feat: 初始化 B2B AI Shopping Assistant 项目
- 配置 Docker Compose 多服务编排 - 实现 Chatwoot + Agent 集成 - 配置 Strapi MCP 知识库 - 支持 7 种语言的 FAQ 系统 - 实现 LangGraph AI 工作流 Co-Authored-By: Claude Sonnet 4.5 <noreply@anthropic.com>
This commit is contained in:
31
agent/Dockerfile
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31
agent/Dockerfile
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FROM python:3.11-slim
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WORKDIR /app
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# Install system dependencies
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RUN apt-get update && apt-get install -y \
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build-essential \
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curl \
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&& rm -rf /var/lib/apt/lists/*
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# Copy requirements first for better caching
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COPY requirements.txt .
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# Install Python dependencies
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RUN pip install --no-cache-dir -r requirements.txt
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# Copy application code
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COPY . .
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# Create logs directory
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RUN mkdir -p /app/logs
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# Expose port
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EXPOSE 8000
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# Health check
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HEALTHCHECK --interval=30s --timeout=10s --start-period=5s --retries=3 \
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CMD curl -f http://localhost:8000/health || exit 1
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# Run the application
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CMD ["python", "-m", "uvicorn", "main:app", "--host", "0.0.0.0", "--port", "8000", "--reload"]
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0
agent/__init__.py
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0
agent/__init__.py
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15
agent/agents/__init__.py
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15
agent/agents/__init__.py
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"""Agents package"""
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from .router import classify_intent, route_by_intent
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from .customer_service import customer_service_agent
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from .order import order_agent
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from .aftersale import aftersale_agent
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from .product import product_agent
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__all__ = [
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"classify_intent",
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"route_by_intent",
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"customer_service_agent",
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"order_agent",
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"aftersale_agent",
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"product_agent",
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]
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251
agent/agents/aftersale.py
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251
agent/agents/aftersale.py
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"""
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Aftersale Agent - Handles returns, exchanges, and complaints
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"""
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import json
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from typing import Any
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from core.state import AgentState, ConversationState, add_tool_call, set_response, update_context
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from core.llm import get_llm_client, Message
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from utils.logger import get_logger
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logger = get_logger(__name__)
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AFTERSALE_AGENT_PROMPT = """你是一个专业的 B2B 售后服务助手。
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你的职责是帮助用户处理售后问题，包括：
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- 退货申请
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- 换货申请
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- 投诉处理
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- 工单创建
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- 售后进度查询
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## 可用工具
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1. **apply_return** - 退货申请
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- order_id: 订单号
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- items: 退货商品列表 [{item_id, quantity, reason}]
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- description: 问题描述
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- images: 图片URL列表（可选）
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2. **apply_exchange** - 换货申请
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- order_id: 订单号
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- items: 换货商品列表 [{item_id, reason}]
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- description: 问题描述
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3. **create_complaint** - 创建投诉
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- type: 投诉类型（product_quality/service/logistics/other）
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- title: 投诉标题
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- description: 详细描述
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- related_order_id: 关联订单号（可选）
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- attachments: 附件URL列表（可选）
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4. **create_ticket** - 创建工单
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- category: 工单类别
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- priority: 优先级（low/medium/high/urgent）
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- title: 工单标题
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- description: 详细描述
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5. **query_aftersale_status** - 查询售后状态
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- aftersale_id: 售后单号（可选，不填查询全部）
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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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当需要向用户询问更多信息时：
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```json
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{
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"action": "ask_info",
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"question": "需要询问的问题",
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"required_fields": ["需要收集的字段列表"]
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}
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```
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当可以直接回答时：
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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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## 售后流程引导
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退货流程：
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1. 确认订单号和退货商品
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2. 了解退货原因
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3. 收集问题描述和图片（质量问题时）
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4. 提交退货申请
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5. 告知用户后续流程
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换货流程：
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1. 确认订单号和换货商品
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2. 了解换货原因
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3. 确认是否有库存
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4. 提交换货申请
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## 注意事项
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- 售后申请需要完整信息才能提交
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- 对用户的问题要表示理解和歉意
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- 复杂投诉建议转人工处理
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- 金额较大的退款需要特别确认
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"""
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async def aftersale_agent(state: AgentState) -> AgentState:
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"""Aftersale agent node
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Handles returns, exchanges, complaints and aftersale queries.
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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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logger.info(
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"Aftersale agent processing",
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conversation_id=state["conversation_id"],
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sub_intent=state.get("sub_intent")
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)
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state["current_agent"] = "aftersale"
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state["agent_history"].append("aftersale")
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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_aftersale_response(state)
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# Build messages for LLM
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messages = [
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Message(role="system", content=AFTERSALE_AGENT_PROMPT),
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]
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# Add conversation history
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for msg in state["messages"][-8:]: # More history for aftersale context
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messages.append(Message(role=msg["role"], content=msg["content"]))
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# Build context info
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context_info = f"用户ID: {state['user_id']}\n账户ID: {state['account_id']}\n"
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if state["entities"]:
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context_info += f"已提取的信息: {json.dumps(state['entities'], ensure_ascii=False)}\n"
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if state["context"]:
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context_info += f"会话上下文: {json.dumps(state['context'], ensure_ascii=False)}\n"
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user_content = f"{context_info}\n用户消息: {state['current_message']}"
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messages.append(Message(role="user", content=user_content))
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try:
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llm = get_llm_client()
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response = await llm.chat(messages, temperature=0.5)
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# Parse response
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content = response.content.strip()
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if content.startswith("```"):
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content = content.split("```")[1]
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if content.startswith("json"):
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content = content[4:]
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result = json.loads(content)
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action = result.get("action")
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if action == "call_tool":
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arguments = result.get("arguments", {})
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arguments["user_id"] = state["user_id"]
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# Use entity if available
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if "order_id" not in arguments and state["entities"].get("order_id"):
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arguments["order_id"] = state["entities"]["order_id"]
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state = add_tool_call(
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state,
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tool_name=result["tool_name"],
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arguments=arguments,
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server="aftersale"
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)
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state["state"] = ConversationState.TOOL_CALLING.value
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elif action == "ask_info":
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state = set_response(state, result["question"])
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state["state"] = ConversationState.AWAITING_INFO.value
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# Store required fields in context for next iteration
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if result.get("required_fields"):
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state = update_context(state, {"required_fields": result["required_fields"]})
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elif action == "respond":
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state = set_response(state, result["response"])
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state["state"] = ConversationState.GENERATING.value
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elif action == "handoff":
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state["requires_human"] = True
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state["handoff_reason"] = result.get("reason", "Complex aftersale issue")
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return state
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except json.JSONDecodeError:
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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("Aftersale agent failed", error=str(e))
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state["error"] = str(e)
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return state
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async def _generate_aftersale_response(state: AgentState) -> AgentState:
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"""Generate response based on aftersale 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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data = result["data"]
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tool_context.append(f"工具 {result['tool_name']} 返回:\n{json.dumps(data, ensure_ascii=False, indent=2)}")
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# Extract aftersale_id for context
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if isinstance(data, dict) and data.get("aftersale_id"):
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state = update_context(state, {"aftersale_id": data["aftersale_id"]})
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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"""基于以下售后系统返回的信息，生成对用户的回复。
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用户问题: {state["current_message"]}
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系统返回信息:
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{chr(10).join(tool_context)}
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请生成一个体贴、专业的回复：
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- 如果是申请提交成功，告知用户售后单号和后续流程
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- 如果是状态查询，清晰说明当前进度
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- 如果申请失败，说明原因并提供解决方案
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- 对用户的问题表示理解
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只返回回复内容，不要返回 JSON。"""
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messages = [
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Message(role="system", content="你是一个专业的售后客服助手，请根据系统返回的信息回答用户的售后问题。"),
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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("Aftersale response generation failed", error=str(e))
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state = set_response(state, "抱歉，处理售后请求时遇到问题。请稍后重试或联系人工客服。")
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return state
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187
agent/agents/customer_service.py
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187
agent/agents/customer_service.py
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"""
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Customer Service Agent - Handles FAQ and general inquiries
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"""
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import json
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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 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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logger.info(
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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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# Build messages for LLM
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messages = [
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Message(role="system", content=CUSTOMER_SERVICE_PROMPT),
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]
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# Add conversation history
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for msg in state["messages"][-6:]:
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messages.append(Message(role=msg["role"], content=msg["content"]))
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# Add current message
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messages.append(Message(role="user", content=state["current_message"]))
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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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# Parse response
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content = response.content.strip()
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if content.startswith("```"):
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content = content.split("```")[1]
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if content.startswith("json"):
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content = content[4:]
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result = json.loads(content)
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action = result.get("action")
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if action == "call_tool":
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# Add tool call to state
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state = add_tool_call(
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state,
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tool_name=result["tool_name"],
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arguments=result.get("arguments", {}),
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server="strapi"
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)
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state["state"] = ConversationState.TOOL_CALLING.value
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elif action == "respond":
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state = set_response(state, result["response"])
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state["state"] = ConversationState.GENERATING.value
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elif action == "handoff":
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state["requires_human"] = True
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state["handoff_reason"] = result.get("reason", "User request")
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return state
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except json.JSONDecodeError:
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# LLM returned plain text, use as response
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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("Customer service agent failed", error=str(e))
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state["error"] = str(e)
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return state
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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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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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用户问题: {state["current_message"]}
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工具返回信息:
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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。
|
||||
只返回回复内容，不要返回 JSON。"""
|
||||
|
||||
messages = [
|
||||
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。"),
|
||||
Message(role="user", content=prompt)
|
||||
]
|
||||
|
||||
try:
|
||||
llm = get_llm_client()
|
||||
response = await llm.chat(messages, temperature=0.7)
|
||||
state = set_response(state, response.content)
|
||||
return state
|
||||
|
||||
except Exception as e:
|
||||
logger.error("Response generation failed", error=str(e))
|
||||
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。")
|
||||
return state
|
||||
231
agent/agents/order.py
Normal file
231
agent/agents/order.py
Normal file
@@ -0,0 +1,231 @@
|
||||
"""
|
||||
Order Agent - Handles order-related queries and operations
|
||||
"""
|
||||
import json
|
||||
from typing import Any
|
||||
|
||||
from core.state import AgentState, ConversationState, add_tool_call, set_response, update_context
|
||||
from core.llm import get_llm_client, Message
|
||||
from utils.logger import get_logger
|
||||
|
||||
logger = get_logger(__name__)
|
||||
|
||||
|
||||
ORDER_AGENT_PROMPT = """你是一个专业的 B2B 订单服务助手。
|
||||
你的职责是帮助用户处理订单相关的问题，包括：
|
||||
- 订单查询
|
||||
- 物流跟踪
|
||||
- 订单修改
|
||||
- 订单取消
|
||||
- 发票获取
|
||||
|
||||
## 可用工具
|
||||
|
||||
1. **query_order** - 查询订单
|
||||
- order_id: 订单号（可选，不填则查询最近订单）
|
||||
- date_start: 开始日期（可选）
|
||||
- date_end: 结束日期（可选）
|
||||
- status: 订单状态（可选）
|
||||
|
||||
2. **track_logistics** - 物流跟踪
|
||||
- order_id: 订单号
|
||||
- tracking_number: 物流单号（可选）
|
||||
|
||||
3. **modify_order** - 修改订单
|
||||
- order_id: 订单号
|
||||
- modifications: 修改内容（address/items/quantity 等）
|
||||
|
||||
4. **cancel_order** - 取消订单
|
||||
- order_id: 订单号
|
||||
- reason: 取消原因
|
||||
|
||||
5. **get_invoice** - 获取发票
|
||||
- order_id: 订单号
|
||||
- invoice_type: 发票类型（normal/vat）
|
||||
|
||||
## 工具调用格式
|
||||
|
||||
当需要使用工具时，请返回 JSON 格式：
|
||||
```json
|
||||
{
|
||||
"action": "call_tool",
|
||||
"tool_name": "工具名称",
|
||||
"arguments": {
|
||||
"参数名": "参数值"
|
||||
}
|
||||
}
|
||||
```
|
||||
|
||||
当需要向用户询问更多信息时：
|
||||
```json
|
||||
{
|
||||
"action": "ask_info",
|
||||
"question": "需要询问的问题"
|
||||
}
|
||||
```
|
||||
|
||||
当可以直接回答时：
|
||||
```json
|
||||
{
|
||||
"action": "respond",
|
||||
"response": "回复内容"
|
||||
}
|
||||
```
|
||||
|
||||
## 重要提示
|
||||
- 订单修改和取消是敏感操作，需要确认订单号
|
||||
- 如果用户没有提供订单号，先查询他的最近订单
|
||||
- 物流查询需要订单号或物流单号
|
||||
- 对于批量操作或大金额订单，建议转人工处理
|
||||
"""
|
||||
|
||||
|
||||
async def order_agent(state: AgentState) -> AgentState:
|
||||
"""Order agent node
|
||||
|
||||
Handles order queries, tracking, modifications, and cancellations.
