570 lines
21 KiB
Python
570 lines
21 KiB
Python
"""
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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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- 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 5，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. **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（心动清单/猜你喜欢）
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- page_size: 推荐数量（默认 6，最大 100）
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- page: 页码（默认 1）
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- warehouse_id: 仓库ID（默认 2）
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- 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
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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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**示例**：
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用户说："搜索 ring"
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返回：
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```json
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{
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"action": "call_tool",
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"tool_name": "search_products",
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"arguments": {
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"keyword": "ring"
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}
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}
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```
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用户说："查找手机"
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返回：
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```json
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{
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"action": "call_tool",
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"tool_name": "search_products",
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"arguments": {
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"keyword": "手机"
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}
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}
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```
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用户说："推荐一些商品"
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返回：
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```json
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{
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"action": "call_tool",
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"tool_name": "recommend_products",
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"arguments": {
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"page_size": 6
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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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**重要规则：推荐 vs 搜索**
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- **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
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- **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)
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- **商品搜索**（"搜索ring"、"找ring商品"） → 使用 search_products
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**说明**：
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- 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
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- 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）
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**其他推荐依据**：
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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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# ========== FAST PATH: Image Search ==========
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# Check if this is an image search request
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image_search_url = state.get("context", {}).get("image_search_url")
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if image_search_url:
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logger.info(
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"Image search detected, calling search_products_by_image",
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conversation_id=state["conversation_id"],
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image_url=image_search_url[:100] + "..." if len(image_search_url) > 100 else image_search_url
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)
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# 直接调用图片搜索工具
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state = add_tool_call(
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state,
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tool_name="search_products_by_image",
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arguments={
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"image_url": image_search_url,
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"page_size": 6,
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"page": 1
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},
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server="product"
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)
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# 清除 image_search_url 防止无限循环
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state["context"]["image_search_url"] = None
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state["state"] = ConversationState.TOOL_CALLING.value
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return state
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# ==============================================
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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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# 只保留最近 2 条历史消息以减少 token 数量和响应时间
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for msg in state["messages"][-2:]:
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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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# Log raw LLM response for debugging
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logger.info(
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"Product agent LLM response",
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response_length=len(content),
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response_preview=content[:200],
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conversation_id=state["conversation_id"]
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)
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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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# Remove leading/trailing whitespace after removing code block markers
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content = content.strip()
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# Handle non-JSON format: "tool_name\n{args}"
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if '\n' in content and not content.startswith('{'):
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lines = content.split('\n', 1)
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tool_name = lines[0].strip()
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args_json = lines[1].strip() if len(lines) > 1 else '{}'
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try:
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arguments = json.loads(args_json) if args_json else {}
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result = {
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"action": "call_tool",
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"tool_name": tool_name,
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"arguments": arguments
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}
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except json.JSONDecodeError:
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# If args parsing fails, use empty dict
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result = {
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"action": "call_tool",
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"tool_name": tool_name,
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"arguments": {}
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}
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else:
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# Standard JSON format
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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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tool_name = result.get("tool_name", "")
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logger.info(
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"Product agent calling tool",
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tool_name=tool_name,
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arguments=arguments,
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conversation_id=state["conversation_id"]
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)
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# Inject context for product search (Mall API)
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if tool_name == "search_products":
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arguments["user_token"] = state.get("user_token")
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arguments["user_id"] = state["user_id"]
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arguments["account_id"] = state["account_id"]
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# Set default page_size if not provided
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if "page_size" not in arguments:
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arguments["page_size"] = 6
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# Set default page if not provided
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if "page" not in arguments:
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arguments["page"] = 1
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# Map "query" parameter to "keyword" for compatibility
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if "query" in arguments and "keyword" not in arguments:
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arguments["keyword"] = arguments.pop("query")
