Merge pull request #6136 from MicrosoftDocs/main

Auto Publish – main to live - 2025-07-21 17:12 UTC

Author: learn-build-service-prod[bot]

articles/ai-foundry/foundry-models/how-to/quickstart-github-models.md

@@ -35,7 +35,7 @@ The rate limits for the playground and free API usage are intended to help you e
 
 To obtain the key and endpoint:
 
-1. Got to [GitHub Models](https://github.com/marketplace/models) and select the model you're interested in.
+1. Go to [GitHub Models](https://github.com/marketplace/models) and select the model you're interested in.
 
 1. In the playground for your model, select **Get API key**.
 

articles/ai-foundry/foundry-models/quotas-limits.md

@@ -19,13 +19,14 @@ This article contains a quick reference and a detailed description of the quotas
 
 ## Quotas and limits reference
 
-Azure uses quotas and limits to prevent budget overruns due to fraud, and to honor Azure capacity constraints. Consider these limits as you scale for production workloads. The following sections provide you with a quick guide to the default quotas and limits that apply to Azure AI model's inference service in Azure AI services:
+Azure uses quotas and limits to prevent budget overruns due to fraud, and to honor Azure capacity constraints. Consider these limits as you scale for production workloads. The following sections provide you with a quick guide to the default quotas and limits that apply to Azure AI model's inference service in Azure AI Foundry:
 
 ### Resource limits
 
 | Limit name | Limit value |
 |--|--|
-| Azure AI services resources per region per Azure subscription | 30 |
+| Azure AI Foundry resources per region per Azure subscription | 30 |
+| Max projects per resource | 250 |
 | Max deployments per resource | 32 | 
 
 ### Rate limits

articles/ai-foundry/what-is-azure-ai-foundry.md

@@ -6,9 +6,10 @@ author: sdgilley
 ms.author: sgilley
 manager: scottpolly
 ms.reviewer: sgilley
-ms.date: 06/12/2025
+ms.date: 07/01/2025
 ms.service: azure-ai-foundry
 ms.topic: overview
+ai-usage: ai-assisted
 ms.custom:
   - ignite-2023
   - build-2024
@@ -23,23 +24,39 @@ keywords:
 
 # What is Azure AI Foundry?
 
-[Azure AI Foundry](https://ai.azure.com/?cid=learnDocs) provides a unified platform for enterprise AI operations, model builders, and application development. This foundation combines production-grade infrastructure with friendly interfaces, ensuring organizations can build and operate AI applications with confidence. 
+[Azure AI Foundry](https://ai.azure.com/?cid=learnDocs) is a unified Azure platform-as-a-service offering for enterprise AI operations, model builders, and application development. This foundation combines production-grade infrastructure with friendly interfaces, enabling developers to focus on building applications rather than managing infrastructure.
+
+Azure AI Foundry unifies agents, models, and tools under a single management grouping with built-in enterprise-readiness capabilities including tracing, monitoring, evaluations, and customizable enterprise setup configurations. The platform provides streamlined management through unified Role-based access control (RBAC), networking, and policies under one Azure resource provider namespace.
 
 [Azure AI Foundry](https://ai.azure.com/?cid=learnDocs) is designed for developers to:
 
-- Build generative AI applications on an enterprise-grade platform.
+- Build generative AI applications and AI agents on an enterprise-grade platform.
 - Explore, build, test, and deploy using cutting-edge AI tools and ML models, grounded in responsible AI practices.
 - Collaborate with a team for the full life-cycle of application development.
+- Work across model providers with a consistent API contract.
 
 With Azure AI Foundry, you can explore a wide variety of models, services and capabilities, and get to building AI applications that best serve your goals. Azure AI Foundry facilitates scalability for transforming proof of concepts into full-fledged production applications with ease. Continuous monitoring and refinement support long-term success.  
 
 ## Work in an Azure AI Foundry project
 
 An Azure AI Foundry project is where you do most of your development work. You can work with your project in the Azure AI Foundry portal, or use the SDK in your preferred development environment.
 
