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

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"
+    # }
 ]
 
 
@@ -56,36 +58,36 @@ async def setup_test_environment():
     print("=" * 60)
     print("设置测试环境...")
     print("=" * 60)
-    
+
     # 获取 LLM 服务
     chat_services = get_all_chat_services()
     if not chat_services:
         raise RuntimeError("没有可用的 LLM 服务")
-    
+
     llm = list(chat_services.values())[0]
     print(f"✓ 使用 LLM: {list(chat_services.keys())[0]}")
-    
+
     # 初始化 RAG 工具
     def create_local_llm():
         return llm
-    
+
     rag_tool = await init_rag_tool(create_local_llm)
     tools = AVAILABLE_TOOLS.copy()
     if rag_tool:
         tools.append(rag_tool)
         print(f"✓ RAG 工具初始化成功")
-    
+
     # 构建图
     graph = build_react_main_graph(
         llm=llm,
         tools=tools,
         use_hybrid_router=True
     ).compile()
-    
+
     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}")