@@ -2,13 +2,13 @@
 title: Hybrid Search
 titleSuffix: Azure AI Search
 description: Describes concepts and architecture of hybrid query processing and document retrieval. Hybrid queries combine vector search and full-text search.
-author: robertklee
-ms.author: robertlee
+author: haileytap
+ms.author: haileytapia
 ms.service: azure-ai-search
 ms.custom:
   - ignite-2023
 ms.topic: concept-article
-ms.date: 11/21/2025
+ms.date: 02/19/2026
 ---
 
 # Hybrid search using vectors and full text in Azure AI Search
@@ -91,7 +91,7 @@ content-type: application/JSON
 
 Filters and facets target data structures within the index that are distinct from the inverted indexes used for full-text search and the vector indexes used for vector search. As such, when filters and faceted operations execute, the search engine can apply the operational result to the hybrid search results in the response.
 
-Notice how there's no `orderby` in the query. Explicit sort orders override relevanced-ranked results, so if you want similarity and BM25 relevance, omit sorting in your query.
+Notice how there's no `orderby` in the query. Explicit sort orders override relevance-ranked results, so if you want similarity and BM25 relevance, omit sorting in your query.
 
 A response from the query might look like the following JSON.
 

@@ -1,15 +1,15 @@
 ---
-title: Lucene query syntax
+title: Lucene Query Syntax
 titleSuffix: Azure AI Search
 description: Reference for the full Lucene query syntax, as used in Azure AI Search for wildcard, fuzzy search, RegEx, and other advanced query constructs.
 manager: nitinme
-author: bevloh
-ms.author: beloh
+author: haileytap
+ms.author: haileytapia
 ms.service: azure-ai-search
 ms.custom:
   - ignite-2023
 ms.topic: concept-article
-ms.date: 12/11/2024
+ms.date: 02/19/2026
 ms.update-cycle: 365-days
 ---
 

@@ -1,12 +1,12 @@
 ---
-title: OData language overview
+title: OData Language Overview
 titleSuffix: Azure AI Search
 description: OData language overview for filters, select, and order-by for Azure AI Search keyword search.
-author: bevloh
-ms.author: beloh
+author: haileytap
+ms.author: haileytapia
 ms.service: azure-ai-search
 ms.topic: concept-article
-ms.date: 12/10/2024
+ms.date: 02/19/2026
 ms.update-cycle: 365-days
 ---
 

@@ -1,15 +1,15 @@
 ---
-title: Simple query syntax
+title: Simple Query Syntax
 titleSuffix: Azure AI Search
 description: Reference for the simple query syntax used for full text search queries in Azure AI Search.
 manager: nitinme
-author: bevloh
-ms.author: beloh
+author: haileytap
+ms.author: haileytapia
 ms.service: azure-ai-search
 ms.custom:
   - ignite-2023
 ms.topic: concept-article
-ms.date: 12/10/2024
+ms.date: 02/19/2026
 ms.update-cycle: 365-days
 ---
 

@@ -9,7 +9,7 @@ ms.service: azure-ai-search
 ms.custom:
   - ignite-2023
 ms.topic: concept-article
-ms.date: 05/16/2022
+ms.date: 02/19/2026
 ms.update-cycle: 365-days
 ---
 

@@ -1,23 +1,23 @@
 ---
-title: Upgrade management SDKs
+title: Upgrade Management SDKs
 titleSuffix: Azure AI Search
 description: Learn about the management libraries and packages used for control plane operations in Azure AI Search.
 manager: nitinme
-author: bevloh
-ms.author: beloh
+author: haileytap
+ms.author: haileytapia
 ms.service: azure-ai-search
 ms.devlang: csharp
 ms.custom:
   - devx-track-dotnet
   - ignite-2023
 ms.topic: upgrade-and-migration-article
-ms.date: 02/24/2025
+ms.date: 02/19/2026
 ms.update-cycle: 365-days
 ---
 
 # Upgrade versions of the Azure Search .NET Management SDK
 
-This article points you to libraries in the Azure SDK for .NET for managing a search service. These libraries provide the APIs used to create, configure, and delete search services. They also provide APIS used to adjust capacity, manage API keys, and configure network security.
+This article points you to libraries in the Azure SDK for .NET for managing a search service. These libraries provide the APIs used to create, configure, and delete search services. They also provide APIs used to adjust capacity, manage API keys, and configure network security.
 
 Management SDKs target a specific version of the Management REST API. Release notes for each library indicate which REST API version is the target for each package. For more information about concepts and operations, see [Search Management (REST)](/rest/api/searchmanagement/).
 
@@ -27,7 +27,7 @@ The following table lists the client libraries used to provision a search servic
 
 | Namespace | Version| Status | Change log |
 |-----------|--------|--------|------------|
-| [Azure.ResourceManager.Search](/dotnet/api/overview/azure/resourcemanager.search-readme?view=azure-dotnet&preserve-view=true) | [Package versions](https://www.nuget.org/packages/Azure.ResourceManager.Search) | **Current** | [Change Lot](https://github.com/Azure/azure-sdk-for-net/blob/main/sdk/search/Azure.ResourceManager.Search/CHANGELOG.md) |
+| [Azure.ResourceManager.Search](/dotnet/api/overview/azure/resourcemanager.search-readme?view=azure-dotnet&preserve-view=true) | [Package versions](https://www.nuget.org/packages/Azure.ResourceManager.Search) | **Current** | [Change log](https://github.com/Azure/azure-sdk-for-net/blob/main/sdk/search/Azure.ResourceManager.Search/CHANGELOG.md) |
 | [Microsoft.Azure.Management.Search](/dotnet/api/overview/azure/search/management/management-cognitivesearch(deprecated)?view=azure-dotnet&preserve-view=true) | [Package versions](https://www.nuget.org/packages/Microsoft.Azure.Management.Search#versions-body-tab) | **Deprecated** | [Release notes](https://www.nuget.org/packages/Microsoft.Azure.Management.Search#release-body-tab) |
 
 ## Checklist for upgrade

@@ -1,9 +1,9 @@
 ---
-title: Search over Markdown blobs
+title: Search Over Markdown Blobs
 titleSuffix: Azure AI Search
 description: Extract searchable text from Markdown blobs using the blob indexer in Azure AI Search. Indexers provide indexing automation for supported data sources like Azure Blob Storage.
-author: mdonovan
-ms.author: mdonovan
+author: haileytap
+ms.author: haileytapia
 ms.service: azure-ai-search
 ms.custom:
   - ignite-2024

@@ -226,7 +226,7 @@ Both wizards follow a similar high-level workflow:
 
 1. Run the wizard to create objects, optionally vectorize data, load data into an index, set a schedule, and configure other options.
 
