Merge branch 'main' into release-aisvcs-move-rai-docs

Author: v-alje

articles/ai-foundry/agents/how-to/tools/openapi-spec-samples.md

@@ -5,7 +5,7 @@ description: Find code samples to use OpenAPI tools with agents.
 author: aahill
 ms.author: aahi
 manager: nitinme
-ms.date: 04/09/2025
+ms.date: 06/18/2025
 ms.service: azure-ai-agent-service
 ms.topic: how-to
 ms.custom:
@@ -33,8 +33,8 @@ Use this article to find step-by-step instructions and code samples for using Op
 
    :::image type="content" source="../../media\tools\open-api-details.png" alt-text="A screenshot showing the openAPI tool details in the Azure AI Foundry portal." lightbox="../../media\tools\open-api-details.png":::
 
-1. Select **Next** and select your authentication method. Choose `connection` for `API key`.
-   1. If you choose `connection`, you need to select the custom keys connection you have created before.
+1. Select **Next** and select your authentication method.
+   1. If you choose `connection`, you need to select the custom connection you have created before.
    1. If you choose `managed identity`, you need to input the audience to get your token. An example of an audience would be `https://cognitiveservices.azure.com/` to connect to Azure AI Services. Make sure you have already set up authentication and role assignment (as described in the [section](./openapi-spec.md#authenticating-with-managed-identity-microsoft-entra-id) above).
 
 1. Copy and paste your OpenAPI specification in the text box.
@@ -70,18 +70,14 @@ with AIProjectClient(
 ) as project_client:
 ```
 
-## Countries Tool Setup
-Similarly, the OpenAPI specification for the countries service is loaded from `countries.json`. An anonymous authentication object (`OpenApiAnonymousAuthDetails`) is created, as this specific API doesn't require authentication in this example.
+## Tool setup
+Similarly, the OpenAPI specification is loaded from `weather.json`. An anonymous authentication object (`OpenApiAnonymousAuthDetails`) is created, as this specific API doesn't require authentication in this example. You can find an example OpenAPI spec on [GitHub](https://github.com/azure-ai-foundry/foundry-samples/blob/main/samples/microsoft/python/getting-started-agents/openapi/weather_openapi.json).
 
 ```python
-    # Load the OpenAPI specification for the countries service from a local JSON file
+    # Load the OpenAPI specification for the weather service from a local JSON file
     with open(os.path.join(os.path.dirname(__file__), "weather.json"), "r") as f:
          openapi_weather = jsonref.loads(f.read())
 
-    # Load the OpenAPI specification for the countries service from a local JSON file
-    with open(os.path.join(os.path.dirname(__file__), "countries.json"), "r") as f:
-         openapi_countries = jsonref.loads(f.read())
-
     # Create Auth object for the OpenApiTool (note: using anonymous auth here; connection or managed identity requires additional setup)
     auth = OpenApiAnonymousAuthDetails()
     # for connection setup
@@ -93,10 +89,6 @@ Similarly, the OpenAPI specification for the countries service is loaded from `c
     openapi_tool = OpenApiTool(
         name="get_weather", spec=openapi_weather, description="Retrieve weather information for a location", auth=auth
     )
-    # Add the countries API definition to the same tool object
-    openapi_tool.add_definition(
-        name="get_countries", spec=openapi_countries, description="Retrieve a list of countries", auth=auth
-    )
 ```
 
 ## Agent Creation
@@ -125,7 +117,7 @@ Create the thread and add the initial user message.
     message = project_client.agents.messages.create(
         thread_id=thread.id,
         role="user",
-        content="What's the weather in Seattle and What is the name and population of the country that uses currency with abbreviation THB?",
+        content="What's the weather in Seattle?",
     )
     print(f"Created message, ID: {message.id}")
 ```
@@ -201,6 +193,8 @@ const client = new AgentsClient(projectEndpoint, new DefaultAzureCredential());
 