+Azure AI Foundry projects provide developers with self-serve capabilities to independently create new environments for exploring ideas and building prototypes, while managing data in isolation. Projects act as secure units of isolation and collaboration where agents share file storage, thread storage (conversation history), and search indexes. You can also bring your own Azure resources for compliance and control over sensitive data.
+
+## Azure AI Foundry API and SDKs
+
+The [Azure AI Foundry API](/rest/api/aifoundry/) is designed specifically for building agentic applications and provides a consistent contract for working across different model providers. The API is complemented by SDKs to make it easy to integrate AI capabilities into your applications. [SDK Client libraries](how-to/develop/sdk-overview.md) are available for:
+
+- Python
+- C#
+- JavaScript/TypeScript (preview)
+- Java (preview)
+
+The [Azure AI Foundry for VS Code Extension](how-to/develop/get-started-projects-vs-code.md) helps you explore models and develop agents directly in your development environment.
+
 ## <a name="project-types"></a> Types of projects
 
-Azure AI Foundry supports two types of projects: a **[!INCLUDE [hub](includes/hub-project-name.md)]** and a **[!INCLUDE [fdp](includes/fdp-project-name.md)]**. In most cases, you'll want to use a [!INCLUDE [fdp-project-name](includes/fdp-project-name.md)].
+Azure AI Foundry supports two types of projects: a **[!INCLUDE [hub](includes/hub-project-name.md)]** and a **[!INCLUDE [fdp](includes/fdp-project-name.md)]**. In most cases, you want to use a [!INCLUDE [fdp-project-name](includes/fdp-project-name.md)].
 
 * [!INCLUDE [fdp-description](includes/fdp-description.md)]
 
@@ -48,8 +65,11 @@ Azure AI Foundry supports two types of projects: a **[!INCLUDE [hub](includes/hu
 
 ### Which type of project do I need?
 
-* In general, you should use a [!INCLUDE [fdp-project-name](includes/fdp-project-name.md)] if you are looking to build agents or work with models. 
-* Use a [!INCLUDE [hub-project-name](includes/hub-project-name.md)] when you need features that are not available in a [!INCLUDE [fdp-project-name](includes/fdp-project-name.md)]. See the following table for more on feature availability.
+- In general, you should use a [!INCLUDE [fdp-project-name](includes/fdp-project-name.md)] if you're looking to build agents or work with models.
+- Use a [!INCLUDE [hub-project-name](includes/hub-project-name.md)] when you need features that aren't available in a [!INCLUDE [fdp-project-name](includes/fdp-project-name.md)]. See the following table for more on feature availability.
+
+> [!NOTE]
+> New agents and model-centric capabilities are only available on [!INCLUDE [fdp-project-name](includes/fdp-project-name.md)]s, including access to the Azure AI Foundry API and Azure AI Foundry Agent Service in general availability.
 
 
 This table summarizes features available in the two project types:  
@@ -61,7 +81,7 @@ This table summarizes features available in the two project types:
 | AI Foundry API to work with agents and across models| ✅ (Native support) | Available via connections |
 | Models sold directly by Azure - Azure OpenAI, DeepSeek, xAI, etc. | ✅ | Available via connections |
 | Partner & Community Models sold through Marketplace - Stability, Bria, Cohere, etc. | ✅ | Available via connections |
-| Open source models e.g. HuggingFace  |  | ✅ |
+| Open source models such as HuggingFace  |  | ✅ |
 | Evaluations | ✅ | ✅ |
 | Playground | ✅ | ✅ |
 | Prompt flow |  | ✅ |
@@ -115,11 +135,11 @@ If you're an admin, or leading a development team, and need to manage the team's
 
 The left pane of the Azure AI Foundry portal is your main navigation tool. Customize this area to show the parts of the portal you want to use.
 
-Pin or unpin items into the left pane. When you unpin an item, it is hidden from the left pane but can be found again in the **...More** menu.
+Pin or unpin items into the left pane. When you unpin an item, it's hidden from the left pane but can be found again in the **...More** menu.
 
 * Select **... More** at the bottom of the pane to see items to pin and unpin.
-* Customize each project separately. The left pane is not shared across projects.
-* The left pane is not shared across users. Each user customizes their own left pane for each project. 
+* Customize each project separately. The left pane isn't shared across projects.
+* The left pane isn't shared across users. Each user customizes their own left pane for each project. 
 