-The workflow is a one-way pipeline. You can't use the wizard to edit any of the objects that were created, but you can use other portal tools, such as the index designer, indexer designer, or JSON editors, to make allowed updates.
+The workflow is a one-way pipeline. You can't use the wizard to edit any of the objects that were created, but you can use other portal tools, such as the index designer or JSON editors, to make allowed updates.
 
 ### Starting the wizards
 

@@ -1,18 +1,18 @@
 ---
-title: moreLikeThis (preview) query feature
+title: moreLikeThis Query Feature
 titleSuffix: Azure AI Search
-description: Describes the moreLikeThis (preview) feature, which is available in preview versions of the Azure AI Search REST API.
-author: bevloh
-ms.author: beloh
+description: Describes the moreLikeThis feature, which is available in preview versions of the Azure AI Search REST API.
+author: haileytap
+ms.author: haileytapia
 ms.service: azure-ai-search
 ms.custom:
   - ignite-2023
 ms.topic: concept-article
-ms.date: 02/20/2025
+ms.date: 02/19/2026
 ms.update-cycle: 365-days
 ---
 
-# moreLikeThis (preview) in Azure AI Search
+# moreLikeThis in Azure AI Search
 
 > [!IMPORTANT] 
 > This feature is in public preview under [Supplemental Terms of Use](https://azure.microsoft.com/support/legal/preview-supplemental-terms/). The [preview REST API](/rest/api/searchservice/index-preview) supports this feature.

@@ -12,7 +12,7 @@ ms.update-cycle: 365-days
 
 # Multi-region deployments in Azure AI Search
 
-Although Azure AI Search is a single-region service, you can achieve higher availability and resiliency by deploying multiple search services with identical configurations and content across multiple regions.
+Although Azure AI Search is a single-region service, you can achieve higher reliability by deploying multiple search services with identical configurations and content across multiple regions.
 
 This article describes the components of a multi-region solution, which relies on your custom script or code to handle failover if a service becomes unavailable.
 
@@ -120,4 +120,4 @@ As you evaluate these load-balancing options, consider the following points:
 ## Related content
 
 + [Reliability in Azure AI Search](/azure/reliability/reliability-ai-search)
-+ [Design reliable Azure applications](/azure/architecture/framework/resiliency/app-design)
++ [Design reliable Azure applications](/azure/well-architected/reliability/checklist)

@@ -1,15 +1,15 @@
 ---
-title: OData collection operator reference
+title: OData Collection Operator Reference
 titleSuffix: Azure AI Search
 description: When creating filter expressions in Azure AI Search queries, use "any" and "all" operators in lambda expressions when the filter is on a collection or complex collection field.
 manager: nitinme
-author: bevloh
-ms.author: beloh
+author: haileytap
+ms.author: haileytapia
 ms.service: azure-ai-search
 ms.custom:
   - ignite-2023
 ms.topic: concept-article
-ms.date: 02/07/2023
+ms.date: 02/19/2026
 ms.update-cycle: 365-days
 ---
 

@@ -1,15 +1,15 @@
 ---
-title: OData comparison operator reference
+title: OData Comparison Operator Reference
 titleSuffix: Azure AI Search
 description: Syntax and reference documentation for using OData comparison operators (eq, ne, gt, lt, ge, and le) in Azure AI Search queries.
 manager: nitinme
-author: bevloh
-ms.author: beloh
+author: haileytap
+ms.author: haileytapia
 ms.service: azure-ai-search
 ms.custom:
   - ignite-2023
 ms.topic: concept-article
-ms.date: 09/16/2021
+ms.date: 02/19/2026
 ms.update-cycle: 365-days
 translation.priority.mt:
   - "de-de"
@@ -23,6 +23,7 @@ translation.priority.mt:
   - "zh-cn"
   - "zh-tw"
 ---
+
 # OData comparison operators in Azure AI Search - `eq`, `ne`, `gt`, `lt`, `ge`, and `le`
 
 The most basic operation in an [OData filter expression](query-odata-filter-orderby-syntax.md) in Azure AI Search is to compare a field to a given value. Two types of comparison are possible -- equality comparison, and range comparison. You can use the following operators to compare a field to a constant value:

@@ -1,15 +1,15 @@
 ---
-title: OData filter reference
+title: OData Filter Reference
 titleSuffix: Azure AI Search
 description: OData language reference and full syntax used for creating filter expressions in Azure AI Search queries.
 manager: nitinme
-author: bevloh
-ms.author: beloh
+author: haileytap
+ms.author: haileytapia
 ms.service: azure-ai-search
 ms.custom:
   - ignite-2023
 ms.topic: concept-article
-ms.date: 07/18/2022
+ms.date: 02/19/2026
 ms.update-cycle: 365-days
 ---
 

