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构建并部署 AI Agent 服务 / deploy (push) Successful in 5m41s
主要变更: - 简化 agent_service:移除复杂双协程,只用 stream_mode=["updates"] - stream_context:提供更清晰的 API (set_stream_queue/get_stream_queue) - main_graph_builder:简化图结构,移除 tools 节点和条件边 - agent 节点:包含完整 ReAct 循环 + 流式 Tool Calling 拼接 - 前端:适配新的事件格式 - 添加测试文件:test_full_react_streaming.py, test_stream.py
189 lines
4.9 KiB
Python
189 lines
4.9 KiB
Python
"""
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Agent Tools - 封装所有功能为 @tool 函数
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"""
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from langchain_core.tools import tool
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from typing import Optional
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from backend.app.logger import info
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# ========== RAG Pipeline(复用现有)
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_rag_pipeline = None
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def _get_rag_pipeline():
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"""获取 RAG Pipeline 实例(复用 rag_nodes.py 的逻辑)"""
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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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当用户询问关于系统、业务、文档相关的问题时使用此工具。
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Args:
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query: 用户的问题或搜索关键词
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Returns:
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检索到的相关文档内容
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"""
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info(f"[RAG Tool] 开始检索: {query[:50]}...")
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try:
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pipeline = _get_rag_pipeline()
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documents = await pipeline.aretrieve(query)
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rag_context = pipeline.format_context(documents)
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info(f"[RAG Tool] 检索完成,得到 {len(documents)} 个文档")
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if rag_context:
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return rag_context
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else:
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return "知识库中没有找到相关内容。"
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except Exception as e:
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info(f"[RAG Tool] 检索失败: {e}")
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return f"知识库检索失败: {str(e)}"
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@tool
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def web_search(query: str) -> str:
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"""
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联网搜索获取最新信息。
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当用户询问实时新闻、热点事件、最新资讯或知识库中没有的内容时使用此工具。
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Args:
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query: 搜索关键词
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Returns:
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搜索结果摘要
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"""
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info(f"[WebSearch Tool] 开始搜索: {query[:50]}...")
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try:
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from backend.app.core import web_search as core_web_search
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search_result = core_web_search(query, max_results=5)
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info(f"[WebSearch Tool] 搜索完成")
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return search_result
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except Exception as e:
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info(f"[WebSearch Tool] 搜索失败: {e}")
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return f"联网搜索失败: {str(e)}"
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# ====== 子图工具封装器
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async def _invoke_subgraph(subgraph_builder, query: str, state_class) -> str:
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"""
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通用子图调用函数
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Args:
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subgraph_builder: 子图构建函数
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query: 用户查询
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state_class: 子图状态类
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Returns:
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子图执行结果
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"""
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try:
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graph = subgraph_builder()
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compiled_graph = graph.compile()
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# 构造初始状态
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initial_state = state_class(user_query=query)
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# 调用子图
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result = await compiled_graph.ainvoke(initial_state)
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# 返回结果
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return result.get("final_result", "子图执行完成")
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except Exception as e:
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info(f"[Subgraph Tool] 执行失败: {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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"""
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查询通讯录信息。
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当用户询问联系人、邮箱、联系方式、发送邮件时使用此工具。
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Args:
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query: 用户查询,描述需要的操作
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Returns:
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联系人信息或操作结果
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"""
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info(f"[Contact Tool] 查询: {query[:50]}...")
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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 _invoke_subgraph(build_contact_subgraph, query, ContactState)
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@tool
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async def dictionary_lookup(word: str) -> str:
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"""
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查询词典,获取单词释义、翻译等。
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当用户询问单词、翻译、生词时使用此工具。
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Args:
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word: 需要查询的单词或短语
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Returns:
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单词释义和翻译
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"""
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info(f"[Dictionary Tool] 查询: {word}")
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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 _invoke_subgraph(build_dictionary_subgraph, word, DictionaryState)
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@tool
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async def news_analysis(topic: str) -> str:
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"""
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分析热点新闻和资讯。
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当用户询问新闻分析、热点解读时使用此工具。
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Args:
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topic: 新闻主题或关键词
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Returns:
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新闻分析结果
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"""
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info(f"[NewsAnalysis Tool] 分析: {topic}")
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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 _invoke_subgraph(build_news_analysis_subgraph, topic, NewsAnalysisState)
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# ====== 导出所有工具
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ALL_TOOLS = [
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rag_search,
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web_search,
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contact_lookup,
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dictionary_lookup,
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news_analysis,
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]
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