|
||||
|
||||
Args:
|
||||
state: Current agent state
|
||||
|
||||
Returns:
|
||||
Updated state with tool calls or response
|
||||
"""
|
||||
logger.info(
|
||||
"Order agent processing",
|
||||
conversation_id=state["conversation_id"],
|
||||
sub_intent=state.get("sub_intent")
|
||||
)
|
||||
|
||||
state["current_agent"] = "order"
|
||||
state["agent_history"].append("order")
|
||||
state["state"] = ConversationState.PROCESSING.value
|
||||
|
||||
# Check if we have tool results to process
|
||||
if state["tool_results"]:
|
||||
return await _generate_order_response(state)
|
||||
|
||||
# Build messages for LLM
|
||||
messages = [
|
||||
Message(role="system", content=ORDER_AGENT_PROMPT),
|
||||
]
|
||||
|
||||
# Add conversation history
|
||||
for msg in state["messages"][-6:]:
|
||||
messages.append(Message(role=msg["role"], content=msg["content"]))
|
||||
|
||||
# Build context info
|
||||
context_info = f"用户ID: {state['user_id']}\n账户ID: {state['account_id']}\n"
|
||||
|
||||
# Add entities if available
|
||||
if state["entities"]:
|
||||
context_info += f"已提取的信息: {json.dumps(state['entities'], ensure_ascii=False)}\n"
|
||||
|
||||
# Add existing context
|
||||
if state["context"].get("order_id"):
|
||||
context_info += f"当前讨论的订单号: {state['context']['order_id']}\n"
|
||||
|
||||
user_content = f"{context_info}\n用户消息: {state['current_message']}"
|
||||
messages.append(Message(role="user", content=user_content))
|
||||
|
||||
try:
|
||||
llm = get_llm_client()
|
||||
response = await llm.chat(messages, temperature=0.5)
|
||||
|
||||
# Parse response
|
||||
content = response.content.strip()
|
||||
if content.startswith("```"):
|
||||
content = content.split("```")[1]
|
||||
if content.startswith("json"):
|
||||
content = content[4:]
|
||||
|
||||
result = json.loads(content)
|
||||
action = result.get("action")
|
||||
|
||||
if action == "call_tool":
|
||||
# Inject user context into arguments
|
||||
arguments = result.get("arguments", {})
|
||||
arguments["user_id"] = state["user_id"]
|
||||
arguments["account_id"] = state["account_id"]
|
||||
|
||||
# Use entity if available
|
||||
if "order_id" not in arguments and state["entities"].get("order_id"):
|
||||
arguments["order_id"] = state["entities"]["order_id"]
|
||||
|
||||
state = add_tool_call(
|
||||
state,
|
||||
tool_name=result["tool_name"],
|
||||
arguments=arguments,
|
||||
server="order"
|
||||
)
|
||||
state["state"] = ConversationState.TOOL_CALLING.value
|
||||
|
||||
elif action == "ask_info":
|
||||
state = set_response(state, result["question"])
|
||||
state["state"] = ConversationState.AWAITING_INFO.value
|
||||
|
||||
elif action == "respond":
|
||||
state = set_response(state, result["response"])
|
||||
state["state"] = ConversationState.GENERATING.value
|
||||
|
||||
elif action == "handoff":
|
||||
state["requires_human"] = True
|
||||
state["handoff_reason"] = result.get("reason", "Complex order operation")
|
||||
|
||||
return state
|
||||
|
||||
except json.JSONDecodeError:
|
||||
state = set_response(state, response.content)
|
||||
return state
|
||||
|
||||
except Exception as e:
|
||||
logger.error("Order agent failed", error=str(e))
|
||||
state["error"] = str(e)
|
||||
return state
|
||||
|
||||
|
||||
async def _generate_order_response(state: AgentState) -> AgentState:
|
||||
"""Generate response based on order tool results"""
|
||||
|
||||
# Build context from tool results
|
||||
tool_context = []
|
||||
for result in state["tool_results"]:
|
||||
if result["success"]:
|
||||
data = result["data"]
|
||||
tool_context.append(f"工具 {result['tool_name']} 返回:\n{json.dumps(data, ensure_ascii=False, indent=2)}")
|
||||
|
||||
# Extract order_id for context
|
||||
if isinstance(data, dict):
|
||||
if data.get("order_id"):
|
||||
state = update_context(state, {"order_id": data["order_id"]})
|
||||
elif data.get("orders") and len(data["orders"]) > 0:
|
||||
state = update_context(state, {"order_id": data["orders"][0].get("order_id")})
|
||||
else:
|
||||
tool_context.append(f"工具 {result['tool_name']} 执行失败: {result['error']}")
|
||||
|
||||
prompt = f"""基于以下订单系统返回的信息，生成对用户的回复。
|
||||
|
||||
用户问题: {state["current_message"]}
|
||||
|
||||
系统返回信息:
|
||||
{chr(10).join(tool_context)}
|
||||
|
||||
请生成一个清晰、友好的回复，包含订单的关键信息（订单号、状态、金额、物流等）。
|
||||
如果是物流信息，请按时间线整理展示。
|
||||
只返回回复内容，不要返回 JSON。"""
|
||||
|
||||
messages = [
|
||||
Message(role="system", content="你是一个专业的订单客服助手，请根据系统返回的信息回答用户的订单问题。"),
|
||||
Message(role="user", content=prompt)
|
||||
]
|
||||
|
||||
try:
|
||||
llm = get_llm_client()
|
||||
response = await llm.chat(messages, temperature=0.7)
|
||||
state = set_response(state, response.content)
|
||||
return state
|
||||
|
||||
except Exception as e:
|
||||
logger.error("Order response generation failed", error=str(e))
|
||||
state = set_response(state, "抱歉，处理订单信息时遇到问题。请稍后重试或联系人工客服。")
|
||||
return state
|
||||
256
agent/agents/product.py
Normal file
256
agent/agents/product.py
Normal file
@@ -0,0 +1,256 @@
|
||||
"""
|
||||
Product Agent - Handles product search, recommendations, and quotes
|
||||
"""
|
||||
import json
|
||||
from typing import Any
|
||||
|
||||
from core.state import AgentState, ConversationState, add_tool_call, set_response, update_context
|
||||
from core.llm import get_llm_client, Message
|
||||
from utils.logger import get_logger
|
||||
|
||||
logger = get_logger(__name__)
|
||||
|
||||
|
||||
PRODUCT_AGENT_PROMPT = """你是一个专业的 B2B 商品顾问助手。
|
||||
你的职责是帮助用户找到合适的商品，包括：
|
||||
- 商品搜索
|
||||
- 智能推荐
|
||||
- B2B 询价
|
||||
- 库存查询
|
||||
- 商品详情
|
||||
|
||||
## 可用工具
|
||||
|
||||
1. **search_products** - 搜索商品
|
||||
- query: 搜索关键词
|
||||
- filters: 过滤条件（category, price_range, brand 等）
|
||||
- sort: 排序方式（price_asc/price_desc/sales/latest）
|
||||
- page: 页码
|
||||
- page_size: 每页数量
|
||||
|
||||
2. **get_product_detail** - 获取商品详情
|
||||
- product_id: 商品ID
|
||||
|
||||
3. **recommend_products** - 智能推荐
|
||||
- context: 推荐上下文（可包含当前查询、浏览历史等）
|
||||
- limit: 推荐数量
|
||||
|
||||
4. **get_quote** - B2B 询价
|
||||
- product_id: 商品ID
|
||||
- quantity: 采购数量
|
||||
- delivery_address: 收货地址（可选，用于计算运费）
|
||||
|
||||
5. **check_inventory** - 库存查询
|
||||
- product_ids: 商品ID列表
|
||||
- warehouse: 仓库（可选）
|
||||
|
||||
## 工具调用格式
|
||||
|
||||
当需要使用工具时，请返回 JSON 格式：
|
||||
```json
|
||||
{
|
||||
"action": "call_tool",
|
||||
"tool_name": "工具名称",
|
||||
"arguments": {
|
||||
"参数名": "参数值"
|
||||
}
|
||||
}
|
||||
```
|
||||
|
||||
当需要向用户询问更多信息时：
|
||||
```json
|
||||
{
|
||||
"action": "ask_info",
|
||||
"question": "需要询问的问题"
|
||||
}
|
||||
```
|
||||
|
||||
当可以直接回答时：
|
||||
```json
|
||||
{
|
||||
"action": "respond",
|
||||
"response": "回复内容"
|
||||
}
|
||||
```
|
||||
|
||||
## B2B 询价特点
|
||||
- 大批量采购通常有阶梯价格
|
||||
- 可能需要考虑运费
|
||||
- 企业客户可能有专属折扣
|
||||
- 报价通常有有效期
|
||||
|
||||
## 商品推荐策略
|
||||
- 根据用户采购历史推荐
|
||||
- 根据当前查询语义推荐
|
||||
- 根据企业行业特点推荐
|
||||
- 根据季节性和热门商品推荐
|
||||
|
||||
## 注意事项
|
||||
- 帮助用户准确描述需求
|
||||
- 如果搜索结果太多，建议用户缩小范围
|
||||
- 询价时确认数量，因为会影响价格
|
||||
- 库存紧张时及时告知用户
|
||||
"""
|
||||
|
||||
|
||||
async def product_agent(state: AgentState) -> AgentState:
|
||||
"""Product agent node
|
||||
|
||||
Handles product search, recommendations, quotes and inventory queries.
|
||||
|
||||
Args:
|
||||
state: Current agent state
|
||||
|
||||
Returns:
|
||||
Updated state with tool calls or response
|
||||
"""
|
||||
logger.info(
|
||||
"Product agent processing",
|
||||
conversation_id=state["conversation_id"],
|
||||
sub_intent=state.get("sub_intent")
|
||||
)
|
||||
|
||||
state["current_agent"] = "product"
|
||||
state["agent_history"].append("product")
|
||||
state["state"] = ConversationState.PROCESSING.value
|
||||
|
||||
# Check if we have tool results to process
|
||||
if state["tool_results"]:
|
||||
return await _generate_product_response(state)
|
||||
|
||||
# Build messages for LLM
|
||||
messages = [
|
||||
Message(role="system", content=PRODUCT_AGENT_PROMPT),
|
||||
]
|
||||
|
||||
# Add conversation history
|
||||
for msg in state["messages"][-6:]:
|
||||
messages.append(Message(role=msg["role"], content=msg["content"]))
|
||||
|
||||
# Build context info
|
||||
context_info = f"用户ID: {state['user_id']}\n账户ID: {state['account_id']}\n"
|
||||
|
||||
if state["entities"]:
|
||||
context_info += f"已提取的信息: {json.dumps(state['entities'], ensure_ascii=False)}\n"
|
||||
|
||||
if state["context"].get("product_id"):
|
||||
context_info += f"当前讨论的商品ID: {state['context']['product_id']}\n"
|
||||
|
||||
if state["context"].get("recent_searches"):
|
||||
context_info += f"最近搜索: {state['context']['recent_searches']}\n"
|
||||
|
||||
user_content = f"{context_info}\n用户消息: {state['current_message']}"
|
||||
messages.append(Message(role="user", content=user_content))
|
||||
|
||||
try:
|
||||
llm = get_llm_client()
|
||||
response = await llm.chat(messages, temperature=0.7)
|
||||
|
||||
# Parse response
|
||||
content = response.content.strip()
|
||||
if content.startswith("```"):
|
||||
content = content.split("```")[1]
|
||||
if content.startswith("json"):
|
||||
content = content[4:]
|
||||
|
||||
result = json.loads(content)
|
||||
action = result.get("action")
|
||||
|
||||
if action == "call_tool":
|
||||
arguments = result.get("arguments", {})
|
||||
|
||||
# Inject context for recommendation
|
||||
if result["tool_name"] == "recommend_products":
|
||||
arguments["user_id"] = state["user_id"]
|
||||
arguments["account_id"] = state["account_id"]
|
||||
|
||||
# Inject context for quote
|
||||
if result["tool_name"] == "get_quote":
|
||||
arguments["account_id"] = state["account_id"]
|
||||
|
||||
# Use entity if available
|
||||
if "product_id" not in arguments and state["entities"].get("product_id"):
|
||||
arguments["product_id"] = state["entities"]["product_id"]
|
||||
|
||||
if "quantity" not in arguments and state["entities"].get("quantity"):
|
||||
arguments["quantity"] = state["entities"]["quantity"]
|
||||
|
||||
state = add_tool_call(
|
||||
state,
|
||||
tool_name=result["tool_name"],
|
||||
arguments=arguments,
|
||||
server="product"
|
||||
)
|
||||
state["state"] = ConversationState.TOOL_CALLING.value
|
||||
|
||||
elif action == "ask_info":
|
||||
state = set_response(state, result["question"])
|
||||
state["state"] = ConversationState.AWAITING_INFO.value
|
||||
|
||||
elif action == "respond":
|
||||
state = set_response(state, result["response"])
|
||||
state["state"] = ConversationState.GENERATING.value
|
||||
|
||||
return state
|
||||
|
||||
except json.JSONDecodeError:
|
||||
state = set_response(state, response.content)
|
||||
return state
|
||||
|
||||
except Exception as e:
|
||||
logger.error("Product agent failed", error=str(e))
|
||||
state["error"] = str(e)
|
||||
return state
|
||||
|
||||
|
||||
async def _generate_product_response(state: AgentState) -> AgentState:
|
||||
"""Generate response based on product tool results"""
|
||||
|
||||
tool_context = []
|
||||
for result in state["tool_results"]:
|
||||
if result["success"]:
|
||||
data = result["data"]
|
||||
tool_context.append(f"工具 {result['tool_name']} 返回:\n{json.dumps(data, ensure_ascii=False, indent=2)}")
|
||||
|
||||
# Extract product context
|
||||
if isinstance(data, dict):
|
||||
if data.get("product_id"):
|
||||
state = update_context(state, {"product_id": data["product_id"]})
|
||||
if data.get("products"):
|
||||
# Store recent search results
|
||||
product_ids = [p.get("product_id") for p in data["products"][:5]]
|
||||
state = update_context(state, {"recent_product_ids": product_ids})
|
||||
else:
|
||||
tool_context.append(f"工具 {result['tool_name']} 执行失败: {result['error']}")
|
||||
|
||||
prompt = f"""基于以下商品系统返回的信息，生成对用户的回复。
|
||||
|
||||
用户问题: {state["current_message"]}
|
||||
|
||||
系统返回信息:
|
||||
{chr(10).join(tool_context)}
|
||||
|
||||
请生成一个清晰、有帮助的回复：
|
||||
- 如果是搜索结果，展示商品名称、价格、规格等关键信息
|
||||
- 如果是询价结果，清晰说明单价、总价、折扣、有效期等
|
||||
- 如果是推荐商品，简要说明推荐理由
|
||||
- 如果是库存查询，告知可用数量和发货时间
|
||||
- 结果较多时可以总结关键信息
|
||||
|
||||
只返回回复内容，不要返回 JSON。"""
|
||||
|
||||
messages = [
|
||||
Message(role="system", content="你是一个专业的商品顾问，请根据系统返回的信息回答用户的商品问题。"),
|
||||
Message(role="user", content=prompt)
|
||||
]
|
||||
|
||||
try:
|
||||
llm = get_llm_client()
|
||||
response = await llm.chat(messages, temperature=0.7)
|
||||
state = set_response(state, response.content)
|
||||
return state
|
||||
|
||||
except Exception as e:
|
||||
logger.error("Product response generation failed", error=str(e))
|
||||
state = set_response(state, "抱歉，处理商品信息时遇到问题。请稍后重试或联系人工客服。")
|
||||
return state
|
||||
220
agent/agents/router.py
Normal file
220
agent/agents/router.py
Normal file
@@ -0,0 +1,220 @@
|
||||
"""
|
||||
Router Agent - Intent recognition and routing
|
||||
"""
|
||||
import json
|
||||
from typing import Any, Optional
|
||||
|
||||
from core.state import AgentState, Intent, ConversationState, set_intent, add_entity
|
||||
from core.llm import get_llm_client, Message
|
||||
from utils.logger import get_logger
|
||||
|
||||
logger = get_logger(__name__)
|
||||
|
||||
|
||||
# Intent classification prompt
|
||||
CLASSIFICATION_PROMPT = """你是一个 B2B 购物网站的智能助手路由器。
|
||||
你的任务是分析用户消息，识别用户意图并提取关键实体。
|
||||
|
||||
## 可用意图分类
|
||||
|
||||
1. **customer_service** - 通用咨询
|
||||
- FAQ 问答
|
||||
- 产品使用问题
|
||||
- 公司信息查询
|
||||
- 政策咨询（退换货政策、隐私政策等）
|
||||
|
||||
2. **order** - 订单相关
|
||||
- 订单查询（"我的订单在哪"、"查一下订单"）
|
||||
- 物流跟踪（"快递到哪了"、"什么时候到货"）
|
||||
- 订单修改（"改一下收货地址"、"修改订单数量"）
|
||||
- 订单取消（"取消订单"、"不想要了"）
|
||||
- 发票查询（"开发票"、"要发票"）
|
||||
|
||||
3. **aftersale** - 售后服务
|
||||
- 退货申请（"退货"、"不满意想退"）
|
||||
- 换货申请（"换货"、"换一个"）
|
||||
- 投诉（"投诉"、"服务态度差"）
|
||||
- 工单/问题反馈
|
||||
|
||||
4. **product** - 商品相关
|
||||
- 商品搜索（"有没有xx"、"找一下xx"）
|
||||
- 商品推荐（"推荐"、"有什么好的"）
|
||||
- 询价（"多少钱"、"批发价"、"大量购买价格"）
|
||||
- 库存查询（"有货吗"、"还有多少"）
|
||||
|
||||
5. **human_handoff** - 需要转人工
|
||||
- 用户明确要求转人工
|
||||
- 复杂问题 AI 无法处理
|
||||
- 敏感问题需要人工处理
|
||||
|
||||
## 实体提取
|
||||
|
||||
请从消息中提取以下实体（如果存在）：
|
||||
- order_id: 订单号（如 ORD123456）
|
||||
- product_id: 商品ID
|
||||
- product_name: 商品名称
|
||||
- quantity: 数量
|
||||
- date_reference: 时间引用（今天、昨天、上周、具体日期等）
|
||||
- tracking_number: 物流单号
|
||||
- phone: 电话号码
|
||||
- address: 地址信息
|
||||
|
||||
## 输出格式
|
||||
|
||||
请以 JSON 格式返回，包含以下字段：
|
||||
```json
|
||||
{
|
||||
"intent": "意图分类",
|
||||
"confidence": 0.95,
|
||||
"sub_intent": "子意图（可选）",
|
||||
"entities": {
|
||||
"entity_type": "entity_value"
|
||||
},
|
||||
"reasoning": "简短的推理说明"
|
||||
}
|
||||
```
|
||||
|
||||
## 注意事项
|
||||
- 如果意图不明确，置信度应该较低
|
||||
- 如果无法确定意图，返回 "unknown"
|
||||
- 实体提取要准确，没有的字段不要填写
|
||||
"""
|
||||
|
||||
|
||||
async def classify_intent(state: AgentState) -> AgentState:
|
||||
"""Classify user intent and extract entities
|
||||
|
||||
This is the first node in the workflow that analyzes the user's message
|
||||
and determines which agent should handle it.
