fixing merge conflict

Author: aahill

.openpublishing.redirection.json

@@ -294,6 +294,31 @@
       "source_path_from_root": "/articles/ai-services/speech-service/text-to-speech-avatar/custom-avatar-endpoint.md",
       "redirect_url": "/azure/ai-services/speech-service/custom-avatar-create",
       "redirect_document_id": false
+    },
+    {
+      "source_path_from_root": "/articles/ai-services/speech-service/migration-overview-neural-voice.md",
+      "redirect_url": "/azure/ai-services/speech-service/custom-neural-voice",
+      "redirect_document_id": false
+    },
+    {
+      "source_path_from_root": "/articles/ai-services/speech-service/how-to-migrate-to-custom-neural-voice.md",
+      "redirect_url": "/azure/ai-services/speech-service/custom-neural-voice",
+      "redirect_document_id": false
+    },
+    {
+      "source_path_from_root": "/articles/ai-services/speech-service/how-to-migrate-to-prebuilt-neural-voice.md",
+      "redirect_url": "/azure/ai-services/speech-service/custom-neural-voice",
+      "redirect_document_id": false
+    },
+    {
+      "source_path_from_root": "/articles/ai-foundry/quickstarts/hear-speak-playground.md",
+      "redirect_url": "/azure/ai-foundry/quickstarts/get-started-playground",
+      "redirect_document_id": false
+    },
+    {
+      "source_path_from_root": "/articles/ai-services/language-service/tutorials/prompt-flow.md",
+      "redirect_url": "/azure/ai-services/language-service/tutorials/power-automate",
+      "redirect_document_id": false
     }
   ]
 }

articles/ai-foundry/concepts/concept-model-distillation.md

@@ -5,7 +5,7 @@ description: Learn how to do distillation in Azure AI Foundry portal.
 manager: scottpolly
 ms.service: azure-ai-foundry
 ms.topic: how-to
-ms.date: 03/09/2025
+ms.date: 05/20/2025
 ms.reviewer: vkann
 reviewer: anshirga
 ms.author: ssalgado

articles/ai-foundry/concepts/concept-synthetic-data.md

@@ -5,7 +5,7 @@ description: Learn how to generate a synthetic dataset in Azure AI Foundry porta
 manager: scottpolly
 ms.service: azure-ai-foundry
 ms.topic: how-to
-ms.date: 03/11/2025
+ms.date: 05/20/2025
 ms.reviewer: vkann
 reviewer: anshirga
 ms.author: ssalgado

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

@@ -6,7 +6,7 @@ manager: scottpolly
 keywords: Azure AI services, cognitive, AI models, local inference
 ms.service: azure-ai-foundry
 ms.topic: quickstart
-ms.date: 05/20/2025
+ms.date: 05/23/2025
 ms.reviewer: samkemp
 ms.author: samkemp
 author: samuel100
@@ -24,7 +24,7 @@ This guide walks you through setting up Foundry Local to run AI models on your d
 
 Your system must meet the following requirements to run Foundry Local:
 
-- **Operating System**: Windows 10 (x64), Windows 11 (x64/ARM), macOS.
+- **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.

articles/ai-foundry/how-to/configure-managed-network.md

@@ -855,8 +855,33 @@ When you create a private endpoint for hub dependency resources, such as Azure S
 
 A private endpoint is automatically created for a connection if the target resource is an Azure resource listed previously. A valid target ID is expected for the private endpoint. A valid target ID for the connection can be the Azure Resource Manager ID of a parent resource. The target ID is also expected in the target of the connection or in `metadata.resourceid`. For more on connections, see [How to add a new connection in Azure AI Foundry portal](connections-add.md).
 
