faiss

FAISS enables fast similarity search over large-scale dense vectors by creating an index for your vector dimension, adding your vectors, and querying for nearest neighbors. It supports billions of vectors across AI pipelines.

Approximate nearest-neighbor search is needed for billion-scale embedding retrieval to balance speed and accuracy. Exact search uses Flat indices, while IVF, HNSW, and PQ indices provide approximate search for faster, scalable querying.

You do not need faiss-gpu installed to run vector search; faiss-cpu is fully supported for CPU deployment. FAISS offers GPU acceleration as a flexible option for faster processing, requiring faiss-gpu and numpy.

The best way to scale vector similarity search across billions of vectors is using FAISS with IVF, HNSW, or PQ indices. These approximate search structures reduce computational load while maintaining retrieval accuracy on large collections.

FAISS supports GPU acceleration for recommendation and clustering pipelines via the faiss-gpu backend. This provides flexible deployment options for processing large vector collections alongside standard CPU operations.

FAISS is used for vector similarity search because it handles exact and approximate search across Flat, IVF, HNSW, and PQ indices. It provides specialized, fast nearest-neighbor retrieval for dense embeddings at a billion-vector scale.