Merge pull request #5311 from MicrosoftDocs/main

Taojunshen · web-flow · commit ca0291aea0b1 · 2025-05-31T06:01:04.000+08:00
5/30/2025 PM Publish
diff --git a/articles/ai-services/agents/how-to/virtual-networks.md b/articles/ai-services/agents/how-to/virtual-networks.md
@@ -80,7 +80,7 @@ For customers without an existing virtual network, the Standard Setup with Priva
 ### Option 1: manually deploy the bicep template
 
 1. To deploy and customize the bicep templates, [download the template from GitHub](https://github.com/azure-ai-foundry/foundry-samples/tree/main/samples/microsoft/infrastructure-setup/15-private-network-standard-agent-setup). Download the following from the `private-network-standard-agent-setup` folder:
-    1. `main.bicep`
+    1. `main-create.bicep`
     1. `azuredeploy.parameters.json`
     1. `modules-network-secured folder`
 1. To authenticate to your Azure subscription from the Azure CLI, use the following command: 
@@ -102,7 +102,7 @@ For customers without an existing virtual network, the Standard Setup with Priva
     1. To use default resource names, run:
 
         ```console
-        az deployment group create --resource-group {my_resource_group} --template-file main.bicep
+        az deployment group create --resource-group {my_resource_group} --template-file main-create.bicep
         ```
 For more details, see the [README](https://github.com/azure-ai-foundry/foundry-samples/tree/main/samples/microsoft/infrastructure-setup/15-private-network-standard-agent-setup).
 
@@ -154,7 +154,7 @@ For Agents, private endpoints ensure secure, internal-only connectivity for the
 | **Azure Cosmos DB**        | Sql          | `privatelink.documents.azure.com` | `documents.azure.com` |
 | **Azure Storage**          | blob         | `privatelink.blob.core.windows.net` | `blob.core.windows.net` |
 
-
+To create a conditional forwarder in the DNS Server to the Azure DNS Virtual Server, use the list of zones mentioned in the above table. The Azure DNS Virtual Server IP address is 168.63.129.16.
 
 ### Access your secured agents
 
@@ -180,4 +180,4 @@ Our goal is to accelerate the development and deployment of AI agents without co
 
 
 ## What's next?
-You’ve now successfully configured a Network Secure Account and project, use the [quickstart](../quickstart.md) to create your first agent. 
+You’ve now successfully configured a Network Secure Account and project, use the [quickstart](../quickstart.md) to create your first agent. 
diff --git a/articles/ai-services/agents/toc.yml b/articles/ai-services/agents/toc.yml
@@ -133,20 +133,12 @@ items:
       href: /rest/api/aifoundry/aiagents
     - name: SDK
       items:
-      - name: Azure SDK
-        items:
-        - name: C#
-          href: /dotnet/api/overview/azure/ai.agents.persistent-readme
-        - name: Python
-          href: https://aka.ms/azsdk/azure-ai-projects/python/reference
-        - name: JavaScript
-          href: /javascript/api/overview/azure/ai-projects-readme
-      - name: OpenAI SDK
-        items:
-        - name: C#
-          href: https://github.com/openai/openai-dotnet/blob/main/README.md
-        - name: Python
-          href: https://github.com/openai/openai-python/blob/main/README.md
+      - name: C#
+        href: /dotnet/api/overview/azure/ai.agents.persistent-readme
+      - name: Python
+        href: https://aka.ms/azsdk/azure-ai-projects/python/reference
+      - name: JavaScript
+        href: /javascript/api/overview/azure/ai-projects-readme
     - name: Data monitoring reference
       href: reference/monitor-service.md
 - name: Resources
diff --git a/articles/ai-services/content-understanding/concepts/analyzer-templates.md b/articles/ai-services/content-understanding/concepts/analyzer-templates.md
@@ -27,7 +27,7 @@ The following analyzer templates are available for use in the [Azure AI Foundry
 |Document analysis |Analyze documents to extract text, layout, structured fields, and more.|
 |Invoice analysis |Analyze invoice as prebuilt template and extract structured fields and tables.|
 
