chore: init monorepo snapshot
This commit is contained in:
liaibo
@@ -0,0 +1,567 @@
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import os
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from dataclasses import dataclass
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from typing import final
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from lightrag.types import KnowledgeGraph, KnowledgeGraphNode, KnowledgeGraphEdge
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from lightrag.utils import logger
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from lightrag.base import BaseGraphStorage
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import networkx as nx
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from .shared_storage import (
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get_storage_lock,
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get_update_flag,
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set_all_update_flags,
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)
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from dotenv import load_dotenv
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# use the .env that is inside the current folder
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# allows to use different .env file for each lightrag instance
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# the OS environment variables take precedence over the .env file
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load_dotenv(dotenv_path=".env", override=False)
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@final
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@dataclass
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class NetworkXStorage(BaseGraphStorage):
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@staticmethod
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def load_nx_graph(file_name) -> nx.Graph:
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if os.path.exists(file_name):
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return nx.read_graphml(file_name)
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return None
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@staticmethod
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def write_nx_graph(graph: nx.Graph, file_name, workspace="_"):
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logger.info(
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f"[{workspace}] Writing graph with {graph.number_of_nodes()} nodes, {graph.number_of_edges()} edges"
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)
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nx.write_graphml(graph, file_name)
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def __post_init__(self):
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working_dir = self.global_config["working_dir"]
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if self.workspace:
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# Include workspace in the file path for data isolation
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workspace_dir = os.path.join(working_dir, self.workspace)
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self.final_namespace = f"{self.workspace}_{self.namespace}"
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else:
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# Default behavior when workspace is empty
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self.final_namespace = self.namespace
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workspace_dir = working_dir
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self.workspace = "_"
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os.makedirs(workspace_dir, exist_ok=True)
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self._graphml_xml_file = os.path.join(
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workspace_dir, f"graph_{self.namespace}.graphml"
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)
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self._storage_lock = None
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self.storage_updated = None
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self._graph = None
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# Load initial graph
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preloaded_graph = NetworkXStorage.load_nx_graph(self._graphml_xml_file)
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if preloaded_graph is not None:
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logger.info(
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f"[{self.workspace}] Loaded graph from {self._graphml_xml_file} with {preloaded_graph.number_of_nodes()} nodes, {preloaded_graph.number_of_edges()} edges"
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)
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else:
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logger.info(
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f"[{self.workspace}] Created new empty graph file: {self._graphml_xml_file}"
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)
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self._graph = preloaded_graph or nx.Graph()
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async def initialize(self):
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"""Initialize storage data"""
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# Get the update flag for cross-process update notification
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self.storage_updated = await get_update_flag(self.final_namespace)
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# Get the storage lock for use in other methods
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self._storage_lock = get_storage_lock()
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async def _get_graph(self):
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"""Check if the storage should be reloaded"""
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# Acquire lock to prevent concurrent read and write
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async with self._storage_lock:
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# Check if data needs to be reloaded
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if self.storage_updated.value:
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logger.info(
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f"[{self.workspace}] Process {os.getpid()} reloading graph {self._graphml_xml_file} due to modifications by another process"
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)
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# Reload data
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self._graph = (
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NetworkXStorage.load_nx_graph(self._graphml_xml_file) or nx.Graph()
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)
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# Reset update flag
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self.storage_updated.value = False
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return self._graph
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async def has_node(self, node_id: str) -> bool:
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graph = await self._get_graph()
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return graph.has_node(node_id)
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async def has_edge(self, source_node_id: str, target_node_id: str) -> bool:
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graph = await self._get_graph()
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return graph.has_edge(source_node_id, target_node_id)
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async def get_node(self, node_id: str) -> dict[str, str] | None:
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graph = await self._get_graph()
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return graph.nodes.get(node_id)
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async def node_degree(self, node_id: str) -> int:
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graph = await self._get_graph()
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return graph.degree(node_id)
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async def edge_degree(self, src_id: str, tgt_id: str) -> int:
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graph = await self._get_graph()
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src_degree = graph.degree(src_id) if graph.has_node(src_id) else 0
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tgt_degree = graph.degree(tgt_id) if graph.has_node(tgt_id) else 0
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return src_degree + tgt_degree
