智能客服多智能体系统实战
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1. 项目概述
1.1 项目背景
随着客户服务需求的不断增长，传统的客服系统已经难以满足企业的需求。智能客服系统通过引入人工智能技术，能够24小时不间断地为客户提供服务，提高客户满意度，同时降低企业运营成本。
多智能体系统（MAS）在智能客服领域的应用具有显著优势：
- 分工协作：不同智能体负责不同类型的任务，提高处理效率
- 知识共享：智能体之间可以共享知识和经验，提升整体服务质量
- 灵活性：系统可以根据业务需求灵活调整智能体的数量和职责
- 可扩展性：易于添加新的智能体和功能，适应业务发展
1.2 项目目标
本项目旨在构建一个基于多智能体系统的智能客服平台，实现以下目标：
- 多渠道接入：支持网站、APP、微信、电话等多种渠道
- 智能分流：根据客户问题类型自动分流到相应的智能体
- 智能问答：基于知识库和历史数据，提供准确的回答
- 多轮对话：支持复杂问题的多轮对话处理
- 业务办理：支持常见业务的自动化办理
- 人工协作：在智能体无法解决问题时，无缝转接人工客服
- 数据分析：收集和分析客户反馈，持续优化系统
1.3 技术栈选择
| 类别 | 技术/框架 | 版本 | 用途 |
|---|---|---|---|
| 开发语言 | Python | 3.12+ | 智能体开发 |
| AI框架 | LangChain | 0.3.x | LLM集成、工具链 |
| LangGraph | 0.2.x | 多智能体协作管理 | |
| 大语言模型 | OpenAI GPT-4o | - | 核心对话能力 |
| 阿里云通义千问 | - | 中文语境优化 | |
| 向量数据库 | ChromaDB | 0.4.x | 知识库存储和检索 |
| 缓存 | Redis | 7.0+ | 会话管理、临时数据存储 |
| 数据库 | PostgreSQL | 15.0+ | 结构化数据存储 |
| 消息队列 | RabbitMQ | 3.12+ | 任务分发、异步处理 |
| Web框架 | FastAPI | 0.104+ | API接口开发 |
| 容器化 | Docker | 20.10+ | 环境隔离、部署一致性 |
| 编排 | Kubernetes | 1.26+ | 容器编排、自动扩缩容 |
| 监控 | Prometheus + Grafana | 2.40+ / 9.0+ | 系统监控、可视化 |
2. 系统架构设计
2.1 整体架构
2.2 智能体角色设计
| 智能体角色 | 职责 | 核心功能 | 技术实现 |
|---|---|---|---|
| 接入智能体 | 统一接入管理 | 多渠道消息接收与发送、格式转换、会话管理 | FastAPI + WebSocket |
| 意图识别智能体 | 客户意图分析 | 文本分类、意图识别、实体提取 | LLM + 微调模型 |
| 分流智能体 | 智能任务分发 | 基于意图和上下文的任务分配、优先级管理 | LangGraph路由节点 |
| 问答智能体 | 知识型问题解答 | 知识库检索、答案生成、多轮对话管理 | RAG + LLM |
| 业务办理智能体 | 业务流程处理 | 表单收集、业务系统调用、结果反馈 | 工具调用 + 状态管理 |
| 情绪管理智能体 | 客户情绪分析 | 情绪识别、情绪调节、危机干预 | NLP模型 + 规则引擎 |
| 人工协作智能体 | 人工客服协作 | 人工转接、上下文传递、协作管理 | WebSocket + 队列 |
2.3 数据流设计
-
客户请求流
- 客户通过多渠道发送请求
- 接入智能体接收并标准化请求
- 意图识别智能体分析客户意图
- 分流智能体将请求分发到对应智能体
- 专业智能体处理请求并生成响应
- 接入智能体将响应返回给客户
-
知识流
- 知识库定期更新和维护
- 向量数据库存储知识嵌入
- 问答智能体检索相关知识
- 智能体间共享处理经验
- 系统从对话中学习新知识
-
监控流
- 各智能体实时上报状态和性能指标
- 监控系统收集和分析指标
- 异常情况触发告警
- 运维人员处理告警和故障
3. 核心功能实现
3.1 接入层实现
3.1.1 多渠道接入
# 接入智能体实现
from fastapi import FastAPI, WebSocket, WebSocketDisconnect
from fastapi.middleware.cors import CORSMiddleware
import json
import asyncio
from typing import Dict, Set
class ConnectionManager:
def __init__(self):
self.active_connections: Dict[str, Dict[str, WebSocket]] = {}
async def connect(self, websocket: WebSocket, channel: str, user_id: str):
await websocket.accept()
if channel not in self.active_connections:
self.active_connections[channel] = {}
self.active_connections[channel][user_id] = websocket
def disconnect(self, channel: str, user_id: str):
if channel in self.active_connections and user_id in self.active_connections[channel]:
del self.active_connections[channel][user_id]
async def send_personal_message(self, message: str, channel: str, user_id: str):
if channel in self.active_connections and user_id in self.active_connections[channel]:
await self.active_connections[channel][user_id].send_text(message)
async def broadcast(self, message: str, channel: str):
if channel in self.active_connections:
for connection in self.active_connections[channel].values():
await connection.send_text(message)
app = FastAPI()
app.add_middleware(
CORSMiddleware,
allow_origins=["*"],
allow_credentials=True,
allow_methods=["*"],
allow_headers=["*"],
)
manager = ConnectionManager()
@app.websocket("/ws/{channel}/{user_id}")
async def websocket_endpoint(websocket: WebSocket, channel: str, user_id: str):
await manager.connect(websocket, channel, user_id)
try:
while True:
data = await websocket.receive_text()
# 处理接收到的消息
message_data = json.loads(data)
# 消息格式转换和标准化
standardized_message = {
"channel": channel,
"user_id": user_id,
"message_type": message_data.get("type", "text"),
"content": message_data.get("content", ""),
"timestamp": message_data.get("timestamp", time.time()),
"metadata": message_data.get("metadata", {})
}
# 转发给意图识别智能体
response = await process_message(standardized_message)
# 返回响应给客户端
await manager.send_personal_message(json.dumps(response), channel, user_id)
except WebSocketDisconnect:
manager.disconnect(channel, user_id)
# 处理断开连接的逻辑
async def process_message(message):
"""处理消息并返回响应"""
# 这里将消息转发给意图识别智能体
# 实际实现中应使用消息队列或直接调用
from agents.intent_agent import IntentRecognitionAgent
intent_agent = IntentRecognitionAgent()
result = await intent_agent.analyze_intent(message)
return result
if __name__ == "__main__":
import uvicorn
uvicorn.run(app, host="0.0.0.0", port=8000)
3.1.2 会话管理
# 会话管理服务
import redis
import json
import time
from typing import Dict, Any, Optional
class SessionManager:
def __init__(self, redis_url: str = "redis://localhost:6379/0"):
self.redis_client = redis.from_url(redis_url, decode_responses=True)
self.session_ttl = 3600 # 会话过期时间（秒）
def create_session(self, user_id: str, channel: str) -> str:
"""创建新会话"""
session_id = f"session:{user_id}:{int(time.time())}"
session_data = {
"session_id": session_id,
"user_id": user_id,
"channel": channel,
"created_at": time.time(),
"last_activity": time.time(),
"messages": [],
"context": {}
}
self.redis_client.setex(
session_id,
self.session_ttl,
json.dumps(session_data, ensure_ascii=False)
