71 lines
2.0 KiB
Python
71 lines
2.0 KiB
Python
"""
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RAG 检索工具
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"""
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from langchain_core.tools import tool
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from backend.app.logger import info
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_rag_pipeline = None
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def _get_rag_pipeline():
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"""获取或创建 RAG pipeline 单例"""
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global _rag_pipeline
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if _rag_pipeline is None:
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from backend.app.rag.pipeline import RAGPipeline
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_rag_pipeline = RAGPipeline(
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num_queries=3,
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rerank_top_n=5,
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use_rerank=True,
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return_parent_docs=True,
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)
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return _rag_pipeline
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def _format_confidence(result) -> str:
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"""格式化置信度描述"""
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if result.confidence < 0.4:
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return "低"
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elif result.confidence < 0.6:
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return "中"
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return "高"
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@tool
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async def rag_search(query: str) -> str:
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"""
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检索知识库获取相关信息
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Returns:
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包含检索结果和置信度的结构化回复
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"""
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info(f"[Tool] rag_search: {query[:30]}...")
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try:
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pipeline = _get_rag_pipeline()
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result = await pipeline.aretrieve_with_confidence(query, original_query=query)
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if not result.content:
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return "【RAG检索结果】\n未在知识库中找到相关内容。\n置信度：0.0\n建议：可尝试联网搜索获取信息。"
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confidence_desc = _format_confidence(result)
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is_useful_note = "✅ 检索结果可信，可直接使用" if result.is_useful else "⚠️ 检索结果置信度较低，可能需要联网搜索补充"
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response = f"""【RAG检索结果】
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{result.content}
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【置信度评估】
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- 综合置信度：{result.confidence:.2f}（{confidence_desc}）
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- 向量相似度：{result.scores['embedding']:.2f}
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- 重排分数：{result.scores['rerank']:.2f}
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- LLM评估：{result.scores['llm']:.2f}
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{is_useful_note}"""
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info(f"[Tool] rag_search 完成: confidence={result.confidence:.3f}, is_useful={result.is_useful}")
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return response
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except Exception as e:
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info(f"[Tool] rag_search 失败: {e}")
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return f"【RAG检索失败】\n错误：{str(e)}\n建议：请稍后重试或使用联网搜索"
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