@@ -9,23 +9,27 @@ ms.custom:
   - ignite-2023
   - build-2024
 ms.topic: reference
-ms.date: 07/29/2025
+ms.date: 09/12/2025
 ---
 
 #	Azure OpenAI Embedding skill
 
-The **Azure OpenAI Embedding** skill connects to a deployed embedding model on your [Azure OpenAI](/azure/ai-services/openai/overview) resource to generate embeddings during indexing. Your data is processed in the [Geo](https://azure.microsoft.com/explore/global-infrastructure/data-residency/) where your model is deployed.
+The **Azure OpenAI Embedding** skill connects to an embedding model deployed to your [Azure OpenAI](/azure/ai-services/openai/overview) resource or [Azure AI Foundry](/azure/ai-foundry/what-is-azure-ai-foundry) project to generate embeddings during indexing. Your data is processed in the [Geo](https://azure.microsoft.com/explore/global-infrastructure/data-residency/) where your model is deployed.
+
+The [Import and vectorize data wizard](search-get-started-portal-import-vectors.md) in the Azure portal uses the Azure OpenAI Embedding skill to vectorize content. You can run the wizard and review the generated skillset to see how the wizard builds the skill for embedding models.
+
+> [!NOTE]
+> This skill is bound to Azure OpenAI and is charged at the existing [Azure OpenAI Standard price](https://azure.microsoft.com/pricing/details/cognitive-services/openai-service/#pricing).
 
 ## Prerequisites
 
-Your Azure OpenAI in Azure AI Foundry Models resource must have an associated [custom subdomain](/azure/ai-services/cognitive-services-custom-subdomains). If the service was created through the Azure portal, this subdomain is automatically generated as part of your service setup. Ensure that your service includes a custom subdomain before using it with the Azure AI Search integration.
++ An [Azure OpenAI in Azure AI Foundry Models resource](/azure/ai-foundry/openai/how-to/create-resource) or [Azure AI Foundry project](/azure/ai-foundry/how-to/create-projects).
 
-If you create an AI Service using Azure AI Foundry, it will automatically generate an endpoint with the domain cognitiveservices.azure.com. When you deploy an Azure OpenAI embedding model in this service, you can change the endpoint to use openai.azure.com domain instead, when using it in this skill. For example, change your endpoint from `https://<yourendpoint>.cognitiveservices.azure.com` to `https://<yourendpoint>.openai.azure.com`. Use this updated endpoint as the value for the `resourceUri` property for this skill. 
+  + Your Azure OpenAI resource must have a [custom subdomain](/azure/ai-services/cognitive-services-custom-subdomains), such as `https://<resourcename>.openai.azure.com`. If you created the resource in the Azure portal, this subdomain was automatically generated during resource setup.
 
-The [Import and vectorize data wizard](search-get-started-portal-import-vectors.md) in the Azure portal uses the **Azure OpenAI Embedding** skill to vectorize content. You can run the wizard and review the generated skillset to see how the wizard builds the skill for embedding models.
+  + Your Azure AI Foundry project should have an Azure AI services endpoint with the `cognitiveservices.azure.com` domain. After you deploy an Azure OpenAI embedding model to the project, you must change the endpoint to use the `openai.azure.com` domain. For example, change the endpoint from `https://<resourcename>.cognitiveservices.azure.com` to `https://<resourcename>.openai.azure.com`. You can then use this updated endpoint for the `resourceUri` property in this skill.
 
-> [!NOTE]
-> This skill is bound to Azure OpenAI and is charged at the existing [Azure OpenAI Standard price](https://azure.microsoft.com/pricing/details/cognitive-services/openai-service/#pricing).
++ An [Azure OpenAI embedding model](/azure/ai-services/openai/concepts/models) deployed to your resource or project.
 
