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
from integrations.chatwoot import ChatwootClient

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. **get_mall_order** - 从商城 API 查询订单（推荐使用）
   - order_id: 订单号（必需）
   - 说明：此工具会自动使用用户的身份 token 查询商城订单详情

2. **get_logistics** - 从商城 API 查询物流信息
   - order_id: 订单号（必需）
   - 说明：查询订单的物流轨迹和配送状态

3. **query_order** - 查询历史订单
   - user_id: 用户 ID（自动注入）
   - account_id: 账户 ID（自动注入）
   - 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）

4. **modify_order** - 修改订单
   - order_id: 订单号
   - user_id: 用户 ID（自动注入）
   - modifications: 修改内容（address/items/quantity 等）

5. **cancel_order** - 取消订单
   - order_id: 订单号
   - user_id: 用户 ID（自动注入）
   - reason: 取消原因

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

## 回复格式要求

**重要**：你必须始终返回完整的 JSON 对象，不要包含任何其他文本或解释。

### 格式 1：调用工具
当需要使用工具查询信息时，返回：
```json
{
  "action": "call_tool",
  "tool_name": "get_mall_order",
  "arguments": {
    "order_id": "202071324"
  }
}
```

### 格式 2：询问信息
当需要向用户询问更多信息时，返回：
```json
{
  "action": "ask_info",
  "question": "请提供您的订单号"
}
```

### 格式 3：直接回复
当可以直接回答时，返回：
```json
{
  "action": "respond",
  "response": "您的订单已发货，预计3天内到达"
}
```

## 示例对话

用户: "查询订单 202071324"
回复:
```json
{
  "action": "call_tool",
  "tool_name": "get_mall_order",
  "arguments": {
    "order_id": "202071324"
  }
}
```

用户: "我的订单发货了吗？"
回复:
```json
{
  "action": "ask_info",
  "question": "请提供您的订单号，以便查询订单状态"
}
```

用户: "帮我查一下订单 202071324 的物流"
回复:
```json
{
  "action": "call_tool",
  "tool_name": "get_logistics",
  "arguments": {
    "order_id": "202071324"
  }
}
```

## 重要约束
- **必须返回完整的 JSON 对象**，不要只返回部分内容
- **不要添加任何 markdown 代码块标记**（如 \`\`\`json）
- **不要添加任何解释性文字**，只返回 JSON
- user_id 和 account_id 会自动注入到 arguments 中，无需手动添加
- 如果用户提供了订单号，优先使用 get_mall_order 工具
- 如果用户想查询物流状态，使用 get_logistics 工具
- 对于敏感操作（取消、修改），确保有明确的订单号
"""


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()
        logger.info(
            "LLM response received",
            conversation_id=state["conversation_id"],
            response_length=len(content),
            response_preview=content[:300]
        )

        # 检查是否是简化的工具调用格式：工具名称\n{参数}
        # 例如：get_mall_order\n{"order_id": "202071324"}
        if "\n" in content and "{" in content:
            lines = content.split("\n")
            if len(lines) >= 2:
                tool_name_line = lines[0].strip()
                json_line = "\n".join(lines[1:]).strip()

                # 如果第一行看起来像工具名称（不包含 {），且第二行是 JSON
                if "{" not in tool_name_line and "{" in json_line:
                    logger.info(
                        "Detected simplified tool call format",
                        tool_name=tool_name_line,
                        json_preview=json_line[:200]
                    )

                    try:
                        arguments = json.loads(json_line)
                        # 直接构建工具调用
                        arguments["user_id"] = state["user_id"]
                        arguments["account_id"] = state["account_id"]

                        # Inject user_token if available
                        if state.get("user_token"):
                            arguments["user_token"] = state["user_token"]
                            logger.info(
                                "Injected user_token into tool call",
                                token_prefix=state["user_token"][:20] if state["user_token"] else None
                            )
                        else:
                            logger.warning(
                                "No user_token available in state, MCP will use default token",
                                conversation_id=state["conversation_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=tool_name_line,
                            arguments=arguments,
                            server="order"
                        )
                        state["state"] = ConversationState.TOOL_CALLING.value

