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Very happy to read this from you, Simon!

Where would a Redis vector store play a part though? Maybe you'd load up relevant embeddings for a particular user while they're interacting with their dataset, to make their responses quicker? You've already spent the effort on hydrating their data out of persistence though. I guess step one is likely being a more trusted alternative to the in-memory vector solutions like HNSW, Faiss, and a potentially faster engine than pg_vector. I've always seen Redis as an augmentation, but maybe in this role it can take the helm?

It's exactly that. Redis is an in-memory data structure server that you can outsource index-style operations to. Vector similarity is a type of index search. I think it's an exact fit for Redis.

Cool. Redis in front of Postgres always brought peace-of-mind that will likely be welcome for the vector data use-case.

P.s.: Appreciate the llm command line tool.

yeah! much better than FT.SEARCH monstrosity...