 ## @odata.type  
 
@@ -41,7 +45,7 @@ Parameters are case-sensitive.
 
 | Inputs | Description |
 |---------------------|-------------|
-| `resourceUri` | The URI of the model provider, in this case, an Azure OpenAI resource. This parameter only supports URLs with domain `openai.azure.com`, such as `https://<resourcename>.openai.azure.com`. If the Azure OpenAI endpoint has a URL with domain `cognitiveservices.azure.com`, like `https://<resourcename>.cognitiveservices.azure.com`, a [custom subdomain](/azure/ai-services/openai/how-to/use-your-data-securely#enabled-custom-subdomain) with `openai.azure.com` must be created first for the Azure OpenAI resource and use `https://<resourcename>.openai.azure.com` instead. This field is required if your Azure OpenAI resource is deployed behind a Private Endpoint or uses Virtual Network (VNet) integration.  |
+| `resourceUri` | The URI of the model provider. This parameter only supports URLs with the `openai.azure.com` domain, such as `https://<resourcename>.openai.azure.com`. If your Azure OpenAI endpoint has a URL with the `cognitiveservices.azure.com` domain, such as `https://<resourcename>.cognitiveservices.azure.com`, you must create a [custom subdomain](/azure/ai-services/openai/how-to/use-your-data-securely#enabled-custom-subdomain) with `openai.azure.com` for the Azure OpenAI resource and use `https://<resourcename>.openai.azure.com` instead. This field is required if your Azure OpenAI resource is deployed behind a private endpoint or uses Virtual Network (VNet) integration.  |
 | `apiKey`   |  The secret key used to access the model. If you provide a key, leave `authIdentity` empty. If you set both the `apiKey` and `authIdentity`, the `apiKey` is used on the connection. |
 | `deploymentId`   | The name of the deployed Azure OpenAI embedding model. The model should be an embedding model, such as text-embedding-ada-002. See the [List of Azure OpenAI models](/azure/ai-services/openai/concepts/models) for supported models.|
 | `authIdentity`   | A user-managed identity used by the search service for connecting to Azure OpenAI. You can use either a [system or user managed identity](search-how-to-managed-identities.md). To use a system managed identity, leave `apiKey` and `authIdentity` blank. The system-managed identity is used automatically. A managed identity must have [Cognitive Services OpenAI User](/azure/ai-services/openai/how-to/role-based-access-control#azure-openai-roles) permissions to send text to Azure OpenAI. |

@@ -6,7 +6,7 @@ author: haileytap
 ms.author: haileytapia
 ms.service: azure-ai-search
 ms.topic: quickstart
-ms.date: 07/22/2025
+ms.date: 09/12/2025
 ms.custom:
   - references_regions
 ---
@@ -55,13 +55,11 @@ For content embedding, you can choose either image verbalization (followed by te
 
 <sup>1</sup> The endpoint of your Azure OpenAI resource must have a [custom subdomain](/azure/ai-services/cognitive-services-custom-subdomains), such as `https://my-unique-name.openai.azure.com`. If you created your resource in the [Azure portal](https://portal.azure.com/), this subdomain was automatically generated during resource setup.
 
-<sup>2</sup> Azure OpenAI resources (with access to embedding models) that were created in the [Azure AI Foundry portal](https://ai.azure.com/?cid=learnDocs) aren't supported. You must create an Azure OpenAI resource in the Azure portal.
+<sup>2</sup> For billing purposes, you must [attach your Azure AI multi-service resource](cognitive-search-attach-cognitive-services.md) to the skillset in your Azure AI Search service. Unless you use a [keyless connection (preview)](cognitive-search-attach-cognitive-services.md#bill-through-a-keyless-connection) to create the skillset, both resources must be in the same region.
 
-<sup>3</sup> For billing purposes, you must [attach your Azure AI multi-service resource](cognitive-search-attach-cognitive-services.md) to the skillset in your Azure AI Search service. Unless you use a [keyless connection (preview)](cognitive-search-attach-cognitive-services.md#bill-through-a-keyless-connection) to create the skillset, both resources must be in the same region.
+<sup>3</sup> `phi-4` is only available to Azure AI Foundry projects.
 
