About FAISS
A research-grade open-source library (from Meta/Fair) for efficient similarity search and clustering of dense vectors, with CPU and GPU implementations of many ANN algorithms.
Key Features
- Collection of high-performance indexing algorithms (IVF, PQ, HNSW, binary/vector quantization) for large-scale similarity search.
- GPU-accelerated implementations for billion-scale nearest-neighbor search and tooling for evaluation/tuning.
- C++ core with Python bindings and extensive research-backed algorithms used in production and experiments.
Use Cases & Best For
Researchers and engineers who need low-level, high-performance vector indexing/search algorithms for custom pipelines.
Teams integrating GPU-accelerated ANN search into ML pipelines or building custom vector search engines.
About Vector Databases
Storage and retrieval for embeddings