feat: 添加图片搜索功能和 Qwen 模型支持

图片搜索功能（以图搜图）：
- Chatwoot webhook 检测图片搜索消息 (content_type="search_image")
- 从 content_attributes.url 提取图片 URL
- 调用 Mall API 图片搜索接口 (/mall/api/spu?searchImageUrl=...)
- 支持嵌套和顶层 URL 位置提取
- Product Agent 添加 fast path 直接调用图片搜索工具
- 防止无限循环（使用后清除 context.image_search_url）

Qwen 模型支持：
- 添加 LLM provider 选择（zhipu/qwen）
- 实现 QwenLLMClient 类（基于 DashScope SDK）
- 添加 dashscope>=1.14.0 依赖
- 修复 API key 设置（直接设置 dashscope.api_key）
- 更新 .env.example 和 docker-compose.yml 配置

其他优化：
- 重构 Chatwoot 集成代码（删除冗余）
- 优化 Product Agent prompt
- 增强 Customer Service Agent 多语言支持

Co-Authored-By: Claude Sonnet 4.5 <noreply@anthropic.com>

agent/agents/customer_service.py

@@ -322,16 +322,51 @@ async def customer_service_agent(state: AgentState) -> AgentState:
             return state
 
         except json.JSONDecodeError as e:
-            # JSON parsing failed
+            # JSON parsing failed - try alternative format: "tool_name\n{args}"
             logger.error(
                 "Failed to parse LLM response as JSON",
                 error=str(e),
                 raw_content=content[:500],
                 conversation_id=state["conversation_id"]
             )
-            # Don't use raw content as response - use fallback instead
-            state = set_response(state, "抱歉，我无法理解您的请求。请尝试重新表述或联系人工客服。")
-            return state
+
+            # Handle non-JSON format: "tool_name\n{args}"
+            if '\n' in content and not content.startswith('{'):
+                lines = content.split('\n', 1)
+                tool_name = lines[0].strip()
+                args_json = lines[1].strip() if len(lines) > 1 else '{}'
+
+                try:
+                    arguments = json.loads(args_json) if args_json else {}
+                    logger.info(
+                        "Customer service agent calling tool (alternative format)",
+                        tool_name=tool_name,
+                        arguments=arguments,
+                        conversation_id=state["conversation_id"]
+                    )
+
+                    state = add_tool_call(
+                        state,
+                        tool_name=tool_name,
+                        arguments=arguments,
+                        server="strapi"
+                    )
+                    state["state"] = ConversationState.TOOL_CALLING.value
+                    return state
+                except json.JSONDecodeError:
+                    # Args parsing also failed
+                    logger.warning(
+                        "Failed to parse tool arguments",
+                        tool_name=tool_name,
+                        args_json=args_json[:200],
+                        conversation_id=state["conversation_id"]
+                    )
+                    state = set_response(state, "抱歉，我无法理解您的请求。请尝试重新表述或联系人工客服。")
+                    return state
+            else:
+                # Not a recognized format
+                state = set_response(state, "抱歉，我无法理解您的请求。请尝试重新表述或联系人工客服。")
+                return state
 
     except Exception as e:
         logger.error("Customer service agent failed", error=str(e), exc_info=True)
@@ -342,11 +377,59 @@ async def customer_service_agent(state: AgentState) -> AgentState:
 async def _generate_response_from_results(state: AgentState) -> AgentState:
     """Generate response based on tool results"""
 
-    # Build context from tool results
+    # Build context from tool results - extract only essential info to reduce prompt size
     tool_context = []
     for result in state["tool_results"]:
         if result["success"]:
-            tool_context.append(f"Tool {result['tool_name']} returned:\n{json.dumps(result['data'], ensure_ascii=False, indent=2)}")
+            tool_name = result['tool_name']
+            data = result['data']
+
+            # Extract only essential information based on tool type
+            if tool_name == "get_company_info":
+                # Extract key contact info only
+                contact = data.get('contact', {})
+                emails = contact.get('email', [])
+                if isinstance(emails, list) and emails:
+                    email_str = ", ".join(emails[:3])  # Max 3 emails
+                else:
+                    email_str = str(emails) if emails else "N/A"
+
+                phones = contact.get('phone', [])
+                if isinstance(phones, list) and phones:
+                    phone_str = ", ".join(phones[:2])  # Max 2 phones
+                else:
+                    phone_str = str(phones) if phones else "N/A"
+
+                address = contact.get('address', {})
+                address_str = f"{address.get('city', '')}, {address.get('country', '')}".strip(', ')
+
+                summary = f"Contact Information: Emails: {email_str} | Phones: {phone_str} | Address: {address_str} | Working hours: {contact.get('working_hours', 'N/A')}"
+                tool_context.append(summary)
+
+            elif tool_name == "query_faq" or tool_name == "search_knowledge_base":
+                # Extract FAQ items summary
+                faqs = data.get('faqs', []) if isinstance(data, dict) else []
+                if faqs:
+                    faq_summaries = [f"- Q: {faq.get('question', '')[:50]}... A: {faq.get('answer', '')[:50]}..." for faq in faqs[:3]]
+                    summary = f"Found {len(faqs)} FAQ items:\n" + "\n".join(faq_summaries)
+                    tool_context.append(summary)
+                else:
+                    tool_context.append("No FAQ items found")
+
+            elif tool_name == "get_categories":
+                # Extract category names only
+                categories = data.get('categories', []) if isinstance(data, dict) else []
+                category_names = [cat.get('name', '') for cat in categories[:5] if cat.get('name')]
+                summary = f"Available categories: {', '.join(category_names)}"
+                if len(categories) > 5:
+                    summary += f" (and {len(categories) - 5} more)"
+                tool_context.append(summary)
+
+            else:
+                # For other tools, include concise summary (limit to 200 chars)
+                data_str = json.dumps(data, ensure_ascii=False)[:200]
+                tool_context.append(f"Tool {tool_name} returned: {data_str}...")
+
         else:
             tool_context.append(f"Tool {result['tool_name']} failed: {result['error']}")
 