-<sup>4</sup> `phi-4` is only available to Azure AI Foundry projects.
-
-<sup>5</sup> The Azure portal doesn't support `embed-v-4-0` for vectorization, so don't use it for this quickstart. Instead, use the [AML skill](cognitive-search-aml-skill.md) or [Azure AI Foundry model catalog vectorizer](vector-search-vectorizer-azure-machine-learning-ai-studio-catalog.md) to programmatically specify this model. You can then use the portal to manage the skillset or vectorizer.
+<sup>4</sup> The Azure portal doesn't support `embed-v-4-0` for vectorization, so don't use it for this quickstart. Instead, use the [AML skill](cognitive-search-aml-skill.md) or [Azure AI Foundry model catalog vectorizer](vector-search-vectorizer-azure-machine-learning-ai-studio-catalog.md) to programmatically specify this model. You can then use the portal to manage the skillset or vectorizer.
 
 ### Public endpoint requirements
 

@@ -9,7 +9,7 @@ ms.custom:
   - build-2024
   - ignite-2024
 ms.topic: quickstart
-ms.date: 07/22/2025
+ms.date: 09/12/2025
 ---
 
 # Quickstart: Vectorize text in the Azure portal
@@ -52,13 +52,11 @@ For integrated vectorization, you must use one of the following embedding models
 
 <sup>1</sup> The endpoint of your Azure OpenAI resource must have a [custom subdomain](/azure/ai-services/cognitive-services-custom-subdomains), such as `https://my-unique-name.openai.azure.com`. If you created your resource in the [Azure portal](https://portal.azure.com/), this subdomain was automatically generated during resource setup.
 
-<sup>2</sup> Azure OpenAI resources (with access to embedding models) that were created in the [Azure AI Foundry portal](https://ai.azure.com/?cid=learnDocs) aren't supported. Only Azure OpenAI resources created in the Azure portal are compatible with the [Azure OpenAI Embedding skill](cognitive-search-skill-azure-openai-embedding.md).
+<sup>2</sup> For billing purposes, you must [attach your Azure AI multi-service resource](cognitive-search-attach-cognitive-services.md) to the skillset in your Azure AI Search service. Unless you use a [keyless connection (preview)](cognitive-search-attach-cognitive-services.md#bill-through-a-keyless-connection) to create the skillset, both resources must be in the same region.
 
-<sup>3</sup> For billing purposes, you must [attach your Azure AI multi-service resource](cognitive-search-attach-cognitive-services.md) to the skillset in your Azure AI Search service. Unless you use a [keyless connection (preview)](cognitive-search-attach-cognitive-services.md#bill-through-a-keyless-connection) to create the skillset, both resources must be in the same region.
+<sup>3</sup> The Azure AI Vision multimodal embedding model is available in [select regions](/azure/ai-services/computer-vision/overview-image-analysis#region-availability).
 
-<sup>4</sup> The Azure AI Vision multimodal embedding model is available in [select regions](/azure/ai-services/computer-vision/overview-image-analysis#region-availability).
-
-<sup>5</sup> The Azure portal doesn't support `embed-v-4-0` for vectorization, so don't use it for this quickstart. Instead, use the [AML skill](cognitive-search-aml-skill.md) or [Azure AI Foundry model catalog vectorizer](vector-search-vectorizer-azure-machine-learning-ai-studio-catalog.md) to programmatically specify this model. You can then use the portal to manage the skillset or vectorizer.
+<sup>4</sup> The Azure portal doesn't support `embed-v-4-0` for vectorization, so don't use it for this quickstart. Instead, use the [AML skill](cognitive-search-aml-skill.md) or [Azure AI Foundry model catalog vectorizer](vector-search-vectorizer-azure-machine-learning-ai-studio-catalog.md) to programmatically specify this model. You can then use the portal to manage the skillset or vectorizer.
 