)
return session_id
def get_session(self, session_id: str) -> Optional[Dict[str, Any]]:
"""获取会话信息"""
session_data = self.redis_client.get(session_id)
if not session_data:
return None
# 更新最后活动时间
session = json.loads(session_data)
session["last_activity"] = time.time()
self.redis_client.setex(
session_id,
self.session_ttl,
json.dumps(session, ensure_ascii=False)
)
return session
def update_session(self, session_id: str, updates: Dict[str, Any]) -> bool:
"""更新会话信息"""
session = self.get_session(session_id)
if not session:
return False
# 更新会话数据
session.update(updates)
session["last_activity"] = time.time()
self.redis_client.setex(
session_id,
self.session_ttl,
json.dumps(session, ensure_ascii=False)
)
return True
def add_message(self, session_id: str, message: Dict[str, Any]) -> bool:
"""添加消息到会话"""
session = self.get_session(session_id)
if not session:
return False
# 添加消息
if "messages" not in session:
session["messages"] = []
session["messages"].append({
"timestamp": time.time(),
"role": message.get("role"),
"content": message.get("content"),
"message_type": message.get("type", "text")
})
# 限制消息数量，避免内存占用过大
if len(session["messages"]) > 50:
session["messages"] = session["messages"][-50:]
return self.update_session(session_id, session)
def set_context(self, session_id: str, context: Dict[str, Any]) -> bool:
"""设置会话上下文"""
return self.update_session(session_id, {"context": context})
def get_context(self, session_id: str) -> Dict[str, Any]:
"""获取会话上下文"""
session = self.get_session(session_id)
if not session:
return {}
return session.get("context", {})
def end_session(self, session_id: str) -> bool:
"""结束会话"""
return bool(self.redis_client.delete(session_id))
# 示例用法
session_manager = SessionManager()
def handle_user_message(user_id: str, channel: str, message: str):
"""处理用户消息"""
# 查找或创建会话
session_id = find_or_create_session(user_id, channel)
# 添加用户消息
session_manager.add_message(session_id, {
"role": "user",
"content": message,
"type": "text"
})
# 处理消息...
# 添加系统回复
session_manager.add_message(session_id, {
"role": "assistant",
"content": "这是系统回复",
"type": "text"
})
def find_or_create_session(user_id: str, channel: str) -> str:
"""查找或创建会话"""
# 实际实现中应根据业务逻辑查找现有会话
# 这里简化处理，直接创建新会话
return session_manager.create_session(user_id, channel)
3.2 智能体核心实现
3.2.1 意图识别智能体
# 意图识别智能体
from langchain_openai import ChatOpenAI
from langchain.prompts import PromptTemplate
from langchain_core.output_parsers import JsonOutputParser
from pydantic import BaseModel, Field
from typing import List, Optional
class IntentResult(BaseModel):
intent: str = Field(..., description="客户意图类型")
confidence: float = Field(..., description="意图识别置信度")
entities: List[dict] = Field(default_factory=list, description="提取的实体")
sentiment: str = Field(default="neutral", description="客户情绪")
need_human: bool = Field(default=False, description="是否需要人工客服")
class IntentRecognitionAgent:
def __init__(self):
self.llm = ChatOpenAI(
model="gpt-4o",
temperature=0.1,
max_tokens=500
)
self.prompt_template = PromptTemplate(
template="""你是一个专业的智能客服意图识别系统，请分析以下客户消息，识别客户的意图和情绪。
客户消息：{message}
请按照以下格式输出JSON格式的结果：
{{
"intent": "意图类型",
"confidence": 置信度,
"entities": [
{{
"type": "实体类型",
"value": "实体值"
}}
],
"sentiment": "情绪类型",
"need_human": 是否需要人工客服
}}
意图类型包括：
- 产品咨询
- 订单查询
- 故障报修
- 投诉建议
- 业务办理
- 其他
情绪类型包括：
- positive（积极）
- neutral（中性）
- negative（消极）
如果客户消息中包含以下内容，need_human 应设为 true：
- 明确要求转接人工客服
- 情绪非常激动或愤怒
- 涉及复杂的个人问题
- 智能系统无法处理的特殊情况
请确保输出格式正确，只包含JSON内容，不要添加其他文本。""",
input_variables=["message"]
)
self.parser = JsonOutputParser(pydantic_object=IntentResult)
self.chain = self.prompt_template | self.llm | self.parser
async def analyze_intent(self, message: dict) -> dict:
"""分析客户意图"""
content = message.get("content", "")
try:
result = await self.chain.ainvoke({"message": content})
# 构建响应
response = {
"type": "intent_analysis",
"intent": result.intent,
"confidence": result.confidence,
"entities": result.entities,
"sentiment": result.sentiment,
"need_human": result.need_human,
"session_id": message.get("session_id"),
"user_id": message.get("user_id")
}
return response
except Exception as e:
# 错误处理
return {
"type": "error",
"message": f"意图识别失败: {str(e)}",
"intent": "其他",
"confidence": 0.5,
"entities": [],
"sentiment": "neutral",
"need_human": False
}
# 示例用法
async def test_intent_agent():
agent = IntentRecognitionAgent()
message = {
"content": "你好，我想查询我昨天下单的订单状态，订单号是123456789",
"user_id": "user123",
"session_id": "session123"
}
result = await agent.analyze_intent(message)
print(result)
if __name__ == "__main__":
import asyncio
asyncio.run(test_intent_agent())
3.2.2 分流智能体
# 分流智能体
from langgraph.graph import StateGraph, END
from langgraph.graph.state import CompiledStateGraph
from typing import Dict, Any, Optional
class RouterState(Dict[str, Any]):
"""分流智能体状态"""
pass
def route_message(state: RouterState) -> str:
"""根据意图分流消息"""
intent = state.get("intent", "")
need_human = state.get("need_human", False)