-   :::image type="content" source="../media/analyzer-template/document-analyzers.png" alt-text="Screenshot of document analyzer template.":::
+   :::image type="content" source="../media/analyzer-template/document-analyzers.png" alt-text="Screenshot of document analyzer template." lightbox="../media/analyzer-template/document-analyzers.png":::
 
 # [Image](#tab/image)
 
@@ -37,7 +37,7 @@ The following analyzer templates are available for use in the [Azure AI Foundry
 |Retail inventory management |Retail inventory management for monitoring of products on shelves.|
 |Defect detection |Identify potential defects in provided images of metal plates.|
 
-   :::image type="content" source="../media/analyzer-template/image-analyzers.png" alt-text="Screenshot of image analyzer template.":::
+   :::image type="content" source="../media/analyzer-template/image-analyzers.png" alt-text="Screenshot of image analyzer template." lightbox="../media/analyzer-template/image-analyzers.png":::
 
 # [Audio](#tab/audio)
 
@@ -48,7 +48,7 @@ The following analyzer templates are available for use in the [Azure AI Foundry
 |Post call analytics |Analyze call center conversations to extract transcripts, summaries, sentiment, and more.|
 
 
-   :::image type="content" source="../media/analyzer-template/audio-analyzers.png" alt-text="Screenshot of audio analyzer template.":::
+   :::image type="content" source="../media/analyzer-template/audio-analyzers.png" alt-text="Screenshot of audio analyzer template." lightbox="../media/analyzer-template/audio-analyzers.png":::
 
 # [Video](#tab/video)
 
@@ -60,6 +60,6 @@ The following analyzer templates are available for use in the [Azure AI Foundry
 |Advertising |Advertising analysis and moderation.|
 
 
-   :::image type="content" source="../media/analyzer-template/video-analyzers.png" alt-text="Screenshot of video analyzer template.":::
+   :::image type="content" source="../media/analyzer-template/video-analyzers.png" alt-text="Screenshot of video analyzer template." lightbox="../media/analyzer-template/video-analyzers.png":::
 
 ---
diff --git a/articles/ai-services/content-understanding/index.yml b/articles/ai-services/content-understanding/index.yml
@@ -32,8 +32,11 @@ conceptualContent:
     - title: AI Foundry
       links:
         - itemType: quickstart
-          text: Quickstart
+          text: "Quickstart: Content Understanding with a single file"
           url: quickstart/use-ai-foundry.md
+        - itemType: quickstart
+          text: "Quickstart: Content Understanding with multiple files"
+          url: quickstart/use-ai-foundry-pro-mode.md  
         - itemType: concept
           text: Analyzer templates
           url: concepts/analyzer-templates.md
diff --git a/articles/ai-services/content-understanding/quickstart/use-ai-foundry-pro-mode.md b/articles/ai-services/content-understanding/quickstart/use-ai-foundry-pro-mode.md
@@ -3,15 +3,16 @@ title: "Create an Azure AI Content Understanding multi-file task in the Azure AI
 titleSuffix: Azure AI services
 description: Create an Azure AI Content Understanding multi-file task in the Azure AI Foundry portal
 author: laujan
+ms.author: kabrow
 manager: nitinme
 ms.service: azure-ai-content-understanding
 ms.topic: quickstart
 ms.date: 05/29/2025
 ---
 
-# Try out Azure AI Content Understanding on multiple files in the Azure AI Foundry portal
+# Quickstart: Use Content Understanding with multiple files
 
-This quickstart shows you how to use the Content Understanding service in the Azure AI Foundry portal to extract structured information from your data. Azure AI Foundry enables you to build and deploy generative AI applications and APIs responsibly.
+This quickstart shows you how to use the Content Understanding service in the [**Azure AI Foundry portal**](https://ai.azure.com/explore/aiservices/vision/contentunderstanding) to extract structured information from your data. Azure AI Foundry enables you to build and deploy generative AI applications and APIs responsibly.
 