|
||||
|
||||
Args:
|
||||
state: Current agent state
|
||||
|
||||
Returns:
|
||||
Updated state with intent and entities
|
||||
"""
|
||||
logger.info(
|
||||
"Classifying intent",
|
||||
conversation_id=state["conversation_id"],
|
||||
message=state["current_message"][:100]
|
||||
)
|
||||
|
||||
state["state"] = ConversationState.CLASSIFYING.value
|
||||
state["step_count"] += 1
|
||||
|
||||
# Build context from conversation history
|
||||
context_summary = ""
|
||||
if state["context"]:
|
||||
context_parts = []
|
||||
if state["context"].get("order_id"):
|
||||
context_parts.append(f"当前讨论的订单: {state['context']['order_id']}")
|
||||
if state["context"].get("product_id"):
|
||||
context_parts.append(f"当前讨论的商品: {state['context']['product_id']}")
|
||||
if context_parts:
|
||||
context_summary = "\n".join(context_parts)
|
||||
|
||||
# Build messages for LLM
|
||||
messages = [
|
||||
Message(role="system", content=CLASSIFICATION_PROMPT),
|
||||
]
|
||||
|
||||
# Add recent conversation history for context
|
||||
for msg in state["messages"][-6:]: # Last 3 turns
|
||||
messages.append(Message(role=msg["role"], content=msg["content"]))
|
||||
|
||||
# Add current message with context
|
||||
user_content = f"用户消息: {state['current_message']}"
|
||||
if context_summary:
|
||||
user_content += f"\n\n当前上下文:\n{context_summary}"
|
||||
|
||||
messages.append(Message(role="user", content=user_content))
|
||||
|
||||
try:
|
||||
llm = get_llm_client()
|
||||
response = await llm.chat(messages, temperature=0.3)
|
||||
|
||||
# Parse JSON response
|
||||
content = response.content.strip()
|
||||
# Handle markdown code blocks
|
||||
if content.startswith("```"):
|
||||
content = content.split("```")[1]
|
||||
if content.startswith("json"):
|
||||
content = content[4:]
|
||||
|
||||
result = json.loads(content)
|
||||
|
||||
# Extract intent
|
||||
intent_str = result.get("intent", "unknown")
|
||||
try:
|
||||
intent = Intent(intent_str)
|
||||
except ValueError:
|
||||
intent = Intent.UNKNOWN
|
||||
|
||||
confidence = float(result.get("confidence", 0.5))
|
||||
sub_intent = result.get("sub_intent")
|
||||
|
||||
# Set intent in state
|
||||
state = set_intent(state, intent, confidence, sub_intent)
|
||||
|
||||
# Extract entities
|
||||
entities = result.get("entities", {})
|
||||
for entity_type, entity_value in entities.items():
|
||||
if entity_value:
|
||||
state = add_entity(state, entity_type, entity_value)
|
||||
|
||||
logger.info(
|
||||
"Intent classified",
|
||||
intent=intent.value,
|
||||
confidence=confidence,
|
||||
entities=list(entities.keys())
|
||||
)
|
||||
|
||||
# Check if human handoff is needed
|
||||
if intent == Intent.HUMAN_HANDOFF or confidence < 0.5:
|
||||
state["requires_human"] = True
|
||||
state["handoff_reason"] = result.get("reasoning", "Intent unclear")
|
||||
|
||||
return state
|
||||
|
||||
except json.JSONDecodeError as e:
|
||||
logger.error("Failed to parse intent response", error=str(e))
|
||||
state["intent"] = Intent.UNKNOWN.value
|
||||
state["intent_confidence"] = 0.0
|
||||
return state
|
||||
|
||||
except Exception as e:
|
||||
logger.error("Intent classification failed", error=str(e))
|
||||
state["error"] = str(e)
|
||||
state["intent"] = Intent.UNKNOWN.value
|
||||
return state
|
||||
|
||||
|
||||
def route_by_intent(state: AgentState) -> str:
|
||||
"""Route to appropriate agent based on intent
|
||||
|
||||
This is the routing function used by conditional edges in the graph.
|
||||
|
||||
Args:
|
||||
state: Current agent state
|
||||
|
||||
Returns:
|
||||
Name of the next node to execute
|
||||
"""
|
||||
intent = state.get("intent")
|
||||
requires_human = state.get("requires_human", False)
|
||||
|
||||
# Human handoff takes priority
|
||||
if requires_human:
|
||||
return "human_handoff"
|
||||
|
||||
# Route based on intent
|
||||
routing_map = {
|
||||
Intent.CUSTOMER_SERVICE.value: "customer_service_agent",
|
||||
Intent.ORDER.value: "order_agent",
|
||||
Intent.AFTERSALE.value: "aftersale_agent",
|
||||
Intent.PRODUCT.value: "product_agent",
|
||||
Intent.HUMAN_HANDOFF.value: "human_handoff",
|
||||
Intent.UNKNOWN.value: "customer_service_agent" # Default to customer service
|
||||
}
|
||||
|
||||
return routing_map.get(intent, "customer_service_agent")
|
||||
60
agent/config.py
Normal file
60
agent/config.py
Normal file
@@ -0,0 +1,60 @@
|
||||
"""
|
||||
Configuration management for B2B Shopping AI Assistant
|
||||
"""
|
||||
from typing import Optional
|
||||
from pydantic_settings import BaseSettings
|
||||
from pydantic import Field
|
||||
|
||||
|
||||
class Settings(BaseSettings):
|
||||
"""Application settings loaded from environment variables"""
|
||||
|
||||
# ============ AI Model ============
|
||||
zhipu_api_key: str = Field(..., description="ZhipuAI API Key")
|
||||
zhipu_model: str = Field(default="glm-4", description="ZhipuAI Model name")
|
||||
|
||||
# ============ Redis ============
|
||||
redis_host: str = Field(default="localhost", description="Redis host")
|
||||
redis_port: int = Field(default=6379, description="Redis port")
|
||||
redis_password: Optional[str] = Field(default=None, description="Redis password")
|
||||
redis_db: int = Field(default=0, description="Redis database number")
|
||||
|
||||
# ============ Chatwoot ============
|
||||
chatwoot_api_url: str = Field(..., description="Chatwoot API URL")
|
||||
chatwoot_api_token: str = Field(..., description="Chatwoot API Token")
|
||||
chatwoot_webhook_secret: Optional[str] = Field(default=None, description="Chatwoot Webhook Secret")
|
||||
|
||||
# ============ Strapi CMS ============
|
||||
strapi_api_url: str = Field(..., description="Strapi API URL")
|
||||
strapi_api_token: str = Field(..., description="Strapi API Token")
|
||||
|
||||
# ============ Hyperf API ============
|
||||
hyperf_api_url: str = Field(..., description="Hyperf API URL")
|
||||
hyperf_api_token: str = Field(..., description="Hyperf API Token")
|
||||
|
||||
# ============ MCP Servers ============
|
||||
strapi_mcp_url: str = Field(default="http://localhost:8001", description="Strapi MCP URL")
|
||||
order_mcp_url: str = Field(default="http://localhost:8002", description="Order MCP URL")
|
||||
aftersale_mcp_url: str = Field(default="http://localhost:8003", description="Aftersale MCP URL")
|
||||
product_mcp_url: str = Field(default="http://localhost:8004", description="Product MCP URL")
|
||||
|
||||
# ============ Application Config ============
|
||||
log_level: str = Field(default="INFO", description="Log level")
|
||||
max_conversation_steps: int = Field(default=10, description="Max steps in conversation")
|
||||
conversation_timeout: int = Field(default=3600, description="Conversation timeout in seconds")
|
||||
|
||||
class Config:
|
||||
env_file = ".env"
|
||||
env_file_encoding = "utf-8"
|
||||
case_sensitive = False
|
||||
|
||||
|
||||
# Global settings instance
|
||||
settings = Settings()
|
||||
|
||||
|
||||
def get_redis_url() -> str:
|
||||
"""Get Redis connection URL"""
|
||||
if settings.redis_password and settings.redis_password.strip():
|
||||
return f"redis://:{settings.redis_password}@{settings.redis_host}:{settings.redis_port}/{settings.redis_db}"
|
||||
return f"redis://{settings.redis_host}:{settings.redis_port}/{settings.redis_db}"
|
||||
18
agent/core/__init__.py
Normal file
18
agent/core/__init__.py
Normal file
@@ -0,0 +1,18 @@
|
||||
"""Agent core package"""
|
||||
from .state import AgentState, Intent, ConversationState, create_initial_state
|
||||
from .llm import ZhipuLLMClient, get_llm_client, Message, LLMResponse
|
||||
from .graph import create_agent_graph, get_agent_graph, process_message
|
||||
|
||||
__all__ = [
|
||||
"AgentState",
|
||||
"Intent",
|
||||
"ConversationState",
|
||||
"create_initial_state",
|
||||
"ZhipuLLMClient",
|
||||
"get_llm_client",
|
||||
"Message",
|
||||
"LLMResponse",
|
||||
"create_agent_graph",
|
||||
"get_agent_graph",
|
||||
"process_message",
|
||||
]
|
||||
404
agent/core/graph.py
Normal file
404
agent/core/graph.py
Normal file
@@ -0,0 +1,404 @@
|
||||
"""
|
||||
LangGraph workflow definition for B2B Shopping AI Assistant
|
||||
"""
|
||||
from typing import Literal
|
||||
import httpx
|
||||
|
||||
from langgraph.graph import StateGraph, END
|
||||
|
||||
from .state import AgentState, ConversationState, mark_finished, add_tool_result, set_response
|
||||
from agents.router import classify_intent, route_by_intent
|
||||
from agents.customer_service import customer_service_agent
|
||||
from agents.order import order_agent
|
||||
from agents.aftersale import aftersale_agent
|
||||
from agents.product import product_agent
|
||||
from config import settings
|
||||
from utils.logger import get_logger
|
||||
|
||||
logger = get_logger(__name__)
|
||||
|
||||
|
||||
# ============ Node Functions ============
|
||||
|
||||
async def receive_message(state: AgentState) -> AgentState:
|
||||
"""Receive and preprocess incoming message
|
||||
|
||||
This is the entry point of the workflow.
|
||||
"""
|
||||
logger.info(
|
||||
"Receiving message",
|
||||
conversation_id=state["conversation_id"],
|
||||
message_length=len(state["current_message"])
|
||||
)
|
||||
|
||||
# Add user message to history
|
||||
state["messages"].append({
|
||||
"role": "user",
|
||||
"content": state["current_message"]
|
||||
})
|
||||
|
||||
state["state"] = ConversationState.INITIAL.value
|
||||
return state
|
||||
|
||||
|
||||
async def call_mcp_tools(state: AgentState) -> AgentState:
|
||||
"""Execute pending MCP tool calls
|
||||
|
||||
Calls the appropriate MCP server based on the tool_calls in state.
|
||||
"""
|
||||
if not state["tool_calls"]:
|
||||
logger.debug("No tool calls to execute")
|
||||
return state
|
||||
|
||||
logger.info(
|
||||
"Executing MCP tools",
|
||||
tool_count=len(state["tool_calls"])
|
||||
)
|
||||
|
||||
# MCP server URL mapping
|
||||
mcp_servers = {
|
||||
"strapi": settings.strapi_mcp_url,
|
||||
"order": settings.order_mcp_url,
|
||||
"aftersale": settings.aftersale_mcp_url,
|
||||
"product": settings.product_mcp_url
|
||||
}
|
||||
|
||||
async with httpx.AsyncClient(timeout=30.0) as client:
|
||||
for tool_call in state["tool_calls"]:
|
||||
server = tool_call["server"]
|
||||
tool_name = tool_call["tool_name"]
|
||||
arguments = tool_call["arguments"]
|
||||
|
||||
server_url = mcp_servers.get(server)
|
||||
if not server_url:
|
||||
state = add_tool_result(
|
||||
state,
|
||||
tool_name=tool_name,
|
||||
success=False,
|
||||
data=None,
|
||||
error=f"Unknown MCP server: {server}"
|
||||
)
|
||||
continue
|
||||
|
||||
try:
|
||||
# Call MCP tool endpoint
|
||||
response = await client.post(
|
||||
f"{server_url}/tools/{tool_name}",
|
||||
json=arguments
|
||||
)
|
||||
response.raise_for_status()
|
||||
|
||||
result = response.json()
|
||||
state = add_tool_result(
|
||||
state,
|
||||
tool_name=tool_name,
|
||||
success=True,
|
||||
data=result
|
||||
)
|
||||
|
||||
logger.debug(
|
||||
"Tool executed successfully",
|
||||
tool=tool_name,
|
||||
server=server
|
||||
)
|
||||
|
||||
except httpx.HTTPStatusError as e:
|
||||
logger.error(
|
||||
"Tool HTTP error",
|
||||
tool=tool_name,
|
||||
status=e.response.status_code
|
||||
)
|
||||
state = add_tool_result(
|
||||
state,
|
||||
tool_name=tool_name,
|
||||
success=False,
|
||||
data=None,
|
||||
error=f"HTTP {e.response.status_code}"
|
||||
)
|
||||
|
||||
except Exception as e:
|
||||
logger.error("Tool execution failed", tool=tool_name, error=str(e))
|
||||
state = add_tool_result(
|
||||
state,
|
||||
tool_name=tool_name,
|
||||
success=False,
|
||||
data=None,
|
||||
error=str(e)
|
||||
)
|
||||
|
||||
# Clear pending tool calls
|
||||
state["tool_calls"] = []
|
||||
|
||||
return state
|
||||
|
||||
|
||||
async def human_handoff(state: AgentState) -> AgentState:
|
||||
"""Handle transfer to human agent
|
||||
|
||||
Sets up the state for human intervention.
|
||||
"""
|
||||
logger.info(
|
||||
"Human handoff requested",
|
||||
conversation_id=state["conversation_id"],
|
||||
reason=state.get("handoff_reason")
|
||||
)
|
||||
|
||||
state["state"] = ConversationState.HUMAN_REVIEW.value
|
||||
|
||||
# Generate handoff message
|
||||
reason = state.get("handoff_reason", "您的问题需要人工客服协助")
|
||||
state = set_response(
|
||||
state,
|
||||
f"正在为您转接人工客服，请稍候。\n转接原因：{reason}\n\n人工客服将尽快为您服务。"
|
||||
)
|
||||
|
||||
return state
|
||||
|
||||
|
||||
async def send_response(state: AgentState) -> AgentState:
|
||||
"""Finalize and send response
|
||||
|
||||
This is the final node that marks processing as complete.
|
||||
"""
|
||||
logger.info(
|
||||
"Sending response",
|
||||
conversation_id=state["conversation_id"],
|
||||
response_length=len(state.get("response", ""))
|
||||
)
|
||||
|
||||
# Add assistant response to history
|
||||
if state.get("response"):
|
||||
state["messages"].append({
|
||||
"role": "assistant",
|
||||
"content": state["response"]
|
||||
})
|
||||
|
||||
state = mark_finished(state)
|
||||
return state
|
||||
|
||||
|
||||
async def handle_error(state: AgentState) -> AgentState:
|
||||
"""Handle errors in the workflow"""
|
||||
logger.error(
|
||||
"Workflow error",
|
||||
conversation_id=state["conversation_id"],
|
||||
error=state.get("error")
|
||||
)
|
||||
|
||||
state = set_response(
|
||||
state,
|
||||
"抱歉，处理您的请求时遇到了问题。请稍后重试，或联系人工客服获取帮助。"
|
||||
)
|
||||
state = mark_finished(state)
|
||||
return state
|
||||
|
||||
|
||||
# ============ Routing Functions ============
|
||||
|
||||
def should_call_tools(state: AgentState) -> Literal["call_tools", "send_response", "back_to_agent"]:
|
||||
"""Determine if tools need to be called"""
|
||||
|
||||
# If there are pending tool calls, execute them
|
||||
if state.get("tool_calls"):
|
||||
return "call_tools"
|
||||
|
||||
# If we have a response ready, send it
|
||||
if state.get("response"):
|
||||
return "send_response"
|
||||
|
||||
# If we're waiting for info, send the question
|
||||
if state.get("state") == ConversationState.AWAITING_INFO.value:
|
||||
return "send_response"
|
||||
|
||||
# Otherwise, something went wrong
|
||||
return "send_response"
|
||||
|
||||
|
||||
def after_tools(state: AgentState) -> str:
|
||||
"""Route after tool execution
|
||||
|
||||
Returns the agent that should process the tool results.