sentiment = state.get("sentiment", "neutral")
# 优先处理需要人工客服的情况
if need_human:
return "human_agent"
# 根据意图分流
intent_route_map = {
"产品咨询": "qa_agent",
"订单查询": "qa_agent",
"故障报修": "qa_agent",
"投诉建议": "emotion_agent",
"业务办理": "business_agent",
"其他": "qa_agent"
}
# 情绪特别消极时，优先转到情绪管理智能体
if sentiment == "negative" and intent not in ["业务办理"]:
return "emotion_agent"
return intent_route_map.get(intent, "qa_agent")
def handle_qa_agent(state: RouterState) -> RouterState:
"""问答智能体处理"""
# 实际实现中应调用问答智能体
from agents.qa_agent import QAAgent
agent = QAAgent()
result = agent.process_message(state)
state["response"] = result
state["agent"] = "qa_agent"
return state
def handle_business_agent(state: RouterState) -> RouterState:
"""业务办理智能体处理"""
# 实际实现中应调用业务办理智能体
from agents.business_agent import BusinessAgent
agent = BusinessAgent()
result = agent.process_message(state)
state["response"] = result
state["agent"] = "business_agent"
return state
def handle_emotion_agent(state: RouterState) -> RouterState:
"""情绪管理智能体处理"""
# 实际实现中应调用情绪管理智能体
from agents.emotion_agent import EmotionAgent
agent = EmotionAgent()
result = agent.process_message(state)
state["response"] = result
state["agent"] = "emotion_agent"
return state
def handle_human_agent(state: RouterState) -> RouterState:
"""人工协作智能体处理"""
# 实际实现中应调用人工协作智能体
from agents.human_agent import HumanAgent
agent = HumanAgent()
result = agent.process_message(state)
state["response"] = result
state["agent"] = "human_agent"
return state
def create_router_graph() -> CompiledStateGraph:
"""创建分流智能体图"""
graph = StateGraph(RouterState)
# 添加节点
graph.add_node("route", route_message)
graph.add_node("qa_agent", handle_qa_agent)
graph.add_node("business_agent", handle_business_agent)
graph.add_node("emotion_agent", handle_emotion_agent)
graph.add_node("human_agent", handle_human_agent)
# 添加边
graph.add_conditional_edges(
"route",
lambda state: state,
{
"qa_agent": "qa_agent",
"business_agent": "business_agent",
"emotion_agent": "emotion_agent",
"human_agent": "human_agent"
}
)
# 所有智能体处理完成后结束
graph.add_edge("qa_agent", END)
graph.add_edge("business_agent", END)
graph.add_edge("emotion_agent", END)
graph.add_edge("human_agent", END)
# 设置入口点
graph.set_entry_point("route")
return graph.compile()
# 示例用法
async def test_router_agent():
"""测试分流智能体"""
graph = create_router_graph()
# 测试订单查询
state = {
"content": "我想查询我的订单状态",
"intent": "订单查询",
"confidence": 0.95,
"entities": [],
"sentiment": "neutral",
"need_human": False,
"user_id": "user123",
"session_id": "session123"
}
result = await graph.ainvoke(state)
print("订单查询测试结果:", result)
# 测试业务办理
state = {
"content": "我想办理会员升级",
"intent": "业务办理",
"confidence": 0.9,
"entities": [],
"sentiment": "neutral",
"need_human": False,
"user_id": "user123",
"session_id": "session123"
}
result = await graph.ainvoke(state)
print("业务办理测试结果:", result)
# 测试需要人工客服的情况
state = {
"content": "我要投诉，转接人工客服",
"intent": "投诉建议",
"confidence": 0.95,
"entities": [],
"sentiment": "negative",
"need_human": True,
"user_id": "user123",
"session_id": "session123"
}
result = await graph.ainvoke(state)
print("人工客服测试结果:", result)
if __name__ == "__main__":
import asyncio
asyncio.run(test_router_agent())
3.2.3 问答智能体
# 问答智能体
from langchain_openai import ChatOpenAI, OpenAIEmbeddings
from langchain_chroma import Chroma
from langchain.prompts import PromptTemplate
from langchain.chains import RetrievalQA
from typing import Dict, Any
class QAAgent:
def __init__(self):
# 初始化嵌入模型
self.embeddings = OpenAIEmbeddings(model="text-embedding-3-large")
# 初始化向量数据库
self.vector_db = Chroma(
persist_directory="./vector_db",
embedding_function=self.embeddings
)
# 初始化LLM
self.llm = ChatOpenAI(
model="gpt-4o",
temperature=0.1
)
# 初始化检索器
self.retriever = self.vector_db.as_retriever(
search_kwargs={"k": 5}
)
# 初始化提示模板
self.prompt_template = PromptTemplate(
template="""你是一个专业的客服智能助手，请根据以下上下文信息回答客户的问题。
上下文信息：
{context}
客户问题：
{question}
请遵循以下要求：
1. 基于上下文信息回答问题，不要添加无关内容
2. 回答要准确、简洁、专业
3. 如果上下文信息不足以回答问题，请明确说明
4. 保持友好的语气和专业的态度
回答：""",
input_variables=["context", "question"]
)
# 初始化问答链
self.qa_chain = RetrievalQA.from_chain_type(
llm=self.llm,
chain_type="stuff",
retriever=self.retriever,
chain_type_kwargs={
"prompt": self.prompt_template
},
return_source_documents=True
)
def process_message(self, state: Dict[str, Any]) -> Dict[str, Any]:
"""处理问答消息"""
question = state.get("content", "")
try:
result = self.qa_chain.invoke({"query": question})
# 构建响应
response = {
"type": "answer",
"content": result["result"],
"sources": [doc.metadata for doc in result.get("source_documents", [])],
"confidence": self._calculate_confidence(result),
"follow_up": self._generate_follow_up(question, result["result"])
}
return response
except Exception as e:
# 错误处理
return {
"type": "error",
"content": "抱歉，我暂时无法回答这个问题，请稍后再试。",
"error": str(e)
}
def _calculate_confidence(self, result: Dict[str, Any]) -> float:
"""计算回答的置信度"""
# 实际实现中应基于检索结果和模型输出计算置信度
# 这里简化处理，返回固定值
return 0.85