@@ -357,7 +440,8 @@ User question: {state["current_message"]}
 Tool returned information:
 {chr(10).join(tool_context)}
 
-Please generate a friendly and professional response. If the tool did not return useful information, honestly inform the user and suggest other ways to get help.
+Please generate a friendly and professional response in Chinese. Keep it concise but informative.
+If the tool did not return useful information, honestly inform the user and suggest other ways to get help.
 Return only the response content, do not return JSON."""
 
     messages = [
@@ -367,11 +451,12 @@ Return only the response content, do not return JSON."""
 
     try:
         llm = get_llm_client()
-        response = await llm.chat(messages, temperature=0.7)
+        # Lower temperature for faster response
+        response = await llm.chat(messages, temperature=0.3)
         state = set_response(state, response.content)
         return state
 
     except Exception as e:
         logger.error("Response generation failed", error=str(e))
-        state = set_response(state, "Sorry, there was a problem processing your request. Please try again later or contact customer support.")
+        state = set_response(state, "抱歉，处理您的请求时出现问题。请稍后重试或联系人工客服。")
         return state

agent/agents/product.py

@@ -30,9 +30,11 @@ PRODUCT_AGENT_PROMPT = """你是一个专业的 B2B 商品顾问助手。
 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
+3. **recommend_products** - 智能推荐（心动清单/猜你喜欢）
+   - page_size: 推荐数量（默认 6，最大 100）
+   - page: 页码（默认 1）
+   - warehouse_id: 仓库ID（默认 2）
+   - 说明：此工具使用 Mall API /mall/api/loveList 接口，需要用户 token 认证，系统会自动注入用户 token
 
 4. **get_quote** - B2B 询价
    - product_id: 商品ID
@@ -81,6 +83,18 @@ PRODUCT_AGENT_PROMPT = """你是一个专业的 B2B 商品顾问助手。
 }
 ```
 
+用户说："推荐一些商品"
+返回：
+```json
+{
+  "action": "call_tool",
+  "tool_name": "recommend_products",
+  "arguments": {
+    "page_size": 6
+  }
+}
+```
+
 当需要向用户询问更多信息时：
 ```json
 {
@@ -104,6 +118,17 @@ PRODUCT_AGENT_PROMPT = """你是一个专业的 B2B 商品顾问助手。
 - 报价通常有有效期
 
 ## 商品推荐策略
+
+**重要规则：推荐 vs 搜索**
+- **泛泛推荐**（"推荐一些商品"、"推荐一下"、"有什么好推荐的"） → 使用 recommend_products
+- **具体商品推荐**（"推荐ring相关的商品"、"推荐手机"、"推荐一些珠宝"） → 使用 search_products (提取关键词：ring、手机、珠宝)
+- **商品搜索**（"搜索ring"、"找ring商品"） → 使用 search_products
+
+**说明**：
+- 如果用户推荐请求中包含具体的商品关键词（如 ring、手机、珠宝等），使用 search_products 进行精准搜索
+- 只有在泛泛请求推荐时才使用 recommend_products（基于用户行为的个性化推荐）
+
+**其他推荐依据**：
 - 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
@@ -119,12 +144,12 @@ PRODUCT_AGENT_PROMPT = """你是一个专业的 B2B 商品顾问助手。
 
 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
     """
@@ -133,11 +158,40 @@ async def product_agent(state: AgentState) -> AgentState:
         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
-    
+
+    # ========== FAST PATH: Image Search ==========
+    # Check if this is an image search request
+    image_search_url = state.get("context", {}).get("image_search_url")
+    if image_search_url:
+        logger.info(
+            "Image search detected, calling search_products_by_image",
+            conversation_id=state["conversation_id"],
+            image_url=image_search_url[:100] + "..." if len(image_search_url) > 100 else image_search_url
+        )
+
+        # 直接调用图片搜索工具
+        state = add_tool_call(
+            state,
+            tool_name="search_products_by_image",
+            arguments={
+                "image_url": image_search_url,
+                "page_size": 6,
+                "page": 1
+            },
+            server="product"
+        )
+
+        # 清除 image_search_url 防止无限循环
+        state["context"]["image_search_url"] = None
+
+        state["state"] = ConversationState.TOOL_CALLING.value
+        return state
+    # ==============================================
+
     # Check if we have tool results to process
     if state["tool_results"]:
         return await _generate_product_response(state)
@@ -148,7 +202,8 @@ async def product_agent(state: AgentState) -> AgentState:
     ]
     