 Suppose you have document files and you want to automatically extract key information from them, while also comparing to reference data to infer conclusions from your files. Using Content Understanding, you can create a task to streamline your data processing, define a field schema to specify the information to extract or generate, and develop an analyzer that applies reasoning to your data, delivering key insights and conclusions. The analyzer becomes an API endpoint that you can integrate into your applications or workflows.
 
diff --git a/articles/ai-services/content-understanding/quickstart/use-ai-foundry.md b/articles/ai-services/content-understanding/quickstart/use-ai-foundry.md
@@ -4,14 +4,15 @@ titleSuffix: Azure AI services
 description: Create an Azure AI Content Understanding single-file task in the Azure AI Foundry portal
 author: laujan
 manager: nitinme
+ms.author: kabrow
 ms.service: azure-ai-content-understanding
 ms.topic: quickstart
 ms.date: 05/19/2025
 ---
 
-# Try out Azure AI Content Understanding with a single file in the Azure AI Foundry portal
- 
-This quickstart shows you how to use the Content Understanding service in the Azure AI Foundry portal to extract structured information from your data. Azure AI Foundry enables you to build and deploy generative AI applications and APIs responsibly.
+# Quickstart: Use Content Understanding with a single file
+
+This quickstart shows you how to use the Content Understanding service in the [**Azure AI Foundry portal**](https://ai.azure.com/explore/aiservices/vision/contentunderstanding) to extract structured information from your data. Azure AI Foundry enables you to build and deploy generative AI applications and APIs responsibly.
  
 Suppose you have files—such as documents, images, audio, or video—and you want to automatically extract key information from them. With Content Understanding, you can create a task to organize your data processing, define a field schema that specifies the information to extract or generate, and then build an analyzer. The analyzer becomes an API endpoint that you can integrate into your applications or workflows.
  
diff --git a/articles/ai-services/content-understanding/quickstart/use-rest-api.md b/articles/ai-services/content-understanding/quickstart/use-rest-api.md
@@ -10,7 +10,7 @@ ms.topic: quickstart
 ms.date: 05/19/2025
 ---
 
-# Quickstart: Azure AI Content Understanding REST APIs
+# Quickstart: Use Azure AI Content Understanding REST API
 
 * This quickstart shows you how to use the [Content Understanding REST API](/rest/api/contentunderstanding/content-analyzers?view=rest-contentunderstanding-2025-05-01-preview&preserve-view=true) to get structured data from multimodal content in document, image, audio, and video files.
 
diff --git a/articles/ai-services/content-understanding/toc.yml b/articles/ai-services/content-understanding/toc.yml
@@ -26,22 +26,22 @@ items:
       - name: Glossary
         displayName: glossary, definition, updates, previews
         href: glossary.md
-  - name: "AI Foundry portal"
+  - name: Azure AI Foundry portal
     items:
-      - name: "AI Foundry portal"
+      - name: Analyzer templates
+        displayName: analyzer, templates, document, text, images, video, audio, multimodal, visual, structured, content, field, extraction
+        href: concepts/analyzer-templates.md
+      - name: Quickstarts
         items:
-          - name: "Quickstart: Try Content Understanding with a single file"
+          - name: Try Content Understanding with a single file"
             displayName: quickstart, extract, text, images, OCR, optical character recognition, foundry, standard, mode
             href: quickstart/use-ai-foundry.md
-          - name: "Quickstart: Try Content Understanding with multiple files"
+          - name: Try Content Understanding with multiple files"
             displayName: quickstart, extract, text, images, OCR, optical character recognition, foundry, pro, mode
             href: quickstart/use-ai-foundry-pro-mode.md
-          - name: Analyzer templates
-            displayName: analyzer, templates, document, text, images, video, audio, multimodal, visual, structured, content, field, extraction
-            href: concepts/analyzer-templates.md
   - name: Analyzers
     items:
-      - name: "Quickstart"
+      - name: Quickstart
         displayName: quickstart, extract, text, images, OCR, optical character recognition, content filtering, filter
         href: quickstart/use-rest-api.md
       - name: Modalities
diff --git a/articles/search/includes/quickstarts/agentic-retrieval-python.md b/articles/search/includes/quickstarts/agentic-retrieval-python.md
@@ -4,7 +4,7 @@ author: haileytap
 ms.author: haileytapia
 ms.service: azure-ai-search
 ms.topic: include
-ms.date: 05/29/2025
+ms.date: 05/30/2025
 ---
 