@@ -1,15 +1,15 @@
 ---
-title: OData geo-spatial function reference
+title: OData Geo-Spatial Function Reference
 titleSuffix: Azure AI Search
 description: Syntax and reference documentation for using OData geo-spatial functions, geo.distance and geo.intersects, in Azure AI Search queries.
 manager: nitinme
-author: bevloh
-ms.author: beloh
+author: haileytap
+ms.author: haileytapia
 ms.service: azure-ai-search
 ms.custom:
   - ignite-2023
 ms.topic: concept-article
-ms.date: 09/16/2021
+ms.date: 02/19/2026
 ms.update-cycle: 365-days
 translation.priority.mt:
   - "de-de"
@@ -23,6 +23,7 @@ translation.priority.mt:
   - "zh-cn"
   - "zh-tw"
 ---
+
 # OData geo-spatial functions in Azure AI Search - `geo.distance` and `geo.intersects`
 
 Azure AI Search supports geo-spatial queries in [OData filter expressions](query-odata-filter-orderby-syntax.md) via the `geo.distance` and `geo.intersects` functions. The `geo.distance` function returns the distance in kilometers between two points, one being a field or range variable, and one being a constant passed as part of the filter. The `geo.intersects` function returns `true` if a given point is within a given polygon, where the point is a field or range variable and the polygon is specified as a constant passed as part of the filter.

@@ -1,15 +1,15 @@
 ---
-title: OData logical operator reference
+title: OData Logical Operator Reference
 titleSuffix: Azure AI Search
 description: Syntax and reference documentation for using OData logical operators, and, or, and not, in Azure AI Search queries.
 manager: nitinme
-author: bevloh
-ms.author: beloh
+author: haileytap
+ms.author: haileytapia
 ms.service: azure-ai-search
 ms.custom:
   - ignite-2023
 ms.topic: concept-article
-ms.date: 09/16/2021
+ms.date: 02/19/2026
 ms.update-cycle: 365-days
 translation.priority.mt:
   - "de-de"
@@ -23,6 +23,7 @@ translation.priority.mt:
   - "zh-cn"
   - "zh-tw"
 ---
+
 # OData logical operators in Azure AI Search - `and`, `or`, `not`
 
 [OData filter expressions](query-odata-filter-orderby-syntax.md) in Azure AI Search are Boolean expressions that evaluate to `true` or `false`. You can write a complex filter by writing a series of [simpler filters](search-query-odata-comparison-operators.md) and composing them using the logical operators from [Boolean algebra](https://en.wikipedia.org/wiki/Boolean_algebra):

@@ -1,15 +1,15 @@
 ---
-title: OData order-by reference
+title: OData Order-By Reference
 titleSuffix: Azure AI Search
 description: Syntax and language reference documentation for using order-by in Azure AI Search queries.
 manager: nitinme
-author: bevloh
-ms.author: beloh
+author: haileytap
+ms.author: haileytapia
 ms.service: azure-ai-search
 ms.custom:
   - ignite-2023
 ms.topic: concept-article
-ms.date: 11/02/2022
+ms.date: 02/19/2026
 ms.update-cycle: 365-days
 ---
 

@@ -1,15 +1,15 @@
 ---
-title: OData search.in function reference
+title: OData search.in Function Reference
 titleSuffix: Azure AI Search
 description: Syntax and reference documentation for using the search.in function in Azure AI Search queries.
 manager: nitinme
-author: bevloh
-ms.author: beloh
+author: haileytap
+ms.author: haileytapia
 ms.service: azure-ai-search
 ms.custom:
   - ignite-2023
 ms.topic: concept-article
-ms.date: 09/16/2021
+ms.date: 02/19/2026
 ms.update-cycle: 365-days
 translation.priority.mt:
   - "de-de"
@@ -23,6 +23,7 @@ translation.priority.mt:
   - "zh-cn"
   - "zh-tw"
 ---
+
 # OData `search.in` function in Azure AI Search
 
 A common scenario in [OData filter expressions](query-odata-filter-orderby-syntax.md) is to check whether a single field in each document is equal to one of many possible values. For example, this is how some applications implement [security trimming](search-security-trimming-for-azure-search.md) -- by checking a field containing one or more principal IDs against a list of principal IDs representing the user issuing the query. One way to write a query like this is to use the [`eq`](search-query-odata-comparison-operators.md) and [`or`](search-query-odata-logical-operators.md) operators:

@@ -1,15 +1,15 @@
 ---
-title: OData search.score function reference
+title: OData search.score Function Reference
 titleSuffix: Azure AI Search
 description: Syntax and reference documentation for using the search.score function in Azure AI Search queries.
 manager: nitinme
-author: bevloh
-ms.author: beloh
+author: haileytap
+ms.author: haileytapia
 ms.service: azure-ai-search
 ms.custom:
   - ignite-2023
 ms.topic: concept-article
-ms.date: 04/18/2023
+ms.date: 02/19/2026
 ms.update-cycle: 365-days
 translation.priority.mt:
   - "de-de"
@@ -23,6 +23,7 @@ translation.priority.mt:
   - "zh-cn"
   - "zh-tw"
 ---
+
 # OData `search.score` function in Azure AI Search
 
 When you send a query to Azure AI Search without the [**$orderby** parameter](search-query-odata-orderby.md), the results that come back will be sorted in descending order by relevance score. Even when you do use **$orderby**, the relevance score is used to break ties by default. However, sometimes it's useful to use the relevance score as an initial sort criteria, and some other criteria as the tie-breaker. The example in this article demonstrates using the `search.score` function for sorting.

