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构建并部署 AI Agent 服务 / deploy (push) Successful in 6m8s
1. 双模型服务 (llm + smallLLM) - 增加 get_small_llm_service() 函数 - 支持智谱/DeepSeek 小模型作为轻量级选项 2. 前置混合路由 - 规则快速分流（无 LLM，超快速） - 轻量级意图分类（smallLLM） - 快速路径：fast_chitchat, fast_rag, fast_tool 3. 自动升级机制 - 快速路径失败 → 自动回到 React 循环 - SSE 事件增强：intent_classified, path_decision, fast_path_*, escalation 4. 向后兼容 - build_react_main_graph(use_hybrid_router=True/False) - 可选择启用或禁用混合路由 5. 更新 intent.py - 支持 use_small_llm 参数 - 保留原有完整功能供 React 循环使用
546 lines
18 KiB
Python
546 lines
18 KiB
Python
"""
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混合路由节点模块 - 前置路由 + 快速路径
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"""
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import re
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import json
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from typing import Dict, Any, Optional, List
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from dataclasses import dataclass, field
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from datetime import datetime
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from app.main_graph.state import MainGraphState
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from app.logger import info, debug
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from app.model_services.chat_services import get_small_llm_service, get_chat_service
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from app.main_graph.nodes.rag_nodes import rag_retrieve_node
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# ========== 核心数据类型 ==========
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@dataclass
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class HybridRouterResult:
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"""混合路由结果"""
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intent: str = "complex" # chitchat / knowledge / tool / complex
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confidence: float = 0.0
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suggested_tools: List[str] = field(default_factory=list)
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path: str = "react_loop" # fast_chitchat / fast_rag / fast_tool / react_loop
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reasoning: str = ""
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# ========== 规则分流（无 LLM，<5ms） ==========
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# 问候、感谢等直接返回的关键词
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AL_CHITCHAT = {
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"你好", "您好", "hi", "hello", "hey", "早上好", "晚上好", "下午好",
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"谢谢", "感谢", "多谢", "thanks", "thank you",
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"再见", "拜拜", "goodbye", "bye"
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}
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# 子图关键词映射
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SUBGRAPH_KEYWORDS = {
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"contact": ["通讯录", "联系人", "contact", "email", "邮件", "邮箱"],
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"dictionary": ["词典", "单词", "翻译", "dictionary", "translate", "生词"],
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"news_analysis": ["资讯", "新闻", "分析", "news", "report", "热点"]
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}
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def _rule_based_redirect(query: str) -> Optional[HybridRouterResult]:
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"""
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规则分流：处理明显不需要推理的情况（超快速）
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Args:
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query: 用户查询
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Returns:
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HybridRouterResult 或 None
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"""
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query_clean = query.strip().lower()
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# 1. 检查闲聊
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if query_clean in AL_CHITCHAT or any(keyword in query_clean for keyword in AL_CHITCHAT):
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return HybridRouterResult(
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intent="chitchat",
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confidence=1.0,
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path="fast_chitchat",
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reasoning=f"规则匹配：闲聊类请求"
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)
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# 2. 检查子图关键词（直接调用工具）
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for subgraph_name, keywords in SUBGRAPH_KEYWORDS.items():
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if any(kw in query_clean for kw in keywords):
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return HybridRouterResult(
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intent="tool",
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confidence=0.9,
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suggested_tools=[subgraph_name],
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path="fast_tool",
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reasoning=f"规则匹配：{subgraph_name} 子图关键词"
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)
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# 3. 检查是否是纯问号或很短的问题（可能需要澄清）
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if len(query_clean) < 3 or (query_clean.endswith("?") and len(query_clean) < 5):
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return HybridRouterResult(
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intent="complex",
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confidence=0.3,
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path="react_loop",
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reasoning="规则匹配：问题过于简短或不确定"
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)
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return None
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# ========== 轻量级 LLM 分类 ==========
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async def _classify_with_small_llm(query: str) -> HybridRouterResult:
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"""
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使用轻量级 LLM 进行意图分类
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Args:
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query: 用户查询
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Returns:
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HybridRouterResult
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"""
