Ubuntu
187 lines
7.3 KiB
Python
187 lines
7.3 KiB
Python
"""
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混合模式客服系统 - 支持业务知识+通用知识+实时工具
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"""
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from typing import TypedDict, Literal
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from langchain_core.messages import HumanMessage, AIMessage, SystemMessage
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from langchain_openai import ChatOpenAI
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from langgraph.graph import StateGraph, END
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from langgraph.checkpoint.memory import MemorySaver
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from config import LLM_API_KEY, LLM_BASE_URL, LLM_MODEL, SYSTEM_PROMPT
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from knowledge import get_knowledge_base
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class HybridState(TypedDict):
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"""混合模式状态"""
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messages: list
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intent: Literal["medical", "general", "realtime", "greeting"]
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context: str
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tool_result: str
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session_id: str
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class HybridChatbot:
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"""混合客服：垂直领域 + 通用知识 + 实时工具"""
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def __init__(self):
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self.llm = ChatOpenAI(
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model=LLM_MODEL,
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api_key=LLM_API_KEY,
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base_url=LLM_BASE_URL,
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temperature=0.7
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)
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self.knowledge_base = get_knowledge_base()
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self.workflow = self._build_workflow()
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def _classify_intent(self, state: HybridState) -> HybridState:
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"""意图识别 - 判断问题类型"""
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query = state["messages"][-1].content
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# 用LLM快速判断意图（也可以用关键词规则）
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prompt = f"""判断用户意图，只能返回以下之一：
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- medical: 医疗/健康相关问题
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- general: 通用知识（历史、科学、生活常识等）
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- realtime: 需要实时数据（天气、股价、新闻等）
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- greeting: 问候/闲聊
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用户问题：{query}
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意图："""
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response = self.llm.invoke([HumanMessage(content=prompt)])
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intent = response.content.strip().lower()
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# 归一化
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valid_intents = ["medical", "general", "realtime", "greeting"]
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intent = intent if intent in valid_intents else "general"
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print(f"🎯 意图识别: {query} -> {intent}")
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return {"intent": intent, "messages": state["messages"], "session_id": state["session_id"]}
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def _route_by_intent(self, state: HybridState) -> str:
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"""根据意图路由到不同节点"""
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intent = state.get("intent", "general")
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routing_map = {
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"medical": "retrieve_knowledge", # 查医疗知识库
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"general": "general_answer", # 直接问大模型
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"realtime": "call_tool", # 调用实时工具
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"greeting": "general_answer" # 闲聊也走通用
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}
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return routing_map.get(intent, "general_answer")
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def _retrieve_medical(self, state: HybridState) -> HybridState:
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"""检索医疗知识库"""
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query = state["messages"][-1].content
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docs = self.knowledge_base.search(query)
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if docs:
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context = "\n".join([doc.page_content for doc in docs])
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return {"context": context, "messages": state["messages"],
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"intent": state["intent"], "session_id": state["session_id"]}
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else:
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# 医疗问题但知识库没有 → 转人工
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return {"context": "【无相关医疗知识，需转人工】",
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"messages": state["messages"],
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"intent": state["intent"], "session_id": state["session_id"]}
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def _call_realtime_tool(self, state: HybridState) -> HybridState:
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"""调用实时工具（MCP/Function Call）"""
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query = state["messages"][-1].content
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# 示例：天气查询（实际可用 MCP/天气API）
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if "天气" in query:
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# 这里可以调用 MCP 或天气API
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# result = call_mcp("weather", location="北京")
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tool_result = "【实时天气工具】：北京今天晴，25°C，适合出行"
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elif "股价" in query or "股票" in query:
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tool_result = "【实时股价工具】：需要调用股票API获取实时数据"
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else:
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tool_result = "【无可用工具】"
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return {"tool_result": tool_result, "messages": state["messages"],
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"intent": state["intent"], "session_id": state["session_id"]}
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def _generate_answer(self, state: HybridState) -> HybridState:
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"""生成最终回答"""
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intent = state["intent"]
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query = state["messages"][-1].content
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if intent == "medical":
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# 医疗问题：基于知识库回答
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if "无相关医疗知识" in state.get("context", ""):
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response = "抱歉，这个问题我暂时无法回答，为您转接专业医生..."
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else:
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system_msg = f"你是医疗助手，基于以下知识回答：\n{state['context']}"
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messages = [SystemMessage(content=system_msg)] + state["messages"]
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response = self.llm.invoke(messages).content
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elif intent == "realtime":
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# 实时信息：基于工具结果回答
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system_msg = f"基于以下实时信息回答用户：\n{state.get('tool_result', '')}"
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messages = [SystemMessage(content=system_msg)] + state["messages"]
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response = self.llm.invoke(messages).content
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else: # general / greeting
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# 通用问题：直接回答
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system_msg = "你是友好的助手，回答用户的通用问题。"
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messages = [SystemMessage(content=system_msg)] + state["messages"]
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response = self.llm.invoke(messages).content
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return {"messages": state["messages"] + [AIMessage(content=response)],
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"intent": intent, "session_id": state["session_id"]}
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def _build_workflow(self):
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"""构建工作流"""
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workflow = StateGraph(HybridState)
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# 添加节点
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workflow.add_node("classify", self._classify_intent)
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workflow.add_node("retrieve_knowledge", self._retrieve_medical)
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workflow.add_node("call_tool", self._call_realtime_tool)
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workflow.add_node("general_answer", self._generate_answer)
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workflow.add_node("generate", self._generate_answer)
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# 条件路由
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workflow.set_entry_point("classify")
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workflow.add_conditional_edges(
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"classify",
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self._route_by_intent,
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{
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"retrieve_knowledge": "retrieve_knowledge",
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"call_tool": "call_tool",
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"general_answer": "general_answer"
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}
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)
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# 汇聚到生成节点
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workflow.add_edge("retrieve_knowledge", "generate")
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workflow.add_edge("call_tool", "generate")
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workflow.add_edge("generate", END)
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workflow.add_edge("general_answer", END)
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return workflow.compile(checkpointer=MemorySaver())
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async def chat(self, message: str, session_id: str) -> str:
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"""处理消息"""
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config = {"configurable": {"thread_id": session_id}}
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result = await self.workflow.ainvoke(
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{"messages": [HumanMessage(content=message)],
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"intent": "general",
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"context": "",
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"tool_result": "",
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"session_id": session_id},
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config
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)
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return result["messages"][-1].content
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# 单例
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_hybrid_chatbot = None
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def get_hybrid_chatbot():
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global _hybrid_chatbot
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if _hybrid_chatbot is None:
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_hybrid_chatbot = HybridChatbot()
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return _hybrid_chatbot
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