 [!INCLUDE [Feature preview](../previews/preview-generic.md)]
@@ -39,20 +39,19 @@ Agentic retrieval [supports several models](../../search-agentic-retrieval-how-t
 
 To deploy the Azure OpenAI models:
 
-1. Sign in to the [Azure AI Foundry portal](https://ai.azure.com/).
+1. Sign in to the [Azure AI Foundry portal](https://ai.azure.com/) and select your Azure OpenAI resource.
 
-1. On the home page, find the Azure OpenAI tile and select **Let's go**.
+1. From the left pane, select **Model catalog**.
 
-    :::image type="content" source="../../media/search-get-started-agentic-retrieval/azure-openai-lets-go-tile.png" alt-text="Screenshot of the Azure OpenAI tile in the Azure AI Foundry portal." border="true" lightbox="../../media/search-get-started-agentic-retrieval/azure-openai-lets-go-tile.png":::
+1. Select **gpt-4o-mini**, and then select **Use this model**.
 
-   Your most recently used Azure OpenAI resource appears. If you have multiple Azure OpenAI resources, select **All resources** to switch between them.
+1. Specify a deployment name. To simplify your code, we recommend **gpt-4o-mini**.
 
-1. From the left pane, select **Model catalog**.
+1. Accept the defaults.
 
-1. Deploy `gpt-4o-mini` and `text-embedding-3-large` to your Azure OpenAI resource.
+1. Select **Deploy**.
 
-   > [!NOTE]
-   > To simplify your code, don't use a custom deployment name for either model. This quickstart assumes the deployment and model names are the same.
+1. Repeat the previous steps for **text-embedding-3-large**.
 
 ## Configure role-based access
 
diff --git a/articles/search/includes/quickstarts/agentic-retrieval-rest.md b/articles/search/includes/quickstarts/agentic-retrieval-rest.md
@@ -4,7 +4,7 @@ author: haileytap
 ms.author: haileytapia
 ms.service: azure-ai-search
 ms.topic: include
-ms.date: 05/29/2025
+ms.date: 05/30/2025
 ---
 
 [!INCLUDE [Feature preview](../previews/preview-generic.md)]
@@ -40,20 +40,19 @@ Agentic retrieval [supports several models](../../search-agentic-retrieval-how-t
 
 To deploy the Azure OpenAI models:
 
-1. Sign in to the [Azure AI Foundry portal](https://ai.azure.com/).
+1. Sign in to the [Azure AI Foundry portal](https://ai.azure.com/) and select your Azure OpenAI resource.
 
-1. On the home page, find the Azure OpenAI tile and select **Let's go**.
+1. From the left pane, select **Model catalog**.
 
-    :::image type="content" source="../../media/search-get-started-agentic-retrieval/azure-openai-lets-go-tile.png" alt-text="Screenshot of the Azure OpenAI tile in the Azure AI Foundry portal." border="true" lightbox="../../media/search-get-started-agentic-retrieval/azure-openai-lets-go-tile.png":::
+1. Select **gpt-4o-mini**, and then select **Use this model**.
 
-   Your most recently used Azure OpenAI resource appears. If you have multiple Azure OpenAI resources, select **All resources** to switch between them.
+1. Specify a deployment name. To simplify your code, we recommend **gpt-4o-mini**.
 
-1. From the left pane, select **Model catalog**.
+1. Accept the defaults.
 
-1. Deploy `gpt-4o-mini` and `text-embedding-3-large` to your Azure OpenAI resource.
+1. Select **Deploy**.
 
-   > [!NOTE]
-   > To simplify your code, don't use a custom deployment name for either model. This quickstart assumes the deployment and model names are the same.
+1. Repeat the previous steps for **text-embedding-3-large**.
 
