Mark rescore_vector as generally available (#126038)

* Mark rescore_vector as generally available

* Update docs/changelog/126038.yaml

docs/changelog/126038.yaml

@@ -0,0 +1,5 @@
+pr: 126038
+summary: Mark `rescore_vector` as generally available
+area: Vector Search
+type: enhancement
+issues: []

docs/reference/elasticsearch/mapping-reference/dense-vector.md

@@ -288,7 +288,7 @@ $$$dense-vector-index-options$$$
     :   (Optional, float) Only applicable to `int8_hnsw`, `int4_hnsw`, `int8_flat`, and `int4_flat` index types. The confidence interval to use when quantizing the vectors. Can be any value between and including `0.90` and `1.0` or exactly `0`. When the value is `0`, this indicates that dynamic quantiles should be calculated for optimized quantization. When between `0.90` and `1.0`, this value restricts the values used when calculating the quantization thresholds. For example, a value of `0.95` will only use the middle 95% of the values when calculating the quantization thresholds (e.g. the highest and lowest 2.5% of values will be ignored). Defaults to `1/(dims + 1)` for `int8` quantized vectors and `0` for `int4` for dynamic quantile calculation.
 
     `rescore_vector`
-    :   (Optional, object) Functionality in [preview]. An optional section that configures automatic vector rescoring on knn queries for the given field. Only applicable to quantized index types.
+    :   (Optional, object) An optional section that configures automatic vector rescoring on knn queries for the given field. Only applicable to quantized index types.
     :::::{dropdown} Properties of `rescore_vector`
     `oversample`
     :   (required, float) The amount to oversample the search results by. This value should be greater than `1.0` and less than `10.0` or exactly `0` to indicate no oversampling & rescoring should occur. The higher the value, the more vectors will be gathered and rescored with the raw values per shard.

docs/reference/elasticsearch/rest-apis/retrievers.md

@@ -198,7 +198,7 @@ A kNN retriever returns top documents from a [k-nearest neighbor search (kNN)](d
 
 
 `rescore_vector`
-:   (Optional, object) Functionality in [preview]. Apply oversampling and rescoring to quantized vectors.
+:   (Optional, object) Apply oversampling and rescoring to quantized vectors.
 
 ::::{note}
 Rescoring only makes sense for quantized vectors; when [quantization](/reference/elasticsearch/mapping-reference/dense-vector.md#dense-vector-quantization) is not used, the original vectors are used for scoring. Rescore option will be ignored for non-quantized `dense_vector` fields.

docs/reference/query-languages/query-dsl/query-dsl-knn-query.md

@@ -98,7 +98,7 @@ The filter is a pre-filter, meaning that it is applied **during** the approximat
 
 
 `rescore_vector`
-:   (Optional, object) Functionality in [preview]. Apply oversampling and rescoring to quantized vectors.
+:   (Optional, object) Apply oversampling and rescoring to quantized vectors.
 
 ::::{note}
 Rescoring only makes sense for quantized vectors; when [quantization](/reference/elasticsearch/mapping-reference/dense-vector.md#dense-vector-quantization) is not used, the original vectors are used for scoring. Rescore option will be ignored for non-quantized `dense_vector` fields.