@@ -16,17 +16,17 @@ ms.date: 08/05/2024
 # AML skill in an Azure AI Search enrichment pipeline
 
 > [!IMPORTANT]
-> Support for indexer connections to the Azure AI Studio model catalog is in public preview under [supplemental terms of use](https://azure.microsoft.com/support/legal/preview-supplemental-terms/). Preview REST APIs support this skill.
+> Support for indexer connections to the Azure AI Foundry model catalog is in public preview under [supplemental terms of use](https://azure.microsoft.com/support/legal/preview-supplemental-terms/). Preview REST APIs support this skill.
 
 The **AML** skill allows you to extend AI enrichment with a custom [Azure Machine Learning (AML)](../machine-learning/overview-what-is-azure-machine-learning.md) model. Once an AML model is [trained and deployed](../machine-learning/concept-azure-machine-learning-architecture.md#workspace), an **AML** skill integrates it into AI enrichment.
 
 Like other built-in skills, an **AML** skill has inputs and outputs. The inputs are sent to your deployed AML online endpoint as a JSON object, which outputs a JSON payload as a response along with a success status code. Your data is processed in the [Geo](https://azure.microsoft.com/explore/global-infrastructure/data-residency/) where your model is deployed. The response is expected to have the outputs specified by your **AML** skill. Any other response is considered an error and no enrichments are performed.
 
-The **AML** skill can be called with the 2024-07-01 stable API version or the 2024-05-01-preview API version for connections to the model catalog in Azure AI Studio.
+The **AML** skill can be called with the 2024-07-01 stable API version or the 2024-05-01-preview API version for connections to the model catalog in Azure AI Foundry portal.
 
-Starting in 2024-05-01-preview REST API and in the Azure portal (which also targets the 2024-05-01-preview), Azure AI Search introduced the [Azure AI Studio model catalog vectorizer](vector-search-vectorizer-azure-machine-learning-ai-studio-catalog.md) for query time connections to the model catalog in Azure AI Studio. If you want to use that vectorizer for queries, the **AML** skill is the *indexing counterpart* for generating embeddings using a model in the Azure AI Studio model catalog. 
+Starting in 2024-05-01-preview REST API and in the Azure portal (which also targets the 2024-05-01-preview), Azure AI Search introduced the [Azure AI Foundry model catalog vectorizer](vector-search-vectorizer-azure-machine-learning-ai-studio-catalog.md) for query time connections to the model catalog in Azure AI Foundry portal. If you want to use that vectorizer for queries, the **AML** skill is the *indexing counterpart* for generating embeddings using a model in the Azure AI Foundry model catalog. 
 
-During indexing, the **AML** skill can connect to the model catalog to generate vectors for the index. At query time, queries can use a vectorizer to connect to the same model to vectorize text strings for a vector query. In this workflow, the **AML** skill and the model catalog vectorizer should be used together so that you're using the same embedding model for both indexing and queries. See [How to implement integrated vectorization using models from Azure AI Studio](vector-search-integrated-vectorization-ai-studio.md) for details on this workflow.
+During indexing, the **AML** skill can connect to the model catalog to generate vectors for the index. At query time, queries can use a vectorizer to connect to the same model to vectorize text strings for a vector query. In this workflow, the **AML** skill and the model catalog vectorizer should be used together so that you're using the same embedding model for both indexing and queries. See [How to implement integrated vectorization using models from Azure AI Foundry](vector-search-integrated-vectorization-ai-studio.md) for details on this workflow.
 
 > [!NOTE]
 > The indexer will retry twice for certain standard HTTP status codes returned from the AML online endpoint. These HTTP status codes are:
@@ -172,4 +172,4 @@ For cases when the AML online endpoint is unavailable or returns an HTTP error,
 
 + [How to define a skillset](cognitive-search-defining-skillset.md)
 + [AML online endpoint troubleshooting](../machine-learning/how-to-troubleshoot-online-endpoints.md)
-+ [Integrated vectorization with models from Azure AI Studio](vector-search-integrated-vectorization-ai-studio.md)
++ [Integrated vectorization with models from Azure AI Foundry](vector-search-integrated-vectorization-ai-studio.md)

@@ -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 Studio 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 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. 
 

@@ -63,11 +63,11 @@ landingContent:
             url: https://github.com/Azure/azure-search-vector-samples/blob/main/README.md
 
   # Card
-  - title: Azure AI Studio
+  - title: Azure AI Foundry
     linkLists:
       - linkListType: how-to-guide
         links:
-          - text: Create a vector index in AI Studio
+          - text: Create a vector index in 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

@@ -40,7 +40,7 @@ Learning paths are a collection of training modules that are organized around sp
 
 ## RAG-centric modules
 
-+ [Build a RAG-based copilot solution with your own data using Azure AI Studio](/training/modules/build-copilot-ai-studio/)
++ [Build a RAG-based copilot solution with your own data using Azure AI Foundry](/training/modules/build-copilot-ai-studio/)
 + [Implement Retrieval Augmented Generation (RAG) with Azure OpenAI Service](/training/modules/use-own-data-azure-openai/)
 
 ## Pluralsight training

@@ -37,7 +37,7 @@ Azure AI Search is a [proven solution for information retrieval](/azure/develope
 
 Microsoft has several built-in implementations for using Azure AI Search in a RAG solution.
 
-+ Azure AI Studio, [use a vector index and retrieval augmentation](/azure/ai-studio/concepts/retrieval-augmented-generation). 
++ Azure AI Foundry, [use a vector index and retrieval augmentation](/azure/ai-studio/concepts/retrieval-augmented-generation). 
 + Azure OpenAI, [use a search index with or without vectors](/azure/ai-services/openai/concepts/use-your-data).
 + Azure Machine Learning, [use a search index as a vector store in a prompt flow](/azure/machine-learning/how-to-create-vector-index).
 
@@ -78,7 +78,7 @@ The information retrieval system provides the searchable index, query logic, and
 
 The LLM receives the original prompt, plus the results from Azure AI Search. The LLM analyzes the results and formulates a response. If the LLM is ChatGPT, the user interaction might be a back and forth conversation. If you're using Davinci, the prompt might be a fully composed answer. An Azure solution most likely uses Azure OpenAI, but there's no hard dependency on this specific service.
 
-Azure AI Search doesn't provide native LLM integration for prompt flows or chat preservation, so you need to write code that handles orchestration and state. You can review demo source ([Azure-Samples/azure-search-openai-demo](https://github.com/Azure-Samples/azure-search-openai-demo)) for a blueprint of what a full solution entails. We also recommend [Azure AI Studio](/azure/ai-studio/concepts/retrieval-augmented-generation) to create RAG-based Azure AI Search solutions that integrate with LLMs.
+Azure AI Search doesn't provide native LLM integration for prompt flows or chat preservation, so you need to write code that handles orchestration and state. You can review demo source ([Azure-Samples/azure-search-openai-demo](https://github.com/Azure-Samples/azure-search-openai-demo)) for a blueprint of what a full solution entails. We also recommend [Azure AI Foundry](/azure/ai-studio/concepts/retrieval-augmented-generation) to create RAG-based Azure AI Search solutions that integrate with LLMs.
 
 ## Searchable content in Azure AI Search
 
@@ -99,7 +99,7 @@ Since you probably know what kind of content you want to search over, consider t
 
  <sup>1</sup> Azure AI Search provides [integrated data chunking and vectorization](vector-search-integrated-vectorization.md), but you must take a dependency on indexers and skillsets. If you can't use an indexer, Microsoft's [Semantic Kernel](/semantic-kernel/overview/) or other community offerings can help you with a full stack solution. For code samples showing both approaches, see [azure-search-vectors repo](https://github.com/Azure/azure-search-vector-samples).
 
-<sup>2</sup> [Skills](cognitive-search-working-with-skillsets.md) are built-in support for [applied AI](cognitive-search-concept-intro.md). For OCR and Image Analysis, the indexing pipeline makes an internal call to the Azure AI Vision APIs. These skills pass an extracted image to Azure AI for processing, and receive the output as text that's indexed by Azure AI Search. Skills are also used for integrated data chunking (Text Split skill) and integrated embedding (skills that call Azure AI Vision multimodal, Azure OpenAI, and models in the Azure AI Studio model catalog.)
+<sup>2</sup> [Skills](cognitive-search-working-with-skillsets.md) are built-in support for [applied AI](cognitive-search-concept-intro.md). For OCR and Image Analysis, the indexing pipeline makes an internal call to the Azure AI Vision APIs. These skills pass an extracted image to Azure AI for processing, and receive the output as text that's indexed by Azure AI Search. Skills are also used for integrated data chunking (Text Split skill) and integrated embedding (skills that call Azure AI Vision multimodal, Azure OpenAI, and models in the Azure AI Foundry model catalog.)
 
 Vectors provide the best accommodation for dissimilar content (multiple file formats and languages) because content is expressed universally in mathematic representations. Vectors also support similarity search: matching on the coordinates that are most similar to the vector query. Compared to keyword search (or term search) that matches on tokenized terms, similarity search is more nuanced. It's a better choice if there's ambiguity or interpretation requirements in the content or in queries.
 