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try:
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llm = get_small_llm_service()
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prompt = f"""你是一个专业的意图分类助手。请分析用户的查询，并输出 JSON 格式的结果。
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意图类型（4选一）：
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- chitchat: 闲聊、问候、感谢、道别（不需要工具）
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- knowledge: 知识查询（需要查询知识库）
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- tool: 工具操作（需要调用通讯录/词典/新闻等子图）
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- complex: 复杂任务（多步骤、不确定、或需要推理）
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用户查询:
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{query}
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输出格式（仅 JSON，不要其他内容）：
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{{
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"intent": "chitchat|knowledge|tool|complex",
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"confidence": 0.0-1.0,
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"reasoning": "简要说明理由",
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"suggested_tools": ["contact|dictionary|news_analysis", "other"]
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}}
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注意：如果不能100%确定意图，请选择 "complex"，置信度设低一些。
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"""
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response = await llm.ainvoke(prompt)
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content = response.content
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# 解析 JSON
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json_match = re.search(r'(\{[^{}]*\{[^{}]*\}[^{}]*\})|(\{[^{}]*\})', content)
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if json_match:
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try:
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data = json.loads(json_match.group(0))
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intent = data.get("intent", "complex")
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confidence = float(data.get("confidence", 0.3))
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reasoning = data.get("reasoning", "")
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suggested_tools = data.get("suggested_tools", [])
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# 置信度低于 0.5 一律走 complex
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if confidence < 0.5:
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intent = "complex"
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path = "react_loop"
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elif intent == "chitchat":
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path = "fast_chitchat"
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elif intent == "knowledge":
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path = "fast_rag"
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elif intent == "tool":
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path = "fast_tool"
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else:
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intent = "complex"
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path = "react_loop"
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return HybridRouterResult(
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intent=intent,
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confidence=confidence,
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suggested_tools=suggested_tools,
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path=path,
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reasoning=reasoning
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)
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except Exception as e:
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debug(f"轻量 LLM 响应解析失败: {e}")
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pass
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except Exception as e:
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debug(f"轻量 LLM 调用失败: {e}")
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# LLM 失败，降级到规则+默认
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return HybridRouterResult(
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intent="complex",
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confidence=0.3,
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path="react_loop",
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reasoning="LLM 调用失败，降级到 React 循环"
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)
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# ========== 路由决策 ==========
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def _make_decision(classification_result: HybridRouterResult) -> HybridRouterResult:
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"""
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根据分类结果最终决策
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Args:
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classification_result: 分类结果
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Returns:
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最终决策结果
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"""
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if classification_result.confidence < 0.5:
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classification_result.intent = "complex"
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classification_result.path = "react_loop"
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return classification_result
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return classification_result
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# ========== 混合路由主节点 ==========
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async def hybrid_router_node(state: MainGraphState, config: Optional[Dict[str, Any]] = None) -> MainGraphState:
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"""
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混合路由节点：前置路由，决定走快速路径还是 React 循环
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Args:
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state: 当前状态
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config: LangChain 配置（用于发送自定义事件）
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Returns:
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更新后的状态
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"""
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state.current_phase = "hybrid_router"
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query = state.user_query or ""
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info(f"[Hybrid Router] 开始路由: {query[:50]}...")
