[jira] [Created] (SOLR-16675) Introduce the possibility to rerank topK results with vector similarity functions using DenseVectorField

["Elia Porciani (Jira)" <ji...@apache.org>]

Elia Porciani created SOLR-16675:
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             Summary: 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
      Security Level: Public (Default Security Level. Issues are Public)
            Reporter: Elia Porciani


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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