Vector search is commonly implemented using specialized frameworks designed to handle high-dimensional data efficiently. Three widely used frameworks are FAISS, Annoy, and HNSW. FAISS (Facebook AI Similarity Search) is a library optimized for fast similarity search and clustering of dense vectors. Annoy (Approximate Nearest Neighbors Oh Yeah), developed by Spotify, focuses on building tree-based structures for approximate nearest neighbor searches. HNSW (Hierarchical Navigable Small World) is a graph-based indexing method known for balancing speed and accuracy. These tools provide the core algorithms and data structures needed to perform vector search at scale.
FAISS is particularly popular for its GPU acceleration and support for large datasets. For example, it allows developers to create indexes optimized for memory usage or query speed, such as IVF (Inverted File Index) combined with product quantization. Annoy uses random projection trees to partition data, enabling fast approximate searches with tunable trade-offs between precision and speed. HNSW, implemented in libraries like hnswlib, constructs layered graphs to enable efficient traversal during searches, making it suitable for applications requiring high recall. These frameworks are often integrated into larger systems—FAISS is used in recommendation engines, Annoy powers music recommendations at Spotify, and HNSW is a backbone for vector search in databases like Elasticsearch.
Beyond standalone libraries, databases and managed services have adopted these frameworks to simplify vector search. Milvus, an open-source vector database, integrates FAISS, HNSW, and other algorithms to support scalable similarity search. Elasticsearch added native vector search capabilities using HNSW, allowing developers to combine keyword and vector queries. Managed platforms like Pinecone abstract infrastructure complexity, offering FAISS- or HNSW-based solutions with automatic scaling. These tools often include features like metadata filtering, hybrid search, and real-time updates, making them practical for production use cases such as image retrieval, semantic search, or fraud detection. Developers typically choose frameworks based on performance needs, scalability, and integration with existing data pipelines.