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async def get_edge(
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self, source_node_id: str, target_node_id: str
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) -> dict[str, str] | None:
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graph = await self._get_graph()
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return graph.edges.get((source_node_id, target_node_id))
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async def get_node_edges(self, source_node_id: str) -> list[tuple[str, str]] | None:
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graph = await self._get_graph()
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if graph.has_node(source_node_id):
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return list(graph.edges(source_node_id))
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return None
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async def upsert_node(self, node_id: str, node_data: dict[str, str]) -> None:
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"""
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Importance notes:
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1. Changes will be persisted to disk during the next index_done_callback
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2. Only one process should updating the storage at a time before index_done_callback,
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KG-storage-log should be used to avoid data corruption
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"""
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graph = await self._get_graph()
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graph.add_node(node_id, **node_data)
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async def upsert_edge(
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self, source_node_id: str, target_node_id: str, edge_data: dict[str, str]
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) -> None:
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"""
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Importance notes:
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1. Changes will be persisted to disk during the next index_done_callback
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2. Only one process should updating the storage at a time before index_done_callback,
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KG-storage-log should be used to avoid data corruption
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"""
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graph = await self._get_graph()
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graph.add_edge(source_node_id, target_node_id, **edge_data)
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async def delete_node(self, node_id: str) -> None:
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"""
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Importance notes:
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1. Changes will be persisted to disk during the next index_done_callback
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2. Only one process should updating the storage at a time before index_done_callback,
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KG-storage-log should be used to avoid data corruption
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"""
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graph = await self._get_graph()
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if graph.has_node(node_id):
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graph.remove_node(node_id)
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logger.debug(f"[{self.workspace}] Node {node_id} deleted from the graph")
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else:
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logger.warning(
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f"[{self.workspace}] Node {node_id} not found in the graph for deletion"
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)
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async def remove_nodes(self, nodes: list[str]):
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"""Delete multiple nodes
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Importance notes:
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1. Changes will be persisted to disk during the next index_done_callback
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2. Only one process should updating the storage at a time before index_done_callback,
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KG-storage-log should be used to avoid data corruption
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Args:
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nodes: List of node IDs to be deleted
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"""
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graph = await self._get_graph()
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for node in nodes:
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if graph.has_node(node):
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graph.remove_node(node)
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async def remove_edges(self, edges: list[tuple[str, str]]):
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"""Delete multiple edges
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Importance notes:
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1. Changes will be persisted to disk during the next index_done_callback
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2. Only one process should updating the storage at a time before index_done_callback,
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KG-storage-log should be used to avoid data corruption
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Args:
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edges: List of edges to be deleted, each edge is a (source, target) tuple
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"""
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graph = await self._get_graph()
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for source, target in edges:
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if graph.has_edge(source, target):
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graph.remove_edge(source, target)
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async def get_all_labels(self) -> list[str]:
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"""
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Get all node labels in the graph
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Returns:
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[label1, label2, ...] # Alphabetically sorted label list
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"""
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graph = await self._get_graph()
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labels = set()
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for node in graph.nodes():
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labels.add(str(node)) # Add node id as a label
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# Return sorted list
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return sorted(list(labels))
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async def get_popular_labels(self, limit: int = 300) -> list[str]:
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"""
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Get popular labels by node degree (most connected entities)
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Args:
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limit: Maximum number of labels to return
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Returns:
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List of labels sorted by degree (highest first)
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"""
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graph = await self._get_graph()
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# Get degrees of all nodes and sort by degree descending
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degrees = dict(graph.degree())
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sorted_nodes = sorted(degrees.items(), key=lambda x: x[1], reverse=True)
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# Return top labels limited by the specified limit
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popular_labels = [str(node) for node, _ in sorted_nodes[:limit]]
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logger.debug(
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f"[{self.workspace}] Retrieved {len(popular_labels)} popular labels (limit: {limit})"
