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>
206 lines
7.5 KiB
Python
206 lines
7.5 KiB
Python
"""
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Aftersale Agent - Handles returns, exchanges, and complaints
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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 prompts import get_prompt
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from utils.logger import get_logger
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logger = get_logger(__name__)
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async def aftersale_agent(state: AgentState) -> AgentState:
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"""Aftersale agent node
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Handles returns, exchanges, complaints and aftersale 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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"Aftersale 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"] = "aftersale"
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state["agent_history"].append("aftersale")
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state["state"] = ConversationState.PROCESSING.value
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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_aftersale_response(state)
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# Get detected language
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locale = state.get("detected_language", "en")
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# Auto-query FAQ for return-related questions
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message_lower = state["current_message"].lower()
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faq_keywords = ["return", "refund", "defective", "exchange", "complaint", "damaged", "wrong", "missing"]
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# 如果消息包含退货相关关键词，且没有工具调用记录，自动查询 FAQ
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if any(keyword in message_lower for keyword in faq_keywords):
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# 检查是否已经查询过 FAQ
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tool_calls = state.get("tool_calls", [])
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has_faq_query = any(tc.get("tool_name") in ["query_faq", "search_knowledge_base"] for tc in tool_calls)
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if not has_faq_query:
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logger.info(
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"Auto-querying FAQ for return-related question",
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conversation_id=state["conversation_id"]
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)
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# 自动添加 FAQ 工具调用
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state = add_tool_call(
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state,
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tool_name="query_faq",
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arguments={
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"category": "return",
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"locale": locale,
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"limit": 5
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},
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server="strapi"
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)
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state["state"] = ConversationState.TOOL_CALLING.value
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return state
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# Build messages for LLM
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# Load prompt in detected language
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system_prompt = get_prompt("aftersale", locale)
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messages = [
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Message(role="system", content=system_prompt),
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]
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# Add conversation history
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for msg in state["messages"][-8:]: # More history for aftersale context
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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"User ID: {state['user_id']}\nAccount ID: {state['account_id']}\n"
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if state["entities"]:
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context_info += f"Extracted entities: {json.dumps(state['entities'], ensure_ascii=False)}\n"
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if state["context"]:
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context_info += f"Conversation context: {json.dumps(state['context'], ensure_ascii=False)}\n"
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user_content = f"{context_info}\nUser message: {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=0.5)
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# Parse response
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content = response.content.strip()
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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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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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arguments["user_id"] = state["user_id"]
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# Use entity if available
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if "order_id" not in arguments and state["entities"].get("order_id"):
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arguments["order_id"] = state["entities"]["order_id"]
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state = add_tool_call(
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state,
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tool_name=result["tool_name"],
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arguments=arguments,
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server="aftersale"
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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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# Store required fields in context for next iteration
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if result.get("required_fields"):
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state = update_context(state, {"required_fields": result["required_fields"]})
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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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elif action == "handoff":
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state["requires_human"] = True
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state["handoff_reason"] = result.get("reason", "Complex aftersale issue")
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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 aftersale 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("Aftersale 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_aftersale_response(state: AgentState) -> AgentState:
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"""Generate response based on aftersale tool results"""
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tool_context = []
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for result in state["tool_results"]:
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if result["success"]:
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data = result["data"]
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tool_context.append(f"Tool {result['tool_name']} returned:\n{json.dumps(data, ensure_ascii=False, indent=2)}")
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# Extract aftersale_id for context
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if isinstance(data, dict) and data.get("aftersale_id"):
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state = update_context(state, {"aftersale_id": data["aftersale_id"]})
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else:
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tool_context.append(f"Tool {result['tool_name']} failed: {result['error']}")
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prompt = f"""Based on the following aftersale system information, generate a response to the user.
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User question: {state["current_message"]}
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System returned information:
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{chr(10).join(tool_context)}
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Please generate a compassionate and professional response:
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- If application submitted successfully, inform user of aftersale ID and next steps
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- If status query, clearly explain current progress
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- If application failed, explain reason and provide solution
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- Show understanding for user's issue
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Return only the response content, do not return JSON."""
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messages = [
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Message(role="system", content="You are a professional aftersale service assistant, please answer user's aftersale questions based on system returned information."),
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Message(role="user", content=prompt)
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]
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try:
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llm = get_llm_client()
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response = await llm.chat(messages, temperature=0.7)
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state = set_response(state, response.content)
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return state
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except Exception as e:
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logger.error("Aftersale response generation failed", error=str(e))
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state = set_response(state, "Sorry, there was a problem processing your aftersale request. Please try again later or contact customer support.")
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return state
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