Glad I hopped into this thread while your comment was recent enough to be at the top. This is super interesting! Apologies if you went over this in your other post (or the docs, I'll be digging into this over the weekend) but could you share a bit about why you went this route? What you tried, what the hangups were/are with other approaches, and if there are any interesting possibilities with your approach that other vector databases just wouldn't be able to do?

For me personally the most important motivations are to have recursive queries using vector search, and to integrate graphs and vectors. Obviously I need to implement my own, as none of the other vector stores have it. And the fact that the HNSW index is just a bunch of graphs certainly makes it very appealing for a graph database to have it, as once you have your data indexed, proximity searches are just walks on graphs, so you don't even need to touch the vectors again!

Thanks for the links and discussions, I’m keeping an eye on this one it looks really promising, at least in the hybrid area compared to the much hyped surrealDB whose graph implementation looks more like an afterthought when you get down to the technical details, functionality and performance