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)
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return popular_labels
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async def search_labels(self, query: str, limit: int = 50) -> list[str]:
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"""
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Search labels with fuzzy matching
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Args:
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query: Search query string
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limit: Maximum number of results to return
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Returns:
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List of matching labels sorted by relevance
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"""
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graph = await self._get_graph()
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query_lower = query.lower().strip()
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if not query_lower:
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return []
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# Collect matching nodes with relevance scores
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matches = []
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for node in graph.nodes():
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node_str = str(node)
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node_lower = node_str.lower()
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# Skip if no match
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if query_lower not in node_lower:
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continue
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# Calculate relevance score
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# Exact match gets highest score
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if node_lower == query_lower:
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score = 1000
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# Prefix match gets high score
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elif node_lower.startswith(query_lower):
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score = 500
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# Contains match gets base score, with bonus for shorter strings
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else:
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# Shorter strings with matches are more relevant
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score = 100 - len(node_str)
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# Bonus for word boundary matches
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if f" {query_lower}" in node_lower or f"_{query_lower}" in node_lower:
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score += 50
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matches.append((node_str, score))
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# Sort by relevance score (desc) then alphabetically
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matches.sort(key=lambda x: (-x[1], x[0]))
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# Return top matches limited by the specified limit
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search_results = [match[0] for match in matches[:limit]]
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logger.debug(
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f"[{self.workspace}] Search query '{query}' returned {len(search_results)} results (limit: {limit})"
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)
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return search_results
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async def get_knowledge_graph(
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self,
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node_label: str,
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max_depth: int = 3,
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max_nodes: int = None,
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) -> KnowledgeGraph:
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"""
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Retrieve a connected subgraph of nodes where the label includes the specified `node_label`.
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Args:
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node_label: Label of the starting node，* means all nodes
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max_depth: Maximum depth of the subgraph, Defaults to 3
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max_nodes: Maxiumu nodes to return by BFS, Defaults to 1000
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Returns:
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KnowledgeGraph object containing nodes and edges, with an is_truncated flag
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indicating whether the graph was truncated due to max_nodes limit
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"""
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# Get max_nodes from global_config if not provided
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if max_nodes is None:
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max_nodes = self.global_config.get("max_graph_nodes", 1000)
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else:
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# Limit max_nodes to not exceed global_config max_graph_nodes
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max_nodes = min(max_nodes, self.global_config.get("max_graph_nodes", 1000))
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graph = await self._get_graph()
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result = KnowledgeGraph()
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# Handle special case for "*" label
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if node_label == "*":
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# Get degrees of all nodes
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degrees = dict(graph.degree())
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# Sort nodes by degree in descending order and take top max_nodes
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sorted_nodes = sorted(degrees.items(), key=lambda x: x[1], reverse=True)
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# Check if graph is truncated
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if len(sorted_nodes) > max_nodes:
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result.is_truncated = True
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logger.info(
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f"[{self.workspace}] Graph truncated: {len(sorted_nodes)} nodes found, limited to {max_nodes}"
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)
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limited_nodes = [node for node, _ in sorted_nodes[:max_nodes]]
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# Create subgraph with the highest degree nodes
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subgraph = graph.subgraph(limited_nodes)
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else:
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# Check if node exists
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if node_label not in graph:
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logger.warning(
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f"[{self.workspace}] Node {node_label} not found in the graph"
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)
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return KnowledgeGraph() # Return empty graph
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# Use modified BFS to get nodes, prioritizing high-degree nodes at the same depth
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bfs_nodes = []
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visited = set()
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# Store (node, depth, degree) in the queue
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queue = [(node_label, 0, graph.degree(node_label))]
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# Flag to track if there are unexplored neighbors due to depth limit
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has_unexplored_neighbors = False
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# Modified breadth-first search with degree-based prioritization
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while queue and len(bfs_nodes) < max_nodes:
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# Get the current depth from the first node in queue