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# 1. 规则分流（超快速）
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rule_result = _rule_based_redirect(query)
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if rule_result:
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info(f"[Hybrid Router] 规则分流命中: {rule_result.path}")
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decision = rule_result
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else:
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# 2. 轻量 LLM 分类
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info(f"[Hybrid Router] 规则未命中，使用轻量 LLM 分类")
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classification_result = await _classify_with_small_llm(query)
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decision = _make_decision(classification_result)
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# 3. 发送 SSE 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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"intent_classified",
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{
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"intent": decision.intent,
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"confidence": decision.confidence,
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"reasoning": decision.reasoning,
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"suggested_tools": decision.suggested_tools
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},
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callbacks=callbacks
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)
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await adispatch_custom_event(
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"path_decision",
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{
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"path": decision.path,
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"intent": decision.intent,
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"reasoning": decision.reasoning
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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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debug(f"[Hybrid Router] 发送 SSE 事件失败: {e}")
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# 4. 更新状态
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state.debug_info["hybrid_decision"] = decision
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state.debug_info["hybrid_start_time"] = datetime.now().isoformat()
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info(f"[Hybrid Router] 路由决策: {decision.path} (intent={decision.intent}, confidence={decision.confidence})")
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return state
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# ========== 快速路径：闲聊 ==========
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async def fast_chitchat_node(state: MainGraphState, config: Optional[Dict[str, Any]] = None) -> MainGraphState:
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"""
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快速闲聊节点：直接返回回复，不走 RAG/工具/循环
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Args:
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state: 当前状态
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config: LangChain 配置
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Returns:
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更新后的状态
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"""
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state.current_phase = "fast_chitchat"
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query = state.user_query or ""
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info(f"[Fast Chitchat] 处理: {query[:50]}")
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# 发送 SSE 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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"fast_path_start",
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{"path": "fast_chitchat"},
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callbacks=callbacks
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)
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except Exception as e:
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debug(f"[Fast Chitchat] 发送事件失败: {e}")
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# 快速回复（可以扩展为模板库）
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query_clean = query.strip().lower()
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if any(kw in query_clean for kw in ["谢谢", "感谢", "thanks", "thank you"]):
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reply = "不客气！如果还有其他问题，请随时告诉我 😊"
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elif any(kw in query_clean for kw in ["再见", "拜拜", "bye", "goodbye"]):
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reply = "再见！期待下次为您服务 👋"
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elif any(kw in query_clean for kw in ["你好", "您好", "hi", "hello", "hey", "早上好", "晚上好", "下午好"]):
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reply = "你好！有什么我可以帮您的吗？"
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else:
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# 兜底：用轻量 LLM 生成
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try:
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llm = get_small_llm_service()
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response = await llm.ainvoke(f"你是一个友好的助手。用户说：{query}。请简短友好地回复：")
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reply = response.content
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except:
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reply = "你好！有什么我可以帮您的吗？"
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state.final_result = reply
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state.success = True
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state.current_phase = "finalizing"
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state.debug_info["fast_chitchat_success"] = True
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# 发送 fast_path_end 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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"fast_path_end",
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{"path": "fast_chitchat", "success": True},
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callbacks=callbacks
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)
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except Exception as e:
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debug(f"[Fast Chitchat] 发送完成事件失败: {e}")
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return state
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# ========== 快速路径：RAG（带自动升级） ==========
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async def fast_rag_node(state: MainGraphState, config: Optional[Dict[str, Any]] = None) -> MainGraphState:
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"""
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快速 RAG 节点：先尝试快速检索，失败自动升级到 React 循环
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Args:
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state: 当前状态
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config: LangChain 配置
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Returns:
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更新后的状态
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"""
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state.current_phase = "fast_rag"
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query = state.user_query or ""
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info(f"[Fast RAG] 开始处理: {query[:50]}")
