faiss

Similarity search at billion scale requires building approximate nearest-neighbor indexes like IVF or HNSW using FAISS, enabling fast dense-vector retrieval without brute-force scanning. You train the index on a data subset, add embeddings, then query top-k neighbors efficiently.

Use Flat indexes for exact similarity search when accuracy is critical and memory is sufficient. Switch to IVFPQ or HNSW approximate indexes when handling large embedding sets where speed and memory optimization outweigh minor accuracy trade-offs.

Yes, GPU acceleration is supported for RAG retrieval by installing faiss-gpu, which speeds up dense-vector indexing and k-NN search operations. The framework integrates with common vector-store frameworks for retrieval pipelines.

FAISS supports persistence by allowing you to save built indexes to disk and load them back into memory for continuous embedding retrieval. This enables index reuse across sessions without retraining or re-adding vectors.

Retrieve top-k nearest neighbors by building an appropriate FAISS index—Flat for exact matches or IVF and HNSW for approximate search—then querying user or item embeddings to return the most similar vectors efficiently.

Yes, FAISS works directly with numpy arrays, accepting dense vectors as input for indexing, clustering, and recommendation system queries. You generate embeddings as numpy arrays, then train, add, and search the index using standard array formats.