feat: 实现 BM25 稀疏 + 稠密向量混合检索功能
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306
tools/test/test_backend.py
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306
tools/test/test_backend.py
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#!/usr/bin/env python3
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"""
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完整后端测试 - 验证 Agent 所有功能
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包括：短期记忆、长期记忆、工具调用、流式对话、历史查询
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"""
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import asyncio
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import os
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import sys
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import uuid
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from dotenv import load_dotenv
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# 添加项目根目录和 backend 目录到 Python 路径
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project_root = os.path.join(os.path.dirname(__file__), "..")
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backend_dir = os.path.join(project_root, "backend")
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sys.path.insert(0, project_root)
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sys.path.insert(0, backend_dir)
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load_dotenv()
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from backend.app.config import DB_URI
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from langgraph.checkpoint.postgres.aio import AsyncPostgresSaver
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from backend.app.agent.agent_service import AIAgentService
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from backend.app.agent.history import ThreadHistoryService
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from backend.app.logger import info, warning, error
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# PostgreSQL 连接字符串
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async def print_section(title):
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"""打印测试区块标题"""
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print("\n" + "=" * 70)
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print(f" {title}")
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print("=" * 70)
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async def test_short_term_memory(agent_service):
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"""测试短期记忆（同一 thread_id 继续对话）"""
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await print_section("测试 1: 短期记忆（Short-term Memory）")
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thread_id = str(uuid.uuid4())
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user_id = "test_user_memory"
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print(f"\n使用 thread_id: {thread_id[:8]}...")
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print(f"使用 user_id: {user_id}")
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# 第一轮对话
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print("\n[第一轮] 发送消息: '我叫张三，今年28岁'")
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result1 = await agent_service.process_message(
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"我叫张三，今年28岁", thread_id, "local", user_id
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)
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print(f"回复: {result1['reply'][:100]}...")
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# 第二轮对话 - 测试记忆
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print("\n[第二轮] 发送消息: '我叫什么名字？今年多大？'")
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result2 = await agent_service.process_message(
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"我叫什么名字？今年多大？", thread_id, "local", user_id
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)
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print(f"回复: {result2['reply']}")
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# 验证记忆是否存在
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if "张三" in result2['reply'] or "28" in result2['reply']:
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print("\n✅ 短期记忆测试通过！")
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return True
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else:
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print("\n❌ 短期记忆测试失败！")
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return False
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async def test_tool_calling(agent_service):
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"""测试工具调用（RAG 搜索）"""
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await print_section("测试 2: 工具调用（Tool Calling）")
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thread_id = str(uuid.uuid4())
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user_id = "test_user_tools"
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print(f"\n使用 thread_id: {thread_id[:8]}...")
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print(f"使用 user_id: {user_id}")
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# 发送需要 RAG 搜索的问题
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print("\n发送消息: '请告诉我，黄双银在魔王大陆的故事？'")
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result = await agent_service.process_message(
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"请告诉我，黄双银在魔王大陆的故事？", thread_id, "local", user_id
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)
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print(f"回复: {result['reply'][:200]}...")
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# 检查是否调用了 RAG 工具（回复中会有黄双银相关内容）
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if "黄双银" in result['reply']:
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print("\n✅ 工具调用测试通过！")
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return True
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else:
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print("\n⚠️ 工具调用测试结果不确定，需要手动验证")
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return None
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async def test_streaming(agent_service):
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"""测试流式对话"""
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await print_section("测试 3: 流式对话（Streaming）")
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thread_id = str(uuid.uuid4())
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user_id = "test_user_stream"
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print(f"\n使用 thread_id: {thread_id[:8]}...")
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print(f"使用 user_id: {user_id}")
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print("\n发送消息: '用100字介绍一下AI人工智能' (流式)...")