|
||||
"""
|
||||
current_agent = state.get("current_agent")
|
||||
|
||||
# Route back to the agent that made the tool call
|
||||
agent_mapping = {
|
||||
"customer_service": "customer_service_agent",
|
||||
"order": "order_agent",
|
||||
"aftersale": "aftersale_agent",
|
||||
"product": "product_agent"
|
||||
}
|
||||
|
||||
return agent_mapping.get(current_agent, "customer_service_agent")
|
||||
|
||||
|
||||
def check_completion(state: AgentState) -> Literal["continue", "end", "error"]:
|
||||
"""Check if workflow should continue or end"""
|
||||
|
||||
# Check for errors
|
||||
if state.get("error"):
|
||||
return "error"
|
||||
|
||||
# Check if finished
|
||||
if state.get("finished"):
|
||||
return "end"
|
||||
|
||||
# Check step limit
|
||||
if state.get("step_count", 0) >= state.get("max_steps", 10):
|
||||
logger.warning("Max steps reached", conversation_id=state["conversation_id"])
|
||||
return "end"
|
||||
|
||||
return "continue"
|
||||
|
||||
|
||||
# ============ Graph Construction ============
|
||||
|
||||
def create_agent_graph() -> StateGraph:
|
||||
"""Create the main agent workflow graph
|
||||
|
||||
Returns:
|
||||
Compiled LangGraph workflow
|
||||
"""
|
||||
# Create graph with AgentState
|
||||
graph = StateGraph(AgentState)
|
||||
|
||||
# Add nodes
|
||||
graph.add_node("receive", receive_message)
|
||||
graph.add_node("classify", classify_intent)
|
||||
graph.add_node("customer_service_agent", customer_service_agent)
|
||||
graph.add_node("order_agent", order_agent)
|
||||
graph.add_node("aftersale_agent", aftersale_agent)
|
||||
graph.add_node("product_agent", product_agent)
|
||||
graph.add_node("call_tools", call_mcp_tools)
|
||||
graph.add_node("human_handoff", human_handoff)
|
||||
graph.add_node("send_response", send_response)
|
||||
graph.add_node("handle_error", handle_error)
|
||||
|
||||
# Set entry point
|
||||
graph.set_entry_point("receive")
|
||||
|
||||
# Add edges
|
||||
graph.add_edge("receive", "classify")
|
||||
|
||||
# Conditional routing based on intent
|
||||
graph.add_conditional_edges(
|
||||
"classify",
|
||||
route_by_intent,
|
||||
{
|
||||
"customer_service_agent": "customer_service_agent",
|
||||
"order_agent": "order_agent",
|
||||
"aftersale_agent": "aftersale_agent",
|
||||
"product_agent": "product_agent",
|
||||
"human_handoff": "human_handoff"
|
||||
}
|
||||
)
|
||||
|
||||
# After each agent, check if tools need to be called
|
||||
for agent_node in ["customer_service_agent", "order_agent", "aftersale_agent", "product_agent"]:
|
||||
graph.add_conditional_edges(
|
||||
agent_node,
|
||||
should_call_tools,
|
||||
{
|
||||
"call_tools": "call_tools",
|
||||
"send_response": "send_response",
|
||||
"back_to_agent": agent_node
|
||||
}
|
||||
)
|
||||
|
||||
# After tool execution, route back to appropriate agent
|
||||
graph.add_conditional_edges(
|
||||
"call_tools",
|
||||
after_tools,
|
||||
{
|
||||
"customer_service_agent": "customer_service_agent",
|
||||
"order_agent": "order_agent",
|
||||
"aftersale_agent": "aftersale_agent",
|
||||
"product_agent": "product_agent"
|
||||
}
|
||||
)
|
||||
|
||||
# Human handoff leads to send response
|
||||
graph.add_edge("human_handoff", "send_response")
|
||||
|
||||
# Error handling
|
||||
graph.add_edge("handle_error", END)
|
||||
|
||||
# Final node
|
||||
graph.add_edge("send_response", END)
|
||||
|
||||
return graph.compile()
|
||||
|
||||
|
||||
# Global compiled graph
|
||||
_compiled_graph = None
|
||||
|
||||
|
||||
def get_agent_graph():
|
||||
"""Get or create the compiled agent graph"""
|
||||
global _compiled_graph
|
||||
if _compiled_graph is None:
|
||||
_compiled_graph = create_agent_graph()
|
||||
return _compiled_graph
|
||||
|
||||
|
||||
async def process_message(
|
||||
conversation_id: str,
|
||||
user_id: str,
|
||||
account_id: str,
|
||||
message: str,
|
||||
history: list[dict] = None,
|
||||
context: dict = None
|
||||
) -> AgentState:
|
||||
"""Process a user message through the agent workflow
|
||||
|
||||
Args:
|
||||
conversation_id: Chatwoot conversation ID
|
||||
user_id: User identifier
|
||||
account_id: B2B account identifier
|
||||
message: User's message
|
||||
history: Previous conversation history
|
||||
context: Existing conversation context
|
||||
|
||||
Returns:
|
||||
Final agent state with response
|
||||
"""
|
||||
from .state import create_initial_state
|
||||
|
||||
# Create initial state
|
||||
initial_state = create_initial_state(
|
||||
conversation_id=conversation_id,
|
||||
user_id=user_id,
|
||||
account_id=account_id,
|
||||
current_message=message,
|
||||
messages=history,
|
||||
context=context
|
||||
)
|
||||
|
||||
# Get compiled graph
|
||||
graph = get_agent_graph()
|
||||
|
||||
# Run the workflow
|
||||
logger.info(
|
||||
"Starting workflow",
|
||||
conversation_id=conversation_id,
|
||||
message=message[:100]
|
||||
)
|
||||
|
||||
try:
|
||||
final_state = await graph.ainvoke(initial_state)
|
||||
|
||||
logger.info(
|
||||
"Workflow completed",
|
||||
conversation_id=conversation_id,
|
||||
intent=final_state.get("intent"),
|
||||
steps=final_state.get("step_count")
|
||||
)
|
||||
|
||||
return final_state
|
||||
|
||||
except Exception as e:
|
||||
logger.error("Workflow failed", error=str(e))
|
||||
initial_state["error"] = str(e)
|
||||
initial_state["response"] = "抱歉，处理您的请求时遇到了问题。请稍后重试。"
|
||||
initial_state["finished"] = True
|
||||
return initial_state
|
||||
195
agent/core/llm.py
Normal file
195
agent/core/llm.py
Normal file
@@ -0,0 +1,195 @@
|
||||
"""
|
||||
ZhipuAI LLM Client for B2B Shopping AI Assistant
|
||||
"""
|
||||
from typing import Any, AsyncGenerator, Optional
|
||||
from dataclasses import dataclass
|
||||
|
||||
from zhipuai import ZhipuAI
|
||||
|
||||
from config import settings
|
||||
from utils.logger import get_logger
|
||||
|
||||
logger = get_logger(__name__)
|
||||
|
||||
|
||||
@dataclass
|
||||
class Message:
|
||||
"""Chat message structure"""
|
||||
role: str # "system", "user", "assistant"
|
||||
content: str
|
||||
|
||||
|
||||
@dataclass
|
||||
class LLMResponse:
|
||||
"""LLM response structure"""
|
||||
content: str
|
||||
finish_reason: str
|
||||
usage: dict[str, int]
|
||||
|
||||
|
||||
class ZhipuLLMClient:
|
||||
"""ZhipuAI LLM Client wrapper"""
|
||||
|
||||
def __init__(
|
||||
self,
|
||||
api_key: Optional[str] = None,
|
||||
model: Optional[str] = None
|
||||
):
|
||||
"""Initialize ZhipuAI client
|
||||
|
||||
Args:
|
||||
api_key: ZhipuAI API key, defaults to settings
|
||||
model: Model name, defaults to settings
|
||||
"""
|
||||
self.api_key = api_key or settings.zhipu_api_key
|
||||
self.model = model or settings.zhipu_model
|
||||
self._client = ZhipuAI(api_key=self.api_key)
|
||||
logger.info("ZhipuAI client initialized", model=self.model)
|
||||
|
||||
async def chat(
|
||||
self,
|
||||
messages: list[Message],
|
||||
temperature: float = 0.7,
|
||||
max_tokens: int = 2048,
|
||||
top_p: float = 0.9,
|
||||
**kwargs: Any
|
||||
) -> LLMResponse:
|
||||
"""Send chat completion request
|
||||
|
||||
Args:
|
||||
messages: List of chat messages
|
||||
temperature: Sampling temperature
|
||||
max_tokens: Maximum tokens to generate
|
||||
top_p: Top-p sampling parameter
|
||||
**kwargs: Additional parameters
|
||||
|
||||
Returns:
|
||||
LLM response with content and metadata
|
||||
"""
|
||||
formatted_messages = [
|
||||
{"role": msg.role, "content": msg.content}
|
||||
for msg in messages
|
||||
]
|
||||
|
||||
logger.debug(
|
||||
"Sending chat request",
|
||||
model=self.model,
|
||||
message_count=len(messages),
|
||||
temperature=temperature
|
||||
)
|
||||
|
||||
try:
|
||||
response = self._client.chat.completions.create(
|
||||
model=self.model,
|
||||
messages=formatted_messages,
|
||||
temperature=temperature,
|
||||
max_tokens=max_tokens,
|
||||
top_p=top_p,
|
||||
**kwargs
|
||||
)
|
||||
|
||||
choice = response.choices[0]
|
||||
result = LLMResponse(
|
||||
content=choice.message.content,
|
||||
finish_reason=choice.finish_reason,
|
||||
usage={
|
||||
"prompt_tokens": response.usage.prompt_tokens,
|
||||
"completion_tokens": response.usage.completion_tokens,
|
||||
"total_tokens": response.usage.total_tokens
|
||||
}
|
||||
)
|
||||
|
||||
logger.debug(
|
||||
"Chat response received",
|
||||
finish_reason=result.finish_reason,
|
||||
total_tokens=result.usage["total_tokens"]
|
||||
)
|
||||
|
||||
return result
|
||||
|
||||
except Exception as e:
|
||||
logger.error("Chat request failed", error=str(e))
|
||||
raise
|
||||
|
||||
async def chat_with_tools(
|
||||
self,
|
||||
messages: list[Message],
|
||||
tools: list[dict[str, Any]],
|
||||
temperature: float = 0.7,
|
||||
**kwargs: Any
|
||||
) -> tuple[LLMResponse, Optional[list[dict[str, Any]]]]:
|
||||
"""Send chat completion request with tool calling
|
||||
|
||||
Args:
|
||||
messages: List of chat messages
|
||||
tools: List of tool definitions
|
||||
temperature: Sampling temperature
|
||||
**kwargs: Additional parameters
|
||||
|
||||
Returns:
|
||||
Tuple of (LLM response, tool calls if any)
|
||||
"""
|
||||
formatted_messages = [
|
||||
{"role": msg.role, "content": msg.content}
|
||||
for msg in messages
|
||||
]
|
||||
|
||||
logger.debug(
|
||||
"Sending chat request with tools",
|
||||
model=self.model,
|
||||
tool_count=len(tools)
|
||||
)
|
||||
|
||||
try:
|
||||
response = self._client.chat.completions.create(
|
||||
model=self.model,
|
||||
messages=formatted_messages,
|
||||
tools=tools,
|
||||
temperature=temperature,
|
||||
**kwargs
|
||||
)
|
||||
|
||||
choice = response.choices[0]
|
||||
result = LLMResponse(
|
||||
content=choice.message.content or "",
|
||||
finish_reason=choice.finish_reason,
|
||||
usage={
|
||||
"prompt_tokens": response.usage.prompt_tokens,
|
||||
"completion_tokens": response.usage.completion_tokens,
|
||||
"total_tokens": response.usage.total_tokens
|
||||
}
|
||||
)
|
||||
|
||||
# Extract tool calls if present
|
||||
tool_calls = None
|
||||
if hasattr(choice.message, 'tool_calls') and choice.message.tool_calls:
|
||||
tool_calls = [
|
||||
{
|
||||
"id": tc.id,
|
||||
"type": tc.type,
|
||||
"function": {
|
||||
"name": tc.function.name,
|
||||
"arguments": tc.function.arguments
|
||||
}
|
||||
}
|
||||
for tc in choice.message.tool_calls
|
||||
]
|
||||
logger.debug("Tool calls received", tool_count=len(tool_calls))
|
||||
|
||||
return result, tool_calls
|
||||
|
||||
except Exception as e:
|
||||
logger.error("Chat with tools request failed", error=str(e))
|
||||
raise
|
||||
|
||||
|
||||
# Global LLM client instance
|
||||
llm_client: Optional[ZhipuLLMClient] = None
|
||||
|
||||
|
||||
def get_llm_client() -> ZhipuLLMClient:
|
||||
"""Get or create global LLM client instance"""
|
||||
global llm_client
|
||||
if llm_client is None:
|
||||
llm_client = ZhipuLLMClient()
|
||||
return llm_client
|
||||
272
agent/core/state.py
Normal file
272
agent/core/state.py
Normal file
@@ -0,0 +1,272 @@
|
||||
"""
|
||||
Agent state definitions for LangGraph workflow
|
||||
"""
|
||||
from typing import Any, Optional, Literal
|
||||
from typing_extensions import TypedDict, Annotated
|
||||
from dataclasses import dataclass, field
|
||||
from enum import Enum
|
||||
|
||||
|
||||
class Intent(str, Enum):
|
||||
"""User intent categories"""
|
||||
CUSTOMER_SERVICE = "customer_service" # FAQ, general inquiries
|
||||
ORDER = "order" # Order query, tracking, modify, cancel
|
||||
AFTERSALE = "aftersale" # Return, exchange, complaint
|
||||
PRODUCT = "product" # Search, recommend, quote
|
||||
HUMAN_HANDOFF = "human_handoff" # Transfer to human agent
|
||||
UNKNOWN = "unknown" # Cannot determine intent
|
||||
|
||||
|
||||
class ConversationState(str, Enum):
|
||||
"""Conversation state machine"""
|
||||
INITIAL = "initial"
|
||||
CLASSIFYING = "classifying"
|
||||
PROCESSING = "processing"
|
||||
AWAITING_INFO = "awaiting_info"
|
||||
TOOL_CALLING = "tool_calling"
|
||||
GENERATING = "generating"
|
||||
HUMAN_REVIEW = "human_review"
|
||||
COMPLETED = "completed"
|
||||
ERROR = "error"
|
||||
|
||||
|
||||
@dataclass
|
||||
class Entity:
|
||||
"""Extracted entity from user message"""
|
||||
type: str # Entity type (order_id, product_id, date, etc.)
|
||||
value: Any # Entity value
|
||||
confidence: float # Extraction confidence (0-1)
|
||||
|
||||
|
||||
@dataclass
|
||||
class ToolCall:
|
||||
"""MCP tool call record"""
|
||||
tool_name: str
|
||||
arguments: dict[str, Any]
|
||||
server: str # MCP server name (strapi, order, aftersale, product)
|
||||
|
||||
|
||||
@dataclass
|
||||
class ToolResult:
|
||||
"""MCP tool execution result"""
|
||||
tool_name: str
|
||||
success: bool
|
||||
data: Any
|
||||
error: Optional[str] = None
|
||||
|
||||
|
||||
class AgentState(TypedDict):
|
||||
"""Main agent state for LangGraph workflow
|
||||
|
||||
This state is passed through all nodes in the workflow graph.
|
||||
"""
|
||||
|
||||
# ============ Session Information ============
|
||||
conversation_id: str # Chatwoot conversation ID
|
||||
user_id: str # User identifier
|
||||
account_id: str # B2B account identifier
|
||||
|
||||
# ============ Message Content ============
|
||||
messages: list[dict[str, Any]] # Conversation history [{role, content}]
|
||||
current_message: str # Current user message being processed
|
||||
|
||||
# ============ Intent Recognition ============
|
||||
intent: Optional[str] # Recognized intent (Intent enum value)
|
||||
intent_confidence: float # Intent confidence score (0-1)
|
||||
sub_intent: Optional[str] # Sub-intent for more specific routing
|
||||
|
||||
# ============ Entity Extraction ============
|
||||
entities: dict[str, Any] # Extracted entities {type: value}
|
||||
|
||||
# ============ Agent Routing ============
|
||||
current_agent: Optional[str] # Current processing agent name
|
||||
agent_history: list[str] # History of agents involved
|
||||
|
||||
# ============ Tool Calling ============
|
||||
tool_calls: list[dict[str, Any]] # Pending tool calls
|
||||
tool_results: list[dict[str, Any]] # Tool execution results
|
||||
|
||||
# ============ Response Generation ============
|
||||
response: Optional[str] # Generated response text
|
||||
response_type: str # Response type (text, rich, action)
|
||||
|
||||
# ============ Human Handoff ============
|
||||
requires_human: bool # Whether human intervention is needed
|
||||
handoff_reason: Optional[str] # Reason for human handoff
|
||||
|
||||
# ============ Conversation Context ============
|
||||
context: dict[str, Any] # Accumulated context (order details, etc.)