     # Add conversation history
-    for msg in state["messages"][-6:]:
+    # 只保留最近 2 条历史消息以减少 token 数量和响应时间
+    for msg in state["messages"][-2:]:
         messages.append(Message(role=msg["role"], content=msg["content"]))
     
     # Build context info
@@ -233,7 +288,7 @@ async def product_agent(state: AgentState) -> AgentState:
 
                 # Set default page_size if not provided
                 if "page_size" not in arguments:
-                    arguments["page_size"] = 5
+                    arguments["page_size"] = 6
 
                 # Set default page if not provided
                 if "page" not in arguments:
@@ -249,8 +304,21 @@ async def product_agent(state: AgentState) -> AgentState:
 
             # Inject context for recommendation
             if tool_name == "recommend_products":
-                arguments["user_id"] = state["user_id"]
-                arguments["account_id"] = state["account_id"]
+                arguments["user_token"] = state.get("user_token")
+                # 如果没有提供 page_size，使用默认值 6
+                if "page_size" not in arguments:
+                    arguments["page_size"] = 6
+                # 如果没有提供 warehouse_id，使用默认值 2
+                if "warehouse_id" not in arguments:
+                    arguments["warehouse_id"] = 2
+
+                logger.info(
+                    "Product agent recommend_products after injection",
+                    user_token_present="user_token" in arguments,
+                    user_token_preview=arguments.get("user_token", "")[:20] + "..." if arguments.get("user_token") else None,
+                    arguments=arguments,
+                    conversation_id=state["conversation_id"]
+                )
 
             # Inject context for quote
             if tool_name == "get_quote":
@@ -301,25 +369,55 @@ async def product_agent(state: AgentState) -> AgentState:
 async def _generate_product_response(state: AgentState) -> AgentState:
     """Generate response based on product tool results"""
 
-    # 特殊处理：如果是 search_products 工具返回，直接发送商品卡片
-    has_product_search_result = False
+    # 特殊处理：如果是 search_products、recommend_products 或 search_products_by_image 工具返回，直接发送商品卡片
+    has_product_result = False
     products = []
+    result_source = None  # "search", "recommend" 或 "image_search"
+
+    # 添加日志：查看所有工具结果
+    import json as json_module
+    logger.info(
+        "All tool results",
+        tool_results_count=len(state.get("tool_results", [])),
+        tool_results=json_module.dumps(state.get("tool_results", []), ensure_ascii=False, indent=2)[:2000]
+    )
 
     for result in state["tool_results"]:
-        if result["success"] and result["tool_name"] == "search_products":
+        logger.info(
+            "Processing tool result",
+            tool_name=result["tool_name"],
+            success=result["success"],
+            data_keys=list(result.get("data", {}).keys()) if isinstance(result.get("data"), dict) else "not a dict",
+            data_preview=json_module.dumps(result.get("data"), ensure_ascii=False)[:500]
+        )
+
+        if result["success"] and result["tool_name"] in ["search_products", "recommend_products", "search_products_by_image"]:
             data = result["data"]
             if isinstance(data, dict) and data.get("success"):
-                products = data.get("products", [])
-                has_product_search_result = True
+                # MCP 返回的数据结构: {"success": true, "result": {"success": true, "products": [...]}}
+                # 需要从 result.result 中提取实际数据
+                inner_data = data.get("result", data)
+                products = inner_data.get("products", [])
+                keyword = inner_data.get("keyword", "")
+
+                has_product_result = True
+                if result["tool_name"] == "recommend_products":
+                    result_source = "recommend"
+                elif result["tool_name"] == "search_products_by_image":
+                    result_source = "image_search"
+                else:
+                    result_source = "search"
+
                 logger.info(
-                    "Product search results found",
+                    f"Product {result_source} results found",
                     products_count=len(products),
-                    keyword=data.get("keyword", "")
+                    keyword=keyword,
+                    products_preview=json_module.dumps(products[:2], ensure_ascii=False, indent=2) if products else "[]"
                 )
                 break
 
-    # 如果有商品搜索结果，直接发送商品卡片
-    if has_product_search_result and products:
+    # 如果有商品结果，直接发送商品卡片（product_list 格式）
+    if has_product_result and products:
         try:
             from integrations.chatwoot import ChatwootClient
             from core.language_detector import detect_language
@@ -327,7 +425,7 @@ async def _generate_product_response(state: AgentState) -> AgentState:
             # 检测语言
             detected_language = state.get("detected_language", "en")
 
-            # 发送商品卡片
+            # 发送商品列表
             chatwoot = ChatwootClient(account_id=int(state.get("account_id", 1)))
             conversation_id = state.get("conversation_id")
 
@@ -339,10 +437,11 @@ async def _generate_product_response(state: AgentState) -> AgentState:
                 )
 
                 logger.info(
-                    "Product cards sent successfully",
+                    f"Product {result_source} cards sent successfully",
                     conversation_id=conversation_id,
                     products_count=len(products),
-                    language=detected_language
+                    language=detected_language,
+                    result_source=result_source
                 )
 