@@ -36,7 +36,7 @@ Preview features are removed from this list if they're retired or transition to
 | [**Target filters in a hybrid search to just the vector queries**](hybrid-search-how-to-query.md#hybrid-search-with-filters-targeting-vector-subqueries-preview) | Query | A filter on a hybrid query involves all subqueries on the request, regardless of type. You can override the global filter to scope the filter to a specific subquery. A new `filterOverride` parameter provides the behaviors. | [Search Documents (preview)](/rest/api/searchservice/documents/search-post?view=rest-searchservice-2024-09-01-preview&preserve-view=true). |
 | [**Text Split skill (token chunking)**](cognitive-search-skill-textsplit.md) | Applied AI (skills) | This skill has new parameters that improve data chunking for embedding models. A new `unit` parameter lets you specify token chunking. You can now chunk by token length, setting the length to a value that makes sense for your embedding model. You can also specify the tokenizer and any tokens that shouldn't be split during data chunking. | [Create or Update Skillset (preview)](/rest/api/searchservice/skillsets/create-or-update?view=rest-searchservice-2024-09-01-preview&preserve-view=true). |
 | [**Azure AI Vision multimodal embedding skill**](cognitive-search-skill-vision-vectorize.md) | Applied AI (skills) | A new skill type that calls Azure AI Vision multimodal API to generate embeddings for text or images during indexing. | [Create or Update Skillset (preview)](/rest/api/searchservice/skillsets/create-or-update?view=rest-searchservice-2024-05-01-preview&preserve-view=true). |
-| [**Azure Machine Learning (AML) skill**](cognitive-search-aml-skill.md) | Applied AI (skills) | AML skill integrates an inferencing endpoint from Azure Machine Learning. In previous preview APIs, it supports connections to deployed custom models in an AML workspace. Starting in the 2024-05-01-preview, you can use this skill in workflows that connect to embedding models in the Azure AI Studio model catalog. It's also available in the portal, in skillset design, assuming Azure AI Search and Azure Machine Learning services are deployed in the same subscription. | [Create or Update Skillset (preview)](/rest/api/searchservice/skillsets/create-or-update?view=rest-searchservice-2024-05-01-preview&preserve-view=true). |
+| [**Azure Machine Learning (AML) skill**](cognitive-search-aml-skill.md) | Applied AI (skills) | AML skill integrates an inferencing endpoint from Azure Machine Learning. In previous preview APIs, it supports connections to deployed custom models in an AML workspace. Starting in the 2024-05-01-preview, you can use this skill in workflows that connect to embedding models in the Azure AI Foundry model catalog. It's also available in the portal, in skillset design, assuming Azure AI Search and Azure Machine Learning services are deployed in the same subscription. | [Create or Update Skillset (preview)](/rest/api/searchservice/skillsets/create-or-update?view=rest-searchservice-2024-05-01-preview&preserve-view=true). |
 | [**Incremental enrichment cache**](cognitive-search-incremental-indexing-conceptual.md) | Applied AI (skills) | Adds caching to an enrichment pipeline, allowing you to reuse existing output if a targeted modification, such as an update to a skillset or another object, doesn't change the content. Caching applies only to enriched documents produced by a skillset.| [Create or Update Indexer (preview)](/rest/api/searchservice/indexers/create-or-update?view=rest-searchservice-2024-05-01-preview&preserve-view=true). |
 |  [**OneLake files indexer**](search-how-to-index-onelake-files.md) | Indexer data source | New data source for extracting searchable data and metadata data from a [lakehouse](/fabric/onelake/create-lakehouse-onelake) on top of [OneLake](/fabric/onelake/onelake-overview) | [Create or Update Data Source (preview)](/rest/api/searchservice/data-sources/create-or-update?view=rest-searchservice-2024-05-01-preview&preserve-view=true). |
 |  [**Azure Files indexer**](search-file-storage-integration.md) | Indexer data source | New data source for indexer-based indexing from [Azure Files](https://azure.microsoft.com/services/storage/files/) | [Create or Update Data Source (preview)](/rest/api/searchservice/data-sources/create-or-update?view=rest-searchservice-2024-05-01-preview&preserve-view=true). |

@@ -189,7 +189,7 @@ sections:
       - question: |
           Does Azure AI Search process customer data in other regions?
         answer: |
-          Processing (vectorization or applied AI transformations) is performed in the Geo that hosts the Azure AI services used by skills, or the Azure apps or functions hosting custom skills, or the Azure OpenAI or Azure AI Studio region that hosts your deployed models. These resources are specified by you, so you can choose whether to provision them in the same Geo as your search service or not
+          Processing (vectorization or applied AI transformations) is performed in the Geo that hosts the Azure AI services used by skills, or the Azure apps or functions hosting custom skills, or the Azure OpenAI or Azure AI Foundry region that hosts your deployed models. These resources are specified by you, so you can choose whether to provision them in the same Geo as your search service or not
           
           If you send data to external (non-Azure) models or services, the processing location is determined by the external service. 
 

@@ -41,7 +41,7 @@ There's feature parity in all Azure public, private, and sovereign clouds, but s
 | Vector search algorithms | Use [Hierarchical Navigable Small World (HNSW)](vector-search-ranking.md#when-to-use-hnsw) or [exhaustive K-Nearest Neighbors (KNN)](vector-search-ranking.md#when-to-use-exhaustive-knn) to find similar vectors in a search index. |
 | Vector filters | [Apply filters before or after query execution](vector-search-filters.md) for greater precision during information retrieval. |
 | Hybrid information retrieval | Search for concepts and keywords in a single [hybrid query request](hybrid-search-how-to-query.md). </p>[**Hybrid search**](hybrid-search-overview.md) consolidates vector and text search, with optional semantic ranking and relevance tuning for best results.|
-| Integrated data chunking and vectorization | Native data chunking through [Text Split skill](cognitive-search-skill-textsplit.md). Native vectorization through [vectorizers](vector-search-how-to-configure-vectorizer.md) and embedding skills such as [AzureOpenAIEmbeddingModel](cognitive-search-skill-azure-openai-embedding.md), [Azure AI Vision multimodal](cognitive-search-skill-vision-vectorize.md), and the [AML skill](cognitive-search-aml-skill.md) that you can use to connect to endpoints in the Azure AI Studio model catalog. </p>[**Integrated vectorization**](vector-search-integrated-vectorization.md) provides an end-to-end indexing pipeline from source files to queries.|
+| Integrated data chunking and vectorization | Native data chunking through [Text Split skill](cognitive-search-skill-textsplit.md). Native vectorization through [vectorizers](vector-search-how-to-configure-vectorizer.md) and embedding skills such as [AzureOpenAIEmbeddingModel](cognitive-search-skill-azure-openai-embedding.md), [Azure AI Vision multimodal](cognitive-search-skill-vision-vectorize.md), and the [AML skill](cognitive-search-aml-skill.md) that you can use to connect to endpoints in the Azure AI Foundry model catalog. </p>[**Integrated vectorization**](vector-search-integrated-vectorization.md) provides an end-to-end indexing pipeline from source files to queries.|
 | Integrated vector compression and quantization | Use [built-in scalar and binary quantization](vector-search-how-to-quantization.md) to reduce vector index size in memory and on disk. You can also forego storage of vectors you don't need, or assign narrow data types to vector fields for reduced storage requirements. |
 
 ## Applied AI and knowledge mining

@@ -47,12 +47,12 @@ Use an embedding model on an Azure AI platform in the [same region as Azure AI S
 | Provider | Supported models |
 |---|---|
 | [Azure OpenAI Service](https://aka.ms/oai/access) | text-embedding-ada-002, text-embedding-3-large, or text-embedding-3-small. |
-| [Azure AI Studio model catalog](/azure/ai-studio/what-is-ai-studio) |  Azure, Cohere, and Facebook embedding models. |
+| [Azure AI Foundry model catalog](/azure/ai-studio/what-is-ai-studio) |  Azure, Cohere, and Facebook embedding models. |
 | [Azure AI services multi-service account](/azure/ai-services/multi-service-resource) | [Azure AI Vision multimodal](/azure/ai-services/computer-vision/how-to/image-retrieval) for image and text vectorization. Azure AI Vision multimodal is available in selected regions. [Check the documentation](/azure/ai-services/computer-vision/how-to/image-retrieval?tabs=csharp) for an updated list. Depending on how you [attach the multi-service resource](cognitive-search-attach-cognitive-services.md), the account might need to be in the same region as Azure AI Search. |
 
 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 Studio 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 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
 
@@ -157,7 +157,7 @@ This section points you to the content that works for this quickstart.
 
 ## Set up embedding models
 
-The wizard can use embedding models deployed from Azure OpenAI, Azure AI Vision, or from the model catalog in Azure AI Studio.
+The wizard can use embedding models deployed from Azure OpenAI, Azure AI Vision, or from the model catalog in Azure AI Foundry portal.
 
 ### [Azure OpenAI](#tab/model-aoai)
 
@@ -181,7 +181,7 @@ The wizard supports text-embedding-ada-002, text-embedding-3-large, and text-emb
 
 1. On the **Overview** page, select **Click here to view endpoints** or **Click here to manage keys** if you need to copy an endpoint or API key. You can paste these values into the wizard if you're using an Azure OpenAI resource with key-based authentication.
 
-1. Under **Resource Management** and **Model deployments**, select **Manage Deployments** to open Azure AI Studio.
+1. Under **Resource Management** and **Model deployments**, select **Manage Deployments** to open Azure AI Foundry.
 
 1. Copy the deployment name of `text-embedding-ada-002` or another supported embedding model. If you don't have an embedding model, deploy one now.
 
@@ -200,15 +200,15 @@ After you finish these steps, you should be able to select the Azure AI Vision v
 > [!NOTE]
 > If you can't select an Azure AI Vision vectorizer, make sure you have an Azure AI Vision resource in a supported region. Also make sure that your search service's managed identity has **Cognitive Services OpenAI User** permissions.
 
-### [Azure AI Studio model catalog](#tab/model-catalog)
+### [Azure AI Foundry model catalog](#tab/model-catalog)
 
-The wizard supports Azure, Cohere, and Facebook embedding models in the Azure AI Studio model catalog, but it doesn't currently support the OpenAI CLIP model. Internally, the wizard calls the [AML skill](cognitive-search-aml-skill.md) to connect to the catalog.
+The wizard supports Azure, Cohere, and Facebook embedding models in the Azure AI Foundry model catalog, but it doesn't currently support the OpenAI CLIP model. Internally, the wizard calls the [AML skill](cognitive-search-aml-skill.md) to connect to the catalog.
 
-1. For the model catalog, you should have an [Azure OpenAI resource](/azure/ai-services/openai/how-to/create-resource), a [hub in Azure AI Studio](/azure/ai-studio/how-to/create-projects), and a [project](/azure/ai-studio/how-to/create-projects). Hubs and projects having the same name can share connection information and permissions.
+1. For the model catalog, you should have an [Azure OpenAI resource](/azure/ai-services/openai/how-to/create-resource), a [hub in Azure AI Foundry portal](/azure/ai-studio/how-to/create-projects), and a [project](/azure/ai-studio/how-to/create-projects). Hubs and projects having the same name can share connection information and permissions.
 
 1. Deploy a supported embedding model to the model catalog in your project.
 
-1. For role-based connections, create two role assignments: one for Azure AI Search, and another for the AI Studio project. Assign the [Cognitive Services OpenAI User](/azure/ai-services/openai/how-to/role-based-access-control) role for embeddings and vectorization.
+1. For role-based connections, create two role assignments: one for Azure AI Search, and another for the AI Foundry project. Assign the [Cognitive Services OpenAI User](/azure/ai-services/openai/how-to/role-based-access-control) role for embeddings and vectorization.
 
 ---
 
@@ -312,7 +312,7 @@ Chunking is built in and nonconfigurable. The effective settings are:
 1. On the **Vectorize your text** page, choose the source of the embedding model:
 
    + Azure OpenAI
-   + Azure AI Studio model catalog
+   + Azure AI Foundry model catalog
    + An existing Azure AI Vision multimodal resource in the same region as Azure AI Search. If there's no [Azure AI Services multi-service account](/azure/ai-services/multi-service-resource) in the same region, this option isn't available.
 
