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/router.py
+++ b/agent/agents/router.py
@@ -0,0 +1,220 @@
+"""
+Router Agent - Intent recognition and routing
+"""
+import json
+from typing import Any, Optional
+
+from core.state import AgentState, Intent, ConversationState, set_intent, add_entity
+from core.llm import get_llm_client, Message
+from utils.logger import get_logger
+
+logger = get_logger(__name__)
+
+
+# Intent classification prompt
+CLASSIFICATION_PROMPT = """你是一个 B2B 购物网站的智能助手路由器。
+你的任务是分析用户消息，识别用户意图并提取关键实体。
+
+## 可用意图分类
+
+1. **customer_service** - 通用咨询
+   - FAQ 问答
+   - 产品使用问题
+   - 公司信息查询
+   - 政策咨询（退换货政策、隐私政策等）
+   
+2. **order** - 订单相关
+   - 订单查询（"我的订单在哪"、"查一下订单"）
+   - 物流跟踪（"快递到哪了"、"什么时候到货"）
+   - 订单修改（"改一下收货地址"、"修改订单数量"）
+   - 订单取消（"取消订单"、"不想要了"）
+   - 发票查询（"开发票"、"要发票"）
+   
+3. **aftersale** - 售后服务
+   - 退货申请（"退货"、"不满意想退"）
+   - 换货申请（"换货"、"换一个"）
+   - 投诉（"投诉"、"服务态度差"）
+   - 工单/问题反馈
+   
+4. **product** - 商品相关
+   - 商品搜索（"有没有xx"、"找一下xx"）
+   - 商品推荐（"推荐"、"有什么好的"）
+   - 询价（"多少钱"、"批发价"、"大量购买价格"）
+   - 库存查询（"有货吗"、"还有多少"）
+   
+5. **human_handoff** - 需要转人工
+   - 用户明确要求转人工
+   - 复杂问题 AI 无法处理
+   - 敏感问题需要人工处理
+
+## 实体提取
+
+请从消息中提取以下实体（如果存在）：
+- order_id: 订单号（如 ORD123456）
+- product_id: 商品ID
+- product_name: 商品名称
+- quantity: 数量
+- date_reference: 时间引用（今天、昨天、上周、具体日期等）
+- tracking_number: 物流单号
+- phone: 电话号码
+- address: 地址信息
+
+## 输出格式
+
+请以 JSON 格式返回，包含以下字段：
+```json
+{
+  "intent": "意图分类",
+  "confidence": 0.95,
+  "sub_intent": "子意图（可选）",
+  "entities": {
+    "entity_type": "entity_value"
+  },
+  "reasoning": "简短的推理说明"
+}
+```
+
+## 注意事项
+- 如果意图不明确，置信度应该较低
+- 如果无法确定意图，返回 "unknown"
+- 实体提取要准确，没有的字段不要填写
+"""
+
+
+async def classify_intent(state: AgentState) -> AgentState:
+    """Classify user intent and extract entities
+    
+    This is the first node in the workflow that analyzes the user's message
+    and determines which agent should handle it.
+    
+    Args:
+        state: Current agent state
+        
+    Returns:
+        Updated state with intent and entities
+    """
+    logger.info(
+        "Classifying intent",
+        conversation_id=state["conversation_id"],
+        message=state["current_message"][:100]
+    )
+    
+    state["state"] = ConversationState.CLASSIFYING.value
+    state["step_count"] += 1
+    
+    # Build context from conversation history
+    context_summary = ""
+    if state["context"]:
+        context_parts = []
+        if state["context"].get("order_id"):
+            context_parts.append(f"当前讨论的订单: {state['context']['order_id']}")
+        if state["context"].get("product_id"):
+            context_parts.append(f"当前讨论的商品: {state['context']['product_id']}")
+        if context_parts:
+            context_summary = "\n".join(context_parts)
+    
+    # Build messages for LLM
+    messages = [
+        Message(role="system", content=CLASSIFICATION_PROMPT),
+    ]
+    
+    # Add recent conversation history for context
+    for msg in state["messages"][-6:]:  # Last 3 turns
+        messages.append(Message(role=msg["role"], content=msg["content"]))
+    
+    # Add current message with context
+    user_content = f"用户消息: {state['current_message']}"
+    if context_summary:
+        user_content += f"\n\n当前上下文:\n{context_summary}"
+    
+    messages.append(Message(role="user", content=user_content))
+    
+    try:
+        llm = get_llm_client()
+        response = await llm.chat(messages, temperature=0.3)
+        
+        # Parse JSON response
+        content = response.content.strip()
+        # Handle markdown code blocks
+        if content.startswith("```"):
+            content = content.split("```")[1]
+            if content.startswith("json"):
+                content = content[4:]
+        
+        result = json.loads(content)
+        
+        # Extract intent
+        intent_str = result.get("intent", "unknown")
+        try:
+            intent = Intent(intent_str)
+        except ValueError:
+            intent = Intent.UNKNOWN
+        
+        confidence = float(result.get("confidence", 0.5))
+        sub_intent = result.get("sub_intent")
+        
+        # Set intent in state
+        state = set_intent(state, intent, confidence, sub_intent)
+        
+        # Extract entities
+        entities = result.get("entities", {})
+        for entity_type, entity_value in entities.items():
+            if entity_value:
+                state = add_entity(state, entity_type, entity_value)
+        
+        logger.info(
+            "Intent classified",
+            intent=intent.value,
+            confidence=confidence,
+            entities=list(entities.keys())
+        )
+        
+        # Check if human handoff is needed
+        if intent == Intent.HUMAN_HANDOFF or confidence < 0.5:
+            state["requires_human"] = True
+            state["handoff_reason"] = result.get("reasoning", "Intent unclear")
+        
+        return state
+        
+    except json.JSONDecodeError as e:
+        logger.error("Failed to parse intent response", error=str(e))
+        state["intent"] = Intent.UNKNOWN.value
+        state["intent_confidence"] = 0.0
+        return state
+        
+    except Exception as e:
+        logger.error("Intent classification failed", error=str(e))
+        state["error"] = str(e)
+        state["intent"] = Intent.UNKNOWN.value
+        return state
+
+
+def route_by_intent(state: AgentState) -> str:
+    """Route to appropriate agent based on intent
+    
+    This is the routing function used by conditional edges in the graph.
+    
+    Args:
+        state: Current agent state
+        
+    Returns:
+        Name of the next node to execute
+    """
+    intent = state.get("intent")
+    requires_human = state.get("requires_human", False)
+    
+    # Human handoff takes priority
+    if requires_human:
+        return "human_handoff"
+    
+    # Route based on intent
+    routing_map = {
+        Intent.CUSTOMER_SERVICE.value: "customer_service_agent",
+        Intent.ORDER.value: "order_agent",
+        Intent.AFTERSALE.value: "aftersale_agent",
+        Intent.PRODUCT.value: "product_agent",
+        Intent.HUMAN_HANDOFF.value: "human_handoff",
+        Intent.UNKNOWN.value: "customer_service_agent"  # Default to customer service
+    }
+    
+    return routing_map.get(intent, "customer_service_agent")