 ## Configure role-based access
 
diff --git a/articles/search/media/vector-search-integrated-vectorization-ai-studio/use-this-model.png b/articles/search/media/vector-search-integrated-vectorization-ai-studio/use-this-model.png
diff --git a/articles/search/tutorial-rag-build-solution-models.md b/articles/search/tutorial-rag-build-solution-models.md
@@ -8,7 +8,7 @@ ms.author: heidist
 ms.service: azure-ai-search
 ms.topic: tutorial
 ms.custom: references_regions
-ms.date: 05/29/2025
+ms.date: 05/30/2025
 
 ---
 
@@ -99,15 +99,13 @@ This tutorial series uses the following models and model providers:
 
 You must have [**Cognitive Services OpenAI Contributor**]( /azure/ai-services/openai/how-to/role-based-access-control#cognitive-services-openai-contributor) or higher to deploy models in Azure OpenAI.
 
-1. Go to [Azure AI Foundry](https://ai.azure.com/).
+1. Sign in to the [Azure AI Foundry portal](https://ai.azure.com/).
 
-1. Select **Deployments** on the left menu.
+1. From the left pane, select **Model catalog**.
 
-1. Select **Deploy model** > **Deploy base model**.
+1. Select **text-embedding-3-large**, and then select **Use this model**.
 
-1. Select **text-embedding-3-large** from the dropdown list and confirm the selection.
-
-1. Specify a deployment name. We recommend "text-embedding-3-large".
+1. Specify a deployment name. We recommend **text-embedding-3-large**.
 
 1. Accept the defaults.
 
diff --git a/articles/search/vector-search-integrated-vectorization-ai-studio.md b/articles/search/vector-search-integrated-vectorization-ai-studio.md
@@ -8,7 +8,7 @@ ms.service: azure-ai-search
 ms.custom:
   - build-2024
 ms.topic: how-to
-ms.date: 05/08/2025
+ms.date: 05/30/2025
 ---
 
 # Use embedding models from Azure AI Foundry model catalog for integrated vectorization
@@ -49,15 +49,17 @@ For image embeddings:
 
 1. Open the [Azure AI Foundry model catalog](https://ai.azure.com/explore/models). Create a project if you don't have one already.
 
-1. Apply a filter to show just the embedding models. Under **Inference tasks**, select **Embeddings**:
+1. From the left pane, select **Model catalog**.
+
+1. Apply a filter to show just the embedding models. Under **Inference tasks**, select **Embeddings**.
 
    :::image type="content" source="media\vector-search-integrated-vectorization-ai-studio\ai-studio-catalog-embeddings-filter.png" lightbox="media\vector-search-integrated-vectorization-ai-studio\ai-studio-catalog-embeddings-filter.png" alt-text="Screenshot of the Azure AI Foundry model catalog page highlighting how to filter by embeddings models.":::
 
-1. Select a supported model, then select **Deploy**.
+1. Select a supported model, and then select **Use this model**.
 
-   :::image type="content" source="media\vector-search-integrated-vectorization-ai-studio\ai-studio-deploy-endpoint.png" lightbox="media\vector-search-integrated-vectorization-ai-studio\ai-studio-deploy-endpoint.png" alt-text="Screenshot of deploying an endpoint via the Azure AI Foundry model catalog.":::
+   :::image type="content" source="media\vector-search-integrated-vectorization-ai-studio\use-this-model.png" lightbox="media\vector-search-integrated-vectorization-ai-studio\use-this-model.png" alt-text="Screenshot of deploying a model via the Azure AI Foundry model catalog.":::
 
-1. Accept the defaults or modify as needed, and then select **Deploy**. The deployment details vary depending on which model you select. 
+1. Accept the defaults or modify as needed, and then select **Deploy**. The deployment details vary depending on which model you select.
 
 1. Wait for the model to finish deploying by monitoring the **Provisioning State**. It should change from "Provisioning" to "Updating" to "Succeeded". You might need to select **Refresh** every few minutes to see the status update.
 