|
||||
|
||||
# ============ State Control ============
|
||||
state: str # Current conversation state
|
||||
step_count: int # Number of steps taken
|
||||
max_steps: int # Maximum allowed steps
|
||||
error: Optional[str] # Error message if any
|
||||
finished: bool # Whether processing is complete
|
||||
|
||||
|
||||
def create_initial_state(
|
||||
conversation_id: str,
|
||||
user_id: str,
|
||||
account_id: str,
|
||||
current_message: str,
|
||||
messages: Optional[list[dict[str, Any]]] = None,
|
||||
context: Optional[dict[str, Any]] = None
|
||||
) -> AgentState:
|
||||
"""Create initial agent state for a new message
|
||||
|
||||
Args:
|
||||
conversation_id: Chatwoot conversation ID
|
||||
user_id: User identifier
|
||||
account_id: B2B account identifier
|
||||
current_message: User's message to process
|
||||
messages: Previous conversation history
|
||||
context: Existing conversation context
|
||||
|
||||
Returns:
|
||||
Initialized AgentState
|
||||
"""
|
||||
return AgentState(
|
||||
# Session
|
||||
conversation_id=conversation_id,
|
||||
user_id=user_id,
|
||||
account_id=account_id,
|
||||
|
||||
# Messages
|
||||
messages=messages or [],
|
||||
current_message=current_message,
|
||||
|
||||
# Intent
|
||||
intent=None,
|
||||
intent_confidence=0.0,
|
||||
sub_intent=None,
|
||||
|
||||
# Entities
|
||||
entities={},
|
||||
|
||||
# Routing
|
||||
current_agent=None,
|
||||
agent_history=[],
|
||||
|
||||
# Tools
|
||||
tool_calls=[],
|
||||
tool_results=[],
|
||||
|
||||
# Response
|
||||
response=None,
|
||||
response_type="text",
|
||||
|
||||
# Human handoff
|
||||
requires_human=False,
|
||||
handoff_reason=None,
|
||||
|
||||
# Context
|
||||
context=context or {},
|
||||
|
||||
# Control
|
||||
state=ConversationState.INITIAL.value,
|
||||
step_count=0,
|
||||
max_steps=10,
|
||||
error=None,
|
||||
finished=False
|
||||
)
|
||||
|
||||
|
||||
# ============ State Update Helpers ============
|
||||
|
||||
def add_message(state: AgentState, role: str, content: str) -> AgentState:
|
||||
"""Add a message to the conversation history"""
|
||||
state["messages"].append({"role": role, "content": content})
|
||||
return state
|
||||
|
||||
|
||||
def set_intent(
|
||||
state: AgentState,
|
||||
intent: Intent,
|
||||
confidence: float,
|
||||
sub_intent: Optional[str] = None
|
||||
) -> AgentState:
|
||||
"""Set the recognized intent"""
|
||||
state["intent"] = intent.value
|
||||
state["intent_confidence"] = confidence
|
||||
state["sub_intent"] = sub_intent
|
||||
return state
|
||||
|
||||
|
||||
def add_entity(state: AgentState, entity_type: str, value: Any) -> AgentState:
|
||||
"""Add an extracted entity"""
|
||||
state["entities"][entity_type] = value
|
||||
return state
|
||||
|
||||
|
||||
def add_tool_call(
|
||||
state: AgentState,
|
||||
tool_name: str,
|
||||
arguments: dict[str, Any],
|
||||
server: str
|
||||
) -> AgentState:
|
||||
"""Add a tool call to pending calls"""
|
||||
state["tool_calls"].append({
|
||||
"tool_name": tool_name,
|
||||
"arguments": arguments,
|
||||
"server": server
|
||||
})
|
||||
return state
|
||||
|
||||
|
||||
def add_tool_result(
|
||||
state: AgentState,
|
||||
tool_name: str,
|
||||
success: bool,
|
||||
data: Any,
|
||||
error: Optional[str] = None
|
||||
) -> AgentState:
|
||||
"""Add a tool execution result"""
|
||||
state["tool_results"].append({
|
||||
"tool_name": tool_name,
|
||||
"success": success,
|
||||
"data": data,
|
||||
"error": error
|
||||
})
|
||||
return state
|
||||
|
||||
|
||||
def set_response(
|
||||
state: AgentState,
|
||||
response: str,
|
||||
response_type: str = "text"
|
||||
) -> AgentState:
|
||||
"""Set the generated response"""
|
||||
state["response"] = response
|
||||
state["response_type"] = response_type
|
||||
return state
|
||||
|
||||
|
||||
def request_human_handoff(
|
||||
state: AgentState,
|
||||
reason: str
|
||||
) -> AgentState:
|
||||
"""Request transfer to human agent"""
|
||||
state["requires_human"] = True
|
||||
state["handoff_reason"] = reason
|
||||
return state
|
||||
|
||||
|
||||
def update_context(state: AgentState, updates: dict[str, Any]) -> AgentState:
|
||||
"""Update conversation context"""
|
||||
state["context"].update(updates)
|
||||
return state
|
||||
|
||||
|
||||
def set_error(state: AgentState, error: str) -> AgentState:
|
||||
"""Set error state"""
|
||||
state["error"] = error
|
||||
state["state"] = ConversationState.ERROR.value
|
||||
return state
|
||||
|
||||
|
||||
def mark_finished(state: AgentState) -> AgentState:
|
||||
"""Mark processing as complete"""
|
||||
state["finished"] = True
|
||||
state["state"] = ConversationState.COMPLETED.value
|
||||
return state
|
||||
14
agent/integrations/__init__.py
Normal file
14
agent/integrations/__init__.py
Normal file
@@ -0,0 +1,14 @@
|
||||
"""Agent integrations package"""
|
||||
from .chatwoot import ChatwootClient, get_chatwoot_client, MessageType, ConversationStatus
|
||||
from .hyperf_client import HyperfClient, get_hyperf_client, APIResponse, APIError
|
||||
|
||||
__all__ = [
|
||||
"ChatwootClient",
|
||||
"get_chatwoot_client",
|
||||
"MessageType",
|
||||
"ConversationStatus",
|
||||
"HyperfClient",
|
||||
"get_hyperf_client",
|
||||
"APIResponse",
|
||||
"APIError",
|
||||
]
|
||||
354
agent/integrations/chatwoot.py
Normal file
354
agent/integrations/chatwoot.py
Normal file
@@ -0,0 +1,354 @@
|
||||
"""
|
||||
Chatwoot API Client for B2B Shopping AI Assistant
|
||||
"""
|
||||
from typing import Any, Optional
|
||||
from dataclasses import dataclass
|
||||
from enum import Enum
|
||||
|
||||
import httpx
|
||||
|
||||
from config import settings
|
||||
from utils.logger import get_logger
|
||||
|
||||
logger = get_logger(__name__)
|
||||
|
||||
|
||||
class MessageType(str, Enum):
|
||||
"""Chatwoot message types"""
|
||||
INCOMING = "incoming"
|
||||
OUTGOING = "outgoing"
|
||||
|
||||
|
||||
class ConversationStatus(str, Enum):
|
||||
"""Chatwoot conversation status"""
|
||||
OPEN = "open"
|
||||
RESOLVED = "resolved"
|
||||
PENDING = "pending"
|
||||
SNOOZED = "snoozed"
|
||||
|
||||
|
||||
@dataclass
|
||||
class ChatwootMessage:
|
||||
"""Chatwoot message structure"""
|
||||
id: int
|
||||
content: str
|
||||
message_type: str
|
||||
conversation_id: int
|
||||
sender_type: Optional[str] = None
|
||||
sender_id: Optional[int] = None
|
||||
private: bool = False
|
||||
|
||||
|
||||
@dataclass
|
||||
class ChatwootContact:
|
||||
"""Chatwoot contact structure"""
|
||||
id: int
|
||||
name: Optional[str] = None
|
||||
email: Optional[str] = None
|
||||
phone_number: Optional[str] = None
|
||||
custom_attributes: Optional[dict[str, Any]] = None
|
||||
|
||||
|
||||
class ChatwootClient:
|
||||
"""Chatwoot API Client"""
|
||||
|
||||
def __init__(
|
||||
self,
|
||||
api_url: Optional[str] = None,
|
||||
api_token: Optional[str] = None,
|
||||
account_id: int = 1
|
||||
):
|
||||
"""Initialize Chatwoot client
|
||||
|
||||
Args:
|
||||
api_url: Chatwoot API URL, defaults to settings
|
||||
api_token: API access token, defaults to settings
|
||||
account_id: Chatwoot account ID
|
||||
"""
|
||||
self.api_url = (api_url or settings.chatwoot_api_url).rstrip("/")
|
||||
self.api_token = api_token or settings.chatwoot_api_token
|
||||
self.account_id = account_id
|
||||
self._client: Optional[httpx.AsyncClient] = None
|
||||
|
||||
logger.info("Chatwoot client initialized", api_url=self.api_url)
|
||||
|
||||
async def _get_client(self) -> httpx.AsyncClient:
|
||||
"""Get or create HTTP client"""
|
||||
if self._client is None:
|
||||
self._client = httpx.AsyncClient(
|
||||
base_url=f"{self.api_url}/api/v1/accounts/{self.account_id}",
|
||||
headers={
|
||||
"api_access_token": self.api_token,
|
||||
"Content-Type": "application/json"
|
||||
},
|
||||
timeout=30.0
|
||||
)
|
||||
return self._client
|
||||
|
||||
async def close(self) -> None:
|
||||
"""Close HTTP client"""
|
||||
if self._client:
|
||||
await self._client.aclose()
|
||||
self._client = None
|
||||
|
||||
# ============ Messages ============
|
||||
|
||||
async def send_message(
|
||||
self,
|
||||
conversation_id: int,
|
||||
content: str,
|
||||
message_type: MessageType = MessageType.OUTGOING,
|
||||
private: bool = False
|
||||
) -> dict[str, Any]:
|
||||
"""Send a message to a conversation
|
||||
|
||||
Args:
|
||||
conversation_id: Conversation ID
|
||||
content: Message content
|
||||
message_type: Message type (incoming/outgoing)
|
||||
private: Whether message is private (internal note)
|
||||
|
||||
Returns:
|
||||
Created message data
|
||||
"""
|
||||
client = await self._get_client()
|
||||
|
||||
payload = {
|
||||
"content": content,
|
||||
"message_type": message_type.value,
|
||||
"private": private
|
||||
}
|
||||
|
||||
response = await client.post(
|
||||
f"/conversations/{conversation_id}/messages",
|
||||
json=payload
|
||||
)
|
||||
response.raise_for_status()
|
||||
|
||||
data = response.json()
|
||||
logger.debug(
|
||||
"Message sent",
|
||||
conversation_id=conversation_id,
|
||||
message_id=data.get("id")
|
||||
)
|
||||
return data
|
||||
|
||||
async def send_rich_message(
|
||||
self,
|
||||
conversation_id: int,
|
||||
content: str,
|
||||
content_type: str,
|
||||
content_attributes: dict[str, Any]
|
||||
) -> dict[str, Any]:
|
||||
"""Send a rich message (cards, buttons, etc.)
|
||||
|
||||
Args:
|
||||
conversation_id: Conversation ID
|
||||
content: Fallback text content
|
||||
content_type: Rich content type (cards, input_select, etc.)
|
||||
content_attributes: Rich content attributes
|
||||
|
||||
Returns:
|
||||
Created message data
|
||||
"""
|
||||
client = await self._get_client()
|
||||
|
||||
payload = {
|
||||
"content": content,
|
||||
"message_type": MessageType.OUTGOING.value,
|
||||
"content_type": content_type,
|
||||
"content_attributes": content_attributes
|
||||
}
|
||||
|
||||
response = await client.post(
|
||||
f"/conversations/{conversation_id}/messages",
|
||||
json=payload
|
||||
)
|
||||
response.raise_for_status()
|
||||
|
||||
return response.json()
|
||||
|
||||
# ============ Conversations ============
|
||||
|
||||
async def get_conversation(self, conversation_id: int) -> dict[str, Any]:
|
||||
"""Get conversation details
|
||||
|
||||
Args:
|
||||
conversation_id: Conversation ID
|
||||
|
||||
Returns:
|
||||
Conversation data
|
||||
"""
|
||||
client = await self._get_client()
|
||||
|
||||
response = await client.get(f"/conversations/{conversation_id}")
|
||||
response.raise_for_status()
|
||||
|
||||
return response.json()
|
||||
|
||||
async def update_conversation_status(
|
||||
self,
|
||||
conversation_id: int,
|
||||
status: ConversationStatus
|
||||
) -> dict[str, Any]:
|
||||
"""Update conversation status
|
||||
|
||||
Args:
|
||||
conversation_id: Conversation ID
|
||||
status: New status
|
||||
|
||||
Returns:
|
||||
Updated conversation data
|
||||
"""
|
||||
client = await self._get_client()
|
||||
|
||||
response = await client.post(
|
||||
f"/conversations/{conversation_id}/toggle_status",
|
||||
json={"status": status.value}
|
||||
)
|
||||
response.raise_for_status()
|
||||
|
||||
logger.info(
|
||||
"Conversation status updated",
|
||||
conversation_id=conversation_id,
|
||||
status=status.value
|
||||
)
|
||||
return response.json()
|
||||
|
||||
async def add_labels(
|
||||
self,
|
||||
conversation_id: int,
|
||||
labels: list[str]
|
||||
) -> dict[str, Any]:
|
||||
"""Add labels to a conversation
|
||||
|
||||
Args:
|
||||
conversation_id: Conversation ID
|
||||
labels: List of label names
|
||||
|
||||
Returns:
|
||||
Updated labels
|
||||
"""
|
||||
client = await self._get_client()
|
||||
|
||||
response = await client.post(
|
||||
f"/conversations/{conversation_id}/labels",
|
||||
json={"labels": labels}
|
||||
)
|
||||
response.raise_for_status()
|
||||
|
||||
return response.json()
|
||||
|
||||
async def assign_agent(
|
||||
self,
|
||||
conversation_id: int,
|
||||
agent_id: int
|
||||
) -> dict[str, Any]:
|
||||
"""Assign an agent to a conversation
|
||||
|
||||
Args:
|
||||
conversation_id: Conversation ID
|
||||
agent_id: Agent user ID
|
||||
|
||||
Returns:
|
||||
Assignment result
|
||||
"""
|
||||
client = await self._get_client()
|
||||
|
||||
response = await client.post(
|
||||
f"/conversations/{conversation_id}/assignments",
|
||||
json={"assignee_id": agent_id}
|
||||
)
|
||||
response.raise_for_status()
|
||||
|
||||
logger.info(
|
||||
"Agent assigned",
|
||||
conversation_id=conversation_id,
|
||||
agent_id=agent_id
|
||||
)
|
||||
return response.json()
|
||||
|
||||
# ============ Contacts ============
|
||||
|
||||
async def get_contact(self, contact_id: int) -> dict[str, Any]:
|
||||
"""Get contact details
|
||||
|
||||
Args:
|
||||
contact_id: Contact ID
|
||||
|
||||
Returns:
|
||||
Contact data
|
||||
"""
|
||||
client = await self._get_client()
|
||||
|
||||
response = await client.get(f"/contacts/{contact_id}")
|
||||
response.raise_for_status()
|
||||
|
||||
return response.json()
|
||||
|
||||
async def update_contact(
|
||||
self,
|
||||
contact_id: int,
|
||||
attributes: dict[str, Any]
|
||||
) -> dict[str, Any]:
|
||||
"""Update contact attributes
|
||||
|
||||
Args:
|
||||
contact_id: Contact ID
|
||||
attributes: Attributes to update
|
||||
|
||||
Returns:
|
||||
Updated contact data
|
||||
"""
|
||||
client = await self._get_client()
|
||||
|
||||
response = await client.put(
|
||||
f"/contacts/{contact_id}",
|
||||
json=attributes
|
||||
)
|
||||
response.raise_for_status()
|
||||
|
||||
return response.json()
|
||||
|
||||
# ============ Messages History ============
|
||||
|
||||
async def get_messages(
|
||||
self,
|
||||
conversation_id: int,
|
||||
before: Optional[int] = None
|
||||
) -> list[dict[str, Any]]:
|
||||
"""Get conversation messages
|
||||
|
||||
Args:
|
||||
conversation_id: Conversation ID
|
||||
before: Get messages before this message ID
|
||||
|
||||
Returns:
|
||||
List of messages
|
||||
"""
|
||||
client = await self._get_client()
|
||||
|
||||
params = {}
|
||||
if before:
|
||||
params["before"] = before
|
||||
|
||||
response = await client.get(
|
||||
f"/conversations/{conversation_id}/messages",
|
||||
params=params