def _generate_follow_up(self, question: str, answer: str) -> list:
"""生成后续问题建议"""
# 实际实现中应基于问题和回答生成后续问题建议
# 这里简化处理，返回空列表
return []
def add_to_knowledge_base(self, documents: list):
"""添加文档到知识库"""
# 实际实现中应处理文档的嵌入和存储
self.vector_db.add_documents(documents)
self.vector_db.persist()
# 示例用法
def test_qa_agent():
"""测试问答智能体"""
agent = QAAgent()
# 测试问题
state = {
"content": "如何修改密码？",
"user_id": "user123",
"session_id": "session123"
}
result = agent.process_message(state)
print("问答智能体测试结果:")
print(f"回答: {result['content']}")
print(f"来源: {result.get('sources', [])}")
print(f"置信度: {result.get('confidence', 0)}")
if __name__ == "__main__":
test_qa_agent()
3.2.3 问答智能体
# 问答智能体
from langchain_openai import ChatOpenAI, OpenAIEmbeddings
from langchain_chroma import Chroma
from langchain.prompts import PromptTemplate
from langchain.chains import RetrievalQA
from typing import Dict, Any
class QAAgent:
def __init__(self):
# 初始化嵌入模型
self.embeddings = OpenAIEmbeddings(model="text-embedding-3-large")
# 初始化向量数据库
self.vector_db = Chroma(
persist_directory="./vector_db",
embedding_function=self.embeddings
)
# 初始化LLM
self.llm = ChatOpenAI(
model="gpt-4o",
temperature=0.1
)
# 初始化检索器
self.retriever = self.vector_db.as_retriever(
search_kwargs={"k": 5}
)
# 初始化提示模板
self.prompt_template = PromptTemplate(
template="""你是一个专业的客服智能助手，请根据以下上下文信息回答客户的问题。
上下文信息：
{context}
客户问题：
{question}
请遵循以下要求：
1. 基于上下文信息回答问题，不要添加无关内容
2. 回答要准确、简洁、专业
3. 如果上下文信息不足以回答问题，请明确说明
4. 保持友好的语气和专业的态度
回答：""",
input_variables=["context", "question"]
)
# 初始化问答链
self.qa_chain = RetrievalQA.from_chain_type(
llm=self.llm,
chain_type="stuff",
retriever=self.retriever,
chain_type_kwargs={
"prompt": self.prompt_template
},
return_source_documents=True
)
def process_message(self, state: Dict[str, Any]) -> Dict[str, Any]:
"""处理问答消息"""
question = state.get("content", "")
try:
result = self.qa_chain.invoke({"query": question})
# 构建响应
response = {
"type": "answer",
"content": result["result"],
"sources": [doc.metadata for doc in result.get("source_documents", [])],
"confidence": self._calculate_confidence(result),
"follow_up": self._generate_follow_up(question, result["result"])
}
return response
except Exception as e:
# 错误处理
return {
"type": "error",
"content": "抱歉，我暂时无法回答这个问题，请稍后再试。",
"error": str(e)
}
def _calculate_confidence(self, result: Dict[str, Any]) -> float:
"""计算回答的置信度"""
# 实际实现中应基于检索结果和模型输出计算置信度
# 这里简化处理，返回固定值
return 0.85
def _generate_follow_up(self, question: str, answer: str) -> list:
"""生成后续问题建议"""
# 实际实现中应基于问题和回答生成后续问题建议
# 这里简化处理，返回空列表
return []
def add_to_knowledge_base(self, documents: list):
"""添加文档到知识库"""
# 实际实现中应处理文档的嵌入和存储
self.vector_db.add_documents(documents)
self.vector_db.persist()
# 示例用法
def test_qa_agent():
"""测试问答智能体"""
agent = QAAgent()
# 测试问题
state = {
"content": "如何修改密码？",
"user_id": "user123",
"session_id": "session123"
}
result = agent.process_message(state)
print("问答智能体测试结果:")
print(f"回答: {result['content']}")
print(f"来源: {result.get('sources', [])}")
print(f"置信度: {result.get('confidence', 0)}")
if __name__ == "__main__":
test_qa_agent()
3.2.4 业务办理智能体
# 业务办理智能体
from langchain_openai import ChatOpenAI
from langchain.prompts import PromptTemplate
from langchain.tools import tool
from langchain_core.tools import Tool
from langchain.agents import AgentExecutor, create_tool_calling_agent
from typing import Dict, Any, List
class BusinessAgent:
def __init__(self):
self.llm = ChatOpenAI(
model="gpt-4o",
temperature=0.1
)
# 定义工具
self.tools = [
self._create_user_info_tool(),
self._create_order_tool(),
self._create_service_tool()
]
# 初始化提示模板
self.prompt = PromptTemplate(
template="""你是一个业务办理智能助手，负责帮助客户办理各种业务。
当前对话：
{chat_history}
客户请求：
{input}
请根据客户的请求，决定是否需要使用工具来获取更多信息，或者直接回答客户的问题。
可用工具：
{tools}
工具使用格式：
{{
"tool_call": {{
"name": "工具名称",
"arguments": {{
"参数名": "参数值"
}}
}}
}}
直接回答格式：
{{
"direct_answer": "你的回答"
}}
请确保输出格式正确，只包含JSON内容。""",
input_variables=["chat_history", "input", "tools"]
# 初始化智能体
self.agent = create_tool_calling_agent(
llm=self.llm,
tools=self.tools,
prompt=self.prompt
)
self.agent_executor = AgentExecutor(
agent=self.agent,
tools=self.tools,
verbose=True
)
def _create_user_info_tool(self) -> Tool:
"""创建用户信息工具"""
@tool
def get_user_info(user_id: str) -> Dict[str, Any]:
"""获取用户信息
Args:
user_id: 用户ID
Returns:
用户信息字典，包含姓名、手机号、会员等级等信息
"""
# 实际实现中应从数据库或API获取用户信息
return {
"user_id": user_id,
"name": "张三",
"phone": "13800138000",
"member_level": "黄金会员",
"points": 1000
}
return get_user_info
def _create_order_tool(self) -> Tool:
"""创建订单工具"""
@tool
def get_order_info(order_id: str) -> Dict[str, Any]:
"""获取订单信息
Args:
order_id: 订单ID
Returns:
订单信息字典，包含订单状态、商品信息、物流信息等
"""
# 实际实现中应从数据库或API获取订单信息
return {
"order_id": order_id,
"status": "已发货",
"items": [{
"name": "商品1",
"quantity": 1,
"price": 100
}],
"shipping_info": {
"company": "顺丰速运",
"tracking_number": "SF1234567890"
},
"total_amount": 100
}
return get_order_info
def _create_service_tool(self) -> Tool:
"""创建服务工具"""
@tool
