Merge pull request #6350 from MicrosoftDocs/main

Auto Publish – main to live - 2025-08-01 22:07 UTC

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

articles/ai-foundry/agents/concepts/model-region-support.md

@@ -6,10 +6,10 @@ services: cognitive-services
 manager: nitinme
 ms.service: azure-ai-agent-service
 ms.topic: how-to
-ms.date: 07/17/2025
+ms.date: 08/01/2025
 author: aahill
 ms.author: aahi
-ms.custom: azure-ai-agents
+ms.custom: azure-ai-agents, references_regions
 ---
 
 # Azure AI Foundry Agent Service file search tool
@@ -30,6 +30,11 @@ File search augments agents with knowledge from outside its model, such as propr
 
 ### Usage support
 
+> [!NOTE]
+> The file search tool is currently unavailable in the following regions:
+>    * Italy north
+>    * Brazil south
+
 |Azure AI foundry support  | Python SDK |	C# SDK | JavaScript SDK | REST API | Basic agent setup | Standard agent setup |
 |---------|---------|---------|---------|---------|---------|---------|
 | ✔️  | ✔️ | ✔️ | ✔️ | ✔️ | File upload only | File upload and using  bring-your-own blob storage | 
@@ -74,7 +79,7 @@ Vector store objects give the file search tool the ability to search your files.
 Similarly, these files can be removed from a vector store by either:
 
 * Deleting the vector store file object or,
-* By deleting the underlying file object, which removes the file it from all vector_store and code_interpreter configurations across all agents and threads in your organization
+* By deleting the underlying file object, which removes the file from all vector_store and code_interpreter configurations across all agents and threads in your organization
 
 The maximum file size is 512 MB. Each file should contain no more than 5,000,000 tokens per file (computed automatically when you attach a file).
 

articles/ai-foundry/agents/how-to/tools/file-search.md

@@ -6,7 +6,7 @@ services: cognitive-services
 manager: nitinme
 ms.service: azure-ai-agent-service
 ms.topic: how-to
-ms.date: 05/12/2025
+ms.date: 08/01/2025
 author: aahill
 ms.author: aahi
 ms.reviewer: fosteramanda
@@ -15,7 +15,7 @@ ms.custom: azure-ai-agents
 
 # Create a new network-secured environment with user-managed identity
 
-Azure AI Foundry Agent Service offers **Standard Setup with private networking** environment setup, allowing you to bring your own (BYO) private virtual network. This set up creates an isolated network environment that lets you securely access data and perform actions while maintaining full control over your network infrastructure. This guide provides a step-by-step walkthrough of the setup process and outlines all necessary requirements.
+Azure AI Foundry Agent Service offers **Standard Setup with private networking** environment setup, allowing you to bring your own (BYO) private virtual network. This setup creates an isolated network environment that lets you securely access data and perform actions while maintaining full control over your network infrastructure. This guide provides a step-by-step walkthrough of the setup process and outlines all necessary requirements.
 
 ## Security features
 
@@ -39,7 +39,7 @@ For customers without an existing virtual network, the Standard Setup with Priva
     - **Exception:** You may connect your Foundry Project to models deployed in a different region (on another AI Foundry or Azure OpenAI resource) by configuring an appropriate AI Services connection on the Project's [capability host](/azure/templates/microsoft.cognitiveservices/accounts/projects/capabilityhosts).
 - **Region availability**:
   - For supported regions for Foundry workspace resources, see: [Azure AI Foundry project region availability](../../reference/region-support.md#azure-ai-foundry-projects).
-  - For supported regions for model deployments, see: [Azure OpenAI model region support](../concepts/model-region-support.md#azure-openai-models).
+  - For supported regions for model deployments, see: [Azure OpenAI model region support](../concepts/model-region-support.md#available-models).
 - **Azure Blob Storage**: using Azure Blob Storage files with the File Search tool isn't supported.
 
