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>
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agent/agents/product.py
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agent/agents/product.py
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"""
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Product Agent - Handles product search, recommendations, and quotes
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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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PRODUCT_AGENT_PROMPT = """你是一个专业的 B2B 商品顾问助手。
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你的职责是帮助用户找到合适的商品，包括：
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- 商品搜索
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- 智能推荐
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- B2B 询价
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- 库存查询
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- 商品详情
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## 可用工具
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1. **search_products** - 搜索商品
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- query: 搜索关键词
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- filters: 过滤条件（category, price_range, brand 等）
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- sort: 排序方式（price_asc/price_desc/sales/latest）
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- page: 页码
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- page_size: 每页数量
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2. **get_product_detail** - 获取商品详情
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- product_id: 商品ID
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3. **recommend_products** - 智能推荐
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- context: 推荐上下文（可包含当前查询、浏览历史等）
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- limit: 推荐数量
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4. **get_quote** - B2B 询价
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- product_id: 商品ID
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- quantity: 采购数量
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- delivery_address: 收货地址（可选，用于计算运费）
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5. **check_inventory** - 库存查询
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- product_ids: 商品ID列表
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- warehouse: 仓库（可选）
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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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}
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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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## B2B 询价特点
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- 大批量采购通常有阶梯价格
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- 可能需要考虑运费
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- 企业客户可能有专属折扣
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- 报价通常有有效期
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## 商品推荐策略
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- 根据用户采购历史推荐
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- 根据当前查询语义推荐
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- 根据企业行业特点推荐
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- 根据季节性和热门商品推荐
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## 注意事项
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- 帮助用户准确描述需求
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- 如果搜索结果太多，建议用户缩小范围
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- 询价时确认数量，因为会影响价格
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- 库存紧张时及时告知用户
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"""
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async def product_agent(state: AgentState) -> AgentState:
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"""Product agent node
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Handles product search, recommendations, quotes and inventory 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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"Product 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"] = "product"
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state["agent_history"].append("product")
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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_product_response(state)
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# Build messages for LLM
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messages = [
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Message(role="system", content=PRODUCT_AGENT_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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# 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"].get("product_id"):
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context_info += f"当前讨论的商品ID: {state['context']['product_id']}\n"
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if state["context"].get("recent_searches"):
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context_info += f"最近搜索: {state['context']['recent_searches']}\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=0.7)
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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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arguments = result.get("arguments", {})
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# Inject context for recommendation
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if result["tool_name"] == "recommend_products":
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arguments["user_id"] = state["user_id"]
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arguments["account_id"] = state["account_id"]
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# Inject context for quote
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if result["tool_name"] == "get_quote":
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arguments["account_id"] = state["account_id"]
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# Use entity if available
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if "product_id" not in arguments and state["entities"].get("product_id"):
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arguments["product_id"] = state["entities"]["product_id"]
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if "quantity" not in arguments and state["entities"].get("quantity"):
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arguments["quantity"] = state["entities"]["quantity"]
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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="product"
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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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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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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("Product 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_product_response(state: AgentState) -> AgentState:
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"""Generate response based on product 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 product context
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if isinstance(data, dict):
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if data.get("product_id"):
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state = update_context(state, {"product_id": data["product_id"]})
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if data.get("products"):
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# Store recent search results
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product_ids = [p.get("product_id") for p in data["products"][:5]]
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state = update_context(state, {"recent_product_ids": product_ids})
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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"""基于以下商品系统返回的信息，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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请生成一个清晰、有帮助的回复：
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- 如果是搜索结果，展示商品名称、价格、规格等关键信息
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- 如果是询价结果，清晰说明单价、总价、折扣、有效期等
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- 如果是推荐商品，简要说明推荐理由
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- 如果是库存查询，告知可用数量和发货时间
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- 结果较多时可以总结关键信息
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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="你是一个专业的商品顾问，请根据系统返回的信息回答用户的商品问题。"),
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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("Product response generation failed", error=str(e))
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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。")
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return state
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