I don't have much experience in dedicated vector databases, I've only used pgvector, so pardon me if there's an obvious answer to this, but how do people do similarity search combined with other filters and pagination with separate vector DB? It's a pretty common use case at least in my circles.

For example, give me product listings that match the search term (by vector search), and are made by company X (copanies being a separate table). Sort by vector similarity of the search term and give me top 100?.

We have even largely moved away from ElasticSearch to Postgres where we can, because it's just so much easier to implement with new complex filters without needing to add those other tables' data to the index of e.g. "products" every time.

Edit: Ah I guess this is touched a bit in the article with "Pre- vs. Post-Filtering" - I guess you just do the same as with ElasticSearch, predict what you'll want to filter with, add all of that to metadata and keep it up to date.