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print("流式输出: ", end="", flush=True)
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full_reply = ""
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chunk_count = 0
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try:
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async for chunk in agent_service.process_message_stream(
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"用100字介绍一下AI人工智能", thread_id, "local", user_id
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):
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chunk_count += 1
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if chunk.get("type") == "llm_token":
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token = chunk.get("token", "")
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print(token, end="", flush=True)
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full_reply += token
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elif chunk.get("type") == "state_update":
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pass # 状态更新不显示
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print(f"\n\n共收到 {chunk_count} 个 chunk")
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print(f"完整回复长度: {len(full_reply)} 字")
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if chunk_count > 0 and len(full_reply) > 10:
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print("\n✅ 流式对话测试通过！")
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return True
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else:
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print("\n❌ 流式对话测试失败！")
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return False
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except Exception as e:
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print(f"\n❌ 流式对话异常: {e}")
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return False
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async def test_history_service(agent_service, history_service):
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"""测试历史查询服务"""
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await print_section("测试 4: 历史查询服务（History Service）")
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user_id = "test_user_history"
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# 先创建几个对话
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print(f"\n为 user_id={user_id} 创建测试对话...")
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thread_ids = []
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for i in range(3):
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thread_id = str(uuid.uuid4())
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thread_ids.append(thread_id)
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await agent_service.process_message(
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f"这是第 {i+1} 个测试对话", thread_id, "local", user_id
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)
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print(f" 创建线程 {i+1}: {thread_id[:8]}...")
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# 1. 测试获取用户线程列表
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print("\n[4.1] 测试获取用户线程列表...")
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threads = await history_service.get_user_threads(user_id, limit=10)
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print(f" 找到 {len(threads)} 个线程")
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if len(threads) >= 3:
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print(" ✅ 线程列表查询通过")
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else:
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print(" ⚠️ 线程数量少于预期")
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# 2. 测试获取单个线程的消息历史
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if thread_ids:
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test_thread_id = thread_ids[0]
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print(f"\n[4.2] 测试获取线程消息历史 (thread_id={test_thread_id[:8]}...)")
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messages = await history_service.get_thread_messages(test_thread_id)
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print(f" 找到 {len(messages)} 条消息")
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if len(messages) >= 2: # 至少有一问一答
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print(" ✅ 消息历史查询通过")
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else:
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print(" ⚠️ 消息数量少于预期")
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# 3. 测试获取线程摘要
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print(f"\n[4.3] 测试获取线程摘要...")
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summary = await history_service.get_thread_summary(test_thread_id)
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print(f" 摘要: {summary.get('summary', '')[:50]}...")
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print(f" 消息数: {summary.get('message_count', 0)}")
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if summary.get('message_count', 0) > 0:
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print(" ✅ 线程摘要查询通过")
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else:
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print(" ⚠️ 摘要查询结果不确定")
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return len(threads) >= 3
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async def test_long_term_memory(agent_service):
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"""测试长期记忆（mem0）"""
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await print_section("测试 5: 长期记忆（Long-term Memory - mem0）")
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thread_id1 = str(uuid.uuid4())
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thread_id2 = str(uuid.uuid4()) # 不同的线程
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user_id = "test_user_longterm"
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print(f"\n使用 user_id: {user_id}")
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print(f"线程 1: {thread_id1[:8]}...")
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print(f"线程 2: {thread_id2[:8]}...")
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# 在第一个线程中保存信息
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print("\n[线程 1] 发送消息: '记住，我的宠物名字叫小白，是一只猫'")
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result1 = await agent_service.process_message(
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"记住，我的宠物名字叫小白，是一只猫", thread_id1, "local", user_id
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)
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print(f"回复: {result1['reply'][:100]}...")
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# 等待一下，让 mem0 保存
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await asyncio.sleep(1)
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# 在第二个线程中询问（不同的 thread_id）
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print("\n[线程 2] 发送消息: '我的宠物叫什么名字？是什么动物？'")
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result2 = await agent_service.process_message(
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"我的宠物叫什么名字？是什么动物？", thread_id2, "local", user_id
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)
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print(f"回复: {result2['reply']}")
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# 验证长期记忆
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if "小白" in result2['reply'] or "猫" in result2['reply']:
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print("\n✅ 长期记忆测试通过！")
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return True
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else:
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print("\n⚠️ 长期记忆可能未启用，或需要手动验证")
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return None
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async def main():
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"""主测试函数"""
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print("\n" + "=" * 70)
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print(" 后端完整功能测试")
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print("=" * 70)
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results = {}
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try:
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# 创建数据库连接和服务
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print("\n正在初始化数据库连接...")
