refactor: 重构快速路径流程，统一通过 llm_call 输出

- 重构 fast_paths.py，让 fast_chitchat 和 fast_rag 都进入 llm_call 而不是直接设置 final_result
- 修改 check_fast_path_success 函数返回 'llm_call' 而不是 'success'
- 更新 main_graph_builder.py 的条件边配置，支持路由到 llm_call
- 在快速路径节点中添加清除 state.final_result 的逻辑，避免复用旧结果
- 重构 RAG 工具初始化方式，使用模块级变量管理
- 修改 finalize.py 让它返回 final_result
- 更新 agent_service.py 的 RAG 工具注入方式
- 简化 hybrid_router.py 的代码结构
- 清理 rag_nodes.py 的全局变量相关代码
- 更新相关测试文件

backend/app/agent/agent_service.py

@@ -48,9 +48,7 @@ class AIAgentService:
         if rag_tool:
             self.tools.append(rag_tool)
             self.tools_by_name[rag_tool.name] = rag_tool
-            # 关键：设置全局 RAG 工具，供 rag_nodes.py 使用
-            from ..main_graph.nodes.rag_nodes import set_global_rag_tool
-            set_global_rag_tool(rag_tool)
+            self.rag_tool = rag_tool  # 保存到实例变量，供 config 注入
 
         # 2. 构建各模型的 Graph（使用新版 React 模式）
         for name, llm in chat_services.items():
@@ -82,7 +80,10 @@ class AIAgentService:
 
         graph = self.graphs[model]
         config = {
-            "configurable": {"thread_id": thread_id},
+            "configurable": {
+                "thread_id": thread_id,
+                "rag_tool": getattr(self, "rag_tool", None),  # 注入 RAG 工具
+            },
             "metadata": {"user_id": user_id}
         }
         # 新版状态输入：传入完整的 MainGraphState，关键是 user_query
@@ -136,7 +137,10 @@ class AIAgentService:
             raise ValueError(f"模型 '{model_name}' 未找到或未初始化")
 
         config = {
-            "configurable": {"thread_id": thread_id},
+            "configurable": {
+                "thread_id": thread_id,
+                "rag_tool": getattr(self, "rag_tool", None),  # 注入 RAG 工具
+            },
             "metadata": {"user_id": user_id}
         }
         input_state = {
@@ -250,9 +254,11 @@ class AIAgentService:
 
                 elif chunk_type == "updates":
                     updates_data = chunk["data"]
+                    info(f"[Stream] updates 数据: {list(updates_data.keys()) if isinstance(updates_data, dict) else type(updates_data)}")
+                    # 特别检查 final_result
+                    if isinstance(updates_data, dict) and "final_result" in updates_data:
+                        info(f"[Stream] 收到 final_result: {str(updates_data['final_result'])[:100]}...")
                     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", "")

backend/app/main_graph/nodes/__init__.py

@@ -19,6 +19,10 @@ from .finalize import finalize_node
 # 混合路由节点
 from .hybrid_router import (
     hybrid_router_node,
+    route_from_hybrid_decision,
+    check_fast_path_success,
+)
+from .fast_paths import (
     fast_chitchat_node,
     fast_rag_node,
     fast_tool_node,
@@ -45,6 +49,8 @@ __all__ = [
     "finalize_node",
     # 混合路由节点
     "hybrid_router_node",
+    "route_from_hybrid_decision",
+    "check_fast_path_success",
     "fast_chitchat_node",
     "fast_rag_node",
     "fast_tool_node",