 ### Public endpoint requirements
 

@@ -7,7 +7,7 @@ author: haileytap
 ms.author: haileytapia
 ms.service: azure-ai-search
 ms.topic: how-to
-ms.date: 07/17/2025
+ms.date: 09/12/2025
 ---
 
 # Set up integrated vectorization in Azure AI Search using REST
@@ -52,11 +52,9 @@ For integrated vectorization, you must use one of the following embedding models
 
 <sup>1</sup> The endpoint of your Azure OpenAI resource must have a [custom subdomain](/azure/ai-services/cognitive-services-custom-subdomains), such as `https://my-unique-name.openai.azure.com`. If you created your resource in the [Azure portal](https://portal.azure.com/), this subdomain was automatically generated during resource setup.
 
-<sup>2</sup> Azure OpenAI resources (with access to embedding models) that were created in the [Azure AI Foundry portal](https://ai.azure.com/?cid=learnDocs) aren't supported. Only Azure OpenAI resources created in the Azure portal are compatible with the [Azure OpenAI Embedding skill](cognitive-search-skill-azure-openai-embedding.md).
+<sup>2</sup> For billing purposes, you must [attach your Azure AI multi-service resource](cognitive-search-attach-cognitive-services.md) to the skillset in your Azure AI Search service. Unless you use a [keyless connection (preview)](cognitive-search-attach-cognitive-services.md#bill-through-a-keyless-connection) to create the skillset, both resources must be in the same region.
 
-<sup>3</sup> For billing purposes, you must [attach your Azure AI multi-service resource](cognitive-search-attach-cognitive-services.md) to the skillset in your Azure AI Search service. Unless you use a [keyless connection (preview)](cognitive-search-attach-cognitive-services.md#bill-through-a-keyless-connection) to create the skillset, both resources must be in the same region.
-
-<sup>4</sup> The Azure AI Vision multimodal embedding model is available in [select regions](/azure/ai-services/computer-vision/overview-image-analysis#region-availability).
+<sup>3</sup> The Azure AI Vision multimodal embedding model is available in [select regions](/azure/ai-services/computer-vision/overview-image-analysis#region-availability).
 
 ### Role-based access
 

@@ -6,7 +6,7 @@ author: gmndrg
 ms.author: gimondra
 ms.service: azure-ai-search
 ms.topic: how-to
-ms.date: 08/27/2025
+ms.date: 09/12/2025
 ms.custom:
   - ignite-2023
   - sfi-image-nochange
@@ -125,14 +125,16 @@ The SharePoint Online indexer uses a Microsoft Entra application for authenticat
 
 1. On the navigation pane under **Manage**, select **API permissions**, then **Add a permission**, then **Microsoft Graph**.
 
-    + If the indexer is using application API permissions, select **Application permissions**, and then select **Application.Read.All**.
+    + If the indexer is using application API permissions, select **Application permissions**, and then select `Files.Read.All` and `Sites.Read.All`.
 
+      <!-- RESTORE THIS SCREENSHOT -->
       :::image type="content" source="media/search-howto-index-sharepoint-online/application-api-permissions.png" alt-text="Screenshot of application API permissions." lightbox="media/search-howto-index-sharepoint-online/application-api-permissions.png":::
 
       Using application permissions means that the indexer accesses the SharePoint site in a service context. So when you run the indexer, it has access to all content in the SharePoint tenant, which requires tenant admin approval. A client secret is also required for authentication. Setting up the client secret is described later in this article.
 
-    + If the indexer is using delegated API permissions, select **Delegated permissions** and then select  **Application.Read.All**.
+    + If the indexer is using delegated API permissions, select **Delegated permissions** and then select `Delegated - Files.Read.All`, `Delegated - Sites.Read.All`, and `Delegated - User.Read`.
 
+      <!-- RESTORE THIS SCREENSHOT -->
       :::image type="content" source="media/search-howto-index-sharepoint-online/delegated-api-permissions.png" alt-text="Screenshot showing delegated API permissions." lightbox="media/search-howto-index-sharepoint-online/delegated-api-permissions.png":::
 
       Delegated permissions allow the search client to connect to SharePoint under the security identity of the current user.
@@ -141,21 +143,21 @@ The SharePoint Online indexer uses a Microsoft Entra application for authenticat
 
     Tenant admin consent is required when using application API permissions. Some tenants are locked down in such a way that tenant admin consent is required for delegated API permissions as well. If either of these conditions apply, you’ll need to have a tenant admin grant consent for this Microsoft Entra application before creating the indexer.
 
