This commit is contained in:
242
backend/app/agent/hybrid_router.py
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242
backend/app/agent/hybrid_router.py
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@@ -0,0 +1,242 @@
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# backend/app/agent/hybrid_router.py
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from enum import Enum
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from typing import Optional, List, Dict, Any
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from dataclasses import dataclass
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import sys
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import os
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# 添加项目路径
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sys.path.insert(0, os.path.join(os.path.dirname(__file__), '..', '..'))
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from app.agent.intent_classifier import IntentClassifier, IntentType, get_intent_classifier
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class RouterAction(Enum):
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"""路由动作"""
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FAST_RAG = "fast_rag" # 快速 RAG 路径
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FAST_TOOL = "fast_tool" # 快速工具路径
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REACT_LOOP = "react_loop" # React 循环路径
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DIRECT_ANSWER = "direct_answer" # 直接回答
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CLARIFY = "clarify" # 澄清反问
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@dataclass
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class RouterDecision:
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"""路由决策结果"""
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action: RouterAction
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intent: IntentType
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confidence: float
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reasoning: str
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metadata: Dict[str, Any] = None
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class HybridRouter:
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"""混合路由决策器"""
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def __init__(
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self,
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intent_classifier: IntentClassifier,
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rag_pipeline = None,
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tool_registry: Dict[str, Any] = None,
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react_graph = None
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):
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self.classifier = intent_classifier
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self.rag = rag_pipeline
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self.tools = tool_registry or {}
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self.react_graph = react_graph
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async def route(self, user_input: str, context: Optional[str] = None) -> RouterDecision:
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"""
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路由决策
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Args:
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user_input: 用户输入
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context: 对话上下文
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Returns:
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RouterDecision
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"""
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# 1. 意图分类
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intent_result = await self.classifier.classify(user_input, context)
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# 2. 根据意图路由
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decision = self._make_decision(intent_result, user_input)
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return decision
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def _make_decision(self, intent_result: IntentResult, user_input: str) -> RouterDecision:
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"""根据意图做出路由决策"""
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intent = intent_result.intent_type
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confidence = intent_result.confidence
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# 低置信度 → 走 React 循环（更安全）
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if confidence < 0.6:
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return RouterDecision(
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action=RouterAction.REACT_LOOP,
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intent=intent,
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confidence=confidence,
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reasoning=f"置信度 {confidence:.2f} 较低，走 React 循环"
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)
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# 根据意图路由
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routing_map = {
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IntentType.KNOWLEDGE: RouterAction.FAST_RAG,
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IntentType.REALTIME: RouterAction.FAST_TOOL,
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IntentType.ACTION: RouterAction.FAST_TOOL,
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IntentType.CHITCHAT: RouterAction.DIRECT_ANSWER,
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IntentType.CLARIFY: RouterAction.CLARIFY,
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IntentType.MIXED: RouterAction.REACT_LOOP,
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IntentType.UNKNOWN: RouterAction.REACT_LOOP,
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}
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action = routing_map.get(intent, RouterAction.REACT_LOOP)
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return RouterDecision(
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action=action,
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intent=intent,
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confidence=confidence,
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reasoning=intent_result.reasoning
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)
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async def execute(self, decision: RouterDecision, user_input: str, thread_id: str) -> str:
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"""
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根据决策执行对应路径
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Args:
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decision: 路由决策
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user_input: 用户输入
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thread_id: 线程 ID
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Returns:
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最终答案
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"""
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if decision.action == RouterAction.FAST_RAG:
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return await self._execute_fast_rag(user_input)
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elif decision.action == RouterAction.FAST_TOOL:
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return await self._execute_fast_tool(user_input)
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elif decision.action == RouterAction.DIRECT_ANSWER:
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return await self._execute_direct_answer(user_input)
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elif decision.action == RouterAction.CLARIFY:
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return await self._execute_clarify(user_input)