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async with AsyncPostgresSaver.from_conn_string(DB_URI) as checkpointer:
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await checkpointer.setup()
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print("✅ 数据库连接成功")
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# 创建服务实例
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print("\n正在初始化 Agent 服务...")
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agent_service = AIAgentService(checkpointer)
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await agent_service.initialize()
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print("✅ Agent 服务初始化成功")
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history_service = ThreadHistoryService(checkpointer)
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print("✅ 历史服务初始化成功")
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print(f"\n可用模型: {list(agent_service.graphs.keys())}")
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# 运行测试
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results["短期记忆"] = await test_short_term_memory(agent_service)
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await asyncio.sleep(1)
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results["工具调用"] = await test_tool_calling(agent_service)
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await asyncio.sleep(1)
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results["流式对话"] = await test_streaming(agent_service)
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await asyncio.sleep(1)
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results["历史查询"] = await test_history_service(agent_service, history_service)
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await asyncio.sleep(1)
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results["长期记忆"] = await test_long_term_memory(agent_service)
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await asyncio.sleep(1)
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# 打印总结
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await print_section("测试总结")
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print("\n测试结果:")
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print("-" * 40)
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pass_count = 0
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fail_count = 0
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skip_count = 0
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for test_name, result in results.items():
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if result is True:
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status = "✅ 通过"
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pass_count += 1
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elif result is False:
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status = "❌ 失败"
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fail_count += 1
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else:
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status = "⚠️ 待验证"
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skip_count += 1
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print(f" {test_name:12s}: {status}")
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print("-" * 40)
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print(f"总计: {len(results)} 个测试")
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print(f"通过: {pass_count}, 失败: {fail_count}, 待验证: {skip_count}")
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if fail_count == 0:
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print("\n🎉 所有核心测试通过！")
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else:
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print(f"\n⚠️ 有 {fail_count} 个测试失败")
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except Exception as e:
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error(f"\n❌ 测试运行异常: {e}")
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import traceback
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traceback.print_exc()
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return 1
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return 0 if fail_count == 0 else 1
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if __name__ == "__main__":
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exit_code = asyncio.run(main())
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sys.exit(exit_code)
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66
tools/test/test_dqrant.py
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66
tools/test/test_dqrant.py
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"""检查 Qdrant 中存储的向量质量。"""
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import os
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import sys
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import numpy as np
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from dotenv import load_dotenv
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from qdrant_client import QdrantClient
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# 添加项目根目录和 backend 目录到 Python 路径
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project_root = os.path.join(os.path.dirname(__file__), "..")