                 # 清空响应，避免重复发送
@@ -352,17 +451,56 @@ async def _generate_product_response(state: AgentState) -> AgentState:
 
         except Exception as e:
             logger.error(
-                "Failed to send product cards, falling back to text response",
+                f"Failed to send product {result_source} cards, falling back to text response",
                 error=str(e),
-                products_count=len(products)
+                products_count=len(products),
+                result_source=result_source
             )
 
     # 常规处理：生成文本响应
     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)}")
+            tool_name = result['tool_name']
+            data = result['data']
+
+            # Extract only essential information based on tool type
+            if tool_name == "search_products" or tool_name == "recommend_products":
+                products = data.get("products", []) if isinstance(data, dict) else []
+                if products:
+                    product_summaries = [f"- {p.get('product_name', 'N/A')}: {p.get('price', 'N/A')}" for p in products[:3]]
+                    summary = f"Found {len(products)} products:\n" + "\n".join(product_summaries)
+                    if len(products) > 3:
+                        summary += f"\n(and {len(products) - 3} more)"
+                    tool_context.append(summary)
+                else:
+                    tool_context.append("No products found")
+
+            elif tool_name == "get_product_detail":
+                product = data.get("product", {}) if isinstance(data, dict) else {}
+                name = product.get("product_name", product.get("name", "N/A"))
+                price = product.get("price", "N/A")
+                stock = product.get("stock", product.get("stock_status", "N/A"))
+                summary = f"Product: {name} | Price: {price} | Stock: {stock}"
+                tool_context.append(summary)
+
+            elif tool_name == "check_inventory":
+                inventory = data.get("inventory", []) if isinstance(data, dict) else []
+                inv_summaries = [f"{inv.get('product_id', 'N/A')}: {inv.get('quantity', 'N/A')} available" for inv in inventory[:3]]
+                summary = "Inventory status:\n" + "\n".join(inv_summaries)
+                tool_context.append(summary)
+
+            elif tool_name == "get_pricing":
+                product_id = data.get("product_id", "N/A")
+                unit_price = data.get("unit_price", "N/A")
+                total_price = data.get("total_price", "N/A")
+                summary = f"Quote for {product_id}: Unit: {unit_price} | Total: {total_price}"
+                tool_context.append(summary)
+
+            else:
+                # For other tools, include concise summary (limit to 200 chars)
+                data_str = json.dumps(data, ensure_ascii=False)[:200]
+                tool_context.append(f"工具 {tool_name} 返回: {data_str}...")
 
             # Extract product context
             if isinstance(data, dict):
@@ -398,7 +536,8 @@ async def _generate_product_response(state: AgentState) -> AgentState:
 
     try:
         llm = get_llm_client()
-        response = await llm.chat(messages, temperature=0.7)
+        # Lower temperature for faster response
+        response = await llm.chat(messages, temperature=0.3)
         state = set_response(state, response.content)
         return state
 

agent/config.py

@@ -10,8 +10,17 @@ 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")
+    llm_provider: str = Field(default="zhipu", description="LLM provider: 'zhipu' or 'qwen'")
+
+    # ZhipuAI (智谱 AI)
+    zhipu_api_key: str = Field(default="", description="ZhipuAI API Key")
+    zhipu_model: str = Field(default="glm-4-flash", description="ZhipuAI Model name")
+
+    # Qwen (通义千问)
+    qwen_api_key: str = Field(default="", description="Qwen/DashScope API Key")
+    qwen_model: str = Field(default="qwen-omni-turbo", description="Qwen Model name")
+
+    # 通用配置
     enable_reasoning_mode: bool = Field(default=False, description="Enable AI reasoning/thinking mode (slower but more thoughtful)")
     reasoning_mode_for_complex: bool = Field(default=True, description="Enable reasoning mode only for complex queries")
     

agent/core/llm.py

@@ -1,8 +1,9 @@
 """
-ZhipuAI LLM Client for B2B Shopping AI Assistant
+LLM Client for B2B Shopping AI Assistant
+Supports both ZhipuAI and Qwen (DashScope)
 """
 import concurrent.futures
-from typing import Any, Optional
+from typing import Any, Optional, Union
 from dataclasses import dataclass
 
 from zhipuai import ZhipuAI
@@ -276,12 +277,168 @@ class ZhipuLLMClient:
             raise
 
 
-llm_client: Optional[ZhipuLLMClient] = None
+llm_client: Optional[Union[ZhipuLLMClient, "QwenLLMClient"]] = None
 
