage_key = make_relation_chunk_key(normalized_src, normalized_tgt) + + await relation_chunks_storage.delete([storage_key]) + logger.info( + f"Relation Delete: removed chunk tracking for `{source_entity}`~`{target_entity}`" + ) + + # Delete relation from vector database + rel_ids_to_delete = [ + compute_mdhash_id(source_entity + target_entity, prefix="rel-"), + compute_mdhash_id(target_entity + source_entity, prefix="rel-"), + ] + + await relationships_vdb.delete(rel_ids_to_delete) + + # Delete relation from knowledge graph + await chunk_entity_relation_graph.remove_edges( + [(source_entity, target_entity)] + ) + + message = f"Relation Delete: `{source_entity}`~`{target_entity}` deleted successfully" + logger.info(message) + await _persist_graph_updates( + relationships_vdb=relationships_vdb, + chunk_entity_relation_graph=chunk_entity_relation_graph, + relation_chunks_storage=relation_chunks_storage, + ) + return DeletionResult( + status="success", + doc_id=relation_str, + message=message, + status_code=200, + ) + except Exception as e: + error_message = f"Error while deleting relation from '{source_entity}' to '{target_entity}': {e}" + logger.error(error_message) + return DeletionResult( + status="fail", + doc_id=relation_str, + message=error_message, + status_code=500, + ) + + +async def _edit_entity_impl( + chunk_entity_relation_graph, + entities_vdb, + relationships_vdb, + entity_name: str, + updated_data: dict[str, str], + *, + entity_chunks_storage=None, + relation_chunks_storage=None, +) -> dict[str, Any]: + """Internal helper that edits an entity without acquiring storage locks. + + This function performs the actual entity edit operations without lock management. + It should only be called by public APIs that have already acquired necessary locks. + + Args: + chunk_entity_relation_graph: Graph storage instance + entities_vdb: Vector database storage for entities + relationships_vdb: Vector database storage for relationships + entity_name: Name of the entity to edit + updated_data: Dictionary containing updated attributes (including optional entity_name for renaming) + entity_chunks_storage: Optional KV storage for tracking chunks + relation_chunks_storage: Optional KV storage for tracking relation chunks + + Returns: + Dictionary containing updated entity information + + Note: + Caller must acquire appropriate locks before calling this function. + If renaming (entity_name in updated_data), this function will check if the new name exists. + """ + new_entity_name = updated_data.get("entity_name", entity_name) + is_renaming = new_entity_name != entity_name + + original_entity_name = entity_name + + node_exists = await chunk_entity_relation_graph.has_node(entity_name) + if not node_exists: + raise ValueError(f"Entity '{entity_name}' does not exist") + node_data = await chunk_entity_relation_graph.get_node(entity_name) + + if is_renaming: + existing_node = await chunk_entity_relation_graph.has_node(new_entity_name) + if existing_node: + raise ValueError( + f"Entity name '{new_entity_name}' already exists, cannot rename" + ) + + new_node_data = {**node_data, **updated_data} + new_node_data["entity_id"] = new_entity_name + + if "entity_name" in new_node_data: + del new_node_data[ + "entity_name" + ] # Node data should not contain entity_name field + + if is_renaming: + logger.info(f"Entity Edit: renaming `{entity_name}` to `{new_entity_name}`") + + await chunk_entity_relation_graph.upsert_node(new_entity_name, new_node_data) + + relations_to_update = [] + relations_to_delete = [] + edges = await chunk_entity_relation_graph.get_node_edges(entity_name) + if edges: + for source, target in edges: + edge_data = await chunk_entity_relation_graph.get_edge(source, target) + if edge_data: + relations_to_delete.append( + compute_mdhash_id(source + target, prefix="rel-") + ) + relations_to_delete.append( + compute_mdhash_id(target + source, prefix="rel-") + ) + if source == entity_name: + await chunk_entity_relation_graph.upsert_edge( + new_entity_name, target, edge_data + ) + relations_to_update.append((new_entity_name, target, edge_data)) + else: # target == entity_name + await chunk_entity_relation_graph.upsert_edge( + source, new_entity_name, edge_data + ) + relations_to_update.append((source, new_entity_name, edge_data)) + + await chunk_entity_relation_graph.delete_node(entity_name) + + old_entity_id = compute_mdhash_id(entity_name, prefix="ent-") + await entities_vdb.delete([old_entity_id]) + + await relationships_vdb.delete(relations_to_delete) + + for src, tgt, edge_data in relations_to_update: + normalized_src, normalized_tgt = sorted([src, tgt]) + + description = edge_data.get("description", "") + keywords = edge_data.get("keywords", "") + source_id = edge_data.get("source_id", "") + weight = float(edge_data.get("weight", 1.0)) + + content = f"{normalized_src}\t{normalized_tgt}\n{keywords}\n{description}" + + relation_id = compute_mdhash_id( + normalized_src + normalized_tgt, prefix="rel-" + ) + + relation_data = { + relation_id: { + "content": content, + "src_id": normalized_src, + "tgt_id": normalized_tgt, + "source_id": source_id, + "description": description, + "keywords": keywords, + "weight": weight, + } + } + + await relationships_vdb.upsert(relation_data) + + entity_name = new_entity_name + else: + await chunk_entity_relation_graph.upsert_node(entity_name, new_node_data) + + description = new_node_data.get("description", "") + source_id = new_node_data.get("source_id", "") + entity_type = new_node_data.get("entity_type", "") + content = entity_name + "\n" + description + + entity_id = compute_mdhash_id(entity_name, prefix="ent-") + + entity_data = { + entity_id: { + "content": content, + "entity_name": entity_name, + "source_id": source_id, + "description": description, + "entity_type": entity_type, + } + } + + await entities_vdb.upsert(entity_data) + + if entity_chunks_storage is not None or relation_chunks_storage is not None: + from .utils import make_relation_chunk_key, compute_incremental_chunk_ids + + if entity_chunks_storage is not None: + storage_key = original_entity_name if is_renaming else entity_name + stored_data = await entity_chunks_storage.get_by_id(storage_key) + has_stored_data = ( + stored_data + and isinstance(stored_data, dict) + and stored_data.get("chunk_ids") + ) + + old_source_id = node_data.get("source_id", "") + old_chunk_ids = [cid for cid in old_source_id.split(GRAPH_FIELD_SEP) if cid] + + new_source_id = new_node_data.get("source_id", "") + new_chunk_ids = [cid for cid in new_source_id.split(GRAPH_FIELD_SEP) if cid] + + source_id_changed = set(new_chunk_ids) != set(old_chunk_ids) + + if source_id_changed or not has_stored_data or is_renaming: + existing_full_chunk_ids = [] + if has_stored_data: + existing_full_chunk_ids = [ + cid for cid in stored_data.get("chunk_ids", []) if cid + ] + + if not existing_full_chunk_ids: + existing_full_chunk_ids = old_chunk_ids.copy() + + updated_chunk_ids = compute_incremental_chunk_ids( + existing_full_chunk_ids, old_chunk_ids, new_chunk_ids + ) + + if is_renaming: + await entity_chunks_storage.delete([original_entity_name]) + await entity_chunks_storage.upsert( + { + entity_name: { + "chunk_ids": updated_chunk_ids, + "count": len(updated_chunk_ids), + } + } + ) + else: + await entity_chunks_storage.upsert( + { + entity_name: { + "chunk_ids": updated_chunk_ids, + "count": len(updated_chunk_ids), + } + } + ) + + logger.info( + f"Entity Edit: find {len(updated_chunk_ids)} chunks related to `{entity_name}`" + ) + + if is_renaming and relation_chunks_storage is not None and relations_to_update: + for src, tgt, edge_data in relations_to_update: + old_src = original_entity_name if src == entity_name else src + old_tgt = original_entity_name if tgt == entity_name else tgt + + old_normalized_src, old_normalized_tgt = sorted([old_src, old_tgt]) + new_normalized_src, new_normalized_tgt = sorted([src, tgt]) + + old_storage_key = make_relation_chunk_key( + old_normalized_src, old_normalized_tgt + ) + new_storage_key = make_relation_chunk_key( + new_normalized_src, new_normalized_tgt + ) + + if old_storage_key != new_storage_key: + old_stored_data = await relation_chunks_storage.get_by_id( + old_storage_key + ) + relation_chunk_ids = [] + + if old_stored_data and isinstance(old_stored_data, dict): + relation_chunk_ids = [ + cid for cid in old_stored_data.get("chunk_ids", []) if cid + ] + else: + relation_source_id = edge_data.get("source_id", "") + relation_chunk_ids = [ + cid + for cid in relation_source_id.split(GRAPH_FIELD_SEP) + if cid + ] + + await relation_chunks_storage.delete([old_storage_key]) + + if relation_chunk_ids: + await relation_chunks_storage.upsert( + { + new_storage_key: { + "chunk_ids": relation_chunk_ids, + "count": len(relation_chunk_ids), + } + } + ) + logger.info( + f"Entity Edit: migrate {len(relations_to_update)} relations after rename" + ) + + await _persist_graph_updates( + entities_vdb=entities_vdb, + relationships_vdb=relationships_vdb, + chunk_entity_relation_graph=chunk_entity_relation_graph, + entity_chunks_storage=entity_chunks_storage, + relation_chunks_storage=relation_chunks_storage, + ) + + logger.info(f"Entity Edit: `{entity_name}` successfully updated") + return await get_entity_info( + chunk_entity_relation_graph, + entities_vdb, + entity_name, + include_vector_data=True, + ) + + +async def aedit_entity( + chunk_entity_relation_graph, + entities_vdb, + relationships_vdb, + entity_name: str, + updated_data: dict[str, str], + allow_rename: bool = True, + allow_merge: bool = False, + entity_chunks_storage=None, + relation_chunks_storage=None, +) -> dict[str, Any]: + """Asynchronously edit entity information. + + Updates entity information in the knowledge graph and re-embeds the entity in the vector database. + Also synchronizes entity_chunks_storage and relation_chunks_storage to track chunk references. + + Args: + chunk_entity_relation_graph: Graph storage instance + entities_vdb: Vector database storage for entities + relationships_vdb: Vector database storage for relationships + entity_name: Name of the entity to edit + updated_data: Dictionary containing updated attributes, e.g. {"description": "new description", "entity_type": "new type"} + allow_rename: Whether to allow entity renaming, defaults to True + allow_merge: Whether to merge into an existing entity when renaming to an existing name, defaults to False + entity_chunks_storage: Optional KV storage for tracking chunks that reference this entity + relation_chunks_storage: Optional KV storage for tracking chunks that reference relations + + Returns: + Dictionary containing updated entity information and operation summary with the following structure: + { + "entity_name": str, # Name of the entity + "description": str, # Entity description + "entity_type": str, # Entity type + "source_id": str, # Source chunk IDs + ... # Other entity properties + "operation_summary": { + "merged": bool, # Whether entity was merged + "merge_status": str, # "success" | "failed" | "not_attempted" + "merge_error": str | None, # Error message if merge failed + "operation_status": str, # "success" | "partial_success" | "failure" + "target_entity": str | None, # Target entity name if renaming/merging + "final_entity": str, # Final entity name after operation + "renamed": bool # Whether entity was renamed + } + } + + operation_status values: + - "success": Operation completed successfully (update/rename/merge all succeeded) + - "partial_success": Non-name updates succeeded but merge failed + - "failure": Operation failed completely + + merge_status values: + - "success": Entity successfully merged into target + - "failed": Merge operation failed + - "not_attempted": No merge was attempted (normal update/rename) + """ + new_entity_name = updated_data.get("entity_name", entity_name) + is_renaming = new_entity_name != entity_name + + lock_keys = sorted({entity_name, new_entity_name}) if is_renaming else [entity_name] + + workspace = entities_vdb.global_config.get("workspace", "") + namespace = f"{workspace}:GraphDB" if workspace else "GraphDB" + + operation_summary: dict[str, Any] = { + "merged": False, + "merge_status": "not_attempted", + "merge_error": None, + "operation_status": "success", + "target_entity": None, + "final_entity": new_entity_name if is_renaming else entity_name, + "renamed": is_renaming, + } + async with get_storage_keyed_lock( + lock_keys, namespace=namespace, enable_logging=False + ): + try: + if is_renaming and not allow_rename: + raise ValueError( + "Entity renaming is not allowed. Set allow_rename=True to enable this feature" + ) + + if is_renaming: + target_exists = await chunk_entity_relation_graph.has_node( + new_entity_name + ) + if target_exists: + if not allow_merge: + raise ValueError( + f"Entity name '{new_entity_name}' already exists, cannot rename" + ) + + logger.info( + f"Entity Edit: `{entity_name}` will be merged into `{new_entity_name}`" + ) + + # Track whether non-name updates were applied + non_name_updates_applied = False + non_name_updates = { + key: value + for key, value in updated_data.items() + if key != "entity_name" + } + + # Apply non-name updates first + if non_name_updates: + try: + logger.info( + "Entity Edit: applying non-name updates before merge" + ) + await _edit_entity_impl( + chunk_entity_relation_graph, + entities_vdb, + relationships_vdb, + entity_name, + non_name_updates, + entity_chunks_storage=entity_chunks_storage, + relation_chunks_storage=relation_chunks_storage, + ) + non_name_updates_applied = True + except Exception as update_error: + # If update fails, re-raise immediately + logger.error( + f"Entity Edit: non-name updates failed: {update_error}" + ) + raise + + # Attempt to merge entities + try: + merge_result = await _merge_entities_impl( + chunk_entity_relation_graph, + entities_vdb, + relationships_vdb, + [entity_name], + new_entity_name, + merge_strategy=None, + target_entity_data=None, + entity_chunks_storage=entity_chunks_storage, + relation_chunks_storage=relation_chunks_storage, + ) + + # Merge succeeded + operation_summary.update( + { + "merged": True, + "merge_status": "success", + "merge_error": None, + "operation_status": "success", + "target_entity": new_entity_name, + "final_entity": new_entity_name, + } + ) + return {**merge_result, "operation_summary": operation_summary} + + except Exception as merge_error: + # Merge failed, but update may have succeeded + logger.error(f"Entity Edit: merge failed: {merge_error}") + + # Return partial success status (update succeeded but merge failed) + operation_summary.update( + { + "merged": False, + "merge_status": "failed", + "merge_error": str(merge_error), + "operation_status": "partial_success" + if non_name_updates_applied + else "failure", + "target_entity": new_entity_name, + "final_entity": entity_name, # Keep source entity name + } + ) + + # Get current entity info (with applied updates if any) + entity_info = await get_entity_info( + chunk_entity_relation_graph, + entities_vdb, + entity_name, + include_vector_data=True, + ) + return {**entity_info, "operation_summary": operation_summary} + + # Normal edit flow (no merge involved) + edit_result = await _edit_entity_impl( + chunk_entity_relation_graph, + entities_vdb, + relationships_vdb, + entity_name, + updated_data, + entity_chunks_storage=entity_chunks_storage, + relation_chunks_storage=relation_chunks_storage, + ) + operation_summary["operation_status"] = "success" + return {**edit_result, "operation_summary": operation_summary} + + except Exception as e: + logger.error(f"Error while editing entity '{entity_name}': {e}") + raise + + +async def aedit_relation( + chunk_entity_relation_graph, + entities_vdb, + relationships_vdb, + source_entity: str, + target_entity: str, + updated_data: dict[str, Any], + relation_chunks_storage=None, +) -> dict[str, Any]: + """Asynchronously edit relation information. + + Updates relation (edge) information in the knowledge graph and re-embeds the relation in the vector database. + Also synchronizes the relation_chunks_storage to track which chunks reference this relation. + + Args: + chunk_entity_relation_graph: Graph storage instance + entities_vdb: Vector database storage for entities + relationships_vdb: Vector database storage for relationships + source_entity: Name of the source entity + target_entity: Name of the target entity + updated_data: Dictionary containing updated attributes, e.g. {"description": "new description", "keywords": "new keywords"} + relation_chunks_storage: Optional KV storage for tracking chunks that reference this relation + + Returns: + Dictionary containing updated relation information + """ + # Normalize entity order for undirected graph (ensures consistent key generation) + if source_entity > target_entity: + source_entity, target_entity = target_entity, source_entity + + # Use keyed lock for relation to ensure atomic graph and vector db operations + workspace = relationships_vdb.global_config.get("workspace", "") + namespace = f"{workspace}:GraphDB" if workspace else "GraphDB" + sorted_edge_key = sorted([source_entity, target_entity]) + async with get_storage_keyed_lock( + sorted_edge_key, namespace=namespace, enable_logging=False + ): + try: + # 1. Get current relation information + edge_exists = await chunk_entity_relation_graph.has_edge( + source_entity, target_entity + ) + if not edge_exists: + raise ValueError( + f"Relation from '{source_entity}' to '{target_entity}' does not exist" + ) + edge_data = await chunk_entity_relation_graph.get_edge( + source_entity, target_entity + ) + # Important: First delete the old relation record from the vector database + # Delete both permutations to handle relationships created before normalization + rel_ids_to_delete = [ + compute_mdhash_id(source_entity + target_entity, prefix="rel-"), + compute_mdhash_id(target_entity + source_entity, prefix="rel-"), + ] + await relationships_vdb.delete(rel_ids_to_delete) + logger.debug( + f"Relation Delete: delete vdb for `{source_entity}`~`{target_entity}`" + ) + + # 2. Update relation information in the graph + new_edge_data = {**edge_data, **updated_data} + await chunk_entity_relation_graph.upsert_edge( + source_entity, target_entity, new_edge_data + ) + + # 3. Recalculate relation's vector representation and update vector database + description = new_edge_data.get("description", "") + keywords = new_edge_data.get("keywords", "") + source_id = new_edge_data.get("source_id", "") + weight = float(new_edge_data.get("weight", 1.0)) + + # Create content for embedding + content = f"{source_entity}\t{target_entity}\n{keywords}\n{description}" + + # Calculate relation ID + relation_id = compute_mdhash_id( + source_entity + target_entity, prefix="rel-" + ) + + # Prepare data for vector database update + relation_data = { + relation_id: { + "content": content, + "src_id": source_entity, + "tgt_id": target_entity, + "source_id": source_id, + "description": description, + "keywords": keywords, + "weight": weight, + } + } + + # Update vector database + await relationships_vdb.upsert(relation_data) + + # 4. Update relation_chunks_storage in two scenarios: + # - source_id has changed (edit scenario) + # - relation_chunks_storage has no existing data (migration/initialization scenario) + if relation_chunks_storage is not None: + from .utils import ( + make_relation_chunk_key, + compute_incremental_chunk_ids, + ) + + storage_key = make_relation_chunk_key(source_entity, target_entity) + + # Check if storage has existing data + stored_data = await relation_chunks_storage.get_by_id(storage_key) + has_stored_data = ( + stored_data + and isinstance(stored_data, dict) + and stored_data.get("chunk_ids") + ) + + # Get old and new source_id + old_source_id = edge_data.get("source_id", "") + old_chunk_ids = [ + cid for cid in old_source_id.split(GRAPH_FIELD_SEP) if cid + ] + + new_source_id = new_edge_data.get("source_id", "") + new_chunk_ids = [ + cid for cid in new_source_id.split(GRAPH_FIELD_SEP) if cid + ] + + source_id_changed = set(new_chunk_ids) != set(old_chunk_ids) + + # Update if: source_id changed OR storage has no data + if source_id_changed or not has_stored_data: + # Get existing full chunk_ids from storage + existing_full_chunk_ids = [] + if has_stored_data: + existing_full_chunk_ids = [ + cid for cid in stored_data.get("chunk_ids", []) if cid + ] + + # If no stored data exists, use old source_id as baseline + if not existing_full_chunk_ids: + existing_full_chunk_ids = old_chunk_ids.copy() + + # Use utility function to compute incremental updates + updated_chunk_ids = compute_incremental_chunk_ids( + existing_full_chunk_ids, old_chunk_ids, new_chunk_ids + ) + + # Update storage (Update even if updated_chunk_ids is empty) + await relation_chunks_storage.upsert( + { + storage_key: { + "chunk_ids": updated_chunk_ids, + "count": len(updated_chunk_ids), + } + } + ) + + logger.info( + f"Relation Delete: update chunk tracking for `{source_entity}`~`{target_entity}`" + ) + + # 5. Save changes + await _persist_graph_updates( + relationships_vdb=relationships_vdb, + chunk_entity_relation_graph=chunk_entity_relation_graph, + relation_chunks_storage=relation_chunks_storage, + ) + + logger.info( + f"Relation Delete: `{source_entity}`~`{target_entity}`' successfully updated" + ) + return await get_relation_info( + chunk_entity_relation_graph, + relationships_vdb, + source_entity, + target_entity, + include_vector_data=True, + ) + except Exception as e: + logger.error( + f"Error while editing relation from '{source_entity}' to '{target_entity}': {e}" + ) + raise + + +async def acreate_entity( + chunk_entity_relation_graph, + entities_vdb, + relationships_vdb, + entity_name: str, + entity_data: dict[str, Any], + entity_chunks_storage=None, + relation_chunks_storage=None, +) -> dict[str, Any]: + """Asynchronously create a new entity. + + Creates a new entity in the knowledge graph and adds it to the vector database. + Also synchronizes entity_chunks_storage to track chunk references. + + Args: + chunk_entity_relation_graph: Graph storage instance + entities_vdb: Vector database storage for entities + relationships_vdb: Vector database storage for relationships + entity_name: Name of the new entity + entity_data: Dictionary containing entity attributes, e.g. {"description": "description", "entity_type": "type"} + entity_chunks_storage: Optional KV storage for tracking chunks that reference this entity + relation_chunks_storage: Optional KV storage for tracking chunks that reference relations + + Returns: + Dictionary containing created entity information + """ + # Use keyed lock for entity to ensure atomic graph and vector db operations + workspace = entities_vdb.global_config.get("workspace", "") + namespace = f"{workspace}:GraphDB" if workspace else "GraphDB" + async with get_storage_keyed_lock( + [entity_name], namespace=namespace, enable_logging=False + ): + try: + # Check if entity already exists + existing_node = await chunk_entity_relation_graph.has_node(entity_name) + if existing_node: + raise ValueError(f"Entity '{entity_name}' already exists") + + # Prepare node data with defaults if missing + node_data = { + "entity_id": entity_name, + "entity_type": entity_data.get("entity_type", "UNKNOWN"), + "description": entity_data.get("description", ""), + "source_id": entity_data.get("source_id", "manual_creation"), + "file_path": entity_data.get("file_path", "manual_creation"), + "created_at": int(time.time()), + } + + # Add entity to knowledge graph + await chunk_entity_relation_graph.upsert_node(entity_name, node_data) + + # Prepare content for entity + description = node_data.get("description", "") + source_id = node_data.get("source_id", "") + entity_type = node_data.get("entity_type", "") + content = entity_name + "\n" + description + + # Calculate entity ID + entity_id = compute_mdhash_id(entity_name, prefix="ent-") + + # Prepare data for vector database update + entity_data_for_vdb = { + entity_id: { + "content": content, + "entity_name": entity_name, + "source_id": source_id, + "description": description, + "entity_type": entity_type, + "file_path": entity_data.get("file_path", "manual_creation"), + } + } + + # Update vector database + await entities_vdb.upsert(entity_data_for_vdb) + + # Update entity_chunks_storage to track chunk references + if entity_chunks_storage is not None: + source_id = node_data.get("source_id", "") + chunk_ids = [cid for cid in source_id.split(GRAPH_FIELD_SEP) if cid] + + if chunk_ids: + await entity_chunks_storage.upsert( + { + entity_name: { + "chunk_ids": chunk_ids, + "count": len(chunk_ids), + } + } + ) + logger.info( + f"Entity Create: tracked {len(chunk_ids)} chunks for `{entity_name}`" + ) + + # Save changes + await _persist_graph_updates( + entities_vdb=entities_vdb, + relationships_vdb=relationships_vdb, + chunk_entity_relation_graph=chunk_entity_relation_graph, + entity_chunks_storage=entity_chunks_storage, + relation_chunks_storage=relation_chunks_storage, + ) + + logger.info(f"Entity Create: '{entity_name}' successfully created") + return await get_entity_info( + chunk_entity_relation_graph, + entities_vdb, + entity_name, + include_vector_data=True, + ) + except Exception as e: + logger.error(f"Error while creating entity '{entity_name}': {e}") + raise + + +async def acreate_relation( + chunk_entity_relation_graph, + entities_vdb, + relationships_vdb, + source_entity: str, + target_entity: str, + relation_data: dict[str, Any], + relation_chunks_storage=None, +) -> dict[str, Any]: + """Asynchronously create a new relation between entities. + + Creates a new relation (edge) in the knowledge graph and adds it to the vector database. + Also synchronizes relation_chunks_storage to track chunk references. + + Args: + chunk_entity_relation_graph: Graph storage instance + entities_vdb: Vector database storage for entities + relationships_vdb: Vector database storage for relationships + source_entity: Name of the source entity + target_entity: Name of the target entity + relation_data: Dictionary containing relation attributes, e.g. {"description": "description", "keywords": "keywords"} + relation_chunks_storage: Optional KV storage for tracking chunks that reference this relation + + Returns: + Dictionary containing created relation information + """ + # Use keyed lock for relation to ensure atomic graph and vector db operations + workspace = relationships_vdb.global_config.get("workspace", "") + namespace = f"{workspace}:GraphDB" if workspace else "GraphDB" + sorted_edge_key = sorted([source_entity, target_entity]) + async with get_storage_keyed_lock( + sorted_edge_key, namespace=namespace, enable_logging=False + ): + try: + # Check if both entities exist + source_exists = await chunk_entity_relation_graph.has_node(source_entity) + target_exists = await chunk_entity_relation_graph.has_node(target_entity) + + if not source_exists: + raise ValueError(f"Source entity '{source_entity}' does not exist") + if not target_exists: + raise ValueError(f"Target entity '{target_entity}' does not exist") + + # Check if relation already exists + existing_edge = await chunk_entity_relation_graph.has_edge( + source_entity, target_entity + ) + if existing_edge: + raise ValueError( + f"Relation from '{source_entity}' to '{target_entity}' already exists" + ) + + # Prepare edge data with defaults if missing + edge_data = { + "description": relation_data.get("description", ""), + "keywords": relation_data.get("keywords", ""), + "source_id": relation_data.get("source_id", "manual_creation"), + "weight": float(relation_data.get("weight", 1.0)), + "file_path": relation_data.get("file_path", "manual_creation"), + "created_at": int(time.time()), + } + + # Add relation to knowledge graph + await chunk_entity_relation_graph.upsert_edge( + source_entity, target_entity, edge_data + ) + + # Normalize entity order for undirected relation vector (ensures consistent key generation) + if