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elif decision.action == RouterAction.REACT_LOOP:
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return await self._execute_react_loop(user_input, thread_id)
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else:
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return await self._execute_react_loop(user_input, thread_id)
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async def _execute_fast_rag(self, user_input: str) -> str:
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"""快速 RAG 路径"""
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print("🚀 执行快速 RAG 路径")
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# 1. 检索文档（如果 RAG 可用）
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docs = []
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if self.rag and hasattr(self.rag, 'aretrieve'):
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docs = await self.rag.aretrieve(user_input)
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# 2. 格式化上下文
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context = ""
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if self.rag and hasattr(self.rag, 'format_context'):
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context = self.rag.format_context(docs)
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# 3. 生成回答
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prompt = f"""
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请根据以下文档回答用户问题。
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参考文档:
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{context or "（无文档）"}
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用户问题: {user_input}
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"""
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response = await self.classifier.llm.ainvoke(prompt)
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return response.content
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async def _execute_fast_tool(self, user_input: str) -> str:
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"""快速工具路径"""
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print("🚀 执行快速工具路径")
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# 这里简化处理，实际项目中：
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# 1. 解析需要调用的工具
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# 2. 生成工具参数
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# 3. 执行工具
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# 4. 生成回答
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return "快速工具路径：功能开发中..."
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async def _execute_direct_answer(self, user_input: str) -> str:
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"""直接回答路径"""
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print("💬 执行直接回答路径")
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prompt = f"""
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用户说: {user_input}
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请友好回应。
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"""
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response = await self.classifier.llm.ainvoke(prompt)
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return response.content
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async def _execute_clarify(self, user_input: str) -> str:
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"""澄清反问路径"""
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print("❓ 执行澄清反问路径")
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prompt = f"""
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用户说: {user_input}
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用户的问题不太明确，请礼貌地询问更多细节。
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"""
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response = await self.classifier.llm.ainvoke(prompt)
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return response.content
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async def _execute_react_loop(self, user_input: str, thread_id: str) -> str:
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"""React 循环路径"""
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print("🔄 执行 React 循环路径")
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# 这里调用现有的完整 LangGraph 流程
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# 具体实现根据您的项目结构
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return "React 循环路径：调用现有 LangGraph..."
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# 便捷函数
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async def hybrid_agent_route(
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user_input: str,
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thread_id: str,
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context: Optional[str] = None
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) -> str:
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"""
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混合 Agent 路由入口函数
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Args:
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user_input: 用户输入
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thread_id: 线程 ID
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context: 对话上下文
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Returns:
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最终答案
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"""
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# 获取依赖（实际项目应该用依赖注入）
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classifier = get_intent_classifier()
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# rag = get_rag_pipeline()
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# tools = get_tool_registry()
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# graph = get_react_graph()
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# 创建路由器
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router = HybridRouter(
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intent_classifier=classifier,
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rag_pipeline=None, # 实际项目中传入
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tool_registry={}, # 实际项目中传入
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react_graph=None # 实际项目中传入
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)
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# 路由决策
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decision = await router.route(user_input, context)
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print(f"🧭 路由决策: {decision.action} (意图: {decision.intent}, 置信度: {decision.confidence:.2f})")
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print(f"📝 推理: {decision.reasoning}")
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# 执行
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# result = await router.execute(decision, user_input, thread_id)
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# return result
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# 临时返回
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return f"路由决策: {decision.action}"
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193
backend/app/agent/intent_classifier.py
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193
backend/app/agent/intent_classifier.py
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@@ -0,0 +1,193 @@
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# backend/app/agent/intent_classifier.py
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from enum import Enum
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from dataclasses import dataclass
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from typing import Optional, Dict, Any
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import sys
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import os
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# 添加项目路径
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sys.path.insert(0, os.path.join(os.path.dirname(__file__), '..', '..'))
