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https://issues.apache.org/jira/browse/SOLR-16675?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=17877178#comment-17877178
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Alexey Serba commented on SOLR-16675:
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[~abenedetti] [~eliaporciani] Is it possible to use new `vectorSimilarity`
function for exact KNN filtering (without HNSW index) similar to e.g.
[OpenSearch knn scoring
script|https://opensearch.org/docs/latest/search-plugins/knn/knn-score-script/]?
It doesn't look that exact knn filtering is supported because DenseVectorField
[does not
store|https://github.com/apache/solr/blob/8ec3876dc6d096d3906ef08a5c7d8be54d489ee7/solr/core/src/java/org/apache/solr/schema/DenseVectorField.java#L60]
vectors in DocValues which I believe is needed for exact filtering, right?
> Introduce the possibility to rerank topK results with vector similarity
> functions using DenseVectorField
> --------------------------------------------------------------------------------------------------------
>
> Key: SOLR-16675
> URL: https://issues.apache.org/jira/browse/SOLR-16675
> Project: Solr
> Issue Type: Task
> Reporter: Elia Porciani
> Assignee: Alessandro Benedetti
> Priority: Blocker
> Labels: vector-based-search
> Fix For: 9.3
>
> Time Spent: 0.5h
> Remaining Estimate: 0h
>
> When using knnQParser in reranking pay attention to the top-K parameter.
> The second pass score(deriving from KNN search) is calculated only if the
> document d from the first pass is within the K nearest neighbors(in the whole
> index) of the target vector to search.
> This is a current limitation.
> The final ranked list of results will have the first pass score(main query q)
> combined with the second pass score(the approximated similarity function
> distance to the target vector to search).
> Ideally, it should be possible to:
> * Rerank top K results with vector similarity. We should compute the vector
> similarity function using the DenseVectorField value of all the documents in
> top K results without the need of running a KNN query.
> * Use only the second pass score as the final score
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