source_entity > target_entity: + source_entity, target_entity = target_entity, source_entity + + # Prepare content for embedding + description = edge_data.get("description", "") + keywords = edge_data.get("keywords", "") + source_id = edge_data.get("source_id", "") + weight = edge_data.get("weight", 1.0) + + # Create content for embedding + content = f"{keywords}\t{source_entity}\n{target_entity}\n{description}" + + # Calculate relation ID + relation_id = compute_mdhash_id( + source_entity + target_entity, prefix="rel-" + ) + + # Prepare data for vector database update + relation_data_for_vdb = { + relation_id: { + "content": content, + "src_id": source_entity, + "tgt_id": target_entity, + "source_id": source_id, + "description": description, + "keywords": keywords, + "weight": weight, + "file_path": relation_data.get("file_path", "manual_creation"), + } + } + + # Update vector database + await relationships_vdb.upsert(relation_data_for_vdb) + + # Update relation_chunks_storage to track chunk references + if relation_chunks_storage is not None: + from .utils import make_relation_chunk_key + + # Normalize entity order for consistent key generation + normalized_src, normalized_tgt = sorted([source_entity, target_entity]) + storage_key = make_relation_chunk_key(normalized_src, normalized_tgt) + + source_id = edge_data.get("source_id", "") + chunk_ids = [cid for cid in source_id.split(GRAPH_FIELD_SEP) if cid] + + if chunk_ids: + await relation_chunks_storage.upsert( + { + storage_key: { + "chunk_ids": chunk_ids, + "count": len(chunk_ids), + } + } + ) + logger.info( + f"Relation Create: tracked {len(chunk_ids)} chunks for `{source_entity}`~`{target_entity}`" + ) + + # Save changes + await _persist_graph_updates( + relationships_vdb=relationships_vdb, + chunk_entity_relation_graph=chunk_entity_relation_graph, + relation_chunks_storage=relation_chunks_storage, + ) + + logger.info( + f"Relation Create: `{source_entity}`~`{target_entity}` successfully created" + ) + return await get_relation_info( + chunk_entity_relation_graph, + relationships_vdb, + source_entity, + target_entity, + include_vector_data=True, + ) + except Exception as e: + logger.error( + f"Error while creating relation from '{source_entity}' to '{target_entity}': {e}" + ) + raise + + +async def _merge_entities_impl( + chunk_entity_relation_graph, + entities_vdb, + relationships_vdb, + source_entities: list[str], + target_entity: str, + *, + merge_strategy: dict[str, str] = None, + target_entity_data: dict[str, Any] = None, + entity_chunks_storage=None, + relation_chunks_storage=None, +) -> dict[str, Any]: + """Internal helper that merges entities without acquiring storage locks. + + This function performs the actual entity merge operations without lock management. + It should only be called by public APIs that have already acquired necessary locks. + + Args: + chunk_entity_relation_graph: Graph storage instance + entities_vdb: Vector database storage for entities + relationships_vdb: Vector database storage for relationships + source_entities: List of source entity names to merge + target_entity: Name of the target entity after merging + merge_strategy: Deprecated. Merge strategy for each field (optional) + target_entity_data: Dictionary of specific values to set for target entity (optional) + entity_chunks_storage: Optional KV storage for tracking chunks + relation_chunks_storage: Optional KV storage for tracking relation chunks + + Returns: + Dictionary containing the merged entity information + + Note: + Caller must acquire appropriate locks before calling this function. + All source entities and the target entity should be locked together. + """ + # Default merge strategy for entities + default_entity_merge_strategy = { + "description": "concatenate", + "entity_type": "keep_first", + "source_id": "join_unique", + "file_path": "join_unique", + } + effective_entity_merge_strategy = default_entity_merge_strategy + if merge_strategy: + logger.warning( + "Entity Merge: merge_strategy parameter is deprecated and will be ignored in a future release." + ) + effective_entity_merge_strategy = { + **default_entity_merge_strategy, + **merge_strategy, + } + target_entity_data = {} if target_entity_data is None else target_entity_data + + # 1. Check if all source entities exist + source_entities_data = {} + for entity_name in source_entities: + node_exists = await chunk_entity_relation_graph.has_node(entity_name) + if not node_exists: + raise ValueError(f"Source entity '{entity_name}' does not exist") + node_data = await chunk_entity_relation_graph.get_node(entity_name) + source_entities_data[entity_name] = node_data + + # 2. Check if target entity exists and get its data if it does + target_exists = await chunk_entity_relation_graph.has_node(target_entity) + existing_target_entity_data = {} + if target_exists: + existing_target_entity_data = await chunk_entity_relation_graph.get_node( + target_entity + ) + + # 3. Merge entity data + merged_entity_data = _merge_attributes( + list(source_entities_data.values()) + + ([existing_target_entity_data] if target_exists else []), + effective_entity_merge_strategy, + filter_none_only=False, # Use entity behavior: filter falsy values + ) + + # Apply any explicitly provided target entity data (overrides merged data) + for key, value in target_entity_data.items(): + merged_entity_data[key] = value + + # 4. Get all relationships of the source entities and target entity (if exists) + all_relations = [] + entities_to_collect = source_entities.copy() + + # If target entity exists and not already in source_entities, add it + if target_exists and target_entity not in source_entities: + entities_to_collect.append(target_entity) + + for entity_name in entities_to_collect: + # Get all relationships of the entities + edges = await chunk_entity_relation_graph.get_node_edges(entity_name) + if edges: + for src, tgt in edges: + # Ensure src is the current entity + if src == entity_name: + edge_data = await chunk_entity_relation_graph.get_edge(src, tgt) + all_relations.append((src, tgt, edge_data)) + + # 5. Create or update the target entity + merged_entity_data["entity_id"] = target_entity + if not target_exists: + await chunk_entity_relation_graph.upsert_node(target_entity, merged_entity_data) + logger.info(f"Entity Merge: created target '{target_entity}'") + else: + await chunk_entity_relation_graph.upsert_node(target_entity, merged_entity_data) + logger.info(f"Entity Merge: Updated target '{target_entity}'") + + # 6. Recreate all relations pointing to the target entity in KG + # Also collect chunk tracking information in the same loop + relation_updates = {} # Track relationships that need to be merged + relations_to_delete = [] + + # Initialize chunk tracking variables + relation_chunk_tracking = {} # key: storage_key, value: list of chunk_ids + old_relation_keys_to_delete = [] + + for src, tgt, edge_data in all_relations: + relations_to_delete.append(compute_mdhash_id(src + tgt, prefix="rel-")) + relations_to_delete.append(compute_mdhash_id(tgt + src, prefix="rel-")) + + # Collect old chunk tracking key for deletion + if relation_chunks_storage is not None: + from .utils import make_relation_chunk_key + + old_storage_key = make_relation_chunk_key(src, tgt) + old_relation_keys_to_delete.append(old_storage_key) + + new_src = target_entity if src in source_entities else src + new_tgt = target_entity if tgt in source_entities else tgt + + # Skip relationships between source entities to avoid self-loops + if new_src == new_tgt: + logger.info(f"Entity Merge: skipping `{src}`~`{tgt}` to avoid self-loop") + continue + + # Normalize entity order for consistent duplicate detection (undirected relationships) + normalized_src, normalized_tgt = sorted([new_src, new_tgt]) + relation_key = f"{normalized_src}|{normalized_tgt}" + + # Process chunk tracking for this relation + if relation_chunks_storage is not None: + storage_key = make_relation_chunk_key(normalized_src, normalized_tgt) + + # Get chunk_ids from storage for this original relation + stored = await relation_chunks_storage.get_by_id(old_storage_key) + + if stored is not None and isinstance(stored, dict): + chunk_ids = [cid for cid in stored.get("chunk_ids", []) if cid] + else: + # Fallback to source_id from graph + source_id = edge_data.get("source_id", "") + chunk_ids = [cid for cid in source_id.split(GRAPH_FIELD_SEP) if cid] + + # Accumulate chunk_ids with ordered deduplication + if storage_key not in relation_chunk_tracking: + relation_chunk_tracking[storage_key] = [] + + existing_chunks = set(relation_chunk_tracking[storage_key]) + for chunk_id in chunk_ids: + if chunk_id not in existing_chunks: + existing_chunks.add(chunk_id) + relation_chunk_tracking[storage_key].append(chunk_id) + + if relation_key in relation_updates: + # Merge relationship data + existing_data = relation_updates[relation_key]["data"] + merged_relation = _merge_attributes( + [existing_data, edge_data], + { + "description": "concatenate", + "keywords": "join_unique_comma", + "source_id": "join_unique", + "file_path": "join_unique", + "weight": "max", + }, + filter_none_only=True, # Use relation behavior: only filter None + ) + relation_updates[relation_key]["data"] = merged_relation + logger.debug( + f"Entity Merge: deduplicating relation `{normalized_src}`~`{normalized_tgt}`" + ) + else: + relation_updates[relation_key] = { + "graph_src": new_src, + "graph_tgt": new_tgt, + "norm_src": normalized_src, + "norm_tgt": normalized_tgt, + "data": edge_data.copy(), + } + + # Apply relationship updates + logger.info(f"Entity Merge: updatign {len(relation_updates)} relations") + for rel_data in relation_updates.values(): + await chunk_entity_relation_graph.upsert_edge( + rel_data["graph_src"], rel_data["graph_tgt"], rel_data["data"] + ) + logger.info( + f"Entity Merge: updating relation `{rel_data['graph_src']}`~`{rel_data['graph_tgt']}`" + ) + + # Update relation chunk tracking storage + if relation_chunks_storage is not None and all_relations: + if old_relation_keys_to_delete: + await relation_chunks_storage.delete(old_relation_keys_to_delete) + + if relation_chunk_tracking: + updates = {} + for storage_key, chunk_ids in relation_chunk_tracking.items(): + updates[storage_key] = { + "chunk_ids": chunk_ids, + "count": len(chunk_ids), + } + + await relation_chunks_storage.upsert(updates) + logger.info( + f"Entity Merge: {len(updates)} relation chunk tracking records updated" + ) + + # 7. Update relationship vector representations + logger.debug( + f"Entity Merge: deleting {len(relations_to_delete)} relations from vdb" + ) + await relationships_vdb.delete(relations_to_delete) + + for rel_data in relation_updates.values(): + edge_data = rel_data["data"] + normalized_src = rel_data["norm_src"] + normalized_tgt = rel_data["norm_tgt"] + + description = edge_data.get("description", "") + keywords = edge_data.get("keywords", "") + source_id = edge_data.get("source_id", "") + weight = float(edge_data.get("weight", 1.0)) + + # Use normalized order for content and relation ID + content = f"{keywords}\t{normalized_src}\n{normalized_tgt}\n{description}" + relation_id = compute_mdhash_id(normalized_src + normalized_tgt, prefix="rel-") + + relation_data_for_vdb = { + relation_id: { + "content": content, + "src_id": normalized_src, + "tgt_id": normalized_tgt, + "source_id": source_id, + "description": description, + "keywords": keywords, + "weight": weight, + } + } + await relationships_vdb.upsert(relation_data_for_vdb) + logger.debug( + f"Entity Merge: updating vdb `{normalized_src}`~`{normalized_tgt}`" + ) + + logger.info(f"Entity Merge: {len(relation_updates)} relations in vdb updated") + + # 8. Update entity vector representation + description = merged_entity_data.get("description", "") + source_id = merged_entity_data.get("source_id", "") + entity_type = merged_entity_data.get("entity_type", "") + content = target_entity + "\n" + description + + entity_id = compute_mdhash_id(target_entity, prefix="ent-") + entity_data_for_vdb = { + entity_id: { + "content": content, + "entity_name": target_entity, + "source_id": source_id, + "description": description, + "entity_type": entity_type, + } + } + await entities_vdb.upsert(entity_data_for_vdb) + logger.info(f"Entity Merge: updating vdb `{target_entity}`") + + # 9. Merge entity chunk tracking (source entities first, then target entity) + if entity_chunks_storage is not None: + all_chunk_id_lists = [] + + # Build list of entities to process (source entities first, then target entity) + entities_to_process = [] + + # Add source entities first (excluding target if it's already in source list) + for entity_name in source_entities: + if entity_name != target_entity: + entities_to_process.append(entity_name) + + # Add target entity last (if it exists) + if target_exists: + entities_to_process.append(target_entity) + + # Process all entities in order with unified logic + for entity_name in entities_to_process: + stored = await entity_chunks_storage.get_by_id(entity_name) + if stored and isinstance(stored, dict): + chunk_ids = [cid for cid in stored.get("chunk_ids", []) if cid] + if chunk_ids: + all_chunk_id_lists.append(chunk_ids) + + # Merge chunk_ids with ordered deduplication (preserves order, source entities first) + merged_chunk_ids = [] + seen = set() + for chunk_id_list in all_chunk_id_lists: + for chunk_id in chunk_id_list: + if chunk_id not in seen: + seen.add(chunk_id) + merged_chunk_ids.append(chunk_id) + + # Delete source entities' chunk tracking records + entity_keys_to_delete = [e for e in source_entities if e != target_entity] + if entity_keys_to_delete: + await entity_chunks_storage.delete(entity_keys_to_delete) + + # Update target entity's chunk tracking + if merged_chunk_ids: + await entity_chunks_storage.upsert( + { + target_entity: { + "chunk_ids": merged_chunk_ids, + "count": len(merged_chunk_ids), + } + } + ) + logger.info( + f"Entity Merge: find {len(merged_chunk_ids)} chunks related to '{target_entity}'" + ) + + # 10. Delete source entities + for entity_name in source_entities: + if entity_name == target_entity: + logger.warning( + f"Entity Merge: source entity'{entity_name}' is same as target entity" + ) + continue + + logger.info(f"Entity Merge: deleting '{entity_name}' from KG and vdb") + + # Delete entity node and related edges from knowledge graph + await chunk_entity_relation_graph.delete_node(entity_name) + + # Delete entity record from vector database + entity_id = compute_mdhash_id(entity_name, prefix="ent-") + await entities_vdb.delete([entity_id]) + + # 11. Save changes + await _persist_graph_updates( + entities_vdb=entities_vdb, + relationships_vdb=relationships_vdb, + chunk_entity_relation_graph=chunk_entity_relation_graph, + entity_chunks_storage=entity_chunks_storage, + relation_chunks_storage=relation_chunks_storage, + ) + + logger.info( + f"Entity Merge: successfully merged {len(source_entities)} entities into '{target_entity}'" + ) + return await get_entity_info( + chunk_entity_relation_graph, + entities_vdb, + target_entity, + include_vector_data=True, + ) + + +async def amerge_entities( + chunk_entity_relation_graph, + entities_vdb, + relationships_vdb, + source_entities: list[str], + target_entity: str, + merge_strategy: dict[str, str] = None, + target_entity_data: dict[str, Any] = None, + entity_chunks_storage=None, + relation_chunks_storage=None, +) -> dict[str, Any]: + """Asynchronously merge multiple entities into one entity. + + Merges multiple source entities into a target entity, handling all relationships, + and updating both the knowledge graph and vector database. + Also merges chunk tracking information from entity_chunks_storage and relation_chunks_storage. + + Args: + chunk_entity_relation_graph: Graph storage instance + entities_vdb: Vector database storage for entities + relationships_vdb: Vector database storage for relationships + source_entities: List of source entity names to merge + target_entity: Name of the target entity after merging + merge_strategy: Deprecated (Each field uses its own default strategy). If provided, + customizations are applied but a warning is logged. + target_entity_data: Dictionary of specific values to set for the target entity, + overriding any merged values, e.g. {"description": "custom description", "entity_type": "PERSON"} + entity_chunks_storage: Optional KV storage for tracking chunks that reference entities + relation_chunks_storage: Optional KV storage for tracking chunks that reference relations + + Returns: + Dictionary containing the merged entity information + """ + # Collect all entities involved (source + target) and lock them all in sorted order + all_entities = set(source_entities) + all_entities.add(target_entity) + lock_keys = sorted(all_entities) + + workspace = entities_vdb.global_config.get("workspace", "") + namespace = f"{workspace}:GraphDB" if workspace else "GraphDB" + async with get_storage_keyed_lock( + lock_keys, namespace=namespace, enable_logging=False + ): + try: + return await _merge_entities_impl( + chunk_entity_relation_graph, + entities_vdb, + relationships_vdb, + source_entities, + target_entity, + merge_strategy=merge_strategy, + target_entity_data=target_entity_data, + entity_chunks_storage=entity_chunks_storage, + relation_chunks_storage=relation_chunks_storage, + ) + except Exception as e: + logger.error(f"Error merging entities: {e}") + raise + + +def _merge_attributes( + data_list: list[dict[str, Any]], + merge_strategy: dict[str, str], + filter_none_only: bool = False, +) -> dict[str, Any]: + """Merge attributes from multiple entities or relationships. + + This unified function handles merging of both entity and relationship attributes, + applying different merge strategies per field. + + Args: + data_list: List of dictionaries containing entity or relationship data + merge_strategy: Merge strategy for each field. Supported strategies: + - "concatenate": Join all values with GRAPH_FIELD_SEP + - "keep_first": Keep the first non-empty value + - "keep_last": Keep the last non-empty value + - "join_unique": Join unique items separated by GRAPH_FIELD_SEP + - "join_unique_comma": Join unique items separated by comma and space + - "max": Keep the maximum numeric value (for numeric fields) + filter_none_only: If True, only filter None values (keep empty strings, 0, etc.). + If False, filter all falsy values. Default is False for backward compatibility. + + Returns: + Dictionary containing merged data + """ + merged_data = {} + + # Collect all possible keys + all_keys = set() + for data in data_list: + all_keys.update(data.keys()) + + # Merge values for each key + for key in all_keys: + # Get all values for this key based on filtering mode + if filter_none_only: + values = [data.get(key) for data in data_list if data.get(key) is not None] + else: + values = [data.get(key) for data in data_list if data.get(key)] + + if not values: + continue + + # Merge values according to strategy + strategy = merge_strategy.get(key, "keep_first") + + if strategy == "concatenate": + # Convert all values to strings and join with GRAPH_FIELD_SEP + merged_data[key] = GRAPH_FIELD_SEP.join(str(v) for v in values) + elif strategy == "keep_first": + merged_data[key] = values[0] + elif strategy == "keep_last": + merged_data[key] = values[-1] + elif strategy == "join_unique": + # Handle fields separated by GRAPH_FIELD_SEP + unique_items = set() + for value in values: + items = str(value).split(GRAPH_FIELD_SEP) + unique_items.update(items) + merged_data[key] = GRAPH_FIELD_SEP.join(unique_items) + elif strategy == "join_unique_comma": + # Handle fields separated by comma, join unique items with comma + unique_items = set() + for value in values: + items = str(value).split(",") + unique_items.update(item.strip() for item in items if item.strip()) + merged_data[key] = ",".join(sorted(unique_items)) + elif strategy == "max": + # For numeric fields like weight + try: + merged_data[key] = max(float(v) for v in values) + except (ValueError, TypeError): + # Fallback to first value if conversion fails + merged_data[key] = values[0] + else: + # Default strategy: keep first value + merged_data[key] = values[0] + + return merged_data + + +async def get_entity_info( + chunk_entity_relation_graph, + entities_vdb, + entity_name: str, + include_vector_data: bool = False, +) -> dict[str, str | None | dict[str, str]]: + """Get detailed information of an entity""" + + # Get information from the graph + node_data = await chunk_entity_relation_graph.get_node(entity_name) + source_id = node_data.get("source_id") if node_data else None + + result: dict[str, str | None | dict[str, str]] = { + "entity_name": entity_name, + "source_id": source_id, + "graph_data": node_data, + } + + # Optional: Get vector database information + if include_vector_data: + entity_id = compute_mdhash_id(entity_name, prefix="ent-") + vector_data = await entities_vdb.get_by_id(entity_id) + result["vector_data"] = vector_data + + return result + + +async def get_relation_info( + chunk_entity_relation_graph, + relationships_vdb, + src_entity: str, + tgt_entity: str, + include_vector_data: bool = False, +) -> dict[str, str | None | dict[str, str]]: + """ + Get detailed information of a relationship between two entities. + Relationship is unidirectional, swap src_entity and tgt_entity does not change the relationship. + + Args: + src_entity: Source entity name + tgt_entity: Target entity name + include_vector_data: Whether to include vector database information + + Returns: + Dictionary containing relationship information + """ + + # Get information from the graph + edge_data = await chunk_entity_relation_graph.get_edge(src_entity, tgt_entity) + source_id = edge_data.get("source_id") if edge_data else None + + result: dict[str, str | None | dict[str, str]] = { + "src_entity": src_entity, + "tgt_entity": tgt_entity, + "source_id": source_id, + "graph_data": edge_data, + } + + # Optional: Get vector database information + if include_vector_data: + rel_id = compute_mdhash_id(src_entity + tgt_entity, prefix="rel-") + vector_data = await relationships_vdb.get_by_id(rel_id) + result["vector_data"] = vector_data + + return result diff --git a/LightRAG/lightrag_webui/.gitignore b/LightRAG/lightrag_webui/.gitignore new file mode 100644 index 00000000..a547bf36 --- /dev/null +++ b/LightRAG/lightrag_webui/.gitignore @@ -0,0 +1,24 @@ +# Logs +logs +*.log +npm-debug.log* +yarn-debug.log* +yarn-error.log* +pnpm-debug.log* +lerna-debug.log* + +node_modules +dist +dist-ssr +*.local + +# Editor directories and files +.vscode/* +!.vscode/extensions.json +.idea +.DS_Store +*.suo +*.ntvs* +*.njsproj +*.sln +*.sw? diff --git a/LightRAG/lightrag_webui/.prettierrc.json b/LightRAG/lightrag_webui/.prettierrc.json new file mode 100644 index 00000000..635143ee --- /dev/null +++ b/LightRAG/lightrag_webui/.prettierrc.json @@ -0,0 +1,10 @@ +{ + "$schema": "https://json.schemastore.org/prettierrc", + "semi": false, + "tabWidth": 2, + "singleQuote": true, + "printWidth": 100, + "trailingComma": "none", + "endOfLine": "crlf", + "plugins": ["prettier-plugin-tailwindcss"] +} diff --git a/LightRAG/lightrag_webui/README.md b/LightRAG/lightrag_webui/README.md new file mode 100644 index 00000000..00e537ba --- /dev/null +++ b/LightRAG/lightrag_webui/README.md @@ -0,0 +1,46 @@ +# LightRAG WebUI + +LightRAG WebUI is a React-based web interface for interacting with the LightRAG system. It provides a user-friendly interface for querying, managing, and exploring LightRAG's functionalities. + +## Installation + +1. **Install Bun:** + + If you haven't already installed Bun, follow the official documentation: [https://bun.sh/docs/installation](https://bun.sh/docs/installation) + +2. **Install Dependencies:** + + In the `lightrag_webui` directory, run the following command to install project dependencies: + + ```bash + bun install --frozen-lockfile + ``` + +3. **Build the Project:** + + Run the following command to build the project: + + ```bash + bun run build + ``` + + This command will bundle the project and output the built files to the `lightrag/api/webui` directory. + +## Development + +- **Start the Development Server:** + + If you want to run the WebUI in development mode, use the following command: + + ```bash + bun run dev + ``` + +## Script Commands + +The following are some commonly used script commands defined in `package.json`: + +- `bun install`: Installs project dependencies. +- `bun run dev`: Starts the development server. +- `bun run build`: Builds the project. +- `bun run lint`: Runs the linter. diff --git a/LightRAG/lightrag_webui/bun.lock b/LightRAG/lightrag_webui/bun.lock new file mode 100644 index 00000000..661837c4 --- /dev/null +++ b/LightRAG/lightrag_webui/bun.lock @@ -0,0 +1,1837 @@ +{ + "lockfileVersion": 1, + "workspaces": { + "": { + "name": "lightrag-webui", + "dependencies": { + "@faker-js/faker": "^9.9.0", + "@radix-ui/react-alert-dialog": "^1.1.15", + "@radix-ui/react-checkbox": "^1.3.3", + "@radix-ui/react-dialog": "^1.1.15", + "@radix-ui/react-popover": "^1.1.15", + "@radix-ui/react-progress": "^1.1.7", + "@radix-ui/react-scroll-area": "^1.2.10", + "@radix-ui/react-select": "^2.2.6", + "@radix-ui/react-separator": "^1.1.7", + "@radix-ui/react-slot": "^1.2.3", + "@radix-ui/react-tabs": "^1.1.13", + "@radix-ui/react-tooltip": "^1.2.8", + "@radix-ui/react-use-controllable-state": "^1.2.2", + "@react-sigma/core": 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"iconLibrary": "lucide" +} diff --git a/LightRAG/lightrag_webui/env.development.smaple b/LightRAG/lightrag_webui/env.development.smaple new file mode 100644 index 00000000..501be53c --- /dev/null +++ b/LightRAG/lightrag_webui/env.development.smaple @@ -0,0 +1,4 @@ +# Development environment configuration +VITE_BACKEND_URL=http://localhost:9621 +VITE_API_PROXY=true +VITE_API_ENDPOINTS=/api,/documents,/graphs,/graph,/health,/query,/docs,/redoc,/openapi.json,/login,/auth-status,/static diff --git a/LightRAG/lightrag_webui/env.local.sample b/LightRAG/lightrag_webui/env.local.sample new file mode 100644 index 00000000..d8920437 --- /dev/null +++ b/LightRAG/lightrag_webui/env.local.sample @@ -0,0 +1,3 @@ +VITE_BACKEND_URL=http://localhost:9621 +VITE_API_PROXY=true +VITE_API_ENDPOINTS=/api,/documents,/graphs,/graph,/health,/query,/docs,/redoc,/openapi.json,/login,/auth-status diff --git a/LightRAG/lightrag_webui/eslint.config.js b/LightRAG/lightrag_webui/eslint.config.js new file mode 100644 index 00000000..7f42e8b1 --- /dev/null +++ b/LightRAG/lightrag_webui/eslint.config.js @@ -0,0 +1,36 @@ +import js from '@eslint/js' +import globals from 'globals' +import reactHooks from 'eslint-plugin-react-hooks' +import reactRefresh from 'eslint-plugin-react-refresh' +import stylisticJs from '@stylistic/eslint-plugin-js' +import tseslint from 'typescript-eslint' +import prettier from 'eslint-config-prettier' +import react from 'eslint-plugin-react' + +export default tseslint.config( + { ignores: ['dist'] }, + { + extends: [js.configs.recommended, ...tseslint.configs.recommended, prettier], + files: ['**/*.{ts,tsx,js,jsx}'], + languageOptions: { + ecmaVersion: 2020, + globals: globals.browser + }, + settings: { react: { version: '19.0' } }, + plugins: { + 'react-hooks': reactHooks, + 'react-refresh': reactRefresh, + '@stylistic/js': stylisticJs, + react + }, + rules: { + ...reactHooks.configs.recommended.rules, + 'react-refresh/only-export-components': ['warn', { allowConstantExport: true }], + ...react.configs.recommended.rules, + ...react.configs['jsx-runtime'].rules, + '@stylistic/js/indent': ['error', 2], + '@stylistic/js/quotes': ['error', 'single'], + '@typescript-eslint/no-explicit-any': ['off'] + } + } +) diff --git a/LightRAG/lightrag_webui/index.html b/LightRAG/lightrag_webui/index.html new file mode 100644 index 00000000..ebbcce35 --- /dev/null +++ b/LightRAG/lightrag_webui/index.html @@ -0,0 +1,16 @@ + + + + + + + + + + Lightrag + + +

