fix: 修复 RAG 无限循环问题和导入错误

主要修复：
1. 修复 RAG 推理无限循环问题（大小写不匹配 + 缺少已检索结果检查）
2. 修复 intent_classifier.py 的绝对导入错误
3. 删除旧的 start.sh 脚本，添加新的启动脚本
4. 优化路由逻辑和状态管理

.gitignore

@@ -11,8 +11,6 @@
 !backend/**
 !frontend/
 !frontend/**
-!scripts/
-!scripts/**
 !rag_indexer/
 !rag_indexer/**
 !docker/

backend/app/agent/agent_service.py

@@ -7,14 +7,14 @@ import json
 import asyncio
 
 # 本地模块
-from app.main_graph.utils.main_graph_builder import build_react_main_graph
-from app.main_graph.tools.graph_tools import AVAILABLE_TOOLS, TOOLS_BY_NAME
-from app.main_graph.config import set_stream_writer
+from ..main_graph.utils.main_graph_builder import build_react_main_graph
+from ..main_graph.tools.graph_tools import AVAILABLE_TOOLS, TOOLS_BY_NAME
+from ..main_graph.config import set_stream_writer
 from ..model_services.chat_services import get_all_chat_services, LocalVLLMChatProvider
-from app.main_graph.utils.rag_initializer import init_rag_tool
-from app.core.intent_classifier import get_intent_classifier
-from app.logger import info, warning, error
-from app.main_graph.state import MainGraphState, CurrentAction
+from ..main_graph.utils.rag_initializer import init_rag_tool
+from ..core.intent_classifier import get_intent_classifier
+from ..logger import info, warning, error
+from ..main_graph.state import MainGraphState, CurrentAction
 
 
 class AIAgentService:
@@ -32,7 +32,7 @@ class AIAgentService:
 
     async def initialize(self):
         # 0. 初始化 Mem0 客户端
-        from app.memory.mem0_client import Mem0Client
+        from ..memory.mem0_client import Mem0Client
         # 创建一个临时的 LLM 用于 Mem0（用第一个可用的）
         chat_services = get_all_chat_services()
         temp_llm = None
@@ -49,7 +49,7 @@ class AIAgentService:
             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 app.main_graph.nodes.rag_nodes import set_global_rag_tool
+            from ..main_graph.nodes.rag_nodes import set_global_rag_tool
             set_global_rag_tool(rag_tool)
 
         # 2. 构建各模型的 Graph（使用新版 React 模式）
@@ -86,7 +86,7 @@ class AIAgentService:
             "metadata": {"user_id": user_id}
         }
         # 新版状态输入：传入完整的 MainGraphState，关键是 user_query
-        from app.main_graph.state import MainGraphState, CurrentAction
+        from ..main_graph.state import MainGraphState, CurrentAction
         input_state = {
             "user_query": message,
             "messages": [{"role": "user", "content": message}],

backend/app/core/intent.py

@@ -132,8 +132,17 @@ class ReactIntentReasoner:
         
         # 关键修改：不要在第一次 rag_retrieve 后就直接回答，允许再推理一次
         # 让推理逻辑有机会判断 RAG 结果好不好，要不要再检索或转 web search
-        rag_count = previous_actions.count("rag_retrieve")
+        previous_actions = context.get("previous_actions", [])
+        rag_count = previous_actions.count("RETRIEVE_RAG")  # 修复：大写
         web_search_count = previous_actions.count("web_search")
+        retrieved_docs = context.get("retrieved_docs", [])
+        
+        # 如果已经有检索文档了，直接回答
+        if retrieved_docs and len(retrieved_docs) > 0:
+            result.action = ReasoningAction.DIRECT_RESPONSE
+            result.confidence = 0.95
+            result.reasoning = "已获取检索文档，直接回答"
+            return result
         
