@@ -20,7 +20,7 @@ The **Azure OpenAI Embedding** skill connects to a deployed embedding model on y
 
 Your Azure OpenAI Service 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.
 
-Azure OpenAI Service resources (with access to embedding models) that were created in AI Foundry portal aren't supported. Only the Azure OpenAI Service resources created in the Azure portal are compatible with the **Azure OpenAI Embedding** skill integration.
+Azure OpenAI Service resources (with access to embedding models) that were created in Azure AI Foundry portal aren't supported. Only the Azure OpenAI Service resources created in the Azure portal are compatible with the **Azure OpenAI Embedding** skill integration.
 
 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. 
 

@@ -67,7 +67,7 @@ landingContent:
     linkLists:
       - linkListType: how-to-guide
         links:
-          - text: Create a vector index in AI Foundry portal
+          - text: Create a vector index in Azure AI Foundry portal
             url: /azure/ai-studio/how-to/index-add
           - text: Chat with your data using Azure OpenAI
             url: /azure/ai-services/openai/use-your-data-quickstart

@@ -52,7 +52,7 @@ Use an embedding model on an Azure AI platform in the [same region as Azure AI S
 
 If you use the Azure OpenAI Service, the endpoint must have an associated [custom subdomain](/azure/ai-services/cognitive-services-custom-subdomains). A custom subdomain is an endpoint that includes a unique name (for example, `https://hereismyuniquename.cognitiveservices.azure.com`). 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.
 
-Azure OpenAI Service resources (with access to embedding models) that were created in AI Foundry portal aren't supported. Only the Azure OpenAI Service resources created in the Azure portal are compatible with the **Azure OpenAI Embedding** skill integration.
+Azure OpenAI Service resources (with access to embedding models) that were created in Azure AI Foundry portal aren't supported. Only the Azure OpenAI Service resources created in the Azure portal are compatible with the **Azure OpenAI Embedding** skill integration.
 
 ### Public endpoint requirements
 
@@ -323,7 +323,7 @@ Chunking is built in and nonconfigurable. The effective settings are:
 
    + For Azure OpenAI, choose an existing deployment of text-embedding-ada-002, text-embedding-3-large, or text-embedding-3-small.
 
-   + For AI Foundry catalog, choose an existing deployment of an Azure or Cohere embedding model.
+   + For Azure AI Foundry catalog, choose an existing deployment of an Azure or Cohere embedding model.
 
    + For AI Vision multimodal embeddings, select the account.
 

@@ -72,7 +72,7 @@ Here are some points to keep in mind about the skills in the following list:
 |------|--------------------|----------------------------------|
 | [AI Vision multimodal](cognitive-search-skill-vision-vectorize.md)  | ❌ | ✅ |
 | [Azure OpenAI embedding](cognitive-search-skill-azure-openai-embedding.md)  | ❌ | ✅ |
-| [Azure Machine Learning (AI Foundry model catalog)](cognitive-search-aml-skill.md)  | ❌ | ✅ |
+| [Azure Machine Learning (Azure AI Foundry model catalog)](cognitive-search-aml-skill.md)  | ❌ | ✅ |
 | [Document layout](cognitive-search-skill-document-intelligence-layout.md)  | ❌ | ✅ |
 | [Entity recognition](cognitive-search-skill-entity-recognition-v3.md)  | ✅ | ❌ |
 | [Image analysis (applies to blobs, default parsing, whole file indexing](cognitive-search-skill-image-analysis.md)  | ✅ | ❌ |

@@ -1,7 +1,7 @@
 ---
 title: Integrated vectorization with models from Azure AI Foundry
 titleSuffix: Azure AI Search
-description: Learn  how to vectorize content during indexing on Azure AI Search with an AI Foundry model.
+description: Learn  how to vectorize content during indexing on Azure AI Search with an Azure AI Foundry model.
 author: gmndrg
 ms.author: gimondra
 ms.service: azure-ai-search
@@ -20,7 +20,7 @@ In this article, learn how to access the embedding models in the [Azure AI Found
 
 The workflow includes model deployment steps. The model catalog includes embedding models from Microsoft and other companies. Deploying a model is billable per the billing structure of each provider. 
 
-After the model is deployed, you can use it for [integrated vectorization](vector-search-integrated-vectorization.md) during indexing, or with the [AI Foundry vectorizer](vector-search-vectorizer-azure-machine-learning-ai-studio-catalog.md) for queries.
+After the model is deployed, you can use it for [integrated vectorization](vector-search-integrated-vectorization.md) during indexing, or with the [Azure AI Foundry vectorizer](vector-search-vectorizer-azure-machine-learning-ai-studio-catalog.md) for queries.
 