@@ -1,15 +1,15 @@
 ---
-title: OData select reference
+title: OData Select Reference
 titleSuffix: Azure AI Search
 description: Syntax and language reference for explicit selection of fields to return in the search results of Azure AI Search queries.
 manager: nitinme
-author: bevloh
-ms.author: beloh
+author: haileytap
+ms.author: haileytapia
 ms.service: azure-ai-search
 ms.custom:
   - ignite-2023
 ms.topic: concept-article
-ms.date: 07/18/2022
+ms.date: 02/19/2026
 ms.update-cycle: 365-days
 ---
 

@@ -1,15 +1,15 @@
 ---
-title: OData expression syntax reference
+title: OData Expression Syntax Reference
 titleSuffix: Azure AI Search
 description: Formal grammar and syntax specification for OData expressions in Azure AI Search queries.
 manager: nitinme
-author: bevloh
-ms.author: beloh
+author: haileytap
+ms.author: haileytapia
 ms.service: azure-ai-search
 ms.custom:
   - ignite-2023
 ms.topic: concept-article
-ms.date: 07/18/2022
+ms.date: 02/19/2026
 ms.update-cycle: 365-days
 ---
 # OData expression syntax reference for Azure AI Search

@@ -1,84 +1,111 @@
 ---
-title: Configure a vectorizer
+title: Configure Vectorizer
 titleSuffix: Azure AI Search
-description: Steps for adding a vectorizer to a search index in Azure AI Search. A vectorizer calls an embedding model that generates embeddings from text.
+description: Add a vectorizer to an Azure AI Search index so the search service converts text queries to vectors at query time using Microsoft-hosted or custom embedding models.
 author: haileytap
 ms.author: haileytapia
 ms.service: azure-ai-search
 ms.update-cycle: 180-days
 ms.custom:
   - build-2024
 ms.topic: how-to
-ms.date: 07/17/2025
+ms.date: 02/19/2026
+ai-usage: ai-assisted
 ---
 
 # Configure a vectorizer in a search index
 
-In Azure AI Search, a *vectorizer* is a component that performs vectorization using a deployed embedding model on Azure OpenAI or Azure Vision in Foundry Tools. It converts text (or images) to vectors during query execution.
+In Azure AI Search, a *vectorizer* converts text or images into vectors during query execution, allowing you to submit plain-text queries against vector fields without computing embeddings yourself.
 
-It's defined in a [search index](search-what-is-an-index.md), it applies to searchable vector fields, and it's used at query time to generate an embedding for a text or image query input. If instead you need to vectorize content as part of the indexing process, refer to [integrated vectorization](vector-search-integrated-vectorization.md). For built-in vectorization during indexing, you can configure an indexer and skillset that calls an embedding model for your raw text or image content.
+A vectorizer is defined in a search index and assigned to vector fields through a vector profile. At query time, the vectorizer calls an embedding model to generate a vector from your query input. For more information, see [Using integrated vectorization in queries](vector-search-integrated-vectorization.md#using-integrated-vectorization-in-queries).
 
-To add a vectorizer to search index, you can use the index designer in Azure portal, or call the [Create or Update Index](/rest/api/searchservice/indexes/create-or-update) REST API, or use any Azure  SDK package that's updated to provide this feature.
+To add a vectorizer to an index, use the import wizard or index designer in the Azure portal, [Indexes - Create Or Update](/rest/api/searchservice/indexes/create-or-update) (REST API), or an Azure SDK package. This article uses REST for illustration.
 
-Vectorizers are now generally available as long as you use a generally available skill-vectorizer pair. [AzureOpenAIEmbedding vectorizer](vector-search-vectorizer-azure-open-ai.md) and [AzureOpenAIEmbedding skill](cognitive-search-skill-azure-openai-embedding.md) are generally available. The custom [Web API vectorizer](/rest/api/searchservice/indexes/create-or-update#webapivectorizer) is also generally available.
-
-[Azure Vision vectorizer](vector-search-vectorizer-ai-services-vision.md), [Microsoft Foundry model catalog vectorizer](vector-search-vectorizer-azure-machine-learning-ai-studio-catalog.md), and their equivalent skills are still in preview. Your skillset must specify [2024-05-01-preview REST API](/rest/api/searchservice/operation-groups?view=rest-searchservice-2024-05-01-preview&preserve-view=true) to use preview skills and vectorizers.
+> [!TIP]
+> A vectorizer handles query-time vectorization. To also vectorize content during indexing, set up an indexer and skillset with an embedding skill. For more information, see [Using integrated vectorization during indexing](vector-search-integrated-vectorization.md#using-integrated-vectorization-during-indexing).
 
 ## Prerequisites
 
-+ [An index with searchable vector fields](vector-search-how-to-create-index.md) on Azure AI Search.
++ An [index with searchable vector fields](vector-search-how-to-create-index.md) on your search service.
+
++ (Optional) [Diagnostic logging enabled](search-monitor-enable-logging.md) on your search service to confirm vector query execution.
 
-+ A deployed embedding model (see the next section).
++ A [supported embedding model](#supported-embedding-models) deployment for your vectorizer.
 
-+ Permissions to use the embedding model. On Azure OpenAI, the caller must have [Cognitive Services OpenAI User](/azure/ai-services/openai/how-to/role-based-access-control#azure-openai-roles) permissions. Or, you can provide an API key.
++ Permissions to update and query the index. This article uses the recommended [keyless authentication](search-get-started-rbac.md). Assign the **Search Service Contributor** and **Search Index Data Reader** roles to your user account, sign in to Azure, and get an access token. If you use [API keys](search-security-api-keys.md) instead, get an admin key for update operations and a query key for search operations.
 
-+ [Visual Studio Code](https://code.visualstudio.com/download) with a [REST client](https://marketplace.visualstudio.com/items?itemName=humao.rest-client) to send the query and accept a response.
++ Permissions to use the embedding model. For example, with Azure OpenAI, the caller must have [**Cognitive Services OpenAI User**](/azure/ai-services/openai/how-to/role-based-access-control#azure-openai-roles) permissions. You can also use an API key.
 
-We recommend that you [enable diagnostic logging](search-monitor-enable-logging.md) on your search service to confirm vector query execution.
++ [Visual Studio Code](https://code.visualstudio.com/download) with the [REST Client extension](https://marketplace.visualstudio.com/items?itemName=humao.rest-client). Create a `.rest` or `.http` file to send each REST request in this article directly from the editor.
 