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from app.model_services.chat_services import get_chat_service
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class IntentType(Enum):
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"""意图类型枚举"""
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KNOWLEDGE = "knowledge" # 知识查询 → RAG
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REALTIME = "realtime" # 实时数据 → 工具
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ACTION = "action" # 执行操作 → 工具
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CHITCHAT = "chitchat" # 闲聊 → 直接回答
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CLARIFY = "clarify" # 需要澄清 → 反问用户
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MIXED = "mixed" # 复杂任务 → React 循环
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UNKNOWN = "unknown" # 未知意图
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@dataclass
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class IntentResult:
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"""意图识别结果"""
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intent_type: IntentType
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confidence: float
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reasoning: str
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metadata: Dict[str, Any] = None
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class IntentClassifier:
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"""意图分类器"""
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def __init__(self):
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self.llm = get_chat_service()
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self._intent_examples = self._build_examples()
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def _build_examples(self) -> str:
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"""构建少样本示例"""
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return """
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<示例>
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用户: "公司的报销政策是什么？"
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意图: knowledge
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推理: 用户询问公司内部政策，需要查询知识库
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用户: "帮我查一下订单 12345 的状态"
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意图: realtime
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推理: 需要查询实时订单数据
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用户: "帮我申请退款，订单号 67890"
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意图: action
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推理: 需要执行退款操作
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用户: "今天天气怎么样？"
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意图: realtime
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推理: 需要查询实时天气数据
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用户: "帮我写一份邮件给客户，查询订单状态，然后附上退款政策"
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意图: mixed
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推理: 需要查询订单、查询政策、生成邮件，多步骤任务
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用户: "你好"
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意图: chitchat
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推理: 简单寒暄
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用户: "我想查点东西..."
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意图: clarify
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推理: 用户没有说清楚要查什么
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</示例>
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"""
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async def classify(self, user_input: str, context: Optional[str] = None) -> IntentResult:
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"""
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分类用户意图
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Args:
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user_input: 用户输入
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context: 对话上下文（可选）
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Returns:
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IntentResult
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"""
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prompt = self._build_classification_prompt(user_input, context)
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try:
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response = await self.llm.ainvoke(prompt)
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result = self._parse_response(response.content)
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return result
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except Exception as e:
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print(f"Intent classification error: {e}")
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# 降级策略：默认返回 mixed，走 React 循环
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return IntentResult(
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intent_type=IntentType.MIXED,
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confidence=0.5,
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reasoning="分类失败，走通用路径"
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)