backend/app/main_graph/nodes/fast_paths.py

@@ -0,0 +1,203 @@
+"""
+快速路径节点模块
+包含闲聊、RAG、工具等快速处理节点
+"""
+
+from typing import Optional
+
+from ..state import MainGraphState
+from ...logger import info, debug
+from ...model_services.chat_services import get_small_llm_service, get_chat_service
+from .rag_nodes import rag_retrieve_node
+from ._utils import dispatch_custom_event
+
+
+# ========== 闲聊回复模板 ==========
+CHITCHAT_TEMPLATES = {
+    "谢谢": "不客气！如果还有其他问题，请随时告诉我 😊",
+    "再见": "再见！期待下次为您服务 👋",
+    "你好": "你好！有什么我可以帮您的吗？",
+    "默认": None  # 使用 LLM 生成
+}
+
+CHITCHAT_KEYWORDS = {
+    "谢谢": ["谢谢", "感谢", "thanks", "thank you"],
+    "再见": ["再见", "拜拜", "bye", "goodbye"],
+    "你好": ["你好", "您好", "hi", "hello", "hey", "早上好", "晚上好", "下午好"],
+}
+
+
+# ========== 闲聊节点 ==========
+async def fast_chitchat_node(state: MainGraphState, config: Optional[dict] = None) -> MainGraphState:
+    """快速闲聊节点"""
+    state.current_phase = "fast_chitchat"
+    query = state.user_query or ""
+    info(f"[Fast Chitchat] 处理: {query[:50]}")
+
+    # 发送开始事件
+    await dispatch_custom_event("fast_path_start", {"path": "fast_chitchat"}, config)
+
+    # 清除之前的 final_result，让 llm_call 生成新回答
+    state.final_result = None
+    
+    # 标记快速路径成功，但不设置 final_result，让 llm_call 生成回答
+    state.success = True
+    state.current_phase = "llm_call"
+    state.debug_info["fast_chitchat_success"] = True
+
+    # 发送完成事件
+    await dispatch_custom_event("fast_path_end", {"path": "fast_chitchat", "success": True}, config)
+
+    return state
+
+
+def _match_chitchat_template(query: str) -> str:
+    """匹配闲聊模板"""
+    query_clean = query.strip().lower()
+
+    for intent, keywords in CHITCHAT_KEYWORDS.items():
+        if any(kw in query_clean for kw in keywords):
+            return CHITCHAT_TEMPLATES[intent]
+
+    # 默认：使用 LLM 生成
+    try:
+        llm = get_small_llm_service()
+        response = llm.invoke(f"你是一个友好的助手。用户说：{query}。请简短友好地回复：")
+        return response.content
+    except Exception:
+        return "你好！有什么我可以帮您的吗？"
+
+
+# ========== 快速 RAG 节点 ==========
+async def fast_rag_node(state: MainGraphState, config: Optional[dict] = None) -> MainGraphState:
+    """快速 RAG 节点：只负责 RAG 检索，然后交给 llm_call 生成回答"""
+    state.current_phase = "fast_rag"
+    query = state.user_query or ""
+    info(f"[Fast RAG] 开始处理: {query[:50]}")
+
+    # 获取 RAG 工具
+    from app.main_graph.utils.rag_initializer import get_rag_tool
+    rag_tool = get_rag_tool()
+    info(f"[Fast RAG] 获取到 rag_tool: {rag_tool is not None}")
+
+    # 发送开始事件
+    await dispatch_custom_event("fast_path_start", {"path": "fast_rag"}, config)
+
+    # 清除之前的 final_result，让 llm_call 生成新回答
+    state.final_result = None
+
+    # 如果没有 rag_tool，升级到 React 循环
+    if not rag_tool:
+        info("[Fast RAG] 未找到 RAG 工具，升级到 React 循环")
+        return _mark_fast_path_failed(state, "未找到 RAG 工具")
+
+    try:
+        # 尝试 RAG 检索
+        state = await rag_retrieve_node(state, config)
+
+        # 检查检索结果
+        if _has_valid_rag_results(state):
+            info(f"[Fast RAG] 检索有效，进入 llm_call 生成回答")
+            await dispatch_custom_event("fast_path_end", {"path": "fast_rag", "success": True}, config)
+            # 注意：这里不设置 final_result，让 llm_call 节点处理
+            return state
+
+        # 无效结果：升级到 React 循环
+        info("[Fast RAG] 无有效检索结果，升级到 React 循环")
+        return _mark_fast_path_failed(state, "无有效检索结果")
+
+    except Exception as e:
+        info(f"[Fast RAG] 执行失败: {e}")
+        return _mark_fast_path_failed(state, str(e))
+
+
+def _has_valid_rag_results(state: MainGraphState) -> bool:
+    """检查 RAG 结果是否有效"""
+    rag_docs = getattr(state, "rag_docs", [])
+    rag_context = getattr(state, "rag_context", "")
+    return (rag_docs and len(rag_docs) > 0) or (rag_context and len(rag_context) > 10)
+
+
+async def _generate_fast_answer(state: MainGraphState, query: str) -> MainGraphState:
+    """使用小模型快速生成回答"""
+    try:
+        chat_llm = get_chat_service()
+        rag_context = state.rag_context or str(state.rag_docs)[:2000]
+
+        prompt = f"""请根据以下信息回答用户问题：
+
+检索到的信息：
+{rag_context}
+
+用户问题：{query}
+
+请给出简洁、准确的回答："""
+
+        # 使用流式输出
+        from app.main_graph.config import get_stream_writer
+        writer = get_stream_writer()
+
+        full_content = ""
+        async for chunk in chat_llm.astream(prompt):
+            content = getattr(chunk, 'content', '')
+            if content:
+                full_content += content
+                # 流式输出
+                if writer and hasattr(writer, '__call__'):
+                    try:
+                        writer({
+                            "type": "llm_token",
+                            "token": content
+                        })
+                    except Exception:
+                        pass
+
+        state.final_result = full_content
+        state.success = True
+        state.current_phase = "finalizing"
+        state.debug_info["fast_rag_success"] = True
+        return state
+
+    except Exception as e:
+        info(f"[Fast RAG] 快速回答生成失败: {e}")
+        return _mark_fast_path_failed(state, "回答生成失败")
+
+
+# ========== 快速工具节点 ==========
+async def fast_tool_node(state: MainGraphState, config: Optional[dict] = None) -> MainGraphState:
+    """快速工具节点"""
+    state.current_phase = "fast_tool"
+
+    decision = state.debug_info.get("hybrid_decision", {})
+    suggested_tools = decision.get("suggested_tools", [])
+    info(f"[Fast Tool] 开始处理，建议工具: {suggested_tools}")
+
+    await dispatch_custom_event("fast_path_start", {"path": "fast_tool", "suggested_tools": suggested_tools}, config)
+
+    # 无明确工具建议，升级到 React 循环
+    if not suggested_tools:
+        info("[Fast Tool] 无明确工具建议，升级到 React 循环")
+        return _mark_fast_path_failed(state, "无明确工具建议")
+
+    # 当前版本暂不支持快速工具调用，升级到 React 循环
+    info("[Fast Tool] 快速工具调用暂未完善，升级到 React 循环")
+    return _mark_fast_path_failed(state, "快速工具调用暂未完善")
+
+
+# ========== 公共函数 ==========
+def _mark_fast_path_failed(state: MainGraphState, reason: str = "") -> MainGraphState:
+    """标记快速路径失败，准备升级到 React 循环"""
+    state.debug_info["fast_path_failed"] = True
+    state.debug_info["fast_path_fail_reason"] = reason
+    state.success = False
+    info(f"[Fast Path] 标记失败，准备升级: {reason}")
+    return state
+
+
+# ========== 导出 ==========
+__all__ = [
+    "fast_chitchat_node",
+    "fast_rag_node",
+    "fast_tool_node",
+    "_mark_fast_path_failed",
+]

backend/app/main_graph/nodes/finalize.py

@@ -22,10 +22,17 @@ async def finalize_node(state: MainGraphState, config: RunnableConfig) -> Dict[s
         config: 运行时配置
 
     Returns:
-        空字典（完成节点，无状态更新）
+        更新后的状态（包含 final_result）
     """
     log_state_change("finalize", state, "进入")
 
+    # 确保 final_result 被传递出去
+    result = {
+        "final_result": state.final_result,
+        "success": state.success,
+        "current_phase": "done"
+    }
+
     try:
         # 获取流式写入器并发送完成事件
         from app.main_graph.config import get_stream_writer
@@ -39,14 +46,15 @@ async def finalize_node(state: MainGraphState, config: RunnableConfig) -> Dict[s
                     "data": {
                         "type": "done",
                         "token_usage": state.last_token_usage,
-                        "elapsed_time": state.last_elapsed_time
+                        "elapsed_time": state.last_elapsed_time,
+                        "final_result": state.final_result
                     }
                 })
-                info("🏁 [完成事件] 已发送完成事件，包含token使用情况和耗时信息")
+                info("🏁 [完成事件] 已发送完成事件")
             except Exception as e:
                 warning(f"⚠️ [完成事件] 发送完成事件失败 (非致命): {e}")
     except Exception as e:
         warning(f"⚠️ [完成事件] 处理失败 (非致命): {e}")
 
     log_state_change("finalize", state, "离开")
-    return {}
+    return result

backend/app/main_graph/nodes/hybrid_router.py

@@ -1,107 +1,47 @@
 """
-混合路由节点模块 - 前置路由 + 快速路径
+混合路由节点模块 - 前置路由决策
+负责决定走快速路径还是 React 循环
 """
 
 import re
 import json
-from typing import Dict, Any, Optional, List
+from typing import Optional
 from dataclasses import dataclass, field
 from datetime import datetime
 
 from ..state import MainGraphState
 from ...logger import info, debug
-from ...model_services.chat_services import get_small_llm_service, get_chat_service
-from .rag_nodes import rag_retrieve_node
+from ...model_services.chat_services import get_small_llm_service
+from ._utils import dispatch_custom_event
 
 
 # ========== 核心数据类型 ==========
-
 @dataclass
 class HybridRouterResult:
     """混合路由结果"""
     intent: str = "complex"  # chitchat / knowledge / tool / complex
     confidence: float = 0.0
-    suggested_tools: List[str] = field(default_factory=list)
+    suggested_tools: list = field(default_factory=list)
     path: str = "react_loop"  # fast_chitchat / fast_rag / fast_tool / react_loop
     reasoning: str = ""
 
 
-# ========== 规则分流（无 LLM，<5ms） ==========
-
-# 问候、感谢等直接返回的关键词
-AL_CHITCHAT = {
+# ========== 规则配置 ==========
+CHITCHAT_KEYWORDS = {
     "你好", "您好", "hi", "hello", "hey", "早上好", "晚上好", "下午好",
     "谢谢", "感谢", "多谢", "thanks", "thank you",
     "再见", "拜拜", "goodbye", "bye"
 }
 