+    <!-- RESTORE THIS SCREENSHOT -->
     :::image type="content" source="media/search-howto-index-sharepoint-online/aad-app-grant-admin-consent.png" alt-text="Screenshot showing Microsoft Entra app grant admin consent." lightbox="media/search-howto-index-sharepoint-online/aad-app-grant-admin-consent.png":::
 
-1. From the menu, select **Authentication (Preview)**.
+1. Select the **Authentication** tab.
 
-1. On the **Redirect URI configuration** tab, select **+ Add Redirect URI**, then **Mobile and desktop applications**, then check `https://login.microsoftonline.com/common/oauth2/nativeclient`, then **Configure**.
+1. Set **Allow public client flows** to **Yes** then select **Save**.
 
-    :::image type="content" source="media/search-howto-index-sharepoint-online/aad-app-authentication-configuration.png" alt-text="Screenshot showing Microsoft Entra app authentication configuration." lightbox="media/search-howto-index-sharepoint-online/aad-app-authentication-configuration.png" :::
-
-1. Select the **Settings** tab. 
+1. Select **+ Add a platform**, then **Mobile and desktop applications**, then check `https://login.microsoftonline.com/common/oauth2/nativeclient`, then **Configure**.
 
-1. Enable **Allow public client flows**. Save your changes.
+    <!-- RESTORE THIS SCREENSHOT -->
+    :::image type="content" source="media/search-howto-index-sharepoint-online/aad-app-authentication-configuration.png" alt-text="Screenshot showing Microsoft Entra app authentication configuration." lightbox="media/search-howto-index-sharepoint-online/aad-app-authentication-configuration.png" :::
 
 1. (Application API Permissions only) To authenticate to the Microsoft Entra application using application permissions, the indexer requires a client secret.
 
-    + From the menu, select **Certificates & Secrets**, then **Client secrets**, then **New client secret**.
+    + Select **Certificates & Secrets** from the menu on the left, then **Client secrets**, then **New client secret**.
 
       :::image type="content" source="media/search-howto-index-sharepoint-online/application-client-secret.png" alt-text="Screenshot showing new client secret." lightbox="media/search-howto-index-sharepoint-online/application-client-secret.png" :::
 
@@ -165,6 +167,7 @@ The SharePoint Online indexer uses a Microsoft Entra application for authenticat
 
     + The new client secret appears in the secret list. Once you navigate away from the page, the secret is no longer be visible, so copy the value using the copy button and save it in a secure location.
 
+      <!-- RESTORE THIS SCREENSHOT -->
       :::image type="content" source="media/search-howto-index-sharepoint-online/application-client-secret-copy.png" alt-text="Screenshot showing where to copy a client secret.":::
 
 <a name="create-data-source"></a>
@@ -265,6 +268,7 @@ There are a few steps to creating the indexer:
         "parameters": {
         "batchSize": null,
         "maxFailedItems": null,
+        "base64EncodeKeys": null,
         "maxFailedItemsPerBatch": null,
         "configuration": {
             "indexedFileNameExtensions" : ".pdf, .docx",

@@ -8,42 +8,42 @@ ms.service: azure-ai-search
 ms.custom:
   - build-2024
 ms.topic: reference
-ms.date: 07/29/2025
+ms.date: 09/12/2025
 ms.update-cycle: 365-days
 ---
 
 # Azure OpenAI vectorizer
 
-The **Azure OpenAI** vectorizer connects to a deployed embedding model on your [Azure OpenAI](/azure/ai-services/openai/overview) resource to generate embeddings at query time. Your data is processed in the [Geo](https://azure.microsoft.com/explore/global-infrastructure/data-residency/) where your model is deployed. 
+The **Azure OpenAI** vectorizer connects to an embedding model deployed to your [Azure OpenAI](/azure/ai-services/openai/overview) resource or [Azure AI Foundry](/azure/ai-foundry/what-is-azure-ai-foundry) project to generate embeddings at query time. Your data is processed in the [Geo](https://azure.microsoft.com/explore/global-infrastructure/data-residency/) where your model is deployed.
 
