[jira] [Commented] (LUCENE-9614) Implement KNN Query

["ASF subversion and git services (Jira)" <ji...@apache.org>]

    [ https://issues.apache.org/jira/browse/LUCENE-9614?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=17509091#comment-17509091 ] 

ASF subversion and git services commented on LUCENE-9614:
---------------------------------------------------------

Commit 18f9d31608654f88b2f048c4fd6fbdfedb3b6f95 in lucene's branch refs/heads/main from Julie Tibshirani
[ https://gitbox.apache.org/repos/asf?p=lucene.git;h=18f9d31 ]

LUCENE-9614: Fix rare TestKnnVectorQuery failures

Some of our checks relied on doc IDs corresponding to the order in which docs
were passed to IndexWriter. This is fragile and sometimes resulted in failures.
Now we check against an "id" field instead.


> 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: 5h 20m
>  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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