 1. Choose the Azure subscription.
@@ -321,7 +321,7 @@ 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 Studio catalog, choose an existing deployment of an Azure, Cohere, and Facebook embedding model.
+   + For AI Foundry catalog, choose an existing deployment of an Azure, Cohere, and Facebook embedding model.
 
    + For AI Vision multimodal embeddings, select the account.
 
@@ -349,11 +349,11 @@ However, if you work with content that includes useful images, you can apply AI
 
 Azure AI Search and your Azure AI resource must be in the same region or configured for [keyless billing connections](cognitive-search-attach-cognitive-services.md).
 
-1. On the **Vectorize your images** page, specify the kind of connection the wizard should make. For image vectorization, the wizard can connect to embedding models in Azure AI Studio or Azure AI Vision.
+1. On the **Vectorize your images** page, specify the kind of connection the wizard should make. For image vectorization, the wizard can connect to embedding models in Azure AI Foundry portal or Azure AI Vision.
 
 1. Specify the subscription.
 
-1. For the Azure AI Studio model catalog, specify the project and deployment. For more information, see [Set up embedding models](#set-up-embedding-models) earlier in this article.
+1. For the Azure AI Foundry model catalog, specify the project and deployment. For more information, see [Set up embedding models](#set-up-embedding-models) earlier in this article.
 
 1. Optionally, you can crack binary images (for example, scanned document files) and [use OCR](cognitive-search-skill-ocr.md) to recognize text.
 
@@ -413,7 +413,7 @@ When the wizard completes the configuration, it creates the following objects:
 
 + Index with vector fields, vectorizers, vector profiles, and vector algorithms. You can't design or modify the default index during the wizard workflow. Indexes conform to the [2024-05-01-preview REST API](/rest/api/searchservice/indexes/create-or-update?view=rest-searchservice-2024-05-01-preview&preserve-view=true).
 
-+ Skillset with the [Text Split skill](cognitive-search-skill-textsplit.md) for chunking and an embedding skill for vectorization. The embedding skill is either the [AzureOpenAIEmbeddingModel skill](cognitive-search-skill-azure-openai-embedding.md) for Azure OpenAI or the [AML skill](cognitive-search-aml-skill.md) for the Azure AI Studio model catalog. The skillset also has the [index projections](index-projections-concept-intro.md) configuration that allows data to be mapped from one document in the data source to its corresponding chunks in a "child" index.
++ Skillset with the [Text Split skill](cognitive-search-skill-textsplit.md) for chunking and an embedding skill for vectorization. The embedding skill is either the [AzureOpenAIEmbeddingModel skill](cognitive-search-skill-azure-openai-embedding.md) for Azure OpenAI or the [AML skill](cognitive-search-aml-skill.md) for the Azure AI Foundry model catalog. The skillset also has the [index projections](index-projections-concept-intro.md) configuration that allows data to be mapped from one document in the data source to its corresponding chunks in a "child" index.
 
 + Indexer with field mappings and output field mappings (if applicable).
 
@@ -481,7 +481,7 @@ Search Explorer accepts text strings as input and then vectorizes the text for v
 
    Each document is a chunk of the original PDF. The `title` field shows which PDF the chunk comes from. Each `chunk` is quite long. You can copy and paste one into a text editor to read the entire value.
 
-1. To see all of the chunks from a specific document, add a filter for the `text_parent_id` field for a specific PDF. You can check the **Fields** tab of your index to confirm this field is filterable.
+1. To see all of the chunks from a specific document, add a filter for the `title_parent_id` field for a specific PDF. You can check the **Fields** tab of your index to confirm this field is filterable.
 
    ```json
    {

@@ -64,7 +64,7 @@ Here are the limitations of this feature:
 
 + Indexing sub-sites recursively from a specific site provided isn't supported.
 
-+ SharePoint Online indexer isn't supported when [Microsoft ENTRA ID Conditional Access](/entra/identity/conditional-access/overview) is enabled.
++ SharePoint Online indexer isn't supported when [Microsoft Entra ID Conditional Access](/entra/identity/conditional-access/overview) is enabled.
 
 Here are the considerations when using this feature:
 
@@ -74,7 +74,7 @@ Here are the considerations when using this feature:
 
 <!-- + There could be Microsoft 365 processes that update SharePoint file system-metadata (based on different configurations in SharePoint) and will cause the SharePoint Online indexer to trigger. Make sure that you test your setup and understand the document processing count prior to using any AI enrichment. Since this is a third-party connector to Azure (SharePoint is located in Microsoft 365), SharePoint configuration is not checked by the indexer. -->
 
-+ If your SharePoint configuration allows Microsoft 365 processes to update SharePoint file system metadata, be aware that these updates can trigger the SharePoint Online indexer, causing the indexer to ingest documents multiple times. Because the SharePoint Online indexer is a third-party connector to Azure, the indexer can't read the configuration or vary its behavior. It responds to changes in new and changed content, regardless of how those updates are made. For this reason, make sure that you test your setup and understand the document processing count prior to using the indexer and any AI enrichment.
++ If your SharePoint configuration allows Microsoft 365 processes to update SharePoint file system metadata, be aware that these updates can trigger the SharePoint Online indexer, causing the indexer to ingest documents multiple times. Because the SharePoint Online indexer is a non-Microsoft connector to Azure, the indexer can't read the configuration or vary its behavior. It responds to changes in new and changed content, regardless of how those updates are made. For this reason, make sure that you test your setup and understand the document processing count prior to using the indexer and any AI enrichment.
 
 
 
@@ -107,7 +107,7 @@ We recommend app-based permissions. See [limitations](#limitations-and-considera
 
 + Application permissions (recommended), where the indexer runs under the [identity of the SharePoint tenant](/sharepoint/dev/solution-guidance/security-apponly-azureacs) with access to all sites and files. The indexer requires a [client secret](/azure/active-directory/develop/v2-oauth2-client-creds-grant-flow). The indexer will also require [tenant admin approval](/azure/active-directory/manage-apps/grant-admin-consent) before it can index any content.
 
-+ Delegated permissions, where the indexer runs under the identity of the user or app sending the request. Data access is limited to the sites and files to which the caller has access. To support delegated permissions, the indexer requires a [device code prompt](/azure/active-directory/develop/v2-oauth2-device-code) to sign in on behalf of the user. User-delegated permissions enforces token expiration every 75 minutes, per the most recent security libraries used to implement this authentication type. This is not a behavior that can be adjusted. An expired token requires manual indexing using [Run Indexer (preview)](/rest/api/searchservice/indexers/run?view=rest-searchservice-2024-05-01-preview&tabs=HTTP&preserve-view=true). For this reason, you might want app-based permissions instead.
++ Delegated permissions, where the indexer runs under the identity of the user or app sending the request. Data access is limited to the sites and files to which the caller has access. To support delegated permissions, the indexer requires a [device code prompt](/azure/active-directory/develop/v2-oauth2-device-code) to sign in on behalf of the user. User-delegated permissions enforce token expiration every 75 minutes, per the most recent security libraries used to implement this authentication type. This isn't a behavior that can be adjusted. An expired token requires manual indexing using [Run Indexer (preview)](/rest/api/searchservice/indexers/run?view=rest-searchservice-2024-05-01-preview&tabs=HTTP&preserve-view=true). For this reason, you might want app-based permissions instead.
 
 
 <a name='step-3-create-an-azure-ad-application'></a>
@@ -253,7 +253,7 @@ api-key: [admin key]
 
 An indexer connects a data source with a target search index and provides a schedule to automate the data refresh. Once the index and data source are created, you can create the indexer.
 
-If you are using delegated permissions, during this step, you’re asked to sign in with organization credentials that have access to the SharePoint site. If possible, we recommend creating a new organizational user account and giving that new user the exact permissions that you want the indexer to have. 
+If you're using delegated permissions, during this step, you’re asked to sign in with organization credentials that have access to the SharePoint site. If possible, we recommend creating a new organizational user account and giving that new user the exact permissions that you want the indexer to have. 
 
 There are a few steps to creating the indexer:
 
@@ -292,7 +292,7 @@ There are a few steps to creating the indexer:
     }
     ```
 
-   If you're using application permissions, it's necessary to wait until the initial run is complete before starting to query your index. The following instructions provided in this step pertain specifically to delegated permissions, and are not applicable to application permissions.
+   If you're using application permissions, it's necessary to wait until the initial run is complete before starting to query your index. The following instructions provided in this step pertain specifically to delegated permissions, and aren't applicable to application permissions.
 
 1. When you create the indexer for the first time, the [Create Indexer (preview)](/rest/api/searchservice/indexers/create-or-update?view=rest-searchservice-2024-05-01-preview&tabs=HTTP&preserve-view=true) request waits until you complete the next step. You must call [Get Indexer Status](/rest/api/searchservice/indexers/get-status?view=rest-searchservice-2024-05-01-preview&tabs=HTTP&preserve-view=true) to get the link and enter your new device code. 
 
@@ -377,7 +377,7 @@ Here are the steps for updating a data source, assuming an expired device code:
 
 ## Indexing document metadata
 
-If you're indexing document metadata (`"dataToExtract": "contentAndMetadata"`), the following metadata will be available to index.
+If you're indexing document metadata (`"dataToExtract": "contentAndMetadata"`), the following metadata is available to index.
 
 | Identifier | Type | Description | 
 | ------------- | -------------- | ----------- |
@@ -487,7 +487,7 @@ You can also continue indexing if errors happen at any point of processing, eith
 }
 ```
 
-If a file on the SharePoint site has encryption enabled, an error message similar to the following may be encountered:
+If a file on the SharePoint site has encryption enabled, you might see the following error message:
  
 ```
 Code: resourceModified Message: The resource has changed since the caller last read it; usually an eTag mismatch Inner error: Code: irmEncryptFailedToFindProtector

@@ -48,13 +48,13 @@ A search service uses Azure Storage as an indexer data source and as a data sink
 | [Debug sessions (hosted in Azure Storage)](cognitive-search-debug-session.md)	<sup>1</sup> | Yes | No |
 | [Enrichment cache (hosted in Azure Storage)](search-howto-incremental-index.md) <sup>1,</sup> <sup>2</sup> | Yes | Yes |
 | [Knowledge Store (hosted in Azure Storage)](knowledge-store-create-rest.md) <sup>1</sup>| Yes | Yes |
-| Connections to Azure OpenAI, Azure AI Studio and Azure Functions via skills/vectorizers <sup>3</sup> | Yes | Yes |
+| Connections to Azure OpenAI, Azure AI Foundry and Azure Functions via skills/vectorizers <sup>3</sup> | Yes | Yes |
 
 <sup>1</sup> For connectivity between search and storage, your network security configuration imposes constraints on which type of managed identity you can use. Only a system managed identity can be used for a same-region connection to storage via the trusted service exception or resource instance rule. See [Access to a network-protected storage account](search-indexer-securing-resources.md#access-to-a-network-protected-storage-account) for details.
 