 
-def get_llm_client() -> ZhipuLLMClient:
-    """Get or create global LLM client instance"""
+def get_llm_client() -> Union[ZhipuLLMClient, "QwenLLMClient"]:
+    """Get or create global LLM client instance based on provider setting"""
     global llm_client
     if llm_client is None:
-        llm_client = ZhipuLLMClient()
+        provider = settings.llm_provider.lower()
+        if provider == "qwen":
+            llm_client = QwenLLMClient()
+        else:
+            llm_client = ZhipuLLMClient()
     return llm_client
+
+
+# ============ Qwen (DashScope) LLM Client ============
+
+try:
+    from dashscope import Generation
+    DASHSCOPE_AVAILABLE = True
+except ImportError:
+    DASHSCOPE_AVAILABLE = False
+    logger.warning("DashScope SDK not installed. Qwen models will not be available.")
+
+
+class QwenLLMClient:
+    """Qwen (DashScope) LLM Client wrapper"""
+
+    DEFAULT_TIMEOUT = 60  # seconds
+
+    def __init__(
+        self,
+        api_key: Optional[str] = None,
+        model: Optional[str] = None,
+        timeout: Optional[int] = None
+    ):
+        if not DASHSCOPE_AVAILABLE:
+            raise ImportError("DashScope SDK is not installed. Install it with: pip install dashscope")
+
+        self.api_key = api_key or settings.qwen_api_key
+        self.model = model or settings.qwen_model
+        self.timeout = timeout or self.DEFAULT_TIMEOUT
+
+        # 设置 API key 到 DashScope SDK
+        # 必须直接设置 dashscope.api_key，环境变量可能不够
+        import dashscope
+        dashscope.api_key = self.api_key
+
+        logger.info(
+            "Qwen client initialized",
+            model=self.model,
+            timeout=self.timeout,
+            api_key_prefix=self.api_key[:10] + "..." if len(self.api_key) > 10 else self.api_key
+        )
+
+    async def chat(
+        self,
+        messages: list[Message],
+        temperature: float = 0.7,
+        max_tokens: int = 2048,
+        top_p: float = 0.9,
+        use_cache: bool = True,
+        **kwargs: Any
+    ) -> LLMResponse:
+        """Send chat completion request with caching support"""
+        formatted_messages = [
+            {"role": msg.role, "content": msg.content}
+            for msg in messages
+        ]
+
+        # Try cache first
+        if use_cache:
+            try:
+                cache = get_response_cache()
+                cached_response = await cache.get(
+                    model=self.model,
+                    messages=formatted_messages,
+                    temperature=temperature
+                )
+                if cached_response is not None:
+                    logger.info(
+                        "Returning cached response",
+                        model=self.model,
+                        response_length=len(cached_response)
+                    )
+                    return LLMResponse(
+                        content=cached_response,
+                        finish_reason="cache_hit",
+                        usage={"prompt_tokens": 0, "completion_tokens": 0, "total_tokens": 0}
+                    )
+            except Exception as e:
+                logger.warning("Cache check failed", error=str(e))
+
+        logger.info(
+            "Sending chat request",
+            model=self.model,
+            message_count=len(messages),
+            temperature=temperature
+        )
+
+        def _make_request():
+            response = Generation.call(
+                model=self.model,
+                messages=formatted_messages,
+                temperature=temperature,
+                max_tokens=max_tokens,
+                top_p=top_p,
+                result_format="message",
+                **kwargs
+            )
+            return response
+
+        try:
+            with concurrent.futures.ThreadPoolExecutor(max_workers=1) as executor:
+                future = executor.submit(_make_request)
+                response = future.result(timeout=self.timeout)
+
+            # Qwen API 响应格式
+            if response.status_code != 200:
+                raise Exception(f"Qwen API error: {response.message}")
+
+            content = response.output.choices[0].message.content
+            finish_reason = response.output.choices[0].finish_reason
+            usage = response.usage
+
+            logger.info(
+                "Chat response received",
+                finish_reason=finish_reason,
+                content_length=len(content) if content else 0,
+                usage=usage
+            )
+
+            if not content:
+                logger.warning("LLM returned empty content")
+
+            # Cache the response
+            if use_cache and content:
+                try:
+                    cache = get_response_cache()
+                    await cache.set(
+                        model=self.model,
+                        messages=formatted_messages,
+                        response=content,
+                        temperature=temperature
+                    )
+                except Exception as e:
+                    logger.warning("Failed to cache response", error=str(e))
+
+            return LLMResponse(
+                content=content or "",
+                finish_reason=finish_reason,
+                usage={
+                    "prompt_tokens": usage.input_tokens,
+                    "completion_tokens": usage.output_tokens,
+                    "total_tokens": usage.total_tokens
+                }
+            )
+
+        except concurrent.futures.TimeoutError:
+            logger.error("Chat request timed out", timeout=self.timeout)
+            raise TimeoutError(f"Request timed out after {self.timeout} seconds")
+
+        except Exception as e:
+            logger.error("Chat request failed", error=str(e))
+            raise

agent/integrations/chatwoot.py

@@ -1089,7 +1089,7 @@ class ChatwootClient:
         products: list[dict[str, Any]],
         language: str = "en"
     ) -> dict[str, Any]:
-        """发送商品搜索结果（使用 cards 格式）
+        """发送商品搜索结果（使用 product_list 格式）
 
         Args:
             conversation_id: 会话 ID
@@ -1100,8 +1100,7 @@ class ChatwootClient:
                 - product_image: 商品图片 URL
                 - price: 价格
                 - special_price: 特价（可选）
-                - stock: 库存
-                - sales_count: 销量
+                - href: 商品链接路径（可选）
             language: 语言代码（en, nl, de, es, fr, it, tr, zh），默认 en
 
