root
2893accbc4
- 子图执行后直接进入finalize，避免回到react_reason循环 - llm_call节点检查是否已有final_result，避免重复调用LLM - 直接在react_reason_node中通过adispatch_custom_event发送推理事件，避免通过state传递导致重复
286 lines
9.7 KiB
Python
286 lines
9.7 KiB
Python
"""
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整合后的完整主图构建器 - 所有节点都直接操作 MainGraphState
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"""
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from app.main_graph.graph import StateGraph, START, END
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from typing import Dict, Any, Optional
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from langchain_core.runnables.config import RunnableConfig
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from app.main_graph.state import MainGraphState
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from app.main_graph.nodes.react_nodes import (
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init_state_node,
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react_reason_node,
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web_search_node,
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error_handling_node,
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route_by_reasoning
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)
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from app.main_graph.nodes.llm_call import create_llm_call_node
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from app.main_graph.nodes.rag_nodes import rag_retrieve_node
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from app.main_graph.nodes.retrieve_memory import create_retrieve_memory_node
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from app.main_graph.nodes.memory_trigger import memory_trigger_node, set_mem0_client
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from app.main_graph.nodes.summarize import create_summarize_node
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from app.main_graph.nodes.finalize import finalize_node
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from app.subgraphs.contact import build_contact_subgraph
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from app.subgraphs.dictionary import build_dictionary_subgraph
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from app.subgraphs.news_analysis import build_news_analysis_subgraph
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from app.memory.mem0_client import Mem0Client
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from app.logger import info, debug
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# ========== 检查是否需要总结 ==========
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def should_summarize(state: MainGraphState) -> str:
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"""
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检查是否需要总结对话（对话足够长时）
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Args:
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state: 当前图状态
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Returns:
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"summarize" 或 "finalize"
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"""
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if state.turns_since_last_summary >= 5: # 每5轮对话总结一次
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return "summarize"
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else:
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return "finalize"
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# ========== 子图包装器（处理子图错误传递）==========
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def wrap_subgraph_for_error_handling(subgraph, name: str):
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"""
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包装子图，使其错误能传递给主图
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Args:
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subgraph: 编译好的子图
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name: 子图名称（用于错误标识）
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Returns: 包装后的节点函数
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"""
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def wrapped_node(state: MainGraphState) -> MainGraphState:
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try:
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# 调用子图
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result = subgraph.invoke(state)
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# 更新主图状态
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subgraph_result = None
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if name == "contact":
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state.contact_result = result
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subgraph_result = result.get("final_result", "")
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elif name == "dictionary":
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state.dictionary_result = result
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subgraph_result = result.get("final_result", "")
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elif name == "news_analysis":
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state.news_result = result
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subgraph_result = result.get("final_result", "")
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# 关键：设置最终结果，这样就不需要再回到 react_reason 了
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if subgraph_result:
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state.final_result = subgraph_result
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else:
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state.final_result = "子图执行完成"
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# 标记成功
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state.success = True
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state.current_phase = "done"
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# 标记不再需要推理，避免循环
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state.reasoning_history.append({
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"step": state.reasoning_step,
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"action": "subgraph_completed",
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"confidence": 1.0,
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"reasoning": f"{name}子图执行完成",
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"timestamp": datetime.now().isoformat()
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})
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return state
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except Exception as e:
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# 捕获子图错误，传递给主图
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from app.main_graph.state import ErrorRecord, ErrorSeverity
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from datetime import datetime
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error_record = ErrorRecord(
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error_type=f"{name}SubgraphError",
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error_message=str(e),
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severity=ErrorSeverity.WARNING,
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source=f"{name}_subgraph",
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timestamp=datetime.now().isoformat(),
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retry_count=0,
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max_retries=1,
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context={"user_query": state.user_query}
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)
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state.errors.append(error_record)
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state.current_error = error_record
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state.current_phase = "error_handling"
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state.success = False
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return state
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return wrapped_node
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# ========== 主图构建 ==========
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def build_react_main_graph(llm=None, tools=None, mem0_client=None) -> StateGraph:
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"""
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构建整合后的完整主图
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完整流程：
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START
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↓
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retrieve_memory (从Mem0检索长期记忆)
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↓
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memory_trigger (记忆触发器)
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↓
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init_state (初始化)
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↓
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react_reason (推理) ←───────────────────────┐
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↓ │
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条件路由 │
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├─ rag_retrieve →─────────────────────────┤
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├─ contact_subgraph →─────────────────────┤
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├─ dictionary_subgraph →──────────────────┤
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├─ news_analysis_subgraph →───────────────┤