         # 只有当 rag 或 web search 已经超过 1 次，或者已经有推理在 rag 之后，才直接回答
         if rag_count >= 2 or web_search_count >= 1:

backend/app/core/intent_classifier.py

@@ -6,7 +6,7 @@ from typing import Optional, Dict, Any
 import sys
 import os
 
-from backend.app.model_services.chat_services import get_small_llm_service
+from ..model_services.chat_services import get_small_llm_service
 
 
 class IntentType(Enum):

backend/app/main_graph/nodes/hybrid_router.py

@@ -8,10 +8,10 @@ from typing import Dict, Any, Optional, List
 from dataclasses import dataclass, field
 from datetime import datetime
 
-from app.main_graph.state import MainGraphState
-from app.logger import info, debug
-from app.model_services.chat_services import get_small_llm_service, get_chat_service
-from app.main_graph.nodes.rag_nodes import rag_retrieve_node
+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
 
 
 # ========== 核心数据类型 ==========
@@ -367,8 +367,8 @@ async def fast_rag_node(state: MainGraphState, config: Optional[Dict[str, Any]]
             debug(f"[Fast RAG] 发送事件失败: {e}")
     
     try:
-        # 先尝试 RAG 检索
-        state = rag_retrieve_node(state, config)
+        # 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", [])

backend/app/main_graph/nodes/react_nodes.py

@@ -364,11 +364,15 @@ def route_by_reasoning(state: MainGraphState) -> str:
     if "subgraph_completed" in previous_actions or state.final_result:
         return "llm_call"
     
-    # 关键修复：如果已经执行过 rag_retrieve 并且又执行过推理，直接去 LLM_CALL
-    # 这样的流程：推理1 → RAG → 推理2（判断 RAG 结果） → LLM_CALL
-    rag_count = previous_actions.count("rag_retrieve")
-    if rag_count >= 1 and len(previous_actions) >= rag_count + 1:
-        info(f"[route_by_reasoning] 已完成 RAG 检索和结果判断，直接去 llm_call")
+    # 关键修复：检测 RAG 重复循环 - 如果发现"RETRIEVE_RAG"出现超过1次，直接去 LLM
+    rag_count = previous_actions.count("RETRIEVE_RAG")
+    if rag_count >= 2:
+        info(f"[route_by_reasoning] 检测到 RAG 重复循环（{rag_count}次），直接去 llm_call")
+        return "llm_call"
+    
+    # 关键修复：如果已经有 rag_docs 或 rag_context，说明已经检索过了，直接去 LLM
+    if (state.rag_docs and len(state.rag_docs) > 0) or (state.rag_context and len(state.rag_context) > 0):
+        info(f"[route_by_reasoning] 检测到已存在 RAG 检索结果，直接去 llm_call")
         return "llm_call"
     
     # 关键修复：限制最多 3 次推理，避免无限循环

backend/app/main_graph/utils/main_graph_builder.py

@@ -2,19 +2,19 @@
 整合后的完整主图构建器 - 所有节点都直接操作 MainGraphState
 """
 
-from app.main_graph.graph import StateGraph, START, END
+from ..graph import StateGraph, START, END
 from typing import Dict, Any, Optional
 from langchain_core.runnables.config import RunnableConfig
 