                        logger.info(
                            "Tool call added from simplified format",
                            tool_name=tool_name_line,
                            arguments_keys=list(arguments.keys())
                        )

                        return state
                    except json.JSONDecodeError as e:
                        logger.warning(
                            "Failed to parse simplified format",
                            error=str(e),
                            json_line=json_line[:200]
                        )

        # 清理内容，去除可能的 markdown 代码块标记
        # 例如：```json\n{...}\n``` 或 ```\n{...}\n```
        if "```" in content:
            # 找到第一个 ``` 后的内容
            parts = content.split("```")
            if len(parts) >= 2:
                content = parts[1].strip()
                # 去掉可能的 "json" 标记
                if content.startswith("json"):
                    content = content[4:].strip()
                # 去掉结尾的 ``` 标记
                if content.endswith("```"):
                    content = content[:-3].strip()

        # 尝试提取 JSON 对象（处理周围可能有文本的情况）
        json_start = content.find("{")
        json_end = content.rfind("}")
        if json_start != -1 and json_end != -1 and json_end > json_start:
            content = content[json_start:json_end + 1]

        logger.info(
            "Cleaned content for JSON parsing",
            conversation_id=state["conversation_id"],
            content_length=len(content),
            content_preview=content[:500]
        )

        try:
            result = json.loads(content)
        except json.JSONDecodeError as e:
            logger.error(
                "Failed to parse LLM response as JSON",
                conversation_id=state["conversation_id"],
                error=str(e),
                content_preview=content[:500]
            )
            # 如果解析失败，尝试将原始内容作为直接回复
            state = set_response(state, response.content)
            return state

        action = result.get("action")

        logger.info(
            "LLM action parsed",
            conversation_id=state["conversation_id"],
            action=action,
            tool_name=result.get("tool_name")
        )

        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"]

            # Inject user_token if available (for Mall API calls)
            if state.get("user_token"):
                arguments["user_token"] = state["user_token"]
                logger.debug(
                    "Injected user_token into tool call",
                    token_prefix=state["user_token"][:20] if state["user_token"] else None
                )
            else:
                logger.warning(
                    "No user_token available in state, MCP will use default token",
                    conversation_id=state["conversation_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

            logger.info(
                "Tool call added",
                tool_name=result["tool_name"],
                arguments_keys=list(arguments.keys())
            )
            
        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 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"""

    # 解析订单数据并尝试使用 form 格式发送
    order_data = None
    user_message = ""
    logistics_data = None

    for result in state["tool_results"]:
        if result["success"]:
            data = result["data"]
            tool_name = result["tool_name"]

            # 提取订单ID到上下文
            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")})

            # 处理 get_mall_order 返回的订单数据
            if tool_name == "get_mall_order" and isinstance(data, dict):
                # MCP 返回结构: {"success": true, "result": {...}}
                # result 可能包含: {"success": bool, "order": {...}, "order_id": "...", "error": "..."}
                mcp_result = data.get("result", {})

                # 检查是否有错误（如未登录）
                if mcp_result.get("error") or not mcp_result.get("success"):
                    logger.warning(
                        "get_mall_order returned error",
                        error=mcp_result.get("error"),
                        require_login=mcp_result.get("require_login")
                    )
                    # 设置错误消息到状态中
                    if mcp_result.get("require_login"):
                        user_message = mcp_result.get("error", "请先登录账户以查询订单信息")
                elif mcp_result.get("order"):
                    # 有订单数据
                    order_data = _parse_mall_order_data(mcp_result["order"])
                    # 如果 order_data 中没有 order_id，从外层获取
                    if not order_data.get("order_id") and mcp_result.get("order_id"):
                        order_data["order_id"] = mcp_result["order_id"]
                else:
                    logger.warning(
                        "get_mall_order returned success but no order data",
                        data_keys=list(data.keys()),
                        result_keys=list(mcp_result.keys()) if isinstance(mcp_result, dict) else None
                    )