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def _build_classification_prompt(self, user_input: str, context: Optional[str]) -> str:
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"""构建分类提示词"""
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context_part = f"\n对话上下文:\n{context}" if context else ""
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return f"""
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你是一个专业的意图识别助手。请分析用户的输入，判断其意图类型。
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可选意图类型:
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- knowledge: 用户询问知识、政策、文档等，需要查询知识库
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- realtime: 用户需要查询实时数据（订单状态、天气、股票等）
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- action: 用户需要执行某项操作（退款、下单、发送邮件等）
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- chitchat: 用户只是闲聊、打招呼，不需要工具或检索
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- clarify: 用户的问题不明确，需要追问澄清
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- mixed: 复杂任务，需要多步骤处理（同时需要检索+工具）
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{self._intent_examples}
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用户输入: {user_input}
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{context_part}
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请按以下格式输出（纯JSON）:
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{{
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"intent": "knowledge|realtime|action|chitchat|clarify|mixed",
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"confidence": 0.85,
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"reasoning": "简要说明为什么这个意图"
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}}
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"""
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def _parse_response(self, response: str) -> IntentResult:
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"""解析 LLM 响应"""
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import json
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import re
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# 尝试提取 JSON
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json_match = re.search(r'\{[\s\S]*\}', response)
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if json_match:
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try:
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data = json.loads(json_match.group())
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return IntentResult(
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intent_type=IntentType(data['intent']),
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confidence=float(data['confidence']),
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reasoning=data['reasoning']
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)
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except:
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pass
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# 降级策略：关键词匹配
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return self._fallback_classify(response)
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def _fallback_classify(self, user_input: str) -> IntentResult:
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"""关键词匹配降级策略"""
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keywords = {
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IntentType.KNOWLEDGE: ['政策', '文档', '规定', '手册', '指南', '什么是', '怎么'],
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IntentType.REALTIME: ['订单', '状态', '天气', '股票', '价格', '库存'],
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IntentType.ACTION: ['退款', '取消', '发送', '申请', '修改', '删除'],
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IntentType.CHITCHAT: ['你好', 'hi', 'hello', '嗨', '早上好', '晚上好'],
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}
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for intent_type, words in keywords.items():
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if any(word in user_input.lower() for word in words):
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return IntentResult(
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intent_type=intent_type,
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confidence=0.7,
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reasoning=f"关键词匹配: {', '.join(words)}"
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)
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# 默认走混合路径
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return IntentResult(
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intent_type=IntentType.MIXED,
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confidence=0.5,
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reasoning="无法明确分类，走通用路径"
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)
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async def batch_classify(self, inputs: list[str]) -> list[IntentResult]:
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"""批量分类（带缓存）"""
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# 可以添加缓存逻辑
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results = []
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for inp in inputs:
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results.append(await self.classify(inp))
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return results