-from app.main_graph.state import MainGraphState
-from app.main_graph.nodes.react_nodes import (
+from ..state import MainGraphState
+from ..nodes.react_nodes import (
     init_state_node,
     react_reason_node,
     web_search_node,
     error_handling_node,
     route_by_reasoning
 )
-from app.main_graph.nodes.hybrid_router import (
+from ..nodes.hybrid_router import (
     hybrid_router_node,
     fast_chitchat_node,
     fast_rag_node,
@@ -22,17 +22,17 @@ from app.main_graph.nodes.hybrid_router import (
     route_from_hybrid_decision,
     check_fast_path_success
 )
-from app.main_graph.nodes.llm_call import create_llm_call_node
-from app.main_graph.nodes.rag_nodes import rag_retrieve_node
-from app.main_graph.nodes.retrieve_memory import create_retrieve_memory_node
-from app.main_graph.nodes.memory_trigger import memory_trigger_node, set_mem0_client
-from app.main_graph.nodes.summarize import create_summarize_node
-from app.main_graph.nodes.finalize import finalize_node
-from app.subgraphs.contact import build_contact_subgraph
-from app.subgraphs.dictionary import build_dictionary_subgraph
-from app.subgraphs.news_analysis import build_news_analysis_subgraph
-from app.memory.mem0_client import Mem0Client
-from app.logger import info, debug
+from ..nodes.llm_call import create_llm_call_node
+from ..nodes.rag_nodes import rag_retrieve_node
+from ..nodes.retrieve_memory import create_retrieve_memory_node
+from ..nodes.memory_trigger import memory_trigger_node, set_mem0_client
+from ..nodes.summarize import create_summarize_node
+from ..nodes.finalize import finalize_node
+from ...subgraphs.contact import build_contact_subgraph
+from ...subgraphs.dictionary import build_dictionary_subgraph
+from ...subgraphs.news_analysis import build_news_analysis_subgraph
+from ...memory.mem0_client import Mem0Client
+from ...logger import info, debug
 
 
 # ========== 检查是否需要总结 ==========
@@ -140,7 +140,7 @@ def wrap_subgraph_for_error_handling(subgraph, name: str):
             
         except Exception as e:
             # 捕获子图错误，传递给主图
-            from app.main_graph.state import ErrorRecord, ErrorSeverity
+            from ..state import ErrorRecord, ErrorSeverity
             from datetime import datetime
             
             error_record = ErrorRecord(

scripts/start.sh

@@ -1,117 +0,0 @@
-#!/bin/bash
-# =============================================================================
-# AI Agent 启动与管理脚本 - 简化版
-# 用法: ./scripts/start.sh [check|backend|frontend|both]
-# =============================================================================
-
-set -e
-
-# 颜色定义
-GREEN='\033[0;32m'
-BLUE='\033[0;34m'
-RED='\033[0;31m'
-YELLOW='\033[1;33m'
-NC='\033[0m' # No Color
-
-# 项目根目录
-PROJECT_DIR="$(cd "$(dirname "${BASH_SOURCE[0]}")/.." && pwd)"
-
-echo -e "${BLUE}========================================${NC}"