 <sup>2</sup> AI search service currently can't connect to tables on a storage account that has [shared key access turned off](/azure/storage/common/shared-key-authorization-prevent).
 
-<sup>3</sup> Connections to Azure OpenAI,  Azure AI Studio and Azure Functions via skills/vectorizers include: [Custom skill](cognitive-search-custom-skill-interface.md), [Custom vectorizer](vector-search-vectorizer-custom-web-api.md), [Azure OpenAI embedding skill](cognitive-search-skill-azure-openai-embedding.md), [Azure OpenAI vectorizer](vector-search-how-to-configure-vectorizer.md), [AML skill](cognitive-search-aml-skill.md) and [Azure AI Studio model catalog vectorizer](vector-search-vectorizer-azure-machine-learning-ai-studio-catalog.md).
+<sup>3</sup> Connections to Azure OpenAI,  Azure AI Foundry and Azure Functions via skills/vectorizers include: [Custom skill](cognitive-search-custom-skill-interface.md), [Custom vectorizer](vector-search-vectorizer-custom-web-api.md), [Azure OpenAI embedding skill](cognitive-search-skill-azure-openai-embedding.md), [Azure OpenAI vectorizer](vector-search-how-to-configure-vectorizer.md), [AML skill](cognitive-search-aml-skill.md) and [Azure AI Foundry model catalog vectorizer](vector-search-vectorizer-azure-machine-learning-ai-studio-catalog.md).
 
 ## Create a system managed identity
 

@@ -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 Studio model catalog)](cognitive-search-aml-skill.md)  | ❌ | ✅ |
+| [Azure Machine Learning (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)  | ✅ | ❌ |
@@ -109,7 +109,7 @@ To view these objects after the wizard runs:
 | [Indexer](/rest/api/searchservice/indexers/create)  | A configuration object specifying a data source, target index, an optional skillset, optional schedule, and optional configuration settings for error handing and base-64 encoding. |
 | [Data Source](/rest/api/searchservice/data-sources/create)  | Persists connection information to a [supported data source](search-indexer-overview.md#supported-data-sources) on Azure. A data source object is used exclusively with indexers. | 
 | [Index](/rest/api/searchservice/indexes/create) | Physical data structure used for full text search and other queries. | 
-| [Skillset](/rest/api/searchservice/skillsets/create) | Optional. A complete set of instructions for manipulating, transforming, and shaping content, including analyzing and extracting information from image files. Skillsets are also used for integrated vectorization. Unless the volume of work fall under the limit of 20 transactions per indexer per day, the skillset must include a reference to an Azure AI multiservice resource that provides enrichment. For integrated vectorization, you can use either Azure AI Vision or an embedding model in the Azure AI Studio model catalog. | 
+| [Skillset](/rest/api/searchservice/skillsets/create) | Optional. A complete set of instructions for manipulating, transforming, and shaping content, including analyzing and extracting information from image files. Skillsets are also used for integrated vectorization. Unless the volume of work fall under the limit of 20 transactions per indexer per day, the skillset must include a reference to an Azure AI multiservice resource that provides enrichment. For integrated vectorization, you can use either Azure AI Vision or an embedding model in the Azure AI Foundry model catalog. | 
 | [Knowledge store](knowledge-store-concept-intro.md) | Optional. Available only in the **Import data** wizard. Stores enriched skillset output from in tables and blobs in Azure Storage for independent analysis or downstream processing in nonsearch scenarios. |
 
 ## Benefits
@@ -150,7 +150,7 @@ You can use the wizards over restricted public connections, but not all function
 
   The Azure resource must admit network requests from the IP address of the device used on the connection. You should also list Azure AI Search as a trusted service on the resource's network configuration. For example, in Azure Storage, you can list `Microsoft.Search/searchServices` as a trusted service.
 
-+ On connections to an Azure AI multi-service account that you provide, or on connections to embedding models deployed in Azure AI Studio or Azure OpenAI, public internet access must be enabled unless your search service meets the creation date, tier, and region requirements for private connections. For more information about these requirements, see [Make outbound connections through a shared private link](search-indexer-howto-access-private.md).
++ On connections to an Azure AI multi-service account that you provide, or on connections to embedding models deployed in Azure AI Foundry portal or Azure OpenAI, public internet access must be enabled unless your search service meets the creation date, tier, and region requirements for private connections. For more information about these requirements, see [Make outbound connections through a shared private link](search-indexer-howto-access-private.md).
 
   Connections to Azure AI multi-service are for [billing purposes](cognitive-search-attach-cognitive-services.md). Billing occurs when API calls exceed the free transaction count (20 per indexer run) for built-in skills called by the **Import data** wizard or integrated vectorization in the **Import and vectorize data** wizard. 
 

@@ -122,7 +122,7 @@ You can create a shared private link for the following resources.
 
 <sup>5</sup> See [Create a shared private link for a SQL Managed Instance](search-indexer-how-to-access-private-sql.md) for instructions.
 
-<sup>6</sup> The `Microsoft.CognitiveServices/accounts` resource type is used for vectorizer and indexer connections to Azure OpenAI embedding models when implementing [integrated Vectorization](vector-search-integrated-vectorization.md). As of November 19, 2024, there's now support for shared private link to embedding models in the Azure AI Studio model catalog or to the Azure AI Vision multimodal API.
+<sup>6</sup> The `Microsoft.CognitiveServices/accounts` resource type is used for vectorizer and indexer connections to Azure OpenAI embedding models when implementing [integrated Vectorization](vector-search-integrated-vectorization.md). As of November 19, 2024, there's now support for shared private link to embedding models in the Azure AI Foundry model catalog or to the Azure AI Vision multimodal API.
 
 <sup>7</sup> Shared private link for Azure OpenAI is only supported in public cloud. Other cloud offerings such as [Microsoft Azure Government](https://azure.microsoft.com/explore/global-infrastructure/government/) don't have support for shared private links for `openai_account` Group ID.
 

@@ -25,7 +25,7 @@ This article identifies the cloud regions in which Azure AI Search is available.
 | [Availability zones](search-reliability.md#availability-zone-support) | Divides a region's data centers into distinct physical location groups, providing high-availability within the same geo. Regional support is noted in this article. |
 | [AI service integration](cognitive-search-concept-intro.md) | Refers to skills that make internal calls to Azure AI for enrichment and transformation during indexing. Integration requires that Azure AI Search coexists with an [Azure AI multi-service account](/azure/ai-services/multi-service-resource) in the same physical region. Regional support is noted in this article. |
 | [Azure OpenAI integration](vector-search-integrated-vectorization.md)  | Refers to skills and vectorizers that make internal calls to deployed embedding and chat models on Azure OpenAI. Check [Azure OpenAI model region availability](/azure/ai-services/openai/concepts/models#model-summary-table-and-region-availability) for the most current list of regions for each embedding and chat model. Specific Azure OpenAI models are in fewer regions, so be sure to check for joint regional availability before installing.|
-| [Azure AI Studio integration](vector-search-integrated-vectorization-ai-studio.md) | Refers to skills and vectorizers that make internal calls to the models hosted in the model catalog. Check [Azure AI Studio region availability](/azure/ai-studio/reference/region-support) for the most current list of regions. |
+| [Azure AI Foundry integration](vector-search-integrated-vectorization-ai-studio.md) | Refers to skills and vectorizers that make internal calls to the models hosted in the model catalog. Check [Azure AI Foundry region availability](/azure/ai-studio/reference/region-support) for the most current list of regions. |
 | [Azure AI Vision 4.0 multimodal APIs for image vectorization](search-get-started-portal-image-search.md) | Refers to skills and vectorizers that call the multimodal embedding API. Check the [Azure AI Vision region list](/azure/ai-services/computer-vision/overview-image-analysis#region-availability) for joint regional availability. |
 | [Semantic ranker](semantic-search-overview.md) | Takes a dependency on Microsoft-hosted models in specific regions. Regional support is noted in this article. |
 
@@ -129,7 +129,7 @@ AI service integration refers to internal connections to an Azure AI multi-servi
 
 ## See also
 
-- [Azure AI Studio region availability](/azure/ai-studio/reference/region-support)
+- [Azure AI Foundry region availability](/azure/ai-studio/reference/region-support)
 - [Azure OpenAI model region availability](/azure/ai-services/openai/concepts/models#model-summary-table-and-region-availability)
 - [Azure AI Vision region list](/azure/ai-services/computer-vision/overview-image-analysis#region-availability)
 - [Availability zone region availability](/azure/reliability/availability-zones-region-support)

@@ -166,7 +166,7 @@ Within the perimeter, all resources have mutual access at the network level. You
 
 For resources outside of the network security perimeter, you must specify inbound and outbound access rules. Inbound rules specify which connections to allow in, and outbound rules specify which requests are allowed out.
 
-A search service accepts inbound requests from apps like Azure AI Studio, Azure OpenAI Studio, Azure Machine Learning prompt flow, and any app that sends indexing or query requests. A search service sends outbound requests during indexer-based indexing and skillset execution. This section explains how to set up inbound and outbound access rules for Azure AI Search scenarios.
+A search service accepts inbound requests from apps like Azure AI Foundry, Azure OpenAI Studio, Azure Machine Learning prompt flow, and any app that sends indexing or query requests. A search service sends outbound requests during indexer-based indexing and skillset execution. This section explains how to set up inbound and outbound access rules for Azure AI Search scenarios.
 
    > [!NOTE]
    > Any service associated with a network security perimeter implicitly allows inbound and outbound access to any other service associated with the same network security perimeter when that access is authenticated using [managed identities and role assignments](/entra/identity/managed-identities-azure-resources/overview). Access rules only need to be created when allowing access outside of the network security perimeter, or for access authenticated using API keys.

@@ -31,17 +31,17 @@ Once you sign up, you can immediately use either of these links to access Azure
 
 + [Sign in to Azure portal](https://portal.azure.com/) to view, manage, and create more resources. You can also use the portal to track your credits and projected costs.
 
-+ [Sign in to Azure AI Studio](https://ai.azure.com) for a no-code approach to deploying models on Azure OpenAI and using Azure AI Search for information retrieval. **We recommend you start here first.**
++ [Sign in to Azure AI Foundry](https://ai.azure.com) for a no-code approach to deploying models on Azure OpenAI and using Azure AI Search for information retrieval. **We recommend you start here first.**
 
 <!-- Although you can create a free search service that doesn't use up your credits, we recommend provisioning the **Basic** tier so that you can work with larger indexes, more indexes, and premium features like semantic ranking.
 
