use the new folder for AI foundry docs

Author: eric-urban

articles/search/index.yml

@@ -71,8 +71,8 @@ landingContent:
             url: /azure/ai-foundry/how-to/index-add
           - text: Chat with your data using Azure OpenAI
             url: /azure/ai-services/openai/use-your-data-quickstart
-          - text: Build a question and answer copilot
-            url: /azure/ai-foundry/tutorials/deploy-copilot-ai-studio
+          - text: Build a custom RAG app using Azure AI Foundry SDK
+            url: /azure/ai-foundry/tutorials/copilot-sdk-build-rag
 
   # Card
   - title: Index data

articles/search/search-get-started-portal-import-vectors.md

@@ -47,7 +47,7 @@ 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 <br>text-embedding-3-large <br>text-embedding-3-small |
-| [Azure AI Foundry model catalog](/azure/ai-foundry/what-is-ai-studio) | For text: <br>Cohere-embed-v3-english <br>Cohere-embed-v3-multilingual <br>For images: <br>Facebook-DinoV2-Image-Embeddings-ViT-Base <br>Facebook-DinoV2-Image-Embeddings-ViT-Giant |
+| [Azure AI Foundry model catalog](/azure/ai-foundry/what-is-ai-foundry) | For text: <br>Cohere-embed-v3-english <br>Cohere-embed-v3-multilingual <br>For images: <br>Facebook-DinoV2-Image-Embeddings-ViT-Base <br>Facebook-DinoV2-Image-Embeddings-ViT-Giant |
 | [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, [available in selected regions](/azure/ai-services/computer-vision/how-to/image-retrieval?tabs=csharp). Depending on how you [attach the multi-service resource](cognitive-search-attach-cognitive-services.md), the multi-service 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.

articles/search/vector-search-integrated-vectorization-ai-studio.md

@@ -16,7 +16,7 @@ ms.date: 12/03/2024
 > [!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 Foundry model catalog](/azure/ai-foundry/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-foundry/how-to/model-catalog-overview) 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 Microsoft and other companies. Deploying a model is billable per the billing structure of each provider. 
 

articles/search/vector-search-vectorizer-azure-machine-learning-ai-studio-catalog.md

@@ -16,7 +16,7 @@ ms.date: 12/03/2024
 > [!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 Foundry model catalog** vectorizer connects to an embedding model that was deployed via [the Azure AI Foundry model catalog](/azure/ai-foundry/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-foundry/how-to/model-catalog-overview) 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 Foundry portal](cognitive-search-aml-skill.md).
 
@@ -94,4 +94,4 @@ Suggested model names in the Azure AI Foundry model catalog consist of the base
 + [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 Foundry model catalog](/azure/ai-foundry/how-to/model-catalog)
++ [Azure AI Foundry model catalog](/azure/ai-foundry/how-to/model-catalog-overview)

articles/search/whats-new.md

@@ -99,7 +99,7 @@ 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 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-foundry/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).|
+| [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-foundry/how-to/model-catalog-overview). 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 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.|