-# 子图关键词映射
 SUBGRAPH_KEYWORDS = {
     "contact": ["通讯录", "联系人", "contact", "email", "邮件", "邮箱"],
     "dictionary": ["词典", "单词", "翻译", "dictionary", "translate", "生词"],
     "news_analysis": ["资讯", "新闻", "分析", "news", "report", "热点"]
 }
 
-def _rule_based_redirect(query: str) -> Optional[HybridRouterResult]:
-    """
-    规则分流：处理明显不需要推理的情况（超快速）
 
-    Args:
-        query: 用户查询
-    
-    Returns:
-        HybridRouterResult 或 None
-    """
-    query_clean = query.strip().lower()
-    
-    # 1. 检查闲聊
-    if query_clean in AL_CHITCHAT or any(keyword in query_clean for keyword in AL_CHITCHAT):
-        return HybridRouterResult(
-            intent="chitchat",
-            confidence=1.0,
-            path="fast_chitchat",
-            reasoning=f"规则匹配：闲聊类请求"
-        )
-    
-    # 2. 检查子图关键词（直接调用工具）
-    for subgraph_name, keywords in SUBGRAPH_KEYWORDS.items():
-        if any(kw in query_clean for kw in keywords):
-            return HybridRouterResult(
-                intent="tool",
-                confidence=0.9,
-                suggested_tools=[subgraph_name],
-                path="fast_tool",
-                reasoning=f"规则匹配：{subgraph_name} 子图关键词"
-            )
-    
-    # 3. 检查是否是纯问号或很短的问题（可能需要澄清）
-    if len(query_clean) < 3 or (query_clean.endswith("?") and len(query_clean) < 5):
-        return HybridRouterResult(
-            intent="complex",
-            confidence=0.3,
-            path="react_loop",
-            reasoning="规则匹配：问题过于简短或不确定"
-        )
-    
-    return None
-
-
-# ========== 轻量级 LLM 分类 ==========
-
-async def _classify_with_small_llm(query: str) -> HybridRouterResult:
-    """
-    使用轻量级 LLM 进行意图分类
-    
-    Args:
-        query: 用户查询
-    
-    Returns:
-        HybridRouterResult
-    """
-    try:
-        llm = get_small_llm_service()
-        
-        prompt = f"""你是一个专业的意图分类助手。请分析用户的查询，并输出 JSON 格式的结果。
+# ========== 意图分类 Prompt 模板 ==========
+INTENT_CLASSIFICATION_PROMPT = """你是一个专业的意图分类助手。请分析用户的查询，并输出 JSON 格式的结果。
 
 意图类型（4选一）：
 - chitchat: 闲聊、问候、感谢、道别（不需要工具）
@@ -120,52 +60,94 @@ async def _classify_with_small_llm(query: str) -> HybridRouterResult:
     "suggested_tools": ["contact|dictionary|news_analysis", "other"]
 }}
 
-注意：如果不能100%确定意图，请选择 "complex"，置信度设低一些。
-"""
+注意：如果不能100%确定意图，请选择 "complex"，置信度设低一些。"""
 
+
+# ========== 规则分流（<5ms） ==========
+def _rule_based_redirect(query: str) -> Optional[HybridRouterResult]:
+    """规则分流：处理明显不需要推理的情况"""
+    query_clean = query.strip().lower()
+
+    # 1. 闲聊
+    if query_clean in CHITCHAT_KEYWORDS or any(kw in query_clean for kw in CHITCHAT_KEYWORDS):
+        return HybridRouterResult(
+            intent="chitchat",
+            confidence=1.0,
+            path="fast_chitchat",
+            reasoning="规则匹配：闲聊类请求"
+        )
+
+    # 2. 子图关键词
+    for subgraph_name, keywords in SUBGRAPH_KEYWORDS.items():
+        if any(kw in query_clean for kw in keywords):
+            return HybridRouterResult(
+                intent="tool",
+                confidence=0.9,
+                suggested_tools=[subgraph_name],
+                path="fast_tool",
+                reasoning=f"规则匹配：{subgraph_name} 子图关键词"
+            )
+
+    # 3. 短问题
+    if len(query_clean) < 3 or (query_clean.endswith("?") and len(query_clean) < 5):
+        return HybridRouterResult(
+            intent="complex",
+            confidence=0.3,
+            path="react_loop",
+            reasoning="规则匹配：问题过于简短"
+        )
+
+    return None
+
+
+# ========== LLM 分类 ==========
+async def _classify_with_llm(query: str) -> HybridRouterResult:
+    """使用轻量级 LLM 进行意图分类"""
+    try:
+        llm = get_small_llm_service()
+        prompt = INTENT_CLASSIFICATION_PROMPT.format(query=query)
         response = await llm.ainvoke(prompt)
-        content = response.content
 
         # 解析 JSON
-        json_match = re.search(r'(\{[^{}]*\{[^{}]*\}[^{}]*\})|(\{[^{}]*\})', content)
-        if json_match:
-            try:
-                data = json.loads(json_match.group(0))
+        json_match = re.search(r'\{[\s\S]*?\}', response.content)
+        if not json_match:
+            return _default_result()
 
+        data = json.loads(json_match.group())
+        return _parse_classification_result(data)
+
+    except Exception as e:
+        debug(f"LLM 分类失败: {e}")
+        return _default_result()
+
+
+def _parse_classification_result(data: dict) -> HybridRouterResult:
+    """解析分类结果"""
     intent = data.get("intent", "complex")
     confidence = float(data.get("confidence", 0.3))
-                reasoning = data.get("reasoning", "")
-                suggested_tools = data.get("suggested_tools", [])
 
-                # 置信度低于 0.5 一律走 complex
+    # 置信度低于阈值，走 complex
     if confidence < 0.5:
         intent = "complex"
-                    path = "react_loop"
-                elif intent == "chitchat":
-                    path = "fast_chitchat"
-                elif intent == "knowledge":
-                    path = "fast_rag"
-                elif intent == "tool":
-                    path = "fast_tool"
-                else:
-                    intent = "complex"
-                    path = "react_loop"
+
+    # intent -> path 映射
+    path_map = {
+        "chitchat": "fast_chitchat",
+        "knowledge": "fast_rag",
+        "tool": "fast_tool",
+    }
 
     return HybridRouterResult(
         intent=intent,
         confidence=confidence,
-                    suggested_tools=suggested_tools,
-                    path=path,
-                    reasoning=reasoning
+        suggested_tools=data.get("suggested_tools", []),
+        path=path_map.get(intent, "react_loop"),
+        reasoning=data.get("reasoning", "")
     )
-            except Exception as e:
-                debug(f"轻量 LLM 响应解析失败: {e}")
-                pass
 
-    except Exception as e:
-        debug(f"轻量 LLM 调用失败: {e}")
 
-    # LLM 失败，降级到规则+默认
+def _default_result() -> HybridRouterResult:
+    """默认结果（LLM 失败时）"""
     return HybridRouterResult(
         intent="complex",
         confidence=0.3,
@@ -174,372 +156,73 @@ async def _classify_with_small_llm(query: str) -> HybridRouterResult:
     )
 