 The [Azure portal](https://portal.azure.com/) is the easiest approach for first-time users who want to create and use Azure resources. You can access and manage all of your subscriptions and resources from the portal. For Azure AI Search, you can use the portal to build components for classic search scenarios and generative search (RAG) workloads. -->
 
 ## Step two: "Day One" tasks
 
-[**How to build and consume vector indexes in Azure AI Studio**](/azure/ai-studio/how-to/index-add) is a great place to start.
+[**How to build and consume vector indexes in Azure AI Foundry portal**](/azure/ai-studio/how-to/index-add) is a great place to start.
 
-1. [Sign in to Azure AI Studio](https://ai.azure.com).
+1. [Sign in to Azure AI Foundry](https://ai.azure.com).
 
 1. Create a new hub and project.
 
@@ -70,7 +70,7 @@ For a next step evaluation of [RAG scenarios](retrieval-augmented-generation-ove
 
 Many of our quickstarts and tutorials use Azure Storage, so we recommend creating an Azure Storage account for getting started.
 
-Generative search requires embedding and chat models. The Azure cloud provides Azure OpenAI, but you can also use Azure AI Vision for multimodal embeddings (but not chat). Another model provider is Azure AI Studio and deploying chat and embedding models into the model catalog. However, for initial exploration, we recommend Azure OpenAI for its familiarity and mainstream offerings.
+Generative search requires embedding and chat models. The Azure cloud provides Azure OpenAI, but you can also use Azure AI Vision for multimodal embeddings (but not chat). Another model provider is Azure AI Foundry and deploying chat and embedding models into the model catalog. However, for initial exploration, we recommend Azure OpenAI for its familiarity and mainstream offerings.
 
 Application frontends are useful if you're prototyping a solution for a wider audience. You can use Azure Web apps or build an ASP.NET MVC application for this task. Otherwise, if you're working locally, you can view output in Jupyter notebooks in Visual Studio Code or another IDE. Or view results in console apps or other apps that run on localhost.
 
@@ -90,7 +90,7 @@ Continue with the following links to review which regions also provide the model
 
 - [Azure OpenAI region list](/azure/ai-services/openai/concepts/models#model-summary-table-and-region-availability)
 - [Azure AI Vision region list](/azure/ai-services/computer-vision/overview-image-analysis?tabs=4-0#region-availability)
-- [Azure AI Studio region list](/azure/ai-studio/reference/region-support)
+- [Azure AI Foundry region list](/azure/ai-studio/reference/region-support)
 
 > [!TIP]
 > Currently, these regions provide the most overlap and capacity: **East US**, **East US2**, and **South Central** in the Americas; **France Central** or **Switzerland North** in Europe; **Australia East** in Asia Pacific.
@@ -122,7 +122,7 @@ Try the portal quickstarts for Azure AI Search or quickstarts that use Visual St
 - [Quickstart: Generative search (RAG) using a Python client](search-get-started-rag.md)
 - [Quickstart: Vector search using a REST client](search-get-started-vector.md)
 
-Azure AI Studio supports connecting to content in Azure AI Search.
+Azure AI Foundry supports connecting to content in Azure AI Search.
 
 - [Quickstart: Chat using your own data with Azure OpenAI](/azure/ai-services/openai/use-your-data-quickstart)
 - [Tutorial: Build a custom chat app with the prompt flow SDK](/azure/ai-studio/tutorials/copilot-sdk-create-resources)

@@ -49,7 +49,7 @@ On the search service itself, the two primary workloads are *indexing* and *quer
 
   [Applied AI](cognitive-search-concept-intro.md) through a [skillset](cognitive-search-working-with-skillsets.md) extends indexing with image and language models. If you have images or large unstructured text in source document, you can attach skills that perform OCR, analyze and describe images, infer structure, translate text and more. Output is text that can be serialized into JSON and ingested into a search index.
 
-  Skillsets can also perform [data chunking and vectorization during indexing](vector-search-integrated-vectorization.md). Skills that attach to Azure OpenAI, the model catalog in Azure AI Studio, or custom skills that attach to any external chunking and embedding model can be used during indexing to create vector data. Output is chunked vector content that can be ingested into a search index.
+  Skillsets can also perform [data chunking and vectorization during indexing](vector-search-integrated-vectorization.md). Skills that attach to Azure OpenAI, the model catalog in Azure AI Foundry portal, or custom skills that attach to any external chunking and embedding model can be used during indexing to create vector data. Output is chunked vector content that can be ingested into a search index.
 
 + [**Querying**](search-query-overview.md) can happen once an index is populated with searchable content, when your client app sends query requests to a search service and handles responses. All query execution is over a search index that you control.
 

@@ -133,7 +133,7 @@ The trusted service list for Azure AI Search includes:
 + `Microsoft.CognitiveServices` for Azure OpenAI and Azure AI services
 + `Microsoft.MachineLearningServices` for Azure Machine Learning
 
-Workflows for this network exception are requests originating from Azure AI Studio or other AML features to Azure AI Search. The trusted services exception is typically for [Azure OpenAI On Your Data](/azure/ai-services/openai/concepts/use-your-data) scenarios for retrieval augmented generation (RAG) and playground environments.
+Workflows for this network exception are requests originating from Azure AI Foundry or other AML features to Azure AI Search. The trusted services exception is typically for [Azure OpenAI On Your Data](/azure/ai-services/openai/concepts/use-your-data) scenarios for retrieval augmented generation (RAG) and playground environments.
 
 ### Trusted resources must have a managed identity
 

@@ -21,7 +21,7 @@ This article explains how to configure a private connection to Azure AI Search s
 + [Create an Azure virtual machine in the same virtual network](#create-a-virtual-machine)
 + [Test using a browser session on the virtual machine](#connect-to-the-vm)
 
-Other Azure resources that might privately connect to Azure AI Search include Azure OpenAI for "use your own data" scenarios. Azure AI Studio doesn't run in a virtual network, but it can be configured on the backend to send requests over the Microsoft backbone network. Configuration for this traffic pattern is enabled by Microsoft when your request is submitted and approved. For this scenario:
+Other Azure resources that might privately connect to Azure AI Search include Azure OpenAI for "use your own data" scenarios. Azure AI Foundry doesn't run in a virtual network, but it can be configured on the backend to send requests over the Microsoft backbone network. Configuration for this traffic pattern is enabled by Microsoft when your request is submitted and approved. For this scenario:
 
 + Follow the instructions in this article to set up the private endpoint.
 + [Enable trusted service](/azure/ai-services/openai/how-to/use-your-data-securely#enable-trusted-service-1) of your search resource from the Azure portal.

@@ -299,7 +299,7 @@ items:
             href: search-how-to-index-sql-server.md
         - name: OneLake files
           href: search-how-to-index-onelake-files.md
-        - name: SharePoint in Microsoft 365
+        - name: SharePoint and OneDrive
           href: search-howto-index-sharepoint-online.md
     - name: Skillsets
       items:
@@ -341,7 +341,7 @@ items:
       href: search-how-to-semantic-chunking.md   
     - name: Generate embeddings
       href: vector-search-how-to-generate-embeddings.md
-    - name: Use embedding models from Azure AI Studio
+    - name: Use embedding models from Azure AI Foundry
       href: vector-search-integrated-vectorization-ai-studio.md
     - name: Reduce vector size
       items:
@@ -357,7 +357,7 @@ items:
         href: vector-search-how-to-assign-narrow-data-types.md
       - name: Eliminate redundant storage
         href: vector-search-how-to-storage-options.md
-      - name: Truncate dimensions (preview)
+      - name: Truncate dimensions
         href: vector-search-how-to-truncate-dimensions.md
     - name: Query vectors
       href: vector-search-how-to-query.md
@@ -592,7 +592,7 @@ items:
       href: query-simple-syntax.md
     - name: Full Lucene query syntax
       href: query-lucene-syntax.md
-    - name: moreLikeThis (preview)
+    - name: moreLikeThis
       href: search-more-like-this.md
     - name: OData language reference
       items:
@@ -641,7 +641,7 @@ items:
       href: cognitive-search-skill-annotation-language.md
     - name: Azure AI resource skills
       items:
-      - name: Document Layout skill (preview)
+      - name: Document Layout skill
         href: cognitive-search-skill-document-intelligence-layout.md  
       - name: Entity Linking (v3)
         href: cognitive-search-skill-entity-linking-v3.md
@@ -661,7 +661,7 @@ items:
         href: cognitive-search-skill-sentiment-v3.md
       - name: Text Translation
         href: cognitive-search-skill-text-translation.md
-      - name: AI Vision multimodal embeddings (preview)
+      - name: AI Vision multimodal embeddings
         href: cognitive-search-skill-vision-vectorize.md      
     - name: Azure AI Search utility skills (nonbillable)
       items:
@@ -677,7 +677,7 @@ items:
         href: cognitive-search-skill-textsplit.md
     - name: Azure OpenAI skills
       items:
-      - name: Azure OpenAI Embedding (preview)
+      - name: Azure OpenAI Embedding
         href: cognitive-search-skill-azure-openai-embedding.md 
     - name: Custom skills
       items:
@@ -702,7 +702,7 @@ items:
       href: vector-search-vectorizer-azure-open-ai.md
     - name: Azure AI Vision
       href: vector-search-vectorizer-ai-services-vision.md
-    - name: Azure AI Studio model catalog
+    - name: Azure AI Foundry model catalog
       href: vector-search-vectorizer-azure-machine-learning-ai-studio-catalog.md
     - name: Custom Web API
       href: vector-search-vectorizer-custom-web-api.md

@@ -33,7 +33,7 @@ If you don't have an Azure subscription, create a [free account](https://azure.m
 
 - An **Owner** or **User Access Administrator** role on your Azure subscription, necessary for creating role assignments. You use at least three Azure resources in this tutorial. The connections are authenticated using Microsoft Entra ID, which requires the ability to create roles. Role assignments for connecting to models are documented in this article. If you can't create roles, you can use [API keys](search-security-api-keys.md) instead.
 
-- A model provider, such as [Azure OpenAI](/azure/ai-services/openai/how-to/create-resource), Azure AI Vision via an [Azure AI services multi-service resource](/azure/ai-services/multi-service-resource), or [Azure AI Studio](https://ai.azure.com/).
+- A model provider, such as [Azure OpenAI](/azure/ai-services/openai/how-to/create-resource), Azure AI Vision via an [Azure AI services multi-service resource](/azure/ai-services/multi-service-resource), or [Azure AI Foundry](https://ai.azure.com/).
 
   We use Azure OpenAI in this tutorial. Other providers are listed so that you know your options for integrated vectorization.
 