            # 处理 query_order 返回的订单数据
            elif tool_name == "query_order" and isinstance(data, dict):
                if data.get("orders") and len(data["orders"]) > 0:
                    order_data = _parse_order_data(data["orders"][0])
                    if len(data["orders"]) > 1:
                        user_message = f"找到 {len(data['orders'])} 个订单，显示最新的一个："

            # 处理 get_logistics 返回的物流数据
            elif tool_name == "get_logistics" and isinstance(data, dict):
                logistics_data = _parse_logistics_data(data)
                # 如果之前有订单数据，添加物流信息
                if order_data:
                    order_data["logistics"] = logistics_data

    # 尝试使用 Chatwoot cards 格式发送
    if order_data:
        try:
            # 检查是否有有效的 order_id
            if not order_data.get("order_id"):
                logger.warning(
                    "No valid order_id in order_data, falling back to text response",
                    order_data=order_data
                )
                return await _generate_text_response(state)

            chatwoot = ChatwootClient()
            conversation_id = state.get("conversation_id")

            if conversation_id:
                # 记录订单数据（用于调试）
                logger.info(
                    "Preparing to send order card",
                    conversation_id=conversation_id,
                    order_id=order_data.get("order_id"),
                    items_count=len(order_data.get("items", []))
                )

                # 发送订单卡片（使用默认的"查看订单详情"按钮）
                await chatwoot.send_order_card(
                    conversation_id=conversation_id,
                    order_data=order_data
                )

                logger.info(
                    "Order card sent successfully",
                    conversation_id=conversation_id,
                    order_id=order_data.get("order_id")
                )

                # 设置确认消息
                response_text = user_message or "订单详情如下"
                state = set_response(state, response_text)
                state["state"] = ConversationState.GENERATING.value
                return state

        except Exception as e:
            logger.error(
                "Failed to send order card, falling back to text response",
                error=str(e),
                order_id=order_data.get("order_id")
            )

    # 降级处理：使用原来的 LLM 生成逻辑
    return await _generate_text_response(state)


def _parse_mall_order_data(data: dict[str, Any]) -> dict[str, Any]:
    """解析商城 API 返回的订单数据"""
    # 记录原始数据结构（用于调试）
    logger.info(
        "Parsing mall order data",
        data_keys=list(data.keys()),
        has_order_id=bool(data.get("order_id")),
        has_order_sn=bool(data.get("order_sn")),
        has_nested_order=bool(data.get("order")),
        order_id_preview=data.get("order_id", data.get("order_sn", "")),
        # 如果有 order 字段，记录其内容类型和键
        nested_order_type=type(data.get("order")).__name__ if data.get("order") else None,
        nested_order_keys=list(data.get("order", {}).keys()) if isinstance(data.get("order"), dict) else None
    )

    # Mall API 返回结构：外层包含 userId, reqContext 等，实际的订单数据在 order 字段中
    # 如果有嵌套的 order 字段，提取出来
    actual_order_data = data.get("order", data) if data.get("order") else data

    # 记录提取的订单数据结构（用于调试）
    logger.info(
        "Extracted order data structure",
        actual_order_keys=list(actual_order_data.keys()) if isinstance(actual_order_data, dict) else type(actual_order_data).__name__,
        has_items=bool(actual_order_data.get("items")),
        has_order_items=bool(actual_order_data.get("order_items")),
        has_products=bool(actual_order_data.get("products")),
        has_orderProduct=bool(actual_order_data.get("orderProduct")),
        has_orderGoods=bool(actual_order_data.get("orderGoods")),
        has_goods=bool(actual_order_data.get("goods"))
    )

    order_data = {
        "order_id": actual_order_data.get("orderId", actual_order_data.get("order_id", actual_order_data.get("order_sn", ""))),
        "status": actual_order_data.get("orderStatusId", actual_order_data.get("status", "unknown")),
        "status_text": actual_order_data.get("statusText", actual_order_data.get("status_text", actual_order_data.get("status", ""))),
        "total_amount": actual_order_data.get("total", actual_order_data.get("total_amount", actual_order_data.get("order_amount", "0.00"))),
        "shipping_fee": actual_order_data.get("shipping_fee", actual_order_data.get("freight_amount", "0")),
    }