 ## Read in the OpenAPI spec
 
+You can find an example OpenAPI spec on [GitHub](https://github.com/azure-ai-foundry/foundry-samples/blob/main/samples/microsoft/python/getting-started-agents/openapi/weather_openapi.json).
+
 ```javascript
 // Read in OpenApi spec
 const filePath = "./data/weatherOpenApi.json";
@@ -283,7 +277,7 @@ console.log(`Deleted agent, agent ID: ${agent.id}`);
 :::zone pivot="csharp"
 
 ## Configure client and OpenAPI tool
-First, retrieve configuration details and create a `PersistentAgentsClient`, then define the `OpenApiToolDefinition` using the OpenAPI specification.
+First, retrieve configuration details and create a `PersistentAgentsClient`, then define the `OpenApiToolDefinition` using the OpenAPI specification. You can find an example OpenAPI spec on [GitHub](https://github.com/azure-ai-foundry/foundry-samples/blob/main/samples/microsoft/python/getting-started-agents/openapi/weather_openapi.json).
 
 ```csharp
 using Azure;
@@ -392,7 +386,7 @@ Follow the [REST API Quickstart](../../quickstart.md?pivots=rest-api#api-call-in
 
 ## Create the OpenAPI Spec tool definition, agent, and thread
 
-You might want to store the OpenAPI specification in another file and import the content to initialize the tool. This example is using `anonymous` as the authentication type.
+You might want to store the OpenAPI specification in another file and import the content to initialize the tool. This example is using `anonymous` as the authentication type. You can find an example OpenAPI spec on [GitHub](https://github.com/azure-ai-foundry/foundry-samples/blob/main/samples/microsoft/python/getting-started-agents/openapi/weather_openapi.json).
 
 ```bash
 curl --request POST \

articles/ai-foundry/how-to/develop/vscode.md

@@ -1,5 +1,5 @@
 ---
-title: Work with Azure AI Foundry projects in VS Code containers
+title: Use VS Code containers in hub based projects (Preview)
 titleSuffix: Azure AI Foundry
 description: This article provides instructions on how to get started with Azure AI Foundry projects in VS Code containers.
 manager: scottpolly
@@ -9,18 +9,18 @@ ms.custom:
   - build-2024
   - ignite-2024
 ms.topic: how-to
-ms.date: 02/14/2025
+ms.date: 06/18/2025
 ms.reviewer: lebaro
 ms.author: sgilley
 author: sdgilley
 # customer intent: As a Developer, I want to use Azure AI Foundry projects in VS Code containers.
 ---
 
-# Get started with Azure AI Foundry projects in VS Code containers (Preview)
+# Use VS Code containers in [!INCLUDE [hub-project-name](../../includes/hub-project-name.md)]s (Preview)
 
 [!INCLUDE [feature-preview](../../includes/feature-preview.md)]
 
-[Azure AI Foundry](https://ai.azure.com/?cid=learnDocs) supports developing in VS Code - Desktop and Web. In each scenario, your VS Code instance is remotely connected to a prebuilt custom container running on a virtual machine, also known as a compute instance.
+[Azure AI Foundry](https://ai.azure.com/?cid=learnDocs) supports developing in VS Code - Desktop and Web for [!INCLUDE [hub-project-name](../../includes/hub-project-name.md)]s. In each scenario, your VS Code instance is remotely connected to a prebuilt custom container running on a virtual machine, also known as a compute instance.
 
 ## Prerequisites
 
@@ -35,15 +35,16 @@ author: sdgilley
 1. On the left menu, select **Templates**.
 1. Select **VS Code container**.
 1. For **Compute**, select an existing compute instance or create a new one.
-    * Select a compute instance to use. If it's stopped, select **Start compute** and wait for it to switch to **Running**. You'll see a **Ready** status when the compute is ready for use.
-    * If you don't have a compute instance, select **Create compute**.  Then enter a name, compute details, and select **Create compute**. Wait until the compute instance is ready.
+    * Select a compute instance to use. If it's stopped, select **Start compute** and wait for it to switch to **Running**. You see a **Ready** status when the compute is ready for use.
+    * If you don't have a compute instance, select **Create compute**. Then enter a name, compute details, and select **Create compute**. Wait until the compute instance is ready.