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z()TS%>^icr*rMLI2FK=3{LZ?>Yq>Aw4hy1~7{*8q#~@?J0j?u0B|OuHIhV9yX8c(yZ$qn#=Jf?CCwCmsvTw5#rD$&!1cas%Jqq*a8?;P$(!DoPJpO^&Nhhq=?N i?*|G`8~_{i4pA$0J%Q4Q84uis21$s>2v-XJ^8J5n_GKdg literal 0 HcmV?d00001 diff --git a/LightRAG/lightrag_webui/public/logo.svg b/LightRAG/lightrag_webui/public/logo.svg new file mode 100644 index 00000000..fd32836b --- /dev/null +++ b/LightRAG/lightrag_webui/public/logo.svg @@ -0,0 +1 @@ + diff --git a/LightRAG/lightrag_webui/src/App.tsx b/LightRAG/lightrag_webui/src/App.tsx new file mode 100644 index 00000000..b8ae023d --- /dev/null +++ b/LightRAG/lightrag_webui/src/App.tsx @@ -0,0 +1,230 @@ +import { useState, useCallback, useEffect, useRef } from 'react' +import ThemeProvider from '@/components/ThemeProvider' +import TabVisibilityProvider from '@/contexts/TabVisibilityProvider' +import ApiKeyAlert from '@/components/ApiKeyAlert' +import StatusIndicator from '@/components/status/StatusIndicator' +import { SiteInfo, webuiPrefix } from '@/lib/constants' +import { useBackendState, useAuthStore } from '@/stores/state' +import { useSettingsStore } from '@/stores/settings' +import { getAuthStatus } from '@/api/lightrag' +import SiteHeader from '@/features/SiteHeader' +import { InvalidApiKeyError, RequireApiKeError } from '@/api/lightrag' +import { ZapIcon } from 'lucide-react' + +import GraphViewer from '@/features/GraphViewer' +import DocumentManager from '@/features/DocumentManager' +import RetrievalTesting from '@/features/RetrievalTesting' +import ApiSite from '@/features/ApiSite' + +import { Tabs, TabsContent } from '@/components/ui/Tabs' + +function App() { + const message = useBackendState.use.message() + const enableHealthCheck = useSettingsStore.use.enableHealthCheck() + const currentTab = useSettingsStore.use.currentTab() + const [apiKeyAlertOpen, setApiKeyAlertOpen] = useState(false) + const [initializing, setInitializing] = useState(true) // Add initializing state + const versionCheckRef = useRef(false); // Prevent duplicate calls in Vite dev mode + const healthCheckInitializedRef = useRef(false); // Prevent duplicate health checks in Vite dev mode + + const handleApiKeyAlertOpenChange = useCallback((open: boolean) => { + setApiKeyAlertOpen(open) + if (!open) { + useBackendState.getState().clear() + } + }, []) + + // Track component mount status with useRef + const isMountedRef = useRef(true); + + // Set up mount/unmount status tracking + useEffect(() => { + isMountedRef.current = true; + + // Handle page reload/unload + const handleBeforeUnload = () => { + isMountedRef.current = false; + }; + + window.addEventListener('beforeunload', handleBeforeUnload); + + return () => { + isMountedRef.current = false; + window.removeEventListener('beforeunload', handleBeforeUnload); + }; + }, []); + + // Health check - can be disabled + useEffect(() => { + // Health check function + const performHealthCheck = async () => { + try { + // Only perform health check if component is still mounted + if (isMountedRef.current) { + await useBackendState.getState().check(); + } + } catch (error) { + console.error('Health check error:', error); + } + }; + + // Set health check function in the store + useBackendState.getState().setHealthCheckFunction(performHealthCheck); + + if (!enableHealthCheck || apiKeyAlertOpen) { + useBackendState.getState().clearHealthCheckTimer(); + return; + } + + // On first mount or when enableHealthCheck becomes true and apiKeyAlertOpen is false, + // perform an immediate health check and start the timer + if (!healthCheckInitializedRef.current) { + healthCheckInitializedRef.current = true; + } + + // Start/reset the health check timer using the store + useBackendState.getState().resetHealthCheckTimer(); + + // Component unmount cleanup + return () => { + useBackendState.getState().clearHealthCheckTimer(); + }; + }, [enableHealthCheck, apiKeyAlertOpen]); + + // Version check - independent and executed only once + useEffect(() => { + const checkVersion = async () => { + // Prevent duplicate calls in Vite dev mode + if (versionCheckRef.current) return; + versionCheckRef.current = true; + + // Check if version info was already obtained in login page + const versionCheckedFromLogin = sessionStorage.getItem('VERSION_CHECKED_FROM_LOGIN') === 'true'; + if (versionCheckedFromLogin) { + setInitializing(false); // Skip initialization if already checked + return; + } + + try { + setInitializing(true); // Start initialization + + // Get version info + const token = localStorage.getItem('LIGHTRAG-API-TOKEN'); + const status = await getAuthStatus(); + + // If auth is not configured and a new token is returned, use the new token + if (!status.auth_configured && status.access_token) { + useAuthStore.getState().login( + status.access_token, // Use the new token + true, // Guest mode + status.core_version, + status.api_version, + status.webui_title || null, + status.webui_description || null + ); + } else if (token && (status.core_version || status.api_version || status.webui_title || status.webui_description)) { + // Otherwise use the old token (if it exists) + const isGuestMode = status.auth_mode === 'disabled' || useAuthStore.getState().isGuestMode; + useAuthStore.getState().login( + token, + isGuestMode, + status.core_version, + status.api_version, + status.webui_title || null, + status.webui_description || null + ); + } + + // Set flag to indicate version info has been checked + sessionStorage.setItem('VERSION_CHECKED_FROM_LOGIN', 'true'); + } catch (error) { + console.error('Failed to get version info:', error); + } finally { + // Ensure initializing is set to false even if there's an error + setInitializing(false); + } + }; + + // Execute version check + checkVersion(); + }, []); // Empty dependency array ensures it only runs once on mount + + const handleTabChange = useCallback( + (tab: string) => useSettingsStore.getState().setCurrentTab(tab as any), + [] + ) + + useEffect(() => { + if (message) { + if (message.includes(InvalidApiKeyError) || message.includes(RequireApiKeError)) { + setApiKeyAlertOpen(true) + } + } + }, [message]) + + return ( + + + {initializing ? ( + // Loading state while initializing with simplified header +

