Merge pull request #3295 from MicrosoftDocs/main

3/3/2025 AM Publish

Author: Taojunshen

articles/ai-foundry/concepts/encryption-keys-portal.md

@@ -15,7 +15,7 @@ ms.reviewer: deeikele
 
 # Customer-managed keys for encryption with Azure AI Foundry
 
-Customer-managed keys (CMKs) in Azure AI Foundry portal provide enhanced control over the encryption of your data. By using CMKs, you can manage your own encryption keys to add an extra layer of protection and meet compliance requirements more effectively.
+Customer-managed keys (CMKs) in [Azure AI Foundry portal](https://ai.azure.com/) provide enhanced control over the encryption of your data. By using CMKs, you can manage your own encryption keys to add an extra layer of protection and meet compliance requirements more effectively.
 
 ## About encryption in Azure AI Foundry portal
 
@@ -40,7 +40,7 @@ The following data is stored on the managed resources.
 |-----|-----|-----|
 |Azure Cosmos DB|Stores metadata for your Azure AI projects and tools|Index names, tags; Flow creation timestamps; deployment tags; evaluation metrics|
 |Azure AI Search|Stores indices that are used to help query your Azure AI Foundry content.|An index based off your model deployment names|
-|Azure Storage Account|Stores instructions for how customization tasks are orchestrated|JSON representation of flows you create in Azure AI Foundry portal|
+|Azure Storage Account|Stores instructions for how customization tasks are orchestrated|JSON representation of flows you create in [Azure AI Foundry portal](https://ai.azure.com/)|
 
 >[!IMPORTANT]
 > Azure AI Foundry uses Azure compute that is managed in the Microsoft subscription, for example when you fine-tune models or or build flows. Its disks are encrypted with Microsoft-managed keys. Compute is ephemeral, meaning after a task is completed the virtual machine is deprovisioned, and the OS disk is deleted. Compute instance machines used for 'Code' experiences are persistant. Azure Disk Encryption isn't supported for the OS disk. 

articles/ai-foundry/how-to/deploy-models-jamba.md

@@ -50,7 +50,7 @@ To get started with Jamba 1.5 mini deployed as a serverless API, explore our int
 - An Azure subscription with a valid payment method. Free or trial Azure subscriptions won't work. If you don't have an Azure subscription, create a [paid Azure account](https://azure.microsoft.com/pricing/purchase-options/pay-as-you-go) to begin.
 - An [Azure AI Foundry project](../how-to/create-projects.md). The serverless API model deployment offering for Jamba family models is only available with projects created in specific regions. For a list of these regions, see [Region availability for models in serverless API endpoints](deploy-models-serverless-availability.md#ai21-models).
 
-- Azure role-based access controls (Azure RBAC) are used to grant access to operations in Azure AI Foundry portal. To perform the steps in this article, your user account must be assigned the __owner__ or __contributor__ role for the Azure subscription. Alternatively, your account can be assigned a custom role that has the following permissions:
+- Azure role-based access controls (Azure RBAC) are used to grant access to operations in [Azure AI Foundry portal](https://ai.azure.com/). To perform the steps in this article, your user account must be assigned the __owner__ or __contributor__ role for the Azure subscription. Alternatively, your account can be assigned a custom role that has the following permissions:
 
     - On the Azure subscription—to subscribe the Azure AI Foundry project to the Azure Marketplace offering, once for each project, per offering:
       - `Microsoft.MarketplaceOrdering/agreements/offers/plans/read`
@@ -80,7 +80,7 @@ These steps demonstrate the deployment of `AI21 Jamba 1.5 Large` or `AI21 Jamba
 
 1. Select **Deploy** to open a serverless API deployment window for the model.
 
-1. Alternatively, you can initiate a deployment by starting from the **Models + endpoints** page in Azure AI Foundry portal.
+1. Alternatively, you can initiate a deployment by starting from the **Models + endpoints** page in [Azure AI Foundry portal](https://ai.azure.com/).
 
     1. From the left navigation pane of your project, select **My assets** > **Models + endpoints**.
     1. Select **+ Deploy model** > **Deploy base model**.

