feat: 集成混合路由快速路径和前端 SSE 事件支持

backend/app/agent/service.py

@@ -4,6 +4,7 @@ AI Agent 服务类 - 支持多模型动态切换
 """
 
 import json
+import asyncio
 
 # 本地模块
 from ..graph.graph_builder import GraphBuilder, GraphContext
@@ -21,6 +22,8 @@ class AIAgentService:
         self.tools_by_name = TOOLS_BY_NAME.copy()
         # 添加：意图分类器
         self.intent_classifier = get_intent_classifier()
+        # RAG 管道（可选，需要时设置）
+        self.rag_pipeline = None
 
     async def initialize(self):
         # 1. 初始化 RAG 工具（如果需要）
@@ -100,7 +103,7 @@ class AIAgentService:
                 return str(value)
 
     async def process_message_stream(self, message: str, thread_id: str, model_name: str, user_id: str = "default_user"):
-        """流式处理消息，返回异步生成器"""
+        """流式处理消息，返回异步生成器（支持混合路由）"""
         graph = self.graphs.get(model_name)
         if not graph:
             raise ValueError(f"模型 '{model_name}' 未找到或未初始化")
@@ -112,6 +115,38 @@ class AIAgentService:
         input_state = {"messages": [{"role": "user", "content": message}]}
         context = GraphContext(user_id=user_id)
 
+        # ========== 新增：混合路由 ==========
+        intent_result = await self.intent_classifier.classify(message)
+        info(f"🧠 意图识别: {intent_result.intent_type} (置信度: {intent_result.confidence:.2f})")
+        info(f"📝 推理: {intent_result.reasoning}")
+
+        # 发送意图分类事件
+        yield {
+            "type": "intent_classified",
+            "intent": intent_result.intent_type.value,
+            "confidence": intent_result.confidence,
+            "reasoning": intent_result.reasoning
+        }
+
+        # 根据意图决定路径
+        use_react_loop = True
+        if intent_result.confidence >= 0.6:
+            intent_str = intent_result.intent_type.value
+            if intent_str in ["chitchat", "clarify"]:
+                use_react_loop = False
+            elif intent_str == "knowledge" and self.rag_pipeline:
+                use_react_loop = False
+
+        # 发送路径决策事件
+        yield {
+            "type": "path_decision",
+            "path": "react_loop" if use_react_loop else "fast",
+            "intent": intent_result.intent_type.value
+        }
+        # ====================================
+
+        if use_react_loop:
+            # ========== React 循环路径 ==========
             current_node = None
             tool_calls_in_progress = {}
 
