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构建并部署 AI Agent 服务 / deploy (push) Successful in 5m31s
- 重构 fast_paths.py，让 fast_chitchat 和 fast_rag 都进入 llm_call 而不是直接设置 final_result - 修改 check_fast_path_success 函数返回 'llm_call' 而不是 'success' - 更新 main_graph_builder.py 的条件边配置，支持路由到 llm_call - 在快速路径节点中添加清除 state.final_result 的逻辑，避免复用旧结果 - 重构 RAG 工具初始化方式，使用模块级变量管理 - 修改 finalize.py 让它返回 final_result - 更新 agent_service.py 的 RAG 工具注入方式 - 简化 hybrid_router.py 的代码结构 - 清理 rag_nodes.py 的全局变量相关代码 - 更新相关测试文件
371 lines
13 KiB
Python
371 lines
13 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 ..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 ..state import MainGraphState
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from ..nodes.reasoning import react_reason_node
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from ..nodes.web_search import web_search_node
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from ..nodes.error_handling import error_handling_node
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from ..nodes.routing import init_state_node, route_by_reasoning
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from ..nodes.hybrid_router import (
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hybrid_router_node,
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route_from_hybrid_decision,
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check_fast_path_success,
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)
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from ..nodes.fast_paths import (
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fast_chitchat_node,
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fast_rag_node,
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fast_tool_node,
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)
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from ..nodes.llm_call import create_llm_call_node
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from ..nodes.rag_nodes import rag_retrieve_node
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from ..nodes.retrieve_memory import create_retrieve_memory_node
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from ..nodes.memory_trigger import memory_trigger_node, set_mem0_client
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from ..nodes.summarize import create_summarize_node
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from ..nodes.finalize import finalize_node
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from ...subgraphs.contact import build_contact_subgraph
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from ...subgraphs.dictionary import build_dictionary_subgraph
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from ...subgraphs.news_analysis import build_news_analysis_subgraph
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from ...memory.mem0_client import Mem0Client
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from ...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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async def wrapped_node(state: MainGraphState, config: Optional[Dict[str, Any]] = None) -> MainGraphState:
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# 发送子图开始事件
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if config:
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try:
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from langchain_core.callbacks.manager import adispatch_custom_event
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callbacks = config.get("callbacks")
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if callbacks:
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await adispatch_custom_event(
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"react_reasoning",
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{
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"step": state.reasoning_step,
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"action": f"{name}_subgraph_start",
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"confidence": 1.0,
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"reasoning": f"开始执行 {name} 子图..."
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},
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callbacks=callbacks
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)
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except Exception as e:
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info(f"[{name}_subgraph] 无法发送开始事件: {e}")
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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
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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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# 发送子图完成事件
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if config:
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try:
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from langchain_core.callbacks.manager import adispatch_custom_event
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callbacks = config.get("callbacks")
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if callbacks:
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await adispatch_custom_event(
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"react_reasoning",
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{
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"step": state.reasoning_step,
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"action": f"{name}_subgraph_complete",
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"confidence": 1.0,
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"reasoning": f"{name} 子图执行完成"
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},
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callbacks=callbacks
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)
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except Exception as e:
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info(f"[{name}_subgraph] 无法发送完成事件: {e}")
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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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# 发送子图错误事件
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if config:
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try:
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from langchain_core.callbacks.manager import adispatch_custom_event
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callbacks = config.get("callbacks")
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if callbacks:
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await adispatch_custom_event(
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"react_reasoning",
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{
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"step": state.reasoning_step,
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"action": f"{name}_subgraph_error",
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"confidence": 1.0,
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"reasoning": f"{name} 子图执行失败: {str(e)}"
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},
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callbacks=callbacks
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)
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except Exception as e:
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info(f"[{name}_subgraph] 无法发送错误事件: {e}")
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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, use_hybrid_router: bool = True) -> StateGraph:
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"""
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构建整合后的完整主图（支持混合路由）
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Args:
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llm: LangChain ChatModel 实例
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tools: 工具列表
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mem0_client: Mem0 客户端实例
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use_hybrid_router: 是否使用混合路由（快速路径 + React 循环）
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Returns:
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StateGraph: 构建好的图
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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：初始化
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graph.add_node("init_state", init_state_node)
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# ========== 混合路由节点（如果启用） ==========
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if use_hybrid_router:
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graph.add_node("hybrid_router", hybrid_router_node)
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graph.add_node("fast_chitchat", fast_chitchat_node)
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graph.add_node("fast_rag", fast_rag_node)
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graph.add_node("fast_tool", fast_tool_node)
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# 第三阶段：React 循环推理（始终保留）
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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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# 第四阶段：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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# 进入初始化
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graph.add_edge("memory_trigger", "init_state")
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# ========== 混合路由分支（如果启用） ==========
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if use_hybrid_router:
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graph.add_edge("init_state", "hybrid_router")
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# 从 hybrid_router 条件分支
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graph.add_conditional_edges(
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"hybrid_router",
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route_from_hybrid_decision,
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{
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"fast_chitchat": "fast_chitchat",
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"fast_rag": "fast_rag",
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"fast_tool": "fast_tool",
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"react_loop": "react_reason"
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}
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)
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# 快速路径的完成检查
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for fast_node in ["fast_chitchat", "fast_rag", "fast_tool"]:
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graph.add_conditional_edges(
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fast_node,
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check_fast_path_success,
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{
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"llm_call": "llm_call",
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"escalate": "react_reason"
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}
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)
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info(f"✅ [图构建] 混合路由模式已启用")
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else:
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# 无混合路由，直接到 react_reason
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graph.add_edge("init_state", "react_reason")
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info(f"✅ [图构建] 纯 React 模式")
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# ========== React 循环边（始终保留） ==========
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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、子图、error都回到 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("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")
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# ========== 最终完成阶段 ==========
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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(f"✅ [图构建] 整合后的完整主图构建完成（混合路由: {use_hybrid_router}）")
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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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] |