Merge branch 'main' into patch-1

laujan · web-flow · commit 7838ab2d4d9e · 2025-05-28T14:41:02.000-07:00
diff --git a/articles/ai-foundry/includes/create-content-filter.md b/articles/ai-foundry/includes/create-content-filter.md
@@ -35,7 +35,7 @@ Follow these steps to create a content filter:
 
     Now you can configure the input filters (for user prompts) and output filters (for model completion). 
 1. On the **Input filters** page, you can set the filter for the input prompt. For the first four content categories there are three severity levels that are configurable: Low, medium, and high. You can use the sliders to set the severity threshold if you determine that your application or usage scenario requires different filtering than the default values. 
-    Some filters, such as Prompt Shields and Protected material detection, enable you to determine if the model should annotate and/or block content. Selecting **Annotate only** runs the respective model and return annotations via API response, but it will not filter content. In addition to annotate, you can also choose to block content.
+    Some filters, such as Prompt Shields and Protected material detection, enable you to determine if the model should annotate and/or block content. Selecting **Annotate only** runs the respective model and returns annotations via API response, but it will not filter content. In addition to annotate, you can also choose to block content.
 
     If your use case was approved for modified content filters, you receive full control over content filtering configurations and can choose to turn filtering partially or fully off, or enable annotate only for the content harms categories (violence, hate, sexual and self-harm).
 
@@ -80,4 +80,4 @@ Follow these steps to apply a content filter to a deployment:
 Now, you can go to the playground to test whether the content filter works as expected.
 
 > [!TIP]
-> You can also create and update content filters using the REST APIs. For more information, see the [API reference](/rest/api/aiservices/accountmanagement/rai-policies/create-or-update). Content filters can be configured at the resource level. Once a new configuration is created, it can be associated with one or more deployments. For more information about model deployment, see the resource [deployment guide](../../ai-services/openai/how-to/create-resource.md). 
+> You can also create and update content filters using the REST APIs. For more information, see the [API reference](/rest/api/aiservices/accountmanagement/rai-policies/create-or-update). Content filters can be configured at the resource level. Once a new configuration is created, it can be associated with one or more deployments. For more information about model deployment, see the resource [deployment guide](../../ai-services/openai/how-to/create-resource.md). 
diff --git a/articles/ai-services/content-understanding/how-to/create-multi-service-resource.md b/articles/ai-services/content-understanding/how-to/create-multi-service-resource.md
@@ -13,7 +13,11 @@ ms.author: lajanuar
 
 # Create an Azure AI Foundry resource
 
-To use Content Understanding, you need an Azure AI Foundry resource. This multi-service resource enables access to multiple Azure AI services with a single set of credentials.
+To use Content Understanding, you need an Azure AI Foundry resource.
+
+> [!IMPORTANT]
+> The steps below explain how to create a resource for use with the [REST API](../quickstart/use-rest-api.md). To use Content Understanding in the Azure AI Foundry portal, see the [AI Foundry quickstart](../quickstart/use-ai-foundry.md).
+
 
 ## Prerequisites
 
@@ -23,9 +27,6 @@ To use Content Understanding, you need an Azure AI Foundry resource. This multi-
 
     :::image type="content" source="../media/overview/azure-multi-service-resource.png" alt-text="Screenshot of the AI Foundry resource page in the Azure portal.":::
 
-    > [!IMPORTANT]
-    > Azure provides more than one resource type for Azure AI services. Be sure to select the one that is listed under **AI Foundry** > **AI Foundry** with the logo as shown previously.
-
 1. Select the **Create** button.
 
 ## Create a resource
@@ -57,5 +58,5 @@ To use the Azure AI Content Understanding service, you must create your Azure AI
 
 Now that you created your Azure AI Foundry resource, you're ready to try out the Content Understanding service.
 
