I guess if you wanted to do decompounding and stemming you should make the fields with the stemmed values and the decompounded values yourself and ... then implement it for the queries as well? Or is there a way to do that kind of thing somewhere in there?
I guess its Vectorless vector in the same sense that we have Serverless servers?
probably stupid question - is there a way to use this to search over graph data - like some way to do graph embeddings here to map a graph to the vectors?
I get your larger point, but the errors and phrasing are a bit off putting.
Vector similarity alone _IS_ enough for vector search. That's literally what "search" means in this context! Finding another vector within an epsilon bound given a metric. After the 3rd read, I understand the point you're trying to make I think, and I think you might be right.
There might be room in the market for an integrator, an all in one platform. It won't have the best performance or functionality, I doubt it would win in _any_ category. But if you can get the business model working right I could imagine such a product having sizeable market share. Hm...
Edit: I'm also curious about the dimension and metric used. Any numbers about latency or size is kinda pointless without :).
1 point in 1536-D space (what OpenAI uses),4 byte float == 6KB, so even 100 million points is only 600G...