tveasey commented on PR #15903: URL: https://github.com/apache/lucene/pull/15903#issuecomment-4177610692
> For this you'd take a totally different approach -- probably something that looks more like distance computation for product quantization since it uses a codebook in a similar way. This involves generating lookup tables that can be quite large (8KB+) and you would not want to repreat this process on every segment. It can still be very fast but it almost certainly won't be as fast as OSQ's arithmetic comparisons. The paper simply proposes DeQuant and doing things in the original vector space as I read it. This is driven I suspect by targeting mainly GPU where you *really* want to use matmul operations. I agree that for CPU you'd probably want more per query PQ codebook approach, but AFAIK getting these fast requires imposing fairly significant limitations on table sizes which I'm not sure this satisfies. In fairness, I haven't looked at this topic in detail so maybe there are other tricks available. My expectation would be the better route to squeeze more accuracy is use residual quantisation better, since one can centre both the query and document vectors w.r.t. different arbitrary centroids. -- This is an automated message from the Apache Git Service. To respond to the message, please log on to GitHub and use the URL above to go to the specific comment. To unsubscribe, e-mail: [email protected] For queries about this service, please contact Infrastructure at: [email protected] --------------------------------------------------------------------- To unsubscribe, e-mail: [email protected] For additional commands, e-mail: [email protected]
