remove: 移除快速路径逻辑，全部走 React 模式

backend/app/agent/agent_service.py

@@ -16,6 +16,7 @@ from app.core.intent_classifier import get_intent_classifier
 from app.logger import info, warning
 from app.main_graph.state import MainGraphState, CurrentAction
 
+
 class AIAgentService:
     def __init__(self, checkpointer):
         self.checkpointer = checkpointer
@@ -112,7 +113,7 @@ 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（支持混合路由）"""
+        """流式处理消息，返回异步生成器（全部走 React 模式）"""
         graph = self.graphs.get(model_name)
         if not graph:
             raise ValueError(f"模型 '{model_name}' 未找到或未初始化")
@@ -128,7 +129,7 @@ class AIAgentService:
             "current_action": CurrentAction.NONE
         }
 
-        # ========== 新增：混合路由 ==========
+        # ========== 意图识别（保留用于日志） ==========
         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}")
@@ -141,24 +142,14 @@ class AIAgentService:
             "reasoning": intent_result.reasoning
         }
 
-        # 根据意图决定路径
+        # 发送路径决策事件（现在都是 react_loop）
-        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",
+            "path": "react_loop",
             "intent": intent_result.intent_type.value
         }
-        # ====================================
+        # ========================================
 
-        if use_react_loop:
         # ========== React 循环路径 ==========
         current_node = None
         tool_calls_in_progress = {}
@@ -227,7 +218,7 @@ class AIAgentService:
                     processed_event = {
                         "type": "llm_token",
                         "node": node_name,
-                            "token": token_content,  # ✅ 改为 token
+                        "token": token_content,
                         "reasoning_token": reasoning_token
                     }
 
@@ -287,123 +278,3 @@ 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",
-                        "token": char  # ✅ 改为 token
-                    }
-                    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",
-                        "token": char  # ✅ 改为 token
-                    }
-                    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",
-                        "token": char  # ✅ 改为 token
-                    }
-                    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",
-                        "token": char  # ✅ 改为 token
-                    }
-                    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/chat_area.py

@@ -143,7 +143,7 @@ def _handle_ai_response():
             # 1. 处理 LLM Token 流 (打字机效果)
             if event_type == "llm_token":
                 # 确保只处理来自 LLM 的 token，避免将工具的输出作为 token 显示
-                if event.get("node") in ("llm_call", "fallback", "fast_path"):
+                if event.get("node") in ("llm_call", "fallback"):
                     token = str(event.get("token", ""))
                     reasoning_token = str(event.get("reasoning_token", ""))