-> [!IMPORTANT]
-> As of April 30th 2025, the Azure AI Enterprise Network Connection Approver role must be assigned to the Azure AI Foundry hub's managed identity to approve private endpoints to securely access your Azure resources from the managed virtual network. This doesn't impact existing resources with approved private endpoints as the role is correctly assigned by the service. For new resources, ensure the role is assigned to the hub's managed identity. For Azure Data Factory, Azure Databricks, and Azure Function Apps, the Contributor role should instead be assigned to your hub's managed identity. This role assignment is applicable to both User-assigned identity and System-assigned identity workspaces. 
+### Approval of Private Endpoints
+
+To establish Private Endpoint connections in managed virtual networks using Azure AI Foundry, the workspace managed identity, whether system-assigned or user-assigned, must have permissions to approve the Private Endpoint connections on the target resources. Previously, this was done through automatic role assignments by the Azure AI Foundry service. However, there are security concerns about the automatic role assignment. To improve security, starting April 30th, 2025, we will discontinue this automatic permission grant logic. We recommend assigning the [Azure AI Enterprise Network Connection Approver role](/azure/role-based-access-control/built-in-roles/ai-machine-learning) or a custom role with the necessary Private Endpoint connection permissions on the target resource types and grant this role to the Azure Machine Learning workspace's managed identity to allow Azure AI Foundry services to approve Private Endpoint connections to the target Azure resources.
+
+Here's the list of private endpoint target resource types covered by covered by the Azure AI Enterprise Network Connection Approver role:
+
+* Azure Application Gateway
+* Azure Monitor
+* Azure AI Search
+* Event Hubs
+* Azure SQL Database
+* Azure Storage
+* Azure Machine Learning workspace
+* Azure Machine Learning registry
+* Azure AI Foundry
+* Azure Key Vault
+* Azure CosmosDB
+* Azure Database for MySQL
+* Azure Database for PostgreSQL
+* Azure AI Services
+* Azure Cache for Redis
+* Container Registry
+* API Management
+
+For creating Private Endpoint outbound rules to target resource types not covered by the Azure AI Enterprise Network Connection Approver role, such as Azure Data Factory, Azure Databricks, and Azure Function Apps, a custom scoped-down role is recommended, defined only by the actions necessary to approve private endpoint connections on the target resource types.
+
+For creating Private Endpoint outbound rules to default workspace resources, the required permissions are automatically covered by the role assignments granted during workspace creation, so no additional action is needed.
 
 ## Select an Azure Firewall version for allowed only approved outbound
 

articles/ai-foundry/how-to/connections-add.md

@@ -53,7 +53,7 @@ Here's a table of some of the available connection types in Azure AI Foundry por
 |-------------------------------|:-------:|:--------------------------------------:|---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
 | Azure AI Search               |         | ✓                                      | Azure AI Search is an Azure resource that supports information retrieval over your vector and textual data stored in search indexes.                                                   |
 | Azure Storage                 |         | ✓                                      | Azure Storage is a cloud storage solution for storing unstructured data like documents, images, videos, and application installers.                                                   |
-| Azure Cosmos DB                |         | ✓                                      | Azure Cosmos DB is a globally distributed, multi-model database service that offers low latency, high availability, and scalability across multiple geographical regions.                |
+| Azure Cosmos DB               | ✓       | ✓                                      | Azure Cosmos DB is a globally distributed, multi-model database service that offers low latency, high availability, and scalability across multiple geographical regions.                |
 | Azure OpenAI                  |         |                                        | Azure OpenAI is a service that provides access to OpenAI's models including the GPT-4o, GPT-4o mini, GPT-4, GPT-4 Turbo with Vision, GPT-3.5-Turbo, DALLE-3, and Embeddings model series with the security and enterprise capabilities of Azure. |
 | Application Insights          |         |                                        | Azure Application Insights is a service within Azure Monitor that enables developers and DevOps teams to automatically detect performance anomalies, diagnose issues, and gain deep insights into application usage and behavior through powerful telemetry and analytics tools. |
 | API key                       |         |                                        | API Key connections handle authentication to your specified target on an individual basis. |

articles/ai-foundry/how-to/create-projects.md

@@ -9,7 +9,7 @@ ms.custom:
   - build-2024
   - ignite-2024
 ms.topic: how-to
-ms.date: 04/11/2025
+ms.date: 05/20/2025
 ms.reviewer: deeikele
 ms.author: sgilley
 author: sdgilley
@@ -19,7 +19,7 @@ zone_pivot_groups: project-type
 
 # Create a project for Azure AI Foundry
 
-This article describes how to create an [Azure AI Foundry](https://ai.azure.com) project. Projects are folders that let you organize your work for exploring new ideas, and as you're prototyping on a particular use cases.
+This article describes how to create an [Azure AI Foundry](https://ai.azure.com) project. Projects let you organize your work for exploring new ideas and as you prototype on a particular use case.
 