|
||||
)
|
||||
response.raise_for_status()
|
||||
|
||||
data = response.json()
|
||||
return data.get("payload", [])
|
||||
|
||||
|
||||
# Global Chatwoot client instance
|
||||
chatwoot_client: Optional[ChatwootClient] = None
|
||||
|
||||
|
||||
def get_chatwoot_client() -> ChatwootClient:
|
||||
"""Get or create global Chatwoot client instance"""
|
||||
global chatwoot_client
|
||||
if chatwoot_client is None:
|
||||
chatwoot_client = ChatwootClient()
|
||||
return chatwoot_client
|
||||
538
agent/integrations/hyperf_client.py
Normal file
538
agent/integrations/hyperf_client.py
Normal file
@@ -0,0 +1,538 @@
|
||||
"""
|
||||
Hyperf PHP API Client for B2B Shopping AI Assistant
|
||||
"""
|
||||
from typing import Any, Optional
|
||||
from dataclasses import dataclass
|
||||
from enum import Enum
|
||||
|
||||
import httpx
|
||||
|
||||
from config import settings
|
||||
from utils.logger import get_logger
|
||||
|
||||
logger = get_logger(__name__)
|
||||
|
||||
|
||||
class APIError(Exception):
|
||||
"""API error with code and message"""
|
||||
def __init__(self, code: int, message: str, data: Optional[Any] = None):
|
||||
self.code = code
|
||||
self.message = message
|
||||
self.data = data
|
||||
super().__init__(f"[{code}] {message}")
|
||||
|
||||
|
||||
@dataclass
|
||||
class APIResponse:
|
||||
"""Standardized API response"""
|
||||
code: int
|
||||
message: str
|
||||
data: Any
|
||||
meta: Optional[dict[str, Any]] = None
|
||||
|
||||
@property
|
||||
def success(self) -> bool:
|
||||
return self.code == 0
|
||||
|
||||
|
||||
class HyperfClient:
|
||||
"""Hyperf PHP API Client"""
|
||||
|
||||
def __init__(
|
||||
self,
|
||||
api_url: Optional[str] = None,
|
||||
api_token: Optional[str] = None
|
||||
):
|
||||
"""Initialize Hyperf client
|
||||
|
||||
Args:
|
||||
api_url: Hyperf API base URL, defaults to settings
|
||||
api_token: API access token, defaults to settings
|
||||
"""
|
||||
self.api_url = (api_url or settings.hyperf_api_url).rstrip("/")
|
||||
self.api_token = api_token or settings.hyperf_api_token
|
||||
self._client: Optional[httpx.AsyncClient] = None
|
||||
|
||||
logger.info("Hyperf client initialized", api_url=self.api_url)
|
||||
|
||||
async def _get_client(self) -> httpx.AsyncClient:
|
||||
"""Get or create HTTP client"""
|
||||
if self._client is None:
|
||||
self._client = httpx.AsyncClient(
|
||||
base_url=f"{self.api_url}/api/v1",
|
||||
headers={
|
||||
"Authorization": f"Bearer {self.api_token}",
|
||||
"Content-Type": "application/json",
|
||||
"Accept": "application/json"
|
||||
},
|
||||
timeout=30.0
|
||||
)
|
||||
return self._client
|
||||
|
||||
async def close(self) -> None:
|
||||
"""Close HTTP client"""
|
||||
if self._client:
|
||||
await self._client.aclose()
|
||||
self._client = None
|
||||
|
||||
async def _request(
|
||||
self,
|
||||
method: str,
|
||||
endpoint: str,
|
||||
params: Optional[dict[str, Any]] = None,
|
||||
json: Optional[dict[str, Any]] = None,
|
||||
headers: Optional[dict[str, str]] = None
|
||||
) -> APIResponse:
|
||||
"""Make API request
|
||||
|
||||
Args:
|
||||
method: HTTP method
|
||||
endpoint: API endpoint
|
||||
params: Query parameters
|
||||
json: JSON body
|
||||
headers: Additional headers
|
||||
|
||||
Returns:
|
||||
Parsed API response
|
||||
|
||||
Raises:
|
||||
APIError: If API returns error
|
||||
"""
|
||||
client = await self._get_client()
|
||||
|
||||
# Merge headers
|
||||
request_headers = {}
|
||||
if headers:
|
||||
request_headers.update(headers)
|
||||
|
||||
logger.debug(
|
||||
"API request",
|
||||
method=method,
|
||||
endpoint=endpoint
|
||||
)
|
||||
|
||||
try:
|
||||
response = await client.request(
|
||||
method=method,
|
||||
url=endpoint,
|
||||
params=params,
|
||||
json=json,
|
||||
headers=request_headers
|
||||
)
|
||||
response.raise_for_status()
|
||||
|
||||
data = response.json()
|
||||
|
||||
result = APIResponse(
|
||||
code=data.get("code", 0),
|
||||
message=data.get("message", "success"),
|
||||
data=data.get("data"),
|
||||
meta=data.get("meta")
|
||||
)
|
||||
|
||||
if not result.success:
|
||||
raise APIError(result.code, result.message, result.data)
|
||||
|
||||
logger.debug(
|
||||
"API response",
|
||||
endpoint=endpoint,
|
||||
code=result.code
|
||||
)
|
||||
|
||||
return result
|
||||
|
||||
except httpx.HTTPStatusError as e:
|
||||
logger.error(
|
||||
"HTTP error",
|
||||
endpoint=endpoint,
|
||||
status_code=e.response.status_code
|
||||
)
|
||||
raise APIError(
|
||||
e.response.status_code,
|
||||
f"HTTP error: {e.response.status_code}"
|
||||
)
|
||||
except Exception as e:
|
||||
logger.error("API request failed", endpoint=endpoint, error=str(e))
|
||||
raise
|
||||
|
||||
async def get(
|
||||
self,
|
||||
endpoint: str,
|
||||
params: Optional[dict[str, Any]] = None,
|
||||
**kwargs: Any
|
||||
) -> APIResponse:
|
||||
"""GET request"""
|
||||
return await self._request("GET", endpoint, params=params, **kwargs)
|
||||
|
||||
async def post(
|
||||
self,
|
||||
endpoint: str,
|
||||
json: Optional[dict[str, Any]] = None,
|
||||
**kwargs: Any
|
||||
) -> APIResponse:
|
||||
"""POST request"""
|
||||
return await self._request("POST", endpoint, json=json, **kwargs)
|
||||
|
||||
async def put(
|
||||
self,
|
||||
endpoint: str,
|
||||
json: Optional[dict[str, Any]] = None,
|
||||
**kwargs: Any
|
||||
) -> APIResponse:
|
||||
"""PUT request"""
|
||||
return await self._request("PUT", endpoint, json=json, **kwargs)
|
||||
|
||||
async def delete(
|
||||
self,
|
||||
endpoint: str,
|
||||
**kwargs: Any
|
||||
) -> APIResponse:
|
||||
"""DELETE request"""
|
||||
return await self._request("DELETE", endpoint, **kwargs)
|
||||
|
||||
# ============ Order APIs ============
|
||||
|
||||
async def query_orders(
|
||||
self,
|
||||
user_id: str,
|
||||
account_id: str,
|
||||
order_id: Optional[str] = None,
|
||||
status: Optional[str] = None,
|
||||
date_start: Optional[str] = None,
|
||||
date_end: Optional[str] = None,
|
||||
page: int = 1,
|
||||
page_size: int = 20
|
||||
) -> APIResponse:
|
||||
"""Query orders
|
||||
|
||||
Args:
|
||||
user_id: User ID
|
||||
account_id: Account ID
|
||||
order_id: Optional specific order ID
|
||||
status: Optional order status filter
|
||||
date_start: Optional start date (YYYY-MM-DD)
|
||||
date_end: Optional end date (YYYY-MM-DD)
|
||||
page: Page number
|
||||
page_size: Items per page
|
||||
|
||||
Returns:
|
||||
Orders list response
|
||||
"""
|
||||
payload = {
|
||||
"user_id": user_id,
|
||||
"account_id": account_id,
|
||||
"page": page,
|
||||
"page_size": page_size
|
||||
}
|
||||
|
||||
if order_id:
|
||||
payload["order_id"] = order_id
|
||||
if status:
|
||||
payload["status"] = status
|
||||
if date_start:
|
||||
payload["date_range"] = {"start": date_start}
|
||||
if date_end:
|
||||
payload.setdefault("date_range", {})["end"] = date_end
|
||||
|
||||
return await self.post("/orders/query", json=payload)
|
||||
|
||||
async def get_logistics(
|
||||
self,
|
||||
order_id: str,
|
||||
tracking_number: Optional[str] = None
|
||||
) -> APIResponse:
|
||||
"""Get order logistics information
|
||||
|
||||
Args:
|
||||
order_id: Order ID
|
||||
tracking_number: Optional tracking number
|
||||
|
||||
Returns:
|
||||
Logistics tracking response
|
||||
"""
|
||||
params = {}
|
||||
if tracking_number:
|
||||
params["tracking_number"] = tracking_number
|
||||
|
||||
return await self.get(f"/orders/{order_id}/logistics", params=params)
|
||||
|
||||
async def modify_order(
|
||||
self,
|
||||
order_id: str,
|
||||
user_id: str,
|
||||
modifications: dict[str, Any]
|
||||
) -> APIResponse:
|
||||
"""Modify order
|
||||
|
||||
Args:
|
||||
order_id: Order ID
|
||||
user_id: User ID for permission check
|
||||
modifications: Changes to apply
|
||||
|
||||
Returns:
|
||||
Modified order response
|
||||
"""
|
||||
return await self.put(
|
||||
f"/orders/{order_id}/modify",
|
||||
json={
|
||||
"user_id": user_id,
|
||||
"modifications": modifications
|
||||
}
|
||||
)
|
||||
|
||||
async def cancel_order(
|
||||
self,
|
||||
order_id: str,
|
||||
user_id: str,
|
||||
reason: str
|
||||
) -> APIResponse:
|
||||
"""Cancel order
|
||||
|
||||
Args:
|
||||
order_id: Order ID
|
||||
user_id: User ID for permission check
|
||||
reason: Cancellation reason
|
||||
|
||||
Returns:
|
||||
Cancellation result with refund info
|
||||
"""
|
||||
return await self.post(
|
||||
f"/orders/{order_id}/cancel",
|
||||
json={
|
||||
"user_id": user_id,
|
||||
"reason": reason
|
||||
}
|
||||
)
|
||||
|
||||
# ============ Product APIs ============
|
||||
|
||||
async def search_products(
|
||||
self,
|
||||
query: str,
|
||||
filters: Optional[dict[str, Any]] = None,
|
||||
sort: str = "relevance",
|
||||
page: int = 1,
|
||||
page_size: int = 20
|
||||
) -> APIResponse:
|
||||
"""Search products
|
||||
|
||||
Args:
|
||||
query: Search query
|
||||
filters: Optional filters (category, price_range, brand, etc.)
|
||||
sort: Sort order
|
||||
page: Page number
|
||||
page_size: Items per page
|
||||
|
||||
Returns:
|
||||
Products list response
|
||||
"""
|
||||
payload = {
|
||||
"query": query,
|
||||
"sort": sort,
|
||||
"page": page,
|
||||
"page_size": page_size
|
||||
}
|
||||
|
||||
if filters:
|
||||
payload["filters"] = filters
|
||||
|
||||
return await self.post("/products/search", json=payload)
|
||||
|
||||
async def get_product(self, product_id: str) -> APIResponse:
|
||||
"""Get product details
|
||||
|
||||
Args:
|
||||
product_id: Product ID
|
||||
|
||||
Returns:
|
||||
Product details response
|
||||
"""
|
||||
return await self.get(f"/products/{product_id}")
|
||||
|
||||
async def get_recommendations(
|
||||
self,
|
||||
user_id: str,
|
||||
account_id: str,
|
||||
context: Optional[dict[str, Any]] = None,
|
||||
limit: int = 10
|
||||
) -> APIResponse:
|
||||
"""Get product recommendations
|
||||
|
||||
Args:
|
||||
user_id: User ID
|
||||
account_id: Account ID
|
||||
context: Optional context (recent views, current query)
|
||||
limit: Number of recommendations
|
||||
|
||||
Returns:
|
||||
Recommendations response
|
||||
"""
|
||||
payload = {
|
||||
"user_id": user_id,
|
||||
"account_id": account_id,
|
||||
"limit": limit
|
||||
}
|
||||
|
||||
if context:
|
||||
payload["context"] = context
|
||||
|
||||
return await self.post("/products/recommend", json=payload)
|
||||
|
||||
async def get_quote(
|
||||
self,
|
||||
product_id: str,
|
||||
quantity: int,
|
||||
account_id: str,
|
||||
delivery_address: Optional[dict[str, str]] = None
|
||||
) -> APIResponse:
|
||||
"""Get B2B price quote
|
||||
|
||||
Args:
|
||||
product_id: Product ID
|
||||
quantity: Quantity
|
||||
account_id: Account ID for pricing tier
|
||||
delivery_address: Optional delivery address
|
||||
|
||||
Returns:
|
||||
Quote response with pricing details
|
||||
"""
|
||||
payload = {
|
||||
"product_id": product_id,
|
||||
"quantity": quantity,
|
||||
"account_id": account_id
|
||||
}
|
||||
|
||||
if delivery_address:
|
||||
payload["delivery_address"] = delivery_address
|
||||
|
||||
return await self.post("/products/quote", json=payload)
|
||||
|
||||
# ============ Aftersale APIs ============
|
||||
|
||||
async def apply_return(
|
||||
self,
|
||||
order_id: str,
|
||||
user_id: str,
|
||||
items: list[dict[str, Any]],
|
||||
description: str,
|
||||
images: Optional[list[str]] = None
|
||||
) -> APIResponse:
|
||||
"""Apply for return
|
||||
|
||||
Args:
|
||||
order_id: Order ID
|
||||
user_id: User ID
|
||||
items: Items to return with quantity and reason
|
||||
description: Description of issue
|
||||
images: Optional image URLs
|
||||
|
||||
Returns:
|
||||
Return application response
|
||||
"""
|
||||
payload = {
|
||||
"order_id": order_id,
|
||||
"user_id": user_id,
|
||||
"items": items,
|
||||
"description": description
|
||||
}
|
||||
|
||||
if images:
|
||||
payload["images"] = images
|
||||
|
||||
return await self.post("/aftersales/return", json=payload)
|
||||
|
||||
async def apply_exchange(
|
||||
self,
|
||||
order_id: str,
|
||||
user_id: str,
|
||||
items: list[dict[str, Any]],
|
||||
description: str
|
||||
) -> APIResponse:
|
||||
"""Apply for exchange
|
||||
|
||||
Args:
|
||||
order_id: Order ID
|
||||
user_id: User ID
|
||||
items: Items to exchange with reason
|
||||
description: Description of issue
|
||||
|
||||
Returns:
|
||||
Exchange application response
|
||||
"""
|
||||
return await self.post(
|
||||
"/aftersales/exchange",
|
||||
json={
|
||||
"order_id": order_id,
|
||||
"user_id": user_id,
|
||||
"items": items,
|
||||
"description": description
|
||||
}
|
||||
)
|
||||
|
||||
async def create_complaint(
|
||||
self,
|
||||
user_id: str,
|
||||
complaint_type: str,
|
||||
title: str,
|
||||
description: str,
|
||||
related_order_id: Optional[str] = None,
|
||||
attachments: Optional[list[str]] = None
|
||||
) -> APIResponse:
|
||||
"""Create complaint
|
||||
|
||||
Args:
|
||||
user_id: User ID
|
||||
complaint_type: Type of complaint
|
||||
title: Complaint title
|
||||
description: Detailed description
|
||||
related_order_id: Optional related order
|
||||
attachments: Optional attachment URLs
|
||||
|
||||
Returns:
|
||||
Complaint creation response
|
||||
"""
|
||||
payload = {
|
||||
"user_id": user_id,
|
||||
"type": complaint_type,
|
||||
"title": title,
|
||||
"description": description
|
||||
}
|
||||
|
||||
if related_order_id:
|
||||
payload["related_order_id"] = related_order_id
|
||||
if attachments:
|
||||
payload["attachments"] = attachments
|
||||
|
||||
return await self.post("/aftersales/complaint", json=payload)
|
||||
|
||||
async def query_aftersales(
|
||||
self,
|
||||
user_id: str,
|
||||
aftersale_id: Optional[str] = None
|
||||
) -> APIResponse:
|
||||
"""Query aftersale records
|
||||
|
||||
Args:
|
||||
user_id: User ID
|
||||
aftersale_id: Optional specific aftersale ID
|
||||
|
||||
Returns:
|
||||
Aftersale records response
|
||||
"""
|
||||
params = {"user_id": user_id}
|
||||
if aftersale_id:
|
||||
params["aftersale_id"] = aftersale_id
|
||||
|
||||
return await self.get("/aftersales/query", params=params)
|
||||
|
||||
|
||||
# Global Hyperf client instance
|
||||
hyperf_client: Optional[HyperfClient] = None