 
-# ========== 路由决策 ==========
-
-def _make_decision(classification_result: HybridRouterResult) -> HybridRouterResult:
-    """
-    根据分类结果最终决策
-    
-    Args:
-        classification_result: 分类结果
-    
-    Returns:
-        最终决策结果
-    """
-    if classification_result.confidence < 0.5:
-        classification_result.intent = "complex"
-        classification_result.path = "react_loop"
-        return classification_result
-    
-    return classification_result
-
-
-# ========== 混合路由主节点 ==========
-
-async def hybrid_router_node(state: MainGraphState, config: Optional[Dict[str, Any]] = None) -> MainGraphState:
-    """
-    混合路由节点：前置路由，决定走快速路径还是 React 循环
-    
-    Args:
-        state: 当前状态
-        config: LangChain 配置（用于发送自定义事件）
-    
-    Returns:
-        更新后的状态
-    """
+# ========== 主路由节点 ==========
+async def hybrid_router_node(state: MainGraphState, config: Optional[dict] = None) -> MainGraphState:
+    """混合路由节点：前置路由，决定走快速路径还是 React 循环"""
     state.current_phase = "hybrid_router"
-    
     query = state.user_query or ""
+
     info(f"[Hybrid Router] 开始路由: {query[:50]}...")
 
-    # 1. 规则分流（超快速）
+    # 1. 规则分流
     rule_result = _rule_based_redirect(query)
     if rule_result:
-        info(f"[Hybrid Router] 规则分流命中: {rule_result.path}")
         decision = rule_result
+        info(f"[Hybrid Router] 规则命中: {decision.path}")
     else:
-        # 2. 轻量 LLM 分类
-        info(f"[Hybrid Router] 规则未命中，使用轻量 LLM 分类")
-        classification_result = await _classify_with_small_llm(query)
-        decision = _make_decision(classification_result)
+        # 2. LLM 分类
+        info("[Hybrid Router] 规则未命中，使用 LLM 分类")
+        decision = await _classify_with_llm(query)
 
-    # 3. 发送 SSE 事件
-    if config:
-        try:
-            from langchain_core.callbacks.manager import adispatch_custom_event
+    # 3. 更新状态
+    state.debug_info["hybrid_decision"] = {
+        "intent": decision.intent,
+        "confidence": decision.confidence,
+        "path": decision.path,
+        "reasoning": decision.reasoning,
+        "suggested_tools": decision.suggested_tools
+    }
+    state.debug_info["hybrid_start_time"] = datetime.now().isoformat()
 
-            callbacks = config.get("callbacks")
-            if callbacks:
-                await adispatch_custom_event(
-                    "intent_classified",
-                    {
+    # 4. 发送事件
+    await dispatch_custom_event("intent_classified", {
         "intent": decision.intent,
         "confidence": decision.confidence,
         "reasoning": decision.reasoning,
         "suggested_tools": decision.suggested_tools
-                    },
-                    callbacks=callbacks
-                )
+    }, config)
 
-                await adispatch_custom_event(
-                    "path_decision",
-                    {
+    await dispatch_custom_event("path_decision", {
         "path": decision.path,
         "intent": decision.intent,
         "reasoning": decision.reasoning
-                    },
-                    callbacks=callbacks
-                )
-        except Exception as e:
-            debug(f"[Hybrid Router] 发送 SSE 事件失败: {e}")
-    
-    # 4. 更新状态
-    state.debug_info["hybrid_decision"] = decision
-    state.debug_info["hybrid_start_time"] = datetime.now().isoformat()
+    }, config)
 
     info(f"[Hybrid Router] 路由决策: {decision.path} (intent={decision.intent}, confidence={decision.confidence})")
-    
     return state
 
