refactor: 重构快速路径流程，统一通过 llm_call 输出
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构建并部署 AI Agent 服务 / deploy (push) Successful in 5m31s
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构建并部署 AI Agent 服务 / deploy (push) Successful in 5m31s
- 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
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@@ -1,88 +0,0 @@
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#!/usr/bin/env python3
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"""
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快速测试 - 测试 fast_rag 路径修复
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"""
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import asyncio
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from backend.app.main_graph.state import MainGraphState, CurrentAction
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from backend.app.main_graph.utils.main_graph_builder import build_react_main_graph
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from backend.app.model_services.chat_services import get_all_chat_services
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from backend.app.main_graph.tools.graph_tools import AVAILABLE_TOOLS
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async def test_fast_rag_path():
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"""测试 fast_rag 路径"""
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print("=" * 60)
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print("测试 fast_rag 路径修复")
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print("=" * 60)
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# 1. 获取 LLM
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chat_services = get_all_chat_services()
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if not chat_services:
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print("✗ 没有可用的 LLM 服务")
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return
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llm = list(chat_services.values())[0]
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print(f"✓ 使用 LLM: {list(chat_services.keys())[0]}")
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# 2. 构建图
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graph = build_react_main_graph(
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llm=llm,
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tools=AVAILABLE_TOOLS,
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use_hybrid_router=True
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).compile()
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print(f"✓ 图构建完成")
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# 3. 测试问题
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test_query = "吕布和张飞谁厉害？"
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print(f"\n测试问题: {test_query}")
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# 4. 创建状态
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input_state = {
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"user_query": test_query,
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"messages": [{"role": "user", "content": test_query}],
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"user_id": "test_user",
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"current_action": CurrentAction.NONE
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}
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# 5. 执行
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print("开始执行...")
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try:
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result = await graph.ainvoke(
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input_state,
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config={"configurable": {"thread_id": "test_fast_rag"}}
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)
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print(f"\n✓ 执行成功！")
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print(f"最终回答: {result.get('final_result', '')[:300]}")
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# 调试信息
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debug_info = result.get("debug_info", {})
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print(f"\n调试信息:")
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if "fast_path_failed" in debug_info:
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print(f" - fast_path_failed: {debug_info['fast_path_failed']}")
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if "fast_path_fail_reason" in debug_info:
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print(f" - fast_path_fail_reason: {debug_info['fast_path_fail_reason']}")
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except Exception as e:
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print(f"\n✗ 执行失败: {e}")
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import traceback
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print(traceback.format_exc())
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return False
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return True
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async def main():
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success = await test_fast_rag_path()
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if success:
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print("\n🎉 测试通过！")
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else:
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print("\n⚠️ 测试失败")
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if __name__ == "__main__":
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try:
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asyncio.run(main())
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except KeyboardInterrupt:
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print("\n测试被中断")
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@@ -7,47 +7,49 @@ import asyncio
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from pathlib import Path
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from dotenv import load_dotenv
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# 添加 backend 到路径
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sys.path.insert(0, str(Path(__file__).parent.parent.parent / "backend"))
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from backend.app.main_graph.state import MainGraphState, CurrentAction
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from backend.app.main_graph.utils.main_graph_builder import build_react_main_graph
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from backend.app.model_services.chat_services import get_all_chat_services
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from backend.app.main_graph.tools.graph_tools import AVAILABLE_TOOLS
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from backend.app.main_graph.utils.rag_initializer import init_rag_tool
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from app.main_graph.state import MainGraphState, CurrentAction
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from app.main_graph.utils.main_graph_builder import build_react_main_graph
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from app.model_services.chat_services import get_all_chat_services
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from app.main_graph.tools.graph_tools import AVAILABLE_TOOLS
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from app.main_graph.utils.rag_initializer import init_rag_tool
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# ========== 测试用例配置 ==========
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TEST_CASES = [
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# 测试1: 简单闲聊 - 应该走 fast_chitchat
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{
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"name": "闲聊测试",
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"query": "你好！",
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"description": "测试快速闲聊分支"
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},
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# # 测试1: 简单闲聊 - 应该走 fast_chitchat
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# {
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# "name": "闲聊测试",
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# "query": "你好！",
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# "description": "测试快速闲聊分支"
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# },