    + {/* Simplified header during initialization - matches SiteHeader structure */} +
    + + + {/* Empty middle section to maintain layout */} +
    +
    + + {/* Empty right section to maintain layout */} + +
    + + {/* Loading indicator in content area */} +
    +
    +
    +

    Initializing...

    +
    +
    +
    + ) : ( + // Main content after initialization +
    + + +
    + + + + + + + + + + + + +
    +     + {enableHealthCheck && } + +
    + )} + + + ) +} + +export default App diff --git a/LightRAG/lightrag_webui/src/AppRouter.tsx b/LightRAG/lightrag_webui/src/AppRouter.tsx new file mode 100644 index 00000000..3d474d2a --- /dev/null +++ b/LightRAG/lightrag_webui/src/AppRouter.tsx @@ -0,0 +1,95 @@ +import '@/lib/extensions'; // Import all global extensions +import { HashRouter as Router, Routes, Route, useNavigate } from 'react-router-dom' +import { useEffect, useState } from 'react' +import { useAuthStore } from '@/stores/state' +import { navigationService } from '@/services/navigation' +import { Toaster } from 'sonner' +import App from './App' +import LoginPage from '@/features/LoginPage' +import ThemeProvider from '@/components/ThemeProvider' + +const AppContent = () => { + const [initializing, setInitializing] = useState(true) + const { isAuthenticated } = useAuthStore() + const navigate = useNavigate() + + // Set navigate function for navigation service + useEffect(() => { + navigationService.setNavigate(navigate) + }, [navigate]) + + // Token validity check + useEffect(() => { + + const checkAuth = async () => { + try { + const token = localStorage.getItem('LIGHTRAG-API-TOKEN') + + if (token && isAuthenticated) { + setInitializing(false); + return; + } + + if (!token) { + useAuthStore.getState().logout() + } + } catch (error) { + console.error('Auth initialization error:', error) + if (!isAuthenticated) { + useAuthStore.getState().logout() + } + } finally { + setInitializing(false) + } + } + + checkAuth() + + return () => { + } + }, [isAuthenticated]) + + // Redirect effect for protected routes + useEffect(() => { + if (!initializing && !isAuthenticated) { + const currentPath = window.location.hash.slice(1); + if (currentPath !== '/login') { + console.log('Not authenticated, redirecting to login'); + navigate('/login'); + } + } + }, [initializing, isAuthenticated, navigate]); + + // Show nothing while initializing + if (initializing) { + return null + } + + return ( + + } /> + : null} + /> + + ) +} + +const AppRouter = () => { + return ( + + + + + + + ) +} + +export default AppRouter diff --git a/LightRAG/lightrag_webui/src/api/lightrag.ts b/LightRAG/lightrag_webui/src/api/lightrag.ts new file mode 100644 index 00000000..7cf1aec6 --- /dev/null +++ b/LightRAG/lightrag_webui/src/api/lightrag.ts @@ -0,0 +1,817 @@ +import axios, { AxiosError } from 'axios' +import { backendBaseUrl, popularLabelsDefaultLimit, searchLabelsDefaultLimit } from '@/lib/constants' +import { errorMessage } from '@/lib/utils' +import { useSettingsStore } from '@/stores/settings' +import { navigationService } from '@/services/navigation' + +// Types +export type LightragNodeType = { + id: string + labels: string[] + properties: Record +} + +export type LightragEdgeType = { + id: string + source: string + target: string + type: string + properties: Record +} + +export type LightragGraphType = { + nodes: LightragNodeType[] + edges: LightragEdgeType[] +} + +export type LightragStatus = { + status: 'healthy' + working_directory: string + input_directory: string + configuration: { + llm_binding: string + llm_binding_host: string + llm_model: string + embedding_binding: string + embedding_binding_host: string + embedding_model: string + kv_storage: string + doc_status_storage: string + graph_storage: string + vector_storage: string + workspace?: string + max_graph_nodes?: string + enable_rerank?: boolean + rerank_binding?: string | null + rerank_model?: string | null + rerank_binding_host?: string | null + summary_language: string + force_llm_summary_on_merge: boolean + max_parallel_insert: number + max_async: number + embedding_func_max_async: number + embedding_batch_num: number + cosine_threshold: number + min_rerank_score: number + related_chunk_number: number + } + update_status?: Record + core_version?: string + api_version?: string + auth_mode?: 'enabled' | 'disabled' + pipeline_busy: boolean + keyed_locks?: { + process_id: number + cleanup_performed: { + mp_cleaned: number + async_cleaned: number + } + current_status: { + total_mp_locks: number + pending_mp_cleanup: number + total_async_locks: number + pending_async_cleanup: number + } + } + webui_title?: string + webui_description?: string +} + +export type LightragDocumentsScanProgress = { + is_scanning: boolean + current_file: string + indexed_count: number + total_files: number + progress: number +} + +/** + * Specifies the retrieval mode: + * - "naive": Performs a basic search without advanced techniques. + * - "local": Focuses on context-dependent information. + * - "global": Utilizes global knowledge. + * - "hybrid": Combines local and global retrieval methods. + * - "mix": Integrates knowledge graph and vector retrieval. + * - "bypass": Bypasses knowledge retrieval and directly uses the LLM. + */ +export type QueryMode = 'naive' | 'local' | 'global' | 'hybrid' | 'mix' | 'bypass' + +export type Message = { + role: 'user' | 'assistant' | 'system' + content: string + thinkingContent?: string + displayContent?: string + thinkingTime?: number | null +} + +export type QueryRequest = { + query: string + /** Specifies the retrieval mode. */ + mode: QueryMode + /** If True, only returns the retrieved context without generating a response. */ + only_need_context?: boolean + /** If True, only returns the generated prompt without producing a response. */ + only_need_prompt?: boolean + /** Defines the response format. Examples: 'Multiple Paragraphs', 'Single Paragraph', 'Bullet Points'. */ + response_type?: string + /** If True, enables streaming output for real-time responses. */ + stream?: boolean + /** Number of top items to retrieve. Represents entities in 'local' mode and relationships in 'global' mode. */ + top_k?: number + /** Maximum number of text chunks to retrieve and keep after reranking. */ + chunk_top_k?: number + /** Maximum number of tokens allocated for entity context in unified token control system. */ + max_entity_tokens?: number + /** Maximum number of tokens allocated for relationship context in unified token control system. */ + max_relation_tokens?: number + /** Maximum total tokens budget for the entire query context (entities + relations + chunks + system prompt). */ + max_total_tokens?: number + /** + * Stores past conversation history to maintain context. + * Format: [{"role": "user/assistant", "content": "message"}]. + */ + conversation_history?: Message[] + /** Number of complete conversation turns (user-assistant pairs) to consider in the response context. */ + history_turns?: number + /** User-provided prompt for the query. If provided, this will be used instead of the default value from prompt template. */ + user_prompt?: string + /** Enable reranking for retrieved text chunks. If True but no rerank model is configured, a warning will be issued. Default is True. */ + enable_rerank?: boolean +} + +export type QueryResponse = { + response: string +} + +export type EntityUpdateResponse = { + status: string + message: string + data: Record + operation_summary?: { + merged: boolean + merge_status: 'success' | 'failed' | 'not_attempted' + merge_error: string | null + operation_status: 'success' | 'partial_success' | 'failure' + target_entity: string | null + final_entity?: string | null + renamed?: boolean + } +} + +export type DocActionResponse = { + status: 'success' | 'partial_success' | 'failure' | 'duplicated' + message: string + track_id?: string +} + +export type ScanResponse = { + status: 'scanning_started' + message: string + track_id: string +} + +export type ReprocessFailedResponse = { + status: 'reprocessing_started' + message: string + track_id: string +} + +export type DeleteDocResponse = { + status: 'deletion_started' | 'busy' | 'not_allowed' + message: string + doc_id: string +} + +export type DocStatus = 'pending' | 'processing' | 'preprocessed' | 'processed' | 'failed' + +export type DocStatusResponse = { + id: string + content_summary: string + content_length: number + status: DocStatus + created_at: string + updated_at: string + track_id?: string + chunks_count?: number + error_msg?: string + metadata?: Record + file_path: string +} + +export type DocsStatusesResponse = { + statuses: Record +} + +export type TrackStatusResponse = { + track_id: string + documents: DocStatusResponse[] + total_count: number + status_summary: Record +} + +export type DocumentsRequest = { + status_filter?: DocStatus | null + page: number + page_size: number + sort_field: 'created_at' | 'updated_at' | 'id' | 'file_path' + sort_direction: 'asc' | 'desc' +} + +export type PaginationInfo = { + page: number + page_size: number + total_count: number + total_pages: number + has_next: boolean + has_prev: boolean +} + +export type PaginatedDocsResponse = { + documents: DocStatusResponse[] + pagination: PaginationInfo + status_counts: Record +} + +export type StatusCountsResponse = { + status_counts: Record +} + +export type AuthStatusResponse = { + auth_configured: boolean + access_token?: string + token_type?: string + auth_mode?: 'enabled' | 'disabled' + message?: string + core_version?: string + api_version?: string + webui_title?: string + webui_description?: string +} + +export type PipelineStatusResponse = { + autoscanned: boolean + busy: boolean + job_name: string + job_start?: string + docs: number + batchs: number + cur_batch: number + request_pending: boolean + cancellation_requested?: boolean + latest_message: string + history_messages?: string[] + update_status?: Record +} + +export type LoginResponse = { + access_token: string + token_type: string + auth_mode?: 'enabled' | 'disabled' // Authentication mode identifier + message?: string // Optional message + core_version?: string + api_version?: string + webui_title?: string + webui_description?: string +} + +export const InvalidApiKeyError = 'Invalid API Key' +export const RequireApiKeError = 'API Key required' + +// Axios instance +const axiosInstance = axios.create({ + baseURL: backendBaseUrl, + headers: { + 'Content-Type': 'application/json' + } +}) + +// Interceptor: add api key and check authentication +axiosInstance.interceptors.request.use((config) => { + const apiKey = useSettingsStore.getState().apiKey + const token = localStorage.getItem('LIGHTRAG-API-TOKEN'); + + // Always include token if it exists, regardless of path + if (token) { + config.headers['Authorization'] = `Bearer ${token}` + } + if (apiKey) { + config.headers['X-API-Key'] = apiKey + } + return config +}) + +// Interceptor：hanle error +axiosInstance.interceptors.response.use( + (response) => response, + (error: AxiosError) => { + if (error.response) { + if (error.response?.status === 401) { + // For login API, throw error directly + if (error.config?.url?.includes('/login')) { + throw error; + } + // For other APIs, navigate to login page + navigationService.navigateToLogin(); + + // return a reject Promise + return Promise.reject(new Error('Authentication required')); + } + throw new Error( + `${error.response.status} ${error.response.statusText}\n${JSON.stringify( + error.response.data + )}\n${error.config?.url}` + ) + } + throw error + } +) + +// API methods +export const queryGraphs = async ( + label: string, + maxDepth: number, + maxNodes: number +): Promise => { + const response = await axiosInstance.get(`/graphs?label=${encodeURIComponent(label)}&max_depth=${maxDepth}&max_nodes=${maxNodes}`) + return response.data +} + +export const getGraphLabels = async (): Promise => { + const response = await axiosInstance.get('/graph/label/list') + return response.data +} + +export const getPopularLabels = async (limit: number = popularLabelsDefaultLimit): Promise => { + const response = await axiosInstance.get(`/graph/label/popular?limit=${limit}`) + return response.data +} + +export const searchLabels = async (query: string, limit: number = searchLabelsDefaultLimit): Promise => { + const response = await axiosInstance.get(`/graph/label/search?q=${encodeURIComponent(query)}&limit=${limit}`) + return response.data +} + +export const checkHealth = async (): Promise< + LightragStatus | { status: 'error'; message: string } +> => { + try { + const response = await axiosInstance.get('/health') + return response.data + } catch (error) { + return { + status: 'error', + message: errorMessage(error) + } + } +} + +export const getDocuments = async (): Promise => { + const response = await axiosInstance.get('/documents') + return response.data +} + +export const scanNewDocuments = async (): Promise => { + const response = await axiosInstance.post('/documents/scan') + return response.data +} + +export const reprocessFailedDocuments = async (): Promise => { + const response = await axiosInstance.post('/documents/reprocess_failed') + return response.data +} + +export const getDocumentsScanProgress = async (): Promise => { + const response = await axiosInstance.get('/documents/scan-progress') + return response.data +} + +export const queryText = async (request: QueryRequest): Promise => { + const response = await axiosInstance.post('/query', request) + return response.data +} + +export const queryTextStream = async ( + request: QueryRequest, + onChunk: (chunk: string) => void, + onError?: (error: string) => void +) => { + const apiKey = useSettingsStore.getState().apiKey; + const token = localStorage.getItem('LIGHTRAG-API-TOKEN'); + const headers: HeadersInit = { + 'Content-Type': 'application/json', + 'Accept': 'application/x-ndjson', + }; + if (token) { + headers['Authorization'] = `Bearer ${token}`; + } + if (apiKey) { + headers['X-API-Key'] = apiKey; + } + + try { + const response = await fetch(`${backendBaseUrl}/query/stream`, { + method: 'POST', + headers: headers, + body: JSON.stringify(request), + }); + + if (!response.ok) { + // Handle 401 Unauthorized error specifically + if (response.status === 401) { + // For consistency with axios interceptor, navigate to login page + navigationService.navigateToLogin(); + + // Create a specific authentication error + const authError = new Error('Authentication required'); + throw authError; + } + + // Handle other common HTTP errors with specific messages + let errorBody = 'Unknown error'; + try { + errorBody = await response.text(); // Try to get error details from body + } catch { /* ignore */ } + + // Format error message similar to axios interceptor for consistency + const url = `${backendBaseUrl}/query/stream`; + throw new Error( + `${response.status} ${response.statusText}\n${JSON.stringify( + { error: errorBody } + )}\n${url}` + ); + } + + if (!response.body) { + throw new Error('Response body is null'); + } + + const reader = response.body.getReader(); + const decoder = new TextDecoder(); + let buffer = ''; + + while (true) { + const { done, value } = await reader.read(); + if (done) { + break; // Stream finished + } + + // Decode the chunk and add to buffer + buffer += decoder.decode(value, { stream: true }); // stream: true handles multi-byte chars split across chunks + + // Process complete lines (NDJSON) + const lines = buffer.split('\n'); + buffer = lines.pop() || ''; // Keep potentially incomplete line in buffer + + for (const line of lines) { + if (line.trim()) { + try { + const parsed = JSON.parse(line); + if (parsed.response) { + onChunk(parsed.response); + } else if (parsed.error && onError) { + onError(parsed.error); + } + } catch (error) { + console.error('Error parsing stream chunk:', line, error); + if (onError) onError(`Error parsing server response: ${line}`); + } + } + } + } + + // Process any remaining data in the buffer after the stream ends + if (buffer.trim()) { + try { + const parsed = JSON.parse(buffer); + if (parsed.response) { + onChunk(parsed.response); + } else if (parsed.error && onError) { + onError(parsed.error); + } + } catch (error) { + console.error('Error parsing final chunk:', buffer, error); + if (onError) onError(`Error parsing final server response: ${buffer}`); + } + } + + } catch (error) { + const message = errorMessage(error); + + // Check if this is an authentication error + if (message === 'Authentication required') { + // Already navigated to login page in the response.status === 401 block + console.error('Authentication required for stream request'); + if (onError) { + onError('Authentication required'); + } + return; // Exit early, no need for further error handling + } + + // Check for specific HTTP error status codes in the error message + const statusCodeMatch = message.match(/^(\d{3})\s/); + if (statusCodeMatch) { + const statusCode = parseInt(statusCodeMatch[1], 10); + + // Handle specific status codes with user-friendly messages + let userMessage = message; + + switch (statusCode) { + case 403: + userMessage = 'You do not have permission to access this resource (403 Forbidden)'; + console.error('Permission denied for stream request:', message); + break; + case 404: + userMessage = 'The requested resource does not exist (404 Not Found)'; + console.error('Resource not found for stream request:', message); + break; + case 429: + userMessage = 'Too many requests, please try again later (429 Too Many Requests)'; + console.error('Rate limited for stream request:', message); + break; + case 500: + case 502: + case 503: + case 504: + userMessage = `Server error, please try again later (${statusCode})`; + console.error('Server error for stream request:', message); + break; + default: + console.error('Stream request failed with status code:', statusCode, message); + } + + if (onError) { + onError(userMessage); + } + return; + } + + // Handle network errors (like connection refused, timeout, etc.) + if (message.includes('NetworkError') || + message.includes('Failed to fetch') || + message.includes('Network request failed')) { + console.error('Network error for stream request:', message); + if (onError) { + onError('Network connection error, please check your internet connection'); + } + return; + } + + // Handle JSON parsing errors during stream processing + if (message.includes('Error parsing') || message.includes('SyntaxError')) { + console.error('JSON parsing error in stream:', message); + if (onError) { + onError('Error processing response data'); + } + return; + } + + // Handle other errors + console.error('Unhandled stream error:', message); + if (onError) { + onError(message); + } else { + console.error('No error handler provided for stream error:', message); + } + } +}; + +export const insertText = async (text: string): Promise => { + const response = await axiosInstance.post('/documents/text', { text }) + return response.data +} + +export const insertTexts = async (texts: string[]): Promise => { + const response = await axiosInstance.post('/documents/texts', { texts }) + return response.data +} + +export const uploadDocument = async ( + file: File, + onUploadProgress?: (percentCompleted: number) => void +): Promise => { + const formData = new FormData() + formData.append('file', file) + + const response = await axiosInstance.post('/documents/upload', formData, { + headers: { + 'Content-Type': 'multipart/form-data' + }, + // prettier-ignore + onUploadProgress: + onUploadProgress !== undefined + ? (progressEvent) => { + const percentCompleted = Math.round((progressEvent.loaded * 100) / progressEvent.total!) + onUploadProgress(percentCompleted) + } + : undefined + }) + return response.data +} + +export const batchUploadDocuments = async ( + files: File[], + onUploadProgress?: (fileName: string, percentCompleted: number) => void +): Promise => { + return await Promise.all( + files.map(async (file) => { + return await uploadDocument(file, (percentCompleted) => { + onUploadProgress?.(file.name, percentCompleted) + }) + }) + ) +} + +export const clearDocuments = async (): Promise => { + const response = await axiosInstance.delete('/documents') + return response.data +} + +export const clearCache = async (): Promise<{ + status: 'success' | 'fail' + message: string +}> => { + const response = await axiosInstance.post('/documents/clear_cache', {}) + return response.data +} + +export const deleteDocuments = async ( + docIds: string[], + deleteFile: boolean = false, + deleteLLMCache: boolean = false +): Promise => { + const response = await axiosInstance.delete('/documents/delete_document', { + data: { doc_ids: docIds, delete_file: deleteFile, delete_llm_cache: deleteLLMCache } + }) + return response.data +} + +export const getAuthStatus = async (): Promise => { + try { + // Add a timeout to the request to prevent hanging + const response = await axiosInstance.get('/auth-status', { + timeout: 5000, // 5 second timeout + headers: { + 'Accept': 'application/json' // Explicitly request JSON + } + }); + + // Check if response is HTML (which indicates a redirect or wrong endpoint) + const contentType = response.headers['content-type'] || ''; + if (contentType.includes('text/html')) { + console.warn('Received HTML response instead of JSON for auth-status endpoint'); + return { + auth_configured: true, + auth_mode: 'enabled' + }; + } + + // Strict validation of the response data + if (response.data && + typeof response.data === 'object' && + 'auth_configured' in response.data && + typeof response.data.auth_configured === 'boolean') { + + // For unconfigured auth, ensure we have an access token + if (!response.data.auth_configured) { + if (response.data.access_token && typeof response.data.access_token === 'string') { + return response.data; + } else { + console.warn('Auth not configured but no valid access token provided'); + } + } else { + // For configured auth, just return the data + return response.data; + } + } + + // If response data is invalid but we got a response, log it + console.warn('Received invalid auth status response:', response.data); + + // Default to auth configured if response is invalid + return { + auth_configured: true, + auth_mode: 'enabled' + }; + } catch (error) { + // If the request fails, assume authentication is configured + console.error('Failed to get auth status:', errorMessage(error)); + return { + auth_configured: true, + auth_mode: 'enabled' + }; + } +} + +export const getPipelineStatus = async (): Promise => { + const response = await axiosInstance.get('/documents/pipeline_status') + return response.data +} + +export const cancelPipeline = async (): Promise<{ + status: 'cancellation_requested' | 'not_busy' + message: string +}> => { + const response = await axiosInstance.post('/documents/cancel_pipeline') + return response.data +} + +export const loginToServer = async (username: string, password: string): Promise => { + const formData = new FormData(); + formData.append('username', username); + formData.append('password', password); + + const response = await axiosInstance.post('/login', formData, { + headers: { + 'Content-Type': 'multipart/form-data' + } + }); + + return response.data; +} + +/** + * Updates an entity's properties in the knowledge graph + * @param entityName The name of the entity to update + * @param updatedData Dictionary containing updated attributes + * @param allowRename Whether to allow renaming the entity (default: false) + * @param allowMerge Whether to merge into an existing entity when renaming to a duplicate name + * @returns Promise with the updated entity information + */ +export const updateEntity = async ( + entityName: string, + updatedData: Record, + allowRename: boolean = false, + allowMerge: boolean = false +): Promise => { + const response = await axiosInstance.post('/graph/entity/edit', { + entity_name: entityName, + updated_data: updatedData, + allow_rename: allowRename, + allow_merge: allowMerge + }) + return response.data +} + +/** + * Updates a relation's properties in the knowledge graph + * @param sourceEntity The source entity name + * @param targetEntity The target entity name + * @param updatedData Dictionary containing updated attributes + * @returns Promise with the updated relation information + */ +export const updateRelation = async ( + sourceEntity: string, + targetEntity: string, + updatedData: Record +): Promise => { + const response = await axiosInstance.post('/graph/relation/edit', { + source_id: sourceEntity, + target_id: targetEntity, + updated_data: updatedData + }) + return response.data +} + +/** + * Checks if an entity name already exists in the knowledge graph + * @param entityName The entity name to check + * @returns Promise with boolean indicating if the entity exists + */ +export const checkEntityNameExists = async (entityName: string): Promise => { + try { + const response = await axiosInstance.get(`/graph/entity/exists?name=${encodeURIComponent(entityName)}`) + return response.data.exists + } catch (error) { + console.error('Error checking entity name:', error) + return false + } +} + +/** + * Get the processing status of documents by tracking ID + * @param trackId The tracking ID returned from upload, text, or texts endpoints + * @returns Promise with the track status response containing documents and summary + */ +export const getTrackStatus = async (trackId: string): Promise => { + const response = await axiosInstance.get(`/documents/track_status/${encodeURIComponent(trackId)}`) + return response.data +} + +/** + * Get documents with pagination support + * @param request The pagination request parameters + * @returns Promise with paginated documents response + */ +export const getDocumentsPaginated = async (request: DocumentsRequest): Promise => { + const response = await axiosInstance.post('/documents/paginated', request) + return response.data +} + +/** + * Get counts of documents by status + * @returns Promise with status counts response + */ +export const getDocumentStatusCounts = async (): Promise => { + const response = await axiosInstance.get('/documents/status_counts') + return response.data +} diff --git a/LightRAG/lightrag_webui/src/components/ApiKeyAlert.tsx b/LightRAG/lightrag_webui/src/components/ApiKeyAlert.tsx new file mode 100644 index 00000000..bbbce1bb --- /dev/null +++ b/LightRAG/lightrag_webui/src/components/ApiKeyAlert.tsx @@ -0,0 +1,86 @@ +import { useState, useCallback, useEffect } from 'react' +import { useTranslation } from 'react-i18next' +import { + AlertDialog, + AlertDialogContent, + AlertDialogDescription, + AlertDialogHeader, + AlertDialogTitle +} from '@/components/ui/AlertDialog' +import Button from '@/components/ui/Button' +import Input from '@/components/ui/Input' +import { useSettingsStore } from '@/stores/settings' +import { useBackendState } from '@/stores/state' +import { InvalidApiKeyError, RequireApiKeError } from '@/api/lightrag' + +interface ApiKeyAlertProps { + open: boolean; + onOpenChange: (open: boolean) => void; +} + +const ApiKeyAlert = ({ open: opened, onOpenChange: setOpened }: ApiKeyAlertProps) => { + const { t } = useTranslation() + const apiKey = useSettingsStore.use.apiKey() + const [tempApiKey, setTempApiKey] = useState('') + const message = useBackendState.use.message() + + useEffect(() => { + setTempApiKey(apiKey || '') + }, [apiKey, opened]) + + useEffect(() => { + if (message) { + if (message.includes(InvalidApiKeyError) || message.includes(RequireApiKeError)) { + setOpened(true) + } + } + }, [message, setOpened]) + + const setApiKey = useCallback(() => { + useSettingsStore.setState({ apiKey: tempApiKey || null }) + setOpened(false) + }, [tempApiKey, setOpened]) + + const handleTempApiKeyChange = useCallback( + (e: React.ChangeEvent) => { + setTempApiKey(e.target.value) + }, + [setTempApiKey] + ) + + return ( + + + + {t('apiKeyAlert.title')} + + {t('apiKeyAlert.description')} + + +