 
-# ========== 快速路径：闲聊 ==========
-
-async def fast_chitchat_node(state: MainGraphState, config: Optional[Dict[str, Any]] = None) -> MainGraphState:
-    """
-    快速闲聊节点：直接返回回复，不走 RAG/工具/循环
-    
-    Args:
-        state: 当前状态
-        config: LangChain 配置
-    
-    Returns:
-        更新后的状态
-    """
-    state.current_phase = "fast_chitchat"
-    
-    query = state.user_query or ""
-    info(f"[Fast Chitchat] 处理: {query[:50]}")
-    
-    # 发送 SSE 事件
-    if config:
-        try:
-            from langchain_core.callbacks.manager import adispatch_custom_event
-            callbacks = config.get("callbacks")
-            if callbacks:
-                await adispatch_custom_event(
-                    "fast_path_start",
-                    {"path": "fast_chitchat"},
-                    callbacks=callbacks
-                )
-        except Exception as e:
-            debug(f"[Fast Chitchat] 发送事件失败: {e}")
-    
-    # 快速回复（可以扩展为模板库）
-    query_clean = query.strip().lower()
-    
-    if any(kw in query_clean for kw in ["谢谢", "感谢", "thanks", "thank you"]):
-        reply = "不客气！如果还有其他问题，请随时告诉我 😊"
-    elif any(kw in query_clean for kw in ["再见", "拜拜", "bye", "goodbye"]):
-        reply = "再见！期待下次为您服务 👋"
-    elif any(kw in query_clean for kw in ["你好", "您好", "hi", "hello", "hey", "早上好", "晚上好", "下午好"]):
-        reply = "你好！有什么我可以帮您的吗？"
-    else:
-        # 兜底：用轻量 LLM 生成
-        try:
-            llm = get_small_llm_service()
-            response = await llm.ainvoke(f"你是一个友好的助手。用户说：{query}。请简短友好地回复：")
-            reply = response.content
-        except:
-            reply = "你好！有什么我可以帮您的吗？"
-    
-    state.final_result = reply
-    state.success = True
-    state.current_phase = "finalizing"
-    state.debug_info["fast_chitchat_success"] = True
-    
-    # 发送 fast_path_end 事件
-    if config:
-        try:
-            from langchain_core.callbacks.manager import adispatch_custom_event
-            callbacks = config.get("callbacks")
-            if callbacks:
-                await adispatch_custom_event(
-                    "fast_path_end",
-                    {"path": "fast_chitchat", "success": True},
-                    callbacks=callbacks
-                )
-        except Exception as e:
-            debug(f"[Fast Chitchat] 发送完成事件失败: {e}")
-    
-    return state
-
-
-# ========== 快速路径：RAG（带自动升级） ==========
-
-async def fast_rag_node(state: MainGraphState, config: Optional[Dict[str, Any]] = None) -> MainGraphState:
-    """
-    快速 RAG 节点：先尝试快速检索，失败自动升级到 React 循环
-    
-    Args:
-        state: 当前状态
-        config: LangChain 配置
-    
-    Returns:
-        更新后的状态
-    """
-    state.current_phase = "fast_rag"
-    
-    query = state.user_query or ""
-    info(f"[Fast RAG] 开始处理: {query[:50]}")
-    
-    # 发送 SSE 事件
-    if config:
-        try:
-            from langchain_core.callbacks.manager import adispatch_custom_event
-            callbacks = config.get("callbacks")
-            if callbacks:
-                await adispatch_custom_event(
-                    "fast_path_start",
-                    {"path": "fast_rag"},
-                    callbacks=callbacks
-                )
-        except Exception as e:
-            debug(f"[Fast RAG] 发送事件失败: {e}")
-    
-    try:
-        # 先尝试 RAG 检索 - 注意：rag_retrieve_node 是异步函数，需要 await
-        state = await rag_retrieve_node(state, config)
-        
-        # 检查检索结果
-        rag_docs = getattr(state, "rag_docs", [])
-        rag_context = getattr(state, "rag_context", "")
-        
-        # 检查是否有有效结果
-        has_valid_results = (rag_docs and len(rag_docs) > 0) or (rag_context and len(rag_context) > 10)
-        
-        if has_valid_results:
-            # 快速 RAG 成功！使用小模型快速生成回答
-            try:
-                llm = get_chat_service()
-                prompt = f"""请根据以下信息回答用户问题：
-
-检索到的信息：
-{rag_context or str(rag_docs)[:2000]}
-
-用户问题：{query}
-
-请给出简洁、准确的回答："""
-                
-                response = await llm.ainvoke(prompt)
-                
-                state.final_result = response.content
-                state.success = True
-                state.current_phase = "finalizing"
-                state.debug_info["fast_rag_success"] = True
-                
-                # 发送成功事件
-                if config:
-                    try:
-                        from langchain_core.callbacks.manager import adispatch_custom_event
-                        callbacks = config.get("callbacks")
-                        if callbacks:
-                            await adispatch_custom_event(
-                                "fast_path_end",
-                                {"path": "fast_rag", "success": True},
-                                callbacks=callbacks
-                            )
-                    except Exception as e:
-                        debug(f"[Fast RAG] 发送完成事件失败: {e}")
-                
-                return state
-            
-            except Exception as e:
-                info(f"[Fast RAG] 快速回答生成失败: {e}")
-                # 继续往下走，升级到 React 循环
-        
-        # RAG 失败或无结果：标记升级
-        info(f"[Fast RAG] 无有效检索结果，升级到 React 循环")
-        return mark_fast_path_failed(state, reason="无有效检索结果")
-    
-    except Exception as e:
-        info(f"[Fast RAG] 执行失败: {e}")
-        return mark_fast_path_failed(state, reason=str(e))
-
-
-# ========== 快速路径：工具（带自动升级） ==========
-
-async def fast_tool_node(state: MainGraphState, config: Optional[Dict[str, Any]] = None) -> MainGraphState:
-    """
-    快速工具节点：尝试直接调用工具，失败自动升级到 React 循环
-    
-    Args:
-        state: 当前状态
-        config: LangChain 配置
-    
-    Returns:
-        更新后的状态
-    """
-    state.current_phase = "fast_tool"
-    
-    decision: HybridRouterResult = state.debug_info.get("hybrid_decision", HybridRouterResult())
-    suggested_tools = decision.suggested_tools or []
-    
-    query = state.user_query or ""
-    info(f"[Fast Tool] 开始处理，建议工具: {suggested_tools}")
-    
-    # 发送 SSE 事件
-    if config:
-        try:
-            from langchain_core.callbacks.manager import adispatch_custom_event
-            callbacks = config.get("callbacks")
-            if callbacks:
-                await adispatch_custom_event(
-                    "fast_path_start",
-                    {"path": "fast_tool", "suggested_tools": suggested_tools},
-                    callbacks=callbacks
-                )
-        except Exception as e:
-            debug(f"[Fast Tool] 发送事件失败: {e}")
-    
-    # 检查是否有明确的工具建议
-    if not suggested_tools:
-        info(f"[Fast Tool] 无明确工具建议，升级到 React 循环")
-        return mark_fast_path_failed(state, reason="无明确工具建议")
-    
-    # 工具调用逻辑（这里暂时先标记升级，让 React 循环去处理）
-    # 后续可以扩展为直接调用子图
-    info(f"[Fast Tool] 快速工具调用暂未完善，升级到 React 循环")
-    return mark_fast_path_failed(state, reason="快速工具调用暂未完善")
-
-
-# ========== 标记快速路径失败（用于自动升级） ==========
-
-def mark_fast_path_failed(state: MainGraphState, reason: str = "") -> MainGraphState:
-    """
-    标记快速路径失败，准备升级到 React 循环
-    
-    Args:
-        state: 当前状态
-        reason: 失败原因
-    
-    Returns:
-        更新后的状态
-    """
-    state.debug_info["fast_path_failed"] = True
-    state.debug_info["fast_path_fail_reason"] = reason
-    state.success = False
-    
-    # 发送 escalation 事件
-    config = state.debug_info.get("config")
-    if config:
-        try:
-            from langchain_core.callbacks.manager import adispatch_custom_event
-            callbacks = config.get("callbacks")
-            if callbacks:
-                # 这里需要在异步上下文中调用
-                pass
-        except Exception as e:
-            debug(f"[Fast Path] 发送升级事件失败: {e}")
-    
-    info(f"[Fast Path] 标记失败，准备升级: {reason}")
-    return state
-
-
-# ========== 快速路径检查器（自动升级机制） ==========
-
+# ========== 条件路由函数 ==========
 def route_from_hybrid_decision(state: MainGraphState) -> str:
-    """
-    从混合路由决策获取下一步的节点名称
-    
-    Args:
-        state: 当前状态
-    
-    Returns:
-        节点名称
-    """
-    decision: HybridRouterResult = state.debug_info.get("hybrid_decision", HybridRouterResult())
-    return decision.path
+    """从混合路由决策获取下一步节点"""
+    decision = state.debug_info.get("hybrid_decision", {})
+    return decision.get("path", "react_loop")
 
 
 def check_fast_path_success(state: MainGraphState) -> str:
-    """
-    检查快速路径是否成功，成功直接到 finalize，失败升级到 react_reason
-    
-    Args:
-        state: 当前状态
-    
-    Returns:
-        "success" 或 "escalate"
-    """
-    # 检查是否有错误标记
+    """检查快速路径是否成功"""
     if state.debug_info.get("fast_path_failed"):
-        info(f"[Fast Path Check] 快速路径失败，升级到 React 循环")
+        info("[Fast Path Check] 快速路径失败，升级到 React 循环")
         return "escalate"
 
-    # 检查是否成功设置了 final_result
-    if state.final_result:
-        info(f"[Fast Path Check] 快速路径成功，进入 finalize")
-        return "success"
+    info("[Fast Path Check] 快速路径成功，进入 llm_call")
+    return "llm_call"
 
-    # 默认：认为成功（某些快速路径可能直接在节点中完成）
-    return "success"
+
+# ========== 导出 ==========
+__all__ = [
+    "hybrid_router_node",
+    "route_from_hybrid_decision",
+    "check_fast_path_success",
+    "HybridRouterResult",
+]

backend/app/main_graph/nodes/rag_nodes.py

@@ -1,11 +1,11 @@
 """
 RAG 检索节点模块
-包含 RAG 检索节点（带超时重试）
+使用模块级变量管理 RAG 工具
 """
 
 import time
 import asyncio
-from typing import Dict, Any, Optional
+from typing import Optional
 from datetime import datetime
 
 from app.main_graph.state import MainGraphState, ErrorRecord, ErrorSeverity
@@ -13,43 +13,15 @@ from app.main_graph.utils.retry_utils import RAG_RETRY_CONFIG
 from app.logger import info
 from ._utils import dispatch_custom_event, make_react_event
 
