""" Aftersale Agent - Handles returns, exchanges, and complaints """ 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 prompts import get_prompt from utils.logger import get_logger logger = get_logger(__name__) async def aftersale_agent(state: AgentState) -> AgentState: """Aftersale agent node Handles returns, exchanges, complaints and aftersale queries. Args: state: Current agent state Returns: Updated state with tool calls or response """ logger.info( "Aftersale agent processing", conversation_id=state["conversation_id"], sub_intent=state.get("sub_intent") ) state["current_agent"] = "aftersale" state["agent_history"].append("aftersale") state["state"] = ConversationState.PROCESSING.value # Check if we have tool results to process if state["tool_results"]: return await _generate_aftersale_response(state) # Get detected language locale = state.get("detected_language", "en") # Auto-query FAQ for return-related questions message_lower = state["current_message"].lower() faq_keywords = ["return", "refund", "defective", "exchange", "complaint", "damaged", "wrong", "missing"] # 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 if any(keyword in message_lower for keyword in faq_keywords): # 检查是否已经查询过 FAQ tool_calls = state.get("tool_calls", []) has_faq_query = any(tc.get("tool_name") in ["query_faq", "search_knowledge_base"] for tc in tool_calls) if not has_faq_query: logger.info( "Auto-querying FAQ for return-related question", conversation_id=state["conversation_id"] ) # 自动添加 FAQ 工具调用 state = add_tool_call( state, tool_name="query_faq", arguments={ "category": "return", "locale": locale, "limit": 5 }, server="strapi" ) state["state"] = ConversationState.TOOL_CALLING.value return state # Build messages for LLM # Load prompt in detected language system_prompt = get_prompt("aftersale", locale) messages = [ Message(role="system", content=system_prompt), ] # Add conversation history for msg in state["messages"][-8:]: # More history for aftersale context messages.append(Message(role=msg["role"], content=msg["content"])) # Build context info context_info = f"User ID: {state['user_id']}\nAccount ID: {state['account_id']}\n" if state["entities"]: context_info += f"Extracted entities: {json.dumps(state['entities'], ensure_ascii=False)}\n" if state["context"]: context_info += f"Conversation context: {json.dumps(state['context'], ensure_ascii=False)}\n" user_content = f"{context_info}\nUser message: {state['current_message']}" messages.append(Message(role="user", content=user_content)) try: llm = get_llm_client() response = await llm.chat(messages, temperature=0.5) # Parse response content = response.content.strip() if content.startswith("```"): content = content.split("```")[1] if content.startswith("json"): content = content[4:] result = json.loads(content) action = result.get("action") if action == "call_tool": arguments = result.get("arguments", {}) arguments["user_id"] = state["user_id"] # Use entity if available if "order_id" not in arguments and state["entities"].get("order_id"): arguments["order_id"] = state["entities"]["order_id"] state = add_tool_call( state, tool_name=result["tool_name"], arguments=arguments, server="aftersale" ) state["state"] = ConversationState.TOOL_CALLING.value elif action == "ask_info": state = set_response(state, result["question"]) state["state"] = ConversationState.AWAITING_INFO.value # Store required fields in context for next iteration if result.get("required_fields"): state = update_context(state, {"required_fields": result["required_fields"]}) elif action == "respond": state = set_response(state, result["response"]) state["state"] = ConversationState.GENERATING.value elif action == "handoff": state["requires_human"] = True state["handoff_reason"] = result.get("reason", "Complex aftersale issue") return state except json.JSONDecodeError as e: logger.error( "Failed to parse aftersale 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("Aftersale agent failed", error=str(e)) state["error"] = str(e) return state async def _generate_aftersale_response(state: AgentState) -> AgentState: """Generate response based on aftersale tool results""" tool_context = [] for result in state["tool_results"]: if result["success"]: data = result["data"] tool_context.append(f"Tool {result['tool_name']} returned:\n{json.dumps(data, ensure_ascii=False, indent=2)}") # Extract aftersale_id for context if isinstance(data, dict) and data.get("aftersale_id"): state = update_context(state, {"aftersale_id": data["aftersale_id"]}) else: tool_context.append(f"Tool {result['tool_name']} failed: {result['error']}") prompt = f"""Based on the following aftersale system information, generate a response to the user. User question: {state["current_message"]} System returned information: {chr(10).join(tool_context)} Please generate a compassionate and professional response: - If application submitted successfully, inform user of aftersale ID and next steps - If status query, clearly explain current progress - If application failed, explain reason and provide solution - Show understanding for user's issue Return only the response content, do not return JSON.""" messages = [ Message(role="system", content="You are a professional aftersale service assistant, please answer user's aftersale questions based on system returned information."), 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("Aftersale response generation failed", error=str(e)) state = set_response(state, "Sorry, there was a problem processing your aftersale request. Please try again later or contact customer support.") return state