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")
@@ -112,130 +115,282 @@ class AIAgentService:
         input_state = {"messages": [{"role": "user", "content": message}]}
         context = GraphContext(user_id=user_id)
 
-        current_node = None
-        tool_calls_in_progress = {}
+        # ========== 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}")
 
-        async for chunk in graph.astream(
-            input_state,
-            config=config,
-            context=context,
-            stream_mode=["messages", "updates", "custom"],
-            version="v2",
-            subgraphs=True
-        ):
-            chunk_type = chunk["type"]
-            processed_event = {}
-
-            if chunk_type == "messages":
-                message_chunk, metadata = chunk["data"]
-                node_name = metadata.get("langgraph_node", "unknown")
-                
-                # 检测节点变化，发送节点开始事件
-                if node_name != current_node:
-                    if current_node:
-                        yield {
-                            "type": "node_end",
-                            "node": current_node
-                        }
-                    yield {
-                        "type": "node_start",
-                        "node": node_name
-                    }
-                    current_node = node_name
-
-                # 处理消息内容
-                token_content = getattr(message_chunk, 'content', str(message_chunk))
-                reasoning_token = ""
-                if hasattr(message_chunk, 'additional_kwargs'):
-                    reasoning_token = message_chunk.additional_kwargs.get("reasoning_content", "")
-
-                # 处理思考过程
-                if reasoning_token:
-                    processed_event = {
-                        "type": "reasoning",
-                        "node": node_name,
-                        "content": reasoning_token
-                    }
-                # 处理工具调用
-                elif hasattr(message_chunk, 'tool_calls') and message_chunk.tool_calls:
-                    for tool_call in message_chunk.tool_calls:
-                        tool_call_id = tool_call.get("id", "")
-                        tool_name = tool_call.get("name", "")
-                        tool_args = tool_call.get("args", {})
-                        
-                        # 记录工具调用开始
-                        if tool_call_id not in tool_calls_in_progress:
-                            tool_calls_in_progress[tool_call_id] = {
-                                "name": tool_name,
-                                "args": tool_args
-                            }
-                            yield {
-                                "type": "tool_call_start",
-                                "tool": tool_name,
-                                "args": tool_args,
-                                "id": tool_call_id
-                            }
-                # 处理普通 token
-                elif token_content:
-                    processed_event = {
-                        "type": "llm_token",
-                        "node": node_name,
-                        "content": token_content
-                    }
-
-            elif chunk_type == "updates":
-                updates_data = chunk["data"]
-                serialized_data = self._serialize_value(updates_data)
-
-                # 检查是否有人工审核请求
-                if "review_pending" in serialized_data and serialized_data["review_pending"]:
-                    review_id = serialized_data.get("review_id", "")
-                    content_to_review = serialized_data.get("content_to_review", "")
-                    yield {
-                        "type": "human_review_request",
-                        "review_id": review_id,
-                        "content": content_to_review
-                    }
-
-                # 检查是否有工具结果
-                if "messages" in serialized_data:
-                    for msg in serialized_data["messages"]:
-                        # 检测工具结果消息
-                        if msg.get("role") == "tool":
-                            tool_call_id = msg.get("tool_call_id", "")
-                            tool_name = msg.get("name", "")
-                            tool_output = msg.get("content", "")
-                            
-                            if tool_call_id in tool_calls_in_progress:
-                                yield {
-                                    "type": "tool_call_end",
-                                    "tool": tool_name,
-                                    "id": tool_call_id,
-                                    "result": tool_output
-                                }
-                                del tool_calls_in_progress[tool_call_id]
-
-                processed_event = {
-                    "type": "state_update",
-                    "data": serialized_data
-                }
-
-            elif chunk_type == "custom":
-                serialized_data = self._serialize_value(chunk["data"])
-                processed_event = {
-                    "type": "custom",
-                    "data": serialized_data
-                }
-
-            if processed_event:
-                yield processed_event
-
-        # 发送结束事件
-        if current_node:
-            yield {
-                "type": "node_end",
-                "node": current_node
-            }
+        # 发送意图分类事件
         yield {
-            "type": "done"
-        }