 Azure AI Foundry supports two types of projects: a **[!INCLUDE [fdp](../includes/fdp-project-name.md)]** and a **[!INCLUDE [hub](../includes/hub-project-name.md)]**. For more information about the differences between these two project types, see [Types of projects](../what-is-azure-ai-foundry.md#project-types).
 
@@ -44,7 +44,6 @@ Azure AI Foundry supports two types of projects: a **[!INCLUDE [fdp](../includes
     * Evaluations
     * Playgrounds
 
-
 ## Prerequisites
 
 Use the following tabs to select the method you plan to use to create a [!INCLUDE [hub](../includes/hub-project-name.md)]:
@@ -70,7 +69,7 @@ Use the following tabs to select the method you plan to use to create a [!INCLUD
 
 ---
 
-## Create a project
+## Create a [!INCLUDE [hub-project-name](../includes/hub-project-name.md)]
 
 # [Azure AI Foundry portal](#tab/ai-foundry)
 
@@ -129,7 +128,7 @@ On the project **Overview** page, you can find information about the project.
 
 :::image type="content" source="../media/how-to/projects/project-settings.png" alt-text="Screenshot of an Azure AI Foundry project settings page." lightbox = "../media/how-to/projects/project-settings.png":::
 
-- Name: The name of the project appears in the top left corner. You can rename the project using the edit tool.
+- Name: The name of the project appears in the top left corner.
 - Subscription: The subscription that hosts the hub that hosts the project.
 - Resource group: The resource group that hosts the hub that hosts the project.
 
@@ -176,9 +175,7 @@ In addition, many resources are only accessible by users in your project workspa
 
 ## Related content
 
-- [Quickstart: Get started with Azure AI Foundry](../quickstarts/get-started-code.md?pivots=hub-project)
-
-- [Learn more about Azure AI Foundry](../what-is-azure-ai-foundry.md)
+- [Quickstart: Use the chat playground in Azure AI Foundry portal](../quickstarts/get-started-playground.md)
 
 - [Learn more about hubs](../concepts/ai-resources.md)
 
@@ -188,4 +185,4 @@ In addition, many resources are only accessible by users in your project workspa
 
 [!INCLUDE [create-project-fdp](../includes/create-project-fdp.md)]
 
-::: zone-end
+::: zone-end

articles/ai-foundry/how-to/data-add.md

@@ -9,9 +9,9 @@ ms.custom:
   - build-2024
   - ignite-2024
 ms.topic: how-to
-ms.date: 02/11/2025
-ms.author: franksolomon
-author: fbsolo-ms1
+ms.date: 05/21/2025
+author: Blackmist
+ms.author: larryfr
 ---
 
 # How to add and manage data in your Azure AI Foundry project
@@ -29,12 +29,10 @@ Data can help when you need these capabilities:
 > - **Lineage:** For any given data, you can view which jobs or prompt flow pipelines consume the data.
 > - **Ease-of-use:** An Azure AI Foundry data resembles web browser bookmarks (favorites). Instead of remembering long storage paths that *reference* your frequently-used data on Azure Storage, you can create a data *version* and then access that version of the asset with a friendly name.
 