-from app.rag.tools import create_rag_tool
-from app.rag.pipeline import RAGPipeline
 
-
-# ========== 全局 RAG 工具实例 ==========
-_GLOBAL_RAG_TOOL: Optional[Any] = None
-_GLOBAL_RAG_PIPELINE: Optional[RAGPipeline] = None
-
-
-def get_global_rag_tool() -> Optional[Any]:
-    return _GLOBAL_RAG_TOOL
-
-
-def set_global_rag_tool(tool: Any) -> None:
-    global _GLOBAL_RAG_TOOL
-    _GLOBAL_RAG_TOOL = tool
-
-
-def set_global_rag_pipeline(pipeline: RAGPipeline) -> None:
-    global _GLOBAL_RAG_PIPELINE
-    _GLOBAL_RAG_PIPELINE = pipeline
-
-
-def get_rag_tool_from_state(state: MainGraphState) -> Optional[Any]:
-    """从状态或全局获取 RAG 工具"""
-    return state.debug_info.get("rag_tool") or get_global_rag_tool()
-
-
-def inject_rag_tool_to_state(state: MainGraphState, rag_tool: Any) -> MainGraphState:
-    """将 RAG 工具注入到状态中"""
-    state.debug_info["rag_tool"] = rag_tool
-    state.debug_info["rag_tool_injected"] = datetime.now().isoformat()
-    return state
+def _get_rag_tool() -> Optional[callable]:
+    """获取 RAG 工具"""
+    from app.main_graph.utils.rag_initializer import get_rag_tool
+    return get_rag_tool()
 
 
 # ========== RAG 检索核心逻辑 ==========
-async def _rag_retrieve_core(state: MainGraphState) -> MainGraphState:
+async def _rag_retrieve_core(state: MainGraphState, rag_tool: callable) -> MainGraphState:
     """执行 RAG 检索的核心逻辑"""
     retrieval_query = state.user_query
 
@@ -60,55 +32,54 @@ async def _rag_retrieve_core(state: MainGraphState) -> MainGraphState:
         if cfg and cfg.retrieval_query:
             retrieval_query = cfg.retrieval_query
 
-    rag_tool = get_rag_tool_from_state(state)
-
-    if rag_tool:
+    # 调用 RAG 工具
     rag_context = await rag_tool.ainvoke(retrieval_query)
+    info(f"[RAG Core] 获取到 rag_context: {type(rag_context)}, 长度={len(rag_context) if rag_context else 0}")
+
     state.rag_context = rag_context
     state.rag_docs = [{"source": "rag_retrieval", "content": rag_context}]
     state.rag_retrieved = True
     state.success = True
-        state.debug_info["rag_source"] = "rag_tool"
-        return state
+    state.debug_info["rag_source"] = "tool"
 
-    if _GLOBAL_RAG_PIPELINE:
-        documents = await _GLOBAL_RAG_PIPELINE.aretrieve(retrieval_query)
-        if documents:
-            rag_context = _GLOBAL_RAG_PIPELINE.format_context(documents)
-            state.rag_context = rag_context
-            state.rag_docs = [
-                {"source": doc.metadata.get("source", "unknown"), "content": doc.page_content}
-                for doc in documents
-            ]
-        else:
-            state.rag_context = f"未找到与 '{retrieval_query}' 相关的知识库信息。"
-            state.rag_docs = []
-        state.rag_retrieved = True
-        state.success = True
-        state.debug_info["rag_source"] = "rag_pipeline"
+    info(f"[RAG Core] state.rag_docs 长度: {len(state.rag_docs)}")
     return state
 
-    raise RuntimeError("RAG 工具未初始化，请先调用 set_global_rag_tool() 或 set_global_rag_pipeline()")
-
 
 # ========== RAG 检索节点 ==========
-async def rag_retrieve_node(state: MainGraphState, config: Optional[Dict[str, Any]] = None) -> MainGraphState:
+async def rag_retrieve_node(state: MainGraphState, config: Optional[dict] = None) -> MainGraphState:
     """RAG 检索节点：带超时和重试"""
     state.current_phase = "rag_retrieving"
     start_time = time.time()
     last_error = None
 
-    # 步骤1: 发送开始事件
+    # 获取 RAG 工具
+    rag_tool = _get_rag_tool()
+
+    if not rag_tool:
+        error_record = ErrorRecord(
+            error_type="RAGRetrievalError",
+            error_message="RAG 工具未初始化",
+            severity=ErrorSeverity.WARNING,
+            source="rag_retrieve_node",
+            timestamp=datetime.now().isoformat(),
+            retry_count=0,
+            max_retries=RAG_RETRY_CONFIG.max_retries,
+        )
+        state.errors.append(error_record)
+        state.current_error = error_record
+        state.current_phase = "error_handling"
+        return state
+
     await dispatch_custom_event(
         "react_reasoning",
         make_react_event(state.reasoning_step, "rag_retrieve_start", 1.0, "开始执行 RAG 检索..."),
         config
     )
 
-    # 步骤2: 执行检索（带重试）
     for attempt in range(RAG_RETRY_CONFIG.max_retries + 1):
         try:
-            result = await _rag_retrieve_core(state)
+            result = await _rag_retrieve_core(state, rag_tool)
 
             info(f"[RAG] 检索成功，上下文长度: {len(result.rag_context)} 字符")
 
@@ -118,7 +89,6 @@ async def rag_retrieve_node(state: MainGraphState, config: Optional[Dict[str, An
                 "time": time.time() - start_time
             }
 
-            # 记录成功到历史
             state.reasoning_history.append({
                 "step": state.reasoning_step,
                 "action": "RETRIEVE_RAG",
@@ -127,7 +97,6 @@ async def rag_retrieve_node(state: MainGraphState, config: Optional[Dict[str, An
                 "timestamp": datetime.now().isoformat()
             })
 
-            # 发送完成事件
             doc_count = len(result.rag_docs) if result.rag_docs else 0
             await dispatch_custom_event(
                 "react_reasoning",
@@ -144,7 +113,6 @@ async def rag_retrieve_node(state: MainGraphState, config: Optional[Dict[str, An
             if attempt >= RAG_RETRY_CONFIG.max_retries:
                 break
 
-            # 发送重试事件
             await dispatch_custom_event(
                 "react_reasoning",
                 make_react_event(state.reasoning_step, "rag_retrieve_retry", 1.0,
@@ -152,11 +120,10 @@ async def rag_retrieve_node(state: MainGraphState, config: Optional[Dict[str, An
                 config
             )
 
-            # 指数退避
             delay = RAG_RETRY_CONFIG.base_delay * (2 ** attempt)
             await asyncio.sleep(min(delay, RAG_RETRY_CONFIG.max_delay))
 
-    # 步骤3: 所有重试失败，记录到历史（避免推理循环）
+    # 失败记录
     state.reasoning_history.append({
         "step": state.reasoning_step,
         "action": "RETRIEVE_RAG",
@@ -165,7 +132,6 @@ async def rag_retrieve_node(state: MainGraphState, config: Optional[Dict[str, An
         "timestamp": datetime.now().isoformat()
     })
 