    +
    e.preventDefault()}> + + + +
    + {message && ( +
    + {message} +
    + )} +
    +     +     + ) +} + +export default ApiKeyAlert diff --git a/LightRAG/lightrag_webui/src/components/AppSettings.tsx b/LightRAG/lightrag_webui/src/components/AppSettings.tsx new file mode 100644 index 00000000..09312794 --- /dev/null +++ b/LightRAG/lightrag_webui/src/components/AppSettings.tsx @@ -0,0 +1,74 @@ +import { useState, useCallback } from 'react' +import { Popover, PopoverContent, PopoverTrigger } from '@/components/ui/Popover' +import Button from '@/components/ui/Button' +import { Select, SelectContent, SelectItem, SelectTrigger, SelectValue } from '@/components/ui/Select' +import { useSettingsStore } from '@/stores/settings' +import { PaletteIcon } from 'lucide-react' +import { useTranslation } from 'react-i18next' +import { cn } from '@/lib/utils' + +interface AppSettingsProps { + className?: string +} + +export default function AppSettings({ className }: AppSettingsProps) { + const [opened, setOpened] = useState(false) + const { t } = useTranslation() + + const language = useSettingsStore.use.language() + const setLanguage = useSettingsStore.use.setLanguage() + + const theme = useSettingsStore.use.theme() + const setTheme = useSettingsStore.use.setTheme() + + const handleLanguageChange = useCallback((value: string) => { + setLanguage(value as 'en' | 'zh' | 'fr' | 'ar' | 'zh_TW') + }, [setLanguage]) + + const handleThemeChange = useCallback((value: string) => { + setTheme(value as 'light' | 'dark' | 'system') + }, [setTheme]) + + return ( + + + + + +
    +
    + + +
    + +
    + + +
    +
    +     +     + ) +} diff --git a/LightRAG/lightrag_webui/src/components/LanguageToggle.tsx b/LightRAG/lightrag_webui/src/components/LanguageToggle.tsx new file mode 100644 index 00000000..0eab780e --- /dev/null +++ b/LightRAG/lightrag_webui/src/components/LanguageToggle.tsx @@ -0,0 +1,49 @@ +import Button from '@/components/ui/Button' +import { useCallback } from 'react' +import { controlButtonVariant } from '@/lib/constants' +import { useTranslation } from 'react-i18next' +import { useSettingsStore } from '@/stores/settings' + +/** + * Component that toggles the language between English and Chinese. + */ +export default function LanguageToggle() { + const { i18n } = useTranslation() + const currentLanguage = i18n.language + const setLanguage = useSettingsStore.use.setLanguage() + + const setEnglish = useCallback(() => { + i18n.changeLanguage('en') + setLanguage('en') + }, [i18n, setLanguage]) + + const setChinese = useCallback(() => { + i18n.changeLanguage('zh') + setLanguage('zh') + }, [i18n, setLanguage]) + + if (currentLanguage === 'zh') { + return ( + + ) + } + return ( + + ) +} diff --git a/LightRAG/lightrag_webui/src/components/Root.tsx b/LightRAG/lightrag_webui/src/components/Root.tsx new file mode 100644 index 00000000..1129e093 --- /dev/null +++ b/LightRAG/lightrag_webui/src/components/Root.tsx @@ -0,0 +1,9 @@ +import { StrictMode } from 'react' +import App from '@/App' +import '@/i18n' + +export const Root = () => ( + + + +) diff --git a/LightRAG/lightrag_webui/src/components/ThemeProvider.tsx b/LightRAG/lightrag_webui/src/components/ThemeProvider.tsx new file mode 100644 index 00000000..df5816c6 --- /dev/null +++ b/LightRAG/lightrag_webui/src/components/ThemeProvider.tsx @@ -0,0 +1,59 @@ +import { createContext, useEffect } from 'react' +import { Theme, useSettingsStore } from '@/stores/settings' + +type ThemeProviderProps = { + children: React.ReactNode +} + +type ThemeProviderState = { + theme: Theme + setTheme: (theme: Theme) => void +} + +const initialState: ThemeProviderState = { + theme: 'system', + setTheme: () => null +} + +const ThemeProviderContext = createContext(initialState) + +/** + * Component that provides the theme state and setter function to its children. + */ +export default function ThemeProvider({ children, ...props }: ThemeProviderProps) { + const theme = useSettingsStore.use.theme() + const setTheme = useSettingsStore.use.setTheme() + + useEffect(() => { + const root = window.document.documentElement + root.classList.remove('light', 'dark') + + if (theme === 'system') { + const mediaQuery = window.matchMedia('(prefers-color-scheme: dark)') + const handleChange = (e: MediaQueryListEvent) => { + root.classList.remove('light', 'dark') + root.classList.add(e.matches ? 'dark' : 'light') + } + + root.classList.add(mediaQuery.matches ? 'dark' : 'light') + mediaQuery.addEventListener('change', handleChange) + + return () => mediaQuery.removeEventListener('change', handleChange) + } else { + root.classList.add(theme) + } + }, [theme]) + + const value = { + theme, + setTheme + } + + return ( + + {children} + + ) +} + +export { ThemeProviderContext } diff --git a/LightRAG/lightrag_webui/src/components/ThemeToggle.tsx b/LightRAG/lightrag_webui/src/components/ThemeToggle.tsx new file mode 100644 index 00000000..ff333ff0 --- /dev/null +++ b/LightRAG/lightrag_webui/src/components/ThemeToggle.tsx @@ -0,0 +1,41 @@ +import Button from '@/components/ui/Button' +import useTheme from '@/hooks/useTheme' +import { MoonIcon, SunIcon } from 'lucide-react' +import { useCallback } from 'react' +import { controlButtonVariant } from '@/lib/constants' +import { useTranslation } from 'react-i18next' + +/** + * Component that toggles the theme between light and dark. + */ +export default function ThemeToggle() { + const { theme, setTheme } = useTheme() + const setLight = useCallback(() => setTheme('light'), [setTheme]) + const setDark = useCallback(() => setTheme('dark'), [setTheme]) + const { t } = useTranslation() + + if (theme === 'dark') { + return ( + + ) + } + return ( + + ) +} diff --git a/LightRAG/lightrag_webui/src/components/documents/ClearDocumentsDialog.tsx b/LightRAG/lightrag_webui/src/components/documents/ClearDocumentsDialog.tsx new file mode 100644 index 00000000..4aaa1f47 --- /dev/null +++ b/LightRAG/lightrag_webui/src/components/documents/ClearDocumentsDialog.tsx @@ -0,0 +1,211 @@ +import { useState, useCallback, useEffect, useRef } from 'react' +import Button from '@/components/ui/Button' +import { + Dialog, + DialogContent, + DialogDescription, + DialogHeader, + DialogTitle, + DialogTrigger, + DialogFooter +} from '@/components/ui/Dialog' +import Input from '@/components/ui/Input' +import Checkbox from '@/components/ui/Checkbox' +import { toast } from 'sonner' +import { errorMessage } from '@/lib/utils' +import { clearDocuments, clearCache } from '@/api/lightrag' + +import { EraserIcon, AlertTriangleIcon, Loader2Icon } from 'lucide-react' +import { useTranslation } from 'react-i18next' + +// Simple Label component +const Label = ({ + htmlFor, + className, + children, + ...props +}: React.LabelHTMLAttributes) => ( + +) + +interface ClearDocumentsDialogProps { + onDocumentsCleared?: () => Promise +} + +export default function ClearDocumentsDialog({ onDocumentsCleared }: ClearDocumentsDialogProps) { + const { t } = useTranslation() + const [open, setOpen] = useState(false) + const [confirmText, setConfirmText] = useState('') + const [clearCacheOption, setClearCacheOption] = useState(false) + const [isClearing, setIsClearing] = useState(false) + const timeoutRef = useRef | null>(null) + const isConfirmEnabled = confirmText.toLowerCase() === 'yes' + + // Timeout constant (30 seconds) + const CLEAR_TIMEOUT = 30000 + + // Reset state when dialog closes + useEffect(() => { + if (!open) { + setConfirmText('') + setClearCacheOption(false) + setIsClearing(false) + + // Clear timeout timer + if (timeoutRef.current) { + clearTimeout(timeoutRef.current) + timeoutRef.current = null + } + } + }, [open]) + + // Cleanup when component unmounts + useEffect(() => { + return () => { + // Clear timeout timer when component unmounts + if (timeoutRef.current) { + clearTimeout(timeoutRef.current) + } + } + }, []) + + const handleClear = useCallback(async () => { + if (!isConfirmEnabled || isClearing) return + + setIsClearing(true) + + // Set timeout protection + timeoutRef.current = setTimeout(() => { + if (isClearing) { + toast.error(t('documentPanel.clearDocuments.timeout')) + setIsClearing(false) + setConfirmText('') // Reset confirmation text after timeout + } + }, CLEAR_TIMEOUT) + + try { + const result = await clearDocuments() + + if (result.status !== 'success') { + toast.error(t('documentPanel.clearDocuments.failed', { message: result.message })) + setConfirmText('') + return + } + + toast.success(t('documentPanel.clearDocuments.success')) + + if (clearCacheOption) { + try { + await clearCache() + toast.success(t('documentPanel.clearDocuments.cacheCleared')) + } catch (cacheErr) { + toast.error(t('documentPanel.clearDocuments.cacheClearFailed', { error: errorMessage(cacheErr) })) + } + } + + // Refresh document list if provided + if (onDocumentsCleared) { + onDocumentsCleared().catch(console.error) + } + + // Close dialog after all operations succeed + setOpen(false) + } catch (err) { + toast.error(t('documentPanel.clearDocuments.error', { error: errorMessage(err) })) + setConfirmText('') + } finally { + // Clear timeout timer + if (timeoutRef.current) { + clearTimeout(timeoutRef.current) + timeoutRef.current = null + } + setIsClearing(false) + } + }, [isConfirmEnabled, isClearing, clearCacheOption, setOpen, t, onDocumentsCleared, CLEAR_TIMEOUT]) + + return ( + + + + + e.preventDefault()}> + + + + {t('documentPanel.clearDocuments.title')} + + + {t('documentPanel.clearDocuments.description')} + + + +
    + {t('documentPanel.clearDocuments.warning')} +
    +
    + {t('documentPanel.clearDocuments.confirm')} +
    + +
    +
    + + ) => setConfirmText(e.target.value)} + placeholder={t('documentPanel.clearDocuments.confirmPlaceholder')} + className="w-full" + disabled={isClearing} + /> +
    + +
    + setClearCacheOption(checked === true)} + disabled={isClearing} + /> + +
    +
    + + + + + +     +     + ) +} diff --git a/LightRAG/lightrag_webui/src/components/documents/DeleteDocumentsDialog.tsx b/LightRAG/lightrag_webui/src/components/documents/DeleteDocumentsDialog.tsx new file mode 100644 index 00000000..eb5b173f --- /dev/null +++ b/LightRAG/lightrag_webui/src/components/documents/DeleteDocumentsDialog.tsx @@ -0,0 +1,191 @@ +import { useState, useCallback, useEffect } from 'react' +import Button from '@/components/ui/Button' +import { + Dialog, + DialogContent, + DialogDescription, + DialogHeader, + DialogTitle, + DialogTrigger, + DialogFooter +} from '@/components/ui/Dialog' +import Input from '@/components/ui/Input' +import { toast } from 'sonner' +import { errorMessage } from '@/lib/utils' +import { deleteDocuments } from '@/api/lightrag' + +import { TrashIcon, AlertTriangleIcon } from 'lucide-react' +import { useTranslation } from 'react-i18next' + +// Simple Label component +const Label = ({ + htmlFor, + className, + children, + ...props +}: React.LabelHTMLAttributes) => ( + +) + +interface DeleteDocumentsDialogProps { + selectedDocIds: string[] + onDocumentsDeleted?: () => Promise +} + +export default function DeleteDocumentsDialog({ selectedDocIds, onDocumentsDeleted }: DeleteDocumentsDialogProps) { + const { t } = useTranslation() + const [open, setOpen] = useState(false) + const [confirmText, setConfirmText] = useState('') + const [deleteFile, setDeleteFile] = useState(false) + const [isDeleting, setIsDeleting] = useState(false) + const [deleteLLMCache, setDeleteLLMCache] = useState(false) + const isConfirmEnabled = confirmText.toLowerCase() === 'yes' && !isDeleting + + // Reset state when dialog closes + useEffect(() => { + if (!open) { + setConfirmText('') + setDeleteFile(false) + setDeleteLLMCache(false) + setIsDeleting(false) + } + }, [open]) + + const handleDelete = useCallback(async () => { + if (!isConfirmEnabled || selectedDocIds.length === 0) return + + setIsDeleting(true) + try { + const result = await deleteDocuments(selectedDocIds, deleteFile, deleteLLMCache) + + if (result.status === 'deletion_started') { + toast.success(t('documentPanel.deleteDocuments.success', { count: selectedDocIds.length })) + } else if (result.status === 'busy') { + toast.error(t('documentPanel.deleteDocuments.busy')) + setConfirmText('') + setIsDeleting(false) + return + } else if (result.status === 'not_allowed') { + toast.error(t('documentPanel.deleteDocuments.notAllowed')) + setConfirmText('') + setIsDeleting(false) + return + } else { + toast.error(t('documentPanel.deleteDocuments.failed', { message: result.message })) + setConfirmText('') + setIsDeleting(false) + return + } + + // Refresh document list if provided + if (onDocumentsDeleted) { + onDocumentsDeleted().catch(console.error) + } + + // Close dialog after successful operation + setOpen(false) + } catch (err) { + toast.error(t('documentPanel.deleteDocuments.error', { error: errorMessage(err) })) + setConfirmText('') + } finally { + setIsDeleting(false) + } + }, [isConfirmEnabled, selectedDocIds, deleteFile, deleteLLMCache, setOpen, t, onDocumentsDeleted]) + + return ( + + + + + e.preventDefault()}> + + + + {t('documentPanel.deleteDocuments.title')} + + + {t('documentPanel.deleteDocuments.description', { count: selectedDocIds.length })} + + + +
    + {t('documentPanel.deleteDocuments.warning')} +
    + +
    + {t('documentPanel.deleteDocuments.confirm', { count: selectedDocIds.length })} +
    + +
    +
    + + ) => setConfirmText(e.target.value)} + placeholder={t('documentPanel.deleteDocuments.confirmPlaceholder')} + className="w-full" + disabled={isDeleting} + /> +
    + +
    + setDeleteFile(e.target.checked)} + disabled={isDeleting} + className="h-4 w-4 text-red-600 focus:ring-red-500 border-gray-300 rounded" + /> + +
    + +
    + setDeleteLLMCache(e.target.checked)} + disabled={isDeleting} + className="h-4 w-4 text-red-600 focus:ring-red-500 border-gray-300 rounded" + /> + +
    +
    + + + + + +     +     + ) +} diff --git a/LightRAG/lightrag_webui/src/components/documents/PipelineStatusDialog.tsx b/LightRAG/lightrag_webui/src/components/documents/PipelineStatusDialog.tsx new file mode 100644 index 00000000..c368d69c --- /dev/null +++ b/LightRAG/lightrag_webui/src/components/documents/PipelineStatusDialog.tsx @@ -0,0 +1,269 @@ +import { useState, useEffect, useRef } from 'react' +import { useTranslation } from 'react-i18next' +import { toast } from 'sonner' +import { AlignLeft, AlignCenter, AlignRight } from 'lucide-react' + +import { + Dialog, + DialogContent, + DialogHeader, + DialogTitle, + DialogDescription +} from '@/components/ui/Dialog' +import Button from '@/components/ui/Button' +import { getPipelineStatus, cancelPipeline, PipelineStatusResponse } from '@/api/lightrag' +import { errorMessage } from '@/lib/utils' +import { cn } from '@/lib/utils' + +type DialogPosition = 'left' | 'center' | 'right' + +interface PipelineStatusDialogProps { + open: boolean + onOpenChange: (open: boolean) => void +} + +export default function PipelineStatusDialog({ + open, + onOpenChange +}: PipelineStatusDialogProps) { + const { t } = useTranslation() + const [status, setStatus] = useState(null) + const [position, setPosition] = useState('center') + const [isUserScrolled, setIsUserScrolled] = useState(false) + const [showCancelConfirm, setShowCancelConfirm] = useState(false) + const historyRef = useRef(null) + + // Reset position when dialog opens + useEffect(() => { + if (open) { + setPosition('center') + setIsUserScrolled(false) + } else { + // Reset confirmation dialog state when main dialog closes + setShowCancelConfirm(false) + } + }, [open]) + + // Handle scroll position + useEffect(() => { + const container = historyRef.current + if (!container || isUserScrolled) return + + container.scrollTop = container.scrollHeight + }, [status?.history_messages, isUserScrolled]) + + const handleScroll = () => { + const container = historyRef.current + if (!container) return + + const isAtBottom = Math.abs( + (container.scrollHeight - container.scrollTop) - container.clientHeight + ) < 1 + + if (isAtBottom) { + setIsUserScrolled(false) + } else { + setIsUserScrolled(true) + } + } + + // Refresh status every 2 seconds + useEffect(() => { + if (!open) return + + const fetchStatus = async () => { + try { + const data = await getPipelineStatus() + setStatus(data) + } catch (err) { + toast.error(t('documentPanel.pipelineStatus.errors.fetchFailed', { error: errorMessage(err) })) + } + } + + fetchStatus() + const interval = setInterval(fetchStatus, 2000) + return () => clearInterval(interval) + }, [open, t]) + + // Handle cancel pipeline confirmation + const handleConfirmCancel = async () => { + setShowCancelConfirm(false) + try { + const result = await cancelPipeline() + if (result.status === 'cancellation_requested') { + toast.success(t('documentPanel.pipelineStatus.cancelSuccess')) + } else if (result.status === 'not_busy') { + toast.info(t('documentPanel.pipelineStatus.cancelNotBusy')) + } + } catch (err) { + toast.error(t('documentPanel.pipelineStatus.cancelFailed', { error: errorMessage(err) })) + } + } + + // Determine if cancel button should be enabled + const canCancel = status?.busy === true && !status?.cancellation_requested + + return ( + + + + {status?.job_name + ? `${t('documentPanel.pipelineStatus.jobName')}: ${status.job_name}, ${t('documentPanel.pipelineStatus.progress')}: ${status.cur_batch}/${status.batchs}` + : t('documentPanel.pipelineStatus.noActiveJob') + } + + + + {t('documentPanel.pipelineStatus.title')} + + + {/* Position control buttons */} +
    + + + +
    +     + + {/* Status Content */} +
    + {/* Pipeline Status - with cancel button */} +
    + {/* Left side: Status indicators */} +
    +
    +
    {t('documentPanel.pipelineStatus.busy')}:
    +
    +
    +
    +
    {t('documentPanel.pipelineStatus.requestPending')}:
    +
    +
    + {/* Only show cancellation status when it's requested */} + {status?.cancellation_requested && ( +
    +
    {t('documentPanel.pipelineStatus.cancellationRequested')}:
    +
    +
    + )} +
    + + {/* Right side: Cancel button - only show when pipeline is busy */} + {status?.busy && ( + + )} +
    + + {/* Job Information */} +
    +
    {t('documentPanel.pipelineStatus.jobName')}: {status?.job_name || '-'}
    +
    + {t('documentPanel.pipelineStatus.startTime')}: {status?.job_start + ? new Date(status.job_start).toLocaleString(undefined, { + year: 'numeric', + month: 'numeric', + day: 'numeric', + hour: 'numeric', + minute: 'numeric', + second: 'numeric' + }) + : '-'} + {t('documentPanel.pipelineStatus.progress')}: {status ? `${status.cur_batch}/${status.batchs} ${t('documentPanel.pipelineStatus.unit')}` : '-'} +
    +
    + + {/* History Messages */} +
    +
    {t('documentPanel.pipelineStatus.pipelineMessages')}:
    +
    + {status?.history_messages?.length ? ( + status.history_messages.map((msg, idx) => ( +
    {msg}
    + )) + ) : '-'} +
    +
    +
    + + + {/* Cancel Confirmation Dialog */} + + + + {t('documentPanel.pipelineStatus.cancelConfirmTitle')} + + {t('documentPanel.pipelineStatus.cancelConfirmDescription')} + + +
    + + +
    +     +     +
    + ) +} diff --git a/LightRAG/lightrag_webui/src/components/documents/UploadDocumentsDialog.tsx b/LightRAG/lightrag_webui/src/components/documents/UploadDocumentsDialog.tsx new file mode 100644 index 00000000..16e21e7d --- /dev/null +++ b/LightRAG/lightrag_webui/src/components/documents/UploadDocumentsDialog.tsx @@ -0,0 +1,220 @@ +import { useState, useCallback } from 'react' +import { FileRejection } from 'react-dropzone' +import Button from '@/components/ui/Button' +import { + Dialog, + DialogContent, + DialogDescription, + DialogHeader, + DialogTitle, + DialogTrigger +} from '@/components/ui/Dialog' +import FileUploader from '@/components/ui/FileUploader' +import { toast } from 'sonner' +import { errorMessage } from '@/lib/utils' +import { uploadDocument } from '@/api/lightrag' + +import { UploadIcon } from 'lucide-react' +import { useTranslation } from 'react-i18next' + +interface UploadDocumentsDialogProps { + onDocumentsUploaded?: () => Promise +} + +export default function UploadDocumentsDialog({ onDocumentsUploaded }: UploadDocumentsDialogProps) { + const { t } = useTranslation() + const [open, setOpen] = useState(false) + const [isUploading, setIsUploading] = useState(false) + const [progresses, setProgresses] = useState>({}) + const [fileErrors, setFileErrors] = useState>({}) + + const handleRejectedFiles = useCallback( + (rejectedFiles: FileRejection[]) => { + // Process rejected files and add them to fileErrors + rejectedFiles.forEach(({ file, errors }) => { + // Get the first error message + let errorMsg = errors[0]?.message || t('documentPanel.uploadDocuments.fileUploader.fileRejected', { name: file.name }) + + // Simplify error message for unsupported file types + if (errorMsg.includes('file-invalid-type')) { + errorMsg = t('documentPanel.uploadDocuments.fileUploader.unsupportedType') + } + + // Set progress to 100% to display error message + setProgresses((pre) => ({ + ...pre, + [file.name]: 100 + })) + + // Add error message to fileErrors + setFileErrors(prev => ({ + ...prev, + [file.name]: errorMsg + })) + }) + }, + [setProgresses, setFileErrors, t] + ) + + const handleDocumentsUpload = useCallback( + async (filesToUpload: File[]) => { + setIsUploading(true) + let hasSuccessfulUpload = false + + // Only clear errors for files that are being uploaded, keep errors for rejected files + setFileErrors(prev => { + const newErrors = { ...prev }; + filesToUpload.forEach(file => { + delete newErrors[file.name]; + }); + return newErrors; + }); + + // Show uploading toast + const toastId = toast.loading(t('documentPanel.uploadDocuments.batch.uploading')) + + try { + // Track errors locally to ensure we have the final state + const uploadErrors: Record = {} + + // Create a collator that supports Chinese sorting + const collator = new Intl.Collator(['zh-CN', 'en'], { + sensitivity: 'accent', // consider basic characters, accents, and case + numeric: true // enable numeric sorting, e.g., "File 10" will be after "File 2" + }); + const sortedFiles = [...filesToUpload].sort((a, b) => + collator.compare(a.name, b.name) + ); + + // Upload files in sequence, not parallel + for (const file of sortedFiles) { + try { + // Initialize upload progress + setProgresses((pre) => ({ + ...pre, + [file.name]: 0 + })) + + const result = await uploadDocument(file, (percentCompleted: number) => { + console.debug(t('documentPanel.uploadDocuments.single.uploading', { name: file.name, percent: percentCompleted })) + setProgresses((pre) => ({ + ...pre, + [file.name]: percentCompleted + })) + }) + + if (result.status === 'duplicated') { + uploadErrors[file.name] = t('documentPanel.uploadDocuments.fileUploader.duplicateFile') + setFileErrors(prev => ({ + ...prev, + [file.name]: t('documentPanel.uploadDocuments.fileUploader.duplicateFile') + })) + } else if (result.status !