Comment by reerdna

Analysis by dhruv___anand in https://twitter.com/dhruv___anand/status/1752641057278550199 suggests that there are three different "resolutions" in the embeddings, for the first 512, 1024 and full 1536 dimensions in text-embedding-3-small.

You can put a subset of the dimensions in your vector database, thus saving a lot of cost by reducing memory/compute when retrieving nearest neighbors.

Then you can optionally even re-rank the most promising top-k candidates by the full embeddings. At least one database supports this natively: https://twitter.com/jobergum/status/1750888083900240182