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backend_dir = os.path.join(project_root, "backend")
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sys.path.insert(0, project_root)
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sys.path.insert(0, backend_dir)
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load_dotenv()
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from rag_core import LlamaCppEmbedder
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QDRANT_URL = os.getenv("QDRANT_URL", "http://127.0.0.1:6333")
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QDRANT_API_KEY = os.getenv("QDRANT_API_KEY")
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COLLECTION_NAME = "rag_documents"
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client = QdrantClient(url=QDRANT_URL, api_key=QDRANT_API_KEY)
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embedder = LlamaCppEmbedder()
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# 获取样本
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points, _ = client.scroll(
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collection_name=COLLECTION_NAME,
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limit=1,
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with_vectors=True,
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with_payload=True,
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)
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if not points:
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print(f"集合 '{COLLECTION_NAME}' 为空")
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exit()
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sample = points[0]
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raw_vec = sample.vector
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if isinstance(raw_vec, dict):
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stored_vec = list(raw_vec.values())[0]
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elif isinstance(raw_vec, list):
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stored_vec = raw_vec
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else:
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stored_vec = []
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stored_payload = sample.payload or {}
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stored_text = str(stored_payload.get("page_content", ""))[:200]
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print(f"内容预览:\n{stored_text}...\n")
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print(f"向量维度: {len(stored_vec)}") # type: ignore
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print(f"前5个值: {stored_vec[:5]}") # type: ignore
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print(f"是否全零: {all(v == 0.0 for v in stored_vec)}") # type: ignore
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# 重新编码对比
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if stored_text:
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new_vec = embedder.embed_query(stored_text)
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similarity = np.dot(stored_vec, new_vec) / (np.linalg.norm(stored_vec) * np.linalg.norm(new_vec)) # type: ignore
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print(f"\n重新编码前5个值: {new_vec[:5]}")
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print(f"余弦相似度: {similarity:.4f}")
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if similarity < 0.8:
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print("\n⚠️ 相似度过低，建议删除集合并重建索引")
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else:
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print("\n✅ 向量一致")
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else:
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print("\n⚠️ 样本无文本内容")
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69
tools/test/test_frontend.py
Normal file
69
tools/test/test_frontend.py
Normal file
@@ -0,0 +1,69 @@
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#!/usr/bin/env python3
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"""
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前端快速测试脚本
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验证前端导入是否正常工作
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"""
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import sys
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import os
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# 添加必要的路径
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project_root = os.path.dirname(os.path.abspath(__file__))
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frontend_src = os.path.join(project_root, "frontend", "src")
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backend_dir = os.path.join(project_root, "backend")
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sys.path.insert(0, project_root)
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sys.path.insert(0, frontend_src)
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sys.path.insert(0, backend_dir)
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print("=" * 60)
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print("前端导入测试")
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print("=" * 60)
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# 测试 1: 直接导入前端模块
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print("\n[测试 1] 直接导入前端模块...")
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try:
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from frontend.src.frontend_main import main
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print("✅ frontend_main 导入成功")
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except Exception as e:
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print(f"❌ 导入失败: {e}")
|
||||
sys.exit(1)
|
||||
|
||||
# 测试 2: 导入配置
|
||||
print("\n[测试 2] 导入配置...")
|
||||
try:
|
||||
from config import config
|
||||
print(f"✅ config 导入成功: page_title={config.page_title}")
|
||||
except Exception as e:
|
||||
print(f"❌ 导入失败: {e}")
|
||||
|
||||
# 测试 3: 导入状态管理
|
||||
print("\n[测试 3] 导入状态管理...")
|
||||
try:
|
||||
from state import AppState
|
||||
print("✅ AppState 导入成功")
|
||||
except Exception as e:
|
||||
print(f"❌ 导入失败: {e}")
|
||||
|
||||
# 测试 4: 导入 API 客户端
|
||||
print("\n[测试 4] 导入 API 客户端...")
|
||||
try:
|
||||
from api_client import api_client
|
||||
print("✅ api_client 导入成功")
|
||||
except Exception as e:
|
||||
print(f"❌ 导入失败: {e}")
|
||||
|
||||
# 测试 5: 导入组件
|
||||
print("\n[测试 5] 导入组件...")
|
||||
try:
|
||||
from components.sidebar import render_sidebar
|
||||
from components.chat_area import render_chat_area
|
||||
from components.info_panel import render_info_panel
|
||||
print("✅ 所有组件导入成功")
|
||||
except Exception as e:
|
||||
print(f"❌ 导入失败: {e}")
|
||||
|
||||
print("\n" + "=" * 60)
|
||||
print("🎉 所有前端导入测试通过！")
|
||||
print("=" * 60)
|
||||
print("\n现在可以使用 ./scripts/start.sh both 启动完整服务")
|
||||
142
tools/test/test_rag.py
Normal file
142
tools/test/test_rag.py
Normal file
@@ -0,0 +1,142 @@
|
||||
#!/usr/bin/env python3
|
||||
"""
|
||||
RAG 系统使用示例（重构版）
|
||||
|
||||
演示：
|
||||
1. 使用 IndexBuilder 获取父子块检索器
|
||||
2. 创建固定流程的 RAGPipeline（多路改写 → RRF融合 → 重排序 → 返回父文档）
|
||||
3. 将流水线封装为 LangChain 工具，供 Agent 调用
|
||||
"""
|
||||
|
||||
import asyncio
|
||||
import sys
|
||||
import os
|
||||
|
||||
from dotenv import load_dotenv
|
||||
|
||||
# 加载环境变量（Qdrant URL、PostgreSQL 连接等）
|
||||
load_dotenv()
|
||||
|
||||
# 添加项目根目录到路径
|
||||
sys.path.insert(0, os.path.join(os.path.dirname(__file__), ".."))