-echo -e "${BLUE}   AI Agent - 个人生活助手${NC}"
-echo -e "${BLUE}========================================${NC}"
-echo ""
-
-# =============================================================================
-# 启动 Python 服务
-# =============================================================================
-start_backend() {
-    echo -e "\n${BLUE}🚀 启动后端服务 (端口 10079)...${NC}"
-    cd "$PROJECT_DIR"
-    
-    # 加载 .env 文件中的环境变量
-    set -a
-    source .env 2>/dev/null || true
-    set +a
-    
-    export PYTHONPATH="$PROJECT_DIR/backend"
-    export BACKEND_PORT=8079
-    python -m app.backend &
-    BACKEND_PID=$!
-    echo -e "${GREEN}✓ 后端服务已启动 (PID: $BACKEND_PID)${NC}"
-    sleep 2
-}
-
-start_frontend() {
-    echo -e "\n${BLUE}🎨 启动前端界面 (端口 10501)...${NC}"
-    cd "$PROJECT_DIR"
-    
-    # 加载 .env 文件中的环境变量
-    set -a
-    source .env 2>/dev/null || true
-    set +a
-    
-    export PYTHONPATH="$PROJECT_DIR/frontend/src"
-    export API_URL="http://127.0.0.1:8079/chat"
-    streamlit run frontend/src/frontend_main.py --server.port 10501 --server.address 0.0.0.0 &
-    FRONTEND_PID=$!
-    echo -e "${GREEN}✓ 前端服务已启动 (PID: $FRONTEND_PID)${NC}"
-    echo -e "${GREEN}✓ 访问地址:${NC}"
-    echo -e "   本地开发: http://127.0.0.1:10501"
-}
-
-# =============================================================================
-# 清理函数
-# =============================================================================
-cleanup() {
-    echo -e "\n${RED}🛑 正在停止 Python 服务...${NC}"
-    if [ ! -z "$BACKEND_PID" ]; then
-        kill $BACKEND_PID 2>/dev/null || true
-        echo -e "${GREEN}✓ 后端服务已停止${NC}"
-    fi
-    if [ ! -z "$FRONTEND_PID" ]; then
-        kill $FRONTEND_PID 2>/dev/null || true
-        echo -e "${GREEN}✓ 前端服务已停止${NC}"
-    fi
-    exit 0
-}
-
-# 捕获 Ctrl+C
-trap cleanup SIGINT SIGTERM
-
-# =============================================================================
-# 主逻辑
-# =============================================================================
-case "${1:-help}" in
-    backend)
-        start_backend
-        echo -e "\n${GREEN}后端服务正在运行，按 Ctrl+C 停止${NC}"
-        wait $BACKEND_PID
-        ;;
-    
-    frontend)
-        start_frontend
-        echo -e "\n${GREEN}前端服务正在运行，按 Ctrl+C 停止${NC}"
-        wait $FRONTEND_PID
-        ;;
-    
-    both)
-        start_backend
-        sleep 3
-        start_frontend
-        echo -e "\n${GREEN}所有服务正在运行，按 Ctrl+C 停止${NC}"
-        wait
-        ;;
-    
-    help|*)
-        echo -e "${BLUE}用法:${NC} $0 [command]"
-        echo ""
-        echo -e "${BLUE}命令:${NC}"
-        echo "  backend     仅启动后端服务"
-        echo "  frontend    仅启动前端服务"
-        echo "  both        启动前后端服务（默认）"
-        echo "  help        显示此帮助信息"
-        echo ""
-        echo -e "${BLUE}示例:${NC}"
-        echo "  $0 both               # 启动本地开发环境"
-        ;;
-esac

