AI 智能客服 Agent

                    ┌──────────────┐
                    │  Web Client  │
                    └──────┬───────┘
                           │ WebSocket
                    ┌──────▼───────┐
                    │  API Gateway │
                    └──────┬───────┘
                           │
              ┌────────────┼────────────┐
              │            │            │
       ┌──────▼─────┐ ┌───▼────┐ ┌────▼────┐
       │Intent Agent│ │QA Agent│ │Ticket   │
       │  (意图识别)  │ │(问答)  │ │Agent    │
       └──────┬─────┘ └───┬────┘ └────┬────┘
              │            │           │
              └────────────┼───────────┘
                           │
                    ┌──────▼──────┐
                    │  Supervisor │
                    │   Agent     │
                    └─────────────┘

from langgraph.graph import StateGraph, END
from langgraph.prebuilt import ToolNode

class CustomerServiceState(TypedDict):
    messages: list
    intent: str
    context: dict
    ticket_id: str | None
    transfer_to_human: bool

def supervisor(state: CustomerServiceState):
    """主管 Agent：路由决策"""
    last_message = state["messages"][-1]

    if "人工" in last_message.content:
        return {"transfer_to_human": True, "next": "human_agent"}

    if state.get("ticket_id"):
        return {"next": "ticket_agent"}

    intent = classify_intent(last_message.content)
    return {"intent": intent, "next": "qa_agent"}

from fastapi import WebSocket

@app.websocket("/ws/chat/{session_id}")
async def chat_websocket(websocket: WebSocket, session_id: str):
    await websocket.accept()

    agent = CustomerServiceAgent(session_id)

    while True:
        # 接收用户消息
        data = await websocket.receive_json()
        user_message = data["message"]

        # Agent 处理
        async for chunk in agent.stream(user_message):
            # 流式返回
            await websocket.send_json({
                "type": "message",
                "content": chunk,
                "role": "assistant"
            })

        await websocket.send_json({"type": "done"})

FROM python:3.11-slim

WORKDIR /app
COPY requirements.txt .
RUN pip install -r requirements.txt

COPY . .

CMD ["uvicorn", "main:app", "--host", "0.0.0.0", "--port", "8000"]

from prometheus_client import Counter, Histogram

# 对话次数
chat_counter = Counter("chat_total", "Total chat messages")

# Agent 延迟
agent_latency = Histogram("agent_latency_seconds", "Agent processing time")

# 转人工率
transfer_rate = Counter("transfer_to_human_total", "Transfers to human")