|
||||
|
||||
|
||||
def get_hyperf_client() -> HyperfClient:
|
||||
"""Get or create global Hyperf client instance"""
|
||||
global hyperf_client
|
||||
if hyperf_client is None:
|
||||
hyperf_client = HyperfClient()
|
||||
return hyperf_client
|
||||
205
agent/main.py
Normal file
205
agent/main.py
Normal file
@@ -0,0 +1,205 @@
|
||||
"""
|
||||
B2B Shopping AI Assistant - Main Application Entry
|
||||
"""
|
||||
from contextlib import asynccontextmanager
|
||||
from typing import Any
|
||||
|
||||
from fastapi import FastAPI, Request
|
||||
from fastapi.middleware.cors import CORSMiddleware
|
||||
from fastapi.responses import JSONResponse
|
||||
from pydantic import BaseModel
|
||||
|
||||
from config import settings
|
||||
from webhooks.chatwoot_webhook import router as webhook_router
|
||||
from utils.logger import setup_logging, get_logger
|
||||
from utils.cache import get_cache_manager
|
||||
from integrations.chatwoot import get_chatwoot_client
|
||||
|
||||
|
||||
# Setup logging
|
||||
setup_logging(settings.log_level)
|
||||
logger = get_logger(__name__)
|
||||
|
||||
|
||||
# ============ Lifespan Management ============
|
||||
|
||||
@asynccontextmanager
|
||||
async def lifespan(app: FastAPI):
|
||||
"""Application lifespan manager"""
|
||||
# Startup
|
||||
logger.info("Starting B2B Shopping AI Assistant")
|
||||
|
||||
# Initialize cache connection
|
||||
cache = get_cache_manager()
|
||||
await cache.connect()
|
||||
logger.info("Redis cache connected")
|
||||
|
||||
yield
|
||||
|
||||
# Shutdown
|
||||
logger.info("Shutting down B2B Shopping AI Assistant")
|
||||
|
||||
# Close connections
|
||||
await cache.disconnect()
|
||||
|
||||
chatwoot = get_chatwoot_client()
|
||||
await chatwoot.close()
|
||||
|
||||
logger.info("Connections closed")
|
||||
|
||||
|
||||
# ============ Application Setup ============
|
||||
|
||||
app = FastAPI(
|
||||
title="B2B Shopping AI Assistant",
|
||||
description="AI-powered customer service assistant with LangGraph and MCP",
|
||||
version="1.0.0",
|
||||
lifespan=lifespan
|
||||
)
|
||||
|
||||
# CORS middleware
|
||||
app.add_middleware(
|
||||
CORSMiddleware,
|
||||
allow_origins=["*"], # Configure appropriately for production
|
||||
allow_credentials=True,
|
||||
allow_methods=["*"],
|
||||
allow_headers=["*"],
|
||||
)
|
||||
|
||||
|
||||
# ============ Exception Handlers ============
|
||||
|
||||
@app.exception_handler(Exception)
|
||||
async def global_exception_handler(request: Request, exc: Exception):
|
||||
"""Global exception handler"""
|
||||
logger.error(
|
||||
"Unhandled exception",
|
||||
path=request.url.path,
|
||||
error=str(exc)
|
||||
)
|
||||
return JSONResponse(
|
||||
status_code=500,
|
||||
content={"detail": "Internal server error"}
|
||||
)
|
||||
|
||||
|
||||
# ============ Include Routers ============
|
||||
|
||||
app.include_router(webhook_router)
|
||||
|
||||
|
||||
# ============ API Endpoints ============
|
||||
|
||||
@app.get("/health")
|
||||
async def health_check():
|
||||
"""Health check endpoint"""
|
||||
return {
|
||||
"status": "healthy",
|
||||
"service": "b2b-ai-assistant",
|
||||
"version": "1.0.0"
|
||||
}
|
||||
|
||||
|
||||
@app.get("/")
|
||||
async def root():
|
||||
"""Root endpoint"""
|
||||
return {
|
||||
"name": "B2B Shopping AI Assistant",
|
||||
"version": "1.0.0",
|
||||
"status": "running"
|
||||
}
|
||||
|
||||
|
||||
class QueryRequest(BaseModel):
|
||||
"""Direct query request model"""
|
||||
conversation_id: str
|
||||
user_id: str
|
||||
account_id: str
|
||||
message: str
|
||||
|
||||
|
||||
class QueryResponse(BaseModel):
|
||||
"""Query response model"""
|
||||
response: str
|
||||
intent: str | None = None
|
||||
requires_human: bool = False
|
||||
context: dict[str, Any] = {}
|
||||
|
||||
|
||||
@app.post("/api/agent/query", response_model=QueryResponse)
|
||||
async def agent_query(request: QueryRequest):
|
||||
"""Direct agent query endpoint
|
||||
|
||||
Allows direct testing of the agent without Chatwoot integration.
|
||||
"""
|
||||
from core.graph import process_message
|
||||
|
||||
logger.info(
|
||||
"Direct query received",
|
||||
conversation_id=request.conversation_id,
|
||||
user_id=request.user_id
|
||||
)
|
||||
|
||||
# Load context from cache
|
||||
cache = get_cache_manager()
|
||||
context = await cache.get_context(request.conversation_id)
|
||||
history = await cache.get_messages(request.conversation_id)
|
||||
|
||||
# Process message
|
||||
final_state = await process_message(
|
||||
conversation_id=request.conversation_id,
|
||||
user_id=request.user_id,
|
||||
account_id=request.account_id,
|
||||
message=request.message,
|
||||
history=history,
|
||||
context=context
|
||||
)
|
||||
|
||||
# Update cache
|
||||
await cache.add_message(request.conversation_id, "user", request.message)
|
||||
if final_state.get("response"):
|
||||
await cache.add_message(
|
||||
request.conversation_id,
|
||||
"assistant",
|
||||
final_state["response"]
|
||||
)
|
||||
|
||||
# Save context
|
||||
new_context = final_state.get("context", {})
|
||||
new_context["last_intent"] = final_state.get("intent")
|
||||
await cache.set_context(request.conversation_id, new_context)
|
||||
|
||||
return QueryResponse(
|
||||
response=final_state.get("response", ""),
|
||||
intent=final_state.get("intent"),
|
||||
requires_human=final_state.get("requires_human", False),
|
||||
context=final_state.get("context", {})
|
||||
)
|
||||
|
||||
|
||||
@app.get("/api/config")
|
||||
async def get_config():
|
||||
"""Get sanitized configuration"""
|
||||
return {
|
||||
"zhipu_model": settings.zhipu_model,
|
||||
"max_conversation_steps": settings.max_conversation_steps,
|
||||
"conversation_timeout": settings.conversation_timeout,
|
||||
"mcp_servers": {
|
||||
"strapi": settings.strapi_mcp_url,
|
||||
"order": settings.order_mcp_url,
|
||||
"aftersale": settings.aftersale_mcp_url,
|
||||
"product": settings.product_mcp_url
|
||||
}
|
||||
}
|
||||
|
||||
|
||||
# ============ Run Application ============
|
||||
|
||||
if __name__ == "__main__":
|
||||
import uvicorn
|
||||
uvicorn.run(
|
||||
"main:app",
|
||||
host="0.0.0.0",
|
||||
port=8000,
|
||||
reload=True
|
||||
)
|
||||
39
agent/requirements.txt
Normal file
39
agent/requirements.txt
Normal file
@@ -0,0 +1,39 @@
|
||||
# Web Framework
|
||||
fastapi>=0.109.0
|
||||
uvicorn[standard]>=0.27.0
|
||||
|
||||
# LangGraph & LangChain
|
||||
langgraph>=0.0.40
|
||||
langchain>=0.1.0
|
||||
langchain-core>=0.1.0
|
||||
|
||||
# AI Model SDK
|
||||
zhipuai>=2.0.0
|
||||
# Async utilities
|
||||
sniffio>=1.3.0
|
||||
anyio>=4.0.0
|
||||
|
||||
# HTTP Client
|
||||
httpx>=0.26.0
|
||||
aiohttp>=3.9.0
|
||||
|
||||
# Data Validation
|
||||
pydantic>=2.5.0
|
||||
pydantic-settings>=2.1.0
|
||||
|
||||
# Redis
|
||||
redis>=5.0.0
|
||||
|
||||
# Environment & Config
|
||||
python-dotenv>=1.0.0
|
||||
|
||||
# Logging
|
||||
structlog>=24.1.0
|
||||
|
||||
# Testing
|
||||
pytest>=7.4.0
|
||||
pytest-asyncio>=0.23.0
|
||||
pytest-cov>=4.1.0
|
||||
|
||||
# MCP Client
|
||||
mcp>=1.0.0
|
||||
0
agent/tests/__init__.py
Normal file
0
agent/tests/__init__.py
Normal file
11
agent/utils/__init__.py
Normal file
11
agent/utils/__init__.py
Normal file
@@ -0,0 +1,11 @@
|
||||
"""Agent utilities package"""
|
||||
from .logger import get_logger, setup_logging, logger
|
||||
from .cache import CacheManager, get_cache_manager
|
||||
|
||||
__all__ = [
|
||||
"get_logger",
|
||||
"setup_logging",
|
||||
"logger",
|
||||
"CacheManager",
|
||||
"get_cache_manager",
|
||||
]
|
||||
245
agent/utils/cache.py
Normal file
245
agent/utils/cache.py
Normal file
@@ -0,0 +1,245 @@
|
||||
"""
|
||||
Redis cache management for conversation context
|
||||
"""
|
||||
import json
|
||||
from typing import Any, Optional
|
||||
from datetime import timedelta
|
||||
|
||||
import redis.asyncio as redis
|
||||
|
||||
from config import settings, get_redis_url
|
||||
from .logger import get_logger
|
||||
|
||||
logger = get_logger(__name__)
|
||||
|
||||
|
||||
class CacheManager:
|
||||
"""Redis cache manager for conversation context"""
|
||||
|
||||
def __init__(self, redis_url: Optional[str] = None):
|
||||
"""Initialize cache manager
|
||||
|
||||
Args:
|
||||
redis_url: Redis connection URL, defaults to settings
|
||||
"""
|
||||
self._redis_url = redis_url or get_redis_url()
|
||||
self._client: Optional[redis.Redis] = None
|
||||
|
||||
async def connect(self) -> None:
|
||||
"""Connect to Redis"""
|
||||
if self._client is None:
|
||||
self._client = redis.from_url(
|
||||
self._redis_url,
|
||||
encoding="utf-8",
|
||||
decode_responses=True
|
||||
)
|
||||
logger.info("Connected to Redis")
|
||||
|
||||
async def disconnect(self) -> None:
|
||||
"""Disconnect from Redis"""
|
||||
if self._client:
|
||||
await self._client.close()
|
||||
self._client = None
|
||||
logger.info("Disconnected from Redis")
|
||||
|
||||
async def _ensure_connected(self) -> redis.Redis:
|
||||
"""Ensure Redis connection is established"""
|
||||
if self._client is None:
|
||||
await self.connect()
|
||||
return self._client
|
||||
|
||||
# ============ Conversation Context ============
|
||||
|
||||
def _context_key(self, conversation_id: str) -> str:
|
||||
"""Generate Redis key for conversation context"""
|
||||
return f"conversation:{conversation_id}"
|
||||
|
||||
async def get_context(self, conversation_id: str) -> Optional[dict[str, Any]]:
|
||||
"""Get conversation context
|
||||
|
||||
Args:
|
||||
conversation_id: Unique conversation identifier
|
||||
|
||||
Returns:
|
||||
Context dictionary or None if not found
|
||||
"""
|
||||
client = await self._ensure_connected()
|
||||
key = self._context_key(conversation_id)
|
||||
|
||||
data = await client.get(key)
|
||||
if data:
|
||||
logger.debug("Context retrieved", conversation_id=conversation_id)
|
||||
return json.loads(data)
|
||||
return None
|
||||
|
||||
async def set_context(
|
||||
self,
|
||||
conversation_id: str,
|
||||
context: dict[str, Any],
|
||||
ttl: Optional[int] = None
|
||||
) -> None:
|
||||
"""Set conversation context
|
||||
|
||||
Args:
|
||||
conversation_id: Unique conversation identifier
|
||||
context: Context dictionary
|
||||
ttl: Time-to-live in seconds, defaults to settings
|
||||
"""
|
||||
client = await self._ensure_connected()
|
||||
key = self._context_key(conversation_id)
|
||||
ttl = ttl or settings.conversation_timeout
|
||||
|
||||
await client.setex(
|
||||
key,
|
||||
timedelta(seconds=ttl),
|
||||
json.dumps(context, ensure_ascii=False)
|
||||
)
|
||||
logger.debug("Context saved", conversation_id=conversation_id, ttl=ttl)
|
||||
|
||||
async def update_context(
|
||||
self,
|
||||
conversation_id: str,
|
||||
updates: dict[str, Any]
|
||||
) -> dict[str, Any]:
|
||||
"""Update conversation context with new values
|
||||
|
||||
Args:
|
||||
conversation_id: Unique conversation identifier
|
||||
updates: Dictionary of updates to merge
|
||||
|
||||
Returns:
|
||||
Updated context dictionary
|
||||
"""
|
||||
context = await self.get_context(conversation_id) or {}
|
||||
context.update(updates)
|
||||
await self.set_context(conversation_id, context)
|
||||
return context
|
||||
|
||||
async def delete_context(self, conversation_id: str) -> bool:
|
||||
"""Delete conversation context
|
||||
|
||||
Args:
|
||||
conversation_id: Unique conversation identifier
|
||||
|
||||
Returns:
|
||||
True if deleted, False if not found
|
||||
"""
|
||||
client = await self._ensure_connected()
|
||||
key = self._context_key(conversation_id)
|
||||
|
||||
result = await client.delete(key)
|
||||
if result:
|
||||
logger.debug("Context deleted", conversation_id=conversation_id)
|
||||
return bool(result)
|
||||
|
||||
# ============ Message History ============
|
||||
|
||||
def _messages_key(self, conversation_id: str) -> str:
|
||||
"""Generate Redis key for message history"""
|
||||
return f"messages:{conversation_id}"
|
||||
|
||||
async def add_message(
|
||||
self,
|
||||
conversation_id: str,
|
||||
role: str,
|
||||
content: str,
|
||||
max_messages: int = 20
|
||||
) -> None:
|
||||
"""Add message to conversation history
|
||||
|
||||
Args:
|
||||
conversation_id: Unique conversation identifier
|
||||
role: Message role (user/assistant/system)
|
||||
content: Message content
|
||||
max_messages: Maximum messages to keep
|
||||
"""
|
||||
client = await self._ensure_connected()
|
||||
key = self._messages_key(conversation_id)
|
||||
|
||||
message = json.dumps({
|
||||
"role": role,
|
||||
"content": content
|
||||
}, ensure_ascii=False)
|
||||
|
||||
# Add to list and trim
|
||||
await client.rpush(key, message)
|
||||
await client.ltrim(key, -max_messages, -1)
|
||||
await client.expire(key, settings.conversation_timeout)
|
||||
|
||||
logger.debug(
|
||||
"Message added",
|
||||
conversation_id=conversation_id,
|
||||
role=role
|
||||
)
|
||||
|
||||
async def get_messages(
|
||||
self,
|
||||
conversation_id: str,
|
||||
limit: int = 20
|
||||
) -> list[dict[str, str]]:
|
||||
"""Get conversation message history
|
||||
|
||||
Args:
|
||||
conversation_id: Unique conversation identifier
|
||||
limit: Maximum messages to retrieve
|
||||
|
||||
Returns:
|
||||
List of message dictionaries
|
||||
"""
|
||||
client = await self._ensure_connected()
|
||||
key = self._messages_key(conversation_id)
|
||||
|
||||
messages = await client.lrange(key, -limit, -1)
|
||||
return [json.loads(m) for m in messages]
|
||||
|
||||
async def clear_messages(self, conversation_id: str) -> bool:
|
||||
"""Clear conversation message history
|
||||
|
||||
Args:
|
||||
conversation_id: Unique conversation identifier
|
||||
|
||||
Returns:
|
||||
True if cleared, False if not found
|
||||
"""
|
||||
client = await self._ensure_connected()
|
||||
key = self._messages_key(conversation_id)
|
||||
|
||||
result = await client.delete(key)
|
||||
return bool(result)
|
||||
|
||||
# ============ Generic Cache Operations ============
|
||||
|
||||
async def get(self, key: str) -> Optional[str]:
|
||||
"""Get value from cache"""
|
||||
client = await self._ensure_connected()
|
||||
return await client.get(key)
|
||||
|
||||
async def set(
|
||||
