302 lines
9.3 KiB
Python
302 lines
9.3 KiB
Python
"""
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测试语义缓存功能
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功能：
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1. 测试缓存的存储和查找
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2. 测试语义匹配效果
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3. 验证缓存性能
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使用方式：
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python test_semantic_cache.py
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作者：AI Assistant
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"""
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import asyncio
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import sys
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import os
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import time
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# 添加项目根目录到Python路径
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sys.path.insert(0, os.path.dirname(os.path.dirname(os.path.abspath(__file__))))
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from utils.semantic_cache_service import (
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SemanticCacheService,
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get_semantic_cache_service,
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lookup_question,
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store_qa_pair,
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clear_cache
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)
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from config.get_redis import RedisUtil
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# 全局Redis客户端
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_redis_client = None
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async def get_redis():
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"""获取Redis客户端"""
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global _redis_client
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if _redis_client is None:
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_redis_client = await RedisUtil.create_redis_pool()
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return _redis_client
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async def test_basic_functionality():
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"""测试基本功能"""
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print("\n" + "=" * 60)
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print("测试1: 基本功能验证")
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print("=" * 60)
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# 获取Redis客户端
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redis = await get_redis()
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# 创建缓存服务实例
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cache = SemanticCacheService(redis)
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# 清理测试缓存
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await cache.clear(chat_id="test_chat_001")
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# 测试1: 精确匹配
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print("\n1. 测试精确匹配...")
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result = await cache.lookup("test_chat_001", "康达公司成立于哪一年？", redis)
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print(f" 首次查找（未命中）: {result}")
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assert result is None, "首次查找应该未命中"
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# 存储问答对
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success = await cache.store(
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"test_chat_001",
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"康达公司成立于哪一年？",
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"康达公司成立于2012年，是一家专注于智能机器人研发的高科技企业。",
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redis
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)
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print(f" 存储问答对: {'成功' if success else '失败'}")
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# 再次查找
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result = await cache.lookup("test_chat_001", "康达公司成立于哪一年？", redis)
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print(f" 精确查找（已缓存）: {'命中' if result else '未命中'}")
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if result:
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print(f" 答案: {result[0][:50]}...")
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print(f" 相似度: {result[1]:.2f}")
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assert result[1] == 1.0, "精确匹配相似度应该为1.0"
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print(" ✓ 精确匹配测试通过")
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async def test_semantic_matching():
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"""测试语义匹配"""
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print("\n" + "=" * 60)
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print("测试2: 语义匹配验证")
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print("=" * 60)
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redis = await get_redis()
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cache = SemanticCacheService(redis)
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# 清理并重新测试
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await cache.clear(chat_id="test_chat_002")
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# 存储原始问答
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original_question = "公司的退货政策是什么？"
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original_answer = "我们支持7天无理由退换货，您可以在收到商品后7天内申请退换。"
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await cache.store("test_chat_002", original_question, original_answer, redis)
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# 测试各种问法
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test_cases = [
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("公司的退货政策是什么？", "原问题"),
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("退货政策是怎么样的？", "同义词替换"),
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("如果我想退货可以吗？", "语义相关"),
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("你们怎么退货？", "口语化表达"),
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("产品质量有问题怎么换？", "相关但不同"),
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("今天天气怎么样？", "完全无关"),
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]
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print("\n语义匹配测试结果:")
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print("-" * 60)
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for question, desc in test_cases:
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result = await cache.lookup("test_chat_002", question, redis)
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if result:
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answer, similarity = result
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print(f" [{desc}]")
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print(f" Q: {question[:30]}...")
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print(f" 命中! 相似度={similarity:.2f}")
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else:
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print(f" [{desc}]")
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print(f" Q: {question[:30]}...")
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print(f" 未命中")
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async def test_performance():
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"""测试缓存性能"""
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print("\n" + "=" * 60)
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print("测试3: 缓存性能验证")
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print("=" * 60)
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redis = await get_redis()
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cache = SemanticCacheService(redis)
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# 清理并准备测试数据
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await cache.clear(chat_id="test_chat_003")
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# 预热缓存
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questions = [
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"你们公司提供哪些服务？",
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"如何联系客服？",
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"产品售后政策是什么？",
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"发货需要多长时间？",
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"支持哪些支付方式？",
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]
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print("\n预热缓存...")