@@ -45,7 +45,7 @@ If you don't have an Azure subscription, create a [free account](https://azure.m
 
   - [Azure AI Vision regions](/azure/ai-services/computer-vision/overview-image-analysis?tabs=4-0#region-availability)
 
-  - [Azure AI Studio](/azure/ai-studio/reference/region-support) regions. 
+  - [Azure AI Foundry](/azure/ai-studio/reference/region-support) regions. 
 
   Azure AI Search is currently facing limited availability in some regions. To confirm region status, check the [Azure AI Search region list](search-region-support.md).
 
@@ -66,7 +66,7 @@ Azure AI Search provides skill and vectorizer support for the following embeddin
 |--------|------------------|-------|------------|
 | Azure OpenAI | text-embedding-ada-002, <br>text-embedding-3-large, <br>text-embedding-3-small | [AzureOpenAIEmbedding](cognitive-search-skill-azure-openai-embedding.md) | [AzureOpenAIEmbedding](vector-search-vectorizer-azure-open-ai.md) |
 | Azure AI Vision | multimodal 4.0 <sup>1</sup> | [AzureAIVision](cognitive-search-skill-vision-vectorize.md) | [AzureAIVision](vector-search-vectorizer-ai-services-vision.md) |
-| Azure AI Studio model catalog | OpenAI-CLIP-Image-Text-Embeddings-vit-base-patch32, <br>OpenAI-CLIP-Image-Text-Embeddings-ViT-Large-Patch14-336, <br>Facebook-DinoV2-Image-Embeddings-ViT-Base, <br>Facebook-DinoV2-Image-Embeddings-ViT-Giant, <br>Cohere-embed-v3-english, <br>Cohere-embed-v3-multilingual | [AML](cognitive-search-aml-skill.md) <sup>2</sup>  | [Azure AI Studio model catalog](vector-search-vectorizer-azure-machine-learning-ai-studio-catalog.md) |
+| Azure AI Foundry model catalog | OpenAI-CLIP-Image-Text-Embeddings-vit-base-patch32, <br>OpenAI-CLIP-Image-Text-Embeddings-ViT-Large-Patch14-336, <br>Facebook-DinoV2-Image-Embeddings-ViT-Base, <br>Facebook-DinoV2-Image-Embeddings-ViT-Giant, <br>Cohere-embed-v3-english, <br>Cohere-embed-v3-multilingual | [AML](cognitive-search-aml-skill.md) <sup>2</sup>  | [Azure AI Foundry model catalog](vector-search-vectorizer-azure-machine-learning-ai-studio-catalog.md) |
 
 <sup>1</sup> Supports image and text vectorization.
 
@@ -102,7 +102,7 @@ This tutorial series uses the following models and model providers:
 
 You must have [**Cognitive Services OpenAI Contributor**]( /azure/ai-services/openai/how-to/role-based-access-control#cognitive-services-openai-contributor) or higher to deploy models in Azure OpenAI.
 
-1. Go to [Azure AI Studio](https://ai.azure.com/).
+1. Go to [Azure AI Foundry](https://ai.azure.com/).
 
 1. Select **Deployments** on the left menu.
 
@@ -146,7 +146,7 @@ Assign yourself and the search service identity permissions on Azure OpenAI. The
 
 1. Select **Review and Assign** to create the role assignments.
 
-For access to models on Azure AI Vision, assign **Cognitive Services OpenAI User**. For Azure AI Studio, assign **Azure AI Developer**.
+For access to models on Azure AI Vision, assign **Cognitive Services OpenAI User**. For Azure AI Foundry, assign **Azure AI Developer**.
 
 ## Use non-Azure models for embeddings
 

@@ -22,13 +22,13 @@ To add a vectorizer to search index, you can use the index designer in Azure por
 
 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 AI Vision vectorizer](vector-search-vectorizer-ai-services-vision.md), [Azure AI Studio 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.
+[Azure AI Vision vectorizer](vector-search-vectorizer-ai-services-vision.md), [Azure AI 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.
 
 ## Prerequisites
 
 + [An index with searchable vector fields](vector-search-how-to-create-index.md) on Azure AI Search.
 
-+ A deployed embedding model, such as **text-embedding-ada-002**, **text-embedding-3-small**, or **text-embedding-3-large** on Azure OpenAI. It's used to vectorize a query. It must be [identical to the embedding model used for the vector field](vector-search-integrated-vectorization.md#using-integrated-vectorization-in-queries) in your index. You can also use [models deployed from the Azure AI Studio model catalog](vector-search-integrated-vectorization-ai-studio.md) or an [Azure AI Vision model](/azure/ai-services/computer-vision/concept-image-retrieval).
++ A deployed embedding model, such as **text-embedding-ada-002**, **text-embedding-3-small**, or **text-embedding-3-large** on Azure OpenAI. It's used to vectorize a query. It must be [identical to the embedding model used for the vector field](vector-search-integrated-vectorization.md#using-integrated-vectorization-in-queries) in your index. You can also use [models deployed from the Azure AI Foundry model catalog](vector-search-integrated-vectorization-ai-studio.md) or an [Azure AI Vision model](/azure/ai-services/computer-vision/concept-image-retrieval).
 
 + Permissions to use the embedding model. If you're using 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.
 

@@ -529,7 +529,7 @@ Pull APIs refer to indexers, which automate multiple indexing steps, from data r
   + [AzureOpenAIEmbedding skill](cognitive-search-skill-azure-openai-embedding.md)
   + [Custom Web API skill](cognitive-search-custom-skill-web-api.md)
   + [Azure AI Vision multimodal embeddings skill (preview)](cognitive-search-skill-vision-vectorize.md)
-  + [AML skill (preview)](cognitive-search-aml-skill.md) to generate embeddings for models hosted in the Azure AI Studio model catalog. See [How to implement integrated vectorization using models from Azure AI Studio](vector-search-integrated-vectorization-ai-studio.md) for details.
+  + [AML skill (preview)](cognitive-search-aml-skill.md) to generate embeddings for models hosted in the Azure AI Foundry model catalog. See [How to implement integrated vectorization using models from Azure AI Foundry](vector-search-integrated-vectorization-ai-studio.md) for details.
 
 + Indexes provide the vector field definitions and vector search configurations. Those definitions are described in this article.
 

@@ -38,7 +38,7 @@ The binary data type is generally available starting with API version 2024-07-01
 ## Limitations
 
 + No Azure portal support in the Import and vectorize data wizard.
-+ No support for binary fields in the [AML skill](cognitive-search-aml-skill.md) that's used for integrated vectorization of models in the Azure AI Studio model catalog.
++ No support for binary fields in the [AML skill](cognitive-search-aml-skill.md) that's used for integrated vectorization of models in the Azure AI Foundry model catalog.
 
 ## Add a vector search algorithm and vector profile
 

@@ -43,7 +43,7 @@ You can use the REST APIs or Azure SDK beta packages to implement MRL compressio
 
 - Check the change logs for each Azure SDK beta package: [Python](https://github.com/Azure/azure-sdk-for-python/blob/main/sdk/search/azure-search-documents/CHANGELOG.md), [.NET](https://github.com/Azure/azure-sdk-for-net/blob/main/sdk/search/Azure.Search.Documents/CHANGELOG.md), [Java](https://github.com/Azure/azure-sdk-for-java/blob/azure-search-documents_11.1.3/sdk/search/azure-search-documents/CHANGELOG.md), [JavaScript](https://github.com/Azure/azure-sdk-for-js/blob/main/sdk/search/search-documents/CHANGELOG.md).
 
-There's no Azure portal or Azure AI Studio support at this time.
+There's no Azure portal or Azure AI Foundry support at this time.
 
 ## How to use MRL-extended text embeddings
 

@@ -1,7 +1,7 @@
 ---
-title: Integrated vectorization with models from Azure AI Studio
+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 Studio model.
+description: Learn  how to vectorize content during indexing on Azure AI Search with an AI Foundry model.
 author: gmndrg
 ms.author: gimondra
 ms.service: azure-ai-search
@@ -11,28 +11,28 @@ ms.topic: how-to
 ms.date: 10/29/2024
 ---
 
-# How to implement integrated vectorization using models from Azure AI Studio
+# How to implement integrated vectorization using models from Azure AI Foundry
 
 > [!IMPORTANT] 
 > This feature is in public preview under [Supplemental Terms of Use](https://azure.microsoft.com/support/legal/preview-supplemental-terms/). The [2024-05-01-Preview REST API](/rest/api/searchservice/skillsets/create-or-update?view=rest-searchservice-2024-05-01-preview&preserve-view=true) supports this feature.
 
-In this article, learn how to access the embedding models in the [Azure AI Studio model catalog](/azure/ai-studio/how-to/model-catalog) for vector conversions during indexing and in queries in Azure AI Search.
+In this article, learn how to access the embedding models in the [Azure AI Foundry model catalog](/azure/ai-studio/how-to/model-catalog) for vector conversions during indexing and in queries in Azure AI Search.
 
 The workflow includes model deployment steps. The model catalog includes embedding models from Azure OpenAI, Cohere, Facebook, and OpenAI. 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 Studio 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 [AI Foundry vectorizer](vector-search-vectorizer-azure-machine-learning-ai-studio-catalog.md) for queries.
 
-## Deploy an embedding model from the Azure AI Studio model catalog
+## Deploy an embedding model from the Azure AI Foundry model catalog
 
-1. Open the [Azure AI Studio model catalog](https://ai.azure.com/explore/models). 
+1. Open the [Azure AI Foundry model catalog](https://ai.azure.com/explore/models). 
 
 1. Apply a filter to show just the embedding models. Under **Inference tasks**, select **Embeddings**:
 
-   :::image type="content" source="media\vector-search-integrated-vectorization-ai-studio\ai-studio-catalog-embeddings-filter.png" lightbox="media\vector-search-integrated-vectorization-ai-studio\ai-studio-catalog-embeddings-filter.png" alt-text="Screenshot of the Azure AI Studio model catalog page highlighting how to filter by embeddings models.":::
+   :::image type="content" source="media\vector-search-integrated-vectorization-ai-studio\ai-studio-catalog-embeddings-filter.png" lightbox="media\vector-search-integrated-vectorization-ai-studio\ai-studio-catalog-embeddings-filter.png" alt-text="Screenshot of the Azure AI Foundry model catalog page highlighting how to filter by embeddings models.":::
 
 1. Select the model you would like to vectorize your content with. Then select **Deploy** and pick a deployment option.
 
-   :::image type="content" source="media\vector-search-integrated-vectorization-ai-studio\ai-studio-deploy-endpoint.png" lightbox="media\vector-search-integrated-vectorization-ai-studio\ai-studio-deploy-endpoint.png" alt-text="Screenshot of deploying an endpoint via the Azure AI Studio model catalog.":::
+   :::image type="content" source="media\vector-search-integrated-vectorization-ai-studio\ai-studio-deploy-endpoint.png" lightbox="media\vector-search-integrated-vectorization-ai-studio\ai-studio-deploy-endpoint.png" alt-text="Screenshot of deploying an endpoint via the Azure AI Foundry model catalog.":::
 
 1. Fill in the requested details. Select or [create a new AI project](/azure/ai-studio/how-to/create-projects), and then select **Deploy**. The deployment details vary depending on which model you select. 
 
