增加FAISS向量数据库检索存储等功能。
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zhanghuan
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"""
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FAISS向量管理器使用示例
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演示如何使用FAISSManager进行向量操作
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"""
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import numpy as np
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import sys
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import os
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sys.path.append(os.path.dirname(os.path.dirname(os.path.abspath(__file__))))
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from faiss_vector_db.faiss_manager import FAISSManager
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def demo_basic_operations():
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"""演示基本操作"""
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print("=== FAISS向量管理器基本操作演示 ===\n")
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# 1. 创建管理器
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print("1. 创建FAISS管理器...")
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manager = FAISSManager(
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dimension=128, # 向量维度
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index_type="IndexFlatIP", # 使用内积索引（适合余弦相似度）
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normalize_vectors=True, # 标准化向量
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metric_type="cosine" # 余弦相似度
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)
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print(f" 管理器创建成功，维度: {manager.dimension}")
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# 2. 添加单个向量
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print("\n2. 添加单个向量...")
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vector1 = np.random.random(128).astype(np.float32)
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success = manager.add_vector(
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vector=vector1,
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vector_id="food_001",
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metadata={"name": "回锅肉", "category": "川菜", "spicy_level": 3}
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)
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print(f" 添加结果: {'成功' if success else '失败'}")
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# 3. 批量添加向量
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print("\n3. 批量添加向量...")
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batch_vectors = np.random.random((5, 128)).astype(np.float32)
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# 向量ID
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batch_ids = ["food_002", "food_003", "food_004", "food_005", "food_006"]
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# 向量元数据
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batch_metadata = [
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{"name": "炒细面", "category": "川菜", "spicy_level": 2},
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{"name": "西红柿鸡蛋", "category": "家常菜", "spicy_level": 0},
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{"name": "麻辣小面", "category": "川菜", "spicy_level": 4},
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{"name": "宫保鸡丁", "category": "川菜", "spicy_level": 3},
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{"name": "糖醋里脊", "category": "鲁菜", "spicy_level": 0}
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]
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results = manager.add_vectors_batch(batch_vectors, batch_ids, batch_metadata)
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success_count = sum(results)
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print(f" 批量添加结果: {success_count}/{len(batch_ids)} 成功")
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# 4. 查看索引信息
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print("\n4. 索引信息:")
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info = manager.get_index_info()
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for key, value in info.items():
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print(f" {key}: {value}")
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return manager
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def demo_search_operations(manager):
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"""演示搜索操作"""
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print("\n=== 搜索操作演示 ===\n")
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# 1. 相似度搜索
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print("1. 相似度搜索...")
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query_vector = np.random.random(128).astype(np.float32)
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# 搜索最相似的3个向量
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results = manager.search_similar(query_vector, k=3)
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print(f" 找到 {len(results)} 个相似向量:")
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for i, (vector_id, similarity, metadata) in enumerate(results, 1):
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print(f" {i}. ID: {vector_id}")
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print(f" 相似度: {similarity:.4f}")
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print(f" 菜名: {metadata.get('name', 'N/A')}")
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print(f" 类别: {metadata.get('category', 'N/A')}")
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print(f" 辣度: {metadata.get('spicy_level', 'N/A')}")
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print()
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# 2. 带阈值的搜索
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print("2. 带阈值的搜索（相似度 > 0.5）...")
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results_with_threshold = manager.search_similar(query_vector, k=10, threshold=0.5)
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print(f" 找到 {len(results_with_threshold)} 个高相似度向量")
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# 3. 根据ID获取向量信息
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print("\n3. 根据ID获取向量信息...")
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vector_info = manager.get_vector_by_id("food_001")
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if vector_info:
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vector_data, metadata = vector_info
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print(f" ID: food_001")
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print(f" 元数据: {metadata}")
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else:
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print(" 向量不存在")
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def demo_update_delete_operations(manager):
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"""演示更新和删除操作"""
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print("\n=== 更新和删除操作演示 ===\n")
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# 1. 更新向量
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print("1. 更新向量...")
