Merge branch 'main' into feature/multi-project/geoip-yaml-tests

Author: samxbr

docs/reference/elasticsearch/configuration-reference/security-settings.md

@@ -1933,7 +1933,7 @@ You can configure the following TLS/SSL settings.
 `xpack.security.transport.ssl.trust_restrictions.x509_fields` ![logo cloud](https://doc-icons.s3.us-east-2.amazonaws.com/logo_cloud.svg "Supported on Elastic Cloud Hosted")
 :   Specifies which field(s) from the TLS certificate is used to match for the restricted trust management that is used for remote clusters connections. This should only be set when a self managed cluster can not create certificates that follow the Elastic Cloud pattern. The default value is ["subjectAltName.otherName.commonName"], the Elastic Cloud pattern. "subjectAltName.dnsName" is also supported and can be configured in addition to or in replacement of the default.
 
-`xpack.security.transport.ssl.handshake_timeout`
+`xpack.security.transport.ssl.handshake_timeout` {applies_to}`stack: ga 9.2`
 :   Specifies the timeout for a TLS handshake when opening a transport connection. Defaults to `10s`.
 
 ### Transport TLS/SSL key and trusted certificate settings [security-transport-tls-ssl-key-trusted-certificate-settings]
@@ -2133,7 +2133,7 @@ You can configure the following TLS/SSL settings.
 
     For more information, see Oracle’s [Java Cryptography Architecture documentation](https://docs.oracle.com/en/java/javase/11/security/java-cryptography-architecture-jca-reference-guide.html).
 
-`xpack.security.remote_cluster_server.ssl.handshake_timeout`
+`xpack.security.remote_cluster_server.ssl.handshake_timeout` {applies_to}`stack: ga 9.2`
 :   Specifies the timeout for a TLS handshake when handling an inbound remote-cluster connection. Defaults to `10s`.
 
 
@@ -2265,7 +2265,7 @@ You can configure the following TLS/SSL settings.
 
     For more information, see Oracle’s [Java Cryptography Architecture documentation](https://docs.oracle.com/en/java/javase/11/security/java-cryptography-architecture-jca-reference-guide.html).
 
-`xpack.security.remote_cluster_client.ssl.handshake_timeout`
+`xpack.security.remote_cluster_client.ssl.handshake_timeout` {applies_to}`stack: ga 9.2`
 :   Specifies the timeout for a TLS handshake when opening a remote-cluster connection. Defaults to `10s`.
 
 

docs/reference/elasticsearch/index-settings/index-modules.md

@@ -256,8 +256,8 @@ $$$index-final-pipeline$$$
 $$$index-hidden$$$ `index.hidden` {applies_to}`serverless: all`
 :   Indicates whether the index should be hidden by default. Hidden indices are not returned by default when using a wildcard expression. This behavior is controlled per request through the use of the `expand_wildcards` parameter. Possible values are `true` and `false` (default).
 
-$$$index-dense-vector-hnsw-filter-heuristic$$$ `index.dense_vector.hnsw_filter_heuristic` {applies_to}`serverless: all`
-:   The heuristic to utilize when executing a filtered search against vectors in an HNSW graph. This setting is in technical preview may be changed or removed in a future release. It can be set to:
+$$$index-dense-vector-hnsw-filter-heuristic$$$ `index.dense_vector.hnsw_filter_heuristic` {applies_to}`serverless: preview` {applies_to}`stack: preview 9.1`
+:   The heuristic to utilize when executing a filtered search against vectors in an HNSW graph. It can be set to:
 
 * `acorn` (default) - Only vectors that match the filter criteria are searched. This is the fastest option, and generally provides faster searches at similar recall to `fanout`, but `num_candidates` might need to be increased for exceptionally high recall requirements.
 * `fanout` - All vectors are compared with the query vector, but only those passing the criteria are added to the search results. Can be slower than `acorn`, but may yield higher recall.

docs/reference/elasticsearch/jvm-settings.md

@@ -139,9 +139,25 @@ If you are running {{es}} as a Windows service, you can change the heap size usi
 
