232 lines
7.2 KiB
Python
232 lines
7.2 KiB
Python
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"""
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Order Agent - Handles order-related queries and operations
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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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ORDER_AGENT_PROMPT = """你是一个专业的 B2B 订单服务助手。
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你的职责是帮助用户处理订单相关的问题，包括：
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- 订单查询
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- 物流跟踪
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- 订单修改
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- 订单取消
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- 发票获取
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## 可用工具
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1. **query_order** - 查询订单
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- order_id: 订单号（可选，不填则查询最近订单）
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- date_start: 开始日期（可选）
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- date_end: 结束日期（可选）
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- status: 订单状态（可选）
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2. **track_logistics** - 物流跟踪
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- order_id: 订单号
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- tracking_number: 物流单号（可选）
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3. **modify_order** - 修改订单
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- order_id: 订单号
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- modifications: 修改内容（address/items/quantity 等）
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4. **cancel_order** - 取消订单
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- order_id: 订单号
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- reason: 取消原因
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5. **get_invoice** - 获取发票
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- order_id: 订单号
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- invoice_type: 发票类型（normal/vat）
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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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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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## 重要提示
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- 订单修改和取消是敏感操作，需要确认订单号
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- 如果用户没有提供订单号，先查询他的最近订单
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- 物流查询需要订单号或物流单号
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- 对于批量操作或大金额订单，建议转人工处理
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"""
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async def order_agent(state: AgentState) -> AgentState:
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"""Order agent node
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Handles order queries, tracking, modifications, and cancellations.
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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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"Order 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"] = "order"
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state["agent_history"].append("order")
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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_order_response(state)
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# Build messages for LLM
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messages = [
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Message(role="system", content=ORDER_AGENT_PROMPT),
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]
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# Add conversation history
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for msg in state["messages"][-6:]:
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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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# Add entities if available
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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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# Add existing context
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if state["context"].get("order_id"):
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context_info += f"当前讨论的订单号: {state['context']['order_id']}\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=0.5)
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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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# Inject user context into arguments
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arguments = result.get("arguments", {})
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arguments["user_id"] = state["user_id"]
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arguments["account_id"] = state["account_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="order"
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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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elif action == "handoff":
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state["requires_human"] = True
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state["handoff_reason"] = result.get("reason", "Complex order operation")
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return state
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except json.JSONDecodeError:
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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("Order 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_order_response(state: AgentState) -> AgentState:
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"""Generate response based on order tool results"""
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# Build context from 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"工具 {result['tool_name']} 返回:\n{json.dumps(data, ensure_ascii=False, indent=2)}")
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# Extract order_id for context
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if isinstance(data, dict):
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if data.get("order_id"):
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state = update_context(state, {"order_id": data["order_id"]})
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elif data.get("orders") and len(data["orders"]) > 0:
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state = update_context(state, {"order_id": data["orders"][0].get("order_id")})
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else:
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tool_context.append(f"工具 {result['tool_name']} 执行失败: {result['error']}")
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prompt = f"""基于以下订单系统返回的信息，生成对用户的回复。
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用户问题: {state["current_message"]}
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系统返回信息:
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{chr(10).join(tool_context)}
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请生成一个清晰、友好的回复，包含订单的关键信息（订单号、状态、金额、物流等）。
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如果是物流信息，请按时间线整理展示。
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只返回回复内容，不要返回 JSON。"""
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messages = [
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Message(role="system", content="你是一个专业的订单客服助手，请根据系统返回的信息回答用户的订单问题。"),
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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("Order response generation failed", error=str(e))
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state = set_response(state, "抱歉，处理订单信息时遇到问题。请稍后重试或联系人工客服。")
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return state
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