refactor: 重构快速路径流程，统一通过 llm_call 输出
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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
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@@ -1,107 +1,47 @@
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"""
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混合路由节点模块 - 前置路由 + 快速路径
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混合路由节点模块 - 前置路由决策
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负责决定走快速路径还是 React 循环
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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 typing import Optional
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from dataclasses import dataclass, field
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from datetime import datetime
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from ..state import MainGraphState
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from ...logger import info, debug
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from ...model_services.chat_services import get_small_llm_service, get_chat_service
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from .rag_nodes import rag_retrieve_node
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from ...model_services.chat_services import get_small_llm_service
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from ._utils import dispatch_custom_event
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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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suggested_tools: list = 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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# ========== 规则配置 ==========
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CHITCHAT_KEYWORDS = {
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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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# ========== 意图分类 Prompt 模板 ==========
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INTENT_CLASSIFICATION_PROMPT = """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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@@ -120,52 +60,94 @@ async def _classify_with_small_llm(query: str) -> HybridRouterResult:
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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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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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# ========== 规则分流（<5ms） ==========
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def _rule_based_redirect(query: str) -> Optional[HybridRouterResult]:
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"""规则分流：处理明显不需要推理的情况"""
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query_clean = query.strip().lower()
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# 1. 闲聊
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if query_clean in CHITCHAT_KEYWORDS or any(kw in query_clean for kw in CHITCHAT_KEYWORDS):
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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="规则匹配：闲聊类请求"
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)
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# 2. 子图关键词
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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. 短问题
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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"
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)
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return None
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# ========== LLM 分类 ==========
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async def _classify_with_llm(query: str) -> HybridRouterResult:
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"""使用轻量级 LLM 进行意图分类"""
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try:
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llm = get_small_llm_service()
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prompt = INTENT_CLASSIFICATION_PROMPT.format(query=query)
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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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json_match = re.search(r'\{[\s\S]*?\}', response.content)
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if not json_match:
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return _default_result()
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data = json.loads(json_match.group())
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return _parse_classification_result(data)
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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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debug(f"LLM 分类失败: {e}")
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return _default_result()
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def _parse_classification_result(data: dict) -> HybridRouterResult:
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"""解析分类结果"""
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intent = data.get("intent", "complex")
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confidence = float(data.get("confidence", 0.3))
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# 置信度低于阈值，走 complex
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if confidence < 0.5:
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intent = "complex"
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# intent -> path 映射
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path_map = {
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"chitchat": "fast_chitchat",
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"knowledge": "fast_rag",
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"tool": "fast_tool",
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}
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return HybridRouterResult(
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intent=intent,
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confidence=confidence,
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suggested_tools=data.get("suggested_tools", []),
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path=path_map.get(intent, "react_loop"),
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reasoning=data.get("reasoning", "")
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)
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def _default_result() -> HybridRouterResult:
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"""默认结果（LLM 失败时）"""
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return HybridRouterResult(
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intent="complex",
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confidence=0.3,
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@@ -174,372 +156,73 @@ async def _classify_with_small_llm(query: str) -> HybridRouterResult:
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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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# ========== 主路由节点 ==========
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async def hybrid_router_node(state: MainGraphState, config: Optional[dict] = None) -> MainGraphState:
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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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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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# 1. 规则分流
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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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info(f"[Hybrid Router] 规则命中: {decision.path}")
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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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# 2. LLM 分类
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info("[Hybrid Router] 规则未命中，使用 LLM 分类")
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decision = await _classify_with_llm(query)
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# 3. 更新状态
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state.debug_info["hybrid_decision"] = {
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"intent": decision.intent,
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"confidence": decision.confidence,
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"path": decision.path,
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"reasoning": decision.reasoning,
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"suggested_tools": decision.suggested_tools
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}
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state.debug_info["hybrid_start_time"] = datetime.now().isoformat()
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# 4. 发送事件
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await dispatch_custom_event("intent_classified", {
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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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}, config)
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await dispatch_custom_event("path_decision", {
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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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}, config)
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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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||||
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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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||||
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||||
# 发送 fast_path_end 事件
|
||||
if config:
|
||||
try:
|
||||
from langchain_core.callbacks.manager import adispatch_custom_event
|
||||
callbacks = config.get("callbacks")
|
||||
if callbacks:
|
||||
await adispatch_custom_event(
|
||||
"fast_path_end",
|
||||
{"path": "fast_chitchat", "success": True},
|
||||
callbacks=callbacks
|
||||
)
|
||||
except Exception as e:
|
||||
debug(f"[Fast Chitchat] 发送完成事件失败: {e}")
|
||||
|
||||
return state
|
||||
|
||||
|
||||
# ========== 快速路径：RAG（带自动升级） ==========
|
||||
|
||||
async def fast_rag_node(state: MainGraphState, config: Optional[Dict[str, Any]] = None) -> MainGraphState:
|
||||
"""
|
||||
快速 RAG 节点：先尝试快速检索，失败自动升级到 React 循环
|
||||
|
||||
Args:
|
||||
state: 当前状态
|
||||
config: LangChain 配置
|
||||
|
||||
Returns:
|
||||
更新后的状态
|
||||
"""
|
||||
state.current_phase = "fast_rag"
|
||||
|
||||
query = state.user_query or ""
|
||||
info(f"[Fast RAG] 开始处理: {query[:50]}")
|
||||
|
||||
# 发送 SSE 事件
|
||||
if config:
|
||||
try:
|
||||
from langchain_core.callbacks.manager import adispatch_custom_event
|
||||
callbacks = config.get("callbacks")
|
||||
if callbacks:
|
||||
await adispatch_custom_event(
|
||||
"fast_path_start",
|
||||
{"path": "fast_rag"},
|
||||
callbacks=callbacks
|
||||
)
|
||||
except Exception as e:
|
||||
debug(f"[Fast RAG] 发送事件失败: {e}")
|
||||
|
||||
try:
|
||||
# 先尝试 RAG 检索 - 注意：rag_retrieve_node 是异步函数，需要 await
|
||||
state = await rag_retrieve_node(state, config)
|
||||
|
||||
# 检查检索结果
|
||||
rag_docs = getattr(state, "rag_docs", [])
|
||||
rag_context = getattr(state, "rag_context", "")
|
||||
|
||||
# 检查是否有有效结果
|
||||
has_valid_results = (rag_docs and len(rag_docs) > 0) or (rag_context and len(rag_context) > 10)
|
||||
|
||||
if has_valid_results:
|
||||
# 快速 RAG 成功！使用小模型快速生成回答
|
||||
try:
|
||||
llm = get_chat_service()
|
||||
prompt = f"""请根据以下信息回答用户问题：
|
||||
|
||||
检索到的信息：
|
||||
{rag_context or str(rag_docs)[:2000]}
|
||||
|
||||
用户问题：{query}
|
||||
|
||||
请给出简洁、准确的回答："""
|
||||
|
||||
response = await llm.ainvoke(prompt)
|
||||
|
||||
state.final_result = response.content
|
||||
state.success = True
|
||||
state.current_phase = "finalizing"
|
||||
state.debug_info["fast_rag_success"] = True
|
||||
|
||||
# 发送成功事件
|
||||
if config:
|
||||
try:
|
||||
from langchain_core.callbacks.manager import adispatch_custom_event
|
||||
callbacks = config.get("callbacks")
|
||||
if callbacks:
|
||||
await adispatch_custom_event(
|
||||
"fast_path_end",
|
||||
{"path": "fast_rag", "success": True},
|
||||
callbacks=callbacks
|
||||
)
|
||||
except Exception as e:
|
||||
debug(f"[Fast RAG] 发送完成事件失败: {e}")
|
||||
|
||||
return state
|
||||
|
||||
except Exception as e:
|
||||
info(f"[Fast RAG] 快速回答生成失败: {e}")
|
||||
# 继续往下走，升级到 React 循环
|
||||
|
||||
# RAG 失败或无结果：标记升级
|
||||
info(f"[Fast RAG] 无有效检索结果，升级到 React 循环")
|
||||
return mark_fast_path_failed(state, reason="无有效检索结果")
|
||||
|
||||
except Exception as e:
|
||||
info(f"[Fast RAG] 执行失败: {e}")
|
||||
return mark_fast_path_failed(state, reason=str(e))
|
||||
|
||||
|
||||
# ========== 快速路径：工具（带自动升级） ==========
|
||||
|
||||
async def fast_tool_node(state: MainGraphState, config: Optional[Dict[str, Any]] = None) -> MainGraphState:
|
||||
"""
|
||||
快速工具节点：尝试直接调用工具，失败自动升级到 React 循环
|
||||
|
||||
Args:
|
||||
state: 当前状态
|
||||
config: LangChain 配置
|
||||
|
||||
Returns:
|
||||
更新后的状态
|
||||
"""
|
||||
state.current_phase = "fast_tool"
|
||||
|
||||
decision: HybridRouterResult = state.debug_info.get("hybrid_decision", HybridRouterResult())
|
||||
suggested_tools = decision.suggested_tools or []
|
||||
|
||||
query = state.user_query or ""
|
||||
info(f"[Fast Tool] 开始处理，建议工具: {suggested_tools}")
|
||||
|
||||
# 发送 SSE 事件
|
||||
if config:
|
||||
try:
|
||||
from langchain_core.callbacks.manager import adispatch_custom_event
|
||||
callbacks = config.get("callbacks")
|
||||
if callbacks:
|
||||
await adispatch_custom_event(
|
||||
"fast_path_start",
|
||||
{"path": "fast_tool", "suggested_tools": suggested_tools},
|
||||
callbacks=callbacks
|
||||
)
|
||||
except Exception as e:
|
||||
debug(f"[Fast Tool] 发送事件失败: {e}")
|
||||
|
||||
# 检查是否有明确的工具建议
|
||||
if not suggested_tools:
|
||||
info(f"[Fast Tool] 无明确工具建议，升级到 React 循环")
|
||||
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） ==========
|
||||
|
||||
# ========== 条件路由函数 ==========
|
||||
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
|
||||
"""从混合路由决策获取下一步节点"""
|
||||
decision = state.debug_info.get("hybrid_decision", {})
|
||||
return decision.get("path", "react_loop")
|
||||
|
||||
|
||||
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
|
||||
"""检查快速路径是否成功"""
|
||||
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")
|
||||
info("[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"
|
||||
|
||||
info("[Fast Path Check] 快速路径成功，进入 llm_call")
|
||||
return "llm_call"
|
||||
|
||||
|
||||
# ========== 导出 ==========
|
||||
__all__ = [
|
||||
"hybrid_router_node",
|
||||
"route_from_hybrid_decision",
|
||||
"check_fast_path_success",
|
||||
"HybridRouterResult",
|
||||
]
|
||||
|
||||
Reference in New Issue
Block a user