-    # 步骤4: 记录错误
     error_record = ErrorRecord(
         error_type="RAGRetrievalError",
         error_message=str(last_error) if last_error else "RAG 检索超时",
@@ -174,19 +140,12 @@ async def rag_retrieve_node(state: MainGraphState, config: Optional[Dict[str, An
         timestamp=datetime.now().isoformat(),
         retry_count=RAG_RETRY_CONFIG.max_retries,
         max_retries=RAG_RETRY_CONFIG.max_retries,
-        context={
-            "query": state.user_query,
-            "total_time": time.time() - start_time,
-            "has_rag_tool": get_global_rag_tool() is not None,
-            "has_rag_pipeline": _GLOBAL_RAG_PIPELINE is not None
-        }
     )
 
     state.errors.append(error_record)
     state.current_error = error_record
     state.current_phase = "error_handling"
 
-    # 发送错误事件
     await dispatch_custom_event(
         "react_reasoning",
         make_react_event(state.reasoning_step, "rag_retrieve_error", 1.0,
@@ -197,8 +156,7 @@ async def rag_retrieve_node(state: MainGraphState, config: Optional[Dict[str, An
     return state
 
 
-# ========== 重新检索节点 ==========
-async def rag_re_retrieve_node(state: MainGraphState, config: Optional[Dict[str, Any]] = None) -> MainGraphState:
+async def rag_re_retrieve_node(state: MainGraphState, config: Optional[dict] = None) -> MainGraphState:
     """重新检索节点"""
     state.current_phase = "rag_re_retrieving"
 
@@ -214,9 +172,4 @@ async def rag_re_retrieve_node(state: MainGraphState, config: Optional[Dict[str,
 __all__ = [
     "rag_retrieve_node",
     "rag_re_retrieve_node",
-    "inject_rag_tool_to_state",
-    "get_rag_tool_from_state",
-    "get_global_rag_tool",
-    "set_global_rag_tool",
-    "set_global_rag_pipeline",
 ]

backend/app/main_graph/utils/main_graph_builder.py

@@ -13,11 +13,13 @@ from ..nodes.error_handling import error_handling_node
 from ..nodes.routing import init_state_node, route_by_reasoning
 from ..nodes.hybrid_router import (
     hybrid_router_node,
+    route_from_hybrid_decision,
+    check_fast_path_success,
+)
+from ..nodes.fast_paths import (
     fast_chitchat_node,
     fast_rag_node,
     fast_tool_node,
-    route_from_hybrid_decision,
-    check_fast_path_success
 )
 from ..nodes.llm_call import create_llm_call_node
 from ..nodes.rag_nodes import rag_retrieve_node
@@ -294,7 +296,7 @@ def build_react_main_graph(llm=None, tools=None, mem0_client=None, use_hybrid_ro
                 fast_node,
                 check_fast_path_success,
                 {
-                    "success": "finalize",
+                    "llm_call": "llm_call",
                     "escalate": "react_reason"
                 }
             )

backend/app/main_graph/utils/rag_initializer.py

@@ -3,12 +3,43 @@ from app.rag.tools import create_rag_tool
 from app.rag.retriever import create_parent_hybrid_retriever
 from app.model_services import get_embedding_service
 from app.logger import info, warning
+import sys
+
+# 全局 RAG 工具
+_rag_tool = None
+_initialized = False
+
+
+def get_rag_tool() -> callable:
+    """获取全局 RAG 工具"""
+    return _rag_tool
+
+
+def is_initialized() -> bool:
+    """检查是否已初始化"""
+    return _initialized
+
+
+async def init_rag_tool(local_llm_creator, force: bool = False):
+    """
+    初始化 RAG 工具（注册到模块级变量）
+
+    Args:
+        local_llm_creator: 返回 LLM 实例的函数
+        force: 是否强制重新初始化
+
+    Returns:
+        RAG 工具（@tool 装饰函数）或 None
+    """
+    global _rag_tool, _initialized
+
+    # 防止重复初始化
+    if _initialized and not force:
+        info("[RAG] 已初始化，跳过")
+        return _rag_tool
 
-async def init_rag_tool(local_llm_creator):
-    """初始化 RAG 工具，失败返回 None"""
     try:
         info("🔄 正在初始化 RAG 检索系统...")
-        # 使用统一的嵌入服务获取接口
         embeddings = get_embedding_service()
         retriever = create_parent_hybrid_retriever(
             collection_name="rag_documents",
@@ -16,12 +47,26 @@ async def init_rag_tool(local_llm_creator):
             embeddings=embeddings
         )
         rewrite_llm = local_llm_creator()
+
         rag_tool = create_rag_tool(
-            retriever, rewrite_llm,
-            num_queries=3, rerank_top_n=5
+            retriever=retriever,
+            llm=rewrite_llm,
+            num_queries=3,
+            rerank_top_n=5
         )
-        info("✅ RAG 检索工具初始化成功（全异步版本）")
+
+        _rag_tool = rag_tool
+        _initialized = True
+        info(f"✅ RAG 检索工具初始化成功 (id={id(rag_tool)})")
         return rag_tool
+
     except Exception as e:
         warning(f"⚠️ RAG 检索工具初始化失败: {e}")
         return None
+
+
+def reset():
+    """重置（用于测试）"""
+    global _rag_tool, _initialized
+    _rag_tool = None
+    _initialized = False

tools/run.py

@@ -1,20 +1,16 @@
 #!/usr/bin/env python3
-"""统一入口：设置路径后运行 RAG 索引构建 CLI"""
+"""统一入口：设置路径后运行测试"""
 import sys
 from pathlib import Path
 from dotenv import load_dotenv
 
-# 路径设置
+# 路径设置 - 只添加 backend 目录
 project_root = Path(__file__).resolve().parent.parent
-sys.path.insert(0, str(project_root))
-sys.path.insert(0, str(project_root / "backend"))
+backend_path = project_root
+sys.path.insert(0, str(backend_path))
 load_dotenv(project_root / ".env")
 
 if __name__ == "__main__":
-    #from rag_indexer.cli import main
-    #from tools.test.test_rag_indexer_result import main
-    #from tools.test.test_rag_pipeline import main
-    from tools.test.test_fast_rag_fix import main
-    #from tools.test.test_graph_branches import main
+    from tools.test.test_graph_branches import main
     import asyncio
     asyncio.run(main())

tools/start.py

@@ -14,7 +14,6 @@ from dotenv import load_dotenv
 # 路径设置
 project_root = Path(__file__).resolve().parent.parent
 sys.path.insert(0, str(project_root))
-sys.path.insert(0, str(project_root / "backend"))
 load_dotenv(project_root / ".env")
 
