mark vector indexes as GA

src/current/v25.4/cockroachdb-feature-availability.md

@@ -83,10 +83,6 @@ The following cluster settings enable the [`database` and `application_name` lab
 
 By default, these cluster settings are disabled.
 
-### Vector indexes
-
-A [vector index]({% link {{ page.version.version }}/vector-indexes.md %}) enables efficient approximate nearest neighbor (ANN) search on high-dimensional [`VECTOR`]({% link {{ page.version.version }}/vector.md %}) columns. Use vector indexes to improve the performance of similarity searches over large datasets, such as embeddings generated by machine learning models.
-
 ### JSONPath queries
 
 [JSONPath]({% link {{ page.version.version }}/jsonpath.md %}) expressions and functions can be used to query and filter [`JSONB`]({% link {{ page.version.version }}/jsonb.md %}) data, using [JSONPath expressions]({% link {{ page.version.version }}/jsonpath.md %}#jsonpath-expression) as arguments in [JSONPath functions]({% link {{ page.version.version }}/jsonpath.md %}#jsonpath-functions).
@@ -111,10 +107,6 @@ The [cluster setting]({% link {{ page.version.version }}/cluster-settings.md %})
 
 CockroachDB {{ site.data.products.standard }} is our new, [enterprise-ready plan](https://www.cockroachlabs.com/pricing), recommended for most applications. You can start small with [provisioned capacity that can scale on demand]({% link cockroachcloud/plan-your-cluster.md %}), along with enterprise-level security and availability. Compute for CockroachDB {{ site.data.products.standard }} is pre-provisioned and storage is usage-based. You can easily switch a CockroachDB {{ site.data.products.basic }} cluster to CockroachDB {{ site.data.products.standard }} in place.
 
-### Vector search
-
-The [`VECTOR`]({% link {{ page.version.version }}/vector.md %}) data type stores fixed-length arrays of floating-point numbers, which represent data points in multi-dimensional space. Vector search is often used in AI applications such as Large Language Models (LLMs) that rely on vector representations.
-
 ### CockroachDB Cloud Folders
 
 [Organizing CockroachDB {{ site.data.products.cloud }} clusters using folders]({% link cockroachcloud/folders.md %}) is in preview. Folders allow you to organize and manage access to your clusters according to your organization's requirements. For example, you can create top-level folders for each business unit in your organization, and within those folders, organize clusters by geographic location and then by  level of maturity, such as production, staging, and testing.

src/current/v25.4/vector-indexes.md

@@ -6,10 +6,6 @@ keywords: vector, vector index, vector search, ann, approximate nearest neighbor
 docs_area: develop
 ---
 
-{{site.data.alerts.callout_info}}
-{% include_cached feature-phases/preview.md %}
-{{site.data.alerts.end}}
-
 A *vector index* enables efficient approximate nearest neighbor (ANN) search on high-dimensional [`VECTOR`]({% link {{ page.version.version }}/vector.md %}) columns. Use vector indexes to improve the performance of similarity searches over large datasets, such as embeddings generated by machine learning models.
 
 This page describes how to create and use vector indexes on CockroachDB.

src/current/v25.4/vector.md

@@ -5,10 +5,6 @@ toc: true
 docs_area: reference.sql
 ---
 
-{{site.data.alerts.callout_info}}
-{% include feature-phases/preview.md %}
-{{site.data.alerts.end}}
-
 The `VECTOR` data type stores fixed-length arrays of floating-point numbers, which represent data points in multi-dimensional space. Vector search is often used in AI applications such as Large Language Models (LLMs) that rely on vector representations. 
 
 For details on valid `VECTOR` comparison operators, refer to [Syntax](#syntax). For the list of supported `VECTOR` functions, refer to [Functions and Operators]({% link {{ page.version.version }}/functions-and-operators.md %}#pgvector-functions).