 ## JVM heap dump path setting [heap-dump-path-setting]
 
-By default, {{es}} configures the JVM to dump the heap on out of memory exceptions to the default logs directory. On [RPM](docs-content://deploy-manage/deploy/self-managed/install-elasticsearch-with-rpm.md) and [Debian](docs-content://deploy-manage/deploy/self-managed/install-elasticsearch-with-debian-package.md) packages, the logs directory is `/var/log/elasticsearch`. On [Linux and MacOS](docs-content://deploy-manage/deploy/self-managed/install-elasticsearch-from-archive-on-linux-macos.md) and [Windows](docs-content://deploy-manage/deploy/self-managed/install-elasticsearch-with-zip-on-windows.md) distributions, the `logs` directory is located under the root of the {{es}} installation.
+Depending on your stack version, {{es}} configures the JVM to dump the heap on out of memory exceptions to the following location by default:
+
+* {applies_to}`stack: ga 9.1` The default logs directory 
+* {applies_to}`stack: ga 9.0` The default data directory
+
+Directory location:
+
+::::{tab-set}
+:::{tab-item} Logs directory
+* [RPM](docs-content://deploy-manage/deploy/self-managed/install-elasticsearch-with-rpm.md) and [Debian](docs-content://deploy-manage/deploy/self-managed/install-elasticsearch-with-debian-package.md) packages: `/var/log/elasticsearch`
+* [Linux and MacOS](docs-content://deploy-manage/deploy/self-managed/install-elasticsearch-from-archive-on-linux-macos.md) and [Windows](docs-content://deploy-manage/deploy/self-managed/install-elasticsearch-with-zip-on-windows.md) distributions: The `logs` directory at the root of the {{es}} installation
+:::
+:::{tab-item} Data directory
+* [RPM](docs-content://deploy-manage/deploy/self-managed/install-elasticsearch-with-rpm.md) and [Debian](docs-content://deploy-manage/deploy/self-managed/install-elasticsearch-with-debian-package.md) packages: `/var/lib/elasticsearch`
+* [Linux and MacOS](docs-content://deploy-manage/deploy/self-managed/install-elasticsearch-from-archive-on-linux-macos.md) and [Windows](docs-content://deploy-manage/deploy/self-managed/install-elasticsearch-with-zip-on-windows.md) distributions: The `data` directory at the root of the {{es}} installation
+:::
+::::
 
-If this path is not suitable for receiving heap dumps, add the `-XX:HeapDumpPath=...` entry in [`jvm.options`](#set-jvm-options):
+If this path is not suitable for receiving heap dumps, modify or add the `-XX:HeapDumpPath=...` entry in [`jvm.options`](#set-jvm-options):
 
 * If you specify a directory, the JVM will generate a filename for the heap dump based on the PID of the running instance.
 * If you specify a fixed filename instead of a directory, the file must not exist when the JVM needs to perform a heap dump on an out of memory exception. Otherwise, the heap dump will fail.

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

@@ -55,7 +55,14 @@ In many cases, a brute-force kNN search is not efficient enough. For this reason
 
 Unmapped array fields of float elements with size between 128 and 4096 are dynamically mapped as `dense_vector` with a default similariy of `cosine`. You can override the default similarity by explicitly mapping the field as `dense_vector` with the desired similarity.
 
