Merge pull request #2136 from MicrosoftDocs/main

12/30/2024 PM Publish

Author: PhilKang0704

articles/ai-studio/how-to/flow-deploy.md

@@ -142,12 +142,12 @@ If you created the associated endpoint with **User Assigned Identity**, the user
 |Azure AI Foundry project|**Azure Machine Learning Workspace Connection Secrets Reader** role **OR** a customized role with `Microsoft.MachineLearningServices/workspaces/connections/listsecrets/action` | Get project connections|
 |Azure AI Foundry project container registry |**ACR pull** |Pull container image |
 |Azure AI Foundry project default storage| **Storage Blob Data Reader**| Load model from storage |
-|Azure AI Foundry project|**Workspace metrics writer**| After you deploy then endpoint, if you want to monitor the endpoint related metrics like CPU/GPU/Disk/Memory utilization, you need to give this permission to the identity.<br/><br/>Optional|
+|Azure AI Foundry project|**AzureML Metrics Writer (preview)**| After you deploy then endpoint, if you want to monitor the endpoint related metrics like CPU/GPU/Disk/Memory utilization, you need to give this permission to the identity.<br/><br/>Optional|
 
 See detailed guidance about how to grant permissions to the endpoint identity in [Grant permissions to the endpoint](#grant-permissions-to-the-endpoint).
 
 > [!IMPORTANT]
-> If your flow uses Microsoft Entra ID based authentication connections, no matter you use system-assigned identity or user-assigned identity, you always need to grant the managed identity appropriate roles of the corresponding resources so that it can make API calls to that resource. For example, if your Azure OpenAI connection uses Microsoft Entra ID based authentication, you need to grant your endpoint managed identity **Cognitive Services OpenAI User or Cognitive Services OpenAI Contributor role** of the corresponding Azure OpenAI resources.
+> If your flow uses Microsoft Entra ID based authentication connections, whether you use system-assigned identity or user-assigned identity, you always need to grant the managed identity appropriate roles of the corresponding resources so that it can make API calls to that resource. For example, if your Azure OpenAI connection uses Microsoft Entra ID based authentication, you need to grant your endpoint managed identity **Cognitive Services OpenAI User or Cognitive Services OpenAI Contributor role** of the corresponding Azure OpenAI resources.
 
 ### Advanced settings - Outputs & Connections
 

articles/ai-studio/includes/assign-rbac.md

@@ -0,0 +1,28 @@
+---
+title: Include file
+description: Include file
+author: sdgilley
+ms.reviewer: sgilley
+ms.author: sgilley
+ms.service: azure-ai-studio
+ms.topic: include
+ms.date: 12/27/2024
+ms.custom: include
+---
+
+The general pattern for assigning role-based access control (RBAC) for any resource is:
+
+1. Navigate to the Azure portal for the given resource. 
+1. From the left page in the Azure portal, select **Access control (IAM)**. 
+1. Select **+ Add** > **Add role assignment**.
+1. Search for the role you need to assign and select it. Then select **Next**.
+1. When assigning a role to yourself: 
+    1. Select **User, group, or service principal**. 
+    1. Select **Select members**.
+    1. Search for your name and select it.
+1. When assigning a role to another resource: 
+    1. Select **Managed identity**. 
+    1. Select **Select members**.
+    1. Use the dropdown to find the type of resource you want to assign. For example, **Azure AI services** or **Search service**.
+    1. Select the resource from the list that appears. There might only be one, but you still need to select it.
+1. Continue through the wizard and select **Review + assign** to add the role assignment.

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

@@ -21,7 +21,7 @@ Follow these steps to add your data in the chat playground to help the assistant
 
     :::image type="content" source="../media/tutorials/chat/playground-chat.png" alt-text="Screenshot of the chat playground with the chat mode and model selected." lightbox="../media/tutorials/chat/playground-chat.png":::
 
-1. On the left side of the chat playground, select **Add your data** > **+ Add a new data source**.
+1. On the left side of the chat playground, select **Add your data (PREVIEW)** > **+ Add a new data source**.
 
     :::image type="content" source="../media/tutorials/chat/add-your-data.png" alt-text="Screenshot of the chat playground with the option to add a data source visible." lightbox="../media/tutorials/chat/add-your-data.png":::
 
@@ -37,13 +37,7 @@ Follow these steps to add your data in the chat playground to help the assistant
 
    :::image type="content" source="../media/tutorials/chat/add-your-data-uploaded.png" alt-text="Screenshot of the dialog to select and upload files." lightbox="../media/tutorials/chat/add-your-data-uploaded.png":::
 
-1. Select an Azure AI Search service. In this example we select **Connect other Azure AI Search resource** from the **Select Azure AI Search service** dropdown. If you don't have a search resource, you can create one by selecting **Create a new Azure AI Search resource**. Then return to this step to connect and select it.
-
-    :::image type="content" source="../media/tutorials/chat/add-your-data-connect-search.png" alt-text="Screenshot of the search resource selection options." lightbox="../media/tutorials/chat/add-your-data-connect-search.png":::
-
-1. Browse for your Azure AI Search service, and select **Add connection**. 
-
-    :::image type="content" source="../media/tutorials/chat/add-your-data-connect-search-add.png" alt-text="Screenshot of the page to add a search service connection." lightbox="../media/tutorials/chat/add-your-data-connect-search-add.png":::
+1. Select your **Azure AI Search** service.
 
 1. For the **Vector index name**, enter *product-info* and select **Next**.
 

articles/ai-studio/media/tutorials/chat/add-your-data-connect-search-add.png

@@ -70,7 +70,7 @@ These steps deploy a model to a real-time endpoint from the Azure AI Foundry por
 
 After you deploy the **gpt-4o-mini**, repeat the steps to deploy the **text-embedding-ada-002** model.
 
-## Create an Azure AI Search service
+## <a name="create-search"></a> Create an Azure AI Search service
 
 The goal with this application is to ground the model responses in your custom data. The search index is used to retrieve relevant documents based on the user's question.