+            "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 = {}
+
+            async for chunk in graph.astream(
+                input_state,
+                config=config,
+                context=context,
+                stream_mode=["messages", "updates", "custom"],
+                version="v2",
+                subgraphs=True
+            ):
+                chunk_type = chunk["type"]
+                processed_event = {}
+
+                if chunk_type == "messages":
+                    message_chunk, metadata = chunk["data"]
+                    node_name = metadata.get("langgraph_node", "unknown")
+
+                    # 检测节点变化，发送节点开始事件
+                    if node_name != current_node:
+                        if current_node:
+                            yield {
+                                "type": "node_end",
+                                "node": current_node
+                            }
+                        yield {
+                            "type": "node_start",
+                            "node": node_name
+                        }
+                        current_node = node_name
+
+                    # 处理消息内容
+                    token_content = getattr(message_chunk, 'content', str(message_chunk))
+                    reasoning_token = ""
+                    if hasattr(message_chunk, 'additional_kwargs'):
+                        reasoning_token = message_chunk.additional_kwargs.get("reasoning_content", "")
+
+                    # 处理思考过程
+                    if reasoning_token:
+                        processed_event = {
+                            "type": "reasoning",
+                            "node": node_name,
+                            "content": reasoning_token
+                        }
+                    # 处理工具调用
+                    elif hasattr(message_chunk, 'tool_calls') and message_chunk.tool_calls:
+                        for tool_call in message_chunk.tool_calls:
+                            tool_call_id = tool_call.get("id", "")
+                            tool_name = tool_call.get("name", "")
+                            tool_args = tool_call.get("args", {})
+
+                            # 记录工具调用开始
+                            if tool_call_id not in tool_calls_in_progress:
+                                tool_calls_in_progress[tool_call_id] = {
+                                    "name": tool_name,
+                                    "args": tool_args
+                                }
+                                yield {
+                                    "type": "tool_call_start",
+                                    "tool": tool_name,
+                                    "args": tool_args,
+                                    "id": tool_call_id
+                                }
+                    # 处理普通 token
+                    elif token_content:
+                        processed_event = {
+                            "type": "llm_token",
+                            "node": node_name,
+                            "content": token_content
+                        }
+
+                elif chunk_type == "updates":
+                    updates_data = chunk["data"]
+                    serialized_data = self._serialize_value(updates_data)
+
+                    # 检查是否有人工审核请求
+                    if "review_pending" in serialized_data and serialized_data["review_pending"]:
+                        review_id = serialized_data.get("review_id", "")
+                        content_to_review = serialized_data.get("content_to_review", "")
+                        yield {
+                            "type": "human_review_request",
+                            "review_id": review_id,
+                            "content": content_to_review
+                        }
+
+                    # 检查是否有工具结果
+                    if "messages" in serialized_data:
+                        for msg in serialized_data["messages"]:
+                            # 检测工具结果消息
+                            if msg.get("role") == "tool":
+                                tool_call_id = msg.get("tool_call_id", "")
+                                tool_name = msg.get("name", "")
+                                tool_output = msg.get("content", "")
+
+                                if tool_call_id in tool_calls_in_progress:
+                                    yield {
+                                        "type": "tool_call_end",
+                                        "tool": tool_name,
+                                        "id": tool_call_id,
+                                        "result": tool_output
+                                    }
+                                    del tool_calls_in_progress[tool_call_id]
+
+                    processed_event = {
+                        "type": "state_update",
+                        "data": serialized_data
+                    }
+
+                elif chunk_type == "custom":
+                    serialized_data = self._serialize_value(chunk["data"])
+                    processed_event = {
+                        "type": "custom",
+                        "data": serialized_data
+                    }
+
+                if processed_event:
+                    yield processed_event
+
+            # 发送结束事件
+            if current_node:
+                yield {
+                    "type": "node_end",
+                    "node": current_node
+                }
+            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)