         Returns:
@@ -1114,124 +1113,89 @@ class ChatwootClient:
             ...         "product_name": "Product A",
             ...         "product_image": "https://...",
             ...         "price": "99.99",
-            ...         "stock": 100
+            ...         "href": "/product/detail/12345"
             ...     }
             ... ]
             >>> await chatwoot.send_product_cards(123, products, language="zh")
         """
-        # 获取前端 URL
+        client = await self._get_client()
+
+        # 获取前端域名
         frontend_url = settings.frontend_url.rstrip('/')
 
-        # 构建商品卡片
-        cards = []
+        # 构建商品列表
+        product_list = []
 
         for product in products:
-            spu_id = product.get("spu_id", "")
-            spu_sn = product.get("spu_sn", "")
             product_name = product.get("product_name", "Unknown Product")
             product_image = product.get("product_image", "")
             price = product.get("price", "0")
             special_price = product.get("special_price")
-            stock = product.get("stock", 0)
-            sales_count = product.get("sales_count", 0)
+            href = product.get("href", "")
 
-            # 价格显示（如果有特价则显示特价）
+            # 价格显示（如果有特价则显示特价，否则显示原价）
             try:
-                price_num = float(price) if price else 0
-                price_text = f"€{price_num:.2f}"
+                if special_price and float(special_price) > 0:
+                    price_num = float(special_price)
+                else:
+                    price_num = float(price) if price else 0
+
+                # 根据语言选择货币符号
+                if language == "zh":
+                    price_text = f"¥{price_num:.2f}"
+                else:
+                    price_text = f"€{price_num:.2f}"
             except (ValueError, TypeError):
-                price_text = str(price) if price else "€0.00"
+                price_text = str(price) if price else ("¥0.00" if language == "zh" else "€0.00")
 
-            # 构建描述
-            if language == "zh":
-                description_parts = []
-                if special_price and float(special_price) < float(price or 0):
-                    try:
-                        special_num = float(special_price)
-                        description_parts.append(f"特价: €{special_num:.2f}")
-                    except:
-                        pass
-                if stock is not None:
-                    description_parts.append(f"库存: {stock}")
-                if sales_count:
-                    description_parts.append(f"已售: {sales_count}")
-                description = " | ".join(description_parts) if description_parts else "暂无详细信息"
-            else:
-                description_parts = []
-                if special_price and float(special_price) < float(price or 0):
-                    try:
-                        special_num = float(special_price)
-                        description_parts.append(f"Special: €{special_num:.2f}")
-                    except:
-                        pass
-                if stock is not None:
-                    description_parts.append(f"Stock: {stock}")
-                if sales_count:
-                    description_parts.append(f"Sold: {sales_count}")
-                description = " | ".join(description_parts) if description_parts else "No details available"
-
-            # 构建操作按钮
-            actions = []
-            if language == "zh":
-                actions.append({
-                    "type": "link",
-                    "text": "查看详情",
-                    "uri": f"{frontend_url}/product/detail?spuId={spu_id}"
-                })
-                if stock and stock > 0:
-                    actions.append({
-                        "type": "link",
-                        "text": "立即购买",
-                        "uri": f"{frontend_url}/product/detail?spuId={spu_id}"
-                    })
-            else:
-                actions.append({
-                    "type": "link",
-                    "text": "View Details",
-                    "uri": f"{frontend_url}/product/detail?spuId={spu_id}"
-                })
-                if stock and stock > 0:
-                    actions.append({
-                        "type": "link",
-                        "text": "Buy Now",
-                        "uri": f"{frontend_url}/product/detail?spuId={spu_id}"
-                    })
-
-            # 构建卡片
-            card = {
-                "title": product_name,
-                "description": description,
-                "media_url": product_image,
-                "actions": actions
+            # 构建商品对象
+            product_obj = {
+                "image": product_image,
+                "name": product_name,
+                "price": price_text,
+                "target": "_blank"
             }
 
-            cards.append(card)
+            # 如果有 href，添加完整 URL
+            if href:
+                product_obj["url"] = f"{frontend_url}{href}"
+            else:
+                # 如果没有 href，使用 spu_id 构建默认链接
+                spu_id = product.get("spu_id", "")
+                if spu_id:
+                    product_obj["url"] = f"{frontend_url}/product/detail?spuId={spu_id}"
 
-        # 发送 cards 类型消息
-        client = await self._get_client()
+            product_list.append(product_obj)
 
+        # 构建标题
+        if language == "zh":
+            title = "找到以下商品"
+        else:
+            title = "Found following products"
+
+        # 构建 content_attributes
         content_attributes = {
-            "items": cards
+            "title": title,
+            "products": product_list,
+            "actions": []
         }
 
-        # 添加标题
-        if language == "zh":
-            content = f"找到 {len(products)} 个商品"
-        else:
-            content = f"Found {len(products)} products"
-
+        # 发送 product_list 类型消息
         payload = {
-            "content": content,
-            "content_type": "cards",
+            "content": "",
+            "content_type": "product_list",
+            "message_type": 1,
             "content_attributes": content_attributes
         }
 