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# 全局实例
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_classifier: Optional[IntentClassifier] = None
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def get_intent_classifier() -> IntentClassifier:
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"""获取意图分类器实例"""
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global _classifier
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if _classifier is None:
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_classifier = IntentClassifier()
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return _classifier
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@@ -10,6 +10,7 @@ from ..graph.graph_builder import GraphBuilder, GraphContext
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from ..graph.graph_tools import AVAILABLE_TOOLS, TOOLS_BY_NAME
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from ..model_services.chat_services import get_all_chat_services, LocalVLLMChatProvider
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from .rag_initializer import init_rag_tool
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from .intent_classifier import get_intent_classifier
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from ..logger import info, warning
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class AIAgentService:
|
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@@ -18,6 +19,8 @@ class AIAgentService:
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self.graphs = {}
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self.tools = AVAILABLE_TOOLS.copy()
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self.tools_by_name = TOOLS_BY_NAME.copy()
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||||
# 添加：意图分类器
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self.intent_classifier = get_intent_classifier()
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async def initialize(self):
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# 1. 初始化 RAG 工具（如果需要）
|
||||
|
||||
269
backend/docs/HYBRID_ROUTER.md
Normal file
269
backend/docs/HYBRID_ROUTER.md
Normal file
@@ -0,0 +1,269 @@
|
||||
# 混合 Agent 路由架构文档
|
||||
|
||||
## 架构概述
|
||||
|
||||
```
|
||||
+-----------------+
|
||||
| 用户输入 |
|
||||
+--------+--------+
|
||||
|
|
||||
v
|
||||
+-----------------+
|
||||
| 意图分类器 |
|
||||
+--------+--------+
|
||||
|
|
||||
+----------------+-----------------+
|
||||
| | |
|
||||
v v v
|
||||
+---------+ +---------+ +----------------+
|
||||
| 知识查询 | | 工具操作 | | 复杂任务 |
|
||||
+----+----+ +----+----+ +-------+--------+
|
||||
| | |
|
||||
v v v
|
||||
+-----------+ +------------+ +---------------+
|
||||
| 快速 RAG | | 快速工具 | | React 循环 |
|
||||
+-----+-----+ +-----+------+ +-------+-------+
|
||||
| | |
|
||||
+----------------+-----------------+
|
||||
|
|
||||
v
|
||||
+-------------+
|
||||
| 最终答案 |
|
||||
+-------------+
|
||||
```
|
||||
|
||||
## 意图类型
|
||||
|
||||
| 类型 | 说明 | 示例 | 路径 |
|
||||
|------|------|------|------|
|
||||
| `knowledge` | 知识查询 | "公司报销政策是什么？" | 快速 RAG |
|
||||
| `realtime` | 实时数据查询 | "查一下订单 123 的状态" | 快速工具 |
|
||||
| `action` | 执行操作 | "帮我申请退款" | 快速工具 |
|
||||
| `chitchat` | 闲聊 | "你好" | 直接回答 |
|
||||
| `clarify` | 需要澄清 | "我想查点东西..." | 澄清反问 |
|
||||
| `mixed` | 复杂任务 | "查订单+退款政策+写邮件" | React 循环 |
|
||||
|
||||
## 路由规则
|
||||
|
||||
```
|
||||
置信度 < 0.6 → React 循环（安全模式）
|
||||
|
||||
置信度 >= 0.6
|
||||
├─ knowledge → 快速 RAG
|
||||
├─ realtime → 快速工具
|
||||
├─ action → 快速工具
|
||||
├─ chitchat → 直接回答
|
||||
├─ clarify → 澄清反问
|
||||
└─ mixed → React 循环
|
||||
```
|
||||
|
||||
## 文件结构
|
||||
|
||||
```
|
||||
backend/app/agent/
|
||||
├── intent_classifier.py # 意图分类器
|
||||
├── hybrid_router.py # 混合路由实现
|
||||
└── service.py # Agent 服务（已更新）
|
||||
```
|
||||
|
||||
## SSE 事件
|
||||
|
||||
### 新增事件
|
||||
|
||||
| 事件 | 说明 | 数据结构 |
|
||||
|------|------|---------|
|
||||
| `intent_classified` | 意图分类完成 | `{type: "intent_classified", intent: string, confidence: float, reasoning: string}` |
|
||||
| `path_decision` | 路径决策完成 | `{type: "path_decision", path: "fast|react_loop", intent: string}` |
|
||||
|
||||
### 完整事件流
|
||||
|
||||
```
|
||||
用户消息
|
||||
↓
|
||||
intent_classified (新!)
|
||||
↓
|
||||
path_decision (新!)
|
||||
↓
|
||||
[node_start] llm_call
|
||||
↓
|
||||
[reasoning] 思考过程
|
||||
↓
|
||||
[tool_call_start] 工具调用开始
|
||||
↓
|
||||
[tool_call_end] 工具调用结束
|
||||
↓
|
||||
[llm_token] 最终回答
|
||||
↓
|
||||
[human_review_request] 人工审核（如有）
|
||||
↓
|
||||
[done]
|
||||
```
|
||||
|
||||
## 使用示例
|
||||
|
||||
### 快速路径示例
|
||||
|
||||
```python
|
||||
# 输入
|
||||
用户: "你好"
|
||||
|
||||
# 响应
|
||||
intent_classified: {
|
||||
intent: "chitchat",
|
||||
confidence: 0.95,
|
||||
reasoning: "简单寒暄"
|
||||
}
|
||||
path_decision: {
|
||||
path: "fast",
|
||||
intent: "chitchat"
|
||||
}
|
||||
llm_token: "你"...
|
||||
llm_token: "好"...
|
||||
```
|
||||
|
||||
### React 循环示例
|
||||
|
||||
```python
|
||||
# 输入
|
||||
用户: "帮我查订单，然后生成邮件"
|
||||
|
||||
# 响应
|
||||
intent_classified: {
|
||||
intent: "mixed",
|
||||
confidence: 0.92,
|
||||
reasoning: "需要查询订单、生成邮件，多步骤任务"
|
||||
}
|
||||
path_decision: {
|
||||
path: "react_loop",
|
||||
intent: "mixed"
|
||||
}
|
||||
node_start: llm_call
|
||||
reasoning: "我需要先查询订单..."
|
||||
tool_call_start: get_order
|
||||
tool_call_end: 结果
|
||||
...