-Vectorizers are used at query time, but specified in index definitions, and referenced on vector fields through a vector profile. The Azure OpenAI vectorizer is called `AzureOpenAIVectorizer` in the API. 
+Although vectorizers are used at query time, you specify them in index definitions and reference them on vector fields through a vector profile. For more information, see [Configure a vectorizer in a search index](vector-search-how-to-configure-vectorizer.md).
 
-+ Use the [**2024-07-01**](/rest/api/searchservice/indexes/create) REST API or an Azure SDK package that's been updated to provide the feature.
-
-+ [Configure a vectorizer in a search index](vector-search-how-to-configure-vectorizer.md)provides usage instructions.
+The Azure OpenAI vectorizer is called `AzureOpenAIVectorizer` in the REST API. Use the latest stable version of [Indexes - Create (REST API)](/rest/api/searchservice/indexes/create) or an Azure SDK package that provides the feature.
 
 > [!NOTE]
 > This vectorizer is bound to Azure OpenAI and is charged at the existing [Azure OpenAI Standard price](https://azure.microsoft.com/pricing/details/cognitive-services/openai-service/#pricing).
 
 ## Prerequisites
 
-Your Azure OpenAI in Azure AI Foundry Models must have an associated [custom subdomain](/azure/ai-services/cognitive-services-custom-subdomains). If the service was created through the Azure portal, this subdomain is automatically generated as part of your service setup. Ensure that your service includes a custom subdomain before using it with the Azure AI Search integration.
++ An [Azure OpenAI in Azure AI Foundry Models resource](/azure/ai-foundry/openai/how-to/create-resource) or [Azure AI Foundry project](/azure/ai-foundry/how-to/create-projects).
+
+  + Your Azure OpenAI resource must have a [custom subdomain](/azure/ai-services/cognitive-services-custom-subdomains), such as `https://<resourcename>.openai.azure.com`. If you created the resource in the Azure portal, this subdomain was automatically generated during resource setup.
 
-If you create an AI Service using Azure AI Foundry, it will automatically generate an endpoint with the domain cognitiveservices.azure.com. When you deploy an Azure OpenAI embedding model in this service, you can change the endpoint to use openai.azure.com domain instead, when using it in this skill. For example, change your endpoint from `https://<yourendpoint>.cognitiveservices.azure.com` to `https://<yourendpoint>.openai.azure.com`. Use this updated endpoint as the value for the `resourceUri` property for this skill. 
+  + Your Azure AI Foundry project should have an Azure AI services endpoint with the `cognitiveservices.azure.com` domain. After you deploy an Azure OpenAI embedding model to the project, you must change the endpoint to use the `openai.azure.com` domain. For example, change the endpoint from `https://<resourcename>.cognitiveservices.azure.com` to `https://<resourcename>.openai.azure.com`. You can then use this updated endpoint for the `resourceUri` property in this vectorizer.
 
-Azure OpenAI resources (with access to embedding models) that were created in Azure AI Foundry portal aren't supported. Only the Azure OpenAI resources created in the Azure portal are compatible with the **Azure OpenAI Embedding** skill integration. 
++ An Azure OpenAI embedding model deployed to your resource or project. For supported models, see the next section.
 
