[ofiryanai]

the interest in vector database rise as an external, specialized service in a system that runs in addition to the "single source of truth" data lake where the data actually resides. like Redis and Memcached before, it solves a specific problem. Redis started to act like a fully fledged DB (with weird persistency method and guarantees) only after it was really wide-spread. sure, every DB will support vector and every vector search engine will act like a DB. But that's missing the point that vector search is an expensive problem with tradoffs that justify a specialized design

btw I'm working in a DB startup - https://hyper-space.io/

[kordlessagain]

We recently added vector support to FeatureBase: https://cloud.featurebase.com/. What's interesting about using it with our existing b-tree storage layer is that I can now do set operations on the record's stringset fields before I do the similarity calculations on the vectors. I've been experimenting with laying out the vector space in a b-tree structure using this technique along with keyterm extraction, allowing for a type of semantic graph to be applied by the LLM to the vector space.