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current_depth = queue[0][1]
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# Collect all nodes at the current depth
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current_level_nodes = []
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while queue and queue[0][1] == current_depth:
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current_level_nodes.append(queue.pop(0))
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# Sort nodes at current depth by degree (highest first)
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current_level_nodes.sort(key=lambda x: x[2], reverse=True)
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# Process all nodes at current depth in order of degree
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for current_node, depth, degree in current_level_nodes:
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if current_node not in visited:
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visited.add(current_node)
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bfs_nodes.append(current_node)
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# Only explore neighbors if we haven't reached max_depth
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if depth < max_depth:
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# Add neighbor nodes to queue with incremented depth
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neighbors = list(graph.neighbors(current_node))
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# Filter out already visited neighbors
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unvisited_neighbors = [
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n for n in neighbors if n not in visited
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]
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# Add neighbors to the queue with their degrees
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for neighbor in unvisited_neighbors:
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neighbor_degree = graph.degree(neighbor)
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queue.append((neighbor, depth + 1, neighbor_degree))
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else:
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# Check if there are unexplored neighbors (skipped due to depth limit)
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neighbors = list(graph.neighbors(current_node))
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unvisited_neighbors = [
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n for n in neighbors if n not in visited
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]
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if unvisited_neighbors:
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has_unexplored_neighbors = True
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# Check if we've reached max_nodes
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if len(bfs_nodes) >= max_nodes:
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break
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# Check if graph is truncated - either due to max_nodes limit or depth limit
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if (queue and len(bfs_nodes) >= max_nodes) or has_unexplored_neighbors:
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if len(bfs_nodes) >= max_nodes:
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result.is_truncated = True
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logger.info(
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f"[{self.workspace}] Graph truncated: max_nodes limit {max_nodes} reached"
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)
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else:
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logger.info(
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f"[{self.workspace}] Graph truncated: found {len(bfs_nodes)} nodes within max_depth {max_depth}"
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)
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# Create subgraph with BFS discovered nodes
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subgraph = graph.subgraph(bfs_nodes)
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# Add nodes to result
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seen_nodes = set()
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seen_edges = set()
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for node in subgraph.nodes():
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if str(node) in seen_nodes:
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continue
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node_data = dict(subgraph.nodes[node])
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# Get entity_type as labels
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labels = []
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if "entity_type" in node_data:
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if isinstance(node_data["entity_type"], list):
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labels.extend(node_data["entity_type"])
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else:
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labels.append(node_data["entity_type"])
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# Create node with properties
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node_properties = {k: v for k, v in node_data.items()}
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result.nodes.append(
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KnowledgeGraphNode(
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id=str(node), labels=[str(node)], properties=node_properties
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)
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)
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seen_nodes.add(str(node))
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# Add edges to result
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for edge in subgraph.edges():
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source, target = edge
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# Esure unique edge_id for undirect graph
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if str(source) > str(target):
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source, target = target, source
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edge_id = f"{source}-{target}"
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if edge_id in seen_edges:
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continue
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edge_data = dict(subgraph.edges[edge])
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# Create edge with complete information
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result.edges.append(
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KnowledgeGraphEdge(
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id=edge_id,
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type="DIRECTED",
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source=str(source),
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target=str(target),
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properties=edge_data,
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)
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)
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seen_edges.add(edge_id)
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logger.info(
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f"[{self.workspace}] Subgraph query successful | Node count: {len(result.nodes)} | Edge count: {len(result.edges)}"
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)
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return result
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async def get_all_nodes(self) -> list[dict]:
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"""Get all nodes in the graph.
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Returns:
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A list of all nodes, where each node is a dictionary of its properties
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||||
"""
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||||
graph = await self._get_graph()
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all_nodes = []
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for node_id, node_data in graph.nodes(data=True):
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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": "<error details>"}
|
||||
"""
|
||||
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)}
|
||||
Reference in New Issue
Block a user