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6 changes: 5 additions & 1 deletion solutions/search/vector/knn.md
Original file line number Diff line number Diff line change
Expand Up @@ -918,7 +918,11 @@ All forms of quantization will result in some accuracy loss and as the quantizat
* `int4` requires some rescoring for higher accuracy and larger recall scenarios. Generally, oversampling by 1.5x-2x recovers most of the accuracy loss.
* `bbq` requires rescoring except on exceptionally large indices or models specifically designed for quantization. We have found that between 3x-5x oversampling is generally sufficient. But for fewer dimensions or vectors that do not quantize well, higher oversampling may be required.

You can use the `rescore_vector` [preview] option to automatically perform reranking. When a rescore `oversample` parameter is specified, the approximate kNN search will:
#### The `rescore_vector` option
```{applies_to}
stack: preview 9.0, ga 9.1
```
You can use the `rescore_vector` option to automatically perform reranking. When a rescore `oversample` parameter is specified, the approximate kNN search will:

* Retrieve `num_candidates` candidates per shard.
* From these candidates, the top `k * oversample` candidates per shard will be rescored using the original vectors.
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