== 'success') { + uploadErrors[file.name] = result.message + setFileErrors(prev => ({ + ...prev, + [file.name]: result.message + })) + } else { + // Mark that we had at least one successful upload + hasSuccessfulUpload = true + } + } catch (err) { + console.error(`Upload failed for ${file.name}:`, err) + + // Handle HTTP errors, including 400 errors + let errorMsg = errorMessage(err) + + // If it's an axios error with response data, try to extract more detailed error info + if (err && typeof err === 'object' && 'response' in err) { + const axiosError = err as { response?: { status: number, data?: { detail?: string } } } + if (axiosError.response?.status === 400) { + // Extract specific error message from backend response + errorMsg = axiosError.response.data?.detail || errorMsg + } + + // Set progress to 100% to display error message + setProgresses((pre) => ({ + ...pre, + [file.name]: 100 + })) + } + + // Record error message in both local tracking and state + uploadErrors[file.name] = errorMsg + setFileErrors(prev => ({ + ...prev, + [file.name]: errorMsg + })) + } + } + + // Check if any files failed to upload using our local tracking + const hasErrors = Object.keys(uploadErrors).length > 0 + + // Update toast status + if (hasErrors) { + toast.error(t('documentPanel.uploadDocuments.batch.error'), { id: toastId }) + } else { + toast.success(t('documentPanel.uploadDocuments.batch.success'), { id: toastId }) + } + + // Only update if at least one file was uploaded successfully + if (hasSuccessfulUpload) { + // Refresh document list + if (onDocumentsUploaded) { + onDocumentsUploaded().catch(err => { + console.error('Error refreshing documents:', err) + }) + } + } + } catch (err) { + console.error('Unexpected error during upload:', err) + toast.error(t('documentPanel.uploadDocuments.generalError', { error: errorMessage(err) }), { id: toastId }) + } finally { + setIsUploading(false) + } + }, + [setIsUploading, setProgresses, setFileErrors, t, onDocumentsUploaded] + ) + + return ( + { + if (isUploading) { + return + } + if (!open) { + setProgresses({}) + setFileErrors({}) + } + setOpen(open) + }} + > + + + + e.preventDefault()}> + + {t('documentPanel.uploadDocuments.title')} + + {t('documentPanel.uploadDocuments.description')} + + + + + + ) +} diff --git a/LightRAG/lightrag_webui/src/components/graph/EditablePropertyRow.tsx b/LightRAG/lightrag_webui/src/components/graph/EditablePropertyRow.tsx new file mode 100644 index 00000000..1604bcdc --- /dev/null +++ b/LightRAG/lightrag_webui/src/components/graph/EditablePropertyRow.tsx @@ -0,0 +1,288 @@ +import { useState, useEffect } from 'react' +import { useTranslation } from 'react-i18next' +import { toast } from 'sonner' +import { updateEntity, updateRelation, checkEntityNameExists } from '@/api/lightrag' +import { useGraphStore } from '@/stores/graph' +import { useSettingsStore } from '@/stores/settings' +import { SearchHistoryManager } from '@/utils/SearchHistoryManager' +import { PropertyName, EditIcon, PropertyValue } from './PropertyRowComponents' +import PropertyEditDialog from './PropertyEditDialog' +import MergeDialog from './MergeDialog' + +/** + * Interface for the EditablePropertyRow component props + */ +interface EditablePropertyRowProps { + name: string // Property name to display and edit + value: any // Initial value of the property + onClick?: () => void // Optional click handler for the property value + nodeId?: string // ID of the node (for node type) + entityId?: string // ID of the entity (for node type) + edgeId?: string // ID of the edge (for edge type) + dynamicId?: string + entityType?: 'node' | 'edge' // Type of graph entity + sourceId?: string // Source node ID (for edge type) + targetId?: string // Target node ID (for edge type) + onValueChange?: (newValue: any) => void // Optional callback when value changes + isEditable?: boolean // Whether this property can be edited + tooltip?: string // Optional tooltip to display on hover +} + +/** + * EditablePropertyRow component that supports editing property values + * This component is used in the graph properties panel to display and edit entity properties + */ +const EditablePropertyRow = ({ + name, + value: initialValue, + onClick, + nodeId, + edgeId, + entityId, + dynamicId, + entityType, + sourceId, + targetId, + onValueChange, + isEditable = false, + tooltip +}: EditablePropertyRowProps) => { + const { t } = useTranslation() + const [isEditing, setIsEditing] = useState(false) + const [isSubmitting, setIsSubmitting] = useState(false) + const [currentValue, setCurrentValue] = useState(initialValue) + const [errorMessage, setErrorMessage] = useState(null) + const [mergeDialogOpen, setMergeDialogOpen] = useState(false) + const [mergeDialogInfo, setMergeDialogInfo] = useState<{ + targetEntity: string + sourceEntity: string + } | null>(null) + + useEffect(() => { + setCurrentValue(initialValue) + }, [initialValue]) + + const handleEditClick = () => { + if (isEditable && !isEditing) { + setIsEditing(true) + setErrorMessage(null) + } + } + + const handleCancel = () => { + setIsEditing(false) + setErrorMessage(null) + } + + const handleSave = async (value: string, options?: { allowMerge?: boolean }) => { + if (isSubmitting || value === String(currentValue)) { + setIsEditing(false) + setErrorMessage(null) + return + } + + setIsSubmitting(true) + setErrorMessage(null) + + try { + if (entityType === 'node' && entityId && nodeId) { + let updatedData = { [name]: value } + const allowMerge = options?.allowMerge ?? false + + if (name === 'entity_id') { + if (!allowMerge) { + const exists = await checkEntityNameExists(value) + if (exists) { + const errorMsg = t('graphPanel.propertiesView.errors.duplicateName') + setErrorMessage(errorMsg) + toast.error(errorMsg) + return + } + } + updatedData = { 'entity_name': value } + } + + const response = await updateEntity(entityId, updatedData, true, allowMerge) + const operationSummary = response.operation_summary + const operationStatus = operationSummary?.operation_status || 'complete_success' + const finalValue = operationSummary?.final_entity ?? value + + // Handle different operation statuses + if (operationStatus === 'success') { + if (operationSummary?.merged) { + // Node was successfully merged into an existing entity + setMergeDialogInfo({ + targetEntity: finalValue, + sourceEntity: entityId, + }) + setMergeDialogOpen(true) + + // Remove old entity name from search history + SearchHistoryManager.removeLabel(entityId) + + // Note: Search Label update is deferred until user clicks refresh button in merge dialog + + toast.success(t('graphPanel.propertiesView.success.entityMerged')) + } else { + // Node was updated/renamed normally + try { + const graphValue = name === 'entity_id' ? finalValue : value + await useGraphStore + .getState() + .updateNodeAndSelect(nodeId, entityId, name, graphValue) + } catch (error) { + console.error('Error updating node in graph:', error) + throw new Error('Failed to update node in graph') + } + + // Update search history: remove old name, add new name + if (name === 'entity_id') { + const currentLabel = useSettingsStore.getState().queryLabel + + SearchHistoryManager.removeLabel(entityId) + SearchHistoryManager.addToHistory(finalValue) + + // Trigger dropdown refresh to show updated search history + useSettingsStore.getState().triggerSearchLabelDropdownRefresh() + + // If current queryLabel is the old entity name, update to new name + if (currentLabel === entityId) { + useSettingsStore.getState().setQueryLabel(finalValue) + } + } + + toast.success(t('graphPanel.propertiesView.success.entityUpdated')) + } + + // Update local state and notify parent component + // For entity_id updates, use finalValue (which may be different due to merging) + // For other properties, use the original value the user entered + const valueToSet = name === 'entity_id' ? finalValue : value + setCurrentValue(valueToSet) + onValueChange?.(valueToSet) + + } else if (operationStatus === 'partial_success') { + // Partial success: update succeeded but merge failed + // Do NOT update graph data to keep frontend in sync with backend + const mergeError = operationSummary?.merge_error || 'Unknown error' + + const errorMsg = t('graphPanel.propertiesView.errors.updateSuccessButMergeFailed', { + error: mergeError + }) + setErrorMessage(errorMsg) + toast.error(errorMsg) + // Do not update currentValue or call onValueChange + return + + } else { + // Complete failure or unknown status + // Check if this was a merge attempt or just a regular update + if (operationSummary?.merge_status === 'failed') { + // Merge operation was attempted but failed + const mergeError = operationSummary?.merge_error || 'Unknown error' + const errorMsg = t('graphPanel.propertiesView.errors.mergeFailed', { + error: mergeError + }) + setErrorMessage(errorMsg) + toast.error(errorMsg) + } else { + // Regular update failed (no merge involved) + const errorMsg = t('graphPanel.propertiesView.errors.updateFailed') + setErrorMessage(errorMsg) + toast.error(errorMsg) + } + // Do not update currentValue or call onValueChange + return + } + } else if (entityType === 'edge' && sourceId && targetId && edgeId && dynamicId) { + const updatedData = { [name]: value } + await updateRelation(sourceId, targetId, updatedData) + try { + await useGraphStore.getState().updateEdgeAndSelect(edgeId, dynamicId, sourceId, targetId, name, value) + } catch (error) { + console.error(`Error updating edge ${sourceId}->${targetId} in graph:`, error) + throw new Error('Failed to update edge in graph') + } + toast.success(t('graphPanel.propertiesView.success.relationUpdated')) + setCurrentValue(value) + onValueChange?.(value) + } + + setIsEditing(false) + } catch (error) { + console.error('Error updating property:', error) + const errorMsg = error instanceof Error ? error.message : t('graphPanel.propertiesView.errors.updateFailed') + setErrorMessage(errorMsg) + toast.error(errorMsg) + return + } finally { + setIsSubmitting(false) + } + } + + const handleMergeRefresh = (useMergedStart: boolean) => { + const info = mergeDialogInfo + const graphState = useGraphStore.getState() + const settingsState = useSettingsStore.getState() + const currentLabel = settingsState.queryLabel + + // Clear graph state + graphState.clearSelection() + graphState.setGraphDataFetchAttempted(false) + graphState.setLastSuccessfulQueryLabel('') + + if (useMergedStart && info?.targetEntity) { + // Use merged entity as new start point (might already be set in handleSave) + settingsState.setQueryLabel(info.targetEntity) + } else { + // Keep current start point - refresh by resetting and restoring label + // This handles the case where user wants to stay with current label + settingsState.setQueryLabel('') + setTimeout(() => { + settingsState.setQueryLabel(currentLabel) + }, 50) + } + + // Force graph re-render and reset zoom/scale (same as refresh button behavior) + graphState.incrementGraphDataVersion() + + setMergeDialogOpen(false) + setMergeDialogInfo(null) + toast.info(t('graphPanel.propertiesView.mergeDialog.refreshing')) + } + + return ( +
    + + : + + + + { + setMergeDialogOpen(open) + if (!open) { + setMergeDialogInfo(null) + } + }} + onRefresh={handleMergeRefresh} + /> +
    + ) +} + +export default EditablePropertyRow diff --git a/LightRAG/lightrag_webui/src/components/graph/FocusOnNode.tsx b/LightRAG/lightrag_webui/src/components/graph/FocusOnNode.tsx new file mode 100644 index 00000000..3f3cf027 --- /dev/null +++ b/LightRAG/lightrag_webui/src/components/graph/FocusOnNode.tsx @@ -0,0 +1,54 @@ +import { useCamera, useSigma } from '@react-sigma/core' +import { useEffect } from 'react' +import { useGraphStore } from '@/stores/graph' + +/** + * Component that highlights a node and centers the camera on it. + */ +const FocusOnNode = ({ node, move }: { node: string | null; move?: boolean }) => { + const sigma = useSigma() + const { gotoNode } = useCamera() + + /** + * When the selected item changes, highlighted the node and center the camera on it. + */ + useEffect(() => { + const graph = sigma.getGraph(); + + if (move) { + if (node && graph.hasNode(node)) { + try { + graph.setNodeAttribute(node, 'highlighted', true); + gotoNode(node); + } catch (error) { + console.error('Error focusing on node:', error); + } + } else { + // If no node is selected but move is true, reset to default view + sigma.setCustomBBox(null); + sigma.getCamera().animate({ x: 0.5, y: 0.5, ratio: 1 }, { duration: 0 }); + } + useGraphStore.getState().setMoveToSelectedNode(false); + } else if (node && graph.hasNode(node)) { + try { + graph.setNodeAttribute(node, 'highlighted', true); + } catch (error) { + console.error('Error highlighting node:', error); + } + } + + return () => { + if (node && graph.hasNode(node)) { + try { + graph.setNodeAttribute(node, 'highlighted', false); + } catch (error) { + console.error('Error cleaning up node highlight:', error); + } + } + } + }, [node, move, sigma, gotoNode]) + + return null +} + +export default FocusOnNode diff --git a/LightRAG/lightrag_webui/src/components/graph/FullScreenControl.tsx b/LightRAG/lightrag_webui/src/components/graph/FullScreenControl.tsx new file mode 100644 index 00000000..dd8e8d8e --- /dev/null +++ b/LightRAG/lightrag_webui/src/components/graph/FullScreenControl.tsx @@ -0,0 +1,29 @@ +import { useFullScreen } from '@react-sigma/core' +import { MaximizeIcon, MinimizeIcon } from 'lucide-react' +import { controlButtonVariant } from '@/lib/constants' +import Button from '@/components/ui/Button' +import { useTranslation } from 'react-i18next' + +/** + * Component that toggles full screen mode. + */ +const FullScreenControl = () => { + const { isFullScreen, toggle } = useFullScreen() + const { t } = useTranslation() + + return ( + <> + {isFullScreen ? ( + + ) : ( + + )} + + ) +} + +export default FullScreenControl diff --git a/LightRAG/lightrag_webui/src/components/graph/GraphControl.tsx b/LightRAG/lightrag_webui/src/components/graph/GraphControl.tsx new file mode 100644 index 00000000..daa55b50 --- /dev/null +++ b/LightRAG/lightrag_webui/src/components/graph/GraphControl.tsx @@ -0,0 +1,362 @@ +import { useRegisterEvents, useSetSettings, useSigma } from '@react-sigma/core' +import { AbstractGraph } from 'graphology-types' +// import { useLayoutCircular } from '@react-sigma/layout-circular' +import { useLayoutForceAtlas2 } from '@react-sigma/layout-forceatlas2' +import { useEffect, useState } from 'react' + +// import useRandomGraph, { EdgeType, NodeType } from '@/hooks/useRandomGraph' +import { EdgeType, NodeType } from '@/hooks/useLightragGraph' +import useTheme from '@/hooks/useTheme' +import * as Constants from '@/lib/constants' + +import { useSettingsStore } from '@/stores/settings' +import { useGraphStore } from '@/stores/graph' + +const isButtonPressed = (ev: MouseEvent | TouchEvent) => { + if (ev.type.startsWith('mouse')) { + if ((ev as MouseEvent).buttons !== 0) { + return true + } + } + return false +} + +const GraphControl = ({ disableHoverEffect }: { disableHoverEffect?: boolean }) => { + const sigma = useSigma() + const registerEvents = useRegisterEvents() + const setSettings = useSetSettings() + + const maxIterations = useSettingsStore.use.graphLayoutMaxIterations() + const { assign: assignLayout } = useLayoutForceAtlas2({ + iterations: maxIterations + }) + + const { theme } = useTheme() + const hideUnselectedEdges = useSettingsStore.use.enableHideUnselectedEdges() + const enableEdgeEvents = useSettingsStore.use.enableEdgeEvents() + const renderEdgeLabels = useSettingsStore.use.showEdgeLabel() + const renderLabels = useSettingsStore.use.showNodeLabel() + const minEdgeSize = useSettingsStore.use.minEdgeSize() + const maxEdgeSize = useSettingsStore.use.maxEdgeSize() + const selectedNode = useGraphStore.use.selectedNode() + const focusedNode = useGraphStore.use.focusedNode() + const selectedEdge = useGraphStore.use.selectedEdge() + const focusedEdge = useGraphStore.use.focusedEdge() + const sigmaGraph = useGraphStore.use.sigmaGraph() + + // Track system theme changes when theme is set to 'system' + const [systemThemeIsDark, setSystemThemeIsDark] = useState(() => + window.matchMedia('(prefers-color-scheme: dark)').matches + ) + + useEffect(() => { + if (theme === 'system') { + const mediaQuery = window.matchMedia('(prefers-color-scheme: dark)') + const handler = (e: MediaQueryListEvent) => setSystemThemeIsDark(e.matches) + mediaQuery.addEventListener('change', handler) + return () => mediaQuery.removeEventListener('change', handler) + } + }, [theme]) + + /** + * When component mount or maxIterations changes + * => ensure graph reference and apply layout + */ + useEffect(() => { + if (sigmaGraph && sigma) { + // Ensure sigma binding to sigmaGraph + try { + if (typeof sigma.setGraph === 'function') { + sigma.setGraph(sigmaGraph as unknown as AbstractGraph); + console.log('Binding graph to sigma instance'); + } else { + (sigma as any).graph = sigmaGraph; + console.warn('Simgma missing setGraph function, set graph property directly'); + } + } catch (error) { + console.error('Error setting graph on sigma instance:', error); + } + + assignLayout(); + console.log('Initial layout applied to graph'); + } + }, [sigma, sigmaGraph, assignLayout, maxIterations]) + + /** + * Ensure the sigma instance is set in the store + * This provides a backup in case the instance wasn't set in GraphViewer + */ + useEffect(() => { + if (sigma) { + // Double-check that the store has the sigma instance + const currentInstance = useGraphStore.getState().sigmaInstance; + if (!currentInstance) { + console.log('Setting sigma instance from GraphControl'); + useGraphStore.getState().setSigmaInstance(sigma); + } + } + }, [sigma]); + + /** + * When component mount + * => register events + */ + useEffect(() => { + const { setFocusedNode, setSelectedNode, setFocusedEdge, setSelectedEdge, clearSelection } = + useGraphStore.getState() + + // Define event types + type NodeEvent = { node: string; event: { original: MouseEvent | TouchEvent } } + type EdgeEvent = { edge: string; event: { original: MouseEvent | TouchEvent } } + + // Register all events, but edge events will only be processed if enableEdgeEvents is true + const events: Record = { + enterNode: (event: NodeEvent) => { + if (!isButtonPressed(event.event.original)) { + const graph = sigma.getGraph() + if (graph.hasNode(event.node)) { + setFocusedNode(event.node) + } + } + }, + leaveNode: (event: NodeEvent) => { + if (!isButtonPressed(event.event.original)) { + setFocusedNode(null) + } + }, + clickNode: (event: NodeEvent) => { + const graph = sigma.getGraph() + if (graph.hasNode(event.node)) { + setSelectedNode(event.node) + setSelectedEdge(null) + } + }, + clickStage: () => clearSelection() + } + + // Only add edge event handlers if enableEdgeEvents is true + if (enableEdgeEvents) { + events.clickEdge = (event: EdgeEvent) => { + setSelectedEdge(event.edge) + setSelectedNode(null) + } + + events.enterEdge = (event: EdgeEvent) => { + if (!isButtonPressed(event.event.original)) { + setFocusedEdge(event.edge) + } + } + + events.leaveEdge = (event: EdgeEvent) => { + if (!isButtonPressed(event.event.original)) { + setFocusedEdge(null) + } + } + } + + // Register the events + registerEvents(events) + + // Cleanup function - basic cleanup without relying on specific APIs + return () => { + try { + console.log('Cleaning up graph event listeners') + } catch (error) { + console.warn('Error cleaning up graph event listeners:', error) + } + } + }, [registerEvents, enableEdgeEvents, sigma]) + + /** + * When edge size settings change, recalculate edge sizes and refresh the sigma instance + * to ensure changes take effect immediately + */ + useEffect(() => { + if (sigma && sigmaGraph) { + // Get the graph from sigma + const graph = sigma.getGraph() + + // Find min and max weight values + let minWeight = Number.MAX_SAFE_INTEGER + let maxWeight = 0 + + graph.forEachEdge(edge => { + // Get original weight (before scaling) + const weight = graph.getEdgeAttribute(edge, 'originalWeight') || 1 + if (typeof weight === 'number') { + minWeight = Math.min(minWeight, weight) + maxWeight = Math.max(maxWeight, weight) + } + }) + + // Scale edge sizes based on weight range and current min/max edge size settings + const weightRange = maxWeight - minWeight + if (weightRange > 0) { + const sizeScale = maxEdgeSize - minEdgeSize + graph.forEachEdge(edge => { + const weight = graph.getEdgeAttribute(edge, 'originalWeight') || 1 + if (typeof weight === 'number') { + const scaledSize = minEdgeSize + sizeScale * Math.pow((weight - minWeight) / weightRange, 0.5) + graph.setEdgeAttribute(edge, 'size', scaledSize) + } + }) + } else { + // If all weights are the same, use default size + graph.forEachEdge(edge => { + graph.setEdgeAttribute(edge, 'size', minEdgeSize) + }) + } + + // Refresh the sigma instance to apply changes + sigma.refresh() + } + }, [sigma, sigmaGraph, minEdgeSize, maxEdgeSize]) + + + /** + * When component mount or hovered node change + * => Setting the sigma reducers + */ + useEffect(() => { + // Check if dark mode is actually applied (handles both 'dark' theme and 'system' theme when OS is dark) + const isDarkTheme = theme === 'dark' || + (theme === 'system' && window.document.documentElement.classList.contains('dark')) + const labelColor = isDarkTheme ? Constants.labelColorDarkTheme : undefined + const edgeColor = isDarkTheme ? Constants.edgeColorDarkTheme : undefined + + // Update all dynamic settings directly without recreating the sigma container + setSettings({ + // Update display settings + enableEdgeEvents, + renderEdgeLabels, + renderLabels, + + // Node reducer for node appearance + nodeReducer: (node, data) => { + const graph = sigma.getGraph() + + // Add defensive check for node existence during theme switching + if (!graph.hasNode(node)) { + console.warn(`Node ${node} not found in graph during theme switch, returning default data`) + return { ...data, highlighted: false, labelColor } + } + + const newData: NodeType & { + labelColor?: string + borderColor?: string + } = { ...data, highlighted: data.highlighted || false, labelColor } + + if (!disableHoverEffect) { + newData.highlighted = false + const _focusedNode = focusedNode || selectedNode + const _focusedEdge = focusedEdge || selectedEdge + + if (_focusedNode && graph.hasNode(_focusedNode)) { + try { + if (node === _focusedNode || graph.neighbors(_focusedNode).includes(node)) { + newData.highlighted = true + if (node === selectedNode) { + newData.borderColor = Constants.nodeBorderColorSelected + } + } + } catch (error) { + console.error('Error in nodeReducer:', error); + return { ...data, highlighted: false, labelColor } + } + } else if (_focusedEdge && graph.hasEdge(_focusedEdge)) { + try { + if (graph.extremities(_focusedEdge).includes(node)) { + newData.highlighted = true + newData.size = 3 + } + } catch (error) { + console.error('Error accessing edge extremities in nodeReducer:', error); + return { ...data, highlighted: false, labelColor } + } + } else { + return newData + } + + if (newData.highlighted) { + if (isDarkTheme) { + newData.labelColor = Constants.LabelColorHighlightedDarkTheme + } + } else { + newData.color = Constants.nodeColorDisabled + } + } + return newData + }, + + // Edge reducer for edge appearance + edgeReducer: (edge, data) => { + const graph = sigma.getGraph() + + // Add defensive check for edge existence during theme switching + if (!graph.hasEdge(edge)) { + console.warn(`Edge ${edge} not found in graph during theme switch, returning default data`) + return { ...data, hidden: false, labelColor, color: edgeColor } + } + + const newData = { ...data, hidden: false, labelColor, color: edgeColor } + + if (!disableHoverEffect) { + const _focusedNode = focusedNode || selectedNode + // Choose edge highlight color based on theme + const edgeHighlightColor = isDarkTheme + ? Constants.edgeColorHighlightedDarkTheme + : Constants.edgeColorHighlightedLightTheme + + if (_focusedNode && graph.hasNode(_focusedNode)) { + try { + if (hideUnselectedEdges) { + if (!graph.extremities(edge).includes(_focusedNode)) { + newData.hidden = true + } + } else { + if (graph.extremities(edge).includes(_focusedNode)) { + newData.color = edgeHighlightColor + } + } + } catch (error) { + console.error('Error in edgeReducer:', error); + return { ...data, hidden: false, labelColor, color: edgeColor } + } + } else { + const _selectedEdge = selectedEdge && graph.hasEdge(selectedEdge) ? selectedEdge : null; + const _focusedEdge = focusedEdge && graph.hasEdge(focusedEdge) ? focusedEdge : null; + + if (_selectedEdge || _focusedEdge) { + if (edge === _selectedEdge) { + newData.color = Constants.edgeColorSelected + } else if (edge === _focusedEdge) { + newData.color = edgeHighlightColor + } else if (hideUnselectedEdges) { + newData.hidden = true + } + } + } + } + return newData + } + }) + }, [ + selectedNode, + focusedNode, + selectedEdge, + focusedEdge, + setSettings, + sigma, + disableHoverEffect, + theme, + systemThemeIsDark, + hideUnselectedEdges, + enableEdgeEvents, + renderEdgeLabels, + renderLabels + ]) + + return null +} + +export default GraphControl diff --git a/LightRAG/lightrag_webui/src/components/graph/GraphLabels.tsx b/LightRAG/lightrag_webui/src/components/graph/GraphLabels.tsx new file mode 100644 index 00000000..e2ac7aae --- /dev/null +++ b/LightRAG/lightrag_webui/src/components/graph/GraphLabels.tsx @@ -0,0 +1,316 @@ +import { useCallback, useEffect, useState, useRef } from 'react' +import { AsyncSelect } from '@/components/ui/AsyncSelect' +import { useSettingsStore } from '@/stores/settings' +import { useGraphStore } from '@/stores/graph' +import { useBackendState } from '@/stores/state' +import { + dropdownDisplayLimit, + controlButtonVariant, + popularLabelsDefaultLimit, + searchLabelsDefaultLimit +} from '@/lib/constants' +import { useTranslation } from 'react-i18next' +import { RefreshCw } from 'lucide-react' +import Button