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new_vector = np.random.random(128).astype(np.float32)
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new_metadata = {"name": "回锅肉（改良版）", "category": "川菜", "spicy_level": 2}
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success = manager.update_vector(new_vector, "food_001", new_metadata)
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print(f" 更新结果: {'成功' if success else '失败'}")
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# 验证更新
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vector_info = manager.get_vector_by_id("food_001")
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if vector_info:
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_, metadata = vector_info
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print(f" 更新后的元数据: {metadata}")
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# 2. 删除单个向量
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print("\n2. 删除单个向量...")
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success = manager.delete_vector("food_006")
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print(f" 删除结果: {'成功' if success else '失败'}")
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# 3. 批量删除向量
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print("\n3. 批量删除向量...")
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delete_ids = ["food_004", "food_005"]
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results = manager.delete_vectors_batch(delete_ids)
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success_count = sum(results)
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print(f" 批量删除结果: {success_count}/{len(delete_ids)} 成功")
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# 4. 查看删除后的索引信息
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print("\n4. 删除后的索引信息:")
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info = manager.get_index_info()
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for key, value in info.items():
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print(f" {key}: {value}")
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def demo_save_load_operations(manager):
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"""演示保存和加载操作"""
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print("\n=== 保存和加载操作演示 ===\n")
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# 1. 保存索引
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print("1. 保存索引...")
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save_path = "./demo_faiss_index"
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success = manager.save_index(save_path)
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print(f" 保存结果: {'成功' if success else '失败'}")
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# 2. 创建新的管理器并加载索引
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print("\n2. 加载索引到新管理器...")
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new_manager = FAISSManager(dimension=128)
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success = new_manager.load_index(save_path)
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print(f" 加载结果: {'成功' if success else '失败'}")
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# 3. 验证加载的索引
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print("\n3. 验证加载的索引...")
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info = new_manager.get_index_info()
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print(f" 加载后的向量数量: {info['active_vectors']}")
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# 4. 测试加载后的搜索功能
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print("\n4. 测试加载后的搜索功能...")
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query_vector = np.random.random(128).astype(np.float32)
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results = new_manager.search_similar(query_vector, k=2)
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print(f" 搜索到 {len(results)} 个结果")
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for vector_id, similarity, metadata in results:
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print(f" - {vector_id}: {metadata.get('name', 'N/A')} (相似度: {similarity:.4f})")
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return new_manager
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def demo_advanced_features():
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"""演示高级功能"""
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print("\n=== 高级功能演示 ===\n")
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# 1. 不同索引类型的比较
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print("1. 不同索引类型的性能比较...")
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# 创建测试数据
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test_vectors = np.random.random((1000, 64)).astype(np.float32)
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test_ids = [f"test_{i}" for i in range(1000)]
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index_types = ["IndexFlatIP", "IndexFlatL2"]
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for index_type in index_types:
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print(f"\n 测试索引类型: {index_type}")
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manager = FAISSManager(dimension=64, index_type=index_type)
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# 批量添加
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import time
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start_time = time.time()
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manager.add_vectors_batch(test_vectors, test_ids)
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add_time = time.time() - start_time
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# 搜索测试
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query = np.random.random(64).astype(np.float32)
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start_time = time.time()
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results = manager.search_similar(query, k=10)
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search_time = time.time() - start_time
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print(f" - 添加1000个向量耗时: {add_time:.4f}秒")
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print(f" - 搜索耗时: {search_time:.6f}秒")
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print(f" - 找到结果数: {len(results)}")
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def main():
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"""主函数"""
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try:
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# 基本操作演示
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manager = demo_basic_operations()
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# 搜索操作演示
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demo_search_operations(manager)
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# 更新删除操作演示
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demo_update_delete_operations(manager)
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# 保存加载操作演示
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loaded_manager = demo_save_load_operations(manager)
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# 高级功能演示
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demo_advanced_features()
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print("\n=== 演示完成 ===")
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print("所有功能测试通过！")
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except Exception as e:
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print(f"演示过程中出现错误: {e}")
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import traceback
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traceback.print_exc()
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if __name__ == "__main__":
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main()
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