 > [!TIP]
 > Use the [**Import and vectorize data**](search-get-started-portal-import-vectors.md) wizard to generate a skillset that includes an AML skill for deployed embedding models on Azure AI Foundry. AML skill definition for inputs, outputs, and mappings are generated by the wizard, which gives you an easy way to test a model before writing any code.
@@ -65,7 +65,7 @@ For image embeddings:
 
     Optionally, you can change your endpoint to use **Token authentication** instead of **Key authentication**. If you enable token authentication, you only need to copy the URI and model name,  but make a note of which region the model is deployed to.
 
-    :::image type="content" source="media\vector-search-integrated-vectorization-ai-studio\ai-studio-fields-to-copy.png" lightbox="media\vector-search-integrated-vectorization-ai-studio\ai-studio-fields-to-copy.png" alt-text="Screenshot of a deployed endpoint in AI Foundry portal highlighting the fields to copy and save for later.":::
+    :::image type="content" source="media\vector-search-integrated-vectorization-ai-studio\ai-studio-fields-to-copy.png" lightbox="media\vector-search-integrated-vectorization-ai-studio\ai-studio-fields-to-copy.png" alt-text="Screenshot of a deployed endpoint in Azure AI Foundry portal highlighting the fields to copy and save for later.":::
 
 1. You can now configure a search index and indexer to use the deployed model. 
 
@@ -81,7 +81,7 @@ This section describes the AML skill definition and index mappings. It includes
 
 <!-- ### [**Text Input for "Inference" API**](#tab/inference-text)
 
-This AML skill payload works with the following models from AI Foundry:
+This AML skill payload works with the following models from Azure AI Foundry:
 
 + Cohere-embed-v3-english
 + Cohere-embed-v3-multilingual
@@ -126,7 +126,7 @@ The URI and key are generated when you deploy the model from the catalog. For mo
 
 ### [**Facebook embedding models**](#tab/inference-image)
 
-This AML skill payload works with the following image embedding models from AI Foundry:
+This AML skill payload works with the following image embedding models from Azure AI Foundry:
 
 + Facebook-DinoV2-Image-Embeddings-ViT-Base
 + Facebook-DinoV2-Image-Embeddings-ViT-Giant
@@ -172,14 +172,14 @@ The URI and key are generated when you deploy the model from the catalog. For mo
 
 ### [**Cohere embedding models**](#tab/cohere)
 
-This AML skill payload works with the following text embedding models from AI Foundry:
+This AML skill payload works with the following text embedding models from Azure AI Foundry:
 
 + Cohere-embed-v3-english
 + Cohere-embed-v3-multilingual
 
 It assumes that you're chunking your content using the Text Split skill and therefore your text to be vectorized is in the `/document/pages/*` path. If your text comes from a different path, update all references to the `/document/pages/*` path accordingly.
 
-You must add the `/v1/embed` path onto the end of the URL that you copied from your AI Foundry deployment. You might also change the values for the `input_type`, `truncate` and `embedding_types` inputs to better fit your use case. For more information on the available options, review the [Cohere Embed API reference](/azure/ai-studio/how-to/deploy-models-cohere-embed).
+You must add the `/v1/embed` path onto the end of the URL that you copied from your Azure AI Foundry deployment. You might also change the values for the `input_type`, `truncate` and `embedding_types` inputs to better fit your use case. For more information on the available options, review the [Cohere Embed API reference](/azure/ai-studio/how-to/deploy-models-cohere-embed).
 
 The URI and key are generated when you deploy the model from the catalog. For more information about these values, see [How to deploy Cohere Embed models with Azure AI Foundry](/azure/ai-studio/how-to/deploy-models-cohere-embed).
 
@@ -243,11 +243,11 @@ If you selected a different `embedding_types` in your skill definition that you
 
 ---
 
-## Sample AI Foundry vectorizer payload
+## Sample Azure AI Foundry vectorizer payload
 
-The [AI Foundry vectorizer](vector-search-vectorizer-azure-machine-learning-ai-studio-catalog.md), unlike the AML skill, is tailored to work only with those embedding models that are deployable via the AI Foundry model catalog. The main difference is that you don't have to worry about the request and response payload, but you do have to provide the `modelName`, which corresponds to the "Model ID" that you copied after deploying the model in AI Foundry portal. 
+The [Azure AI Foundry vectorizer](vector-search-vectorizer-azure-machine-learning-ai-studio-catalog.md), unlike the AML skill, is tailored to work only with those embedding models that are deployable via the Azure AI Foundry model catalog. The main difference is that you don't have to worry about the request and response payload, but you do have to provide the `modelName`, which corresponds to the "Model ID" that you copied after deploying the model in Azure AI Foundry portal. 
 
-Here's a sample payload of how you would configure the vectorizer on your index definition given the properties copied from AI Foundry.
+Here's a sample payload of how you would configure the vectorizer on your index definition given the properties copied from Azure AI Foundry.
 