 ## Supported embedding models
 
-The following table lists the embedding models that can be used with a vectorizer. Because you must use the [same embedding model for indexing and queries](vector-search-integrated-vectorization.md#using-integrated-vectorization-in-queries), vectorizers are paired with skills that generate embeddings during indexing. The table lists the skill associated with a particular vectorizer.
+Azure AI Search offers several types of vectorizers, each paired with a corresponding skill. The skill generates embeddings during indexing, while the vectorizer generates embeddings at query time. You must use the same embedding model for both, so choose a vectorizer–skill pair that points to the same model deployment.
+
+The following table lists the vectorizers and their supported models and associated skills.
+
+| Vectorizer | Supported models | Associated skill |
+|-----------------|------------|------------------|
+| [Azure OpenAI](vector-search-vectorizer-azure-open-ai.md) | text-embedding-ada-002<br>text-embedding-3-large<br>text-embedding-3-small | [Azure OpenAI Embedding](cognitive-search-skill-azure-openai-embedding.md) |
+| [Microsoft Foundry model catalog](vector-search-vectorizer-azure-machine-learning-ai-studio-catalog.md) | Cohere-embed-v3-english<br>Cohere-embed-v3-multilingual<br>Cohere-embed-v4 <sup>1</sup> | [AML](cognitive-search-aml-skill.md) |
+| [Azure Vision](vector-search-vectorizer-ai-services-vision.md) | [Multimodal embeddings 4.0 API](/azure/ai-services/computer-vision/concept-image-retrieval) | [Azure Vision multimodal embeddings](cognitive-search-skill-vision-vectorize.md) |
+| [Custom Web API](vector-search-vectorizer-custom-web-api.md) | Any embedding model (hosted externally) | [Custom Web API](cognitive-search-custom-skill-web-api.md) |
+
+<sup>1</sup> You can only specify `embed-v-4-0` programmatically through the [AML skill](cognitive-search-aml-skill.md) or [Microsoft Foundry model catalog vectorizer](vector-search-vectorizer-azure-machine-learning-ai-studio-catalog.md), not through the Azure portal. However, you can use the portal to manage the skillset or vectorizer afterward.
 
-| Vectorizer kind | Model names | Model provider | Associated skill |
-|-----------------|------------|----------------|------------------|
-| [`azureOpenAI`](vector-search-vectorizer-azure-open-ai.md) | text-embedding-ada-002<br>text-embedding-3 | Azure OpenAI | [AzureOpenAIEmbedding skill](cognitive-search-skill-azure-openai-embedding.md) |
-| [`aml`](vector-search-vectorizer-azure-machine-learning-ai-studio-catalog.md) | Cohere-embed-v3<br>Cohere-embed-v4 <sup>1</sup> | [Microsoft Foundry model catalog](vector-search-integrated-vectorization-ai-studio.md)  | [AML skill](cognitive-search-aml-skill.md) |
-| [`aiServicesVision`](vector-search-vectorizer-ai-services-vision.md) | [Multimodal embeddings 4.0 API](/azure/ai-services/computer-vision/concept-image-retrieval) | Azure Vision (through a Foundry resource) | [Azure Vision multimodal embeddings skill](cognitive-search-skill-vision-vectorize.md) |
-| [`customWebApi`](vector-search-vectorizer-custom-web-api.md) | Any embedding model | Hosted externally | [Custom Web API skill](cognitive-search-custom-skill-web-api.md) |
+> [!NOTE]
+> Vectorizers are generally available as long as you use a generally available skill–vectorizer pair. For the latest availability information, see the documentation for each vectorizer and skill in the previous table.
 
-<sup>1</sup> At this time, you can only specify `embed-v-4-0` programmatically through the [AML skill](cognitive-search-aml-skill.md) or [Microsoft Foundry model catalog vectorizer](vector-search-vectorizer-azure-machine-learning-ai-studio-catalog.md), not through the Azure portal. However, you can use the portal to manage the skillset or vectorizer afterward.
+## Define a vectorizer using a wizard
 
-## Try a vectorizer with sample data
+The **Import data (new)** wizard in the Azure portal can read files from Azure Blob Storage, create an index with chunked and vectorized fields, and add a vectorizer. By design, the wizard-generated vectorizer is set to the same embedding model used to index the blob content.
 
-The [**Import data (new)** wizard](search-get-started-portal-import-vectors.md) reads files from Azure Blob storage, creates an index with chunked and vectorized fields, and adds a vectorizer. By design, the vectorizer that's created by the wizard is set to the same embedding model used to index the blob content.
+To create a sample index with a vectorizer using the wizard:
 
-1. [Upload sample data files](/azure/storage/blobs/storage-quickstart-blobs-portal) to a container on Azure Storage. We used some [small text files from NASA's earth book](https://github.com/Azure-Samples/azure-search-sample-data/tree/main/nasa-e-book/earth-txt-10) to test these instructions on a free search service.
+1. [Upload files](/azure/storage/blobs/storage-quickstart-blobs-portal) to a container in Azure Storage. We used [small text files from NASA's Earth at Night e-book](https://github.com/Azure-Samples/azure-search-sample-data/tree/main/nasa-e-book/earth-txt-10) to test these instructions on a free search service.
 
-1. Run the [**Import data (new)** wizard](search-get-started-portal-import-vectors.md), choosing the blob container for the data source.
+1. Run the [**Import data (new)** wizard](search-get-started-portal-import-vectors.md). Choose the blob container for the data source.
 
    :::image type="content" source="media/vector-search-how-to-configure-vectorizer/connect-to-data.png" lightbox="media/vector-search-how-to-configure-vectorizer/connect-to-data.png" alt-text="Screenshot of the connect to your data page.":::
 
-1. Choose an existing deployment of **text-embedding-ada-002**. This model generates embeddings during indexing and is also used to configure the vectorizer used during queries.
+1. Choose a vectorizer kind and, if applicable, a model deployment. This example uses Azure OpenAI and a **text-embedding-ada-002** deployment.
 