|
||||
from pydantic import SecretStr
|
||||
from langchain_openai import ChatOpenAI
|
||||
from rag_indexer.index_builder import IndexBuilderConfig
|
||||
from rag_indexer.splitters import SplitterType
|
||||
from backend.app.rag.pipeline import RAGPipeline
|
||||
from backend.app.rag.tools import create_rag_tool_sync
|
||||
from backend.rag_core.retriever_factory import create_parent_retriever
|
||||
|
||||
def create_llm():
|
||||
"""创建本地 vLLM 服务 LLM"""
|
||||
vllm_base_url = os.getenv(
|
||||
"VLLM_BASE_URL",
|
||||
"http://127.0.0.1:8081/v1"
|
||||
)
|
||||
|
||||
return ChatOpenAI(
|
||||
base_url=vllm_base_url,
|
||||
api_key=SecretStr(os.getenv("LLAMACPP_API_KEY", "token-abc123")),
|
||||
model="gemma-4-E2B-it",
|
||||
timeout=60.0, # 请求超时时间（秒）
|
||||
max_retries=2, # 失败后自动重试次数
|
||||
streaming=True, # 确保开启流式输出
|
||||
)
|
||||
|
||||
async def demonstrate_full_pipeline():
|
||||
"""
|
||||
完整流水线演示：
|
||||
- 从 IndexBuilder 获取 ParentDocumentRetriever
|
||||
- 创建 RAGPipeline
|
||||
- 执行检索并打印结果
|
||||
"""
|
||||
print("=" * 60)
|
||||
print("演示：固定流程 RAG 检索（多路改写 + RRF + 重排序 + 父文档）")
|
||||
print("=" * 60)
|
||||
|
||||
retriever = create_parent_retriever(collection_name="rag_documents", search_k=5)
|
||||
|
||||
if retriever is None:
|
||||
print("错误：检索器未初始化，请确保索引已构建。")
|
||||
return
|
||||
|
||||
# 3. 创建 LLM 用于查询改写
|
||||
llm = create_llm()
|
||||
|
||||
# 4. 创建 RAGPipeline（固定流程）
|
||||
pipeline = RAGPipeline(
|
||||
retriever=retriever,
|
||||
llm=llm,
|
||||
num_queries=3, # 生成 3 个查询变体
|
||||
rerank_top_n=5, # 最终返回 5 个父文档
|
||||
)
|
||||
|
||||
# 5. 执行检索
|
||||
query = "打虎英雄是谁？"
|
||||
print(f"\n查询: {query}")
|
||||
print("-" * 40)
|
||||
|
||||
try:
|
||||
documents = await pipeline.aretrieve(query)
|
||||
print(f"返回 {len(documents)} 个父文档\n")
|
||||
|
||||
# 打印结果预览
|
||||
for i, doc in enumerate(documents, 1):
|
||||
content_preview = doc.page_content.replace("\n", " ")[:150]
|
||||
source = doc.metadata.get("source", "未知来源")
|
||||
print(f"{i}. 【来源：{source}】")
|
||||
print(f" {content_preview}...\n")
|
||||
|
||||
# 可选：格式化完整上下文
|
||||
# context = pipeline.format_context(documents)
|
||||
# print(context)
|
||||
|
||||
except Exception as e:
|
||||
print(f"检索失败: {e}")
|
||||
import traceback
|
||||
traceback.print_exc()
|
||||
|
||||
async def demonstrate_tool_creation():
|
||||
"""
|
||||
演示创建 RAG 工具（供 Agent 使用）
|
||||
"""
|
||||
print("\n" + "=" * 60)
|
||||
print("演示：创建 RAG 工具（供 LangGraph Agent 调用）")
|
||||
print("=" * 60)
|
||||
|
||||
# 1. 获取检索器（同上）
|
||||
