Merge pull request #6187 from MicrosoftDocs/main

Auto Publish – main to live - 2025-07-23 22:06 UTC

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

articles/ai-foundry/agents/how-to/connected-agents.md

@@ -230,7 +230,7 @@ To create a multi-agent setup, follow these steps:
     ```python
     import os
     from azure.ai.projects import AIProjectClient
-    from azure.ai.projects.models import ConnectedAgentTool, MessageRole
+    from azure.ai.agents.models import ConnectedAgentTool, MessageRole
     from azure.identity import DefaultAzureCredential
 
 

articles/ai-foundry/agents/how-to/tools/azure-functions.md

@@ -14,10 +14,14 @@ ms.custom: azure-ai-agents
 
 # Use Azure Functions with Azure AI Foundry Agent Service
 
-The Azure AI Foundry Agent Service integrates with Azure Functions, enabling you to create intelligent, event-driven applications with minimal overhead. This combination allows AI-driven workflows to leverage the scalability and flexibility of serverless computing, making it easier to build and deploy solutions that respond to real-time events or complex workflows. 
- 
-Azure Functions provide support for triggers and bindings, which simplify how your AI Agents interact with external systems and services. Triggers determine when a function executes—such as an HTTP request, message from a queue, or a file upload to Azure Blob Storage and allows agents to act dynamically based on incoming events. 
- 
+The Azure AI Foundry Agent Service integrates with Azure Functions, enabling you to create intelligent, event-driven applications with minimal overhead. This combination allows AI-driven workflows to leverage the scalability and flexibility of serverless computing, making it easier to build and deploy solutions that respond to real-time events or complex workflows.
+
+Currently, direct integration with Azure Functions is only supported for functions triggered by Azure Storage Queues. Other trigger types, such as HTTP or Blob Storage, are not natively supported at this time.
+
+Azure Functions provide support for triggers and bindings, which simplify how your AI Agents interact with external systems and services. Triggers determine when a function executes—such as an HTTP request, message from a queue, or a file upload to Azure Blob Storage—and allow agents to act dynamically based on incoming events.
+
+For HTTP-triggered Azure Functions, integration is possible by describing the function through an OpenAPI specification and registering it as a callable tool in the agent configuration. Alternatively, you can implement a queue-based wrapper function that receives messages from the agent and internally invokes the HTTP logic, enabling the use of the existing queue-based integration.
+
 Meanwhile, bindings facilitate streamlined connections to input or output data sources, such as databases or APIs, without requiring extensive boilerplate code. For instance, you can configure a trigger to execute an Azure Function whenever a customer message is received in a chatbot and use output bindings to send a response via the Azure AI Agent.
 
 ### Supported models

articles/ai-foundry/foundry-local/get-started.md

@@ -9,7 +9,7 @@ ms.subservice: foundry-local
 ms.topic: quickstart
 ms.reviewer: samkemp
 ms.author: jburchel
-author: jburchel
+author: jonburchel
 reviewer: samuel100
 ms.custom:
   - build-2025
@@ -35,7 +35,7 @@ Your system must meet the following requirements to run Foundry Local:
 - **Operating System**: Windows 10 (x64), Windows 11 (x64/ARM), Windows Server 2025, macOS.
 - **Hardware**: Minimum 8GB RAM, 3GB free disk space. Recommended 16GB RAM, 15GB free disk space.
 - **Network**: Internet connection for initial model download (optional for offline use)
-- **Acceleration (optional)**: NVIDIA GPU (2,000 series or newer), AMD GPU (6,000 series or newer), Qualcomm Snapdragon X Elite (8GB or more of memory), or Apple silicon.
+- **Acceleration (optional)**: NVIDIA GPU (2,000 series or newer), AMD GPU (6,000 series or newer), Intel iGPU, Qualcomm Snapdragon X Elite (8GB or more of memory), or Apple silicon.
 
 Also, ensure you have administrative privileges to install software on your device.
 

articles/ai-foundry/foundry-local/reference/reference-cli.md

@@ -8,7 +8,7 @@ ms.subservice: foundry-local
 ms.custom: build-2025
 ms.author: jburchel
 ms.reviewer: samkemp
-author: jburchel
+author: jonburchel
 reviewer: samuel100
 ms.topic: concept-article
 ms.date: 07/03/2025

articles/ai-foundry/foundry-local/reference/reference-rest.md

@@ -8,7 +8,7 @@ ms.subservice: foundry-local
 ms.custom: build-2025
 ms.author: jburchel
 ms.reviewer: samkemp
-author: jburchel
+author: jonburchel
 reviewer: samuel100
 ms.topic: concept-article
 ms.date: 07/03/2025

articles/ai-foundry/foundry-local/what-is-foundry-local.md

@@ -10,7 +10,7 @@ ms.topic: overview
 ms.date: 07/03/2025
 ms.reviewer: samkemp
 ms.author: jburchel
-author: jburchel
+author: jonburchel
 reviewer: samuel100
 ms.custom: build-2025
 #customer intent: As a developer, I want to understand what Azure AI Foundry Local is so that I can use it to build AI applications.