 For Cohere models, you should NOT add the `/v1/embed` path to the end of your URL like you did with the skill.
 
@@ -267,7 +267,7 @@ For Cohere models, you should NOT add the `/v1/embed` path to the end of your UR
 
 ## Connect using token authentication
 
-If you can't use key-based authentication, you can instead configure the AML skill and AI Foundry vectorizer connection for [token authentication](../machine-learning/how-to-authenticate-online-endpoint.md) via role-based access control on Azure. The search service must have a [system or user-assigned managed identity](search-howto-managed-identities-data-sources.md), and the identity must have Owner or Contributor permissions for your AML project workspace. You can then remove the key field from your skill and vectorizer definition, replacing it with the resourceId field. If your AML project and search service are in different regions, also provide the region field.
+If you can't use key-based authentication, you can instead configure the AML skill and Azure AI Foundry vectorizer connection for [token authentication](../machine-learning/how-to-authenticate-online-endpoint.md) via role-based access control on Azure. The search service must have a [system or user-assigned managed identity](search-howto-managed-identities-data-sources.md), and the identity must have Owner or Contributor permissions for your AML project workspace. You can then remove the key field from your skill and vectorizer definition, replacing it with the resourceId field. If your AML project and search service are in different regions, also provide the region field.
 
 ```json
 "uri": "<YOUR_URL_HERE>",

@@ -27,7 +27,7 @@ Parameters are case-sensitive. Which parameters you choose to use depends on wha
 | Parameter name | Description |
 |--------------------|-------------|
 | `uri` | (Required) The [URI of the AML online endpoint](../machine-learning/how-to-authenticate-online-endpoint.md) to which the _JSON_ payload is sent. Only the **https** URI scheme is allowed. |
-| `modelName` | (Required) The model ID from the AI Foundry model catalog that is deployed at the provided endpoint. Supported models are: <ul><li>Facebook-DinoV2-Image-Embeddings-ViT-Base </li><li>Facebook-DinoV2-Image-Embeddings-ViT-Giant </li><li>Cohere-embed-v3-english </li><li>Cohere-embed-v3-multilingual</ul> |
+| `modelName` | (Required) The model ID from the Azure AI Foundry model catalog that is deployed at the provided endpoint. Supported models are: <ul><li>Facebook-DinoV2-Image-Embeddings-ViT-Base </li><li>Facebook-DinoV2-Image-Embeddings-ViT-Giant </li><li>Cohere-embed-v3-english </li><li>Cohere-embed-v3-multilingual</ul> |
 | `key` | (Required for [key authentication](#WhatParametersToUse)) The [key for the AML online endpoint](../machine-learning/how-to-authenticate-online-endpoint.md). |
 | `resourceId` | (Required for [token authentication](#WhatParametersToUse)). The Azure Resource Manager resource ID of the AML online endpoint. It should be in the format subscriptions/{guid}/resourceGroups/{resource-group-name}/Microsoft.MachineLearningServices/workspaces/{workspace-name}/onlineendpoints/{endpoint_name}. |
 | `region` | (Optional for [token authentication](#WhatParametersToUse)). The [region](https://azure.microsoft.com/global-infrastructure/regions/) the AML online endpoint is deployed in. Needed if the region is different from the region of the search service. |
@@ -47,7 +47,7 @@ Which authentication parameters are required depends on what authentication your
 
 ## Supported vector query types
 
-Which vector query types are supported by the AI Foundry model catalog vectorizer depends on the `modelName` that is configured.
+Which vector query types are supported by the Azure AI Foundry model catalog vectorizer depends on the `modelName` that is configured.
 
 | Embedding model | Supports `text` query | Supports `imageUrl` query | Supports `imageBinary` query |
 |--------------------|-------------|-------------|-------------|
@@ -58,7 +58,7 @@ Which vector query types are supported by the AI Foundry model catalog vectorize
 
 ## Expected field dimensions
 
-The expected field dimensions for a field configured with an AI Foundry model catalog vectorizer depend on the `modelName` that is configured.
+The expected field dimensions for a field configured with an Azure AI Foundry model catalog vectorizer depend on the `modelName` that is configured.
 
 | `modelName` | Expected dimensions |
 |--------------------|-------------|

@@ -29,7 +29,7 @@ Vectorizers are used at query time, but specified in index definitions, and refe
 
 Your Azure OpenAI Service 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.
 
-Azure OpenAI Service resources (with access to embedding models) that were created in AI Foundry portal aren't supported. Only the Azure OpenAI Service resources created in the Azure portal are compatible with the **Azure OpenAI Embedding** skill integration. 
+Azure OpenAI Service resources (with access to embedding models) that were created in Azure AI Foundry portal aren't supported. Only the Azure OpenAI Service resources created in the Azure portal are compatible with the **Azure OpenAI Embedding** skill integration. 
 
 ## Vectorizer parameters