fix: 改进错误处理和清理测试代码
## 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>
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wangliang
@@ -66,7 +66,47 @@ async def customer_service_agent(state: AgentState) -> AgentState:
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# Get detected language
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locale = state.get("detected_language", "en")
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# Auto-detect category and query FAQ
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# Check if we have already queried 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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# ========== ROUTING: Use sub_intent from router if available ==========
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# Router already classified the intent, use it for direct FAQ query
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sub_intent = state.get("sub_intent")
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# Map sub_intent to FAQ category
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sub_intent_to_category = {
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"register_inquiry": "register",
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"order_inquiry": "order",
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"payment_inquiry": "payment",
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"shipment_inquiry": "shipment",
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"return_inquiry": "return",
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"policy_inquiry": "return", # Policy queries use return FAQ
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}
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# Check if we should auto-query FAQ based on sub_intent
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if sub_intent in sub_intent_to_category and not has_faq_query:
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category = sub_intent_to_category[sub_intent]
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logger.info(
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f"Auto-querying FAQ based on sub_intent: {sub_intent} -> category: {category}",
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conversation_id=state["conversation_id"]
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)
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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": category,
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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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# ========================================================================
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# Auto-detect category and query FAQ (fallback if sub_intent not available)
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message_lower = state["current_message"].lower()
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# 定义分类关键词（支持多语言：en, nl, de, es, fr, it, tr, zh）
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@@ -163,17 +203,13 @@ async def customer_service_agent(state: AgentState) -> AgentState:
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],
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}
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# 检测分类
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# 检测分类（仅在未通过 sub_intent 匹配时使用）
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detected_category = None
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for category, keywords in category_keywords.items():
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if any(keyword in message_lower for keyword in keywords):
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detected_category = category
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break
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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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# 如果检测到分类且未查询过 FAQ，自动查询
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if detected_category and not has_faq_query:
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logger.info(
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@@ -232,44 +268,73 @@ async def customer_service_agent(state: AgentState) -> AgentState:
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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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# Log raw response for debugging
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logger.info(
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"Customer service LLM response",
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conversation_id=state["conversation_id"],
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response_preview=response.content[:300] if response.content else "EMPTY",
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response_length=len(response.content) if response.content else 0
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)
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# Parse response
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content = response.content.strip()
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# Handle markdown code blocks
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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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# Add tool call to state
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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=result.get("arguments", {}),
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server="strapi"
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parts = content.split("```")
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if len(parts) >= 2:
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content = parts[1]
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if content.startswith("json"):
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content = content[4:]
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content = content.strip()
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try:
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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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# Add tool call to state
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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=result.get("arguments", {}),
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server="strapi"
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)
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state["state"] = ConversationState.TOOL_CALLING.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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elif action == "handoff":
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state["requires_human"] = True
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state["handoff_reason"] = result.get("reason", "User request")
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else:
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# Unknown action, treat as plain text response
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logger.warning(
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"Unknown action in LLM response",
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action=action,
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conversation_id=state["conversation_id"]
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)
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state = set_response(state, response.content)
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return state
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except json.JSONDecodeError as e:
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# JSON parsing failed
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logger.error(
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"Failed to parse LLM response as JSON",
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error=str(e),
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raw_content=content[:500],
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conversation_id=state["conversation_id"]
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)
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state["state"] = ConversationState.TOOL_CALLING.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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elif action == "handoff":
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state["requires_human"] = True
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state["handoff_reason"] = result.get("reason", "User request")
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return state
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except json.JSONDecodeError:
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# LLM returned plain text, use as response
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state = set_response(state, response.content)
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return state
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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("Customer service agent failed", error=str(e))
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logger.error("Customer service agent failed", error=str(e), exc_info=True)
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state["error"] = str(e)
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return state
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