重构:清理废弃代码 + 优化 Agent 架构
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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>
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@@ -7,8 +7,7 @@ from langgraph.graph import StateGraph, START, END
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from backend.app.main_graph.state import AgentState
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from backend.app.main_graph.nodes.memory_trigger import memory_trigger_node, set_mem0_client
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from backend.app.main_graph.nodes.agent import create_agent_node
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from backend.app.logger import info, warning
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from backend.app.tools import ALL_TOOLS
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from backend.app.logger import info
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def build_agent_graph(
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@@ -27,9 +26,6 @@ def build_agent_graph(
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Returns:
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构建好的 StateGraph(未编译)
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"""
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# 获取主模型
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primary_model = chat_services.get("primary", next(iter(chat_services.values())))
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# ========== 设置全局客户端 ==========
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if mem0_client:
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set_mem0_client(mem0_client)
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@@ -51,9 +47,8 @@ def build_agent_graph(
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except Exception as e:
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info(f"[Graph Builder] 记忆节点初始化失败: {e}")
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# ========== 3. Agent 节点(包含完整 ReAct 循环) ==========
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llm_with_tools = primary_model.bind_tools(ALL_TOOLS)
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agent_node_fn = create_agent_node(llm_with_tools, primary_model)
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# ========== 3. Agent 节点(包含完整 ReAct 循环,支持动态模型切换) ==========
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agent_node_fn = create_agent_node(chat_services)
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# ========== 4. 完成节点 ==========
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async def finalize_node_simple(state: AgentState):
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