articles/ai-foundry/openai/how-to/role-based-access-control.md

@@ -5,7 +5,7 @@ author: mrbullwinkle
 manager: nitinme
 ms.service: azure-ai-openai
 ms.topic: how-to
-ms.date: 07/11/2025
+ms.date: 07/23/2025
 ms.author: mbullwin
 recommendations: false
 ---
@@ -62,8 +62,7 @@ A user with only this role assigned would be unable to:
 ❌ Upload datasets for fine-tuning <br>
 ❌ View, query, filter Stored completions data <br>
 ❌ Access quota <br>
-❌ Create customized content filters <br>
-❌ Add a data source for the use your data feature
+❌ Create customized content filters
 
 ### Cognitive Services OpenAI Contributor
 
@@ -74,15 +73,14 @@ This role has all the permissions of Cognitive Services OpenAI User and is also
 ✅ View, query, filter Stored completions data <br>
 ✅ Create new model deployments or edit existing model deployments **[Added Fall 2023]** <br>
 ✅ Grant access to the Assistants API <br> 
-✅ Add data sources to Azure OpenAI On Your Data. **You must also have the [Cognitive Services Contributor](#cognitive-services-contributor) role as well**.
+✅ Add data sources to Azure OpenAI On Your Data.
 
 A user with only this role assigned would be unable to:
 
 ❌ Create new Azure OpenAI resources <br>
 ❌ View/Copy/Regenerate keys under **Keys and Endpoint** <br>
 ❌ Access quota <br>
 ❌ Create customized content filters <br>
-❌ Add a data source for Azure OpenAI On Your Data
 
 ### Cognitive Services Contributor
 
@@ -95,7 +93,7 @@ This role is typically granted access at the resource group level for a user in
 ✅ Ability to view what models are available for deployment in [Azure AI Foundry portal](https://ai.azure.com/?cid=learnDocs) <br>
 ✅ Use the Chat, Completions, and DALL-E (preview) playground experiences to generate text and images with any models that have already been deployed to this Azure OpenAI resource <br>
 ✅ Create customized content filters <br>
-✅ Add data sources to Azure OpenAI On Your Data. **You must also have the [Cognitive Services OpenAI Contributor](#cognitive-services-openai-contributor) role as well**.
+✅ Add data sources to Azure OpenAI On Your Data. <br>
 ✅ Create new model deployments or edit existing model deployments (via API) <br>
 ✅ Create custom fine-tuned models **[Added Fall 2023]**<br>
 ✅ Upload datasets for fine-tuning **[Added Fall 2023]**<br>
@@ -152,7 +150,7 @@ All the capabilities of Cognitive Services Contributor plus the ability to:
 |Create new Azure OpenAI resources|❌|❌|✅| ➖ |
 |View/Copy/Regenerate keys under “Keys and Endpoint”|❌|❌|✅| ➖ |
 |Create customized content filters|❌|❌|✅| ➖ |
-|Add a data source for the “on your data” feature|❌|❌|✅| ➖ |
+|Add a data source for the "on your data" feature|✅|✅|✅| ➖ |
 |Access quota|❌|❌|❌|✅|
 |Make inference API calls with Microsoft Entra ID| ✅ | ✅ | ❌ |  ➖ | 
 ## Common Issues

articles/ai-services/content-understanding/toc.yml

@@ -96,6 +96,10 @@ items:
       - name: Build a retrieval-augmented solution
         displayName: RAG, retrieval, augmented, generation, knowledge, base, search, index, vector
         href: tutorial/build-rag-solution.md
+  - name: Samples
+    items:
+      - name: Data extraction using Content Understanding
+        href: https://github.com/Azure-Samples/data-extraction-using-azure-content-understanding
   - name: Responsible AI
     items:
       - name: Transparency note

articles/machine-learning/how-to-azure-container-for-pytorch-environment.md

@@ -1,7 +1,7 @@
 ---
-title: How to create Azure Container for PyTorch Custom Curated environment
+title: How to create Azure Container for PyTorch custom curated environments
 titleSuffix: Azure Machine Learning
-description: Create custom curated Azure Container for PyTorch environments in Azure Machine Learning studio to run your machine learning models and reuse it in different scenarios.
+description: Create custom curated Azure Container for PyTorch environments in Azure Machine Learning studio to run your machine learning models and reuse them in different scenarios.
 services: machine-learning
 author: s-polly
 ms.author: scottpolly
@@ -10,12 +10,13 @@ ms.service: azure-machine-learning
 ms.subservice: core
 ms.custom: build-2023, build-2023-dataai
 ms.topic: how-to
-ms.date: 03/04/2024
+ms.date: 07/23/2025
+
 ---
 
