assistant/agent/agents/order.py

"""
Order Agent - Handles order-related queries and operations
"""
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__)


ORDER_AGENT_PROMPT = """你是一个专业的 B2B 订单服务助手。
你的职责是帮助用户处理订单相关的问题，包括：
- 订单查询
- 物流跟踪
- 订单修改
- 订单取消
- 发票获取

## 可用工具

1. **query_order** - 查询订单
   - order_id: 订单号（可选，不填则查询最近订单）
   - date_start: 开始日期（可选）
   - date_end: 结束日期（可选）
   - status: 订单状态（可选）

2. **track_logistics** - 物流跟踪
   - order_id: 订单号
   - tracking_number: 物流单号（可选）

3. **modify_order** - 修改订单
   - order_id: 订单号
   - modifications: 修改内容（address/items/quantity 等）

4. **cancel_order** - 取消订单
   - order_id: 订单号
   - reason: 取消原因

5. **get_invoice** - 获取发票
   - order_id: 订单号
   - invoice_type: 发票类型（normal/vat）

## 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"
  }
}
```

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"
}
```

## 重要提示
- 订单修改和取消是敏感操作，需要确认订单号
- 如果用户没有提供订单号，先查询他的最近订单
- 物流查询需要订单号或物流单号
- 对于批量操作或大金额订单，建议转人工处理
"""


async def order_agent(state: AgentState) -> AgentState:
    """Order agent node
    
    Handles order queries, tracking, modifications, and cancellations.
    
    Args:
        state: Current agent state
        
    Returns:
        Updated state with tool calls or response
    """
    logger.info(
        "Order agent processing",
        conversation_id=state["conversation_id"],
        sub_intent=state.get("sub_intent")
    )
    
    state["current_agent"] = "order"
    state["agent_history"].append("order")
    state["state"] = ConversationState.PROCESSING.value
    
    # Check if we have tool results to process
    if state["tool_results"]:
        return await _generate_order_response(state)
    
    # Build messages for LLM
    messages = [
        Message(role="system", content=ORDER_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"
    
    # Add entities if available
    if state["entities"]:
        context_info += f"已提取的信息: {json.dumps(state['entities'], ensure_ascii=False)}\n"
    
    # Add existing context
    if state["context"].get("order_id"):
        context_info += f"当前讨论的订单号: {state['context']['order_id']}\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=0.5)
        
        # 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":
            # Inject user context into arguments
            arguments = result.get("arguments", {})
            arguments["user_id"] = state["user_id"]
            arguments["account_id"] = state["account_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="order"
            )
            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
            
        elif action == "handoff":
            state["requires_human"] = True
            state["handoff_reason"] = result.get("reason", "Complex order operation")
            
        return state
        
    except json.JSONDecodeError:
        state = set_response(state, response.content)
        return state
        
    except Exception as e:
        logger.error("Order agent failed", error=str(e))
        state["error"] = str(e)
        return state


async def _generate_order_response(state: AgentState) -> AgentState:
    """Generate response based on order tool results"""
    
    # Build context from tool results
    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 order_id for context
            if isinstance(data, dict):
                if data.get("order_id"):
                    state = update_context(state, {"order_id": data["order_id"]})
                elif data.get("orders") and len(data["orders"]) > 0:
                    state = update_context(state, {"order_id": data["orders"][0].get("order_id")})
        else:
            tool_context.append(f"工具 {result['tool_name']} 执行失败: {result['error']}")
    
    prompt = f"""基于以下订单系统返回的信息，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、友好的回复，包含订单的关键信息（订单号、状态、金额、物流等）。
如果是物流信息，请按时间线整理展示。
只返回回复内容，不要返回 JSON。"""
    
    messages = [
        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。"),
        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("Order response generation failed", error=str(e))
        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。")
        return state