 ## Vectorizer parameters
 
 Parameters are case-sensitive.
 
 | Parameter name	 | Description |
 |--------------------|-------------|
-| `resourceUri` | The URI of the model provider, in this case, an Azure OpenAI resource. This parameter only supports URLs with domain `openai.azure.com`, such as `https://<resourcename>.openai.azure.com`. If the Azure OpenAI endpoint has a URL with domain `cognitiveservices.azure.com`, like `https://<resourcename>.cognitiveservices.azure.com`, a [custom subdomain](/azure/ai-services/openai/how-to/use-your-data-securely#enabled-custom-subdomain) with `openai.azure.com` must be created first for the Azure OpenAI resource and use `https://<resourcename>.openai.azure.com` instead.  |
+| `resourceUri` | The URI of the model provider. This parameter only supports URLs with the `openai.azure.com` domain, such as `https://<resourcename>.openai.azure.com`. If your Azure OpenAI endpoint has a URL with the `cognitiveservices.azure.com` domain, such as `https://<resourcename>.cognitiveservices.azure.com`, you must create a [custom subdomain](/azure/ai-services/openai/how-to/use-your-data-securely#enabled-custom-subdomain) with `openai.azure.com` for the Azure OpenAI resource and use `https://<resourcename>.openai.azure.com` instead. |
 | `apiKey`   |  The secret key used to access the model. If you provide a key, leave `authIdentity` empty. If you set both the `apiKey` and `authIdentity`, the `apiKey` is used on the connection. |
 | `deploymentId`   | The name of the deployed Azure OpenAI embedding model. The model should be an embedding model, such as text-embedding-ada-002. See the [List of Azure OpenAI models](/azure/ai-services/openai/concepts/models) for supported models.|
 | `authIdentity`   | A user-managed identity used by the search service for connecting to Azure OpenAI. You can use either a [system or user managed identity](search-how-to-managed-identities.md). To use a system managed identity, leave `apiKey` and `authIdentity` blank. The system-managed identity is used automatically. A managed identity must have [Cognitive Services OpenAI User](/azure/ai-services/openai/how-to/role-based-access-control#azure-openai-roles) permissions to send text to Azure OpenAI. |
-| `modelName` | (Required in API version 2024-05-01-Preview and later). The name of the Azure OpenAI embedding model that is deployed at the provided `resourceUri` and `deploymentId`. Currently supported values are `text-embedding-ada-002`, `text-embedding-3-large`, and `text-embedding-3-small` |
+| `modelName` | (Required in API version 2024-05-01-Preview and later). The name of the Azure OpenAI embedding model that is deployed at the provided `resourceUri` and `deploymentId`. Currently, supported values are `text-embedding-ada-002`, `text-embedding-3-large`, and `text-embedding-3-small`. |
 
 ## Supported vector query types
 

@@ -14,13 +14,11 @@ ms.update-cycle: 365-days
 
 # Custom Web API vectorizer
 
-The **custom web API** vectorizer allows you to configure your search queries to call out to a Web API endpoint to generate embeddings at query time. The structure of the JSON payload required to be implemented in the provided endpoint is described further down in this document. Your data is processed in the [Geo](https://azure.microsoft.com/explore/global-infrastructure/data-residency/) where your model is deployed. 
+The **custom web API** vectorizer allows you to configure your search queries to call out to a Web API endpoint to generate embeddings at query time. The structure of the JSON payload required to be implemented in the provided endpoint is described further down in this document. Your data is processed in the [Geo](https://azure.microsoft.com/explore/global-infrastructure/data-residency/) where your model is deployed.
 
-Vectorizers are used at query time, but specified in index definitions, and referenced on vector fields through a vector profile. The custom web API vectorizer is called `WebApiVectorizer` in the API. 
+Although vectorizers are used at query time, you specify them in index definitions and reference them on vector fields through a vector profile. For more information, see [Configure a vectorizer in a search index](vector-search-how-to-configure-vectorizer.md).
 
-+ Use the [**2024-07-01**](/rest/api/searchservice/indexes/create) REST API or an Azure SDK package that's been updated to provide the feature.
-
-+ [Configure a vectorizer in a search index](vector-search-how-to-configure-vectorizer.md)provides usage instructions.
+The custom web API vectorizer is called `WebApiVectorizer` in the REST API. Use the latest stable version of [Indexes - Create (REST API)](/rest/api/searchservice/indexes/create) or an Azure SDK package that provides the feature.
 
 ## Vectorizer parameters