-Indexing is enabled by default for dense vector fields and indexed as `bbq_hnsw` if dimensions are greater than or equal to 384, otherwise they are indexed as `int8_hnsw`. When indexing is enabled, you can define the vector similarity to use in kNN search:
+Indexing is enabled by default for dense vector fields and indexed as `bbq_hnsw` if dimensions are greater than or equal to 384, otherwise they are indexed as `int8_hnsw`. {applies_to}`stack: ga 9.1`
+
+:::{note}
+In {{stack}} 9.0, dense vector fields are always indexed as `int8_hnsw`.
+:::
+
+
+When indexing is enabled, you can define the vector similarity to use in kNN search:
 
 ```console
 PUT my-index-2
@@ -107,6 +114,10 @@ When using a quantized format, you may want to oversample and rescore the result
 
 To use a quantized index, you can set your index type to `int8_hnsw`, `int4_hnsw`, or `bbq_hnsw`. When indexing `float` vectors, the current default index type is `bbq_hnsw` for vectors with greater than or equal to 384 dimensions, otherwise it's `int8_hnsw`.
 
+:::{note}
+In {{stack}} 9.0, dense vector fields are always indexed as `int8_hnsw`.
+:::
+
 Quantized vectors can use [oversampling and rescoring](docs-content://solutions/search/vector/knn.md#dense-vector-knn-search-rescoring) to improve accuracy on approximate kNN search results.
 
 ::::{note}
@@ -255,9 +266,16 @@ $$$dense-vector-index-options$$$
 `type`
 :   (Required, string) The type of kNN algorithm to use. Can be either any of:
     * `hnsw` - This utilizes the [HNSW algorithm](https://arxiv.org/abs/1603.09320) for scalable approximate kNN search. This supports all `element_type` values.
-    * `int8_hnsw` - The default index type for float vectors with less than 384 dimensions. This utilizes the [HNSW algorithm](https://arxiv.org/abs/1603.09320) in addition to automatically scalar quantization for scalable approximate kNN search with `element_type` of `float`. This can reduce the memory footprint by 4x at the cost of some accuracy. See [Automatically quantize vectors for kNN search](#dense-vector-quantization).
+    * `int8_hnsw` - The default index type for some float vectors: 
+        
+      * {applies_to}`stack: ga 9.1` Default for float vectors with less than 384 dimensions. 
+      * {applies_to}`stack: ga 9.0` Default for float all vectors.
+      
+      This utilizes the [HNSW algorithm](https://arxiv.org/abs/1603.09320) in addition to automatically scalar quantization for scalable approximate kNN search with `element_type` of `float`. This can reduce the memory footprint by 4x at the cost of some accuracy. See [Automatically quantize vectors for kNN search](#dense-vector-quantization).
     * `int4_hnsw` - This utilizes the [HNSW algorithm](https://arxiv.org/abs/1603.09320) in addition to automatically scalar quantization for scalable approximate kNN search with `element_type` of `float`. This can reduce the memory footprint by 8x at the cost of some accuracy. See [Automatically quantize vectors for kNN search](#dense-vector-quantization).
-    * `bbq_hnsw` - The default index type for float vectors with greater than or equal to 384 dimensions. This utilizes the [HNSW algorithm](https://arxiv.org/abs/1603.09320) in addition to automatically binary quantization for scalable approximate kNN search with `element_type` of `float`. This can reduce the memory footprint by 32x at the cost of accuracy. See [Automatically quantize vectors for kNN search](#dense-vector-quantization).
+    * `bbq_hnsw` - This utilizes the [HNSW algorithm](https://arxiv.org/abs/1603.09320) in addition to automatically binary quantization for scalable approximate kNN search with `element_type` of `float`. This can reduce the memory footprint by 32x at the cost of accuracy. See [Automatically quantize vectors for kNN search](#dense-vector-quantization).
+        
+      {applies_to}`stack: ga 9.1` `bbq_hnsw` is the default index type for float vectors with greater than or equal to 384 dimensions. 
     * `flat` - This utilizes a brute-force search algorithm for exact kNN search. This supports all `element_type` values.
     * `int8_flat` - This utilizes a brute-force search algorithm in addition to automatically scalar quantization. Only supports `element_type` of `float`.
     * `int4_flat` - This utilizes a brute-force search algorithm in addition to automatically half-byte scalar quantization. Only supports `element_type` of `float`.
@@ -273,11 +291,14 @@ $$$dense-vector-index-options$$$
 :   (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`
+`rescore_vector` {applies_to}`stack: preview 9.0, ga 9.1`
 :   (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.
+:   (required, float) The amount to oversample the search results by. This value should be one of the following: 
+    * Greater than `1.0` and less than `10.0` 
+    * Exactly `0` to indicate no oversampling and rescoring should occur {applies_to}`stack: ga 9.1`
+    :   The higher the value, the more vectors will be gathered and rescored with the raw values per shard.
     :   In case a knn query specifies a `rescore_vector` parameter, the query `rescore_vector` parameter will be used instead.
     :   See [oversampling and rescoring quantized vectors](docs-content://solutions/search/vector/knn.md#dense-vector-knn-search-rescoring) for details.
 :::::

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

@@ -60,7 +60,7 @@ A kNN retriever returns top documents from a [k-nearest neighbor search (kNN)](d
     Read more here: [knn similarity search](docs-content://solutions/search/vector/knn.md#knn-similarity-search)
 
 
-`rescore_vector`
+`rescore_vector` {applies_to}`stack: preview 9.0, ga 9.1`
 :   (Optional, object) Apply oversampling and rescoring to quantized vectors.
 
 ::::{note}

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

@@ -97,7 +97,7 @@ The filter is a pre-filter, meaning that it is applied **during** the approximat
 :   (Optional, float) The minimum similarity required for a document to be considered a match. The similarity value calculated relates to the raw [`similarity`](/reference/elasticsearch/mapping-reference/dense-vector.md#dense-vector-similarity) used. Not the document score. The matched documents are then scored according to [`similarity`](/reference/elasticsearch/mapping-reference/dense-vector.md#dense-vector-similarity) and the provided `boost` is applied.
 
 
-`rescore_vector`
+`rescore_vector` {applies_to}`stack: preview 9.0, ga 9.1`
 :   (Optional, object) Apply oversampling and rescoring to quantized vectors.
 
 ::::{note}
@@ -113,7 +113,9 @@ Rescoring only makes sense for quantized vectors; when [quantization](/reference
     * Retrieve `num_candidates` candidates per shard.
     * From these candidates, the top `k * oversample` candidates per shard will be rescored using the original vectors.
     * The top `k` rescored candidates will be returned.
-    Must be >= 1f to indicate oversample factor, or exactly `0` to indicate that no oversampling and rescoring should occur.
+    Must be one of the following values: 
+      * \>= 1f to indicate the oversample factor
+      * Exactly `0` to indicate that no oversampling and rescoring should occur. {applies_to}`stack: ga 9.1`
 
 
 See [oversampling and rescoring quantized vectors](docs-content://solutions/search/vector/knn.md#dense-vector-knn-search-rescoring) for details.