 ## Prerequisites
@@ -79,7 +79,7 @@ For customers without an existing virtual network, the Standard Setup with Priva
 ## Configure a new network-secured environment 
 
 > [!NOTE]
-> - Programmatic deployment is required to set up a network-secured environment for Azure AI Foundry Agent Service. Deployment through the Azure portal is currently not supported.
+> - Programmatic deployment is required to setup a network-secured environment for Azure AI Foundry Agent Service. Deployment through the Azure portal is currently not supported.
 > - If you want to delete your Foundry resource and Standard Agent with secured network set-up, delete your AI Foundry resource and virtual network last. Before deleting the virtual network, ensure to delete and [purge](../../../ai-services/recover-purge-resources.md#purge-a-deleted-resource) your AI Foundry resource.
 > - In the Standard Setup, agents use customer-owned, single-tenant resources. You have full control and visibility over these resources, but you incur costs based on your usage.
 
@@ -119,7 +119,7 @@ The following DNS zones are configured:
 Virtual networks enable you to specify which endpoints can make API calls to your resources. The Azure service automatically rejects API calls from devices outside your defined network. You can establish allowed networks using either formula-based definitions or by creating an exhaustive list of permitted endpoints. This security layer can be combined with other security measures for enhanced protection.
 
 > [!NOTE]
-> If you bring your existing virtual network and subnet with the *Microsoft.App/environments* delegation, the minimize size of your subnet should be /27 (32 addresses). We recommend a subnet size of /24 (256 addresses) and is the default subnet size set in the network secured template. 
+> If you bring your existing virtual network and subnet with the *Microsoft.App/environments* delegation, the minimize size of your subnet should be /27 (32 addresses). We recommend a subnet size of /24 (256 addresses), which is the default subnet size set in the network secured template. 
 
 ### Network rules
 
@@ -169,7 +169,7 @@ Once your template deployment is complete, you can access your Foundry project b
 
 This setup enables AI agents to operate entirely within a dedicated, isolated virtual network. By leveraging private network isolation (BYO VNet), organizations can enforce custom security policies, ensuring that AI agents operate within their trusted infrastructure.    
 
-Our goal is to accelerate the development and deployment of AI agents without compromising critical security requirements. With our bicep and ARM templates, you can quickly set up your agent environment while still maintaining full control over their networking and data.   
+Our goal is to accelerate the development and deployment of AI agents without compromising critical security requirements. With our bicep and ARM templates, you can quickly setup your agent environment while still maintaining full control over their networking and data.   
 
 
 ## What's next?

articles/ai-foundry/agents/how-to/virtual-networks.md

@@ -10,7 +10,7 @@ In this example, write your credentials to environment variables on the local ma
 
 [!INCLUDE [find key and endpoint](./find-key.md)]
 
-To set the environment variable for your key and endpoint, open a console window and follow the instructions for your operating system and development environment.
+To set the environment variable for your key and endpoint, open a console window and complete the instructions that follow for your operating system and development environment.
 
 - To set the `FACE_APIKEY` environment variable, replace `<your_key>` with one of the keys for your resource.
 - To set the `FACE_ENDPOINT` environment variable, replace `<your_endpoint>` with the endpoint for your resource.

articles/ai-services/computer-vision/includes/face-environment-variables.md

@@ -12,4 +12,4 @@ ms.update-cycle: 365-days
 ms.author: pafarley
 ---
 
-Go to the Azure portal. If the resource you created in the **Prerequisites** section deployed successfully, select **Go to resource** under **Next Steps**. You can find your key and endpoint under **Resource Management** in the **Keys and Endpoint** page. Your resource key isn't the same as your Azure subscription ID.
+Go to the Azure portal. If the resource you created in the **Prerequisites** section deployed successfully, select **Go to resource** under **Next steps**. You can find your key and endpoint under **Resource Management** on the **Keys and Endpoint** page of the Face resource. Your resource key isn't the same as your Azure subscription ID.

articles/ai-services/computer-vision/includes/find-key.md

@@ -22,8 +22,7 @@ Use Azure AI Foundry portal to detect faces in an image.
 
