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
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