-* Try Content Understanding with no code in [Azure AI Foundry](https://ai.azure.com/explore/aiservices/vision/contentunderstanding).
 * Ready to go straight to code? Follow the [REST API QuickStart](../quickstart/use-rest-api.md).
+* Try Content Understanding with no code in [Azure AI Foundry](https://ai.azure.com/explore/aiservices/vision/contentunderstanding).
diff --git a/articles/ai-services/content-understanding/quickstart/use-rest-api.md b/articles/ai-services/content-understanding/quickstart/use-rest-api.md
@@ -20,18 +20,15 @@ ms.date: 05/19/2025
 
 To get started, you need **an active Azure subscription**. If you don't have an Azure account, [create one for free](https://azure.microsoft.com/free/).
 
-* Once you have your Azure subscription, create an [Azure AI Foundry resource](https://portal.azure.com/#create/Microsoft.CognitiveServicesAIFoundry) in the Azure portal. This multi-service resource enables access to multiple Azure AI services with a single set of credentials.
+* Once you have your Azure subscription, create an [Azure AI Foundry resource](https://portal.azure.com/#create/Microsoft.CognitiveServicesAIFoundry) in the Azure portal.
 
    * This resource is listed under **AI Foundry** > **AI Foundry** in the portal.
 
-    > [!IMPORTANT]
-    > Azure provides more than one resource type for Azure AI services. Make certain that you select the one listed under **AI Foundry** > **AI Foundry** as depicted in the following image. For more information, see [Create an Azure AI Foundry resource](../how-to/create-multi-service-resource.md).
-
      :::image type="content" source="../media/overview/azure-multi-service-resource.png" alt-text="Screenshot of the AI Foundry resource page in the Azure portal.":::
 
-* In this quickstart, we use the cURL command line tool. If it isn't installed, you can download a version for your dev environment: [cURL download page](https://curl.se/download.html).
+In this guide, we use the cURL command line tool. If it isn't installed, you can [download](https://everything.curl.dev/install/index.html) the appropriate version for your dev environment.
 
-## Get Started with a prebuilt analyzer
+## Get started with a prebuilt analyzer
 
 Analyzers define how your content is processed and the insights that are extracted. We offer [prebuilt analyzers](../concepts/prebuilt-analyzers.md) for common use cases. You can [customize prebuilt analyzers](../concepts/prebuilt-analyzers.md) to better fit your specific needs and use cases.
 This quickstart uses prebuilt document, image, audio, and video analyzers to help you get started.
@@ -41,25 +38,25 @@ This quickstart uses prebuilt document, image, audio, and video analyzers to hel
 Before running the following cURL command, make the following changes to the HTTP request:
 # [Document](#tab/document)
 
-1. Replace `{endpoint}` and `{key}` with the corresponding values from your Azure AI Services instance in the Azure portal.
+1. Replace `{endpoint}` and `{key}` with the corresponding values from your Azure AI Foundry instance in the Azure portal.
 2. Replace `{analyzerId}` with  `prebuilt-documentAnalyzer`. This analyzer extracts text and layout elements such as paragraphs, sections, and tables from a document.
 3. Replace `{fileUrl}` with a publicly accessible URL of the file to analyze—such as a path to an Azure Storage Blob with a shared access signature (SAS), or use the sample URL: `https://github.com/Azure-Samples/azure-ai-content-understanding-python/raw/refs/heads/main/data/invoice.pdf`.
 
 # [Image](#tab/image)
 
-1. Replace `{endpoint}` and `{key}` with the corresponding values from your Azure AI Services instance in the Azure portal.
+1. Replace `{endpoint}` and `{key}` with the corresponding values from your Azure AI Foundry instance in the Azure portal.
 2. Replace `{analyzerId}` with  `prebuilt-imageAnalyzer`. This analyzer generates a description of the image.
 3. Replace `{fileUrl}` with a publicly accessible URL of the file to analyze—such as a path to an Azure Storage Blob with a shared access signature (SAS), or use the sample URL: `https://github.com/Azure-Samples/azure-ai-content-understanding-python/raw/refs/heads/main/data/pieChart.jpg`.
 
