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My local index worked fine in testing and then needed far more memory than I budgeted for once it held real data.
What you'll have at the end
The same index rebuilt with int8 and binary vectors, with its memory footprint and its recall against your own labeled queries both measured, on your own corpus, before and after.
You need
A local vector index already holding real embeddings from your own documents, plus the ability to re-run the same embedding model whenever you need a fresh full-precision vector.
Not covered
Choosing which approximate-search index structure sits on top of the vectors, or picking the embedding model itself.
Leans on
Pick an embedding model from the MTEB leaderboard, then verify it on your data
go there first if you haven't settled on which embedding model to index at all, since that choice happens before you decide how hard to compress it
Speed up nearest neighbor search with a FAISS index
go there when the bottleneck is search latency instead of memory, since a faster index structure solves a different problem than a smaller one
Checked 15 Aug 2026
Part of the Embeddings cookbook