You are viewing a plain text version of this content. The canonical link for it is here.
Posted to issues@solr.apache.org by "Houston Putman (Jira)" <ji...@apache.org> on 2023/07/07 17:22:00 UTC
[jira] [Updated] (SOLR-16675) Introduce the possibility to rerank topK results with vector similarity functions using DenseVectorField
[ https://issues.apache.org/jira/browse/SOLR-16675?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel ]
Houston Putman updated SOLR-16675:
----------------------------------
Fix Version/s: 9.3
> 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: 10m
> 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
--
This message was sent by Atlassian Jira
(v8.20.10#820010)
---------------------------------------------------------------------
To unsubscribe, e-mail: issues-unsubscribe@solr.apache.org
For additional commands, e-mail: issues-help@solr.apache.org