-   :::image type="content" source="media/vector-search-how-to-configure-vectorizer/vectorize-enrich-data.png" lightbox="media/vector-search-how-to-configure-vectorizer/vectorize-enrich-data.png" alt-text="Screenshot of the vectorize and enrich data page.":::
+   :::image type="content" source="media/vector-search-how-to-configure-vectorizer/vectorize-enrich-data.png" lightbox="media/vector-search-how-to-configure-vectorizer/vectorize-enrich-data.png" alt-text="Screenshot of the vectorize and enrich data page for configuring a vectorizer embedding model.":::
 
-1. After the wizard is finished and all indexer processing is complete, you should have an index with a searchable vector field. The field's JSON definition looks like this:
+1. After the wizard finishes and all indexer processing is complete, you should have an index with a searchable vector field. The field's JSON definition looks like this:
 
    ```json
     {
-        "name": "vector",
-        "type": "Collection(Edm.Single)",
-        "searchable": true,
-        "retrievable": true,
-        "dimensions": 1536,
-        "vectorSearchProfile": "vector-nasa-ebook-text-profile"
+      "name": "text_vector",
+      "type": "Collection(Edm.Single)",
+      "searchable": true,
+      "filterable": false,
+      "retrievable": true,
+      "stored": true,
+      "sortable": false,
+      "facetable": false,
+      "key": false,
+      "dimensions": 1536,
+      "vectorSearchProfile": "vector-nasa-ebook-text-profile",
+      "synonymMaps": []
     }
    ```
 
-1. You should also have a vector profile and a vectorizer, similar to the following example:
+    You should also have a vector profile, vector search algorithm, and vectorizer. Their JSON definitions look like this:
 
    ```json
+    "algorithms": [
+      {
+        "name": "vector-nasa-ebook-text-algorithm",
+        "kind": "hnsw",
+        "hnswParameters": {
+          "metric": "cosine",
+          "m": 4,
+          "efConstruction": 400,
+          "efSearch": 500
+        }
+      }
+    ],
    "profiles": [
       {
         "name": "vector-nasa-ebook-text-profile",
@@ -91,146 +118,206 @@ The [**Import data (new)** wizard](search-get-started-portal-import-vectors.md)
         "name": "vector-nasa-ebook-text-vectorizer",
         "kind": "azureOpenAI",
         "azureOpenAIParameters": {
-          "resourceUri": "https://my-fake-azure-openai-resource.openai.azure.com",
+          "resourceUri": "https://my-azure-openai-resource.openai.azure.com",
           "deploymentId": "text-embedding-ada-002",
           "modelName": "text-embedding-ada-002",
-          "apiKey": "0000000000000000000000000000000000000",
-          "authIdentity": null
         },
-        "customWebApiParameters": null
       }
     ]
     ```
 
-1. Skip ahead to [test your vectorizer](#test-a-vectorizer) for text-to-vector conversion during query execution.
+## Define a vectorizer programmatically
 
-## Define a vectorizer and vector profile
+If you didn't use the portal wizard or want to add a vectorizer to an existing index, you can define a vectorizer and vector profile programmatically. A vector profile links a vectorizer to one or more vector fields and specifies the vector search algorithm used for navigation structures.
 
-This section explains the modifications to an index schema for defining a vectorizer manually.
+To define a vectorizer and vector profile in an existing index:
 
-1. Use [Create or Update Index](/rest/api/searchservice/indexes/create-or-update) to add `vectorizers` to a search index.
+1. Retrieve the index definition using [Indexes - Get](/rest/api/searchservice/indexes/get) (REST API). Replace the service name, index name, and access token with your own values.
 
-1. Add the following JSON to your index definition. The vectorizers section provides connection information to a deployed embedding model. This step shows two vectorizer examples so that you can compare an Azure OpenAI embedding model and a custom web API side by side.
+    ```http
+    ### Get index definition
+    GET https://my-search-service.search.windows.net/indexes/my-index?api-version=2025-09-01 HTTP/1.1
+    Authorization: Bearer <your-access-token> // For API keys, replace this line with api-key: <your-admin-api-key>
+    ```
+    
+1. Use [Indexes - Create Or Update](/rest/api/searchservice/indexes/create-or-update) (REST API) to update the index definition. Paste the full index definition in the request body.
+
+   ```http
+   ### Update index definition
+   PUT https://my-search-service.search.windows.net/indexes/my-index?api-version=2025-09-01 HTTP/1.1
+   Content-Type: application/json
+   Authorization: Bearer <your-access-token> // For API keys, replace this line with api-key: <your-admin-api-key>
+
+   // Paste your index definition here
+   ```
+
+1. Add a `vectorizers` section to `vectorSearch` object. This section specifies connection information to a deployed embedding model. The following example includes both Azure OpenAI and Custom Web API for comparison.
 
     ```json
-      "vectorizers": [
-        {
-          "name": "my_azure_open_ai_vectorizer",
-          "kind": "azureOpenAI",
-          "azureOpenAIParameters": {
-            "resourceUri": "https://url.openai.azure.com",
-            "deploymentId": "text-embedding-ada-002",
-            "modelName": "text-embedding-ada-002",
-            "apiKey": "mytopsecretkey"
-          }
-        },
-        {
-          "name": "my_custom_vectorizer",
-          "kind": "customWebApi",
-          "customVectorizerParameters": {
-            "uri": "https://my-endpoint",
-            "authResourceId": " ",
-            "authIdentity": " "
+    "vectorSearch": {
+        "vectorizers": [
+          {
+            "name": "my_azure_open_ai_vectorizer",
+            "kind": "azureOpenAI",
+            "azureOpenAIParameters": {
+              "resourceUri": "https://url.openai.azure.com",
+              "deploymentId": "text-embedding-ada-002",
+              "modelName": "text-embedding-ada-002",
+              "apiKey": "mytopsecretkey"
+            }
+          },
+          {
+            "name": "my_custom_vectorizer",
+            "kind": "customWebApi",
+            "customVectorizerParameters": {
+              "uri": "https://my-endpoint",
+              "authResourceId": null,
+              "authIdentity": null
+            }
           }
-        }
-      ]
+        ]
+      }
     ```
 
-1. In the same index, add a vector profiles section that specifies one of your vectorizers. Vector profiles also require a [vector search algorithm](vector-search-ranking.md) used to create navigation structures.
+1. Add an `algorithms` section to `vectorSearch`. This section defines a [vector search algorithm](vector-search-ranking.md) used for navigation structures.
 
