3 ms·
There is an entire section of the post about that. I believe that's more the illusion of a problem because of product design issues than a real challenge since
by antirez 11mo ago
There is an entire section of the post about that. I believe that's more the illusion of a problem because of product design issues than a real challenge since far results that match the filter are totally useless.
- whakim 11mo agoDoesn't this depend on your data to a large extent? In a very dense graph "far" results (in terms of the effort spent searching) that match the filters might actually be quite similar?
- antirez 11mo agoThe "far" here means "with vectors having a very low cosine similarity / very high distance". So in vector use cases where you want near vectors matching a given set of filters, far vectors matching a set of filters are useless. So in Redis Vector Sets you have another "EF" (effort) parameter just for filters, and you can decide in case not enough results are collected so far how much efforts you want to do. If you want to scan all the graph, that's fine, but Redis by default will do the sane thing and early stop when the vectors anyway are already far.