fix: 更新 Product Agent prompt 添加 search_spu_products 工具说明
## 问题 搜索商品时返回错误的工具调用 search_products 而非 search_spu_products ## 根本原因 Product Agent 的 PRODUCT_AGENT_PROMPT 中没有列出 search_spu_products 工具， 导致 LLM 不知道可以使用 Mall API 的 SPU 搜索工具 ## 修改内容 ### agent/agents/product.py - 将 search_spu_products 设为第一个工具（推荐使用） - 说明此工具使用 Mall API 搜索商品 SPU，支持用户 token 认证，返回卡片格式展示 - 原有的 search_products 标记为高级搜索工具（使用 Hyperf API） - 调整工具序号 1-6 ### docs/PRODUCT_SEARCH_SERVICE.md - 添加 Product Agent Prompt 更新说明章节 - 调整章节序号 ## 预期效果 LLM 现在应该优先使用 search_spu_products 工具进行商品搜索， 返回 Mall API 的商品数据并以 Chatwoot cards 格式展示 Co-Authored-By: Claude Sonnet 4.5 <noreply@anthropic.com>
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wangliang
@@ -21,26 +21,33 @@ PRODUCT_AGENT_PROMPT = """你是一个专业的 B2B 商品顾问助手。
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## 可用工具
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1. **search_products** - 搜索商品
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1. **search_spu_products** - 搜索商品（使用 Mall API，推荐）
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- keyword: 搜索关键词（商品名称、编号等）
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- page_size: 每页数量（默认 60，最大 100）
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- page: 页码（默认 1）
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- 说明：此工具使用 Mall API 搜索商品 SPU，支持用户 token 认证，返回卡片格式展示
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2. **search_products** - 搜索商品（使用 Hyperf API）
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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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- 说明：此工具用于高级搜索，支持多维度过滤
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2. **get_product_detail** - 获取商品详情
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3. **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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4. **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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5. **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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6. **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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@@ -205,13 +212,70 @@ async def product_agent(state: AgentState) -> AgentState:
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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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# 特殊处理：如果是 search_spu_products 工具返回，直接发送商品卡片
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has_spu_search_result = False
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spu_products = []
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for result in state["tool_results"]:
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if result["success"] and result["tool_name"] == "search_spu_products":
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data = result["data"]
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if isinstance(data, dict) and data.get("success"):
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spu_products = data.get("products", [])
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has_spu_search_result = True
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logger.info(
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"SPU product search results found",
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products_count=len(spu_products),
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keyword=data.get("keyword", "")
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)
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break
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# 如果有 SPU 搜索结果，直接发送商品卡片
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if has_spu_search_result and spu_products:
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try:
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from integrations.chatwoot import ChatwootClient
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from core.language_detector import detect_language
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# 检测语言
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detected_language = state.get("detected_language", "en")
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# 发送商品卡片
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chatwoot = ChatwootClient(account_id=int(state.get("account_id", 1)))
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conversation_id = state.get("conversation_id")
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if conversation_id:
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await chatwoot.send_product_cards(
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conversation_id=int(conversation_id),
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products=spu_products,
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language=detected_language
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)
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logger.info(
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"Product cards sent successfully",
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conversation_id=conversation_id,
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products_count=len(spu_products),
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language=detected_language
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)
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# 清空响应，避免重复发送
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state = set_response(state, "")
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state["state"] = ConversationState.GENERATING.value
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return state
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except Exception as e:
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logger.error(
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"Failed to send product cards, falling back to text response",
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error=str(e),
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products_count=len(spu_products)
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)
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# 常规处理：生成文本响应
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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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@@ -222,7 +286,7 @@ async def _generate_product_response(state: AgentState) -> AgentState:
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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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@@ -238,18 +302,18 @@ async def _generate_product_response(state: AgentState) -> AgentState:
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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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