+        # 输出完整的 payload 用于调试
+        import json as json_module
         logger.info(
-            "Sending product cards",
+            "Sending product list to Chatwoot",
             conversation_id=conversation_id,
-            products_count=len(products),
+            products_count=len(product_list),
             language=language,
-            payload_preview=str(payload)[:1000]
+            payload=json_module.dumps(payload, ensure_ascii=False, indent=2)
         )
 
         response = await client.post(

agent/prompts/product/en.yaml

@@ -12,10 +12,16 @@ system_prompt: |
 
   ## Available Tools
 
-  **search_products** - Search for products
-  - query: Search keywords
-  - category: Product category (optional)
-  - filters: {attribute: value} (optional)
+  **search_products** - Search for products by keyword
+  - keyword: Search keywords (required)
+  - page_size: Number of results (optional, default: 6)
+  - page: Page number (optional, default: 1)
+
+  **recommend_products** - Get personalized product recommendations (Love List/心动清单)
+  - page_size: Number of recommendations (optional, default: 6)
+  - page: Page number (optional, default: 1)
+  - warehouse_id: Warehouse ID (optional, default: 2)
+  - Note: Requires user authentication token, uses Mall API /mall/api/loveList
 
   **get_product_details** - Get detailed product information
   - product_id: Product ID or SKU
@@ -28,18 +34,22 @@ system_prompt: |
   - product_id: Product ID
   - quantity: Quantity for pricing (optional, for tiered pricing)
 
-  **recommend_products** - Get product recommendations
-  - category: Product category
-  - limit: Number of recommendations
-
   ## Important Rules
 
   1. **Product Recognition**:
-     - Product search/产品搜索/找产品/商品 → Use search_products
+     - 泛泛推荐（推荐/推荐商品/猜你喜欢/心动清单，无具体关键词） → Use recommend_products
+     - 具体商品推荐（推荐ring/推荐手机，有具体关键词） → Use search_products
+     - Product search/产品搜索/找商品/搜索商品 + 具体关键词 → Use search_products
      - Price/价格/报价/多少钱 → Use get_pricing
      - Stock/库存/有没有货/现货 → Use check_stock
      - Product details/产品详情/产品信息/产品规格 → Use get_product_details
-     - Recommendation/推荐/推荐产品 → Use recommend_products
+
+  2. **Recommendation vs Search**:
+     - "推荐一些商品"、"推荐一下"、"有什么好推荐的"（无具体关键词） → Use recommend_products
+     - "推荐ring相关的商品"、"推荐手机"、"推荐一些珠宝"（有具体关键词） → Use search_products (keyword: "ring"/"手机"/"珠宝")
+     - "搜索ring"、"找ring商品" → Use search_products (keyword: "ring")
+
+     **规则**: 如果推荐请求中包含具体的商品关键词（如 ring、手机、珠宝等），使用 search_products 进行精准搜索。只有在泛泛请求推荐时才使用 recommend_products。
 
   2. For B2B customers, prioritize wholesale/bulk pricing information
   3. Always check stock availability before suggesting purchases
@@ -51,17 +61,24 @@ system_prompt: |
      - For Chinese inquiries, respond in Chinese
      - For English inquiries, respond in English
 
+  ## ⚠️ CRITICAL: Response Format
+
+  **You MUST ALWAYS respond with valid JSON. Never respond with plain text.**
+
   ## Tool Call Format
 
+  When you need to use a tool, respond with EXACTLY this JSON format:
   ```json
   {
     "action": "call_tool",
-    "tool_name": "tool_name",
-    "arguments": {"parameter": "value"}
+    "tool_name": "search_products",
+    "arguments": {
+      "query": "product name"
+    }
   }
   ```
 
-  Or to respond directly:
+  When you can answer directly:
   ```json
   {
     "action": "respond",
@@ -69,6 +86,14 @@ system_prompt: |
   }
   ```
 
+  **IMPORTANT:**
+  - Your entire response must be a valid JSON object
+  - Do NOT include any text outside the JSON
+  - Do NOT use markdown code blocks
+  - Always include "action" field
+  - "action" must be either "call_tool" or "respond"
+  - For "call_tool", you must include "tool_name" and "arguments"
+
 tool_descriptions:
   search_products: "Search for products by keywords or category"
   get_product_details: "Get detailed product information"

agent/requirements.txt

@@ -9,6 +9,7 @@ langchain-core>=0.1.0
 
 # AI Model SDK
 zhipuai>=2.0.0
+dashscope>=1.14.0
 # Async utilities
 sniffio>=1.3.0
 anyio>=4.0.0

agent/webhooks/chatwoot_webhook.py

@@ -6,6 +6,7 @@ import hashlib
 from typing import Any, Optional
 
 from fastapi import APIRouter, Request, HTTPException, BackgroundTasks
+from fastapi.responses import JSONResponse
 from pydantic import BaseModel
 
 from config import settings
@@ -75,6 +76,7 @@ class ChatwootWebhookPayload(BaseModel):
     message_type: Optional[str] = None
     content_type: Optional[str] = None
     private: Optional[bool] = False
+    content_attributes: Optional[dict] = None  # 图片搜索 URL 等额外属性
     conversation: Optional[WebhookConversation] = None
     sender: Optional[WebhookSender] = None
     contact: Optional[WebhookContact] = None
@@ -132,7 +134,15 @@ async def handle_incoming_message(payload: ChatwootWebhookPayload, cookie_token:
     conversation_id = str(conversation.id)
     content = payload.content
 
