Merge pull request #3520 from eric-urban/eur/foundry-path

[SCOPED] Use the new folder for AI foundry docs

Author: JamesJBarnett

articles/search/index.yml

@@ -68,11 +68,11 @@ landingContent:
       - linkListType: how-to-guide
         links:
           - text: Create a vector index in Azure AI Foundry portal
-            url: /azure/ai-studio/how-to/index-add
+            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-studio/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/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 Foundry, [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-foundry/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 Foundry](/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-foundry/concepts/retrieval-augmented-generation) to create RAG-based Azure AI Search solutions that integrate with LLMs.
 
 ## Searchable content in Azure AI Search
 

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-studio/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.
@@ -204,7 +204,7 @@ After you finish these steps, you should be able to select the Azure AI Vision v
 
 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 models. 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 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. 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-foundry/how-to/create-projects), and a [project](/azure/ai-foundry/how-to/create-projects). Hubs and projects having the same name can share connection information and permissions.
 
 1. Deploy an embedding model to the model catalog in your project.
 

articles/search/search-get-started-rag.md

@@ -21,7 +21,7 @@ In this quickstart, you send queries to a chat completion model for a conversati
 
 - An [Azure OpenAI resource](/azure/ai-services/openai/how-to/create-resource).
   - [Choose a region](/azure/ai-services/openai/concepts/models?tabs=global-standard%2Cstandard-chat-completions#global-standard-model-availability) that supports the chat completion model you want to use (gpt-4o, gpt-4o-mini, or an equivalent model).
-  - [Deploy the chat completion model](/azure/ai-studio/how-to/deploy-models-openai) in Azure AI Foundry or [use another approach](/azure/ai-services/openai/how-to/working-with-models).
+  - [Deploy the chat completion model](/azure/ai-foundry/how-to/deploy-models-openai) in Azure AI Foundry or [use another approach](/azure/ai-services/openai/how-to/working-with-models).
 
 - An [Azure AI Search resource](search-create-service-portal.md).
   - Use the same region as your Azure OpenAI resource.

articles/search/search-region-support.md

@@ -28,7 +28,7 @@ Some features take a dependency on other Azure services or infrastructure that a
 | [Semantic ranker](semantic-search-overview.md) | Takes a dependency on Microsoft-hosted models in specific regions. | Regional support is noted in this article. |
 | [AI service integration](cognitive-search-concept-intro.md) | Refers to [built-in skills](cognitive-search-predefined-skills.md) 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. You can bypass region requirements if you use [identity-based connections](cognitive-search-attach-cognitive-services.md#bill-through-a-keyless-connection), currently in public review. | Regional support is noted in this article. |
 | [Azure OpenAI integration](vector-search-integrated-vectorization.md)  | Refers to the AzureOpenAIEmbedding skill and vectorizer that make internal calls to deployed embedding 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 check for model availability first, and then verify Azure AI Search is available in the same region.|
-| [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 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-foundry/reference/region-support) for the most current list of regions. |
 | [Azure AI Vision 4.0 multimodal APIs](search-get-started-portal-image-search.md) | Refers to the Azure AI Vision multimodal embeddings skill and vectorizer that call the multimodal embedding API. | Check the [Azure AI Vision region list](/azure/ai-services/computer-vision/overview-image-analysis#region-availability) first, and then verify Azure AI Search is available in the same region.|
 
 ## Azure Public regions
@@ -131,7 +131,7 @@ AI service integration refers to internal connections to an Azure AI multi-servi
 
 ## See also
 
-- [Azure AI Foundry region availability](/azure/ai-studio/reference/region-support)
+- [Azure AI Foundry region availability](/azure/ai-foundry/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)

articles/search/search-try-for-free.md

@@ -35,7 +35,7 @@ Once you sign up, you can immediately use either of these links to access Azure
 
 ## Step two: "Day One" tasks
 
-[**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.
+[**How to build and consume vector indexes in Azure AI Foundry portal**](/azure/ai-foundry/how-to/index-add) is a great place to start.
 
 1. [Sign in to Azure AI Foundry](https://ai.azure.com).
 
@@ -80,7 +80,7 @@ Start here if you want to use built-in vectorization or chat models:
 
 - [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 Foundry region list](/azure/ai-studio/reference/region-support)
+- [Azure AI Foundry region list](/azure/ai-foundry/reference/region-support)
 
 Continue with the following link to confirm region and tier availability for AI Search:
 
@@ -119,7 +119,7 @@ Try the Azure portal quickstarts for Azure AI Search or quickstarts that use Vis
 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)
+- [Tutorial: Build a custom chat app with the prompt flow SDK](/azure/ai-foundry/tutorials/copilot-sdk-create-resources)
 
 Developers should review [azure-search-vector-samples](https://github.com/Azure/azure-search-vector-samples) repository or the solution accelerators. You can deploy and run any of these samples using the Azure trial subscription. 
 

articles/search/tutorial-rag-build-solution-models.md

@@ -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 Foundry](/azure/ai-studio/reference/region-support) regions. 
+  - [Azure AI Foundry](/azure/ai-foundry/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).
 

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-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-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. 
 
@@ -29,7 +29,7 @@ After the model is deployed, you can use it for [integrated vectorization](vecto
 
 + Azure AI Search, any region and tier.
 
-+ Azure AI Foundry and an [Azure AI Foundry project](/azure/ai-studio/how-to/create-projects).
++ Azure AI Foundry and an [Azure AI Foundry project](/azure/ai-foundry/how-to/create-projects).
 
 ## Supported embedding models
 
@@ -88,7 +88,7 @@ This AML skill payload works with the following models from Azure AI Foundry:
 
 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 Foundry](/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-foundry/how-to/deploy-models-open).
 
 ```json
 {
@@ -133,7 +133,7 @@ This AML skill payload works with the following image embedding models from Azur
 
 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 Foundry](/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 [Add and configure models to Azure AI model inference](/azure/ai-foundry/model-inference/how-to/create-model-deployments).
 
 ```json
 {
@@ -179,9 +179,9 @@ This AML skill payload works with the following text embedding models from Azure
 
 It assumes that you're chunking your content using the Text Split skill and therefore your text to be vectorized is in the `/document/pages/*` path. If your text comes from a different path, update all references to the `/document/pages/*` path accordingly.
 
-You must add the `/v1/embed` path onto the end of the URL that you copied from your Azure AI Foundry deployment. You might also change the values for the `input_type`, `truncate` and `embedding_types` inputs to better fit your use case. For more information on the available options, review the [Cohere Embed API reference](/azure/ai-studio/how-to/deploy-models-cohere-embed).
+You must add the `/v1/embed` path onto the end of the URL that you copied from your Azure AI Foundry deployment. You might also change the values for the `input_type`, `truncate` and `embedding_types` inputs to better fit your use case. For more information on the available options, review the [Cohere Embed API reference](/azure/ai-foundry/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).
+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-foundry/how-to/deploy-models-cohere-embed).
 
 Note that image URIs aren't supported by this integration at this time.
 

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-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-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-studio/how-to/model-catalog)
++ [Azure AI Foundry model catalog](/azure/ai-foundry/how-to/model-catalog-overview)