Score Fix for Binary Quantized Vector and Setting Default value in case of shard level rescoring is disabled for oversampling factor #2183
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src/main/java/org/opensearch/knn/index/query/parser/RescoreParser.java
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…se of shard level rescoring is disabled for oversampling factor Signed-off-by: VIKASH TIWARI <[email protected]>
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…se of shard level rescoring is disabled for oversampling factor (#2183) Signed-off-by: VIKASH TIWARI <[email protected]> (cherry picked from commit 01d7981)
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…se of shard level rescoring is disabled for oversampling factor (#2183) Signed-off-by: VIKASH TIWARI <[email protected]> (cherry picked from commit 01d7981) Signed-off-by: John Mazanec <[email protected]>
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…ult value in case of shard level rescoring is disabled for oversampling factor (#2187) * Score Fix for Binary Quantized Vector and Setting Default value in case of shard level rescoring is disabled for oversampling factor (#2183) Signed-off-by: VIKASH TIWARI <[email protected]> (cherry picked from commit 01d7981) Signed-off-by: John Mazanec <[email protected]> * Java Docs Fix for 2.x in Linux Machine (#2191) Signed-off-by: VIKASH TIWARI <[email protected]> Signed-off-by: John Mazanec <[email protected]> --------- Signed-off-by: VIKASH TIWARI <[email protected]> Signed-off-by: John Mazanec <[email protected]> Co-authored-by: Vikasht34 <[email protected]>
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Description
Change1
Problem
In Disk Vector Mode, while calculating the score for a binary quantized vector, the system was incorrectly using Inner Product as the similarity function. However, for binary quantized vectors, the correct distance metric should be Hamming Distance.
Impact:
This incorrect scoring mechanism led to poor results, particularly in cases with multiple segments. During the process of reducing to the top k candidates, the best candidates (those with the most relevant results) were being filtered out. As a result, the recall was zero, meaning no relevant results were being returned, which critically affected the accuracy of search results.
Expected Behavior:
The system should use Hamming Distance for binary quantized vectors, ensuring that the correct similarity metric is applied. This would allow the best candidates to be retained during the top-k reduction process, thereby maintaining high recall and improving overall search performance in multi-segment scenarios.
Change 2:
Problem:
The system already had a setting to disable shard-level rescoring, allowing for rescoring at the segment level. However, with a recent PR, a default oversampling factor was reintroduced to be applied when the user did not explicitly provide one. Unfortunately, this default oversampling factor was being applied in both cases — regardless of whether shard-level rescoring was enabled or disabled. With Segment level , we don't need high oversampling factor.
Solution:
This PR introduces changes to ensure that the default oversampling factor is only applied when shard-level rescoring is disabled. When segment-level rescoring is enabled, the system overrides the default oversampling factor, ensuring the correct sampling factor is applied based on dimesion.
Related Issues
Resolves #[Issue number to be closed when this PR is merged]
Check List
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