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https://issues.apache.org/jira/browse/LUCENE-10147?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=17424680#comment-17424680
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Michael Sokolov commented on LUCENE-10147:
------------------------------------------
Also, I'll just note that we do have this javadoc about the requirement for
unit-length vectors:
{{
/**
* Creates a numeric vector field. Fields are single-valued: each document
has either one value or
* no value. Vectors of a single field share the same dimension and
similarity function. Note that
* some strategies (notably dot-product) require values to be unit-length,
which can be enforced
* using VectorUtil.l2Normalize(float[]).
*
* @param name field name
* @param vector value
* @param similarityFunction a function defining vector proximity.
* @throws IllegalArgumentException if any parameter is null, or the vector
is empty or has
* dimension > 1024.
*/
public KnnVectorField(String name, float[] vector, VectorSimilarityFunction
similarityFunction) {}}
I guess it is pretty oblique though; I think we need more specific guidance,
and something in the VectorSImilarity class too - does that make sense for
docs?
> KnnVectorQuery can produce negative scores
> ------------------------------------------
>
> Key: LUCENE-10147
> URL: https://issues.apache.org/jira/browse/LUCENE-10147
> Project: Lucene - Core
> Issue Type: Bug
> Reporter: Julie Tibshirani
> Priority: Blocker
>
> The cosine similarity of two vectors falls in the range [-1, 1]. So currently
> with cosine similarity, {{KnnVectorQuery}} can produce negative scores. Maybe
> we should just adjust the scores in this case by adding 1, shifting them to
> the range [0, 2].
> As a side note, this made me notice that
> {{VectorSimilarityFunction.DOT_PRODUCT}} is really quite "expert"! Users need
> to know to normalize all document and query vectors to unit length when using
> this similarity. Otherwise the output is unbounded and difficult to handle in
> scoring. Also dot product is not a true metric: for example, it doesn't obey
> the triangle inequality. So many ANN algorithms have trouble supporting it.
> As part of this issue, we could improve the documentation on
> {{VectorSimilarityFunction.DOT_PRODUCT}} to clarify that normalization is
> required.
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