from '@/components/ui/Button' +import { SearchHistoryManager } from '@/utils/SearchHistoryManager' +import { getPopularLabels, searchLabels } from '@/api/lightrag' + +const GraphLabels = () => { + const { t } = useTranslation() + const label = useSettingsStore.use.queryLabel() + const dropdownRefreshTrigger = useSettingsStore.use.searchLabelDropdownRefreshTrigger() + const [isRefreshing, setIsRefreshing] = useState(false) + const [refreshTrigger, setRefreshTrigger] = useState(0) + const [selectKey, setSelectKey] = useState(0) + + // Pipeline state monitoring + const pipelineBusy = useBackendState.use.pipelineBusy() + const prevPipelineBusy = useRef(undefined) + const shouldRefreshPopularLabelsRef = useRef(false) + + // Dynamic tooltip based on current label state + const getRefreshTooltip = useCallback(() => { + if (isRefreshing) { + return t('graphPanel.graphLabels.refreshingTooltip') + } + + if (!label || label === '*') { + return t('graphPanel.graphLabels.refreshGlobalTooltip') + } else { + return t('graphPanel.graphLabels.refreshCurrentLabelTooltip', { label }) + } + }, [label, t, isRefreshing]) + + // Initialize search history on component mount + useEffect(() => { + const initializeHistory = async () => { + const history = SearchHistoryManager.getHistory() + + if (history.length === 0) { + // If no history exists, fetch popular labels and initialize + try { + const popularLabels = await getPopularLabels(popularLabelsDefaultLimit) + await SearchHistoryManager.initializeWithDefaults(popularLabels) + } catch (error) { + console.error('Failed to initialize search history:', error) + // No fallback needed, API is the source of truth + } + } + } + + initializeHistory() + }, []) + + // Force AsyncSelect to re-render when label changes externally (e.g., from entity rename/merge) + useEffect(() => { + setSelectKey(prev => prev + 1) + }, [label]) + + // Force AsyncSelect to re-render when dropdown refresh is triggered (e.g., after entity rename) + useEffect(() => { + if (dropdownRefreshTrigger > 0) { + setSelectKey(prev => prev + 1) + } + }, [dropdownRefreshTrigger]) + + // Monitor pipeline state changes: busy -> idle + useEffect(() => { + if (prevPipelineBusy.current === true && pipelineBusy === false) { + console.log('Pipeline changed from busy to idle, marking for popular labels refresh') + shouldRefreshPopularLabelsRef.current = true + } + prevPipelineBusy.current = pipelineBusy + }, [pipelineBusy]) + + // Helper: Reload popular labels from backend + const reloadPopularLabels = useCallback(async () => { + if (!shouldRefreshPopularLabelsRef.current) return + + console.log('Reloading popular labels (triggered by pipeline idle)') + try { + const popularLabels = await getPopularLabels(popularLabelsDefaultLimit) + SearchHistoryManager.clearHistory() + + if (popularLabels.length === 0) { + const fallbackLabels = ['entity', 'relationship', 'document', 'concept'] + await SearchHistoryManager.initializeWithDefaults(fallbackLabels) + } else { + await SearchHistoryManager.initializeWithDefaults(popularLabels) + } + } catch (error) { + console.error('Failed to reload popular labels:', error) + const fallbackLabels = ['entity', 'relationship', 'document'] + SearchHistoryManager.clearHistory() + await SearchHistoryManager.initializeWithDefaults(fallbackLabels) + } finally { + // Always clear the flag + shouldRefreshPopularLabelsRef.current = false + } + }, []) + + // Helper: Bump dropdown data to trigger refresh + const bumpDropdownData = useCallback(({ forceSelectKey = false } = {}) => { + setRefreshTrigger(prev => prev + 1) + if (forceSelectKey) { + setSelectKey(prev => prev + 1) + } + }, []) + + const fetchData = useCallback( + async (query?: string): Promise => { + let results: string[] = []; + if (!query || query.trim() === '' || query.trim() === '*') { + // Empty query: return search history + results = SearchHistoryManager.getHistoryLabels(dropdownDisplayLimit) + } else { + // Non-empty query: call backend search API + try { + const apiResults = await searchLabels(query.trim(), searchLabelsDefaultLimit) + results = apiResults.length <= dropdownDisplayLimit + ? apiResults + : [...apiResults.slice(0, dropdownDisplayLimit), '...'] + } catch (error) { + console.error('Search API failed, falling back to local history search:', error) + + // Fallback to local history search + const history = SearchHistoryManager.getHistory() + const queryLower = query.toLowerCase().trim() + results = history + .filter(item => item.label.toLowerCase().includes(queryLower)) + .map(item => item.label) + .slice(0, dropdownDisplayLimit) + } + } + // Always show '*' at the top, and remove duplicates + const finalResults = ['*', ...results.filter(label => label !== '*')]; + return finalResults; + }, + // eslint-disable-next-line react-hooks/exhaustive-deps + [refreshTrigger] // Intentionally added to trigger re-creation when data changes + ) + + const handleRefresh = useCallback(async () => { + setIsRefreshing(true) + + // Clear legend cache to ensure legend is re-generated on refresh + useGraphStore.getState().setTypeColorMap(new Map()) + + try { + let currentLabel = label + + // If queryLabel is empty, set it to '*' + if (!currentLabel || currentLabel.trim() === '') { + useSettingsStore.getState().setQueryLabel('*') + currentLabel = '*' + } + + // Scenario 1: Manual refresh - reload popular labels if flag is set (regardless of current label) + if (shouldRefreshPopularLabelsRef.current) { + await reloadPopularLabels() + bumpDropdownData({ forceSelectKey: true }) + } + + if (currentLabel && currentLabel !== '*') { + // Scenario 1: Has specific label, try to refresh current label + console.log(`Refreshing current label: ${currentLabel}`) + + // Reset graph data fetch status to trigger refresh + useGraphStore.getState().setGraphDataFetchAttempted(false) + useGraphStore.getState().setLastSuccessfulQueryLabel('') + + // Force data refresh for current label + useGraphStore.getState().incrementGraphDataVersion() + + // Note: If the current label has no data after refresh, + // the fallback logic would be handled by the graph component itself + // For now, we keep the current label and let the user see the result + + } else { + // Scenario 3: queryLabel is "*", refresh global data and popular labels + console.log('Refreshing global data and popular labels') + + try { + // Re-fetch popular labels and update search history (if not already done) + const popularLabels = await getPopularLabels(popularLabelsDefaultLimit) + SearchHistoryManager.clearHistory() + + if (popularLabels.length === 0) { + // If no popular labels, provide fallback defaults + const fallbackLabels = ['entity', 'relationship', 'document', 'concept'] + await SearchHistoryManager.initializeWithDefaults(fallbackLabels) + } else { + await SearchHistoryManager.initializeWithDefaults(popularLabels) + } + } catch (error) { + console.error('Failed to reload popular labels:', error) + // Provide fallback even if API fails + const fallbackLabels = ['entity', 'relationship', 'document'] + SearchHistoryManager.clearHistory() + await SearchHistoryManager.initializeWithDefaults(fallbackLabels) + } + + // Reset graph data fetch status + useGraphStore.getState().setGraphDataFetchAttempted(false) + useGraphStore.getState().setLastSuccessfulQueryLabel('') + + // Force global data refresh + useGraphStore.getState().incrementGraphDataVersion() + + // Ensure data update completes before triggering UI refresh + await new Promise(resolve => setTimeout(resolve, 0)) + + // Trigger both refresh mechanisms to ensure dropdown updates + setRefreshTrigger(prev => prev + 1) + setSelectKey(prev => prev + 1) + } + } catch (error) { + console.error('Error during refresh:', error) + } finally { + setIsRefreshing(false) + } + }, [label, reloadPopularLabels, bumpDropdownData]) + + // Handle dropdown before open - reload popular labels if needed + const handleDropdownBeforeOpen = useCallback(async () => { + const currentLabel = useSettingsStore.getState().queryLabel + if (shouldRefreshPopularLabelsRef.current && (!currentLabel || currentLabel === '*')) { + await reloadPopularLabels() + bumpDropdownData() + } + }, [reloadPopularLabels, bumpDropdownData]) + + return ( +
    + {/* Always show refresh button */} + +
    + + key={selectKey} // Force re-render when data changes + className="min-w-[300px]" + triggerClassName="max-h-8 w-full overflow-hidden" + searchInputClassName="max-h-8" + triggerTooltip={t('graphPanel.graphLabels.selectTooltip')} + fetcher={fetchData} + onBeforeOpen={handleDropdownBeforeOpen} + renderOption={(item) => ( +
    + {item} +
    + )} + getOptionValue={(item) => item} + getDisplayValue={(item) => ( +
    + {item} +
    + )} + notFound={
    {t('graphPanel.graphLabels.noLabels')}
    } + ariaLabel={t('graphPanel.graphLabels.label')} + placeholder={t('graphPanel.graphLabels.placeholder')} + searchPlaceholder={t('graphPanel.graphLabels.placeholder')} + noResultsMessage={t('graphPanel.graphLabels.noLabels')} + value={label !== null ? label : '*'} + onChange={(newLabel) => { + const currentLabel = useSettingsStore.getState().queryLabel; + + // select the last item means query all + if (newLabel === '...') { + newLabel = '*'; + } + + // Handle reselecting the same label + if (newLabel === currentLabel && newLabel !== '*') { + newLabel = '*'; + } + + // Add selected label to search history (except for special cases) + if (newLabel && newLabel !== '*' && newLabel !== '...' && newLabel.trim() !== '') { + SearchHistoryManager.addToHistory(newLabel); + } + + // Reset graphDataFetchAttempted flag to ensure data fetch is triggered + useGraphStore.getState().setGraphDataFetchAttempted(false); + + // Update the label to trigger data loading + useSettingsStore.getState().setQueryLabel(newLabel); + + // Force graph re-render and reset zoom/scale (must be AFTER setQueryLabel) + useGraphStore.getState().incrementGraphDataVersion(); + }} + clearable={false} // Prevent clearing value on reselect + debounceTime={500} + /> +
    +
    + ) +} + +export default GraphLabels diff --git a/LightRAG/lightrag_webui/src/components/graph/GraphSearch.tsx b/LightRAG/lightrag_webui/src/components/graph/GraphSearch.tsx new file mode 100644 index 00000000..5971bddb --- /dev/null +++ b/LightRAG/lightrag_webui/src/components/graph/GraphSearch.tsx @@ -0,0 +1,232 @@ +import { FC, useCallback, useEffect } from 'react' +import { + EdgeById, + GraphSearchInputProps, + GraphSearchContextProviderProps +} from '@react-sigma/graph-search' +import { AsyncSearch } from '@/components/ui/AsyncSearch' +import { searchResultLimit } from '@/lib/constants' +import { useGraphStore } from '@/stores/graph' +import MiniSearch from 'minisearch' +import { useTranslation } from 'react-i18next' + +// Message item identifier for search results +export const messageId = '__message_item' + +// Search result option item interface +export interface OptionItem { + id: string + type: 'nodes' | 'edges' | 'message' + message?: string +} + +const NodeOption = ({ id }: { id: string }) => { + const graph = useGraphStore.use.sigmaGraph() + + // Early return if no graph or node doesn't exist + if (!graph?.hasNode(id)) { + return null + } + + // Safely get node attributes with fallbacks + const label = graph.getNodeAttribute(id, 'label') || id + const color = graph.getNodeAttribute(id, 'color') || '#666' + const size = graph.getNodeAttribute(id, 'size') || 4 + + // Custom node display component that doesn't rely on @react-sigma/graph-search + return ( +
    +
    + {label} +
    + ) +} + +function OptionComponent(item: OptionItem) { + return ( +
    + {item.type === 'nodes' && } + {item.type === 'edges' && } + {item.type === 'message' &&
    {item.message}
    } +
    + ) +} + + +/** + * Component thats display the search input. + */ +export const GraphSearchInput = ({ + onChange, + onFocus, + value +}: { + onChange: GraphSearchInputProps['onChange'] + onFocus?: GraphSearchInputProps['onFocus'] + value?: GraphSearchInputProps['value'] +}) => { + const { t } = useTranslation() + const graph = useGraphStore.use.sigmaGraph() + const searchEngine = useGraphStore.use.searchEngine() + + // Reset search engine when graph changes + useEffect(() => { + if (graph) { + useGraphStore.getState().resetSearchEngine() + } + }, [graph]); + + // Create search engine when needed + useEffect(() => { + // Skip if no graph, empty graph, or search engine already exists + if (!graph || graph.nodes().length === 0 || searchEngine) { + return + } + + // Create new search engine + const newSearchEngine = new MiniSearch({ + idField: 'id', + fields: ['label'], + searchOptions: { + prefix: true, + fuzzy: 0.2, + boost: { + label: 2 + } + } + }) + + // Add nodes to search engine with safety checks + const documents = graph.nodes() + .filter(id => graph.hasNode(id)) // Ensure node exists before accessing attributes + .map((id: string) => ({ + id: id, + label: graph.getNodeAttribute(id, 'label') + })) + + if (documents.length > 0) { + newSearchEngine.addAll(documents) + } + + // Update search engine in store + useGraphStore.getState().setSearchEngine(newSearchEngine) + }, [graph, searchEngine]) + + /** + * Loading the options while the user is typing. + */ + const loadOptions = useCallback( + async (query?: string): Promise => { + if (onFocus) onFocus(null) + + // Safety checks to prevent crashes + if (!graph || !searchEngine) { + return [] + } + + // Verify graph has nodes before proceeding + if (graph.nodes().length === 0) { + return [] + } + + // If no query, return some nodes for user to select + if (!query) { + const nodeIds = graph.nodes() + .filter(id => graph.hasNode(id)) + .slice(0, searchResultLimit) + return nodeIds.map(id => ({ + id, + type: 'nodes' + })) + } + + // If has query, search nodes and verify they still exist + let result: OptionItem[] = searchEngine.search(query) + .filter((r: { id: string }) => graph.hasNode(r.id)) + .map((r: { id: string }) => ({ + id: r.id, + type: 'nodes' + })) + + // Add middle-content matching if results are few + // This enables matching content in the middle of text, not just from the beginning + if (result.length < 5) { + // Get already matched IDs to avoid duplicates + const matchedIds = new Set(result.map(item => item.id)) + + // Perform middle-content matching on all nodes with safety checks + const middleMatchResults = graph.nodes() + .filter(id => { + // Skip already matched nodes + if (matchedIds.has(id)) return false + + // Ensure node exists before accessing attributes + if (!graph.hasNode(id)) return false + + // Get node label safely + const label = graph.getNodeAttribute(id, 'label') + // Match if label contains query string but doesn't start with it + return label && + typeof label === 'string' && + !label.toLowerCase().startsWith(query.toLowerCase()) && + label.toLowerCase().includes(query.toLowerCase()) + }) + .map(id => ({ + id, + type: 'nodes' as const + })) + + // Merge results + result = [...result, ...middleMatchResults] + } + + // prettier-ignore + return result.length <= searchResultLimit + ? result + : [ + ...result.slice(0, searchResultLimit), + { + type: 'message', + id: messageId, + message: t('graphPanel.search.message', { count: result.length - searchResultLimit }) + } + ] + }, + [graph, searchEngine, onFocus, t] + ) + + return ( + item.id} + value={value && value.type !== 'message' ? value.id : null} + onChange={(id) => { + if (id !== messageId) onChange(id ? { id, type: 'nodes' } : null) + }} + onFocus={(id) => { + if (id !== messageId && onFocus) onFocus(id ? { id, type: 'nodes' } : null) + }} + ariaLabel={t('graphPanel.search.placeholder')} + placeholder={t('graphPanel.search.placeholder')} + noResultsMessage={t('graphPanel.search.placeholder')} + /> + ) +} + +/** + * Component that display the search. + */ +const GraphSearch: FC = ({ ...props }) => { + return +} + +export default GraphSearch diff --git a/LightRAG/lightrag_webui/src/components/graph/LayoutsControl.tsx b/LightRAG/lightrag_webui/src/components/graph/LayoutsControl.tsx new file mode 100644 index 00000000..c4ec67be --- /dev/null +++ b/LightRAG/lightrag_webui/src/components/graph/LayoutsControl.tsx @@ -0,0 +1,345 @@ +import { useSigma } from '@react-sigma/core' +import { animateNodes } from 'sigma/utils' +import { useLayoutCirclepack } from '@react-sigma/layout-circlepack' +import { useLayoutCircular } from '@react-sigma/layout-circular' +import { LayoutHook, LayoutWorkerHook, WorkerLayoutControlProps } from '@react-sigma/layout-core' +import { useLayoutForce, useWorkerLayoutForce } from '@react-sigma/layout-force' +import { useLayoutForceAtlas2, useWorkerLayoutForceAtlas2 } from '@react-sigma/layout-forceatlas2' +import { useLayoutNoverlap, useWorkerLayoutNoverlap } from '@react-sigma/layout-noverlap' +import { useLayoutRandom } from '@react-sigma/layout-random' +import { useCallback, useMemo, useState, useEffect, useRef } from 'react' + +import Button from '@/components/ui/Button' +import { Popover, PopoverContent, PopoverTrigger } from '@/components/ui/Popover' +import { Command, CommandGroup, CommandItem, CommandList } from '@/components/ui/Command' +import { controlButtonVariant } from '@/lib/constants' +import { useSettingsStore } from '@/stores/settings' + +import { GripIcon, PlayIcon, PauseIcon } from 'lucide-react' +import { useTranslation } from 'react-i18next' + +type LayoutName = + | 'Circular' + | 'Circlepack' + | 'Random' + | 'Noverlaps' + | 'Force Directed' + | 'Force Atlas' + +// Extend WorkerLayoutControlProps to include mainLayout +interface ExtendedWorkerLayoutControlProps extends WorkerLayoutControlProps { + mainLayout: LayoutHook; +} + +const WorkerLayoutControl = ({ layout, autoRunFor, mainLayout }: ExtendedWorkerLayoutControlProps) => { + const sigma = useSigma() + // Use local state to track animation running status + const [isRunning, setIsRunning] = useState(false) + // Timer reference for animation + const animationTimerRef = useRef(null) + const { t } = useTranslation() + + // Function to update node positions using the layout algorithm + const updatePositions = useCallback(() => { + if (!sigma) return + + try { + const graph = sigma.getGraph() + if (!graph || graph.order === 0) return + + // Use mainLayout to get positions, similar to refreshLayout function + // console.log('Getting positions from mainLayout') + const positions = mainLayout.positions() + + // Animate nodes to new positions + // console.log('Updating node positions with layout algorithm') + animateNodes(graph, positions, { duration: 300 }) // Reduced duration for more frequent updates + } catch (error) { + console.error('Error updating positions:', error) + // Stop animation if there's an error + if (animationTimerRef.current) { + window.clearInterval(animationTimerRef.current) + animationTimerRef.current = null + setIsRunning(false) + } + } + }, [sigma, mainLayout]) + + // Improved click handler that uses our own animation timer + const handleClick = useCallback(() => { + if (isRunning) { + // Stop the animation + console.log('Stopping layout animation') + if (animationTimerRef.current) { + window.clearInterval(animationTimerRef.current) + animationTimerRef.current = null + } + + // Try to kill the layout algorithm if it's running + try { + if (typeof layout.kill === 'function') { + layout.kill() + console.log('Layout algorithm killed') + } else if (typeof layout.stop === 'function') { + layout.stop() + console.log('Layout algorithm stopped') + } + } catch (error) { + console.error('Error stopping layout algorithm:', error) + } + + setIsRunning(false) + } else { + // Start the animation + console.log('Starting layout animation') + + // Initial position update + updatePositions() + + // Set up interval for continuous updates + animationTimerRef.current = window.setInterval(() => { + updatePositions() + }, 200) // Reduced interval to create overlapping animations for smoother transitions + + setIsRunning(true) + + // Set a timeout to automatically stop the animation after 3 seconds + setTimeout(() => { + if (animationTimerRef.current) { + console.log('Auto-stopping layout animation after 3 seconds') + window.clearInterval(animationTimerRef.current) + animationTimerRef.current = null + setIsRunning(false) + + // Try to stop the layout algorithm + try { + if (typeof layout.kill === 'function') { + layout.kill() + } else if (typeof layout.stop === 'function') { + layout.stop() + } + } catch (error) { + console.error('Error stopping layout algorithm:', error) + } + } + }, 3000) + } + }, [isRunning, layout, updatePositions]) + + /** + * Init component when Sigma or component settings change. + */ + useEffect(() => { + if (!sigma) { + console.log('No sigma instance available') + return + } + + // Auto-run if specified + let timeout: number | null = null + if (autoRunFor !== undefined && autoRunFor > -1 && sigma.getGraph().order > 0) { + console.log('Auto-starting layout animation') + + // Initial position update + updatePositions() + + // Set up interval for continuous updates + animationTimerRef.current = window.setInterval(() => { + updatePositions() + }, 200) // Reduced interval to create overlapping animations for smoother transitions + + setIsRunning(true) + + // Set a timeout to stop it if autoRunFor > 0 + if (autoRunFor > 0) { + timeout = window.setTimeout(() => { + console.log('Auto-stopping layout animation after timeout') + if (animationTimerRef.current) { + window.clearInterval(animationTimerRef.current) + animationTimerRef.current = null + } + setIsRunning(false) + }, autoRunFor) + } + } + + // Cleanup function + return () => { + // console.log('Cleaning up WorkerLayoutControl') + if (animationTimerRef.current) { + window.clearInterval(animationTimerRef.current) + animationTimerRef.current = null + } + if (timeout) { + window.clearTimeout(timeout) + } + setIsRunning(false) + } + }, [autoRunFor, sigma, updatePositions]) + + return ( + + ) +} + +/** + * Component that controls the layout of the graph. + */ +const LayoutsControl = () => { + const sigma = useSigma() + const { t } = useTranslation() + const [layout, setLayout] = useState('Circular') + const [opened, setOpened] = useState(false) + + const maxIterations = useSettingsStore.use.graphLayoutMaxIterations() + + const layoutCircular = useLayoutCircular() + const layoutCirclepack = useLayoutCirclepack() + const layoutRandom = useLayoutRandom() + const layoutNoverlap = useLayoutNoverlap({ + maxIterations: maxIterations, + settings: { + margin: 5, + expansion: 1.1, + gridSize: 1, + ratio: 1, + speed: 3, + } + }) + // Add parameters for Force Directed layout to improve convergence + const layoutForce = useLayoutForce({ + maxIterations: maxIterations, + settings: { + attraction: 0.0003, // Lower attraction force to reduce oscillation + repulsion: 0.02, // Lower repulsion force to reduce oscillation + gravity: 0.02, // Increase gravity to make nodes converge to center faster + inertia: 0.4, // Lower inertia to add damping effect + maxMove: 100 // Limit maximum movement per step to prevent large jumps + } + }) + const layoutForceAtlas2 = useLayoutForceAtlas2({ iterations: maxIterations }) + const workerNoverlap = useWorkerLayoutNoverlap() + const workerForce = useWorkerLayoutForce() + const workerForceAtlas2 = useWorkerLayoutForceAtlas2() + + const layouts = useMemo(() => { + return { + Circular: { + layout: layoutCircular + }, + Circlepack: { + layout: layoutCirclepack + }, + Random: { + layout: layoutRandom + }, + Noverlaps: { + layout: layoutNoverlap, + worker: workerNoverlap + }, + 'Force Directed': { + layout: layoutForce, + worker: workerForce + }, + 'Force Atlas': { + layout: layoutForceAtlas2, + worker: workerForceAtlas2 + } + } as { [key: string]: { layout: LayoutHook; worker?: LayoutWorkerHook } } + }, [ + layoutCirclepack, + layoutCircular, + layoutForce, + layoutForceAtlas2, + layoutNoverlap, + layoutRandom, + workerForce, + workerNoverlap, + workerForceAtlas2 + ]) + + const runLayout = useCallback( + (newLayout: LayoutName) => { + console.debug('Running layout:', newLayout) + const { positions } = layouts[newLayout].layout + + try { + const graph = sigma.getGraph() + if (!graph) { + console.error('No graph available') + return + } + + const pos = positions() + console.log('Positions calculated, animating nodes') + animateNodes(graph, pos, { duration: 400 }) + setLayout(newLayout) + } catch (error) { + console.error('Error running layout:', error) + } + }, + [layouts, sigma] + ) + + return ( +
    +
    + {layouts[layout] && 'worker' in layouts[layout] && ( + + )} +
    +
    + + + + + + + + + {Object.keys(layouts).map((name) => ( + { + runLayout(name as LayoutName) + }} + key={name} + className="cursor-pointer text-xs" + > + {t(`graphPanel.sideBar.layoutsControl.layouts.${name}`)} + + ))} + + + + + +
    +
    + ) +} + +export default LayoutsControl diff --git a/LightRAG/lightrag_webui/src/components/graph/Legend.tsx b/LightRAG/lightrag_webui/src/components/graph/Legend.tsx new file mode 100644 index 00000000..e4a45857 --- /dev/null +++ b/LightRAG/lightrag_webui/src/components/graph/Legend.tsx @@ -0,0 +1,41 @@ +import React from 'react' +import { useTranslation } from 'react-i18next' +import { useGraphStore } from '@/stores/graph' +import { Card } from '@/components/ui/Card' +import { ScrollArea } from '@/components/ui/ScrollArea' + +interface LegendProps { + className?: string +} + +const Legend: React.FC = ({ className }) => { + const { t } = useTranslation() + const typeColorMap = useGraphStore.use.typeColorMap() + + if (!typeColorMap || typeColorMap.size === 0) { + return null + } + + return ( + +

