Speed up nearest neighbor search with a FAISS index

Instant with a hundred vectors. Unusable with real data.

Intermediate to advancedRead 7 minDoing: an afternoon7 steps

▶ Start

What you'll have at the end

A local index that returns the same top matches as brute force, in a fraction of the time.

Before you start

You need

Your own documents already turned into vectors, saved somewhere you can load the whole array back into memory, using a similarity metric you've already settled on.

Not covered

Keeping the index in sync as new documents keep arriving, or shrinking each vector's memory footprint with quantization.

Leans on

7 steps

Where to go next

Choose cosine similarity or dot product for your vectors

go there first if you haven't settled which similarity metric your vectors actually use

Shrink your vector index's memory footprint with quantization

go there once this index is fast but still too large for your memory budget

Checked 15 Aug 2026

Part of the Embeddings cookbook