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start_time = time.time()
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for i, q in enumerate(questions):
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await cache.store(
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"test_chat_003",
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q,
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f"这是针对问题的回答 #{i+1}",
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redis
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)
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warm_time = time.time() - start_time
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print(f" 存储5条问答耗时: {warm_time*1000:.2f}ms")
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# 性能测试：精确匹配 vs 语义匹配
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print("\n性能测试:")
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# 精确匹配测试
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start_time = time.time()
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for _ in range(100):
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await cache.lookup("test_chat_003", "你们公司提供哪些服务？", redis)
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exact_time = time.time() - start_time
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print(f" 100次精确匹配: {exact_time*1000:.2f}ms (平均{exact_time*10:.2f}ms/次)")
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# 语义匹配测试（会扫描所有缓存）
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start_time = time.time()
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for _ in range(10):
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await cache.lookup("test_chat_003", "公司提供什么服务？", redis)
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semantic_time = time.time() - start_time
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print(f" 10次语义匹配: {semantic_time*1000:.2f}ms (平均{semantic_time*100:.2f}ms/次)")
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async def test_cache_statistics():
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"""测试缓存统计"""
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print("\n" + "=" * 60)
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print("测试4: 缓存统计功能")
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print("=" * 60)
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redis = await get_redis()
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cache = SemanticCacheService(redis)
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# 清理并准备数据
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await cache.clear(chat_id="test_chat_stats")
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# 存储一些问答
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await cache.store("test_chat_stats", "问题1", "答案1", redis)
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await cache.store("test_chat_stats", "问题2", "答案2", redis)
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# 命中几次
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await cache.lookup("test_chat_stats", "问题1", redis)
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await cache.lookup("test_chat_stats", "问题1", redis)
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await cache.lookup("test_chat_stats", "问题2", redis)
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# 获取统计
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stats = await cache.get_stats("test_chat_stats", redis)
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print(f"\n缓存统计信息:")
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print(f" 总条目数: {stats.get('total_entries', 0)}")
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print(f" 配置信息:")
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print(f" - 最大缓存大小: {stats['config']['max_cache_size']}")
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print(f" - 相似度阈值: {stats['config']['similarity_threshold']}")
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print(f" - 缓存过期时间: {stats['config']['cache_ttl_hours']}小时")
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async def test_integration_with_ragflow():
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"""集成测试：模拟RAG流程"""
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print("\n" + "=" * 60)
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print("测试5: 集成测试 - 模拟RAG流程")
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print("=" * 60)
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redis = await get_redis()
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cache = SemanticCacheService(redis)
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# 模拟RAG服务
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class MockRAGService:
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async def query(self, question):
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# 模拟RAG耗时
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await asyncio.sleep(0.1)
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return f"RAG生成的答案：{question}的详细回答..."
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rag_service = MockRAGService()
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# 清理测试缓存
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await cache.clear(chat_id="test_integration")
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test_questions = [
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"康达机器人有哪些产品？",
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"康达机器人有哪些产品？", # 重复问题，期望命中缓存
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"你们的机器人产品有哪些？", # 相似问题，期望语义匹配
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"产品价格是多少？", # 新问题，不命中
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]
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print("\n模拟用户连续提问:")
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print("-" * 60)
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for i, question in enumerate(test_questions):
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print(f"\n用户提问 #{i+1}: {question[:30]}...")
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# 1. 先查缓存
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cached = await cache.lookup("test_integration", question, redis)
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if cached:
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answer, similarity = cached
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print(f" ✓ 缓存命中! (相似度={similarity:.2f})")
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print(f" → 直接返回缓存答案")
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else:
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print(f" ✗ 缓存未命中")
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print(f" → 调用RAG服务...")
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# 2. 调用RAG
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answer = await rag_service.query(question)
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print(f" → RAG返回答案")
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# 3. 存储到缓存
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await cache.store("test_integration", question, answer, redis)
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print(f" → 已存入缓存")
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print(f" 答案预览: {answer[:40]}...")
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async def main():
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"""主测试函数"""
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print("\n" + "╔" + "═" * 58 + "╗")
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print("║" + " " * 15 + "语义缓存功能测试" + " " * 20 + "║")
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print("╚" + "═" * 58 + "╝")
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try:
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await test_basic_functionality()
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await test_semantic_matching()
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await test_performance()
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await test_cache_statistics()
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await test_integration_with_ragflow()
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print("\n" + "=" * 60)
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print("✓ 所有测试完成!")
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print("=" * 60)
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except Exception as e:
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print(f"\n✗ 测试失败: {e}")
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import traceback
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traceback.print_exc()
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finally:
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# 清理测试数据
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redis = await get_redis()
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cache = SemanticCacheService(redis)
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await cache.clear(chat_id="test_chat_001")
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await cache.clear(chat_id="test_chat_002")
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await cache.clear(chat_id="test_chat_003")
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await cache.clear(chat_id="test_chat_stats")
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await cache.clear(chat_id="test_integration")
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print("\n测试数据已清理")
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if __name__ == "__main__":
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asyncio.run(main())
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