FAISS

What is FAISS?

In the AI world, text, images and audio all get turned into strings of numbers called vectors. Finding "the ones most like this" by brute force collapses once your data gets big. FAISS is Meta's open-source library for vector similarity search, built for exactly that: it indexes huge collections of vectors so you can pull the most similar batch in milliseconds.

What problem does it solve?

Similarity search
Given a query vector, quickly find the K nearest neighbors among millions or billions of vectors.
Approximate nearest neighbors (ANN)
Exact search is too slow, so FAISS trades a little accuracy for speed — a tiny error buys you a thousands-fold speedup, with practically no loss in results.
GPU and disk support
When data no longer fits in memory, it can accelerate on GPU or keep the index on disk, handling hundreds of millions of vectors.

How does it do it?

Index structures
FAISS ships many index types — brute-force Flat, inverted-file IVF, product-quantized PQ, plus GPU and disk variants — so you pick per scenario.
Compression and pruning
Quantization shrinks the vectors, clustering narrows the search space, and you only do precise math in a small region. That's where the speed comes from.

Where is it used?

The most common use is RAG (retrieval-augmented generation): chunk a knowledge base, turn the chunks into vectors, store them in FAISS, then retrieve relevant pieces when a user asks and feed them to the model. Recommendation systems, image search, deduplication and copyright detection all lean on it too.

Bottom line: FAISS is a search engine for vectors — it indexes millions of them and finds the most similar ones in milliseconds.