     ```json
-    "profiles": [ 
-        { 
-            "name": "my_vector_profile", 
-            "algorithm": "my_hnsw_algorithm", 
-            "vectorizer":"my_azure_open_ai_vectorizer" 
+    "algorithms": [
+      {
+        "name": "my_hnsw_algorithm",
+        "kind": "hnsw",
+        "hnswParameters": {
+          "m": 4,
+          "efConstruction": 400,
+          "efSearch": 500,
+          "metric": "cosine"
         }
+      }
+    ]
+    ```
+  
+1. Add a `profiles` section to `vectorSearch`. This section references the vectorizer and vector search algorithm defined in the previous steps.
+
+    ```json
+    "profiles": [ 
+      { 
+        "name": "my_vector_profile", 
+        "algorithm": "my_hnsw_algorithm", 
+        "vectorizer": "my_azure_open_ai_vectorizer" 
+      }
     ]
     ```
 
-1. Assign a vector profile to a vector field. The following example shows a fields collection with the required key field, a title string field, and two vector fields with a vector profile assignment.
+1. In the `fields` array, assign the vector profile to one or more vector fields by specifying the `vectorSearchProfile` property.
 
     ```json
-    "fields": [ 
-            { 
-                "name": "ID", 
-                "type": "Edm.String", 
-                "key": true, 
-                "sortable": true, 
-                "analyzer": "keyword" 
-            }, 
-            { 
-                "name": "title", 
-                "type": "Edm.String"
-            }, 
-            { 
-                "name": "vector", 
-                "type": "Collection(Edm.Single)", 
-                "dimensions": 1536, 
-                "vectorSearchProfile": "my_vector_profile", 
-                "searchable": true, 
-                "retrievable": true
-            }, 
-            { 
-                "name": "my-second-vector", 
-                "type": "Collection(Edm.Single)", 
-                "dimensions": 1024, 
-                "vectorSearchProfile": "my_vector_profile", 
-                "searchable": true, 
-                "retrievable": true
-            }
+    "fields": [
+        ... // Trimmed for brevity
+        {
+          "name": "vector",
+          "type": "Collection(Edm.Single)",
+          "dimensions": 1536,
+          "vectorSearchProfile": "my_vector_profile",
+          "searchable": true,
+          "retrievable": true
+        },
+        {
+          "name": "my_second_vector",
+          "type": "Collection(Edm.Single)",
+          "dimensions": 1536,
+          "vectorSearchProfile": "my_vector_profile",
+          "searchable": true,
+          "retrievable": true
+        }
     ]
     ```
 
-## Test a vectorizer
+1. Send the PUT request to update the index definition. If the request is successful, you should receive a `204 No Content` response.
 
-Use a search client to send a query through a vectorizer. This example assumes Visual Studio Code with a REST client and a [sample index](#try-a-vectorizer-with-sample-data).
+1. To verify the vectorizer and vector profile, rerun the GET request from the first step. Confirm that:
 
-1. In Visual Studio Code, provide a search endpoint and [search query API key](search-security-api-keys.md#find-existing-keys):
+   + The `vectorSearch.vectorizers` array contains your vectorizer definition with the correct `kind` and connection parameters.
 
-   ```http
-    @baseUrl: 
-    @queryApiKey: 00000000000000000000000
-   ```
+   + The `vectorSearch.profiles` array includes a profile that references your vectorizer by name.
 
-1. Paste in a [vector query request](vector-search-how-to-query.md).
+   + The `vectorSearch.algorithms` array includes the vector search algorithm referenced by your profile.
 
-   ```http
-    ### Run a query
-    POST {{baseUrl}}/indexes/vector-nasa-ebook-txt/docs/search?api-version=2025-09-01 HTTP/1.1
-        Content-Type: application/json
-        api-key: {{queryApiKey}}
-    
+   + The `vectorSearchProfile` property on your vector field(s) in the `fields` array matches the profile name.
+
+## Test a vectorizer
+
+To confirm a vectorizer works, send a [vector query](vector-search-how-to-query.md) that passes a text string instead of a vector. The following example targets the sample index from [Define a vectorizer using a wizard](#define-a-vectorizer-using-a-wizard), but you can test your own index by adjusting the field names and query parameters.
+
+Use [Documents - Search Post](/rest/api/searchservice/documents/search-post) (REST API) to send the request. Replace the service name, index name, and access token with your own values.
+
+```http
+### Test a vectorizer with a vector query
+POST https://my-search-service.search.windows.net/indexes/vector-nasa-ebook-txt/docs/search?api-version=2025-09-01 HTTP/1.1
+Content-Type: application/json
+Authorization: Bearer <your-access-token> // For API keys, replace this line with api-key: <your-query-api-key>
+
+{
+    "count": true,
+    "select": "title, chunk",
+    "vectorQueries": [
         {
-            "count": true,
-            "select": "title,chunk",
-            "vectorQueries": [
-                {
-                    "kind": "text",
-                    "text": "what cloud formations exists in the troposphere",
-                    "fields": "vector",
-                    "k": 3,
-                    "exhaustive": true
-                }
-            ]
+            "kind": "text",
+            "text": "what cloud formations exist in the troposphere",
+            "fields": "text_vector",
+            "k": 3,
+            "exhaustive": true
         }
-   ```
+    ]
+}
+```
+
+**Key points:**
+
++ `"kind": "text"` tells the search engine that the input is a text string and to use the vectorizer associated with the search field.
+
++ `"text"` is the plain-language string to vectorize.
+
++ `"fields": "text_vector"` is the name of the field to query over. If you use the sample index produced by the wizard, the generated vector field is named `text_vector`.
+
++ `"exhaustive": true` bypasses the HNSW graph and performs a brute-force search over all vectors. This setting is useful for testing accuracy but is slower than the default approximate search. Remove this parameter in production queries for better performance.
+
++ The query doesn't set any vectorizer properties. The search engine reads them automatically from the vector profile assigned to the field.
+
+If the vectorizer is configured correctly, the response returns matching documents ranked by similarity. You should get three results (`k`: 3), the first of which is the most relevant.
 
