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https://issues.apache.org/jira/browse/LUCENE-9614?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=17398760#comment-17398760
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ASF subversion and git services commented on LUCENE-9614:
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Commit 624560a3d7060a1ae9f9b647b229590f28c4aa24 in lucene's branch 
refs/heads/main from Michael Sokolov
[ https://gitbox.apache.org/repos/asf?p=lucene.git;h=624560a ]

LUCENE-9614: add KnnVectorQuery implementation



> Implement KNN Query
> -------------------
>
>                 Key: LUCENE-9614
>                 URL: https://issues.apache.org/jira/browse/LUCENE-9614
>             Project: Lucene - Core
>          Issue Type: New Feature
>            Reporter: Michael Sokolov
>            Priority: Major
>          Time Spent: 2h 50m
>  Remaining Estimate: 0h
>
> Now we have a vector index format, and one vector indexing/KNN search 
> implementation, but the interface is low-level: you can search across a 
> single segment only. We would like to expose a Query implementation. 
> Initially, we want to support a usage where the KnnVectorQuery selects the 
> k-nearest neighbors without regard to any other constraints, and these can 
> then be filtered as part of an enclosing Boolean or other query.
> Later we will want to explore some kind of filtering *while* performing 
> vector search, or a re-entrant search process that can yield further results. 
> Because of the nature of knn search (all documents having any vector value 
> match), it is more like a ranking than a filtering operation, and it doesn't 
> really make sense to provide an iterator interface that can be merged in the 
> usual way, in docid order, skipping ahead. It's not yet clear how to satisfy 
> a query that is "k nearest neighbors satsifying some arbitrary Query", at 
> least not without realizing a complete bitset for the Query. But this is for 
> a later issue; *this* issue is just about performing the knn search in 
> isolation, computing a set of (some given) K nearest neighbors, and providing 
> an iterator over those.



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