feat: 初始化 B2B AI Shopping Assistant 项目

- 配置 Docker Compose 多服务编排 - 实现 Chatwoot + Agent 集成 - 配置 Strapi MCP 知识库 - 支持 7 种语言的 FAQ 系统 - 实现 LangGraph AI 工作流 Co-Authored-By: Claude Sonnet 4.5 <noreply@anthropic.com>
2026-01-14 19:25:22 +08:00
commit 3ad6eee0d9
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--- a/agent/agents/order.py
+++ b/agent/agents/order.py
@@ -0,0 +1,231 @@
+"""
+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