def process_service_request(service_type: str, user_id: str, details: Dict[str, Any]) -> Dict[str, Any]:
"""处理服务请求
Args:
service_type: 服务类型，如 "密码重置"、"会员升级" 等
user_id: 用户ID
details: 服务详情
Returns:
服务处理结果
"""
# 实际实现中应调用相应的服务API
return {
"status": "success",
"message": f"{service_type} 处理成功",
"service_id": f"SVC{int(time.time())}",
"user_id": user_id
}
return process_service_request
def process_message(self, state: Dict[str, Any]) -> Dict[str, Any]:
"""处理业务办理消息"""
input_text = state.get("content", "")
chat_history = state.get("chat_history", "")
try:
result = self.agent_executor.invoke({
"input": input_text,
"chat_history": chat_history
})
# 构建响应
response = {
"type": "service_response",
"content": result["output"],
"status": "completed" if "成功" in result["output"] else "pending"
}
return response
except Exception as e:
# 错误处理
return {
"type": "error",
"content": "抱歉，业务办理过程中出现错误，请稍后再试。",
"error": str(e)
}
# 示例用法
import time
def test_business_agent():
"""测试业务办理智能体"""
agent = BusinessAgent()
# 测试订单查询
state = {
"content": "查询订单号123456789的状态",
"user_id": "user123",
"session_id": "session123"
}
result = agent.process_message(state)
print("业务办理智能体测试结果:")
print(f"响应: {result['content']}")
print(f"状态: {result['status']}")
if __name__ == "__main__":
test_business_agent()3.3 知识库管理
# 知识库管理
from langchain_community.document_loaders import TextLoader, PDFLoader, Docx2txtLoader
from langchain.text_splitter import RecursiveCharacterTextSplitter
from langchain_openai import OpenAIEmbeddings
from langchain_chroma import Chroma
from typing import List, Dict, Any
import os
class KnowledgeBaseManager:
def __init__(self, persist_directory="./vector_db"):
self.persist_directory = persist_directory
self.embeddings = OpenAIEmbeddings(model="text-embedding-3-large")
self.text_splitter = RecursiveCharacterTextSplitter(
chunk_size=1000,
chunk_overlap=200
)
self.vector_db = Chroma(
persist_directory=persist_directory,
embedding_function=self.embeddings
)
def add_document(self, file_path: str, metadata: Dict[str, Any] = None) -> int:
"""添加文档到知识库"""
try:
# 根据文件类型选择加载器
if file_path.endswith('.txt'):
loader = TextLoader(file_path)
elif file_path.endswith('.pdf'):
loader = PDFLoader(file_path)
elif file_path.endswith('.docx'):
loader = Docx2txtLoader(file_path)
else:
raise ValueError(f"不支持的文件类型: {file_path}")
# 加载文档
documents = loader.load()
# 分割文档
splits = self.text_splitter.split_documents(documents)
# 添加元数据
if metadata:
for split in splits:
split.metadata.update(metadata)
# 添加到向量数据库
self.vector_db.add_documents(splits)
self.vector_db.persist()
return len(splits)
except Exception as e:
print(f"添加文档失败: {str(e)}")
return 0
def add_text(self, text: str, metadata: Dict[str, Any] = None) -> bool:
"""添加文本到知识库"""
try:
# 创建文档对象
from langchain_core.documents import Document
document = Document(page_content=text, metadata=metadata or {})
# 分割文本
splits = self.text_splitter.split_documents([document])
# 添加到向量数据库
self.vector_db.add_documents(splits)
self.vector_db.persist()
return True
except Exception as e:
print(f"添加文本失败: {str(e)}")
return False
def search(self, query: str, k: int = 5) -> List[Dict[str, Any]]:
"""搜索知识库"""
try:
results = self.vector_db.similarity_search_with_score(query, k=k)
# 格式化结果
formatted_results = []
for doc, score in results:
formatted_results.append({
"content": doc.page_content,
"metadata": doc.metadata,
"score": score
})
return formatted_results
except Exception as e:
print(f"搜索失败: {str(e)}")
return []
def delete_document(self, document_id: str) -> bool:
"""删除文档"""
try:
# 实际实现中应根据文档ID删除
# 这里简化处理
return True
except Exception as e:
print(f"删除文档失败: {str(e)}")
return False
def clear_knowledge_base(self) -> bool:
"""清空知识库"""
try:
# 删除向量数据库文件
import shutil
if os.path.exists(self.persist_directory):
shutil.rmtree(self.persist_directory)
# 重新初始化
self.vector_db = Chroma(
persist_directory=self.persist_directory,
embedding_function=self.embeddings
)
return True
except Exception as e:
print(f"清空知识库失败: {str(e)}")
return False
def get_statistics(self) -> Dict[str, Any]:
"""获取知识库统计信息"""
try:
# 实际实现中应获取更详细的统计信息
return {
"persist_directory": self.persist_directory,
"document_count": self.vector_db._collection.count()
}
except Exception as e:
print(f"获取统计信息失败: {str(e)}")
return {}
# 示例用法
def test_knowledge_base():
"""测试知识库管理"""
manager = KnowledgeBaseManager()
# 添加文档
print("添加文档...")
count = manager.add_document("./docs/faq.txt", {
"source": "faq",
"category": "客户服务",
"update_date": "2024-01-01"
})
print(f"添加了 {count} 个文档片段")
# 添加文本
print("添加文本...")
success = manager.add_text(
"如何修改密码？\n1. 登录账户\n2. 进入个人中心\n3. 点击密码修改\n4. 按照提示操作",
{
"source": "manual",
"category": "账户管理",
"update_date": "2024-01-01"
}
)
print(f"添加文本成功: {success}")
# 搜索
print("搜索知识库...")
results = manager.search("修改密码")
print(f"搜索结果: {len(results)} 条")
for i, result in enumerate(results):
print(f"结果 {i+1}: 相似度 {result['score']:.4f}")
print(f"内容: {result['content'][:100]}...")
print(f"来源: {result['metadata'].get('source')}")
# 获取统计信息
print("获取统计信息...")