config = IndexBuilderConfig(
|
||||
collection_name="rag_documents",
|
||||
splitter_type=SplitterType.PARENT_CHILD,
|
||||
)
|
||||
retriever = create_parent_retriever(collection_name="rag_documents", search_k=5)
|
||||
|
||||
# 2. 创建 LLM
|
||||
llm = create_llm()
|
||||
|
||||
# 3. 创建工具
|
||||
rag_tool = create_rag_tool_sync(
|
||||
retriever=retriever,
|
||||
llm=llm,
|
||||
num_queries=3,
|
||||
rerank_top_n=5,
|
||||
collection_name="rag_documents",
|
||||
)
|
||||
|
||||
print(f"工具名称: {rag_tool.name}")
|
||||
print(f"工具描述: {rag_tool.description[:100]}...")
|
||||
|
||||
# 4. 模拟 Agent 调用工具
|
||||
query = "请告诉我 打虎英雄是谁？"
|
||||
print(f"\n模拟调用: {query}")
|
||||
print("-" * 40)
|
||||
|
||||
result = await rag_tool.ainvoke({"query": query})
|
||||
print(result[:800] + "..." if len(result) > 800 else result)
|
||||
|
||||
async def main():
|
||||
await demonstrate_full_pipeline()
|
||||
await demonstrate_tool_creation()
|
||||
|
||||
if __name__ == "__main__":
|
||||
asyncio.run(main())
|
||||
49
tools/test/test_rag_indexer_result.py
Normal file
49
tools/test/test_rag_indexer_result.py
Normal file
@@ -0,0 +1,49 @@
|
||||
#!/usr/bin/env python3
|
||||
"""
|
||||
测试重构后的 IndexBuilder 和 RAGRetriever
|
||||
"""
|
||||
|
||||
import asyncio
|
||||
import os
|
||||
import sys
|
||||
|
||||
# 添加项目根目录到 Python 路径
|
||||
project_root = os.path.join(os.path.dirname(__file__), "..")
|
||||
sys.path.insert(0, project_root)
|
||||
|
||||
from rag_indexer.index_builder import IndexBuilder
|
||||
from rag_indexer.splitters import SplitterType
|
||||
|
||||
async def test_index_builder():
|
||||
"""测试索引构建功能"""
|
||||
print("测试索引构建功能...")
|
||||
|
||||
# 创建 IndexBuilder 实例
|
||||
builder = IndexBuilder(
|
||||
collection_name="rag_documents",
|
||||
splitter_type=SplitterType.PARENT_CHILD,
|
||||
parent_chunk_size=1000,
|
||||
child_chunk_size=200
|
||||
)
|
||||
|
||||
# 测试文档路径
|
||||
test_file = os.path.join(os.path.dirname(__file__), "..", "data", "user_docs", "doublestory.txt")
|
||||
|
||||
if os.path.exists(test_file):
|
||||
# 构建索引
|
||||
print(f"正在为文件 {test_file} 构建索引...")
|
||||
processed = await builder.build_from_file(test_file)
|
||||
print(f"索引构建完成，处理了 {processed} 个文档")
|
||||
|
||||
# 获取集合信息
|
||||
info = builder.get_collection_info()
|
||||
print(f"集合信息: {info}")
|
||||
else:
|
||||
print(f"测试文件不存在: {test_file}")
|
||||
|
||||
# 关闭资源
|
||||
builder.close()
|
||||
print("\n测试完成")
|
||||
|
||||
if __name__ == "__main__":
|
||||
asyncio.run(test_index_builder())
|
||||
Reference in New Issue
Block a user