tools/run.py

@@ -11,8 +11,10 @@ sys.path.insert(0, str(project_root / "backend"))
 load_dotenv(project_root / ".env")
 
 if __name__ == "__main__":
-    from rag_indexer.cli import 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
     import asyncio
-    asyncio.run(main())
+    asyncio.run(main())

tools/start.py

@@ -0,0 +1,125 @@
+#!/usr/bin/env python3
+"""
+AI Agent 启动与管理脚本 - Python版
+用法: python tools/testrun.py [check|backend|frontend|both]
+"""
+import sys
+import os
+import time
+import signal
+import subprocess
+from pathlib import Path
+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
+processes = []
+
+
+def start_backend():
+    """启动后端服务"""
+    print("\n🚀 启动后端服务 (端口 8079)...")
+    
+    env = os.environ.copy()
+    env["PYTHONPATH"] = str(project_root / "backend")
+    env["BACKEND_PORT"] = "8079"
+    
+    proc = subprocess.Popen(
+        [sys.executable, "-m", "app.backend"],
+        cwd=str(project_root),
+        env=env
+    )
+    processes.append(proc)
+    print(f"✓ 后端服务已启动 (PID: {proc.pid})")
+    time.sleep(2)
+    return proc
+
+
+def start_frontend():
+    """启动前端服务"""
+    print("\n🎨 启动前端界面 (端口 10501)...")
+    
+    env = os.environ.copy()
+    env["PYTHONPATH"] = str(project_root / "frontend/src")
+    env["API_URL"] = "http://127.0.0.1:8079/chat"
+    
+    frontend_main = str(project_root / "frontend" / "src" / "frontend_main.py")
+    proc = subprocess.Popen(
+        [
+            sys.executable, "-m", "streamlit", "run", frontend_main,
+            "--server.port", "10501", "--server.address", "0.0.0.0"
+        ],
+        cwd=str(project_root),
+        env=env
+    )
+    processes.append(proc)
+    print(f"✓ 前端服务已启动 (PID: {proc.pid})")
+    print("✓ 访问地址:")
+    print("   本地开发: http://127.0.0.1:10501")
+    return proc
+
+
+def cleanup(signum, frame):
+    """清理函数 - 停止所有进程"""
+    print("\n🛑 正在停止服务...")
+    for i, proc in enumerate(processes):
+        if proc.poll() is None:  # 进程还在运行
+            proc.terminate()
+            proc.wait(timeout=5)
+            print(f"✓ 服务 {i+1} 已停止")
+    sys.exit(0)
+
+
+def print_help():
+    """显示帮助信息"""
+    print("========================================")
+    print("   AI Agent - 个人生活助手")
+    print("========================================")
+    print("\n用法: python tools/testrun.py [command]")
+    print("\n命令:")
+    print("  backend     仅启动后端服务")
+    print("  frontend    仅启动前端服务")
+    print("  both        启动前后端服务（默认）")
+    print("  help        显示此帮助信息")
+    print("\n示例:")
+    print("  python tools/testrun.py both               # 启动本地开发环境")
+
+
+def main():
+    """主函数"""
+    print("========================================")
+    print("   AI Agent - 个人生活助手")
+    print("========================================")
+    
+    # 捕获信号
+    signal.signal(signal.SIGINT, cleanup)
+    signal.signal(signal.SIGTERM, cleanup)
+    
+    cmd = sys.argv[1] if len(sys.argv) > 1 else "both"
+    
+    if cmd == "backend":
+        start_backend()
+        print("\n后端服务正在运行，按 Ctrl+C 停止")
+        processes[0].wait()
+    elif cmd == "frontend":
+        start_frontend()
+        print("\n前端服务正在运行，按 Ctrl+C 停止")
+        processes[0].wait()
+    elif cmd == "both":
+        start_backend()
+        time.sleep(3)
+        start_frontend()
+        print("\n所有服务正在运行，按 Ctrl+C 停止")
+        for proc in processes:
+            proc.wait()
+    else:
+        print_help()
+
+
+if __name__ == "__main__":
+    main()

tools/test/test_fast_rag_fix.py

@@ -0,0 +1,97 @@
+#!/usr/bin/env python3
+"""
+快速测试 - 测试 fast_rag 路径修复
+"""
+import sys
+import asyncio
+from pathlib import Path
+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")
+
+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
+
+
+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