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# 测试2: 知识查询 - 应该走 fast_rag，然后可能升级到 react
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{
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"name": "知识查询测试",
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"query": "什么是机器学习？",
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"query": "吕布的事迹？",
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"description": "测试快速 RAG 分支"
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},
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# 测试3: 需要推理的复杂问题 - 应该直接到 React 循环
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{
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"name": "复杂推理测试",
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"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？",
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"description": "测试 React 循环推理分支"
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},
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# 测试4: 需要工具调用的问题
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{
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"name": "工具调用测试",
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"query": "搜索一下今天的天气怎么样",
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"description": "测试工具调用分支"
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},
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# 测试5: 带记忆的对话
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{
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"name": "记忆测试",
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"query": "你刚才回答了我什么问题？",
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"description": "测试记忆检索分支",
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"thread_id": "test_memory_thread"
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}
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# # 测试3: 需要推理的复杂问题 - 应该直接到 React 循环
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# {
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# "name": "复杂推理测试",
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# "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？",
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# "description": "测试 React 循环推理分支"
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# },
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# # 测试4: 需要工具调用的问题
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# {
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# "name": "工具调用测试",
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# "query": "搜索一下今天的天气怎么样",
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# "description": "测试工具调用分支"
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# },
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# # 测试5: 带记忆的对话
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# {
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# "name": "记忆测试",
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# "query": "你刚才回答了我什么问题？",
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# "description": "测试记忆检索分支",
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# "thread_id": "test_memory_thread"
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# }
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]
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@@ -56,36 +58,36 @@ async def setup_test_environment():
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print("=" * 60)
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print("设置测试环境...")
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print("=" * 60)
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# 获取 LLM 服务
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chat_services = get_all_chat_services()
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if not chat_services:
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raise RuntimeError("没有可用的 LLM 服务")
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llm = list(chat_services.values())[0]
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print(f"✓ 使用 LLM: {list(chat_services.keys())[0]}")
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# 初始化 RAG 工具
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def create_local_llm():
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return llm
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rag_tool = await init_rag_tool(create_local_llm)
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tools = AVAILABLE_TOOLS.copy()
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if rag_tool:
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tools.append(rag_tool)
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print(f"✓ RAG 工具初始化成功")
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# 构建图
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graph = build_react_main_graph(
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llm=llm,
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tools=tools,
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use_hybrid_router=True
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).compile()
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print(f"✓ 图构建完成")
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print()
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return graph
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return graph, rag_tool
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def create_test_state(query: str, thread_id: str = None) -> dict:
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@@ -98,7 +100,7 @@ def create_test_state(query: str, thread_id: str = None) -> dict:
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}
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async def run_single_test(graph, test_case: dict) -> dict:
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async def run_single_test(graph, rag_tool, test_case: dict) -> dict:
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"""运行单个测试"""
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name = test_case["name"]
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query = test_case["query"]
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@@ -115,9 +117,12 @@ async def run_single_test(graph, test_case: dict) -> dict:
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# 创建初始状态
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input_state = create_test_state(query, thread_id)
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# 配置
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# 配置（注入 RAG 工具）
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config = {
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"configurable": {"thread_id": thread_id}
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"configurable": {
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"thread_id": thread_id,
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"rag_tool": rag_tool
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}
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}
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# 执行图
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@@ -168,12 +173,12 @@ async def main():
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print("=" * 60)
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# 设置环境
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graph = await setup_test_environment()
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graph, rag_tool = await setup_test_environment()
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# 运行所有测试
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results = []
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for test_case in TEST_CASES:
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result = await run_single_test(graph, test_case)
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result = await run_single_test(graph, rag_tool, test_case)
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results.append(result)
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# 稍微间隔一下
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@@ -63,7 +63,6 @@ async def test_rag_tool():
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num_queries=3,
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rerank_top_n=5
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)
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query = "吕布的经历"
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print(f"\n用户查询: {query}")
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