@@ -239,3 +274,123 @@ class AIAgentService:
             yield {
                 "type": "done"
             }
+
+        else:
+            # ========== 快速路径 ==========
+            intent_str = intent_result.intent_type.value
+
+            if intent_str == "chitchat":
+                # 闲聊直接回答
+                reply = await self._generate_fast_reply(
+                    message,
+                    "你是一个友好的助手，请礼貌回应用户的问候或闲聊。"
+                )
+                for char in reply:
+                    yield {
+                        "type": "llm_token",
+                        "node": "fast_path",
+                        "content": char
+                    }
+                    await asyncio.sleep(0.03)
+
+            elif intent_str == "clarify":
+                # 澄清反问
+                reply = await self._generate_fast_reply(
+                    message,
+                    "用户的问题不够明确，请礼貌地询问更多细节，以便更好地帮助用户。"
+                )
+                for char in reply:
+                    yield {
+                        "type": "llm_token",
+                        "node": "fast_path",
+                        "content": char
+                    }
+                    await asyncio.sleep(0.03)
+
+            elif intent_str == "knowledge" and self.rag_pipeline:
+                # 快速 RAG
+                yield {
+                    "type": "node_start",
+                    "node": "fast_rag"
+                }
+                yield {
+                    "type": "reasoning",
+                    "node": "fast_rag",
+                    "content": "正在查询知识库..."
+                }
+
+                # 模拟 RAG 检索
+                await asyncio.sleep(0.3)
+
+                # 使用 RAG 生成回答
+                reply = await self._generate_rag_reply(message)
+
+                yield {
+                    "type": "node_end",
+                    "node": "fast_rag"
+                }
+
+                for char in reply:
+                    yield {
+                        "type": "llm_token",
+                        "node": "fast_path",
+                        "content": char
+                    }
+                    await asyncio.sleep(0.03)
+
+            else:
+                # 兜底：直接回答
+                reply = await self._generate_fast_reply(
+                    message,
+                    "请简洁回答用户的问题。"
+                )
+                for char in reply:
+                    yield {
+                        "type": "llm_token",
+                        "node": "fast_path",
+                        "content": char
+                    }
+                    await asyncio.sleep(0.03)
+
+            yield {
+                "type": "done"
+            }
+
+    async def _generate_fast_reply(self, message: str, system_prompt: str) -> str:
+        """快速生成回复（不经过 React 循环）"""
+        # 使用默认模型生成回复
+        model_name = next(iter(self.graphs.keys()), "zhipu")
+        llm = get_all_chat_services().get(model_name)
+
+        if not llm:
+            return "抱歉，服务暂时不可用。"
+
+        prompt = f"{system_prompt}\n\n用户: {message}"
+        response = await llm.ainvoke(prompt)
+        return response.content if hasattr(response, 'content') else str(response)
+
+    async def _generate_rag_reply(self, message: str) -> str:
+        """使用 RAG 生成回复"""
+        if not self.rag_pipeline:
+            return await self._generate_fast_reply(message, "请简洁回答用户的问题。")
+
+        # 检索文档
+        docs = await self.rag_pipeline.aretrieve(message)
+        context = self.rag_pipeline.format_context(docs)
+
+        # 生成回答
+        model_name = next(iter(self.graphs.keys()), "zhipu")
+        llm = get_all_chat_services().get(model_name)
+
+        if not llm:
+            return "抱歉，服务暂时不可用。"
+
+        prompt = f"""请根据以下参考文档回答用户问题。
+
+参考文档:
+{context or "（无相关文档）"}
+
+用户问题: {message}
+"""
+        response = await llm.ainvoke(prompt)
+        return response.content if hasattr(response, 'content') else str(response)

frontend/src/components/ChatContainer.tsx

@@ -216,6 +216,44 @@ export const HumanReviewCard: React.FC<HumanReviewCardProps> = ({ review, onActi
   );
 };
 
+// 新增：路径指示器组件
+interface PathIndicatorProps {
+  path?: string;
+  intent?: string;
+  confidence?: number;
+}
+
+export const PathIndicator: React.FC<PathIndicatorProps> = ({
+  path,
+  intent,
+  confidence
+}) => {
+  if (!path) return null;
+
+  const getPathIcon = () => path === 'fast' ? '⚡' : '🔄';
+  const getPathText = () => path === 'fast' ? '快速路径' : 'React 循环';
+  const getPathColor = () => path === 'fast' ? 'text-green-600' : 'text-blue-600';
+
+  return (
+    <div className="flex items-center gap-2 text-xs text-gray-500 mb-2 flex-wrap">
+      <span>{getPathIcon()}</span>
+      <span className={getPathColor()}>{getPathText()}</span>
+      {intent && (
+        <>
+          <span>•</span>
+          <span>意图: {intent}</span>
+        </>
+      )}
+      {confidence && (
+        <>
+          <span>•</span>
+          <span>置信度: {(confidence * 100).toFixed(0)}%</span>
+        </>
+      )}
+    </div>
+  );
+};
+
 interface AssistantMessageProps {
   message: Message;
   onReviewAction?: (review: HumanReview, action: 'approve' | 'reject' | 'modify', comment?: string, modifiedContent?: string) => void;
@@ -228,6 +266,15 @@ export const AssistantMessage: React.FC<AssistantMessageProps> = ({ message, onR
         AI
       </div>
       <div className="flex-1">
+        {/* 新增：路径指示器 */}
+        {message.metadata && (
+          <PathIndicator
+            path={message.metadata.path}
+            intent={message.metadata.intent}
+            confidence={message.metadata.confidence}
+          />
+        )}
+
         <ReasoningSection content={message.reasoning} />
 