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logger.info(
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"Parameter mapped: query -> keyword",
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conversation_id=state["conversation_id"]
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)
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# Inject context for recommendation
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if tool_name == "recommend_products":
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arguments["user_token"] = state.get("user_token")
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# 如果没有提供 page_size，使用默认值 6
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if "page_size" not in arguments:
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arguments["page_size"] = 6
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# 如果没有提供 warehouse_id，使用默认值 2
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if "warehouse_id" not in arguments:
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arguments["warehouse_id"] = 2
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logger.info(
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"Product agent recommend_products after injection",
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user_token_present="user_token" in arguments,
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user_token_preview=arguments.get("user_token", "")[:20] + "..." if arguments.get("user_token") else None,
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arguments=arguments,
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conversation_id=state["conversation_id"]
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)
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# Inject context for quote
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if 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=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 as e:
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logger.error(
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"Failed to parse product agent LLM response as JSON",
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error=str(e),
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conversation_id=state.get("conversation_id"),
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raw_content=response.content[:500] if response.content else "EMPTY"
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)
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# Don't use raw content as response - use fallback instead
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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。")
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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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# 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
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has_product_result = False
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products = []
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result_source = None # "search", "recommend" 或 "image_search"
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# 添加日志：查看所有工具结果
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import json as json_module
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logger.info(
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"All tool results",
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tool_results_count=len(state.get("tool_results", [])),
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tool_results=json_module.dumps(state.get("tool_results", []), ensure_ascii=False, indent=2)[:2000]
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)
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for result in state["tool_results"]:
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logger.info(
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"Processing tool result",
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tool_name=result["tool_name"],
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success=result["success"],
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data_keys=list(result.get("data", {}).keys()) if isinstance(result.get("data"), dict) else "not a dict",
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data_preview=json_module.dumps(result.get("data"), ensure_ascii=False)[:500]
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)
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if result["success"] and result["tool_name"] in ["search_products", "recommend_products", "search_products_by_image"]:
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data = result["data"]
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if isinstance(data, dict) and data.get("success"):
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# MCP 返回的数据结构: {"success": true, "result": {"success": true, "products": [...]}}
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# 需要从 result.result 中提取实际数据
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inner_data = data.get("result", data)
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products = inner_data.get("products", [])
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keyword = inner_data.get("keyword", "")
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has_product_result = True
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if result["tool_name"] == "recommend_products":
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result_source = "recommend"
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elif result["tool_name"] == "search_products_by_image":
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result_source = "image_search"
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else:
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result_source = "search"
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logger.info(
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f"Product {result_source} results found",
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products_count=len(products),
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keyword=keyword,
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products_preview=json_module.dumps(products[:2], ensure_ascii=False, indent=2) if products else "[]"
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)
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break
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# 如果有商品结果，直接发送商品卡片（product_list 格式）
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if has_product_result and 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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# 发送商品列表
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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=products,
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language=detected_language
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)
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logger.info(
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f"Product {result_source} cards sent successfully",
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conversation_id=conversation_id,
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products_count=len(products),
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language=detected_language,
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result_source=result_source
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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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f"Failed to send product {result_source} cards, falling back to text response",
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error=str(e),
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products_count=len(products),
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result_source=result_source
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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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tool_name = result['tool_name']
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data = result['data']
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# Extract only essential information based on tool type
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if tool_name == "search_products" or tool_name == "recommend_products":
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products = data.get("products", []) if isinstance(data, dict) else []
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if products:
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# Keep top 5 products with more details
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product_items = []
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for p in products[:5]: # Increased from 3 to 5
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name = p.get('product_name', 'N/A')
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price = p.get('price', 'N/A')
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special_price = p.get('special_price')
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spu_id = p.get('spu_id', '')
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# Show special price if available
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if special_price and float(special_price) > 0:
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price_str = f"原价: {price}, 特价: {special_price}"
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else:
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price_str = str(price)
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# Format: [ID] Name - Price
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if spu_id:
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product_items.append(f"- [{spu_id}] {name} - {price_str}")
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else:
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product_items.append(f"- {name} - {price_str}")
|
||
|
||
summary = f"Found {len(products)} products:\n" + "\n".join(product_items)
|
||
|
||
# Add note if there are more products
|
||
if len(products) > 5:
|
||
summary += f"\n(and {len(products) - 5} more products, visit website for full selection)"
|
||
|
||
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):
|
||
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()
|
||
# 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("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
|