 ## Management center
 

articles/search/search-get-started-portal-image-search.md

@@ -7,7 +7,7 @@ ms.author: haileytapia
 ms.service: azure-ai-search
 ms.update-cycle: 90-days
 ms.topic: quickstart
-ms.date: 06/11/2025
+ms.date: 07/16/2025
 ms.custom:
   - references_regions
 ---
@@ -52,7 +52,7 @@ For content embedding, you can choose either image verbalization (followed by te
 | Method | Description | Supported models |
 |--|--|--|
 | Image verbalization | Uses an LLM to generate natural-language descriptions of images, and then uses an embedding model to vectorize plain text and verbalized images.<br><br>Requires an [Azure OpenAI resource](/azure/ai-services/openai/how-to/create-resource) <sup>1, 2</sup> or [Azure AI Foundry project](/azure/ai-foundry/how-to/create-projects).<br><br>For text vectorization, you can also use an [Azure AI services multi-service resource](/azure/ai-services/multi-service-resource#azure-ai-multi-services-resource-for-azure-ai-search-skills) <sup>3</sup> in a [supported region](cognitive-search-skill-vision-vectorize.md). | LLMs:<br>GPT-4o<br>GPT-4o-mini<br>phi-4 <sup>4</sup><br><br>Embedding models:<br>text-embedding-ada-002<br>text-embedding-3-small<br>text-embedding-3-large |
-| Multimodal embeddings | Uses an embedding model to directly vectorize both text and images.<br><br>Requires an [Azure AI Foundry project](/azure/ai-foundry/how-to/create-projects) or [Azure AI services multi-service resource](/azure/ai-services/multi-service-resource#azure-ai-multi-services-resource-for-azure-ai-search-skills) <sup>3</sup> in a [supported region](cognitive-search-skill-vision-vectorize.md). | Cohere-embed-v3-english<br>Cohere-embed-v3-multilingual |
+| Multimodal embeddings | Uses an embedding model to directly vectorize both text and images.<br><br>Requires an [Azure AI Foundry project](/azure/ai-foundry/how-to/create-projects) or [Azure AI services multi-service resource](/azure/ai-services/multi-service-resource#azure-ai-multi-services-resource-for-azure-ai-search-skills) <sup>3</sup> in a [supported region](cognitive-search-skill-vision-vectorize.md). | Cohere-embed-v3-english<br>Cohere-embed-v3-multilingual<br>Cohere-embed-v4 <sup>5</sup> |
 
 <sup>1</sup> The endpoint of your Azure OpenAI resource must have a [custom subdomain](/azure/ai-services/cognitive-services-custom-subdomains), such as `https://my-unique-name.openai.azure.com`. If you created your resource in the [Azure portal](https://portal.azure.com/), this subdomain was automatically generated during resource setup.
 
@@ -62,6 +62,8 @@ For content embedding, you can choose either image verbalization (followed by te
 
 <sup>4</sup> `phi-4` is only available to Azure AI Foundry projects.
 
+<sup>5</sup> The Azure portal doesn't support `embed-v-4-0` for vectorization, so don't use it for this quickstart. Instead, use the [AML skill](cognitive-search-aml-skill.md) or [Azure AI Foundry model catalog vectorizer](vector-search-vectorizer-azure-machine-learning-ai-studio-catalog.md) to programmatically specify this model. You can then use the portal to manage the skillset or vectorizer.
+
 ### Public endpoint requirements
 
 All of the preceding resources must have public access enabled so that the Azure portal nodes can access them. Otherwise, the wizard fails. After the wizard runs, you can enable firewalls and private endpoints on the integration components for security. For more information, see [Secure connections in the import wizards](search-import-data-portal.md#secure-connections).

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

@@ -10,7 +10,7 @@ ms.custom:
   - build-2024
   - ignite-2024
 ms.topic: quickstart
-ms.date: 06/11/2025
+ms.date: 07/17/2025
 ---
 
 # Quickstart: Vectorize text in the Azure portal
@@ -49,7 +49,7 @@ For integrated vectorization, you must use one of the following embedding models
 |--|--|
 | [Azure OpenAI in Azure AI Foundry Models](/azure/ai-services/openai/how-to/create-resource) <sup>1, 2</sup> | text-embedding-ada-002<br>text-embedding-3-small<br>text-embedding-3-large |
 | [Azure AI services multi-service resource](/azure/ai-services/multi-service-resource#azure-ai-multi-services-resource-for-azure-ai-search-skills) <sup>3</sup> | For text and images: [Azure AI Vision multimodal](/azure/ai-services/computer-vision/how-to/image-retrieval) <sup>4</sup></li> |
-| [Azure AI Foundry model catalog](/azure/ai-foundry/what-is-azure-ai-foundry) | For text:<br>Cohere-embed-v3-english<br>Cohere-embed-v3-multilingual<br><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-azure-ai-foundry) | For images:<br>Facebook-DinoV2-Image-Embeddings-ViT-Base<br>Facebook-DinoV2-Image-Embeddings-ViT-Giant<br><br>For text and images:<br>Cohere-embed-v3-english<br>Cohere-embed-v3-multilingual<br>Cohere-embed-v4 <sup>5</sup> |
 
 <sup>1</sup> The endpoint of your Azure OpenAI resource must have a [custom subdomain](/azure/ai-services/cognitive-services-custom-subdomains), such as `https://my-unique-name.openai.azure.com`. If you created your resource in the [Azure portal](https://portal.azure.com/), this subdomain was automatically generated during resource setup.
 
@@ -59,6 +59,8 @@ For integrated vectorization, you must use one of the following embedding models
 
 <sup>4</sup> The Azure AI Vision multimodal embedding model is available in [select regions](/azure/ai-services/computer-vision/overview-image-analysis#region-availability).
 