    {t('graphPanel.legend')}

    + +
    + {Array.from(typeColorMap.entries()).map(([type, color]) => ( +
    +
    + + {t(`graphPanel.nodeTypes.${type.toLowerCase().replace(/\s+/g, '')}`, type)} + +
    + ))} +
    + + + ) +} + +export default Legend diff --git a/LightRAG/lightrag_webui/src/components/graph/LegendButton.tsx b/LightRAG/lightrag_webui/src/components/graph/LegendButton.tsx new file mode 100644 index 00000000..cf036721 --- /dev/null +++ b/LightRAG/lightrag_webui/src/components/graph/LegendButton.tsx @@ -0,0 +1,32 @@ +import { useCallback } from 'react' +import { BookOpenIcon } from 'lucide-react' +import Button from '@/components/ui/Button' +import { controlButtonVariant } from '@/lib/constants' +import { useSettingsStore } from '@/stores/settings' +import { useTranslation } from 'react-i18next' + +/** + * Component that toggles legend visibility. + */ +const LegendButton = () => { + const { t } = useTranslation() + const showLegend = useSettingsStore.use.showLegend() + const setShowLegend = useSettingsStore.use.setShowLegend() + + const toggleLegend = useCallback(() => { + setShowLegend(!showLegend) + }, [showLegend, setShowLegend]) + + return ( + + ) +} + +export default LegendButton diff --git a/LightRAG/lightrag_webui/src/components/graph/MergeDialog.tsx b/LightRAG/lightrag_webui/src/components/graph/MergeDialog.tsx new file mode 100644 index 00000000..5ba18e3f --- /dev/null +++ b/LightRAG/lightrag_webui/src/components/graph/MergeDialog.tsx @@ -0,0 +1,70 @@ +import { useTranslation } from 'react-i18next' +import { useSettingsStore } from '@/stores/settings' +import { + Dialog, + DialogContent, + DialogDescription, + DialogFooter, + DialogHeader, + DialogTitle +} from '@/components/ui/Dialog' +import Button from '@/components/ui/Button' + +interface MergeDialogProps { + mergeDialogOpen: boolean + mergeDialogInfo: { + targetEntity: string + sourceEntity: string + } | null + onOpenChange: (open: boolean) => void + onRefresh: (useMergedStart: boolean) => void +} + +/** + * MergeDialog component that appears after a successful entity merge + * Allows user to choose whether to use the merged entity or keep current start point + */ +const MergeDialog = ({ + mergeDialogOpen, + mergeDialogInfo, + onOpenChange, + onRefresh +}: MergeDialogProps) => { + const { t } = useTranslation() + const currentQueryLabel = useSettingsStore.use.queryLabel() + + return ( + + + + {t('graphPanel.propertiesView.mergeDialog.title')} + + {t('graphPanel.propertiesView.mergeDialog.description', { + source: mergeDialogInfo?.sourceEntity ?? '', + target: mergeDialogInfo?.targetEntity ?? '', + })} + + +

    + {t('graphPanel.propertiesView.mergeDialog.refreshHint')} +

    + + {currentQueryLabel !== mergeDialogInfo?.sourceEntity && ( + + )} + + +     +     + ) +} + +export default MergeDialog diff --git a/LightRAG/lightrag_webui/src/components/graph/PropertiesView.tsx b/LightRAG/lightrag_webui/src/components/graph/PropertiesView.tsx new file mode 100644 index 00000000..463b49da --- /dev/null +++ b/LightRAG/lightrag_webui/src/components/graph/PropertiesView.tsx @@ -0,0 +1,408 @@ +import { useEffect, useState } from 'react' +import { useGraphStore, RawNodeType, RawEdgeType } from '@/stores/graph' +import Text from '@/components/ui/Text' +import Button from '@/components/ui/Button' +import useLightragGraph from '@/hooks/useLightragGraph' +import { useTranslation } from 'react-i18next' +import { GitBranchPlus, Scissors } from 'lucide-react' +import EditablePropertyRow from './EditablePropertyRow' + +/** + * Component that view properties of elements in graph. + */ +const PropertiesView = () => { + const { getNode, getEdge } = useLightragGraph() + const selectedNode = useGraphStore.use.selectedNode() + const focusedNode = useGraphStore.use.focusedNode() + const selectedEdge = useGraphStore.use.selectedEdge() + const focusedEdge = useGraphStore.use.focusedEdge() + const graphDataVersion = useGraphStore.use.graphDataVersion() + + const [currentElement, setCurrentElement] = useState(null) + const [currentType, setCurrentType] = useState<'node' | 'edge' | null>(null) + + // This effect will run when selection changes or when graph data is updated + useEffect(() => { + let type: 'node' | 'edge' | null = null + let element: RawNodeType | RawEdgeType | null = null + if (focusedNode) { + type = 'node' + element = getNode(focusedNode) + } else if (selectedNode) { + type = 'node' + element = getNode(selectedNode) + } else if (focusedEdge) { + type = 'edge' + element = getEdge(focusedEdge, true) + } else if (selectedEdge) { + type = 'edge' + element = getEdge(selectedEdge, true) + } + + if (element) { + if (type == 'node') { + setCurrentElement(refineNodeProperties(element as any)) + } else { + setCurrentElement(refineEdgeProperties(element as any)) + } + setCurrentType(type) + } else { + setCurrentElement(null) + setCurrentType(null) + } + }, [ + focusedNode, + selectedNode, + focusedEdge, + selectedEdge, + graphDataVersion, // Add dependency on graphDataVersion to refresh when data changes + setCurrentElement, + setCurrentType, + getNode, + getEdge + ]) + + if (!currentElement) { + return <> + } + return ( +
    + {currentType == 'node' ? ( + + ) : ( + + )} +
    + ) +} + +type NodeType = RawNodeType & { + relationships: { + type: string + id: string + label: string + }[] +} + +type EdgeType = RawEdgeType & { + sourceNode?: RawNodeType + targetNode?: RawNodeType +} + +const refineNodeProperties = (node: RawNodeType): NodeType => { + const state = useGraphStore.getState() + const relationships = [] + + if (state.sigmaGraph && state.rawGraph) { + try { + if (!state.sigmaGraph.hasNode(node.id)) { + console.warn('Node not found in sigmaGraph:', node.id) + return { + ...node, + relationships: [] + } + } + + const edges = state.sigmaGraph.edges(node.id) + + for (const edgeId of edges) { + if (!state.sigmaGraph.hasEdge(edgeId)) continue; + + const edge = state.rawGraph.getEdge(edgeId, true) + if (edge) { + const isTarget = node.id === edge.source + const neighbourId = isTarget ? edge.target : edge.source + + if (!state.sigmaGraph.hasNode(neighbourId)) continue; + + const neighbour = state.rawGraph.getNode(neighbourId) + if (neighbour) { + relationships.push({ + type: 'Neighbour', + id: neighbourId, + label: neighbour.properties['entity_id'] ? neighbour.properties['entity_id'] : neighbour.labels.join(', ') + }) + } + } + } + } catch (error) { + console.error('Error refining node properties:', error) + } + } + + return { + ...node, + relationships + } +} + +const refineEdgeProperties = (edge: RawEdgeType): EdgeType => { + const state = useGraphStore.getState() + let sourceNode: RawNodeType | undefined = undefined + let targetNode: RawNodeType | undefined = undefined + + if (state.sigmaGraph && state.rawGraph) { + try { + if (!state.sigmaGraph.hasEdge(edge.dynamicId)) { + console.warn('Edge not found in sigmaGraph:', edge.id, 'dynamicId:', edge.dynamicId) + return { + ...edge, + sourceNode: undefined, + targetNode: undefined + } + } + + if (state.sigmaGraph.hasNode(edge.source)) { + sourceNode = state.rawGraph.getNode(edge.source) + } + + if (state.sigmaGraph.hasNode(edge.target)) { + targetNode = state.rawGraph.getNode(edge.target) + } + } catch (error) { + console.error('Error refining edge properties:', error) + } + } + + return { + ...edge, + sourceNode, + targetNode + } +} + +const PropertyRow = ({ + name, + value, + onClick, + tooltip, + nodeId, + edgeId, + dynamicId, + entityId, + entityType, + sourceId, + targetId, + isEditable = false, + truncate +}: { + name: string + value: any + onClick?: () => void + tooltip?: string + nodeId?: string + entityId?: string + edgeId?: string + dynamicId?: string + entityType?: 'node' | 'edge' + sourceId?: string + targetId?: string + isEditable?: boolean + truncate?: string +}) => { + const { t } = useTranslation() + + const getPropertyNameTranslation = (name: string) => { + const translationKey = `graphPanel.propertiesView.node.propertyNames.${name}` + const translation = t(translationKey) + return translation === translationKey ? name : translation + } + + // Utility function to convert to newlines + const formatValueWithSeparators = (value: any): string => { + if (typeof value === 'string') { + return value.replace(//g, ';\n') + } + return typeof value === 'string' ? value : JSON.stringify(value, null, 2) + } + + // Format the value to convert to newlines + const formattedValue = formatValueWithSeparators(value) + let formattedTooltip = tooltip || formatValueWithSeparators(value) + + // If this is source_id field and truncate info exists, append it to the tooltip + if (name === 'source_id' && truncate) { + formattedTooltip += `\n(Truncated: ${truncate})` + } + + // Use EditablePropertyRow for editable fields (description, entity_id and entity_type) + if (isEditable && (name === 'description' || name === 'entity_id' || name === 'entity_type' || name === 'keywords')) { + return ( + + ) + } + + // For non-editable fields, use the regular Text component + return ( +
    + + {getPropertyNameTranslation(name)} + {name === 'source_id' && truncate && †} + : + +
    + ) +} + +const NodePropertiesView = ({ node }: { node: NodeType }) => { + const { t } = useTranslation() + + const handleExpandNode = () => { + useGraphStore.getState().triggerNodeExpand(node.id) + } + + const handlePruneNode = () => { + useGraphStore.getState().triggerNodePrune(node.id) + } + + return ( +
    +
    +

    {t('graphPanel.propertiesView.node.title')}

    +
    + + +
    +
    +
    + + { + useGraphStore.getState().setSelectedNode(node.id, true) + }} + /> + +
    +

    {t('graphPanel.propertiesView.node.properties')}

    +
    + {Object.keys(node.properties) + .sort() + .map((name) => { + if (name === 'created_at' || name === 'truncate') return null; // Hide created_at and truncate properties + return ( + + ) + })} +
    + {node.relationships.length > 0 && ( + <> +

    + {t('graphPanel.propertiesView.node.relationships')} +

    +
    + {node.relationships.map(({ type, id, label }) => { + return ( + { + useGraphStore.getState().setSelectedNode(id, true) + }} + /> + ) + })} +
    + + )} +
    + ) +} + +const EdgePropertiesView = ({ edge }: { edge: EdgeType }) => { + const { t } = useTranslation() + return ( +
    +

    {t('graphPanel.propertiesView.edge.title')}

    +
    + + {edge.type && } + { + useGraphStore.getState().setSelectedNode(edge.source, true) + }} + /> + { + useGraphStore.getState().setSelectedNode(edge.target, true) + }} + /> +
    +

    {t('graphPanel.propertiesView.edge.properties')}

    +
    + {Object.keys(edge.properties) + .sort() + .map((name) => { + if (name === 'created_at' || name === 'truncate') return null; // Hide created_at and truncate properties + return ( + + ) + })} +
    +
    + ) +} + +export default PropertiesView diff --git a/LightRAG/lightrag_webui/src/components/graph/PropertyEditDialog.tsx b/LightRAG/lightrag_webui/src/components/graph/PropertyEditDialog.tsx new file mode 100644 index 00000000..457b2b48 --- /dev/null +++ b/LightRAG/lightrag_webui/src/components/graph/PropertyEditDialog.tsx @@ -0,0 +1,196 @@ +import { useState, useEffect } from 'react' +import { useTranslation } from 'react-i18next' +import { + Dialog, + DialogContent, + DialogHeader, + DialogTitle, + DialogFooter, + DialogDescription +} from '@/components/ui/Dialog' +import Button from '@/components/ui/Button' +import Checkbox from '@/components/ui/Checkbox' + +interface PropertyEditDialogProps { + isOpen: boolean + onClose: () => void + onSave: (value: string, options?: { allowMerge?: boolean }) => void + propertyName: string + initialValue: string + isSubmitting?: boolean + errorMessage?: string | null +} + +/** + * Dialog component for editing property values + * Provides a modal with a title, multi-line text input, and save/cancel buttons + */ +const PropertyEditDialog = ({ + isOpen, + onClose, + onSave, + propertyName, + initialValue, + isSubmitting = false, + errorMessage = null +}: PropertyEditDialogProps) => { + const { t } = useTranslation() + const [value, setValue] = useState('') + const [allowMerge, setAllowMerge] = useState(false) + + // Initialize value when dialog opens + useEffect(() => { + if (isOpen) { + setValue(initialValue) + setAllowMerge(false) + } + }, [isOpen, initialValue]) + + // Get translated property name + const getPropertyNameTranslation = (name: string) => { + const translationKey = `graphPanel.propertiesView.node.propertyNames.${name}` + const translation = t(translationKey) + return translation === translationKey ? name : translation + } + + // Get textarea configuration based on property name + const getTextareaConfig = (propertyName: string) => { + switch (propertyName) { + case 'description': + return { + // No rows attribute for description to allow auto-sizing + className: 'max-h-[50vh] min-h-[10em] resize-y', // Maximum height 70% of viewport, minimum height ~20 lines, allow vertical resizing + style: { + height: '70vh', // Set initial height to 70% of viewport + minHeight: '20em', // Minimum height ~20 lines + resize: 'vertical' as const // Allow vertical resizing, using 'as const' to fix type + } + }; + case 'entity_id': + return { + rows: 2, + className: '', + style: {} + }; + case 'keywords': + return { + rows: 4, + className: '', + style: {} + }; + default: + return { + rows: 5, + className: '', + style: {} + }; + } + }; + + const handleSave = async () => { + const trimmedValue = value.trim() + if (trimmedValue !== '') { + const options = propertyName === 'entity_id' ? { allowMerge } : undefined + await onSave(trimmedValue, options) + } + } + + return ( + !open && onClose()}> + + + + {t('graphPanel.propertiesView.editProperty', { + property: getPropertyNameTranslation(propertyName) + })} + + + {t('graphPanel.propertiesView.editPropertyDescription')} + + + + {/* Display error message if save fails */} + {errorMessage && ( +
    + {errorMessage} +
    + )} + + {/* Multi-line text input using textarea */} +
    + {(() => { + const config = getTextareaConfig(propertyName); + return propertyName === 'description' ? ( +