 # [Audio](#tab/audio)
 
-1. Replace `{endpoint}` and `{key}` with the corresponding values from your Azure AI Services instance in the Azure portal.
+1. Replace `{endpoint}` and `{key}` with the corresponding values from your Azure AI Foundry instance in the Azure portal.
 2. Replace `{analyzerId}` with  `prebuilt-audioAnalyzer`. This analyzer extracts the audio transcript, generates a summary, and performs speaker labeling.
 3. Replace `{fileUrl}` with a publicly accessible URL of the file to analyze—such as a path to an Azure Storage Blob with a shared access signature (SAS), or use the sample URL: `https://github.com/Azure-Samples/azure-ai-content-understanding-python/raw/refs/heads/main/data/audio.wav`.
 
 # [Video](#tab/video)
 
-1. Replace `{endpoint}` and `{key}` with the corresponding values from your Azure AI Services instance in the Azure portal.
+1. Replace `{endpoint}` and `{key}` with the corresponding values from your Azure AI Foundry instance in the Azure portal.
 2. Replace `{analyzerId}` with  `prebuilt-videoAnalyzer`. This analyzer extracts keyframes, transcript, and chapter segments from video.
 3. Replace `{fileUrl}` with a publicly accessible URL of the file to analyze—such as a path to an Azure Storage Blob with a shared access signature (SAS), or use the sample URL: `https://github.com/Azure-Samples/azure-ai-content-understanding-python/raw/refs/heads/main/data/FlightSimulator.mp4`.
 ---
@@ -96,7 +93,7 @@ Operation-Location: {endpoint}/contentunderstanding/analyzerResults/{resultId}?a
 
 Use the `resultId` from the [`POST` response](#post-response) and retrieve the result of the analysis.
 
-1. Replace `{endpoint}` and `{key}` with the endpoint and key values from your Azure portal Azure AI Services instance.
+1. Replace `{endpoint}` and `{key}` with the endpoint and key values from your Azure portal Azure AI Foundry instance.
 2. Replace `{resultId}` with the `resultId` from the `POST` response.
 
 #### GET request
@@ -348,7 +345,7 @@ The 200 (`OK`) JSON response includes a `status` field indicating the status of
 
 ## Next steps
 
-Learn more about [analyzers](../concepts/analyzer-templates.md) for your use case.
+Learn more about creating [custom analyzers](../tutorial/create-custom-analyzer.md) for your use case.
 
 
 
diff --git a/articles/ai-services/content-understanding/toc.yml b/articles/ai-services/content-understanding/toc.yml
@@ -26,7 +26,7 @@ items:
       - name: Glossary
         displayName: glossary, definition, updates, previews
         href: glossary.md
-  - name: "AI Foundry: Content Understanding"
+  - name: "AI Foundry portal"
     items:
       - name: Quickstart
         displayName: quickstart, extract, text, images, OCR, optical character recognition
diff --git a/articles/ai-services/content-understanding/tutorial/build-rag-solution.md b/articles/ai-services/content-understanding/tutorial/build-rag-solution.md
@@ -27,19 +27,16 @@ This tutorial explains how to create a retrieval-augmented generation (RAG) solu
 
 To get started, you need **An active Azure subscription**. If you don't have an Azure account, you can [create a free subscription](https://azure.microsoft.com/free/).
 
-* Once you have your Azure subscription, create an [Azure AI Foundry resource](https://portal.azure.com/#create/Microsoft.CognitiveServicesAIFoundry) in the Azure portal. This multi-service resource enables access to multiple Azure AI services with a single set of credentials.
+* Once you have your Azure subscription, create an [Azure AI Foundry resource](https://portal.azure.com/#create/Microsoft.CognitiveServicesAIFoundry) in the Azure portal.
 