 1. If prompted, select **Authenticate** to authenticate your compute instance.
-1. For **VS Code container**, select **Set up container** once the button enables. This configures the container on the compute for you. The container setup might take a few minutes to complete. Once you set up the container for the first time, you can directly launch subsequent times. When setup is complete, you'll see **Ready**.
+1. For **VS Code container**, select **Set up container** once the button enables. This setting configures the container on the compute for you. The container setup might take a few minutes to complete. Once you set up the container for the first time, you can directly launch subsequent times. When setup is complete, you see **Ready**.
 
     > [!WARNING]
-     > Even if you [enable idle shutdown on your compute instance](../create-manage-compute.md#configure-idle-shutdown), idle shutdown will not occur for any compute that is set up with this custom VS Code container. This is to ensure the compute doesn't shut down unexpectedly while you're working within a container.
+     > Even if you [enable idle shutdown on your compute instance](../create-manage-compute.md#configure-idle-shutdown), idle shutdown won't occur for any compute that is set up with this custom VS Code container. This is to ensure the compute doesn't shut down unexpectedly while you're working within a container.
 
 1. Once your container is ready, launch VS Code:
+
     * If you want to work in your local VS Code instance, choose **Launch in VS Code (Desktop)**. A new local instance of VS Code opens on your local machine.
     * If you want to work in the browser instead, select the dropdown arrow and choose **Launch in VS Code (Web)**. A new browser tab connected to *vscode.dev* opens.
 
@@ -108,4 +109,3 @@ For app templates and SDK samples in your preferred programming language, see [D
 ## Related content
 
 - [Get started building a chat app using the prompt flow SDK](../../quickstarts/get-started-code.md)
-- [Get started with Azure AI Foundry SDKs](sdk-overview.md)

articles/ai-foundry/tutorials/deploy-chat-web-app.md

@@ -9,7 +9,7 @@ ms.custom:
   - build-2024
   - ignite-2024
 ms.topic: tutorial
-ms.date: 02/13/2025
+ms.date: 06/18/2025
 ms.reviewer: tgokal
 ms.author: sgilley
 author: sdgilley
@@ -73,7 +73,7 @@ To start, identify the resources you need to configure from the Azure AI Foundry
 
 1. For each resource, select the link to open the resource details.  From the details page, select the resource name to open the resource in the Azure portal.  (For the workspaceblobstore, select **View in Azure Portal**). 
 1. After the browser tab opens, go back to the Azure AI Foundry portal and repeat the process for the next resource. 
-1. When you're done, you should have three new browser tabs open, for **Search service**, **Azure AI services**, and **blobstore Container**. Keep all three new tabs open as you'll go back and forth between them to configure the resources.
+1. When you're done, you should have three new browser tabs open, for **Search service**, **Azure AI Foundry**, and **blobstore Container**. Keep all three new tabs open as you'll go back and forth between them to configure the resources.
 
 ### Enable managed identity
 
@@ -83,7 +83,7 @@ On the browser tab for the **Search service** resource in the Azure portal, enab
 1. Switch **Status** to **On**.
 1. Select **Save**.
 
-On the browser tab for the **Azure AI services** resource in the Azure portal, enable the managed identity:
+On the browser tab for the **Azure AI Foundry** resource in the Azure portal, enable the managed identity:
 
 1. From the left pane, under **Resource Management**, select **Identity**.
 1. Switch **Status** to **On**.
@@ -106,18 +106,18 @@ You'll repeat this pattern multiple times in the bulleted items below.
 Use these steps to assign roles for the resources you're configuring in this tutorial:
 
 * Assign the following roles on the browser tab for **Search service** in the Azure portal:
-    * **Search Index Data Reader** to the **Azure AI services** managed identity
-    * **Search Service Contributor** to the **Azure AI services** managed identity
+    * **Search Index Data Reader** to the **Azure AI Foundry** managed identity
+    * **Search Service Contributor** to the **Azure AI Foundry** managed identity
     * **Contributor** to yourself (to find **Contributor**, switch to the **Privileged administrator roles** tab at the top.  All other roles are in the **Job function roles** tab.)
 
-* Assign the following roles on the browser tab for **Azure AI services** in the Azure portal:
+* Assign the following roles on the browser tab for **Azure AI Foundry** in the Azure portal:
 
     * **Cognitive Services OpenAI Contributor** to the **Search service** managed identity
     * **Contributor** to yourself.
 
 * Assign the following roles on the browser tab for **Azure Blob storage** in the Azure portal:
 
-    * **Storage Blob Data Contributor** to the **Azure AI services** managed identity
+    * **Storage Blob Data Contributor** to the **Azure AI Foundry** managed identity
     * **Storage Blob Data Reader** to the **Search service** managed identity
     * **Contributor** to yourself
 

articles/ai-services/cognitive-services-data-loss-prevention.md

@@ -26,7 +26,7 @@ There are two parts to enable data loss prevention. First, the resource property
 >[!NOTE]
 >
 > * The `allowedFqdnList`  property value supports a maximum of 1000 URLs.
-> * The property supports fully qualified domain names (i.e., `www.microsoft.com`) as values.
+> * The property supports fully qualified domain names (i.e., `www.contoso.com`) as values.
 > * It can take up to 15 minutes for the updated list to take effect. 
 
 # [Azure CLI](#tab/azure-cli)
@@ -52,7 +52,7 @@ There are two parts to enable data loss prevention. First, the resource property
     ```azurecli-interactive
     az rest -m patch \
         -u /subscriptions/{subscription ID}/resourceGroups/{resource group}/providers/Microsoft.CognitiveServices/accounts/{account name}?api-version=2024-10-01 \
-        -b '{"properties": { "restrictOutboundNetworkAccess": true, "allowedFqdnList": [ "microsoft.com" ] }}'
+        -b '{"properties": { "restrictOutboundNetworkAccess": true, "allowedFqdnList": [ "contoso.com" ] }}'
     ```
 
 # [PowerShell](#tab/powershell)
@@ -82,7 +82,7 @@ There are two parts to enable data loss prevention. First, the resource property
         ResourceType = 'accounts'
         Name = 'myaccount'
         ApiVersion = '2024-10-01'
-        Payload = '{"properties": { "restrictOutboundNetworkAccess": true, "allowedFqdnList": [ "microsoft.com" ] }}'
+        Payload = '{"properties": { "restrictOutboundNetworkAccess": true, "allowedFqdnList": [ "contoso.com" ] }}'
         Method = 'PATCH'
     }
     Invoke-AzRestMethod @patchParams

articles/ai-services/openai/concepts/content-streaming.md

@@ -37,8 +37,8 @@ To enable Asynchronous Filter in [Azure AI Foundry portal](https://ai.azure.com/
 | Compare | Streaming - Default | Streaming - Asynchronous Filter |
 |---|---|---|
 |Status |GA |Public Preview |
-| Eligibility |All customers |Customers approved for modified content filtering |
-| How to enable | Enabled by default, no action needed |Customers approved for modified content filtering can configure it directly in [Azure AI Foundry portal](https://ai.azure.com/?cid=learnDocs) (as part of a content filtering configuration, applied at the deployment level) |
+| Eligibility |All customers |All customers |
+| How to enable | Enabled by default, no action needed |Customers can configure it directly in [Azure AI Foundry portal](https://ai.azure.com/?cid=learnDocs) (as part of a content filtering configuration, applied at the deployment level) |
 |Modality and availability |Text; all GPT models |Text; all GPT models |
 |Streaming experience |Content is buffered and returned in chunks |Zero latency (no buffering, filters run asynchronously) |
 |Content filtering signal |Immediate filtering signal |Delayed filtering signal (in up to ~1,000-character increments) |