+<sup>5</sup> The Azure portal doesn't support `embed-v-4-0` for vectorization, so don't use it for this quickstart. Instead, use the [AML skill](cognitive-search-aml-skill.md) or [Azure AI Foundry model catalog vectorizer](vector-search-vectorizer-azure-machine-learning-ai-studio-catalog.md) to programmatically specify this model. You can then use the portal to manage the skillset or vectorizer.
+
 ### Public endpoint requirements
 
 For the purposes of this quickstart, all of the preceding resources must have public access enabled so that the Azure portal nodes can access them. Otherwise, the wizard fails. After the wizard runs, you can enable firewalls and private endpoints on the integration components for security. For more information, see [Secure connections in the import wizards](search-import-data-portal.md#secure-connections).

articles/search/search-how-to-integrated-vectorization.md

@@ -7,7 +7,7 @@ author: haileytap
 ms.author: haileytapia
 ms.service: azure-ai-search
 ms.topic: how-to
-ms.date: 06/11/2025
+ms.date: 07/17/2025
 ---
 
 # Set up integrated vectorization in Azure AI Search using REST
@@ -48,7 +48,7 @@ For integrated vectorization, you must use one of the following embedding models
 |--|--|
 | [Azure OpenAI in Azure AI Foundry Models](/azure/ai-services/openai/how-to/create-resource) <sup>1, 2</sup> | text-embedding-ada-002<br>text-embedding-3-small<br>text-embedding-3-large |
 | [Azure AI services multi-service resource](/azure/ai-services/multi-service-resource#azure-ai-services-resource-for-azure-ai-search-skills) <sup>3</sup> | For text and images: [Azure AI Vision multimodal](/azure/ai-services/computer-vision/how-to/image-retrieval) <sup>4</sup></li> |
-<!--| [Azure AI Foundry model catalog](/azure/ai-foundry/what-is-azure-ai-foundry) | For text:<br>Cohere-embed-v3-english<br>Cohere-embed-v3-multilingual<br><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-azure-ai-foundry) | For images:<br>Facebook-DinoV2-Image-Embeddings-ViT-Base<br>Facebook-DinoV2-Image-Embeddings-ViT-Giant<br>For text and images:<br>Cohere-embed-v3-english<br>Cohere-embed-v3-multilingual<br>Cohere-embed-v4 |-->
 
 <sup>1</sup> The endpoint of your Azure OpenAI resource must have a [custom subdomain](/azure/ai-services/cognitive-services-custom-subdomains), such as `https://my-unique-name.openai.azure.com`. If you created your resource in the [Azure portal](https://portal.azure.com/), this subdomain was automatically generated during resource setup.
 

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

@@ -9,7 +9,7 @@ ms.service: azure-ai-search
 ms.update-cycle: 180-days
 ms.topic: tutorial
 ms.custom: references_regions
-ms.date: 06/11/2025
+ms.date: 07/17/2025
 
 ---
 
@@ -52,13 +52,15 @@ Azure AI Search provides skill and vectorizer support for the following embeddin
 
 | Client | Embedding models | Skill | Vectorizer |
 |--------|------------------|-------|------------|
-| 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 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 Foundry model catalog | 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) |
+| Azure AI Foundry model catalog | Facebook-DinoV2-Image-Embeddings-ViT-Base<br>Facebook-DinoV2-Image-Embeddings-ViT-Giant<br>Cohere-embed-v3-english <sup>1</sup><br>Cohere-embed-v3-multilingual <sup>1</sup><br>Cohere-embed-v4 <sup>1, 2</sup> | [AML](cognitive-search-aml-skill.md) <sup>3</sup> | [Azure AI Foundry model catalog](vector-search-vectorizer-azure-machine-learning-ai-studio-catalog.md) |
 
-<sup>1</sup> Supports image and text vectorization.
+<sup>1</sup> Supports text and image vectorization.
 
-<sup>2</sup> Deployed models in the model catalog are accessed over an AML endpoint. We use the existing AML skill for this connection.
+<sup>2</sup> At this time, you can only specify `embed-v-4-0` programmatically through the [AML skill](cognitive-search-aml-skill.md) or [Azure AI Foundry model catalog vectorizer](vector-search-vectorizer-azure-machine-learning-ai-studio-catalog.md), not through the Azure portal. However, you can use the portal to manage the skillset or vectorizer afterward.
+
+<sup>3</sup> Deployed models in the model catalog are accessed over an AML endpoint. We use the existing AML skill for this connection.
 
 You can use other models besides the ones listed here. For more information, see [Use non-Azure models for embeddings](#use-non-azure-models-for-embeddings) in this article.