230 lines
7.3 KiB
Python
230 lines
7.3 KiB
Python
"""
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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 Optional
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from dataclasses import dataclass, field
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from datetime import datetime
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from langchain_core.runnables.config import RunnableConfig
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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
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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 = 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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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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# ========== 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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- 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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# ========== 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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# 解析 JSON
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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"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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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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# ========== 主路由节点 ==========
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async def hybrid_router_node(state: MainGraphState, config: Optional[RunnableConfig] = 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
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rule_result = _rule_based_redirect(query)
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if rule_result:
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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. 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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# ========== 条件路由函数 ==========
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def route_from_hybrid_decision(state: MainGraphState) -> str:
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"""从混合路由决策获取下一步节点"""
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decision = state.debug_info.get("hybrid_decision", {})
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return decision.get("path", "react_loop")
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def check_fast_path_success(state: MainGraphState) -> str:
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"""检查快速路径是否成功"""
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if state.debug_info.get("fast_path_failed"):
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info("[Fast Path Check] 快速路径失败，升级到 React 循环")
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return "escalate"
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info("[Fast Path Check] 快速路径成功，进入 llm_call")
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return "llm_call"
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# ========== 导出 ==========
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__all__ = [
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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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"HybridRouterResult",
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]
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