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构建并部署 AI Agent 服务 / deploy (push) Successful in 5m24s
主要变更： - 删除 deprecated 文件夹（intent/hybrid_router/rag_nodes 等） - 删除 intent_classifier.py（未使用） - 删除 subgraph_wrapper.py（死代码） - 重构 agent.py：简化工厂函数，支持动态模型切换 - 重构 prompts.py：添加信息获取优先级、思维链要求、工具调用约束 - 优化 tools：统一位置，rag_search 返回置信度评估 - 新增 RAG 置信度评估：embedding(25%) + rerank(25%) + LLM(50%) - 添加循环检测：防止工具无限重复调用 Co-Authored-By: Claude Opus 4.7 <noreply@anthropic.com>
126 lines
4.0 KiB
Python
126 lines
4.0 KiB
Python
"""
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Agent Tools - 所有工具统一定义
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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 ==========
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_rag_pipeline = None
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def _get_rag_pipeline():
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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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@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，格式：
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- 内容：检索到的相关信息
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- 置信度评估：基于向量相似度、重排分数、LLM判断的综合评分
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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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# 使用带置信度的检索
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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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# 构建包含置信度的回复
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confidence_desc = "高"
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if result.confidence < 0.4:
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confidence_desc = "低"
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elif result.confidence < 0.6:
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confidence_desc = "中"
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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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{'✅ 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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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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# ========== 联网搜索 ==========
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@tool
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def web_search(query: str) -> str:
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"""联网搜索获取最新信息"""
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info(f"[Tool] web_search: {query[:30]}...")
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try:
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from backend.app.core.web_search import web_search as search_fn
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return search_fn(query, max_results=5)
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except Exception as e:
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info(f"[Tool] web_search 失败: {e}")
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return f"联网搜索失败: {str(e)}"
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# ========== 子图工具 ==========
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async def _call_subgraph(builder_fn, state_cls, query: str) -> str:
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"""通用子图调用"""
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try:
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graph = builder_fn().compile()
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state = state_cls(user_query=query)
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result = await graph.ainvoke(state)
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return result.get("final_result", "执行完成")
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except Exception as e:
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info(f"[Tool] 子图调用失败: {e}")
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return f"执行失败: {str(e)}"
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@tool
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async def contact_lookup(query: str) -> str:
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"""查询通讯录"""
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from backend.app.subgraphs.contact.graph import build_contact_subgraph
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from backend.app.subgraphs.contact.state import ContactState
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return await _call_subgraph(build_contact_subgraph, ContactState, query)
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@tool
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async def dictionary_lookup(word: str) -> str:
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"""查询词典/翻译"""
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from backend.app.subgraphs.dictionary.graph import build_dictionary_subgraph
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from backend.app.subgraphs.dictionary.state import DictionaryState
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return await _call_subgraph(build_dictionary_subgraph, DictionaryState, word)
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@tool
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async def news_analysis(topic: str) -> str:
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"""分析新闻热点"""
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from backend.app.subgraphs.news_analysis.graph import build_news_analysis_subgraph
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from backend.app.subgraphs.news_analysis.state import NewsAnalysisState
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return await _call_subgraph(build_news_analysis_subgraph, NewsAnalysisState, topic)
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# ========== 导出 ==========
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ALL_TOOLS = [rag_search, web_search, contact_lookup, dictionary_lookup, news_analysis]
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