FAISS is an open-source library from Meta for quickly finding the most similar vectors in large collections, a core job of vector databases.
In plain words
Embeddings turn text or images into lists of numbers, called vectors. To find similar items, you look for the nearest vectors, which gets slow when you have millions. FAISS offers many indexing methods that trade some accuracy for speed, and it can also cluster and compress vectors. It works even when the set is too big for memory. It is written in C++ with Python wrappers, and some of its algorithms run on GPUs.
A quick example
Imagine a million product photos, each turned into an embedding. You load the vectors into a FAISS index. When a shopper uploads a picture, you embed it and ask the index for the ten nearest vectors, which point to the ten most visually similar products.
Where you'll meet it
- Semantic search and retrieval for RAG.
- Clustering and compressing large sets of vectors.
- Vector similarity search, a core part of vector databases.
Related
Sources
- Docsfacebookresearch/faiss, Meta (GitHub) · read 29 Sept 2026
- PaperThe Faiss library, Douze et al. (arXiv) · read 29 Sept 2026
Checked by WhatIsAI against the sources above · 29 Sept 2026. Spotted an error? Report it.