stats = manager.get_statistics()
print(f"知识库统计: {stats}")
if __name__ == "__main__":
test_knowledge_base()
3.4 人工协作实现
# 人工协作智能体
import asyncio
import json
from typing import Dict, Any, List
class HumanAgent:
def __init__(self):
self.available_agents = [] # 可用的人工客服
self.queue = [] # 等待队列
self.active_sessions = {} # 活跃会话
def process_message(self, state: Dict[str, Any]) -> Dict[str, Any]:
"""处理需要人工客服的消息"""
session_id = state.get("session_id")
user_id = state.get("user_id")
content = state.get("content", "")
# 检查是否已有活跃的人工会话
if session_id in self.active_sessions:
# 已有活跃会话，直接转发消息
agent_id = self.active_sessions[session_id]
self._forward_to_agent(session_id, agent_id, content)
return {
"type": "human_agent",
"content": "您的消息已转发给人工客服，请稍候...",
"status": "forwarded",
"agent_id": agent_id
}
else:
# 没有活跃会话，尝试分配客服
agent_id = self._assign_agent()
if agent_id:
# 分配成功，创建会话
self.active_sessions[session_id] = agent_id
self._forward_to_agent(session_id, agent_id, content)
return {
"type": "human_agent",
"content": "正在为您转接人工客服，请稍候...",
"status": "assigned",
"agent_id": agent_id
}
else:
# 没有可用客服，加入等待队列
self.queue.append({
"session_id": session_id,
"user_id": user_id,
"content": content,
"timestamp": time.time()
})
return {
"type": "human_agent",
"content": "当前人工客服繁忙，请耐心等待，我们会尽快为您服务。",
"status": "queued",
"queue_position": len(self.queue)
}
def _assign_agent(self) -> str:
"""分配人工客服"""
# 实际实现中应基于客服的工作量、技能等因素分配
# 这里简化处理，返回第一个可用客服
if self.available_agents:
return self.available_agents[0]
return "agent_001" # 模拟返回一个客服ID
def _forward_to_agent(self, session_id: str, agent_id: str, message: str):
"""转发消息给人工客服"""
# 实际实现中应通过WebSocket或消息队列转发消息
print(f"转发消息到客服 {agent_id}: {message}")
def agent_respond(self, session_id: str, agent_id: str, response: str):
"""人工客服回复"""
# 实际实现中应将回复发送给用户
print(f"客服 {agent_id} 回复: {response}")
# 检查会话是否存在
if session_id in self.active_sessions:
# 可以在这里添加回复的处理逻辑
pass
def agent_available(self, agent_id: str):
"""客服可用"""
if agent_id not in self.available_agents:
self.available_agents.append(agent_id)
# 检查是否有等待的会话
if self.queue:
# 分配第一个等待的会话
session_info = self.queue.pop(0)
self.active_sessions[session_info["session_id"]] = agent_id
self._forward_to_agent(
session_info["session_id"],
agent_id,
session_info["content"]
)
def agent_unavailable(self, agent_id: str):
"""客服不可用"""
if agent_id in self.available_agents:
self.available_agents.remove(agent_id)
def end_session(self, session_id: str):
"""结束会话"""
if session_id in self.active_sessions:
agent_id = self.active_sessions[session_id]
del self.active_sessions[session_id]
# 可以在这里添加会话结束的处理逻辑
print(f"会话 {session_id} 已结束，客服 {agent_id} 已释放")
# 示例用法
import time
def test_human_agent():
"""测试人工协作智能体"""
agent = HumanAgent()
# 模拟客服上线
agent.agent_available("agent_001")
agent.agent_available("agent_002")
# 测试消息
state = {
"content": "我要投诉，转接人工客服",
"user_id": "user123",
"session_id": "session123"
}
result = agent.process_message(state)
print("人工协作智能体测试结果:")
print(f"响应: {result['content']}")
print(f"状态: {result['status']}")
print(f"客服ID: {result.get('agent_id')}")
# 模拟客服回复
time.sleep(1)
agent.agent_respond("session123", "agent_001", "您好，我是客服小李，请问有什么可以帮助您的？")
# 模拟结束会话
time.sleep(2)
agent.end_session("session123")
if __name__ == "__main__":
test_human_agent()
4. 系统集成与部署
4.1 容器化部署
4.1.1 Docker Compose配置
version: '3.8'
services:
# 智能客服API服务
api-service:
build: ./api-service
restart: unless-stopped
ports:
- "8000:8000"
volumes:
- ./api-service:/app
environment:
- OPENAI_API_KEY=${OPENAI_API_KEY}
- REDIS_URL=redis://redis:6379
- DATABASE_URL=postgresql://admin:password@postgres:5432/example_db
- CHROMA_DB_PATH=/app/vector_db
depends_on:
- redis
- postgres
networks:
- agent-net
# Redis缓存
redis:
image: redis:7
restart: unless-stopped
volumes:
- redis-data:/data
networks:
- agent-net
# PostgreSQL数据库
postgres:
image: postgres:15
restart: unless-stopped
volumes:
- postgres-data:/var/lib/postgresql/data
environment:
- POSTGRES_USER=admin
- POSTGRES_PASSWORD=password
- POSTGRES_DB=example_db
networks:
- agent-net
# 向量数据库
chroma:
build: ./chroma
restart: unless-stopped
volumes:
- chroma-data:/app/chroma_db
networks:
- agent-net
# 监控服务
prometheus:
image: prom/prometheus:latest
restart: unless-stopped
volumes:
- ./prometheus/prometheus.yml:/etc/prometheus/prometheus.yml
ports:
- "9090:9090"
networks:
- agent-net
# 可视化服务
grafana:
image: grafana/grafana:latest
restart: unless-stopped
ports:
- "3000:3000"
volumes:
- grafana-data:/var/lib/grafana
networks:
- agent-net
volumes:
redis-data:
postgres-data:
chroma-data:
grafana-data:
networks:
agent-net:
driver: bridge
4.1.2 Kubernetes部署
# 智能客服API服务部署
apiVersion: apps/v1
kind: Deployment
metadata:
name: api-service
namespace: smart-customer-service
spec:
replicas: 3
selector:
matchLabels:
app: api-service
template:
metadata:
labels:
app: api-service
spec:
containers:
- name: api-service
image: your-registry/api-service:latest
ports:
- containerPort: 8000
env:
- name: OPENAI_API_KEY
valueFrom:
secretKeyRef:
name: api-keys
key: openai-api-key
- name: REDIS_URL
value: redis://redis:6379
- name: DATABASE_URL
value: postgresql://admin:password@postgres:5432/example_db
- name: CHROMA_DB_PATH
value: /app/vector_db
resources:
requests:
cpu: "500m"
memory: "1Gi"
limits:
cpu: "1"
memory: "2Gi"
---
# 智能客服API服务Service
apiVersion: v1
kind: Service
metadata:
name: api-service
namespace: smart-customer-service
spec:
selector:
app: api-service
ports:
- port: 8000
targetPort: 8000
type: LoadBalancer
---
# Redis部署
apiVersion: apps/v1
kind: Deployment
metadata:
name: redis
namespace: smart-customer-service
spec:
replicas: 1
selector:
matchLabels:
app: redis
template:
metadata:
labels:
app: redis
spec:
containers:
- name: redis
image: redis:7
ports:
- containerPort: 6379
volumeMounts:
- name: redis-data
mountPath: /data
volumes:
- name: redis-data
persistentVolumeClaim:
claimName: redis-pvc
---
# Redis Service
apiVersion: v1
kind: Service
metadata:
name: redis
namespace: smart-customer-service
spec:
selector:
app: redis
ports:
- port: 6379
targetPort: 6379
---
# PostgreSQL部署
apiVersion: apps/v1
kind: Deployment
metadata:
name: postgres
namespace: smart-customer-service
spec:
replicas: 1
selector:
matchLabels:
app: postgres
template:
metadata:
labels:
app: postgres
spec:
containers:
- name: postgres
image: postgres:15
ports:
- containerPort: 5432
env:
- name: POSTGRES_USER
value: admin
- name: POSTGRES_PASSWORD
value: password
- name: POSTGRES_DB
value: example_db
volumeMounts:
- name: postgres-data
mountPath: /var/lib/postgresql/data
volumes:
- name: postgres-data
persistentVolumeClaim:
claimName: postgres-pvc
---
# PostgreSQL Service
apiVersion: v1
kind: Service
metadata:
name: postgres
namespace: smart-customer-service
spec:
selector:
app: postgres
ports:
- port: 5432
targetPort: 5432
4.2 系统集成
4.2.1 与业务系统集成
# 业务系统集成服务
import requests
import json
from typing import Dict, Any, Optional
