Merge branch 'main' into 7507-collapse-search-results

.github/workflows/pr_checklist.yml

@@ -1,7 +1,7 @@
 name: PR Checklist
 
 on:
-  pull_request:
+  pull_request_target:
     types: [opened]
 
 permissions:

_about/version-history.md

@@ -30,6 +30,7 @@ OpenSearch version | Release highlights | Release date
 [2.0.1](https://github.com/opensearch-project/opensearch-build/blob/main/release-notes/opensearch-release-notes-2.0.1.md) | Includes bug fixes and maintenance updates for Alerting and Anomaly Detection. | 16 June 2022
 [2.0.0](https://github.com/opensearch-project/opensearch-build/blob/main/release-notes/opensearch-release-notes-2.0.0.md) | Includes document-level monitors for alerting, OpenSearch Notifications plugins, and Geo Map Tiles in OpenSearch Dashboards. Also adds support for Lucene 9 and bug fixes for all OpenSearch plugins. For a full list of release highlights, see the Release Notes. | 26 May 2022
 [2.0.0-rc1](https://github.com/opensearch-project/opensearch-build/blob/main/release-notes/opensearch-release-notes-2.0.0-rc1.md) | The Release Candidate for 2.0.0. This version allows you to preview the upcoming 2.0.0 release before the GA release. The preview release adds document-level alerting, support for Lucene 9, and the ability to use term lookup queries in document level security. | 03 May 2022
+[1.3.18](https://github.com/opensearch-project/opensearch-build/blob/main/release-notes/opensearch-release-notes-1.3.18.md) | Includes maintenance updates for OpenSearch security. | 16 July 2024
 [1.3.17](https://github.com/opensearch-project/opensearch-build/blob/main/release-notes/opensearch-release-notes-1.3.17.md) | Includes maintenance updates for OpenSearch security and OpenSearch Dashboards security. | 06 June 2024
 [1.3.16](https://github.com/opensearch-project/opensearch-build/blob/main/release-notes/opensearch-release-notes-1.3.16.md) | Includes bug fixes and maintenance updates for OpenSearch security, index management, performance analyzer, and reporting. | 23 April 2024
 [1.3.15](https://github.com/opensearch-project/opensearch-build/blob/main/release-notes/opensearch-release-notes-1.3.15.md) | Includes bug fixes and maintenance updates for cross-cluster replication, SQL, OpenSearch Dashboards reporting, and alerting.  | 05 March 2024

_api-reference/nodes-apis/nodes-stats.md

@@ -44,7 +44,7 @@ thread_pool | Statistics about each thread pool for the node.
 fs | File system statistics, such as read/write statistics, data path, and free disk space.
 transport | Transport layer statistics about send/receive in cluster communication.
 http | Statistics about the HTTP layer.
-breaker | Statistics about the field data circuit breakers.
+breakers | Statistics about the field data circuit breakers.
 script | Statistics about scripts, such as compilations and cache evictions. 
 discovery | Statistics about cluster states.
 ingest | Statistics about ingest pipelines.

_search-plugins/neural-sparse-search.md

@@ -16,8 +16,8 @@ Introduced 2.11
 
 When selecting a model, choose one of the following options:
 
-- Use a sparse encoding model at both ingestion time and search time (high performance, relatively high latency).
-- Use a sparse encoding model at ingestion time and a tokenizer at search time for relatively low performance and low latency. The tokenism doesn't conduct model inference, so you can deploy and invoke a tokenizer using the ML Commons Model API for a more consistent experience.
+- Use a sparse encoding model at both ingestion time and search time for better search relevance at the expense of relatively high latency.
+- Use a sparse encoding model at ingestion time and a tokenizer at search time for lower search latency at the expense of relatively lower search relevance. Tokenization doesn't involve model inference, so you can deploy and invoke a tokenizer using the ML Commons Model API for a more streamlined experience.
 
 **PREREQUISITE**<br>
 Before using neural sparse search, make sure to set up a [pretrained sparse embedding model]({{site.url}}{{site.baseurl}}/ml-commons-plugin/pretrained-models/#sparse-encoding-models) or your own sparse embedding model. For more information, see [Choosing a model]({{site.url}}{{site.baseurl}}/ml-commons-plugin/integrating-ml-models/#choosing-a-model).