92 lines
2.7 KiB
Python
92 lines
2.7 KiB
Python
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from __future__ import annotations
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import json
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from typing import Dict, Optional
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from app.services.supabase_rest import supabase_rest
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class SimpleAIService:
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"""提供在 LightRAG 不可用时的兜底回答。"""
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def summarize_document(
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self,
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*,
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query: str,
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user_id: str,
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document_id: Optional[str],
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workspace_id: Optional[str],
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) -> Dict[str, object]:
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document = None
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if document_id:
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document = supabase_rest.select_one(
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"documents",
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{
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"id": document_id,
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"user_id": user_id,
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},
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)
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title = (document or {}).get("title") or "当前页面"
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raw_text = (document or {}).get("raw_text") or ""
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if not raw_text and document and document.get("content"):
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raw_text = self._blocks_to_text(document["content"])
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snippet = raw_text.strip().replace("\n", " ")
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if len(snippet) > 400:
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snippet = snippet[:400].rstrip() + "…"
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if snippet:
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summary = f"《{title}》当前摘要：{snippet}"
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else:
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summary = f"《{title}》暂未填写正文内容，可直接编辑后再次提问。"
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answer = "\n\n".join(
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[
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summary,
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f"你的问题：{query}",
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"（提示：LightRAG 暂未就绪，已使用本地摘要兜底回答）",
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]
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)
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references = []
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if document_id:
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ref_path = f"doc://{document_id}"
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if title:
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ref_path = f"{ref_path}?title={title}"
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references.append(
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{
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"file_path": ref_path,
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"workspace": workspace_id or "",
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}
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)
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return {"content": answer, "references": references}
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def _blocks_to_text(self, content: object) -> str:
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if isinstance(content, str):
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return content
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if isinstance(content, list):
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parts = []
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for item in content:
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text = self._blocks_to_text(item)
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if text:
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parts.append(text)
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return " ".join(parts)
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if isinstance(content, dict):
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if "text" in content and isinstance(content["text"], str):
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return content["text"]
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parts = []
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for value in content.values():
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text = self._blocks_to_text(value)
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if text:
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parts.append(text)
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return " ".join(parts)
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try:
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return json.dumps(content, ensure_ascii=False)
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except TypeError:
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return ""
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simple_ai_service = SimpleAIService()
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