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

@@ -43,12 +43,12 @@ The Azure AI Agent Service also supports the following models from the Azure AI
 * Cohere-command-r-plus
 * Cohere-command-r
 
-To use these models, you can use Azure AI Foundry portal to make a deployment, and then reference the deployment name in your agent. For example:
+To use these models, you can use [Azure AI Foundry portal](https://ai.azure.com/) to make a deployment, and then reference the deployment name in your agent. For example:
 
 ```python
 agent = project_client.agents.create_agent( model="llama-3", name="my-agent", instructions="You are a helpful agent" ) 
 ```
 
 ## Next steps
 
-[Create a new Agent project](../quickstart.md)
+[Create a new Agent project](../quickstart.md)

articles/ai-services/agents/how-to/tools/bing-grounding.md

@@ -64,7 +64,7 @@ Grounding with Bing returns relevant search results to the customer's model depl
 
     :::image type="content" source="../../media/tools/bing/resource-azure-portal.png" alt-text="A screenshot of the Bing resource in the Azure portal." lightbox="../../media/tools/bing/resource-azure-portal.png":::
 
-1. You can add the Grounding with Bing Search tool to an agent programatically using the code examples listed at the top of this article, or the Azure AI Foundry portal. If you want to use the portal, in the **Create and debug** screen for your agent, scroll down the **Setup** pane on the right to **knowledge**. Then select **Add**.
+1. You can add the Grounding with Bing Search tool to an agent programatically using the code examples listed at the top of this article, or the [Azure AI Foundry portal](https://ai.azure.com/). If you want to use the portal, in the **Create and debug** screen for your agent, scroll down the **Setup** pane on the right to **knowledge**. Then select **Add**.
 
     :::image type="content" source="../../media/tools/knowledge-tools.png" alt-text="A screenshot showing the available tool categories in the Azure AI Foundry portal." lightbox="../../media/tools/knowledge-tools.png":::
 
@@ -169,7 +169,7 @@ Follow the [REST API Quickstart](../../quickstart.md?pivots=rest-api) to set the
 
 ## Step 2: Create an Agent with the Grounding with Bing search tool enabled
 
-To make the Grounding with Bing search tool available to your agent, use a connection to initialize the tool and attach it to the agent. You can find your connection in the **connected resources** section of your project in the Azure AI Foundry portal.
+To make the Grounding with Bing search tool available to your agent, use a connection to initialize the tool and attach it to the agent. You can find your connection in the **connected resources** section of your project in the [Azure AI Foundry portal](https://ai.azure.com/).
 
 # [Python](#tab/python)
 

articles/ai-services/agents/how-to/tools/code-interpreter.md

@@ -38,7 +38,7 @@ We recommend using Agents with the latest models to take advantage of the new fe
 
 ## Using the code interpreter tool with an agent
 
-You can add the code interpreter tool to an agent programatically using the code examples listed at the top of this article, or the Azure AI Foundry portal. If you want to use the portal:
+You can add the code interpreter tool to an agent programatically using the code examples listed at the top of this article, or the [Azure AI Foundry portal](https://ai.azure.com/). If you want to use the portal:
 
 1. In the **Create and debug** screen for your agent, scroll down the **Setup** pane on the right to **action**. Then select **Add**.
 

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

@@ -91,7 +91,7 @@ As a fallback, there's a 60-second maximum wait in the run object when the threa
 
 ## Add file search to an agent using the Azure AI Foundry portal
 
-You can add the Bing Search tool to an agent programatically using the code examples listed at the top of this article, or the Azure AI Foundry portal. If you want to use the portal:
+You can add the Bing Search tool to an agent programatically using the code examples listed at the top of this article, or the [Azure AI Foundry portal](https://ai.azure.com/). If you want to use the portal:
 
 1. In the **Create and debug** screen for your agent, scroll down the **Setup** pane on the right to **knowledge**. Then select **Add**.
 

articles/ai-services/agents/quotas-limits.md

@@ -21,7 +21,7 @@ The following sections provide you with a guide to the default quotas and limits
 
 | Limit Name | Limit Value |
 |--|--|
-| Max files per agent/thread | 10,000 when using the API or Azure AI Foundry portal. In Azure OpenAI Studio the limit was 20.|
+| Max files per agent/thread | 10,000 when using the API or [Azure AI Foundry portal](https://ai.azure.com/). In Azure OpenAI Studio the limit was 20.|
 | Max file size for agents & fine-tuning | 512 MB |
 | Max size for all uploaded files for agents |100 GB |  
 | agents token limit | 2,000,000 token limit |
@@ -32,4 +32,4 @@ See the [Azure OpenAI](../openai/quotas-limits.md) for current quotas and limits
 
 ## Next steps
 
-[Learn about the models available for Azure AI Agent Service](./concepts/model-region-support.md)
+[Learn about the models available for Azure AI Agent Service](./concepts/model-region-support.md)

articles/ai-services/cognitive-services-virtual-networks.md

@@ -52,7 +52,7 @@ Virtual networks are supported in [regions where Azure AI services are available
 > - Translator
 