-    if not content:
+    # 检查是否是图片搜索消息（在过滤空 content 之前）
+    # 图片搜索消息的 content 通常是空的，但需要处理
+    is_image_search = (
+        payload.content_type == "search_image" or
+        payload.content_type == 17
+    )
+
+    # 只有非图片搜索消息才检查 content 是否为空
+    if not content and not is_image_search:
         logger.debug("Empty message content, skipping")
         return
 
@@ -282,19 +292,7 @@ async def handle_incoming_message(payload: ChatwootWebhookPayload, cookie_token:
         channel=conversation.channel if conversation else None
     )
 
-    # 识别消息渠道（邮件、网站等）
-    message_channel = conversation.channel if conversation else "Channel"
-    is_email = message_channel == "Email"
-
-    # 邮件渠道特殊处理
-    if is_email:
-        logger.info(
-            "Email channel detected",
-            conversation_id=conversation_id,
-            sender_email=contact.email if contact else None
-        )
-
-    # Load conversation context from cache
+    # Load conversation context from cache (需要在图片搜索检测之前)
     cache = get_cache_manager()
     await cache.connect()
 
@@ -307,10 +305,125 @@ async def handle_incoming_message(payload: ChatwootWebhookPayload, cookie_token:
     if mall_token:
         context["mall_token"] = mall_token
 
+    # 图片 URL 可能在两个位置：
+    # 1. payload.content_attributes.url (顶层)
+    # 2. payload.conversation.messages[0].content_attributes.url (嵌套)
+    image_url = None
+
+    # 首先尝试从顶层获取
+    if payload.content_attributes:
+        image_url = payload.content_attributes.get("url")
+
+    # 如果顶层没有，尝试从 conversation.messages[0] 获取
+    if not image_url and conversation and hasattr(conversation, 'model_dump'):
+        conv_dict = conversation.model_dump()
+        messages = conv_dict.get('messages', [])
+        if messages and len(messages) > 0:
+            first_message = messages[0]
+            msg_content_attrs = first_message.get('content_attributes', {})
+            image_url = msg_content_attrs.get('url')
+
+    if is_image_search and image_url:
+        logger.info(
+            "Image search detected",
+            conversation_id=conversation_id,
+            image_url=image_url[:100] + "..." if len(image_url) > 100 else image_url
+        )
+
+        # 创建 Chatwoot client
+        from integrations.chatwoot import ChatwootClient
+        chatwoot = ChatwootClient(account_id=int(account_id))
+
+        # 开启 typing status
+        try:
+            await chatwoot.toggle_typing_status(
+                conversation_id=conversation.id,
+                typing_status="on"
+            )
+        except Exception as e:
+            logger.warning(
+                "Failed to enable typing status for image search",
+                conversation_id=conversation_id,
+                error=str(e)
+            )
+
+        # 直接调用图片搜索工具
+        # 修改 content 为图片搜索指令
+        search_content = f"[图片搜索] 请根据这张图片搜索相似的商品"
+
+        # 使用特殊标记让 Product Agent 知道这是图片搜索
+        context["image_search_url"] = image_url
+
+        final_state = await process_message(
+            conversation_id=conversation_id,
+            user_id=user_id,
+            account_id=account_id,
+            message=search_content,
+            history=history,
+            context=context,
+            user_token=user_token,
+            mall_token=mall_token
+        )
+
+        # 获取响应并发送
+        response = final_state.get("response", "")
+
+        if response:
+            await chatwoot.send_message(
+                conversation_id=conversation.id,
+                content=response
+            )
+
+            logger.info(
+                "Image search response sent",
+                conversation_id=conversation_id,
+                response_length=len(response)
+            )
+
+        # 关闭 typing status
+        await chatwoot.toggle_typing_status(
+            conversation_id=conversation.id,
+            typing_status="off"
+        )
+
+        await chatwoot.close()
+
+        # Update cache
+        await cache.add_message(conversation_id, "user", search_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(
+            "Image search message processed successfully",
+            conversation_id=conversation_id,
+            intent=final_state.get("intent")
+        )
+
+        return JSONResponse(
+            content={"status": "success"},
+            status_code=200
+        )
+
+    # 识别消息渠道（邮件、网站等）
+    message_channel = conversation.channel if conversation else "Channel"
+    is_email = message_channel == "Email"
+
     # 添加渠道信息到 context（让 Agent 知道是邮件还是网站）
     context["channel"] = message_channel
     context["is_email"] = is_email
 
+    # 邮件渠道特殊处理
+    if is_email:
+        logger.info(
+            "Email channel detected",
+            conversation_id=conversation_id,
+            sender_email=contact.email if contact else None
+        )
+
     # 创建 Chatwoot client（提前创建以便开启 typing status）
     from integrations.chatwoot import ChatwootClient
     chatwoot = ChatwootClient(account_id=int(account_id))