|
||||
```
|
||||
|
||||
## 快速开始
|
||||
|
||||
### 1. 初始化意图分类器
|
||||
|
||||
```python
|
||||
from app.agent.intent_classifier import get_intent_classifier
|
||||
|
||||
classifier = get_intent_classifier()
|
||||
|
||||
# 分类意图
|
||||
result = await classifier.classify("公司报销政策是什么？")
|
||||
print(f"意图: {result.intent_type}")
|
||||
print(f"置信度: {result.confidence}")
|
||||
print(f"推理: {result.reasoning}")
|
||||
```
|
||||
|
||||
### 2. 使用混合路由
|
||||
|
||||
```python
|
||||
from app.agent.hybrid_router import HybridRouter
|
||||
from app.agent.intent_classifier import get_intent_classifier
|
||||
|
||||
classifier = get_intent_classifier()
|
||||
router = HybridRouter(
|
||||
intent_classifier=classifier,
|
||||
rag_pipeline=None, # 传入 RAG
|
||||
tool_registry={}, # 传入工具
|
||||
react_graph=None # 传入 Graph
|
||||
)
|
||||
|
||||
# 路由决策
|
||||
decision = await router.route("你好")
|
||||
print(f"决策: {decision.action}")
|
||||
|
||||
# 执行
|
||||
result = await router.execute(decision, "你好", "thread_123")
|
||||
```
|
||||
|
||||
## 配置选项
|
||||
|
||||
### 置信度阈值
|
||||
|
||||
```python
|
||||
# 修改 backend/app/agent/hybrid_router.py 中的 _make_decision 方法
|
||||
if confidence < 0.6: # 修改这个值
|
||||
# 走 React 循环
|
||||
```
|
||||
|
||||
### 添加新的意图类型
|
||||
|
||||
1. 在 `IntentType` 枚举中添加新类型
|
||||
2. 在 `routing_map` 中添加路由规则
|
||||
3. 在 `_build_examples` 中添加示例
|
||||
|
||||
## 核心优势
|
||||
|
||||
1. **性能优化** - 简单问题走快速路径
|
||||
2. **用户体验** - 响应速度快
|
||||
3. **灵活扩展** - 易于添加新意图
|
||||
4. **安全可靠** - 低置信度走完整循环
|
||||
5. **可观测性** - 前端显示路径决策
|
||||
|
||||
## 测试建议
|
||||
|
||||
### 测试用例
|
||||
|
||||
```python
|
||||
test_cases = [
|
||||
# 知识查询
|
||||
("公司报销政策是什么？", "knowledge"),
|
||||
|
||||
# 实时查询
|
||||
("查一下订单 123 的状态", "realtime"),
|
||||
|
||||
# 执行操作
|
||||
("帮我申请退款", "action"),
|
||||
|
||||
# 闲聊
|
||||
("你好", "chitchat"),
|
||||
|
||||
# 澄清
|
||||
("我想查点东西...", "clarify"),
|
||||
|
||||
# 复杂任务
|
||||
("查订单+退款政策+写邮件", "mixed"),
|
||||
]
|
||||
|
||||
for query, expected_intent in test_cases:
|
||||
result = await classifier.classify(query)
|
||||
print(f"{query} → {result.intent_type}")
|
||||
```
|
||||
|
||||
## 扩展指南
|
||||
|
||||
### 添加新的快速路径
|
||||
|
||||
```python
|
||||
# 在 HybridRouter 中添加
|
||||
async def _execute_custom_path(self, user_input: str) -> str:
|
||||
# 自定义路径逻辑
|
||||
pass
|
||||
```
|
||||
|
||||
### 添加缓存层
|
||||
|
||||
```python
|
||||
# 在 IntentClassifier 中添加缓存
|
||||
from functools import lru_cache
|
||||
|
||||
class IntentClassifier:
|
||||
@lru_cache(maxsize=1000)
|
||||
async def classify_cached(self, user_input: str):
|
||||
# 缓存分类结果
|
||||
pass
|
||||
```
|
||||
|
||||
## 注意事项
|
||||
|
||||
1. 确保降级策略合理
|
||||
2. 监控意图分类准确率
|
||||
3. 根据实际情况调整置信度阈值
|
||||
4. 前端需要处理新的 SSE 事件
|
||||
5. 保持向后兼容
|
||||
Reference in New Issue
Block a user