 # 全局变量

tools/test/test_fast_rag_fix.py

@@ -1,88 +0,0 @@
-#!/usr/bin/env python3
-"""
-快速测试 - 测试 fast_rag 路径修复
-"""
-
-import asyncio
-from backend.app.main_graph.state import MainGraphState, CurrentAction
-from backend.app.main_graph.utils.main_graph_builder import build_react_main_graph
-from backend.app.model_services.chat_services import get_all_chat_services
-from backend.app.main_graph.tools.graph_tools import AVAILABLE_TOOLS
-
-
-async def test_fast_rag_path():
-    """测试 fast_rag 路径"""
-    print("=" * 60)
-    print("测试 fast_rag 路径修复")
-    print("=" * 60)
-    
-    # 1. 获取 LLM
-    chat_services = get_all_chat_services()
-    if not chat_services:
-        print("✗ 没有可用的 LLM 服务")
-        return
-    
-    llm = list(chat_services.values())[0]
-    print(f"✓ 使用 LLM: {list(chat_services.keys())[0]}")
-    
-    # 2. 构建图
-    graph = build_react_main_graph(
-        llm=llm,
-        tools=AVAILABLE_TOOLS,
-        use_hybrid_router=True
-    ).compile()
-    print(f"✓ 图构建完成")
-    
-    # 3. 测试问题
-    test_query = "吕布和张飞谁厉害？"
-    print(f"\n测试问题: {test_query}")
-    
-    # 4. 创建状态
-    input_state = {
-        "user_query": test_query,
-        "messages": [{"role": "user", "content": test_query}],
-        "user_id": "test_user",
-        "current_action": CurrentAction.NONE
-    }
-    
-    # 5. 执行
-    print("开始执行...")
-    try:
-        result = await graph.ainvoke(
-            input_state,
-            config={"configurable": {"thread_id": "test_fast_rag"}}
-        )
-        
-        print(f"\n✓ 执行成功！")
-        print(f"最终回答: {result.get('final_result', '')[:300]}")
-        
-        # 调试信息
-        debug_info = result.get("debug_info", {})
-        print(f"\n调试信息:")
-        if "fast_path_failed" in debug_info:
-            print(f"  - fast_path_failed: {debug_info['fast_path_failed']}")
-        if "fast_path_fail_reason" in debug_info:
-            print(f"  - fast_path_fail_reason: {debug_info['fast_path_fail_reason']}")
-            
-    except Exception as e:
-        print(f"\n✗ 执行失败: {e}")
-        import traceback
-        print(traceback.format_exc())
-        return False
-    
-    return True
-
-
-async def main():
-    success = await test_fast_rag_path()
-    if success:
-        print("\n🎉 测试通过！")
-    else:
-        print("\n⚠️  测试失败")
-
-
-if __name__ == "__main__":
-    try:
-        asyncio.run(main())
-    except KeyboardInterrupt:
-        print("\n测试被中断")

tools/test/test_graph_branches.py

@@ -7,47 +7,49 @@ import asyncio
 from pathlib import Path
 from dotenv import load_dotenv
 
+# 添加 backend 到路径
+sys.path.insert(0, str(Path(__file__).parent.parent.parent / "backend"))
 
-from backend.app.main_graph.state import MainGraphState, CurrentAction
-from backend.app.main_graph.utils.main_graph_builder import build_react_main_graph
-from backend.app.model_services.chat_services import get_all_chat_services
-from backend.app.main_graph.tools.graph_tools import AVAILABLE_TOOLS
-from backend.app.main_graph.utils.rag_initializer import init_rag_tool
+from app.main_graph.state import MainGraphState, CurrentAction
+from app.main_graph.utils.main_graph_builder import build_react_main_graph
+from app.model_services.chat_services import get_all_chat_services
+from app.main_graph.tools.graph_tools import AVAILABLE_TOOLS
+from app.main_graph.utils.rag_initializer import init_rag_tool
 
 
 # ========== 测试用例配置 ==========
 TEST_CASES = [
-    # 测试1: 简单闲聊 - 应该走 fast_chitchat
-    {
-        "name": "闲聊测试",
-        "query": "你好！",
-        "description": "测试快速闲聊分支"
-    },
+    # # 测试1: 简单闲聊 - 应该走 fast_chitchat
+    # {
+    #     "name": "闲聊测试",
+    #     "query": "你好！",
+    #     "description": "测试快速闲聊分支"
+    # },
     # 测试2: 知识查询 - 应该走 fast_rag，然后可能升级到 react
     {
         "name": "知识查询测试",
-        "query": "什么是机器学习？",
+        "query": "吕布的事迹？",
         "description": "测试快速 RAG 分支"
     },
-    # 测试3: 需要推理的复杂问题 - 应该直接到 React 循环
-    {
-        "name": "复杂推理测试",
-        "query": "请帮我分析：如果我有10万元，想要在一年内获得15%的收益，有哪些低风险的投资方案？",
-        "description": "测试 React 循环推理分支"
-    },
-    # 测试4: 需要工具调用的问题
-    {
-        "name": "工具调用测试",
-        "query": "搜索一下今天的天气怎么样",
-        "description": "测试工具调用分支"
-    },
-    # 测试5: 带记忆的对话
-    {
-        "name": "记忆测试",
-        "query": "你刚才回答了我什么问题？",
-        "description": "测试记忆检索分支",
-        "thread_id": "test_memory_thread"
-    }
+    # # 测试3: 需要推理的复杂问题 - 应该直接到 React 循环
+    # {
+    #     "name": "复杂推理测试",
+    #     "query": "请帮我分析：如果我有10万元，想要在一年内获得15%的收益，有哪些低风险的投资方案？",
+    #     "description": "测试 React 循环推理分支"
+    # },
+    # # 测试4: 需要工具调用的问题
+    # {
+    #     "name": "工具调用测试",
+    #     "query": "搜索一下今天的天气怎么样",
+    #     "description": "测试工具调用分支"
+    # },
+    # # 测试5: 带记忆的对话
+    # {
+    #     "name": "记忆测试",
+    #     "query": "你刚才回答了我什么问题？",
+    #     "description": "测试记忆检索分支",
+    #     "thread_id": "test_memory_thread"
+    # }
 ]
 
 
@@ -85,7 +87,7 @@ async def setup_test_environment():
     print(f"✓ 图构建完成")
     print()
 
-    return graph
+    return graph, rag_tool
 
 
 def create_test_state(query: str, thread_id: str = None) -> dict:
@@ -98,7 +100,7 @@ def create_test_state(query: str, thread_id: str = None) -> dict:
     }
 
 
-async def run_single_test(graph, test_case: dict) -> dict:
+async def run_single_test(graph, rag_tool, test_case: dict) -> dict:
     """运行单个测试"""
     name = test_case["name"]
     query = test_case["query"]
@@ -115,9 +117,12 @@ async def run_single_test(graph, test_case: dict) -> dict:
         # 创建初始状态
         input_state = create_test_state(query, thread_id)
         
-        # 配置
+        # 配置（注入 RAG 工具）
         config = {
-            "configurable": {"thread_id": thread_id}
+            "configurable": {
+                "thread_id": thread_id,
+                "rag_tool": rag_tool
+            }
         }
         
         # 执行图
@@ -168,12 +173,12 @@ async def main():
     print("=" * 60)
     
     # 设置环境
-    graph = await setup_test_environment()
+    graph, rag_tool = await setup_test_environment()
     
     # 运行所有测试
     results = []
     for test_case in TEST_CASES:
-        result = await run_single_test(graph, test_case)
+        result = await run_single_test(graph, rag_tool, test_case)
         results.append(result)
         
         # 稍微间隔一下

tools/test/test_rag_pipeline.py

@@ -63,7 +63,6 @@ async def test_rag_tool():
         num_queries=3,
         rerank_top_n=5
     )
-    
     query = "吕布的经历"
     
     print(f"\n用户查询: {query}")