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MemOS-MCP

by qinshu1109
Apache 2.0
3
  • Linux
  • Apple
test_qwen_integration.py7.18 kB
#!/usr/bin/env python3 """ 测试Qwen模型集成 验证嵌入模型和重排模型的完整功能 """ import os import sys from pathlib import Path # 添加当前目录到路径 sys.path.insert(0, str(Path(__file__).parent)) from usage_examples import load_env_file from mvp_memory import MVPMemoryManager def test_qwen_models_basic(): """测试Qwen模型基础功能""" print("🧪 测试Qwen模型基础功能") print("=" * 60) # 加载环境变量 load_env_file() try: # 测试Qwen配置 from qwen_embedding_config import test_qwen_models success = test_qwen_models() if success: print("✅ Qwen模型基础测试通过") return True else: print("❌ Qwen模型基础测试失败") return False except Exception as e: print(f"❌ Qwen模型测试异常: {e}") return False def test_mvp_with_qwen(): """测试MVP记忆管理器与Qwen模型集成""" print("\n🧪 测试MVP记忆管理器与Qwen模型集成") print("=" * 60) try: # 创建MVP记忆管理器,启用增强版 mvp_manager = MVPMemoryManager("./test_qwen_mvp_data", use_enhanced=True) # 测试连接 if not mvp_manager.test_connection(): print("❌ MVP管理器连接测试失败") return False # 测试添加记忆 print("\n📝 测试添加记忆...") test_memories = [ ("Qwen3-Embedding-0.6B是阿里巴巴开发的高质量嵌入模型", ["Qwen", "嵌入模型", "阿里巴巴"]), ("Qwen3-Reranker-0.6B提供精准的文档重排功能", ["Qwen", "重排模型", "文档检索"]), ("MemOS集成Qwen模型后,搜索精度显著提升", ["MemOS", "Qwen", "搜索优化"]), ("向量数据库使用1024维嵌入向量存储语义信息", ["向量数据库", "嵌入向量", "语义搜索"]), ("SiliconFlow API提供稳定的模型推理服务", ["SiliconFlow", "API", "模型服务"]) ] for content, tags in test_memories: success = mvp_manager.remember(content, tags=tags) if success: print(f"✅ 记忆添加成功: {content[:30]}...") else: print(f"❌ 记忆添加失败: {content[:30]}...") # 测试智能检索 print("\n🔍 测试智能检索...") test_queries = [ "Qwen模型的功能特点", "向量搜索和语义检索", "API服务和模型推理", "文档重排和搜索优化" ] for query in test_queries: print(f"\n查询: {query}") results = mvp_manager.recall(query, top_k=3, use_reranker=True) if results: print(f"找到 {len(results)} 条相关记忆:") for i, result in enumerate(results, 1): print(f" {i}. 向量分数: {result.get('score', 0):.4f}") if 'rerank_score' in result: print(f" 重排分数: {result['rerank_score']:.4f}") print(f" 内容: {result['content'][:50]}...") print(f" 标签: {result.get('tags', [])}") else: print(" 未找到相关记忆") print("\n✅ MVP与Qwen模型集成测试完成") return True except Exception as e: print(f"❌ MVP与Qwen集成测试失败: {e}") import traceback traceback.print_exc() return False def test_performance_comparison(): """测试性能对比:基础版 vs 增强版""" print("\n🧪 测试性能对比:基础版 vs 增强版") print("=" * 60) try: # 测试基础版 print("🔄 测试基础版MemOS...") basic_manager = MVPMemoryManager("./test_basic_data", use_enhanced=False) # 测试增强版 print("🔄 测试增强版MemOS...") enhanced_manager = MVPMemoryManager("./test_enhanced_data", use_enhanced=True) # 添加相同的测试数据 test_content = "这是一个测试记忆,用于比较基础版和增强版的性能差异" test_tags = ["测试", "性能对比"] # 基础版添加记忆 basic_success = basic_manager.remember(test_content, tags=test_tags) print(f"基础版添加记忆: {'成功' if basic_success else '失败'}") # 增强版添加记忆 enhanced_success = enhanced_manager.remember(test_content, tags=test_tags) print(f"增强版添加记忆: {'成功' if enhanced_success else '失败'}") # 搜索测试 query = "性能测试记忆" print(f"\n🔍 搜索查询: {query}") # 基础版搜索 basic_results = basic_manager.recall(query, top_k=3) print(f"基础版搜索结果: {len(basic_results)} 条") # 增强版搜索 enhanced_results = enhanced_manager.recall(query, top_k=3, use_reranker=True) print(f"增强版搜索结果: {len(enhanced_results)} 条") # 显示结果对比 if basic_results: print(f"基础版最佳匹配分数: {basic_results[0].get('score', 0):.4f}") if enhanced_results: print(f"增强版向量分数: {enhanced_results[0].get('score', 0):.4f}") if 'rerank_score' in enhanced_results[0]: print(f"增强版重排分数: {enhanced_results[0]['rerank_score']:.4f}") print("\n✅ 性能对比测试完成") return True except Exception as e: print(f"❌ 性能对比测试失败: {e}") return False def main(): """主测试函数""" print("🚀 Qwen模型集成完整测试") print("=" * 80) # 测试计数 total_tests = 3 passed_tests = 0 # 1. 基础功能测试 if test_qwen_models_basic(): passed_tests += 1 # 2. MVP集成测试 if test_mvp_with_qwen(): passed_tests += 1 # 3. 性能对比测试 if test_performance_comparison(): passed_tests += 1 # 总结 print(f"\n📊 测试总结") print("=" * 80) print(f"总测试数: {total_tests}") print(f"通过测试: {passed_tests}") print(f"失败测试: {total_tests - passed_tests}") print(f"成功率: {passed_tests/total_tests*100:.1f}%") if passed_tests == total_tests: print("\n🎉 所有测试通过!Qwen模型集成成功!") print("现在可以使用以下功能:") print("- Qwen3-Embedding-0.6B 高质量嵌入向量") print("- Qwen3-Reranker-0.6B 精准文档重排") print("- 增强版MVP记忆管理器") print("- 智能检索和语义搜索") return True else: print(f"\n❌ 部分测试失败,请检查配置和环境") return False if __name__ == "__main__": success = main() sys.exit(0 if success else 1)

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