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# 发送 SSE 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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"fast_path_start",
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{"path": "fast_rag"},
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callbacks=callbacks
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)
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except Exception as e:
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debug(f"[Fast RAG] 发送事件失败: {e}")
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try:
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# 先尝试 RAG 检索
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state = rag_retrieve_node(state, config)
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# 检查检索结果
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rag_docs = getattr(state, "rag_docs", [])
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rag_context = getattr(state, "rag_context", "")
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# 检查是否有有效结果
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has_valid_results = (rag_docs and len(rag_docs) > 0) or (rag_context and len(rag_context) > 10)
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if has_valid_results:
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# 快速 RAG 成功！使用小模型快速生成回答
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try:
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llm = get_chat_service()
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prompt = f"""请根据以下信息回答用户问题：
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检索到的信息：
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{rag_context or str(rag_docs)[:2000]}
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用户问题：{query}
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请给出简洁、准确的回答："""
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response = await llm.ainvoke(prompt)
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state.final_result = response.content
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state.success = True
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state.current_phase = "finalizing"
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state.debug_info["fast_rag_success"] = True
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# 发送成功事件
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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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"fast_path_end",
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{"path": "fast_rag", "success": True},
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callbacks=callbacks
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)
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except Exception as e:
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debug(f"[Fast RAG] 发送完成事件失败: {e}")
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return state
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except Exception as e:
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info(f"[Fast RAG] 快速回答生成失败: {e}")
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# 继续往下走，升级到 React 循环
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# RAG 失败或无结果：标记升级
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info(f"[Fast RAG] 无有效检索结果，升级到 React 循环")
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return mark_fast_path_failed(state, reason="无有效检索结果")
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except Exception as e:
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info(f"[Fast RAG] 执行失败: {e}")
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return mark_fast_path_failed(state, reason=str(e))
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# ========== 快速路径：工具（带自动升级） ==========
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async def fast_tool_node(state: MainGraphState, config: Optional[Dict[str, Any]] = None) -> MainGraphState:
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"""
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快速工具节点：尝试直接调用工具，失败自动升级到 React 循环
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Args:
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state: 当前状态
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config: LangChain 配置
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Returns:
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更新后的状态
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"""
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state.current_phase = "fast_tool"
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decision: HybridRouterResult = state.debug_info.get("hybrid_decision", HybridRouterResult())
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suggested_tools = decision.suggested_tools or []
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query = state.user_query or ""
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info(f"[Fast Tool] 开始处理，建议工具: {suggested_tools}")
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# 发送 SSE 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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"fast_path_start",
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{"path": "fast_tool", "suggested_tools": suggested_tools},
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callbacks=callbacks
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)
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except Exception as e:
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debug(f"[Fast Tool] 发送事件失败: {e}")
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# 检查是否有明确的工具建议
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if not suggested_tools:
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info(f"[Fast Tool] 无明确工具建议，升级到 React 循环")
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return mark_fast_path_failed(state, reason="无明确工具建议")
|
||
|
||
# 工具调用逻辑（这里暂时先标记升级，让 React 循环去处理）
|
||
# 后续可以扩展为直接调用子图
|
||
info(f"[Fast Tool] 快速工具调用暂未完善，升级到 React 循环")
|
||
return mark_fast_path_failed(state, reason="快速工具调用暂未完善")
|
||
|
||
|
||
# ========== 标记快速路径失败（用于自动升级） ==========
|
||
|
||
def mark_fast_path_failed(state: MainGraphState, reason: str = "") -> MainGraphState:
|
||
"""
|
||
标记快速路径失败，准备升级到 React 循环
|
||
|
||
Args:
|
||
state: 当前状态
|
||
reason: 失败原因
|
||
|
||
Returns:
|
||
更新后的状态
|
||
"""
|
||
state.debug_info["fast_path_failed"] = True
|
||
state.debug_info["fast_path_fail_reason"] = reason
|
||
state.success = False
|
||
|
||
# 发送 escalation 事件
|
||
config = state.debug_info.get("config")
|
||
if config:
|
||
try:
|
||
from langchain_core.callbacks.manager import adispatch_custom_event
|
||
callbacks = config.get("callbacks")
|
||
if callbacks:
|
||
# 这里需要在异步上下文中调用
|
||
pass
|
||
except Exception as e:
|
||
debug(f"[Fast Path] 发送升级事件失败: {e}")
|
||
|
||
info(f"[Fast Path] 标记失败，准备升级: {reason}")
|
||
return state
|
||
|
||
|
||
# ========== 快速路径检查器（自动升级机制） ==========
|
||
|
||
def route_from_hybrid_decision(state: MainGraphState) -> str:
|
||
"""
|
||
从混合路由决策获取下一步的节点名称
|
||
|
||
Args:
|
||
state: 当前状态
|
||
|
||
Returns:
|
||
节点名称
|
||
"""
|
||
decision: HybridRouterResult = state.debug_info.get("hybrid_decision", HybridRouterResult())
|
||
return decision.path
|
||
|
||
|
||
def check_fast_path_success(state: MainGraphState) -> str:
|
||
"""
|
||
检查快速路径是否成功，成功直接到 finalize，失败升级到 react_reason
|
||
|
||
Args:
|
||
state: 当前状态
|
||
|
||
Returns:
|
||
"success" 或 "escalate"
|
||
"""
|
||
# 检查是否有错误标记
|
||
if state.debug_info.get("fast_path_failed"):
|
||
info(f"[Fast Path Check] 快速路径失败，升级到 React 循环")
|
||
return "escalate"
|
||
|
||
# 检查是否成功设置了 final_result
|
||
if state.final_result:
|
||
info(f"[Fast Path Check] 快速路径成功，进入 finalize")
|
||
return "success"
|
||
|
||
# 默认：认为成功（某些快速路径可能直接在节点中完成）
|
||
return "success"
|