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https://issues.apache.org/jira/browse/LUCENE-9614?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=17404009#comment-17404009
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Mayya Sharipova commented on LUCENE-9614:
-----------------------------------------
This test reproducibly fails on `main` branch:
./gradlew test --tests TestKnnVectorQuery.testRandom
-Dtests.seed=3B6CE0E105431E18 -Dtests.slow=true -Dtests.badapples=true
-Dtests.locale=en-NF -Dtests.timezone=Europe/Andorra -Dtests.asserts=true
-Dtests.file.encoding=UTF-8
java.lang.AssertionError: expected:<100> but was:<94>
...
at
org.apache.lucene.search.TestKnnVectorQuery.testRandom(TestKnnVectorQuery.java:298)
> 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: 3h 40m
> 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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