 Go to the [Azure AI Foundry portal](https://ai.azure.com/), and sign in with your Azure account that has the AI Foundry resource.
 
-On the left nav, select **AI Services**. On the next page, select **Vision + Document**. Go to the **Face** tab and select **Detect faces in an image**.
-
+Select **Azure AI Foundry** in the top-left corner, scroll down and select **Explore Azure AI Services**, and then select **Vision + Document**. On the **Face** tab, select **Detect faces in an image**.
 
 ## Detect faces
 
@@ -33,7 +32,7 @@ Select an image from the available set, or upload your own. The service will det
 
 ## Next steps
 
-In this quickstart, you did face detection using the Azure AI Foundry portal. Next, learn about the different face detection models and how to specify the right model for your use case.
+In this quickstart, you did face detection by using the Azure AI Foundry portal. Next, learn about the different face detection models and how to specify the right model for your use case.
 
 > [!div class="nextstepaction"]
 > [Specify a face detection model version](../how-to/specify-detection-model.md)

articles/ai-services/computer-vision/includes/identity-studio-quickstart.md

@@ -18,11 +18,9 @@ Get started with facial recognition using the Face client library for .NET. The
 
 ## Prerequisites
 
-* Azure subscription - [Create one for free](https://azure.microsoft.com/free/cognitive-services/)
+* Azure subscription. [Create one for free](https://azure.microsoft.com/free/cognitive-services/).
 * The [Visual Studio IDE](https://visualstudio.microsoft.com/vs/) or current version of [.NET Core](https://dotnet.microsoft.com/download/dotnet-core).
-* Once you have your Azure subscription, <a href="https://portal.azure.com/#create/Microsoft.CognitiveServicesFace"  title="Create a Face resource"  target="_blank">create a Face resource </a> in the Azure portal to get your key and endpoint. After it deploys, select **Go to resource**.
-    * You'll need the key and endpoint from the resource you create to connect your application to the Face API.
-    * You can use the free pricing tier (`F0`) to try the service, and upgrade later to a paid tier for production.
+* Once you have your Azure subscription, <a href="https://portal.azure.com/#create/Microsoft.CognitiveServicesFace"  title="Create a Face resource"  target="_blank">create a Face resource </a> in the Azure portal to get your key and endpoint. You can use the free pricing tier (`F0`) to try the service, and upgrade later to a paid tier for production.
 
 
 ## Create environment variables
@@ -31,15 +29,15 @@ Get started with facial recognition using the Face client library for .NET. The
 
 ## Identify and verify faces
 
-1. Create a new C# application
+1. Create a new C# application.
 
     #### [Visual Studio IDE](#tab/visual-studio)
 
-    Using Visual Studio, create a new .NET Core application. 
+    Using Visual Studio, create a new .NET Console App. 
 
     ### Install the client library 
 
-    Once you've created a new project, install the client library by right-clicking on the project solution in the **Solution Explorer** and selecting **Manage NuGet Packages**. In the package manager that opens select **Browse**, check **Include prerelease**, and search for `Azure.AI.Vision.Face`. Select the latest version, and then **Install**. 
+    Once you've created a new project, install the client library by right-clicking the project in  **Solution Explorer** and selecting **Manage NuGet Packages**. In the package manager that opens, select **Browse**, select **Include prerelease**, and search for `Azure.AI.Vision.Face`. Select the latest version, and then select **Install**. 
 
     #### [CLI](#tab/cli)
 
@@ -74,10 +72,10 @@ Get started with facial recognition using the Face client library for .NET. The
     ```
 
     ---
-1. Add the following code into the *Program.cs* file.
+1. Add the following code into the *Program.cs* file, replacing the existing code.
 
     > [!NOTE]
-    > If you haven't received access to the Face service using the [intake form](https://aka.ms/facerecognition), some of these functions won't work.
+    > If you haven't received access to the Face service by using the [intake form](https://aka.ms/facerecognition), some of these functions won't work.
     
