4 ms·

As it always happens, people realize there is something new is Redis in 2 years or more. With Streams it tragically took like 4 years and then everybody started

by antirez 7mo ago

As it always happens, people realize there is something new is Redis in 2 years or more. With Streams it tragically took like 4 years and then everybody started to use it for this use case, with a sharp acceleration in the latest few years. I believe this is what is happening for vector sets as well. For a reduced size problem like that you just git clone Redis, add the vectors into a key with VADD, and query with VSIM. It's a 10 lines Python script that will deliver 20k/50k queries per second more or less, out of the box with zero optimizations. But here the problem is: the scale. Billions of vectors. And I wonder if Redis should add on-disk vector sets, which I started to sketch months ago and never implemented. So my question is, the "3B" in Vicky's blog post is theoretical or is a practical need many folks have? I'm asking because at such a scale, the main problem is to generate the embeddings for your items, whatever they are. https://gist.github.com/antirez/b3cc9af4db69b04756606ad91cab1083 https://gist.github.com/antirez/b3cc9af4db69b04756606ad91cab... EDIT: I wonder if it is possible to use in memory vector sets to index discrete on disk dense blobs of nearby vectors to query with an approach like the one described in the post. It's like a H-HNSW, and resembles to certain on-disk approaches for vector similarity indeed.

antonvs 7mo ago
> at such a scale, the main problem is to generate the embeddings for your items Generation is often decoupled from querying, though. Consider LLMs, where training is a very expensive, slow, hardware intensive process, whereas inference is much faster and much less intensive. But the performance of inference is in many ways more important than the performance of training, because inference is what users interact with directly.

4 ms·

As it always happens, people realize there is something new is Redis in 2 years or more. With Streams it tragically took like 4 years and then everybody started

by antirez 7mo ago

antonvs 7mo ago
> at such a scale, the main problem is to generate the embeddings for your items Generation is often decoupled from querying, though. Consider LLMs, where training is a very expensive, slow, hardware intensive process, whereas inference is much faster and much less intensive. But the performance of inference is in many ways more important than the performance of training, because inference is what users interact with directly.