@@ -0,0 +1,221 @@
+#!/usr/bin/env python3
+"""
+主图完整测试 - 覆盖各个分支
+"""
+import sys
+import asyncio
+from pathlib import Path
+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")
+
+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？",
+        "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"
+    }
+]
+
+
+async def setup_test_environment():
+    """设置测试环境"""
+    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
+
+
+def create_test_state(query: str, thread_id: str = None) -> dict:
+    """创建测试状态"""
+    return {
+        "user_query": query,
+        "messages": [{"role": "user", "content": query}],
+        "user_id": "test_user",
+        "current_action": CurrentAction.NONE
+    }
+
+
+async def run_single_test(graph, test_case: dict) -> dict:
+    """运行单个测试"""
+    name = test_case["name"]
+    query = test_case["query"]
+    description = test_case["description"]
+    thread_id = test_case.get("thread_id", "test_thread")
+    
+    print(f"\n{'=' * 60}")
+    print(f"测试: {name}")
+    print(f"描述: {description}")
+    print(f"查询: {query}")
+    print(f"{'=' * 60}")
+    
+    try:
+        # 创建初始状态
+        input_state = create_test_state(query, thread_id)
+        
+        # 配置
+        config = {
+            "configurable": {"thread_id": thread_id}
+        }
+        
+        # 执行图
+        print("开始执行图...")
+        result = await graph.ainvoke(input_state, config=config)
+        
+        # 检查结果
+        success = result.get("success", False)
+        final_result = result.get("final_result", "")
+        
+        print(f"\n结果:")
+        print(f"  成功: {'✓' if success else '✗'}")
+        print(f"  最终回答: {final_result[:200]}{'...' if len(final_result) > 200 else ''}")
+        
+        # 调试信息
+        if "debug_info" in result:
+            debug_info = result["debug_info"]
+            print(f"  调试信息:")
+            if "fast_path_failed" in debug_info:
+                print(f"    - 快速路径失败: {debug_info['fast_path_failed']}")
+            if "fast_path_fail_reason" in debug_info:
+                print(f"    - 失败原因: {debug_info['fast_path_fail_reason']}")
+            if "hybrid_decision" in debug_info:
+                decision = debug_info["hybrid_decision"]
+                print(f"    - 路由决策: {decision.path if hasattr(decision, 'path') else 'unknown'}")
+        
+        return {
+            "name": name,
+            "success": success,
+            "result": result
+        }
+        
+    except Exception as e:
+        print(f"\n✗ 测试失败: {e}")
+        import traceback
+        print(f"堆栈: {traceback.format_exc()}")
+        return {
+            "name": name,
+            "success": False,
+            "error": str(e)
+        }
+
+
+async def main():
+    """主函数"""
+    print("\n" + "=" * 60)
+    print("主图完整测试套件")
+    print("=" * 60)
+    
+    # 设置环境
+    graph = 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)
+        results.append(result)
+        
+        # 稍微间隔一下
+        await asyncio.sleep(1)
+    
+    # 总结
+    print("\n" + "=" * 60)
+    print("测试总结")
+    print("=" * 60)
+    
+    total = len(results)
+    passed = sum(1 for r in results if r["success"])
+    failed = total - passed
+    
+    print(f"\n总测试数: {total}")
+    print(f"通过: {passed}")
+    print(f"失败: {failed}")
+    
+    print("\n详细结果:")
+    for result in results:
+        status = "✓ 通过" if result["success"] else "✗ 失败"
+        print(f"  {result['name']}: {status}")
+    
+    print("\n" + "=" * 60)
+    if failed == 0:
+        print("🎉 所有测试通过！")
+    else:
+        print(f"⚠️  有 {failed} 个测试失败")
+    print("=" * 60)
+
+
+if __name__ == "__main__":
+    try:
+        asyncio.run(main())
+    except KeyboardInterrupt:
+        print("\n\n测试被用户中断")
+    except Exception as e:
+        print(f"\n\n测试运行失败: {e}")
+        import traceback
+        print(traceback.format_exc())

tools/test/test_rag_pipeline.py

@@ -22,7 +22,7 @@ async def test_rag_pipeline_direct():
         rerank_top_n=5
     )
     
-    query = "黄双银的经历"
+    query = "吕布的经历"
     
     print(f"\n用户查询: {query}")
     print("-" * 80)
@@ -64,7 +64,7 @@ async def test_rag_tool():
         rerank_top_n=5
     )
     
-    query = "黄双银的经历"
+    query = "吕布的经历"
     
     print(f"\n用户查询: {query}")
     print("-" * 80)
@@ -91,7 +91,7 @@ async def test_custom_pipeline():
         rerank_top_n=3     # 只返回前 3 个最相关文档
     )
     
-    query = "黄双银的经历"
+    query = "吕布的经历"
     
     print(f"\n用户查询: {query}")
     print(f"配置: num_queries=2, rerank_top_n=3")
@@ -124,7 +124,7 @@ async def main():
     """主测试函数"""
     print("\n" + "="*80)
     print("完整 RAG Pipeline 测试")
-    print("查询: '黄双银的经历'")
+    print("查询: '吕布的经历'")
     print("="*80)
     
     # 测试 1: 直接使用 pipeline