feat: 实现 React 模式循环推理，带超时重试和结构化错误处理
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- 更新 intent.py 为 React 模式推理器 - 新增 react_nodes.py: React 模式节点 - 新增 retry_utils.py: 超时和重试工具 - 更新 state.py: 支持循环步数和错误记录 - 重写 subgraph_builder.py: 完整 React 循环流程 - 结构化错误输出，符合 Agent 执行循环最佳实践 - 限制最大推理步数 ≤40，防止无限循环 - RAG 检索带重试和超时保护 - 子图错误可传递给主图处理
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@@ -1,157 +1,193 @@
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"""
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子图整合主图构建器
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Subgraph Integration Main Graph Builder
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React 模式主图构建器 - 完整循环推理版本
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Main Graph Builder - Full React Mode with Loop Reasoning
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"""
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from langgraph.graph import StateGraph, START, END
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from typing import Dict, Any
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from .state import MainGraphState, CurrentAction
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from .react_nodes import (
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init_state_node,
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react_reason_node,
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rag_retrieve_node,
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error_handling_node,
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final_response_node,
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route_by_reasoning
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)
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from ..agent_subgraphs.contact import build_contact_subgraph
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from ..agent_subgraphs.dictionary import build_dictionary_subgraph
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from ..agent_subgraphs.news_analysis import build_news_analysis_subgraph
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def parse_user_intent(state: MainGraphState) -> MainGraphState:
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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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解析用户意图节点
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包装子图，使其错误能传递给主图
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确定该路由到哪个子图
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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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state.current_phase = "intent_parsing"
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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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if name == "contact":
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state.contact_result = result
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elif name == "dictionary":
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state.dictionary_result = result
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elif name == "news_analysis":
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state.news_result = result
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# 标记成功
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state.success = True
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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 .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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# 从messages中提取用户查询（如果user_query为空）
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if not state.user_query and state.messages:
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# 获取最后一条消息的内容
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last_msg = state.messages[-1]
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state.user_query = last_msg.content
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query_lower = state.user_query.lower()
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# 简单的关键词匹配
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if any(keyword in query_lower for keyword in ["通讯录", "联系人", "contact", "email"]):
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state.current_action = CurrentAction.CONTACT
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state.intent_confidence = 0.9
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elif any(keyword in query_lower for keyword in ["词典", "单词", "翻译", "dictionary", "translate"]):
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state.current_action = CurrentAction.DICTIONARY
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state.intent_confidence = 0.9
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elif any(keyword in query_lower for keyword in ["资讯", "新闻", "分析", "news", "report"]):
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state.current_action = CurrentAction.NEWS_ANALYSIS
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state.intent_confidence = 0.9
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else:
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# 默认是普通聊天
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state.current_action = CurrentAction.GENERAL_CHAT
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state.intent_confidence = 0.8
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return state
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return wrapped_node
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def route_to_subgraph(state: MainGraphState) -> str:
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# ========== 主图构建 ==========
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def build_react_main_graph() -> StateGraph:
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"""
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条件路由：决定路由到哪个子图
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"""
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if state.current_action == CurrentAction.NONE:
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return "general_chat"
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elif state.current_action == CurrentAction.GENERAL_CHAT:
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return "general_chat"
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elif state.current_action == CurrentAction.CONTACT:
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return "contact_subgraph"
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elif state.current_action == CurrentAction.DICTIONARY:
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return "dictionary_subgraph"
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elif state.current_action == CurrentAction.NEWS_ANALYSIS:
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return "news_analysis_subgraph"
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else:
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return "general_chat"
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def general_chat_node(state: MainGraphState) -> MainGraphState:
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"""
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普通聊天节点
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（目前是占位符，后续整合旧的LLM调用逻辑）
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"""
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state.current_phase = "general_chat"
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state.final_result = f"普通聊天模式：{state.user_query}"
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state.success = True
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return state
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def integrate_results(state: MainGraphState) -> MainGraphState:
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"""
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整合子图结果节点
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"""
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state.current_phase = "integrating"
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构建完整的 React 模式主图
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# 整合通讯录子图结果
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if state.contact_result:
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state.final_result = state.contact_result.get("final_result", "")
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# 整合词典子图结果
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elif state.dictionary_result:
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state.final_result = state.dictionary_result.get("final_result", "")
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# 整合资讯子图结果
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elif state.news_result:
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state.final_result = state.news_result.get("final_result", "")
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else:
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# 没有子图结果
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if not state.final_result:
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state.final_result = "处理完成"
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state.current_phase = "done"
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return state
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def build_main_graph() -> StateGraph:
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"""
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构建整合了子图的主图
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Returns:
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配置好的 StateGraph
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流程：
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START
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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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├─→ handle_error → (重试或结束) ──┤
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└─→ final_response
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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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# 添加节点
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graph.add_node("parse_intent", parse_user_intent)
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graph.add_node("general_chat", general_chat_node)
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graph.add_node("integrate_results", integrate_results)
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# ========== 添加节点 ==========
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# 添加子图节点
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# 1. 初始化节点
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graph.add_node("init_state", init_state_node)
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# 2. React 推理节点
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graph.add_node("react_reason", react_reason_node)
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# 3. RAG 检索节点
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graph.add_node("rag_retrieve", rag_retrieve_node)
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# 4. 错误处理节点
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graph.add_node("handle_error", error_handling_node)
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# 5. 最终回答节点
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graph.add_node("final_response", final_response_node)
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# ========== 添加子图节点 ==========
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# 构建并包装子图（带错误处理）
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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("contact_subgraph", contact_graph.compile())
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graph.add_node("dictionary_subgraph", dictionary_graph.compile())
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graph.add_node("news_analysis_subgraph", news_analysis_graph.compile())
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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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# 添加边
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# 从START开始
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graph.add_edge(START, "parse_intent")
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# ========== 添加边 ==========
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# 从parse_intent根据条件路由
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# 1. START → init_state
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graph.add_edge(START, "init_state")
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# 2. init_state → react_reason
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graph.add_edge("init_state", "react_reason")
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# 3. 条件路由：react_reason → 各分支
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graph.add_conditional_edges(
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"parse_intent",
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route_to_subgraph,
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"react_reason",
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route_by_reasoning,
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{
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"general_chat": "general_chat",
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# 检索分支 → 检索后回到推理
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"rag_retrieve": "rag_retrieve",
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# 子图分支 → 子图后回到推理
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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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# 错误处理分支
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"handle_error": "handle_error",
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# 最终回答分支
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"final_response": "final_response",
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}
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)
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# 从普通聊天和子图到结果整合
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graph.add_edge("general_chat", "integrate_results")
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graph.add_edge("contact_subgraph", "integrate_results")
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graph.add_edge("dictionary_subgraph", "integrate_results")
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graph.add_edge("news_analysis_subgraph", "integrate_results")
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# 4. 循环边：检索/子图/错误处理 后 → 回到推理
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graph.add_edge("rag_retrieve", "react_reason")
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graph.add_edge("contact_subgraph", "react_reason")
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graph.add_edge("dictionary_subgraph", "react_reason")
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graph.add_edge("news_analysis_subgraph", "react_reason")
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graph.add_edge("handle_error", "react_reason") # 错误处理后可能重试
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# 最终到END
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graph.add_edge("integrate_results", END)
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# 5. 最终边：final_response → END
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graph.add_edge("final_response", END)
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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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]
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