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https://issues.apache.org/jira/browse/SOLR-18424?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=18112983#comment-18112983
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Kevin Liang commented on SOLR-18424:
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Part of acceptance criteria is that we should have some benchmark of the
latency of proposed solution vs. the generic rerank method
> Add native rerank for dense vectors
> -----------------------------------
>
> Key: SOLR-18424
> URL: https://issues.apache.org/jira/browse/SOLR-18424
> Project: Solr
> Issue Type: Improvement
> Reporter: Kevin Liang
> Priority: Major
>
> Solr 10 has introduced support for binary quantized dense vectors
> (SOLR-17781), however binary quantized vectors by themselves have a pretty
> noticeable recall hit (only achieving ~60% recall compared to non-quantized
> KNN by some benchmarks). The solution is to pair binary quantization with
> oversampling + rerank phase. While it is possible to do this currently with
> Solr's generic rerank function through something like:
> {code:java}
> q={!knn f=vector_quantized topK=100}[...]&rq={!rerank reRankDocs=100
> reRankQuery=$rrq reRankWeight=1.0
> reRankOperator=replace}&rrq={!func}vectorSimilarity(FLOAT32, DOT_PRODUCT,
> vector_quantized, [...])&rows=10{code}
> this is verbose, cumbersome, and easy to get subtley wrong behavior. Propose
> that we support reranking directly in the knn query parser via an
> "oversample" factor param such as
> {code:java}
> q={!knn f=vector_quantized topK=10 oversample=10}[0.12, 0.34, ...]{code}
> "oversample" here meaning retrieve n * k documents, rerank with raw vectors,
> and return top k.
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