# embedding

Library for efficient similarity search and clustering of dense vectors at billion-scale.

FAISS is Meta AI Research's open-source library for efficient similarity search and clustering of dense vectors. It implements approximate nearest-neighbor algorithms designed to scale to billions of vectors, with optimized indexes that fit in RAM and GPU acceleration for the largest workloads. Engineering teams use FAISS as the retrieval primitive underneath custom RAG pipelines, recommendation systems, and large-scale embedding search infrastructure.

Header-only C++ implementation of HNSW for fast approximate nearest-neighbor search.

hnswlib is a header-only C++ library implementing the Hierarchical Navigable Small World (HNSW) graph algorithm for approximate nearest-neighbor search, with Python bindings and a tiny dependency footprint. Originally developed by the nmslib team, it has become the default HNSW implementation embedded inside many vector databases and search products. Engineers use it directly when they want HNSW retrieval without pulling in a heavyweight vector DB.

Embedding-first search and discovery engine for AI-powered product experiences.

Marqo is an open-source tensor search engine that combines embedding generation and vector search in a single API, removing the need to manage separate embedding pipelines and vector databases. Built for product discovery and multi-modal search, it lets teams index text, images, and structured data together, returning ranked results based on semantic similarity rather than keyword overlap.