     [!code-csharp[](~/cognitive-services-quickstart-code/dotnet/Face/Quickstart.cs?name=snippet_single)]
 
@@ -86,7 +84,7 @@ Get started with facial recognition using the Face client library for .NET. The
 
     #### [Visual Studio IDE](#tab/visual-studio)
 
-    Run the application by clicking the **Debug** button at the top of the IDE window.
+    Run the application by selecting the **Debug** button at the top of the IDE window.
 
     #### [CLI](#tab/cli)
 

articles/ai-services/computer-vision/includes/quickstarts-sdk/identity-csharp-sdk.md

@@ -12,17 +12,15 @@ ms.date: 05/03/2022
 ms.author: pafarley
 ---
 
-Get started with facial recognition using the Face client library for JavaScript. Follow these steps to install the package and try out the example code for basic tasks. The Face service provides you with access to advanced algorithms for detecting and recognizing human faces in images. Follow these steps to install the package and try out the example code for basic face identification using remote images.
+Get started with facial recognition using the Face client library for JavaScript. The Face service provides you with access to advanced algorithms for detecting and recognizing human faces in images. Follow these steps to install the package and try out the example code for basic face identification using remote images.
 
 [Reference documentation](https://aka.ms/azsdk-javascript-face-ref) | [Library source code](https://github.com/Azure/azure-sdk-for-js/tree/main/sdk/face/ai-vision-face-rest) | [Package (npm)](https://www.npmjs.com/package/@azure-rest/ai-vision-face) | [Samples](https://github.com/Azure/azure-sdk-for-js/tree/main/sdk/face/ai-vision-face-rest/samples)
 
 ## Prerequisites
 
-* Azure subscription - [Create one for free](https://azure.microsoft.com/free/cognitive-services/)
+* Azure subscription - [Create one for free](https://azure.microsoft.com/free/cognitive-services/).
 * The latest version of [Node.js](https://nodejs.org/en/)
-* Once you have your Azure subscription, [Create a Face resource](https://portal.azure.com/#create/Microsoft.CognitiveServicesFace) in the Azure portal to get your key and endpoint. After it deploys, select **Go to resource**.
-    * You'll need the key and endpoint from the resource you create to connect your application to the Face API.
-    * You can use the free pricing tier (`F0`) to try the service, and upgrade later to a paid tier for production.
+* Once you have your Azure subscription, [Create a Face resource](https://portal.azure.com/#create/Microsoft.CognitiveServicesFace).You can use the free pricing tier (`F0`) to try the service, and upgrade later to a paid tier for production.
 
 
 ## Create environment variables
@@ -54,7 +52,7 @@ Get started with facial recognition using the Face client library for JavaScript
 
     Your app's `package.json` file is updated with the dependencies.
 
-1. Create a file named `index.js`, open it in a text editor, and paste in the following code:
+1. Create a file named `index.js`, open it in a text editor, paste in the following code, and save it to the folder that contains the app. 
 
     > [!NOTE]
     > If you haven't received access to the Face service using the [intake form](https://aka.ms/facerecognition), some of these functions won't work.

articles/ai-services/computer-vision/includes/quickstarts-sdk/identity-javascript-sdk.md

@@ -20,8 +20,9 @@ feedback_help_link_url: https://learn.microsoft.com/answers/tags/156/azure-face
 # Quickstart: Use the Face service
 
 [!INCLUDE [GDPR-related guidance](../includes/identity-data-notice.md)]
-[!INCLUDE [Gate notice](../includes/identity-gate-notice.md)]
 
+> [!CAUTION]
+> Face service access is limited based on eligibility and usage criteria in order to support our Responsible AI principles. Face service is only available to Microsoft managed customers and partners. Use the [Face Recognition intake form](https://aka.ms/facerecognition) to apply for access. To complete the steps in this quickstart, request the **[Face API] Facial Identification (1:N or 1:1 matching) with optional facial liveness detection for touchless access control** use case. For more information, see the [Face limited access](/azure/ai-foundry/responsible-ai/computer-vision/limited-access-identity) page.
 
 ::: zone pivot="foundry-portal"