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├─ web_search →───────────────────────────┤
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├─ handle_error → (重试或结束) ────────────┤
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└─ llm_call (大模型调用) ←────────────────┘
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↓
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检查：需要总结吗？
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├─ 是 → summarize (提交给Mem0存储)
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└─ 否 → (跳过)
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↓
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finalize (发送完成事件)
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↓
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END
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"""
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# 创建图
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graph = StateGraph(MainGraphState)
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# 设置全局 mem0_client
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if mem0_client:
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set_mem0_client(mem0_client)
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# 创建节点
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llm_node = None
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if llm is not None:
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llm_node = create_llm_call_node(llm, tools or [])
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retrieve_memory_node = None
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summarize_node = None
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if mem0_client:
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retrieve_memory_node = create_retrieve_memory_node(mem0_client)
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summarize_node = create_summarize_node(mem0_client)
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# ========== 添加节点 ==========
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# 第一阶段：记忆检索
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if retrieve_memory_node:
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graph.add_node("retrieve_memory", retrieve_memory_node)
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graph.add_node("memory_trigger", memory_trigger_node)
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# 第二阶段：React 循环推理
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graph.add_node("init_state", init_state_node)
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graph.add_node("react_reason", react_reason_node)
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graph.add_node("rag_retrieve", rag_retrieve_node)
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graph.add_node("web_search", web_search_node)
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graph.add_node("handle_error", error_handling_node)
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if llm_node is not None:
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graph.add_node("llm_call", llm_node)
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# 子图节点
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contact_graph = build_contact_subgraph()
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dictionary_graph = build_dictionary_subgraph()
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news_analysis_graph = build_news_analysis_subgraph()
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graph.add_node(
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"contact_subgraph",
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wrap_subgraph_for_error_handling(contact_graph.compile(), "contact")
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)
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graph.add_node(
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"dictionary_subgraph",
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wrap_subgraph_for_error_handling(dictionary_graph.compile(), "dictionary")
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)
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graph.add_node(
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"news_analysis_subgraph",
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wrap_subgraph_for_error_handling(news_analysis_graph.compile(), "news_analysis")
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)
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# 第三阶段：完成处理
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if summarize_node:
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graph.add_node("summarize", summarize_node)
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graph.add_node("finalize", finalize_node)
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# ========== 添加边 ==========
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# 第一阶段：记忆检索
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if retrieve_memory_node:
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graph.add_edge(START, "retrieve_memory")
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graph.add_edge("retrieve_memory", "memory_trigger")
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else:
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graph.add_edge(START, "memory_trigger")
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# 进入第二阶段
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graph.add_edge("memory_trigger", "init_state")
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graph.add_edge("init_state", "react_reason")
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# 第二阶段：React 循环推理
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graph.add_conditional_edges(
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"react_reason",
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route_by_reasoning,
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{
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"rag_retrieve": "rag_retrieve",
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"web_search": "web_search",
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"contact_subgraph": "contact_subgraph",
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"dictionary_subgraph": "dictionary_subgraph",
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"news_analysis_subgraph": "news_analysis_subgraph",
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"handle_error": "handle_error",
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"llm_call": "llm_call"
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}
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)
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# 循环边（rag、web_search、error 回到 reason）
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graph.add_edge("rag_retrieve", "react_reason")
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graph.add_edge("web_search", "react_reason")
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graph.add_edge("handle_error", "react_reason")
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# 关键修改：子图执行完后直接去 finalize（避免循环）
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graph.add_edge("contact_subgraph", "finalize")
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graph.add_edge("dictionary_subgraph", "finalize")
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graph.add_edge("news_analysis_subgraph", "finalize")
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# 第三阶段：llm_call 后进入完成处理
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if llm_node is not None:
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if summarize_node:
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# 检查是否需要总结
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graph.add_conditional_edges(
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"llm_call",
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should_summarize,
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{
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"summarize": "summarize",
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"finalize": "finalize"
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}
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)
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graph.add_edge("summarize", "finalize")
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else:
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# 没有 summarize 节点，直接 finalize
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graph.add_edge("llm_call", "finalize")
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# 完成
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graph.add_edge("finalize", END)
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info("✅ [图构建] 整合后的完整主图构建完成")
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return graph
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# ========== 兼容性：保留旧的函数名 ==========
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def build_main_graph() -> StateGraph:
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"""
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兼容性函数：旧代码调用 build_main_graph() 时返回 React 版本
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"""
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return build_react_main_graph()
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# ========== 导出 ==========
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__all__ = [
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"build_react_main_graph",
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"build_main_graph",
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"wrap_subgraph_for_error_handling"
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] |