Adding Support to Enable/Disble Share level Rescoring and Update Oversampling Factor #2172
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Vikasht34
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…sampling Factor Signed-off-by: VIKASH TIWARI <[email protected]>
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| @@ -88,6 +88,7 @@ public class KNNSettings { | |||
| public static final String QUANTIZATION_STATE_CACHE_SIZE_LIMIT = "knn.quantization.cache.size.limit"; | |||
| public static final String QUANTIZATION_STATE_CACHE_EXPIRY_TIME_MINUTES = "knn.quantization.cache.expiry.minutes"; | |||
| public static final String KNN_FAISS_AVX512_DISABLED = "knn.faiss.avx512.disabled"; | |||
| public static final String KNN_DISK_VECTOR_SHARD_LEVEL_RESCORING_DISABLED = "index.knn.disk.vector.shard_level_rescoring_disabled"; | |||
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nit: A disabled suffix might cause confusion here if we ever expose this to customers. consider index.knn.shard_level_rescoring and the default can be false
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…sampling Factor (#2172) Signed-off-by: VIKASH TIWARI <[email protected]> (cherry picked from commit 07f4df2)
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…sampling Factor (#2172) Signed-off-by: VIKASH TIWARI <[email protected]> Signed-off-by: John Mazanec <[email protected]>
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…sampling Factor (#2172) (#2173) 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
Issue:- During rescoring at the shard level in a multi-segment case, the recall of the search results drops to zero.
Fix :- To address the issue of reduced recall during shard-level rescoring in multi-segment cases, we are introducing an index-level setting that allows users to enable or disable shard-level rescoring. This setting gives finer control over whether rescoring should be performed at the shard level or if the system should rely on a more global rescoring process across all segments. By disabling shard-level rescoring, the recall issue is mitigated, as the rescoring process will take into account the complete set of results from all segments, rather than focusing on top k results.
Oversampling Update :- With this change we are updating default oversampling factor as well. For dimensions less than 768, an oversample factor of 3.0x is applied to ensure higher recall for smaller vector spaces. For dimensions between 768 and 1000, the oversampling factor is set to 2.0x to balance recall and performance. For dimensions above 1000, no oversampling (1.0x) is applied.
Related Issues
Resolves #[Issue number to be closed when this PR is merged]
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