-## Prerequisites
 
-To create and work with data, you need:
+## Prerequisites
 
-- An Azure subscription. If you don't have one, create a [free account](https://azure.microsoft.com/free/).
-- An [Azure AI Foundry project](../how-to/create-projects.md).
+[!INCLUDE [hub-only-prereq](../includes/hub-only-prereq.md)]
 
 ## Create data
 

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

@@ -51,8 +51,11 @@ To use LLMs deployed in Azure AI Foundry portal, you need the endpoint and crede
 [!INCLUDE [tip-left-pane](../../includes/tip-left-pane.md)]
 
 1. Go to the [Azure AI Foundry](https://ai.azure.com/).
+
 1. Open the project where the model is deployed, if it isn't already open.
+
 1. Go to **Models + endpoints** and select the model you deployed as indicated in the prerequisites.
+
 1. Copy the endpoint URL and the key.
 
     :::image type="content" source="../../media/how-to/inference/serverless-endpoint-url-keys.png" alt-text="Screenshot of the option to copy endpoint URI and keys from an endpoint." lightbox="../../media/how-to/inference/serverless-endpoint-url-keys.png":::
@@ -63,11 +66,19 @@ To use LLMs deployed in Azure AI Foundry portal, you need the endpoint and crede
 In this scenario, we placed both the endpoint URL and key in the following environment variables:
 
 ```bash
-export AZURE_INFERENCE_ENDPOINT="<your-model-endpoint-goes-here>"
+export AZURE_INFERENCE_ENDPOINT="https://<resource>.services.ai.azure.com/models"
 export AZURE_INFERENCE_CREDENTIAL="<your-key-goes-here>"
 ```
 
-Once configured, create a client to connect to the endpoint. In this case, we're working with a chat completions model hence we import the class `AzureAIChatCompletionsModel`.
+Once configured, create a client to connect with the chat model by using the `init_chat_model`. For Azure OpenAI models, configure the client as indicated at [Using Azure OpenAI models](#using-azure-openai-models).
+
+```python
+from langchain.chat_models import init_chat_model
+
+llm = init_chat_model(model="mistral-large-2407", model_provider="azure_ai")
+```
+
+You can also use the class `AzureAIChatCompletionsModel` directly.
 
 ```python
 import os
@@ -80,8 +91,8 @@ model = AzureAIChatCompletionsModel(
 )
 ```
 
-> [!TIP]
-> For Azure OpenAI models, configure the client as indicated at [Using Azure OpenAI models](#using-azure-openai-models).
+> [!CAUTION]
+> **Breaking change:** Parameter `model_name` was renamed `model` in version `0.1.3`.
 
 You can use the following code to create the client if your endpoint supports Microsoft Entra ID:
 
@@ -93,7 +104,7 @@ from langchain_azure_ai.chat_models import AzureAIChatCompletionsModel
 model = AzureAIChatCompletionsModel(
     endpoint=os.environ["AZURE_INFERENCE_ENDPOINT"],
     credential=DefaultAzureCredential(),
-    model_name="mistral-large-2407",
+    model="mistral-large-2407",
 )
 ```
 
@@ -111,11 +122,11 @@ from langchain_azure_ai.chat_models import AzureAIChatCompletionsModel
 model = AzureAIChatCompletionsModel(
     endpoint=os.environ["AZURE_INFERENCE_ENDPOINT"],
     credential=DefaultAzureCredentialAsync(),
-    model_name="mistral-large-2407",
+    model="mistral-large-2407",
 )
 ```
 
-If your endpoint is serving one model, like with the standard deployment, you don't have to indicate `model_name` parameter:
+If your endpoint is serving one model, like with the standard deployment, you don't have to indicate `model` parameter:
 
 ```python
 import os
@@ -180,21 +191,21 @@ chain.invoke({"language": "italian", "text": "hi"})
 
 Models deployed to Azure AI Foundry support the Foundry Models API, which is standard across all the models. Chain multiple LLM operations based on the capabilities of each model so you can optimize for the right model based on capabilities. 
 
-In the following example, we create two model clients. One is a producer and another one is a verifier. To make the distinction clear, we're using a multi-model endpoint like the [Foundry Models API](../../model-inference/overview.md) and hence we're passing the parameter `model_name` to use a `Mistral-Large` and a `Mistral-Small` model, quoting the fact that **producing content is more complex than verifying it**.
+In the following example, we create two model clients. One is a producer and another one is a verifier. To make the distinction clear, we're using a multi-model endpoint like the [Foundry Models API](../../model-inference/overview.md) and hence we're passing the parameter `model` to use a `Mistral-Large` and a `Mistral-Small` model, quoting the fact that **producing content is more complex than verifying it**.
 