    * This resource is listed under **AI Foundry** > **AI Foundry** in the portal.
 
-    > [!IMPORTANT]
-    > Azure provides more than one resource type for Azure AI services. Make certain that you select the one listed under **AI Foundry** > **AI Foundry** as depicted in the following image. For more information, see [Create an Azure AI Foundry resource](../how-to/create-multi-service-resource.md).
-
      :::image type="content" source="../media/overview/azure-multi-service-resource.png" alt-text="Screenshot of the multi-service resource page in the Azure portal.":::
 
 * **Azure AI Search Resource:** Set up an [Azure AI Search resource](../../../search/search-create-service-portal.md) to enable indexing and retrieval of multimodal data.
 * **Azure OpenAI Chat Model Deployment:** Deploy an [Azure OpenAI chat model](../../openai/how-to/deployment-types.md) that enables conversational interactions.
 * **Embedding Model Deployment:** Ensure you have an embedding model deployed to generate vector representations for semantic search.
-* **API Version:** This tutorial uses the latest preview [API version](/rest/api/contentunderstanding/analyzers?view=rest-contentunderstanding-2024-12-01-preview&preview&preserve-view=true): `2024-12-01-preview`.
+* **API Version:** This tutorial uses the latest preview [API version](/rest/api/contentunderstanding/operation-groups?preserve-view=true).
 * **Python Environment:** Install [Python 3.11](https://www.python.org/downloads/) to execute the provided code samples and scripts.
 * This tutorial follows this sample code can be found in our [Python notebook](https://github.com/Azure-Samples/azure-ai-search-with-content-understanding-python#samples). Follow the [README](https://github.com/Azure-Samples/azure-ai-search-with-content-understanding-python/blob/main/README.md) to create essential resources, grant resources the right Access control(IAM) roles and install all packages needed for this tutorial.
 * The multimodal data used in this tutorial consists of documents, images, audio, and video. They're designed to guide you through the process of building a robust RAG solution with Azure AI Content Understanding.
diff --git a/articles/ai-services/content-understanding/tutorial/create-custom-analyzer.md b/articles/ai-services/content-understanding/tutorial/create-custom-analyzer.md
@@ -14,10 +14,7 @@ ms.date: 05/19/2025
 
 Content Understanding analyzers define how to process and extract insights from your content. They ensure uniform processing and output structure across all your content to deliver reliable and predictable results. We offer [prebuilt analyzers](../concepts/prebuilt-analyzers.md) for common use cases. This guide shows how these analyzers can be customized to better fit your needs.
 
-In this guide, we use the cURL command line tool. If it isn't installed, you can download the appropriate version for your dev environment:
-
-  * [Windows](https://curl.haxx.se/windows/)
-  * [Mac or Linux](https://learn2torials.com/thread/how-to-install-curl-on-mac-or-linux-(ubuntu)-or-windows)
+In this guide, we use the cURL command line tool. If it isn't installed, you can [download](https://everything.curl.dev/install/index.html) the appropriate version for your dev environment.
 
 ## Define an analyzer schema
 
@@ -222,25 +219,25 @@ Before running the cURL command, make the following changes to the HTTP request:
 
 # [Document](#tab/document)
 
-1. Replace `{endpoint}` and `{key}` with the endpoint and key values from your Azure portal Azure AI Services instance.
+1. Replace `{endpoint}` and `{key}` with the endpoint and key values from your Azure portal Azure AI Foundry instance.
 1. Replace `{analyzerId}` with the name of the custom analyzer created earlier.
 1. Replace `{fileUrl}` with a publicly accessible URL of the file to analyze, such as a path to an Azure Storage Blob with a shared access signature (SAS) or the sample URL `https://github.com/Azure-Samples/azure-ai-content-understanding-python/raw/refs/heads/main/data/receipt.png`.
 
