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https://issues.apache.org/jira/browse/SOLR-16675?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
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Alessandro Benedetti resolved SOLR-16675.
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Resolution: Done
> 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
> Priority: Blocker
> 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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