@@ -42,30 +42,30 @@ After the model is deployed, you can use it for [integrated vectorization](vecto
 
     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 URL and Model ID, and also 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 Studio 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 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. 
 
    + To use the model during indexing, see [steps to enable integrated vectorization](vector-search-integrated-vectorization.md#how-to-use-integrated-vectorization). Be sure to use the [Azure Machine Learning (AML) skill](cognitive-search-aml-skill.md), and not the [AzureOpenAIEmbedding skill](cognitive-search-skill-azure-openai-embedding.md). The next section describes the skill configuration.
 
-   + To use the model as a vectorizer at query time, see [Configure a vectorizer](vector-search-how-to-configure-vectorizer.md). Be sure to use the [Azure AI Studio model catalog vectorizer](vector-search-vectorizer-azure-machine-learning-ai-studio-catalog.md) for this step.
+   + To use the model as a vectorizer at query time, see [Configure a vectorizer](vector-search-how-to-configure-vectorizer.md). Be sure to use the [Azure AI Foundry model catalog vectorizer](vector-search-vectorizer-azure-machine-learning-ai-studio-catalog.md) for this step.
 
 ## Sample AML skill payloads
 
-When you deploy embedding models from the [Azure AI Studio model catalog](https://ai.azure.com/explore/models) you connect to them using the [AML skill](cognitive-search-aml-skill.md) in Azure AI Search for indexing workloads.
+When you deploy embedding models from the [Azure AI Foundry model catalog](https://ai.azure.com/explore/models) you connect to them using the [AML skill](cognitive-search-aml-skill.md) in Azure AI Search for indexing workloads.
 
 This section describes the AML skill definition and index mappings. It includes sample payloads that are already configured to work with their corresponding deployed endpoints. For more technical details on how these payloads work, read about the [Skill context and input annotation language](cognitive-search-skill-annotation-language.md).
 
 ### [**Text Input for "Inference" API**](#tab/inference-text)
 
-This AML skill payload works with the following models from AI Studio:
+This AML skill payload works with the following models from AI Foundry:
 
 + OpenAI-CLIP-Image-Text-Embeddings-vit-base-patch32
 + OpenAI-CLIP-Image-Text-Embeddings-ViT-Large-Patch14-336
 
 It assumes that you're chunking your content using the [Text Split skill](cognitive-search-skill-textsplit.md) and that the 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.
 
-The URI and key are generated when you deploy the model from the catalog. For more information about these values, see [How to deploy large language models with Azure AI Studio](/azure/ai-studio/how-to/deploy-models-open).
+The URI and key are generated when you deploy the model from the catalog. For more information about these values, see [How to deploy large language models with Azure AI Foundry](/azure/ai-studio/how-to/deploy-models-open).
 
 ```json
 {
@@ -103,7 +103,7 @@ The URI and key are generated when you deploy the model from the catalog. For mo
 
 ### [**Image Input for "Inference" API**](#tab/inference-image)
 
-This AML skill payload works with the following models from AI Studio:
+This AML skill payload works with the following models from AI Foundry:
 
 + OpenAI-CLIP-Image-Text-Embeddings-vit-base-patch32
 + OpenAI-CLIP-Image-Text-Embeddings-ViT-Large-Patch14-336
@@ -112,7 +112,7 @@ This AML skill payload works with the following models from AI Studio:
 
 It assumes that your images come from the `/document/normalized_images/*` path that is created by enabling [built in image extraction](cognitive-search-concept-image-scenarios.md). If your images come from a different path or are stored as URLs, update all references to the `/document/normalized_images/*` path according.
 
-The URI and key are generated when you deploy the model from the catalog. For more information about these values, see [How to deploy large language models with Azure AI Studio](/azure/ai-studio/how-to/deploy-models-open).
+The URI and key are generated when you deploy the model from the catalog. For more information about these values, see [How to deploy large language models with Azure AI Foundry](/azure/ai-studio/how-to/deploy-models-open).
 
 ```json
 {
@@ -150,16 +150,16 @@ The URI and key are generated when you deploy the model from the catalog. For mo
 
 ### [**Cohere**](#tab/cohere)
 
-This AML skill payload works with the following models from AI Studio:
+This AML skill payload works with the following models from AI Foundry:
 
 + Cohere-embed-v3-english
 + Cohere-embed-v3-multilingual
 
 It assumes that you're chunking your content using the SplitSkill 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 according.
 
-You must add the `/v1/embed` path onto the end of the URL that you copied from your AI Studio 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 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 Studio](/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).
 
 Note that image URIs are not supported by this integration at this time.
 
@@ -221,11 +221,11 @@ If you selected a different `embedding_types` in your skill definition that you
 
 ---
 
-## Sample AI Studio vectorizer payload
+## Sample AI Foundry vectorizer payload
 
-The [AI Studio 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 Studio 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 Studio. 
+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. 
 
-Here's a sample payload of how you would configure the vectorizer on your index definition given the properties copied from AI Studio.
+Here's a sample payload of how you would configure the vectorizer on your index definition given the properties copied from 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.
 
@@ -245,7 +245,7 @@ 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 Studio 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 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>",
@@ -258,5 +258,5 @@ If you can't use key-based authentication, you can instead configure the AML ski
 + [Configure a vectorizer in a search index](vector-search-how-to-configure-vectorizer.md)
 + [Configure index projections in a skillset](index-projections-concept-intro.md)
 + [AML skill](cognitive-search-aml-skill.md)
-+ [Azure AI Studio vectorizer](vector-search-vectorizer-azure-machine-learning-ai-studio-catalog.md)
++ [Azure AI Foundry vectorizer](vector-search-vectorizer-azure-machine-learning-ai-studio-catalog.md)
 + [Skill context and input annotation language](cognitive-search-skill-annotation-language.md)

@@ -46,7 +46,7 @@ For data chunking and text-to-vector conversions, you're taking a dependency on
  
     + [Azure AI Vision skill (preview)](cognitive-search-skill-vision-vectorize.md) that points to the multimodal API for Azure AI Vision.
 
-    + [AML skill pointing to the model catalog in Azure AI Studio](cognitive-search-aml-skill.md) that points to selected models in the model catalog.
+    + [AML skill pointing to the model catalog in Azure AI Foundry portal](cognitive-search-aml-skill.md) that points to selected models in the model catalog.
 
 ## Using integrated vectorization in queries
 
@@ -63,7 +63,7 @@ For text-to-vector conversion during queries, you take a dependency on these com
     | [AzureOpenAIEmbedding skill](cognitive-search-skill-azure-openai-embedding.md) | [Azure OpenAI vectorizer](vector-search-vectorizer-azure-open-ai.md) |
     | [Custom skill](cognitive-search-custom-skill-web-api.md) | [Custom Web API vectorizer](vector-search-vectorizer-custom-web-api.md) |
     | [Azure AI Vision skill (preview)](cognitive-search-skill-vision-vectorize.md)  | [Azure AI Vision vectorizer](vector-search-vectorizer-ai-services-vision.md) |
-    | [AML skill pointing to the model catalog in Azure AI Studio](cognitive-search-aml-skill.md) | [Azure AI Studio model catalog vectorizer](vector-search-vectorizer-azure-machine-learning-ai-studio-catalog.md) |
+    | [AML skill pointing to the model catalog in Azure AI Foundry portal](cognitive-search-aml-skill.md) | [Azure AI Foundry model catalog vectorizer](vector-search-vectorizer-azure-machine-learning-ai-studio-catalog.md) |
 
 ## Component diagram
 
@@ -96,7 +96,7 @@ Data chunking (Text Split skill) is free and available on all Azure AI services
 
 ## When to use integrated vectorization
 
-We recommend using the built-in vectorization support of Azure AI Studio. If this approach doesn't meet your needs, you can create indexers and skillsets that invoke integrated vectorization using the programmatic interfaces of Azure AI Search.
+We recommend using the built-in vectorization support of Azure AI Foundry. If this approach doesn't meet your needs, you can create indexers and skillsets that invoke integrated vectorization using the programmatic interfaces of Azure AI Search.
 
 ## How to use integrated vectorization
 

@@ -74,7 +74,7 @@ Vector search is available in:
 + [Azure portal: Import and vectorize data wizard](search-get-started-portal-import-vectors.md)
 + [Azure REST APIs](/rest/api/searchservice/operation-groups)
 + [Azure SDKs for .NET](https://www.nuget.org/packages/Azure.Search.Documents), [Python](https://pypi.org/project/azure-search-documents), and [JavaScript](https://www.npmjs.com/package/@azure/search-documents)
-+ Other Azure offerings such as Azure AI Studio.
++ Other Azure offerings such as Azure AI Foundry.
 
 > [!NOTE]
 > Some older search services created before January 1, 2019 are deployed on infrastructure that doesn't support vector workloads. If you try to add a vector field to a schema and get an error, it's a result of outdated services. In this situation, you must create a new search service to try out the vector feature.
@@ -85,7 +85,7 @@ Azure AI Search is deeply integrated across the Azure AI platform. The following
 
 | Product | Integration |
 |---------|-------------|
-| Azure AI Studio | In the chat with your data playground, **Add your own data** uses Azure AI Search for grounding data and conversational search. This is the easiest and fastest approach for chatting with your data. |
+| Azure AI Foundry | In the chat with your data playground, **Add your own data** uses Azure AI Search for grounding data and conversational search. This is the easiest and fastest approach for chatting with your data. |
 | Azure OpenAI | Azure OpenAI provides embedding models and chat models. Demos and samples target the [text-embedding-ada-002](/azure/ai-services/openai/concepts/models#embeddings-models). We recommend Azure OpenAI for generating embeddings for text. |
 | Azure AI Services | [Image Retrieval Vectorize Image API(Preview)](/azure/ai-services/computer-vision/how-to/image-retrieval#call-the-vectorize-image-api) supports vectorization of image content. We recommend this API for generating embeddings for images. |
 | Azure data platforms: Azure Blob Storage, Azure Cosmos DB | You can use [indexers](search-indexer-overview.md) to automate data ingestion, and then use [integrated vectorization](vector-search-integrated-vectorization.md) to generate embeddings. Azure AI Search can automatically index vector data from two data sources: [Azure blob indexers](search-howto-indexing-azure-blob-storage.md) and [Azure Cosmos DB for NoSQL indexers](search-howto-index-cosmosdb.md). For more information, see [Add vector fields to a search index.](vector-search-how-to-create-index.md). |

@@ -1,7 +1,7 @@
 ---
-title: Azure AI Studio model catalog vectorizer
+title: Azure AI Foundry model catalog vectorizer
 titleSuffix: Azure AI Search
-description: Connects to a deployed model from the Azure AI Studio model catalog at query time.
+description: Connects to a deployed model from the Azure AI Foundry model catalog at query time.
 author: gmndrg
 ms.author: gimondra
 ms.service: azure-ai-search
@@ -11,14 +11,14 @@ ms.topic: reference
 ms.date: 08/05/2024
 ---
 
-#	Azure AI Studio model catalog vectorizer
+#	Azure AI Foundry model catalog vectorizer
 
 > [!IMPORTANT] 
 > This vectorizer is in public preview under [Supplemental Terms of Use](https://azure.microsoft.com/support/legal/preview-supplemental-terms/). The [2024-05-01-Preview REST API](/rest/api/searchservice/indexes/create-or-update?view=rest-searchservice-2024-05-01-Preview&preserve-view=true) supports this feature.
 