-   Key points about the query include:
+```json
+{
+    "@odata.count": 3,
+    "value": [
+        {
+            "@search.score": 0.66195244,
+            "chunk": "Cloud Shadow\tGermany\nIn November 2012, the Earth Observing...",
+            "title": "page-25.txt"
+        },
+        ... // Trimmed for brevity
+    ]
+}
+```
 
-   + `"kind": "text"` tells the search engine that the input is a text string, and to use the vectorizer associated with the search field.
+## Troubleshooting
 
-   + `"text": "what cloud formations exists in the troposphere"` is the text string to vectorize.
+If your vectorizer isn't working as expected, start with the common errors table and then check your diagnostic logs for more detail.
 
-   + `"fields": "vector"` is the name of the field to query over. If you use the sample index produced by the wizard, the generated vector field is named `vector`.
+### Common errors
 
-1. Send the request. You should get three `k` results, where the first result is the most relevant.
+The following table lists common vectorizer errors and how to resolve them.
 
-Notice that there are no vectorizer properties to set at query time. The query reads the vectorizer properties, as per the vector profile field assignment in the index.
+| Error | Cause | Resolution |
+|-------|-------|------------|
+| **Authentication failure** (401/403) | Invalid API key or missing RBAC role assignment for the embedding model. | Verify your API key or confirm that the search service identity has the `Cognitive Services OpenAI User` role on the Azure OpenAI resource. |
+| **Dimension mismatch** | The vectorizer model produces embeddings with a different dimension count than the vector field expects. | Ensure the `dimensions` property on the vector field matches the output dimensions of the embedding model (for example, 1536 for `text-embedding-ada-002`). |
+| **Rate limiting** (429) | The embedding model provider is throttling requests. | Review [Azure OpenAI quota limits](/azure/ai-services/openai/quotas-limits?view=foundry&preserve-view=true) and consider increasing your tokens-per-minute (TPM) allocation or reducing batch size. |
+| **Vectorizer not found** | The vector profile references a vectorizer name that doesn't exist in the index. | Confirm that the `vectorizer` property in the vector profile matches the `name` of a vectorizer in the `vectorizers` array. |
+| **Empty results** | Text-to-vector conversion succeeded but the query returns no matches. | Verify the `fields` parameter in the vector query matches the name of a searchable vector field. Increase `k` to return more results. |
 
-## Check logs
+### Check logs
 
-If you enabled diagnostic logging for your search service, run a Kusto query to confirm query execution on your vector field:
+If you enabled diagnostic logging for your search service, run the following Kusto query to confirm query execution on your vector field.
 
 ```kusto
 OperationEvent
@@ -240,8 +327,18 @@ OperationEvent
 
 ## Best practices
 
-If you're setting up an Azure OpenAI vectorizer, consider the same [best practices](cognitive-search-skill-azure-openai-embedding.md#best-practices) that we recommend for the Azure OpenAI embedding skill.
++ **Use a managed identity instead of API keys in production.** Managed identities are more secure and avoid key rotation overhead. For more information, see [Configure a search service to connect using a managed identity](search-how-to-managed-identities.md).
+
++ **Deploy the embedding model in the same region as your search service.** Colocation reduces latency and improves the speed of data transfer between services. Vectorizers are available in all regions where Azure AI Search is available, but model availability varies by provider.
+
++ **Use separate deployments of the same embedding model for indexing and queries.** Dedicated deployments let you allocate TPM quota independently for each workload and make it easier to identify traffic sources.
+
++ **Monitor your Azure OpenAI TPM quota.** If you're hitting your TPM limit, review the [quota limits](/azure/ai-services/openai/quotas-limits?view=foundry&preserve-view=true) and consider requesting a higher limit through a [support case](/azure/azure-portal/supportability/how-to-create-azure-support-request).
+
++ **Review [best practices](cognitive-search-skill-azure-openai-embedding.md#best-practices) for the Azure OpenAI embedding skill.** The same guidance applies to the Azure OpenAI vectorizer.
 
-## See also
+## Related content
 
-+ [Integrated vectorization (preview)](vector-search-integrated-vectorization.md)
++ [Vector search in Azure AI Search](vector-search-overview.md)
++ [Integrated vector embedding](vector-search-integrated-vectorization.md)
++ [Create a vector query](vector-search-how-to-query.md)

@@ -97,8 +97,8 @@ Data chunking (Text Split skill) is free and available on all Foundry Tools in a
 
 For query-only vectorization:
 
-1. [Add a vectorizer](vector-search-how-to-configure-vectorizer.md#define-a-vectorizer-and-vector-profile) to an index. It should be the same embedding model used to generate vectors in the index.
-1. [Assign the vectorizer](vector-search-how-to-configure-vectorizer.md#define-a-vectorizer-and-vector-profile) to a vector profile, and then assign a vector profile to the vector field.
+1. [Add a vectorizer](vector-search-how-to-configure-vectorizer.md#define-a-vectorizer-programmatically) to an index. It should be the same embedding model used to generate vectors in the index.
+1. [Assign the vectorizer](vector-search-how-to-configure-vectorizer.md#define-a-vectorizer-programmatically) to a vector profile, and then assign a vector profile to the vector field.
 1. [Formulate a vector query](vector-search-how-to-configure-vectorizer.md#test-a-vectorizer) that specifies the text string to vectorize.
 
 A more common scenario - data chunking and vectorization during indexing: