github-mirrors/elasticsearch
1
0
Fork 0
mirror of https://github.com/elastic/elasticsearch.git synced 2025-06-28 09:28:55 -04:00
Code Issues Projects Releases Packages Wiki Activity

fix formatting errors (#116843)

Browse source
This commit is contained in:
shainaraskas 2024-11-14 15:45:16 -05:00 • committed by GitHub
parent 04a804461e
commit 2d2ad00872
No known key found for this signature in database
GPG key ID: B5690EEEBB952194
1 changed files with 6 additions and 9 deletions
Download patch file Download diff file Expand all files Collapse all files

15
docs/reference/mapping/types/dense-vector.asciidoc
View file

@ -117,12 +117,10 @@ that sacrifices result accuracy for improved speed.
The `dense_vector` type supports quantization to reduce the memory footprint required when <<approximate-knn, searching>> `float` vectors.
The three following quantization strategies are supported:
+
--
`int8` - Quantizes each dimension of the vector to 1-byte integers. This reduces the memory footprint by 75% (or 4x) at the cost of some accuracy.
`int4` - Quantizes each dimension of the vector to half-byte integers. This reduces the memory footprint by 87% (or 8x) at the cost of accuracy.
`bbq` - experimental:[] Better binary quantization which reduces each dimension to a single bit precision. This reduces the memory footprint by 96% (or 32x) at a larger cost of accuracy. Generally, oversampling during query time and reranking can help mitigate the accuracy loss.
--
* `int8` - Quantizes each dimension of the vector to 1-byte integers. This reduces the memory footprint by 75% (or 4x) at the cost of some accuracy.
* `int4` - Quantizes each dimension of the vector to half-byte integers. This reduces the memory footprint by 87% (or 8x) at the cost of accuracy.
* `bbq` - experimental:[] Better binary quantization which reduces each dimension to a single bit precision. This reduces the memory footprint by 96% (or 32x) at a larger cost of accuracy. Generally, oversampling during query time and reranking can help mitigate the accuracy loss.
When using a quantized format, you may want to oversample and rescore the results to improve accuracy. See <<dense-vector-knn-search-reranking, oversampling and rescoring>> for more information.
@ -245,12 +243,11 @@ their vector field's similarity to the query vector. The `_score` of each
document will be derived from the similarity, in a way that ensures scores are
positive and that a larger score corresponds to a higher ranking.
Defaults to `l2_norm` when `element_type: bit` otherwise defaults to `cosine`.
NOTE: `bit` vectors only support `l2_norm` as their similarity metric.
+
^*^ This parameter can only be specified when `index` is `true`.
+
NOTE: `bit` vectors only support `l2_norm` as their similarity metric.
.Valid values for `similarity`
[%collapsible%open]
====