assistant/agent/agents/product.py

"""
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
   - keyword: 搜索关键词（商品名称、编号等）
   - page_size: 每页数量（默认 60，最大 100）
   - page: 页码（默认 1）
   - 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

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"
  }
}
```

**示例**：
用户说："搜索 ring"
返回：
```json
{
  "action": "call_tool",
  "tool_name": "search_products",
  "arguments": {
    "keyword": "ring"
  }
}
```

用户说："查找手机"
返回：
```json
{
  "action": "call_tool",
  "tool_name": "search_products",
  "arguments": {
    "keyword": "手机"
  }
}
```

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()

        # Log raw LLM response for debugging
        logger.info(
            "Product agent LLM response",
            response_length=len(content),
            response_preview=content[:200],
            conversation_id=state["conversation_id"]
        )

        if content.startswith("```"):
            content = content.split("```")[1]
            if content.startswith("json"):
                content = content[4:]
            # Remove leading/trailing whitespace after removing code block markers
            content = content.strip()

        # 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 {}
                result = {
                    "action": "call_tool",
                    "tool_name": tool_name,
                    "arguments": arguments
                }
            except json.JSONDecodeError:
                # If args parsing fails, use empty dict
                result = {
                    "action": "call_tool",
                    "tool_name": tool_name,
                    "arguments": {}
                }
        else:
            # Standard JSON format
            result = json.loads(content)

        action = result.get("action")

        if action == "call_tool":
            arguments = result.get("arguments", {})
            tool_name = result.get("tool_name", "")

            logger.info(
                "Product agent calling tool",
                tool_name=tool_name,
                arguments=arguments,
                conversation_id=state["conversation_id"]
            )

            # Inject context for product search (Mall API)
            if tool_name == "search_products":
                arguments["user_token"] = state.get("user_token")
                arguments["user_id"] = state["user_id"]
                arguments["account_id"] = state["account_id"]

                # Map "query" parameter to "keyword" for compatibility
                if "query" in arguments and "keyword" not in arguments:
                    arguments["keyword"] = arguments.pop("query")
                    logger.info(
                        "Parameter mapped: query -> keyword",
                        conversation_id=state["conversation_id"]
                    )

            # Inject context for recommendation
            if tool_name == "recommend_products":
                arguments["user_id"] = state["user_id"]
                arguments["account_id"] = state["account_id"]

            # Inject context for quote
            if 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=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"""

    # 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
    products = []

    for result in state["tool_results"]:
        if result["success"] and result["tool_name"] == "search_products":
            data = result["data"]
            if isinstance(data, dict) and data.get("success"):
                products = data.get("products", [])
                has_product_search_result = True
                logger.info(
                    "Product search results found",
                    products_count=len(products),
                    keyword=data.get("keyword", "")
                )
                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:
        try:
            from integrations.chatwoot import ChatwootClient
            from core.language_detector import detect_language

            # 检测语言
            detected_language = state.get("detected_language", "en")

            # 发送商品卡片
            chatwoot = ChatwootClient(account_id=int(state.get("account_id", 1)))
            conversation_id = state.get("conversation_id")

            if conversation_id:
                await chatwoot.send_product_cards(
                    conversation_id=int(conversation_id),
                    products=products,
                    language=detected_language
                )

                logger.info(
                    "Product cards sent successfully",
                    conversation_id=conversation_id,
                    products_count=len(products),
                    language=detected_language
                )

                # 清空响应，避免重复发送
                state = set_response(state, "")
                state["state"] = ConversationState.GENERATING.value
                return state

        except Exception as e:
            logger.error(
                "Failed to send product cards, falling back to text response",
                error=str(e),
                products_count=len(products)
            )

    # 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)}")

            # 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