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https://issues.apache.org/jira/browse/LUCENE-10191?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=17459633#comment-17459633
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Julie Tibshirani commented on LUCENE-10191:
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I've been pondering this more. It definitely seems possible to move
VectorSimilarityFunction to Lucene90HnswVectorsFormat. Maybe we could start
with something really simple, like a new method
{{VectorValues#computeDistance(float[] query)}} that uses the configured
distance function. I guess {{computeDistance}} could give a simple interface
but do something fancy if it wanted to, since it knows how exactly its vectors
are represented.
My main hesitation is that VectorSimilarityFunction is a cross-cutting concept
that makes sense across format implementations. In fact, I would expect all
KnnVectorsFormat to support dot product, euclidean, and cosine. Could there be
drift across different formats (maybe a vector function is missing, or is named
something different) in a way that hurts users?
> Optimize vector functions by precomputing magnitudes
> ----------------------------------------------------
>
> Key: LUCENE-10191
> URL: https://issues.apache.org/jira/browse/LUCENE-10191
> Project: Lucene - Core
> Issue Type: Improvement
> Reporter: Julie Tibshirani
> Priority: Minor
>
> Both euclidean distance (L2 norm) and cosine similarity can be expressed in
> terms of dot product and vector magnitudes:
> * l2_norm(a, b) = ||a - b|| = sqrt(||a||^2 - 2(a . b) + ||b||^2)
> * cosine(a, b) = a . b / ||a|| ||b||
> We could compute and store each vector's magnitude upfront while indexing,
> and compute the query vector's magnitude once per query. Then we'd calculate
> the distance using our (very optimized) dot product method, plus the
> precomputed values.
> This is an exploratory issue: I haven't tested this out yet, so I'm not sure
> how much it would help. I would at least expect it to help with cosine
> similarity – several months ago we tried out similar ideas in Elasticsearch
> and were able to get a nice boost in cosine performance.
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