0f13102a02
## 主要修复 ### 1. JSON 解析错误处理 - 修复所有 Agent 的 LLM 响应解析失败时返回原始内容的问题 - 当 JSON 解析失败时，返回友好的兜底消息而不是原始文本 - 影响文件: customer_service.py, order.py, product.py, aftersale.py ### 2. FAQ 快速路径修复 - 修复 customer_service.py 中变量定义顺序问题 - has_faq_query 在使用前未定义导致 NameError - 添加详细的错误日志记录 ### 3. Chatwoot 集成改进 - 添加响应内容调试日志 - 改进错误处理和日志记录 ### 4. 订单查询优化 - 将订单列表默认返回数量从 10 条改为 5 条 - 统一 MCP 工具层和 Mall Client 层的默认值 ### 5. 代码清理 - 删除所有测试代码和示例文件 - 刋试文件包括: test_*.py, test_*.html, test_*.sh - 删除测试目录: tests/, agent/tests/, agent/examples/ Co-Authored-By: Claude Sonnet 4.5 <noreply@anthropic.com>
409 lines
13 KiB
Python
409 lines
13 KiB
Python
"""
|
||
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 5，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"]
|
||
|
||
# Set default page_size if not provided
|
||
if "page_size" not in arguments:
|
||
arguments["page_size"] = 5
|
||
|
||
# Set default page if not provided
|
||
if "page" not in arguments:
|
||
arguments["page"] = 1
|
||
|
||
# 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 as e:
|
||
logger.error(
|
||
"Failed to parse product agent LLM response as JSON",
|
||
error=str(e),
|
||
conversation_id=state.get("conversation_id"),
|
||
raw_content=response.content[:500] if response.content else "EMPTY"
|
||
)
|
||
# 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
|
||
|
||
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
|