 # [Image](#tab/image)
 
-1. Replace `{endpoint}` and `{key}` with the endpoint and key values from your Azure portal Azure AI Services instance.
+1. Replace `{endpoint}` and `{key}` with the endpoint and key values from your Azure portal Azure AI Foundry instance.
 1. Replace `{analyzerId}` with the name of the custom analyzer created earlier.
 1. Replace `{fileUrl}` with a publicly accessible URL of the file to analyze, such as a path to an Azure Storage Blob with a shared access signature (SAS) or the sample URL `https://github.com/Azure-Samples/azure-ai-content-understanding-python/raw/refs/heads/main/data/pieChart.jpg`.
 
 # [Audio](#tab/audio)
 
-1. Replace `{endpoint}` and `{key}` with the endpoint and key values from your Azure portal Azure AI Services instance.
+1. Replace `{endpoint}` and `{key}` with the endpoint and key values from your Azure portal Azure AI Foundry instance.
 1. Replace `{analyzerId}` with the name of the custom analyzer created earlier.
 1. Replace `{fileUrl}` with a publicly accessible URL of the file to analyze, such as a path to an Azure Storage Blob with a shared access signature (SAS) or the sample URL `https://github.com/Azure-Samples/azure-ai-content-understanding-python/raw/refs/heads/main/data/audio.wav`.
 
 # [Video](#tab/video)
 
-1. Replace `{endpoint}` and `{key}` with the endpoint and key values from your Azure portal Azure AI Services instance.
+1. Replace `{endpoint}` and `{key}` with the endpoint and key values from your Azure portal Azure AI Foundry instance.
 1. Replace `{analyzerId}` with the name of the custom analyzer created earlier.
 1. Replace `{fileUrl}` with a publicly accessible URL of the file to analyze, such as a path to an Azure Storage Blob with a shared access signature (SAS) or the sample URL `https://github.com/Azure-Samples/azure-ai-content-understanding-python/raw/refs/heads/main/data/FlightSimulator.mp4`.
 
@@ -274,7 +271,7 @@ The `202 Accepted` response includes the `{resultId}` which you can use to track
 
 ### Get Analyze Result
 
-1. Replace `{endpoint}` and `{key}` with the endpoint and key values from your Azure portal Azure AI Services instance.
+1. Replace `{endpoint}` and `{key}` with the endpoint and key values from your Azure portal Azure AI Foundry instance.
 2. Replace `{resultId}` with the `resultId` in `POST` response.
 
 #### GET Request
diff --git a/articles/ai-services/openai/how-to/responses.md b/articles/ai-services/openai/how-to/responses.md
@@ -929,6 +929,7 @@ curl GET https://YOUR-RESOURCE-NAME.openai.azure.com/openai/v1/responses/resp_12
 ```python
 from openai import AzureOpenAI
 from azure.identity import DefaultAzureCredential, get_bearer_token_provider
+from time import sleep
 
 token_provider = get_bearer_token_provider(
     DefaultAzureCredential(), "https://cognitiveservices.azure.com/.default"
@@ -947,7 +948,7 @@ response = client.responses.create(
 )
 
 while response.status in {"queued", "in_progress"}:
-    print(f"Current status: {resp.status}")
+    print(f"Current status: {response.status}")
     sleep(2)
     response = client.responses.retrieve(response.id)
 
@@ -1293,4 +1294,4 @@ For examples of how to use reasoning models with the responses API see the [reas
 
 ## Computer use
 
-Computer use with Playwright has moved to the [dedicated computer use model guide](./computer-use.md#playwright-integration)
+Computer use with Playwright has moved to the [dedicated computer use model guide](./computer-use.md#playwright-integration)
diff --git a/articles/ai-services/openai/references/elasticsearch.md b/articles/ai-services/openai/references/elasticsearch.md
diff --git a/articles/machine-learning/how-to-assign-roles.md b/articles/machine-learning/how-to-assign-roles.md