 ```python
 from langchain_azure_ai.chat_models import AzureAIChatCompletionsModel
 
 producer = AzureAIChatCompletionsModel(
     endpoint=os.environ["AZURE_INFERENCE_ENDPOINT"],
     credential=os.environ["AZURE_INFERENCE_CREDENTIAL"],
-    model_name="mistral-large-2407",
+    model="mistral-large-2407",
 )
 
 verifier = AzureAIChatCompletionsModel(
     endpoint=os.environ["AZURE_INFERENCE_ENDPOINT"],
     credential=os.environ["AZURE_INFERENCE_CREDENTIAL"],
-    model_name="mistral-small",
+    model="mistral-small",
 )
 ```
 
@@ -271,7 +282,7 @@ from langchain_azure_ai.embeddings import AzureAIEmbeddingsModel
 embed_model = AzureAIEmbeddingsModel(
     endpoint=os.environ["AZURE_INFERENCE_ENDPOINT"],
     credential=os.environ['AZURE_INFERENCE_CREDENTIAL'],
-    model_name="text-embedding-3-large",
+    model="text-embedding-3-large",
 )
 ```
 
@@ -305,31 +316,15 @@ for doc in results:
 
 ## Using Azure OpenAI models
 
-If you're using Azure OpenAI in Foundry Models or Foundry Models service with OpenAI models with `langchain-azure-ai` package, you might need to use `api_version` parameter to select a specific API version. The following example shows how to connect to an Azure OpenAI in Foundry Models deployment:
-
-```python
-from langchain_azure_ai.chat_models import AzureAIChatCompletionsModel
-
-llm = AzureAIChatCompletionsModel(
-    endpoint="https://<resource>.openai.azure.com/openai/deployments/<deployment-name>",
-    credential=os.environ["AZURE_INFERENCE_CREDENTIAL"],
-    api_version="2024-05-01-preview",
-)
-```
-
-> [!IMPORTANT]
-> Check which is the API version that your deployment is using. Using a wrong `api_version` or one not supported by the model results in a `ResourceNotFound` exception.
-
-If the deployment is hosted in Azure AI Services, you can use the Foundry Models service:
+If you're using Azure OpenAI models with `langchain-azure-ai` package, use the following URL:
 
 ```python
 from langchain_azure_ai.chat_models import AzureAIChatCompletionsModel
 
 llm = AzureAIChatCompletionsModel(
-    endpoint="https://<resource>.services.ai.azure.com/models",
+    endpoint="https://<resource>.openai.azure.com/openai/v1",
     credential=os.environ["AZURE_INFERENCE_CREDENTIAL"],
-    model_name="<model-name>",
-    api_version="2024-05-01-preview",
+    model="gpt-4o"
 )
 ```
 
@@ -370,7 +365,7 @@ from langchain_azure_ai.chat_models import AzureAIChatCompletionsModel
 model = AzureAIChatCompletionsModel(
     endpoint=os.environ["AZURE_INFERENCE_ENDPOINT"],
     credential=os.environ["AZURE_INFERENCE_CREDENTIAL"],
-    model_name="mistral-large-2407",
+    model="mistral-large-2407",
     client_kwargs={"logging_enable": True},
 )
 ```

articles/ai-foundry/includes/chat-with-data.md

@@ -15,7 +15,7 @@ To complete this section, you need a local copy of product data. The [Azure-Samp
 Follow these steps to add your data in the chat playground to help the assistant answer questions about your products. You're not changing the deployed model itself. Your data is stored separately and securely in your Azure subscription.
 
 1. Go to your project in [Azure AI Foundry](https://ai.azure.com). 
-1. Select **Playgrounds**.
+1. Select **Playgrounds** from the left pane.
 1. Select **Try the chat playground**.
 1. Select your deployed chat model from the **Deployment** dropdown.