e8e89601a5
主要修改： 1. 订单数据解析修复 (agent/agents/order.py) - 修复 Mall API 返回数据的嵌套结构解析 - 更新字段映射：orderId→order_id, orderProduct→items, statusText→status_text - 支持多种商品图片字段：image, pic, thumb, productImg - 添加详细的调试日志 2. 物流查询修复 (mcp_servers/order_mcp/server.py) - 修复物流接口返回数据结构解析 (data[].trackingCode→tracking_number) - 添加 print() 日志用于调试 - 支持多种字段名映射 3. Chatwoot 集成优化 (agent/integrations/chatwoot.py) - 添加 json 模块导入 - 完善订单卡片和表单展示功能 4. API 请求头优化 (mcp_servers/shared/mall_client.py) - 更新 User-Agent 和 Accept 头 - 修正 Origin 和 Referer 大小写 Co-Authored-By: Claude Sonnet 4.5 <noreply@anthropic.com>
656 lines
25 KiB
Python
656 lines
25 KiB
Python
"""
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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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from integrations.chatwoot import ChatwootClient
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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. **get_mall_order** - 从商城 API 查询订单（推荐使用）
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- order_id: 订单号（必需）
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- 说明：此工具会自动使用用户的身份 token 查询商城订单详情
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2. **get_logistics** - 从商城 API 查询物流信息
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- order_id: 订单号（必需）
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- 说明：查询订单的物流轨迹和配送状态
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3. **query_order** - 查询历史订单
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- user_id: 用户 ID（自动注入）
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- account_id: 账户 ID（自动注入）
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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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4. **modify_order** - 修改订单
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- order_id: 订单号
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- user_id: 用户 ID（自动注入）
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- modifications: 修改内容（address/items/quantity 等）
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5. **cancel_order** - 取消订单
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- order_id: 订单号
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- user_id: 用户 ID（自动注入）
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- reason: 取消原因
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6. **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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## 回复格式要求
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**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。
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### 格式 1：调用工具
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当需要使用工具查询信息时，返回：
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```json
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{
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"action": "call_tool",
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"tool_name": "get_mall_order",
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"arguments": {
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"order_id": "202071324"
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}
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}
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```
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### 格式 2：询问信息
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当需要向用户询问更多信息时，返回：
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```json
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{
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"action": "ask_info",
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"question": "请提供您的订单号"
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}
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```
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### 格式 3：直接回复
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当可以直接回答时，返回：
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```json
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{
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"action": "respond",
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"response": "您的订单已发货，预计3天内到达"
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}
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```
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## 示例对话
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用户: "查询订单 202071324"
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回复:
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```json
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{
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"action": "call_tool",
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"tool_name": "get_mall_order",
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"arguments": {
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"order_id": "202071324"
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}
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}
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```
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用户: "我的订单发货了吗？"
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回复:
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```json
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{
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"action": "ask_info",
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"question": "请提供您的订单号，以便查询订单状态"
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}
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```
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用户: "帮我查一下订单 202071324 的物流"
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回复:
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```json
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{
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"action": "call_tool",
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"tool_name": "get_logistics",
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"arguments": {
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"order_id": "202071324"
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}
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}
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```
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## 重要约束
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- **必须返回完整的 JSON 对象**，不要只返回部分内容
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- **不要添加任何 markdown 代码块标记**（如 \`\`\`json）
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- **不要添加任何解释性文字**，只返回 JSON
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- user_id 和 account_id 会自动注入到 arguments 中，无需手动添加
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- 如果用户提供了订单号，优先使用 get_mall_order 工具
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- 如果用户想查询物流状态，使用 get_logistics 工具
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- 对于敏感操作（取消、修改），确保有明确的订单号
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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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logger.info(
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"LLM response received",
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conversation_id=state["conversation_id"],
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response_length=len(content),
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response_preview=content[:300]
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)
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# 检查是否是简化的工具调用格式：工具名称\n{参数}
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# 例如：get_mall_order\n{"order_id": "202071324"}
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if "\n" in content and "{" in content:
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lines = content.split("\n")
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if len(lines) >= 2:
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tool_name_line = lines[0].strip()
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json_line = "\n".join(lines[1:]).strip()
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# 如果第一行看起来像工具名称（不包含 {），且第二行是 JSON
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if "{" not in tool_name_line and "{" in json_line:
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logger.info(
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"Detected simplified tool call format",
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tool_name=tool_name_line,
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json_preview=json_line[:200]
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)
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try:
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arguments = json.loads(json_line)
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# 直接构建工具调用
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arguments["user_id"] = state["user_id"]
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arguments["account_id"] = state["account_id"]
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# Inject user_token if available
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if state.get("user_token"):
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arguments["user_token"] = state["user_token"]
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logger.info(
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"Injected user_token into tool call",
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token_prefix=state["user_token"][:20] if state["user_token"] else None
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)
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else:
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logger.warning(
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"No user_token available in state, MCP will use default token",
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conversation_id=state["conversation_id"]
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)
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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=tool_name_line,
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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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logger.info(
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"Tool call added from simplified format",
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tool_name=tool_name_line,
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arguments_keys=list(arguments.keys())
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)
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return state
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except json.JSONDecodeError as e:
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logger.warning(
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"Failed to parse simplified format",
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error=str(e),
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json_line=json_line[:200]
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)
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# 清理内容，去除可能的 markdown 代码块标记
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# 例如：```json\n{...}\n``` 或 ```\n{...}\n```
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if "```" in content:
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# 找到第一个 ``` 后的内容
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parts = content.split("```")
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if len(parts) >= 2:
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content = parts[1].strip()
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# 去掉可能的 "json" 标记
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if content.startswith("json"):
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content = content[4:].strip()
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# 去掉结尾的 ``` 标记
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if content.endswith("```"):
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content = content[:-3].strip()
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# 尝试提取 JSON 对象（处理周围可能有文本的情况）
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json_start = content.find("{")
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json_end = content.rfind("}")
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if json_start != -1 and json_end != -1 and json_end > json_start:
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content = content[json_start:json_end + 1]
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logger.info(
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"Cleaned content for JSON parsing",
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conversation_id=state["conversation_id"],
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content_length=len(content),
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content_preview=content[:500]
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)
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try:
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result = json.loads(content)
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except json.JSONDecodeError as e:
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logger.error(
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"Failed to parse LLM response as JSON",
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conversation_id=state["conversation_id"],
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error=str(e),
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content_preview=content[:500]
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)
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# 如果解析失败，尝试将原始内容作为直接回复
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state = set_response(state, response.content)
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return state
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action = result.get("action")
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logger.info(
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"LLM action parsed",
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conversation_id=state["conversation_id"],
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action=action,
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tool_name=result.get("tool_name")
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)
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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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# Inject user_token if available (for Mall API calls)
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if state.get("user_token"):
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arguments["user_token"] = state["user_token"]
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logger.debug(
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"Injected user_token into tool call",
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token_prefix=state["user_token"][:20] if state["user_token"] else None
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)
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else:
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logger.warning(
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"No user_token available in state, MCP will use default token",
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conversation_id=state["conversation_id"]
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)
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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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logger.info(
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"Tool call added",
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tool_name=result["tool_name"],
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arguments_keys=list(arguments.keys())
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)
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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 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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# 解析订单数据并尝试使用 form 格式发送
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order_data = None
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user_message = ""
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logistics_data = None
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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_name = result["tool_name"]
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# 提取订单ID到上下文
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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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# 处理 get_mall_order 返回的订单数据
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if tool_name == "get_mall_order" and isinstance(data, dict):
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# MCP 返回结构: {"success": true, "result": {...}}
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# result 可能包含: {"success": bool, "order": {...}, "order_id": "...", "error": "..."}
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mcp_result = data.get("result", {})
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# 检查是否有错误（如未登录）
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if mcp_result.get("error") or not mcp_result.get("success"):
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logger.warning(
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"get_mall_order returned error",
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error=mcp_result.get("error"),
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require_login=mcp_result.get("require_login")
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)
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# 设置错误消息到状态中
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if mcp_result.get("require_login"):
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user_message = mcp_result.get("error", "请先登录账户以查询订单信息")
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elif mcp_result.get("order"):
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# 有订单数据
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order_data = _parse_mall_order_data(mcp_result["order"])
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# 如果 order_data 中没有 order_id，从外层获取
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if not order_data.get("order_id") and mcp_result.get("order_id"):
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order_data["order_id"] = mcp_result["order_id"]
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else:
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logger.warning(
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"get_mall_order returned success but no order data",
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data_keys=list(data.keys()),
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result_keys=list(mcp_result.keys()) if isinstance(mcp_result, dict) else None
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)
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# 处理 query_order 返回的订单数据
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elif tool_name == "query_order" and isinstance(data, dict):
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if data.get("orders") and len(data["orders"]) > 0:
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order_data = _parse_order_data(data["orders"][0])
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if len(data["orders"]) > 1:
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user_message = f"找到 {len(data['orders'])} 个订单，显示最新的一个："
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# 处理 get_logistics 返回的物流数据
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elif tool_name == "get_logistics" and isinstance(data, dict):
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logistics_data = _parse_logistics_data(data)
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# 如果之前有订单数据，添加物流信息
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if order_data:
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order_data["logistics"] = logistics_data
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# 尝试使用 Chatwoot cards 格式发送
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if order_data:
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try:
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# 检查是否有有效的 order_id
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if not order_data.get("order_id"):
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logger.warning(
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"No valid order_id in order_data, falling back to text response",
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order_data=order_data
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)
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return await _generate_text_response(state)
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chatwoot = ChatwootClient()
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conversation_id = state.get("conversation_id")
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if conversation_id:
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# 记录订单数据（用于调试）
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logger.info(
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"Preparing to send order card",
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conversation_id=conversation_id,
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order_id=order_data.get("order_id"),
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items_count=len(order_data.get("items", []))
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)
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# 发送订单卡片（使用默认的"查看订单详情"按钮）
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await chatwoot.send_order_card(
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conversation_id=conversation_id,
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order_data=order_data
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)
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logger.info(
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"Order card sent successfully",
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conversation_id=conversation_id,
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order_id=order_data.get("order_id")
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)
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# 设置确认消息
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response_text = user_message or "订单详情如下"
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state = set_response(state, response_text)
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state["state"] = ConversationState.GENERATING.value
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return state
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except Exception as e:
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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
|