This is really great! It speaks very much to my use-case (building user embeddings and serving them both to analysts + other ML models).

I was wondering if there was a reasonable way to store raw data next to the embeddings such that: 1. Analysts can run queries to filter down to a space they understand (the raw data). 2. Nearest neighbors can be run on top of their selection on the embedding space.

Our main use case is segmentation, so giving analysts access to the raw feature space is very important.