feat: 增强 Agent 系统和完善项目结构
主要改进: - Agent 增强: 订单查询、售后支持、客服路由等功能优化 - 新增语言检测和 Token 管理模块 - 改进 Chatwoot webhook 处理和用户标识 - MCP 服务器增强: 订单 MCP 和 Strapi MCP 功能扩展 - 新增商城客户端、知识库、缓存和同步模块 - 添加多语言提示词系统 (YAML) - 完善项目结构: 整理文档、脚本和测试文件 - 新增调试和测试工具脚本 Co-Authored-By: Claude Sonnet 4.5 <noreply@anthropic.com>
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wangliang
@@ -6,76 +6,20 @@ from typing import Any
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from core.state import AgentState, ConversationState, add_tool_call, set_response
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from core.llm import get_llm_client, Message
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from prompts import get_prompt
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from utils.logger import get_logger
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logger = get_logger(__name__)
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CUSTOMER_SERVICE_PROMPT = """你是一个专业的 B2B 购物网站客服助手。
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你的职责是回答用户的一般性问题，包括：
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- 常见问题解答 (FAQ)
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- 公司信息查询
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- 政策咨询（退换货政策、隐私政策等）
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- 产品使用指南
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- 其他一般性咨询
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## 可用工具
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你可以使用以下工具获取信息：
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1. **query_faq** - 搜索 FAQ 常见问题
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- query: 搜索关键词
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- category: 分类（可选）
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2. **get_company_info** - 获取公司信息
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- section: 信息类别（about_us, contact, etc.）
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3. **get_policy** - 获取政策文档
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- policy_type: 政策类型（return_policy, privacy_policy, etc.）
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## 工具调用格式
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当需要使用工具时，请返回 JSON 格式：
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```json
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{
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"action": "call_tool",
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"tool_name": "工具名称",
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"arguments": {
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"参数名": "参数值"
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}
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}
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```
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当可以直接回答时，请返回：
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```json
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{
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"action": "respond",
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"response": "回复内容"
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}
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```
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当需要转人工时，请返回：
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```json
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{
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"action": "handoff",
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"reason": "转人工原因"
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}
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```
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## 注意事项
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- 保持专业、友好的语气
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- 如果不确定答案，建议用户联系人工客服
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- 不要编造信息，只使用工具返回的数据
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"""
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async def customer_service_agent(state: AgentState) -> AgentState:
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"""Customer service agent node
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Handles FAQ, company info, and general inquiries using Strapi MCP tools.
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Args:
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state: Current agent state
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Returns:
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Updated state with tool calls or response
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"""
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@@ -83,18 +27,87 @@ async def customer_service_agent(state: AgentState) -> AgentState:
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"Customer service agent processing",
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conversation_id=state["conversation_id"]
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)
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state["current_agent"] = "customer_service"
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state["agent_history"].append("customer_service")
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state["state"] = ConversationState.PROCESSING.value
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# Check if we have tool results to process
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if state["tool_results"]:
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return await _generate_response_from_results(state)
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# Get detected language
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locale = state.get("detected_language", "en")
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# Auto-detect category and query FAQ
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message_lower = state["current_message"].lower()
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# 定义分类关键词
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category_keywords = {
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"register": ["register", "sign up", "account", "login", "password", "forgot"],
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"order": ["order", "place order", "cancel order", "modify order", "change order"],
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"payment": ["pay", "payment", "checkout", "voucher", "discount", "promo"],
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"shipment": ["ship", "shipping", "delivery", "courier", "transit", "logistics", "tracking"],
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"return": ["return", "refund", "exchange", "defective", "damaged"],
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}
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# 检测分类
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detected_category = None
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for category, keywords in category_keywords.items():
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if any(keyword in message_lower for keyword in keywords):
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detected_category = category
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break
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# 检查是否已经查询过 FAQ
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tool_calls = state.get("tool_calls", [])
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has_faq_query = any(tc.get("tool_name") in ["query_faq", "search_knowledge_base"] for tc in tool_calls)
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# 如果检测到分类且未查询过 FAQ，自动查询
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if detected_category and not has_faq_query:
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logger.info(
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f"Auto-querying FAQ for category: {detected_category}",
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conversation_id=state["conversation_id"]
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)
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# 自动添加 FAQ 工具调用
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state = add_tool_call(
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state,
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tool_name="query_faq",
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arguments={
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"category": detected_category,
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"locale": locale,
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"limit": 5
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},
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server="strapi"
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)
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state["state"] = ConversationState.TOOL_CALLING.value
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return state
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# 如果询问营业时间或联系方式，自动查询公司信息
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if any(keyword in message_lower for keyword in ["opening hour", "contact", "address", "phone", "email"]) and not has_faq_query:
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logger.info(
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"Auto-querying company info",
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conversation_id=state["conversation_id"]
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)
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state = add_tool_call(
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state,
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tool_name="get_company_info",
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arguments={
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"section": "contact",
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"locale": locale
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},
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server="strapi"
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)
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state["state"] = ConversationState.TOOL_CALLING.value
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return state
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# Build messages for LLM
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# Load prompt in detected language
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system_prompt = get_prompt("customer_service", locale)
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messages = [
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Message(role="system", content=CUSTOMER_SERVICE_PROMPT),
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Message(role="system", content=system_prompt),
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]
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# Add conversation history
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@@ -151,37 +164,37 @@ async def customer_service_agent(state: AgentState) -> AgentState:
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async def _generate_response_from_results(state: AgentState) -> AgentState:
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"""Generate response based on tool results"""
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# Build context from tool results
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tool_context = []
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for result in state["tool_results"]:
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if result["success"]:
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tool_context.append(f"工具 {result['tool_name']} 返回:\n{json.dumps(result['data'], ensure_ascii=False, indent=2)}")
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tool_context.append(f"Tool {result['tool_name']} returned:\n{json.dumps(result['data'], ensure_ascii=False, indent=2)}")
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else:
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tool_context.append(f"工具 {result['tool_name']} 执行失败: {result['error']}")
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prompt = f"""基于以下工具返回的信息，生成对用户的回复。
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tool_context.append(f"Tool {result['tool_name']} failed: {result['error']}")
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用户问题: {state["current_message"]}
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prompt = f"""Based on the following tool returned information, generate a response to the user.
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工具返回信息:
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User question: {state["current_message"]}
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Tool returned information:
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{chr(10).join(tool_context)}
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请生成一个友好、专业的回复。如果工具没有返回有用信息，请诚实告知用户并建议其他方式获取帮助。
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只返回回复内容，不要返回 JSON。"""
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Please generate a friendly and professional response. If the tool did not return useful information, honestly inform the user and suggest other ways to get help.
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Return only the response content, do not return JSON."""
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messages = [
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Message(role="system", content="你是一个专业的 B2B 客服助手，请根据工具返回的信息回答用户问题。"),
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Message(role="system", content="You are a professional B2B customer service assistant, please answer user questions based on tool returned information."),
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Message(role="user", content=prompt)
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]
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try:
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llm = get_llm_client()
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response = await llm.chat(messages, temperature=0.7)
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state = set_response(state, response.content)
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return state
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except Exception as e:
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logger.error("Response generation failed", error=str(e))
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state = set_response(state, "抱歉，处理您的请求时遇到问题。请稍后重试或联系人工客服。")
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state = set_response(state, "Sorry, there was a problem processing your request. Please try again later or contact customer support.")
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return state
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