rebrand other directories

Author: sdgilley

articles/search/cognitive-search-aml-skill.md

@@ -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)

articles/search/cognitive-search-skill-azure-openai-embedding.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. 
 

articles/search/index.yml

@@ -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

articles/search/resource-training.md

@@ -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

articles/search/retrieval-augmented-generation-overview.md

@@ -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.
 

articles/search/search-api-preview.md

@@ -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). |

articles/search/search-faq-frequently-asked-questions.yml

@@ -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. 
 

articles/search/search-features-list.md

@@ -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