         {message.toolCalls.map(toolCall => (
@@ -329,7 +376,7 @@ interface ChatContainerProps {
 
 export const ChatContainer: React.FC<ChatContainerProps> = ({ model = 'zhipu', threadId: propThreadId }) => {
   const [threadId] = useState(() => propThreadId || Date.now().toString());
-  const { messages, isLoading, sendMessage, handleReviewAction } = useChat();
+  const { messages, isLoading, sendMessage, handleReviewAction, lastIntent } = useChat();
 
   const handleSend = (text: string) => {
     sendMessage(text, threadId, model);
@@ -376,4 +423,4 @@ export const ChatContainer: React.FC<ChatContainerProps> = ({ model = 'zhipu', t
   );
 };
 
-export default ChatContainer;
+export default ChatContainer

frontend/src/components/useChat.ts

@@ -5,6 +5,21 @@ export interface SSEEvent {
   [key: string]: any;
 }
 
+// 新增：意图分类事件
+export interface IntentClassifiedEvent extends SSEEvent {
+  type: 'intent_classified';
+  intent: string;
+  confidence: number;
+  reasoning: string;
+}
+
+// 新增：路径决策事件
+export interface PathDecisionEvent extends SSEEvent {
+  type: 'path_decision';
+  path: 'fast' | 'react_loop';
+  intent: string;
+}
+
 export interface ToolCall {
   id: string;
   tool: string;
@@ -30,6 +45,12 @@ export interface Message {
   humanReview?: HumanReview;
   isLoading: boolean;
   timestamp: Date;
+  // 新增：元数据
+  metadata?: {
+    intent?: string;
+    confidence?: number;
+    path?: 'fast' | 'react_loop';
+  };
 }
 
 const API_BASE = 'http://localhost:8079';
@@ -117,6 +138,8 @@ export class ApiClient {
 export function useChat() {
   const [messages, setMessages] = useState<Message[]>([]);
   const [isLoading, setIsLoading] = useState(false);
+  // 新增：最后一次意图识别结果
+  const [lastIntent, setLastIntent] = useState<{ intent: string; confidence: number } | null>(null);
   const [apiClient] = useState(() => new ApiClient());
   const currentMessageRef = useRef<Message | null>(null);
 
@@ -129,6 +152,8 @@ export function useChat() {
       toolCalls: [],
       isLoading: role === 'assistant',
       timestamp: new Date(),
+      // 新增：初始化元数据
+      metadata: undefined,
     };
     setMessages(prev => [...prev, message]);
     currentMessageRef.current = message;
@@ -149,12 +174,39 @@ export function useChat() {
 
   const sendMessage = useCallback(async (text: string, threadId: string, model: string = 'zhipu') => {
     setIsLoading(true);
+    setLastIntent(null); // 重置
     addMessage('user', text);
     addMessage('assistant', '');
 
     try {
       for await (const event of apiClient.chatStream(text, threadId, model)) {
         switch (event.type) {
+          // 新增：处理意图分类事件
+          case 'intent_classified':
+            console.log(`🧠 意图识别: ${event.intent} (置信度: ${event.confidence})`);
+            setLastIntent({
+              intent: event.intent,
+              confidence: event.confidence,
+            });
+            updateCurrentMessage({
+              metadata: {
+                intent: event.intent,
+                confidence: event.confidence,
+              },
+            });
+            break;
+
+          // 新增：处理路径决策事件
+          case 'path_decision':
+            console.log(`🧭 路径决策: ${event.path}`);
+            updateCurrentMessage({
+              metadata: {
+                ...currentMessageRef.current?.metadata,
+                path: event.path,
+              },
+            });
+            break;
+
           case 'node_start':
             console.log('Node started:', event.node);
             break;
@@ -268,5 +320,6 @@ export function useChat() {
     isLoading,
     sendMessage,
     handleReviewAction,
+    lastIntent, // 新增：导出最后意图
   };
 }