 > [!NOTE]
-> If you use Azure OpenAI, LUIS, Speech Services, or Language services, the `CognitiveServicesManagement` tag only enables you to use the service by using the SDK or REST API. To access and use the Azure AI Foundry portal, LUIS portal, Speech Studio, or Language Studio from a virtual network, you need to use the following tags:
+> If you use Azure OpenAI, LUIS, Speech Services, or Language services, the `CognitiveServicesManagement` tag only enables you to use the service by using the SDK or REST API. To access and use the [Azure AI Foundry portal](https://ai.azure.com/), LUIS portal, Speech Studio, or Language Studio from a virtual network, you need to use the following tags:
 >
 > - `AzureActiveDirectory`
 > - `AzureFrontDoor.Frontend`
@@ -61,7 +61,7 @@ Virtual networks are supported in [regions where Azure AI services are available
 > - `CognitiveServicesFrontEnd`
 > - `Storage` (Speech Studio only)
 > 
-> For information on Azure AI Foundry portal configurations, see the [Azure AI Foundry documentation](../ai-studio/how-to/configure-private-link.md).
+> For information on [Azure AI Foundry portal](https://ai.azure.com/) configurations, see the [Azure AI Foundry documentation](../ai-studio/how-to/configure-private-link.md).
 
 ## Change the default network access rule
 

articles/ai-services/content-understanding/glossary.md

@@ -17,12 +17,12 @@ ms.author: lajanuar
 | **File** | Any type of data, including text, documents, images, videos, and audio. |
 | **File type** | The MIME type of a file, such as text/plain, application/pdf, image/jpeg, audio/wav, and video/mp4. Generic categories like *document* refer to all corresponding MIME types supported by the service. |
 | **Analyzer** | A component that processes and extracts content and structured fields from files. Content Understanding offers a few analyzer templates for common scenarios. |
-| **Analyzer template** | A predefined configuration and field schema for an analyzer. It simplifies creating analyzers by allowing modifications to a template instead of starting from scratch. This feature is available only in Azure AI Foundry portal, not via REST API/SDKs. |
+| **Analyzer template** | A predefined configuration and field schema for an analyzer. It simplifies creating analyzers by allowing modifications to a template instead of starting from scratch. This feature is available only in [Azure AI Foundry portal](https://ai.azure.com/), not via REST API/SDKs. |
 | **Analyzer result** | The output generated by an analyzer after processing input data. It typically includes extracted content in Markdown, extracted fields, and optional modality-specific details. |
 | **Add-ons** | Added features that enhance content extraction results, such as layout elements, barcodes, and figures in documents. |
 | **Fields** | List of structured key-value pairs derived from the content, as defined by the field schema. [Learn more about supported field value types.](service-limits.md) |
 | **Field schema** | A formal description of the fields to extract from the input. It specifies the name, description, value type, generation method, and more for each field. |
 | **Generation method** | The process of determining the extracted value of a specified field. Content Understanding supports: <br/> &bullet; **Extract**: Directly extract values from the input content, such as dates from receipts or item details from invoices. <br/> &bullet; **Classify**: Classify content into predefined categories, such as call sentiment or chart type. <br/> &bullet; **Generate**: Generate values from input data, such as summarizing an audio conversation or generating scene descriptions from videos. |
 | **Span** | A reference indicating the location of an element (for example, field, word) within the extracted Markdown content. A character offset and length represent a span. Different programming languages use various character encodings, which can affect the exact offset and length values for Unicode text. To avoid confusion, spans are only returned if the desired encoding is explicitly specified in the request. Some elements can map to multiple spans if they aren't contiguous in the markdown (for example, page). |
 | **Grounding source** | The specific regions in content where a value was generated. It has different representations depending on the file type: <br>&bullet; **Image** - A polygon in the image, often an axis-aligned rectangle (bounding box). <br>&bullet; **PDF/TIFF** - A polygon on a specific page, often a quadrilateral. <br>&bullet; **Audio** - A start and end time range. <br>&bullet; **Video** - A start and end time range with an optional polygon in each frame, often a bounding box.|
-| **Confidence score** | The level of certainty that the extracted data is accurate. |
+| **Confidence score** | The level of certainty that the extracted data is accurate. |

articles/ai-services/document-intelligence/studio-overview.md

@@ -194,7 +194,7 @@ With Document Intelligence, you can quickly automate your data processing in app
 
 ### [**Azure AI Foundry portal**](#tab/ai-studio)
 
-Document Intelligence is part of the Azure AI services offerings in the Azure AI Foundry portal. Each of the Azure AI services helps developers and organizations rapidly create intelligent, cutting-edge, market-ready, and responsible applications with out-of-the-box and prebuilt and customizable APIs and models.
+Document Intelligence is part of the Azure AI services offerings in the [Azure AI Foundry portal](https://ai.azure.com/). Each of the Azure AI services helps developers and organizations rapidly create intelligent, cutting-edge, market-ready, and responsible applications with out-of-the-box and prebuilt and customizable APIs and models.
 
 Learn how to [connect your AI services hub](../../ai-studio/ai-services/how-to/connect-ai-services.md) with AI services and get started using Document Intelligence.