-The **Azure AI Studio model catalog** vectorizer connects to an embedding model that was deployed via [the Azure AI Studio model catalog](/azure/ai-studio/how-to/model-catalog) to an Azure Machine Learning endpoint. Your data is processed in the [Geo](https://azure.microsoft.com/explore/global-infrastructure/data-residency/) where your model is deployed. 
+The **Azure AI Foundry model catalog** vectorizer connects to an embedding model that was deployed via [the Azure AI Foundry model catalog](/azure/ai-studio/how-to/model-catalog) to an Azure Machine Learning endpoint. Your data is processed in the [Geo](https://azure.microsoft.com/explore/global-infrastructure/data-residency/) where your model is deployed. 
 
-If you used integrated vectorization to create the vector arrays, the skillset should include an [AML skill pointing to the model catalog in Azure AI Studio](cognitive-search-aml-skill.md).
+If you used integrated vectorization to create the vector arrays, the skillset should include an [AML skill pointing to the model catalog in Azure AI Foundry portal](cognitive-search-aml-skill.md).
 
 ## Vectorizer parameters
 
@@ -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 Studio model catalog that is deployed at the provided endpoint. Currently supported values are <ul><li>OpenAI-CLIP-Image-Text-Embeddings-vit-base-patch32 </li><li>OpenAI-CLIP-Image-Text-Embeddings-ViT-Large-Patch14-336 </li><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 AI Foundry model catalog that is deployed at the provided endpoint. Currently supported values are <ul><li>OpenAI-CLIP-Image-Text-Embeddings-vit-base-patch32 </li><li>OpenAI-CLIP-Image-Text-Embeddings-ViT-Large-Patch14-336 </li><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 Studio model catalog vectorizer depends on the `modelName` that is configured.
+Which vector query types are supported by the AI Foundry model catalog vectorizer depends on the `modelName` that is configured.
 
 | `modelName` | Supports `text` query | Supports `imageUrl` query | Supports `imageBinary` query |
 |--------------------|-------------|-------------|-------------|
@@ -60,7 +60,7 @@ Which vector query types are supported by the AI Studio model catalog vectorizer
 
 ## Expected field dimensions
 
-The expected field dimensions for a field configured with an AI Studio model catalog vectorizer depend on the `modelName` that is configured.
+The expected field dimensions for a field configured with an AI Foundry model catalog vectorizer depend on the `modelName` that is configured.
 
 | `modelName` | Expected dimensions |
 |--------------------|-------------|
@@ -93,7 +93,7 @@ The expected field dimensions for a field configured with an AI Studio model cat
 ## See also
 
 + [Integrated vectorization](vector-search-integrated-vectorization.md)
-+ [Integrated vectorization with models from Azure AI Studio](vector-search-integrated-vectorization-ai-studio.md)
++ [Integrated vectorization with models from Azure AI Foundry](vector-search-integrated-vectorization-ai-studio.md)
 + [How to configure a vectorizer in a search index](vector-search-how-to-configure-vectorizer.md)
 + [Azure Machine Learning skill](cognitive-search-aml-skill.md)
-+ [Azure AI Studio model catalog](/azure/ai-studio/how-to/model-catalog)
\ No newline at end of file
++ [Azure AI Foundry model catalog](/azure/ai-studio/how-to/model-catalog)
\ No newline at end of file

@@ -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 Studio 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 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
 

@@ -87,9 +87,9 @@ ms.custom:
 | [Binary vectors support](/rest/api/searchservice/supported-data-types) | Feature | `Collection(Edm.Byte)` is a new supported data type. This data type opens up integration with the [Cohere v3 binary embedding models](https://cohere.com/blog/int8-binary-embeddings) and custom binary quantization. Narrow data types lower the cost of large vector datasets. See [Index binary data for vector search](vector-search-how-to-index-binary-data.md) for more information.| 
 | [Azure AI Vision multimodal embeddings skill (preview)](cognitive-search-skill-vision-vectorize.md) | Skill | New skill that's bound to the [multimodal embeddings API of Azure AI Vision](/azure/ai-services/computer-vision/concept-image-retrieval). You can generate embeddings for text or images during indexing. This skill is available through the Azure portal and the [2024-05-01-preview REST API](/rest/api/searchservice/operation-groups?view=rest-searchservice-2024-05-01-preview&preserve-view=true).|
 | [Azure AI Vision vectorizer (preview)](vector-search-vectorizer-ai-services-vision.md) | Vectorizer | New vectorizer connects to an Azure AI Vision resource using the [multimodal embeddings API](/azure/ai-services/computer-vision/concept-image-retrieval) to generate embeddings at query time. This vectorizer is available through the Azure portal and the [2024-05-01-preview REST API](/rest/api/searchservice/operation-groups?view=rest-searchservice-2024-05-01-preview&preserve-view=true). |
-| [Azure AI Studio model catalog vectorizer (preview)](vector-search-vectorizer-azure-machine-learning-ai-studio-catalog.md) | Vectorizer | New vectorizer connects to an embedding model deployed from the [Azure AI Studio model catalog](/azure/ai-studio/how-to/model-catalog). This vectorizer is available through the Azure portal and the [2024-05-01-preview REST API](/rest/api/searchservice/operation-groups?view=rest-searchservice-2024-05-01-preview&preserve-view=true). <br><br>[**How to implement integrated vectorization using models from Azure AI Studio**](vector-search-integrated-vectorization-ai-studio.md).|
+| [Azure AI Foundry model catalog vectorizer (preview)](vector-search-vectorizer-azure-machine-learning-ai-studio-catalog.md) | Vectorizer | New vectorizer connects to an embedding model deployed from the [Azure AI Foundry model catalog](/azure/ai-studio/how-to/model-catalog). This vectorizer is available through the Azure portal and the [2024-05-01-preview REST API](/rest/api/searchservice/operation-groups?view=rest-searchservice-2024-05-01-preview&preserve-view=true). <br><br>[**How to implement integrated vectorization using models from Azure AI Foundry**](vector-search-integrated-vectorization-ai-studio.md).|
 | [AzureOpenAIEmbedding skill (preview) supports more models on Azure OpenAI](cognitive-search-skill-azure-openai-embedding.md) | Skill | Now supports text-embedding-3-large and text-embedding-3-small, along with text-embedding-ada-002 from the previous update. New `dimensions` and `modelName` properties make it possible to specify the various embedding models on Azure OpenAI. Previously, the dimensions limits were fixed at 1,536 dimensions, applicable to text-embedding-ada-002 only. The updated skill is available through the Azure portal and the [2024-05-01-preview REST API](/rest/api/searchservice/operation-groups?view=rest-searchservice-2024-05-01-preview&preserve-view=true).|
-| Azure portal updates | Portal | [Import and vectorize data wizard](search-get-started-portal-import-vectors.md) now supports OneLake indexers as a data source. For embeddings, it also supports connections to Azure AI Vision multimodal, Azure AI Studio model catalog, and more embedding models on Azure OpenAI. <br><br>When adding a field to an index, you can choose a [binary data type](vector-search-how-to-index-binary-data.md). <br><br>[Search explorer](search-explorer.md) now defaults to 2024-05-01-preview and supports the new preview features for vector and hybrid queries.  |
+| Azure portal updates | Portal | [Import and vectorize data wizard](search-get-started-portal-import-vectors.md) now supports OneLake indexers as a data source. For embeddings, it also supports connections to Azure AI Vision multimodal, Azure AI Foundry model catalog, and more embedding models on Azure OpenAI. <br><br>When adding a field to an index, you can choose a [binary data type](vector-search-how-to-index-binary-data.md). <br><br>[Search explorer](search-explorer.md) now defaults to 2024-05-01-preview and supports the new preview features for vector and hybrid queries.  |
 | [2024-05-01-preview](/rest/api/searchservice/search-service-api-versions#2024-05-01-preview) | API | New preview version of the Search REST APIs provides new skills and vectorizers, new binary data type, OneLake files indexer, and new query parameters for more relevant results. See [Upgrade REST APIs](search-api-migration.md) if you have existing code written against the 2023-07-01-preview and need to migrate to this version.|
 | Azure SDK beta packages | API | Review the changelogs of the following Azure SDK beta packages for new feature support: [Azure SDK for Python](https://github.com/Azure/azure-sdk-for-python/blob/main/sdk/search/azure-search-documents/CHANGELOG.md), [Azure SDK for .NET](https://github.com/Azure/azure-sdk-for-net/blob/Azure.Search.Documents_11.6.0-beta.4/sdk/search/Azure.Search.Documents/CHANGELOG.md), [Azure SDK for Java](https://github.com/Azure/azure-sdk-for-java/blob/main/sdk/search/azure-search-documents/CHANGELOG.md) |
 | [Python code samples](https://github.com/Azure/azure-search-vector-samples/blob/main/demo-python/readme.md)  | Samples | New end-to-end samples demonstrate [integration with Cohere Embed v3](https://github.com/Azure/azure-search-vector-samples/blob/main/demo-python/code/community-integration/cohere/azure-search-cohere-embed-v3-sample.ipynb), [integration with OneLake and cloud data platforms on Google and AWS](https://github.com/Azure/azure-search-vector-samples/blob/main/demo-python/code/e2e-demos/azure-ai-search-e2e-build-demo.ipynb), and [integration with Azure AI Vision multimodal APIs](https://github.com/Azure/azure-search-vector-samples/blob/main/demo-python/code/embeddings/multimodal-embeddings/multimodal-embeddings.ipynb). |