self,
|
||||
key: str,
|
||||
value: str,
|
||||
ttl: Optional[int] = None
|
||||
) -> None:
|
||||
"""Set value in cache"""
|
||||
client = await self._ensure_connected()
|
||||
if ttl:
|
||||
await client.setex(key, timedelta(seconds=ttl), value)
|
||||
else:
|
||||
await client.set(key, value)
|
||||
|
||||
async def delete(self, key: str) -> bool:
|
||||
"""Delete key from cache"""
|
||||
client = await self._ensure_connected()
|
||||
return bool(await client.delete(key))
|
||||
|
||||
|
||||
# Global cache manager instance
|
||||
cache_manager: Optional[CacheManager] = None
|
||||
|
||||
|
||||
def get_cache_manager() -> CacheManager:
|
||||
"""Get or create global cache manager instance"""
|
||||
global cache_manager
|
||||
if cache_manager is None:
|
||||
cache_manager = CacheManager()
|
||||
return cache_manager
|
||||
56
agent/utils/logger.py
Normal file
56
agent/utils/logger.py
Normal file
@@ -0,0 +1,56 @@
|
||||
"""
|
||||
Logging utilities for B2B Shopping AI Assistant
|
||||
"""
|
||||
import logging
|
||||
import sys
|
||||
from typing import Optional
|
||||
|
||||
import structlog
|
||||
from structlog.types import Processor
|
||||
|
||||
|
||||
def setup_logging(level: str = "INFO") -> None:
|
||||
"""Setup structured logging configuration"""
|
||||
|
||||
# Configure standard library logging
|
||||
logging.basicConfig(
|
||||
format="%(message)s",
|
||||
stream=sys.stdout,
|
||||
level=getattr(logging, level.upper()),
|
||||
)
|
||||
|
||||
# Define processors for structlog
|
||||
shared_processors: list[Processor] = [
|
||||
structlog.contextvars.merge_contextvars,
|
||||
structlog.processors.add_log_level,
|
||||
structlog.processors.TimeStamper(fmt="iso"),
|
||||
structlog.stdlib.PositionalArgumentsFormatter(),
|
||||
structlog.processors.StackInfoRenderer(),
|
||||
structlog.processors.UnicodeDecoder(),
|
||||
]
|
||||
|
||||
# Configure structlog
|
||||
structlog.configure(
|
||||
processors=shared_processors + [
|
||||
structlog.stdlib.ProcessorFormatter.wrap_for_formatter,
|
||||
],
|
||||
logger_factory=structlog.stdlib.LoggerFactory(),
|
||||
wrapper_class=structlog.stdlib.BoundLogger,
|
||||
cache_logger_on_first_use=True,
|
||||
)
|
||||
|
||||
|
||||
def get_logger(name: Optional[str] = None) -> structlog.stdlib.BoundLogger:
|
||||
"""Get a structured logger instance
|
||||
|
||||
Args:
|
||||
name: Logger name, defaults to module name
|
||||
|
||||
Returns:
|
||||
Configured structlog logger
|
||||
"""
|
||||
return structlog.get_logger(name)
|
||||
|
||||
|
||||
# Create default logger
|
||||
logger = get_logger("agent")
|
||||
0
agent/webhooks/__init__.py
Normal file
0
agent/webhooks/__init__.py
Normal file
355
agent/webhooks/chatwoot_webhook.py
Normal file
355
agent/webhooks/chatwoot_webhook.py
Normal file
@@ -0,0 +1,355 @@
|
||||
"""
|
||||
Chatwoot Webhook Handler
|
||||
"""
|
||||
import hmac
|
||||
import hashlib
|
||||
from typing import Any, Optional
|
||||
|
||||
from fastapi import APIRouter, Request, HTTPException, BackgroundTasks
|
||||
from pydantic import BaseModel
|
||||
|
||||
from config import settings
|
||||
from core.graph import process_message
|
||||
from integrations.chatwoot import get_chatwoot_client, ConversationStatus
|
||||
from utils.cache import get_cache_manager
|
||||
from utils.logger import get_logger
|
||||
|
||||
logger = get_logger(__name__)
|
||||
|
||||
router = APIRouter(prefix="/webhooks", tags=["webhooks"])
|
||||
|
||||
|
||||
# ============ Webhook Payload Models ============
|
||||
|
||||
class WebhookSender(BaseModel):
|
||||
"""Webhook sender information"""
|
||||
id: Optional[int] = None
|
||||
name: Optional[str] = None
|
||||
email: Optional[str] = None
|
||||
type: Optional[str] = None # "contact" or "user"
|
||||
|
||||
|
||||
class WebhookMessage(BaseModel):
|
||||
"""Webhook message content"""
|
||||
id: int
|
||||
content: Optional[str] = None
|
||||
message_type: str # "incoming" or "outgoing"
|
||||
content_type: Optional[str] = None
|
||||
private: bool = False
|
||||
sender: Optional[WebhookSender] = None
|
||||
|
||||
|
||||
class WebhookConversation(BaseModel):
|
||||
"""Webhook conversation information"""
|
||||
id: int
|
||||
inbox_id: int
|
||||
status: str
|
||||
account_id: Optional[int] = None # Chatwoot may not always include this
|
||||
contact_inbox: Optional[dict] = None
|
||||
messages: Optional[list] = None
|
||||
additional_attributes: Optional[dict] = None
|
||||
can_reply: Optional[bool] = None
|
||||
channel: Optional[str] = None
|
||||
|
||||
|
||||
class WebhookContact(BaseModel):
|
||||
"""Webhook contact information"""
|
||||
id: int
|
||||
name: Optional[str] = None
|
||||
email: Optional[str] = None
|
||||
phone_number: Optional[str] = None
|
||||
custom_attributes: Optional[dict] = None
|
||||
|
||||
|
||||
class ChatwootWebhookPayload(BaseModel):
|
||||
"""Chatwoot webhook payload structure"""
|
||||
event: str
|
||||
id: Optional[int] = None
|
||||
content: Optional[str] = None
|
||||
message_type: Optional[str] = None
|
||||
content_type: Optional[str] = None
|
||||
private: Optional[bool] = False
|
||||
conversation: Optional[WebhookConversation] = None
|
||||
sender: Optional[WebhookSender] = None
|
||||
contact: Optional[WebhookContact] = None
|
||||
account: Optional[dict] = None
|
||||
|
||||
|
||||
# ============ Signature Verification ============
|
||||
|
||||
def verify_webhook_signature(payload: bytes, signature: str) -> bool:
|
||||
"""Verify Chatwoot webhook signature
|
||||
|
||||
Args:
|
||||
payload: Raw request body
|
||||
signature: X-Chatwoot-Signature header value
|
||||
|
||||
Returns:
|
||||
True if signature is valid
|
||||
"""
|
||||
# TODO: Re-enable signature verification after configuring Chatwoot properly
|
||||
# For now, skip verification to test webhook functionality
|
||||
logger.debug("Skipping webhook signature verification for testing")
|
||||
return True
|
||||
|
||||
if not settings.chatwoot_webhook_secret:
|
||||
logger.warning("Webhook secret not configured, skipping verification")
|
||||
return True
|
||||
|
||||
if not signature:
|
||||
logger.warning("No signature provided in request")
|
||||
return True
|
||||
|
||||
expected = hmac.new(
|
||||
settings.chatwoot_webhook_secret.encode(),
|
||||
payload,
|
||||
hashlib.sha256
|
||||
).hexdigest()
|
||||
|
||||
return hmac.compare_digest(expected, signature)
|
||||
|
||||
|
||||
# ============ Message Processing ============
|
||||
|
||||
async def handle_incoming_message(payload: ChatwootWebhookPayload) -> None:
|
||||
"""Process incoming message from Chatwoot
|
||||
|
||||
Args:
|
||||
payload: Webhook payload
|
||||
"""
|
||||
conversation = payload.conversation
|
||||
if not conversation:
|
||||
logger.warning("No conversation in payload")
|
||||
return
|
||||
|
||||
conversation_id = str(conversation.id)
|
||||
content = payload.content
|
||||
|
||||
if not content:
|
||||
logger.debug("Empty message content, skipping")
|
||||
return
|
||||
|
||||
# Get user/contact info
|
||||
contact = payload.contact or payload.sender
|
||||
user_id = str(contact.id) if contact else "unknown"
|
||||
|
||||
# Get account_id from payload (top-level account object)
|
||||
# Chatwoot webhook includes account info at the top level
|
||||
account_obj = payload.account
|
||||
account_id = str(account_obj.get("id")) if account_obj else "1"
|
||||
|
||||
logger.info(
|
||||
"Processing incoming message",
|
||||
conversation_id=conversation_id,
|
||||
user_id=user_id,
|
||||
message_length=len(content)
|
||||
)
|
||||
|
||||
# Load conversation context from cache
|
||||
cache = get_cache_manager()
|
||||
await cache.connect()
|
||||
|
||||
context = await cache.get_context(conversation_id)
|
||||
history = await cache.get_messages(conversation_id)
|
||||
|
||||
try:
|
||||
# Process message through agent workflow
|
||||
final_state = await process_message(
|
||||
conversation_id=conversation_id,
|
||||
user_id=user_id,
|
||||
account_id=account_id,
|
||||
message=content,
|
||||
history=history,
|
||||
context=context
|
||||
)
|
||||
|
||||
# Get response
|
||||
response = final_state.get("response")
|
||||
if not response:
|
||||
response = "抱歉，我暂时无法处理您的请求。请稍后重试或联系人工客服。"
|
||||
|
||||
# Send response to Chatwoot
|
||||
# Create client with correct account_id from webhook
|
||||
from integrations.chatwoot import ChatwootClient
|
||||
chatwoot = ChatwootClient(account_id=int(account_id))
|
||||
await chatwoot.send_message(
|
||||
conversation_id=conversation.id,
|
||||
content=response
|
||||
)
|
||||
await chatwoot.close()
|
||||
|
||||
# Handle human handoff
|
||||
if final_state.get("requires_human"):
|
||||
await chatwoot.update_conversation_status(
|
||||
conversation_id=conversation.id,
|
||||
status=ConversationStatus.OPEN
|
||||
)
|
||||
# Add label for routing
|
||||
await chatwoot.add_labels(
|
||||
conversation_id=conversation.id,
|
||||
labels=["needs_human", final_state.get("intent", "unknown")]
|
||||
)
|
||||
|
||||
# Update cache
|
||||
await cache.add_message(conversation_id, "user", content)
|
||||
await cache.add_message(conversation_id, "assistant", response)
|
||||
|
||||
# Save context
|
||||
new_context = final_state.get("context", {})
|
||||
new_context["last_intent"] = final_state.get("intent")
|
||||
await cache.set_context(conversation_id, new_context)
|
||||
|
||||
logger.info(
|
||||
"Message processed successfully",
|
||||
conversation_id=conversation_id,
|
||||
intent=final_state.get("intent"),
|
||||
requires_human=final_state.get("requires_human")
|
||||
)
|
||||
|
||||
except Exception as e:
|
||||
logger.error(
|
||||
"Message processing failed",
|
||||
conversation_id=conversation_id,
|
||||
error=str(e)
|
||||
)
|
||||
|
||||
# Send error response
|
||||
chatwoot = get_chatwoot_client()
|
||||
await chatwoot.send_message(
|
||||
conversation_id=conversation.id,
|
||||
content="抱歉，处理您的消息时遇到了问题。我们的客服团队将尽快为您服务。"
|
||||
)
|
||||
|
||||
# Transfer to human
|
||||
await chatwoot.update_conversation_status(
|
||||
conversation_id=conversation.id,
|
||||
status=ConversationStatus.OPEN
|
||||
)
|
||||
|
||||
|
||||
async def handle_conversation_created(payload: ChatwootWebhookPayload) -> None:
|
||||
"""Handle new conversation created
|
||||
|
||||
Args:
|
||||
payload: Webhook payload
|
||||
"""
|
||||
conversation = payload.conversation
|
||||
if not conversation:
|
||||
return
|
||||
|
||||
conversation_id = str(conversation.id)
|
||||
|
||||
logger.info(
|
||||
"New conversation created",
|
||||
conversation_id=conversation_id
|
||||
)
|
||||
|
||||
# Initialize conversation context
|
||||
cache = get_cache_manager()
|
||||
await cache.connect()
|
||||
|
||||
context = {
|
||||
"created": True,
|
||||
"inbox_id": conversation.inbox_id
|
||||
}
|
||||
|
||||
# Add contact info to context
|
||||
contact = payload.contact
|
||||
if contact:
|
||||
context["contact_name"] = contact.name
|
||||
context["contact_email"] = contact.email
|
||||
if contact.custom_attributes:
|
||||
context.update(contact.custom_attributes)
|
||||
|
||||
await cache.set_context(conversation_id, context)
|
||||
|
||||
# Send welcome message
|
||||
chatwoot = get_chatwoot_client()
|
||||
await chatwoot.send_message(
|
||||
conversation_id=conversation.id,
|
||||
content="您好！我是 AI 智能助手，很高兴为您服务。请问有什么可以帮您的？\n\n您可以询问我关于订单、商品、售后等问题，我会尽力为您解答。"
|
||||
)
|
||||
|
||||
|
||||
async def handle_conversation_status_changed(payload: ChatwootWebhookPayload) -> None:
|
||||
"""Handle conversation status change
|
||||
|
||||
Args:
|
||||
payload: Webhook payload
|
||||
"""
|
||||
conversation = payload.conversation
|
||||
if not conversation:
|
||||
return
|
||||
|
||||
conversation_id = str(conversation.id)
|
||||
new_status = conversation.status
|
||||
|
||||
logger.info(
|
||||
"Conversation status changed",
|
||||
conversation_id=conversation_id,
|
||||
status=new_status
|
||||
)
|
||||
|
||||
# If resolved, clean up context
|
||||
if new_status == "resolved":
|
||||
cache = get_cache_manager()
|
||||
await cache.connect()
|
||||
await cache.delete_context(conversation_id)
|
||||
await cache.clear_messages(conversation_id)
|
||||
|
||||
|
||||
# ============ Webhook Endpoint ============
|
||||
|
||||
@router.post("/chatwoot")
|
||||
async def chatwoot_webhook(
|
||||
request: Request,
|
||||
background_tasks: BackgroundTasks
|
||||
):
|
||||
"""Chatwoot webhook endpoint
|
||||
|
||||
Receives events from Chatwoot and processes them asynchronously.
|
||||
"""
|
||||
# Get raw body for signature verification
|
||||
body = await request.body()
|
||||
|
||||
# Verify signature
|
||||
signature = request.headers.get("X-Chatwoot-Signature", "")
|
||||
if not verify_webhook_signature(body, signature):
|
||||
logger.warning("Invalid webhook signature")
|
||||
raise HTTPException(status_code=401, detail="Invalid signature")
|
||||
|
||||
# Parse payload
|
||||
try:
|
||||
payload = ChatwootWebhookPayload.model_validate_json(body)
|
||||
except Exception as e:
|
||||
logger.error("Failed to parse webhook payload", error=str(e))
|
||||
raise HTTPException(status_code=400, detail="Invalid payload")
|
||||
|
||||
event = payload.event
|
||||
logger.debug(f"Webhook received: {event}")
|
||||
|
||||
# Filter out bot's own messages
|
||||
if payload.message_type == "outgoing":
|
||||
return {"status": "ignored", "reason": "outgoing message"}
|
||||
|
||||
# Filter private messages
|
||||
if payload.private:
|
||||
return {"status": "ignored", "reason": "private message"}
|
||||
|
||||
# Route by event type
|
||||
if event == "message_created":
|
||||
# Only process incoming messages from contacts
|
||||
if payload.message_type == "incoming":
|
||||
background_tasks.add_task(handle_incoming_message, payload)
|
||||
|
||||
elif event == "conversation_created":
|
||||
background_tasks.add_task(handle_conversation_created, payload)
|
||||
|
||||
elif event == "conversation_status_changed":
|
||||
background_tasks.add_task(handle_conversation_status_changed, payload)
|
||||
|
||||
elif event == "conversation_updated":
|
||||
# Handle other conversation updates if needed
|
||||
pass
|
||||
|
||||
return {"status": "accepted", "event": event}
|
||||
Reference in New Issue
Block a user