    # 下单时间
    if actual_order_data.get("created_at"):
        order_data["created_at"] = actual_order_data["created_at"]
    elif actual_order_data.get("add_time"):
        order_data["created_at"] = actual_order_data["add_time"]
    elif actual_order_data.get("dateAdded"):
        order_data["created_at"] = actual_order_data["dateAdded"]

    # 商品列表 - 尝试多种可能的字段名（优先 orderProduct）
    items = (
        actual_order_data.get("orderProduct") or
        actual_order_data.get("items") or
        actual_order_data.get("order_items") or
        actual_order_data.get("products") or
        actual_order_data.get("orderGoods") or
        actual_order_data.get("goods") or
        []
    )

    # 记录商品列表数据结构（用于调试）
    if items and len(items) > 0:
        first_item = items[0]
        logger.info(
            "First item structure",
            first_item_keys=list(first_item.keys()) if isinstance(first_item, dict) else type(first_item).__name__,
            has_image_url=bool(first_item.get("image_url")) if isinstance(first_item, dict) else False,
            has_image=bool(first_item.get("image")) if isinstance(first_item, dict) else False,
            has_pic=bool(first_item.get("pic")) if isinstance(first_item, dict) else False,
            sample_item_data=str(first_item)[:500] if isinstance(first_item, dict) else str(first_item)
        )

    if items:
        order_data["items"] = []
        for item in items:
            item_data = {
                "name": item.get("name", item.get("productName", item.get("product_name", "未知商品"))),
                "quantity": item.get("quantity", item.get("num", item.get("productNum", 1))),
                "price": item.get("price", item.get("total", item.get("productPrice", item.get("product_price", "0.00"))))
            }
            # 添加商品图片（支持多种可能的字段名）
            image_url = (
                item.get("image") or
                item.get("image_url") or
                item.get("pic") or
                item.get("thumb") or
                item.get("product_image") or
                item.get("pic_url") or
                item.get("thumb_url") or
                item.get("img") or
                item.get("productImg") or
                item.get("thumb")
            )
            if image_url:
                item_data["image_url"] = image_url
            else:
                # 记录没有图片的商品（用于调试）
                logger.debug(
                    "No image found for product",
                    product_name=item_data.get("name"),
                    available_keys=list(item.keys())
                )
            order_data["items"].append(item_data)

    # 备注
    if actual_order_data.get("remark") or actual_order_data.get("user_remark"):
        order_data["remark"] = actual_order_data.get("remark", actual_order_data.get("user_remark", ""))

    return order_data


def _parse_order_data(data: dict[str, Any]) -> dict[str, Any]:
    """解析历史订单数据"""
    return _parse_mall_order_data(data)


def _parse_logistics_data(data: dict[str, Any]) -> dict[str, Any]:
    """解析物流数据"""
    # MCP 返回结构: {"success": true, "result": {...物流数据...}}
    mcp_result = data.get("result", data) if data.get("result") else data

    logger.info(
        "Parsing logistics data",
        data_keys=list(data.keys()) if isinstance(data, dict) else None,
        has_result_in_data=bool(data.get("result")),
        mcp_result_keys=list(mcp_result.keys()) if isinstance(mcp_result, dict) else None,
        raw_tracking_number_value=repr(mcp_result.get("tracking_number")) if mcp_result.get("tracking_number") is not None else None,
        raw_courier_value=repr(mcp_result.get("courier")) if mcp_result.get("courier") is not None else None,
        has_tracking_number=bool(mcp_result.get("tracking_number")),
        has_courier=bool(mcp_result.get("courier")),
        has_timeline=bool(mcp_result.get("timeline"))
    )

    return {
        "carrier": mcp_result.get("courier", mcp_result.get("carrier", mcp_result.get("express_name", "未知"))),
        "tracking_number": mcp_result.get("tracking_number") or "",
        "status": mcp_result.get("status"),
        "estimated_delivery": mcp_result.get("estimatedDelivery"),
        "timeline": mcp_result.get("timeline", [])
    }


async def _generate_text_response(state: AgentState) -> AgentState:
    """生成纯文本回复（降级方案）"""

    # 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)}")
        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