 # Create custom curated Azure Container for PyTorch (ACPT) environments in Azure Machine Learning studio
 
-In this article you'll learn to create a custom environment in Azure Machine learning. Custom Environments allow you to extend curated environments and add Hugging Face (HF) transformers, datasets or install any other external packages with Azure Machine Learning. Azure machine Learning offers to create a new environment with docker context containing ACPT curated environment as a base image and additional packages on top of it.
+In this article, you learn how to create a custom environment in Azure Machine Learning. Custom environments allow you to extend curated environments and add Hugging Face (HF) transformers, datasets, or install other external packages with Azure Machine Learning. Azure Machine Learning enables you to create a new environment with Docker context that contains an ACPT curated environment as a base image with additional packages on top of it.
 
 ## Prerequisites
 
@@ -32,14 +33,14 @@ In the [Azure Machine Learning studio](https://ml.azure.com/registries/environme
 
 ## Navigate to curated environments
 
-Navigate to curated environments and search "acpt" to list all the available ACPT curated environments. Selecting the environment shows details of the environment.
+Navigate to curated environments and search for "acpt" to list all available ACPT curated environments. Select an environment to view its details.
 
 :::image type="content" source="./media/how-to-azure-container-for-pytorch-environment/navigate-to-curated-environments.png" alt-text="Screenshot of navigating to curated environments." lightbox= "./media/how-to-azure-container-for-pytorch-environment/navigate-to-curated-environments.png":::
 
 
 ## Get details of the curated environments
 
-To create a custom environment, you need the base docker image repository, which can be found in the **Description** section as **Azure Container Registry**. Copy the **Azure Container Registry** name, which is used later when you create a new custom environment.
+To create a custom environment, you need the base Docker image repository, which you can find in the **Description** section as **Azure Container Registry**. Copy the **Azure Container Registry** name to use later when you create a new custom environment.
 
 :::image type="content" source="./media/how-to-azure-container-for-pytorch-environment/get-details-curated-environments.png" alt-text="Screenshot of getting container registry name." lightbox= "./media/how-to-azure-container-for-pytorch-environment/get-details-curated-environments.png":::
 
@@ -51,22 +52,22 @@ Go back and select the **Custom Environments** tab.
 
 ## Create custom environments
 
-Select **+ Create**. In the "Create Environment" window, name the environment, description, and select **Create a new docker context** in the Select environments type section.
+Select **+ Create**. In the "Create Environment" window, provide a name and description for the environment, and select **Create a new docker context** in the "Select environment type" section.
 
 :::image type="content" source="./media/how-to-azure-container-for-pytorch-environment/create-environment-window.png" alt-text="Screenshot of creating custom environment." lightbox= "./media/how-to-azure-container-for-pytorch-environment/create-environment-window.png":::
 
-Paste the docker image name that you copied in previously. Configure your environment by declaring the base image and add any env variables you want to use and the packages that you want to include.
+Paste the Docker image name that you copied previously. Configure your environment by declaring the base image and adding any environment variables you want to use and the packages that you want to include.
 
 :::image type="content" source="./media/how-to-azure-container-for-pytorch-environment/configure-environment.png" alt-text="Screenshot of configuring the environment with name, packages with docker context." lightbox= "./media/how-to-azure-container-for-pytorch-environment/configure-environment.png":::
 
-Review your environment settings, add any tags if needed and select on the **Create** button to create your custom environment.
+Review your environment settings, add any tags if needed, and select the **Create** button to create your custom environment.
 
-That's it! You've now created a custom environment in Azure Machine Learning studio and can use it to run your machine learning models.
+You've now created a custom environment in Azure Machine Learning studio that you can use to run your machine learning models.
 
 ## Next steps
 
 - Learn more about environment objects:
-    - [What are Azure Machine Learning environments? ](concept-environments.md).
-    -  Learn more about [curated environments](concept-environments.md).
-- Learn more about [training models in Azure Machine Learning](concept-train-machine-learning-model.md).
+    - [What are Azure Machine Learning environments?](concept-environments.md)
+    - Learn more about [curated environments](concept-environments.md)
+- Learn more about [training models in Azure Machine Learning](concept-train-machine-learning-model.md)
 - [Azure Container for PyTorch (ACPT) reference](resource-azure-container-for-pytorch.md)