class BusinessSystemIntegration:
def __init__(self):
self.services = {
"order": {
"base_url": "http://order-service:8000",
"endpoints": {
"get_order": "/api/orders/{order_id}",
"create_order": "/api/orders",
"cancel_order": "/api/orders/{order_id}/cancel"
}
},
"user": {
"base_url": "http://user-service:8000",
"endpoints": {
"get_user": "/api/users/{user_id}",
"update_user": "/api/users/{user_id}",
"reset_password": "/api/users/{user_id}/reset-password"
}
},
"payment": {
"base_url": "http://payment-service:8000",
"endpoints": {
"create_payment": "/api/payments",
"get_payment": "/api/payments/{payment_id}"
}
}
}
def get_order_info(self, order_id: str) -> Optional[Dict[str, Any]]:
"""获取订单信息"""
try:
service = self.services["order"]
endpoint = service["endpoints"]["get_order"].format(order_id=order_id)
url = f"{service['base_url']}{endpoint}"
response = requests.get(url, timeout=10)
response.raise_for_status()
return response.json()
except Exception as e:
print(f"获取订单信息失败: {str(e)}")
return None
def cancel_order(self, order_id: str, user_id: str) -> Optional[Dict[str, Any]]:
"""取消订单"""
try:
service = self.services["order"]
endpoint = service["endpoints"]["cancel_order"].format(order_id=order_id)
url = f"{service['base_url']}{endpoint}"
response = requests.post(url, json={"user_id": user_id}, timeout=10)
response.raise_for_status()
return response.json()
except Exception as e:
print(f"取消订单失败: {str(e)}")
return None
def get_user_info(self, user_id: str) -> Optional[Dict[str, Any]]:
"""获取用户信息"""
try:
service = self.services["user"]
endpoint = service["endpoints"]["get_user"].format(user_id=user_id)
url = f"{service['base_url']}{endpoint}"
response = requests.get(url, timeout=10)
response.raise_for_status()
return response.json()
except Exception as e:
print(f"获取用户信息失败: {str(e)}")
return None
def reset_password(self, user_id: str, phone: str) -> Optional[Dict[str, Any]]:
"""重置密码"""
try:
service = self.services["user"]
endpoint = service["endpoints"]["reset_password"].format(user_id=user_id)
url = f"{service['base_url']}{endpoint}"
response = requests.post(url, json={"phone": phone}, timeout=10)
response.raise_for_status()
return response.json()
except Exception as e:
print(f"重置密码失败: {str(e)}")
return None
def create_payment(self, order_id: str, user_id: str, amount: float) -> Optional[Dict[str, Any]]:
"""创建支付"""
try:
service = self.services["payment"]
endpoint = service["endpoints"]["create_payment"]
url = f"{service['base_url']}{endpoint}"
payload = {
"order_id": order_id,
"user_id": user_id,
"amount": amount,
"payment_method": "online"
}
response = requests.post(url, json=payload, timeout=10)
response.raise_for_status()
return response.json()
except Exception as e:
print(f"创建支付失败: {str(e)}")
return None
# 示例用法
def test_business_integration():
"""测试业务系统集成"""
integration = BusinessSystemIntegration()
# 测试获取订单信息
order_info = integration.get_order_info("123456789")
print("订单信息:", order_info)
# 测试获取用户信息
user_info = integration.get_user_info("user123")
print("用户信息:", user_info)
if __name__ == "__main__":
test_business_integration()
4.2.2 与第三方服务集成
# 第三方服务集成
import requests
import json
from typing import Dict, Any, Optional
class ThirdPartyIntegration:
def __init__(self):
self.services = {
"sms": {
"base_url": "http://sms-service:8000",
"endpoints": {
"send_sms": "/api/sms/send"
}
},
"email": {
"base_url": "http://email-service:8000",
"endpoints": {
"send_email": "/api/email/send"
}
},
"notification": {
"base_url": "http://notification-service:8000",
"endpoints": {
"send_notification": "/api/notifications/send"
}
}
}
def send_sms(self, phone: str, message: str) -> bool:
"""发送短信"""
try:
service = self.services["sms"]
endpoint = service["endpoints"]["send_sms"]
url = f"{service['base_url']}{endpoint}"
payload = {
"phone": phone,
"message": message,
"type": "verification"
}
response = requests.post(url, json=payload, timeout=10)
response.raise_for_status()
return True
except Exception as e:
print(f"发送短信失败: {str(e)}")
return False
def send_email(self, email: str, subject: str, content: str) -> bool:
"""发送邮件"""
try:
service = self.services["email"]
endpoint = service["endpoints"]["send_email"]
url = f"{service['base_url']}{endpoint}"
payload = {
"email": email,
"subject": subject,
"content": content,
"type": "notification"
}
response = requests.post(url, json=payload, timeout=10)
response.raise_for_status()
return True
except Exception as e:
print(f"发送邮件失败: {str(e)}")
return False
def send_notification(self, user_id: str, title: str, content: str, channel: str) -> bool:
"""发送通知"""
try:
service = self.services["notification"]
endpoint = service["endpoints"]["send_notification"]
url = f"{service['base_url']}{endpoint}"
payload = {
"user_id": user_id,
"title": title,
"content": content,
"channel": channel
}
response = requests.post(url, json=payload, timeout=10)
response.raise_for_status()
return True
except Exception as e:
print(f"发送通知失败: {str(e)}")
return False
# 示例用法
def test_third_party_integration():
"""测试第三方服务集成"""
integration = ThirdPartyIntegration()
# 测试发送短信
success = integration.send_sms("13800138000", "您的验证码是123456")
print(f"发送短信成功: {success}")
# 测试发送邮件
success = integration.send_email("user@example.com", "测试邮件", "这是一封测试邮件")
print(f"发送邮件成功: {success}")
if __name__ == "__main__":
test_third_party_integration()
5. 系统监控与运维
5.1 监控系统
# 监控系统集成
import prometheus_client
from prometheus_client import Counter, Gauge, Histogram, Summary
from fastapi import FastAPI, Request
import time
# 定义指标
REQUEST_COUNT = Counter('request_count', 'Total request count', ['endpoint', 'method', 'status'])
REQUEST_LATENCY = Histogram('request_latency_seconds', 'Request latency', ['endpoint'])
AGENT_RESPONSE_TIME = Gauge('agent_response_time_seconds', 'Agent response time', ['agent_type'])
ACTIVE_SESSIONS = Gauge('active_sessions', 'Number of active sessions')
ERROR_COUNT = Counter('error_count', 'Error count', ['error_type'])
app = FastAPI()
# 启动Prometheus指标服务
prometheus_client.start_http_server(8000)
@app.middleware("http")
async def metrics_middleware(request: Request, call_next):
"""请求指标中间件"""
start_time = time.time()
# 处理请求
response = await call_next(request)
# 记录指标
endpoint = request.url.path
method = request.method
status = response.status_code
REQUEST_COUNT.labels(endpoint=endpoint, method=method, status=status).inc()
REQUEST_LATENCY.labels(endpoint=endpoint).observe(time.time() - start_time)
return response
def record_agent_response_time(agent_type: str, response_time: float):
"""记录智能体响应时间"""
AGENT_RESPONSE_TIME.labels(agent_type=agent_type).set(response_time)
def record_error(error_type: str):
"""记录错误"""
ERROR_COUNT.labels(error_type=error_type).inc()
def update_active_sessions(count: int):
"""更新活跃会话数"""
ACTIVE_SESSIONS.set(count)
# 示例用法
def process_user_message(message: dict):
"""处理用户消息"""
start_time = time.time()
try:
# 处理消息...
# 记录智能体响应时间
response_time = time.time() - start_time
record_agent_response_time("qa_agent", response_time)
# 更新活跃会话数
update_active_sessions(10)
except Exception as e:
# 记录错误
record_error("processing_error")
raise
if __name__ == "__main__":
import uvicorn
uvicorn.run(app, host="0.0.0.0", port=8001)
5.2 日志管理
# 日志管理
import logging
import logging.config
import json
import os
from datetime import datetime
# 日志配置
LOG_CONFIG = {
"version": 1,
"disable_existing_loggers": False,
"formatters": {
"standard": {
"format": "%(asctime)s [%(levelname)s] %(name)s: %(message)s"
},
"json": {
"()": "logging.Formatter",
"format": json.dumps({
"timestamp": "%(asctime)s",
"level": "%(levelname)s",
"logger": "%(name)s",
"message": "%(message)s",
"module": "%(module)s",
"function": "%(funcName)s",
"line": "%(lineno)d"
})
}
},
"handlers": {
"console": {
"class": "logging.StreamHandler",
"formatter": "standard",
"level": "INFO"
},
"file": {
"class": "logging.handlers.RotatingFileHandler",
"filename": "logs/agent-system.log",
"maxBytes": 10485760, # 10MB
"backupCount": 5,
"formatter": "json",
"level": "INFO"
},
"error": {
"class": "logging.handlers.RotatingFileHandler",
"filename": "logs/agent-system-error.log",
"maxBytes": 10485760, # 10MB
"backupCount": 5,
"formatter": "json",
"level": "ERROR"
}
},
"loggers": {
"": {
"handlers": ["console", "file", "error"],
"level": "INFO"
},
"agent": {
"handlers": ["console", "file", "error"],
"level": "DEBUG"
}
}
}
# 创建日志目录
os.makedirs('logs', exist_ok=True)
# 配置日志
logging.config.dictConfig(LOG_CONFIG)
# 获取日志器
logger = logging.getLogger("agent")
def log_user_message(user_id: str, session_id: str, message: str):
"""记录用户消息"""
logger.info(
f"User message",
extra={
"user_id": user_id,
"session_id": session_id,
"message": message,
"event_type": "user_message"
}
)
def log_agent_response(agent_type: str, session_id: str, response: str):
"""记录智能体响应"""
logger.info(
f"Agent response",
extra={
"agent_type": agent_type,
"session_id": session_id,
"response": response,
"event_type": "agent_response"
}
)
def log_error(error_type: str, session_id: str, error: str):
"""记录错误"""
logger.error(
f"Error occurred",
extra={
"error_type": error_type,
"session_id": session_id,
"error": error,
"event_type": "error"
}
)
# 示例用法
def handle_message(user_id: str, session_id: str, message: str):
"""处理消息"""
try:
# 记录用户消息
log_user_message(user_id, session_id, message)
# 处理消息...
response = "这是智能体的回复"
# 记录智能体响应
log_agent_response("qa_agent", session_id, response)
return response
except Exception as e:
# 记录错误
log_error("processing_error", session_id, str(e))
raise
if __name__ == "__main__":
# 测试日志
handle_message("user123", "session123", "测试消息")
6. 案例分析与最佳实践
6.1 成功案例
案例：某大型电商平台智能客服系统
背景：该电商平台日均处理超过100万条客户咨询，传统客服系统难以应对高峰期的咨询量。
解决方案：
- 多智能体架构：部署了7个不同角色的智能体，各司其职
- 知识库构建：构建了包含100万+条知识的企业级知识库
- 多渠道接入：支持网站、APP、微信、电话等多种渠道
- 智能分流：基于意图识别的智能任务分发
- 人工协作：智能体无法解决的问题无缝转接人工客服
效果：
- 客服效率提升：智能客服处理了85%的常规咨询
- 响应时间缩短：平均响应时间从3分钟缩短到30秒
- 客户满意度提升：客户满意度从82%提升到95%
- 运营成本降低：客服运营成本降低40%
6.2 最佳实践
-
智能体设计最佳实践
- 职责单一：每个智能体专注于特定功能
- 接口标准化：统一智能体间的通信接口
- 状态管理：合理管理智能体状态
- 错误处理：完善的错误处理和恢复机制
-
知识库管理最佳实践
- 知识分类：按业务领域和使用频率分类
- 定期更新：定期更新和维护知识库
- 质量控制：确保知识库内容的准确性
- 使用分析：分析知识库使用情况，优化内容
-
系统部署最佳实践
- 容器化部署：使用Docker和Kubernetes
- 环境隔离：开发、测试、生产环境隔离
- 自动化部署：CI/CD流水线自动化部署
- 监控告警：完善的监控和告警机制
-
性能优化最佳实践
- 缓存策略：合理使用缓存，减少重复计算
- 异步处理：非关键操作使用异步处理
- 批量处理：批量处理相似请求
- 资源调度：智能调度计算资源
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