Refresh article

Author: cdpark

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

@@ -1,90 +1,99 @@
 ---
-title: How to build and consume vector indexes in Azure AI Foundry portal
+title: Build and Consume Vector Indexes
 titleSuffix: Azure AI Foundry
-description: Learn how to create and use a vector index for performing Retrieval Augmented Generation (RAG).
+description: Learn how to create and use a vector index for performing retrieval-augmented generation (RAG) by using Azure AI Foundry portal.
 ms.service: azure-ai-foundry
 ms.custom:
   - ignite-2023
   - build-2024
   - ignite-2024
 ms.topic: how-to
-ms.date: 06/29/2025
+ms.date: 10/10/2025
 ms.reviewer: estraight
 ms.author: ssalgado
 manager: nitinme
 author: ssalgadodev
 ---
 
-# How to build and consume vector indexes in Azure AI Foundry portal
+# Build and consume vector indexes in Azure AI Foundry portal
 
 [!INCLUDE [feature-preview](../includes/feature-preview.md)]
 
-In this article, you learn how to create and use a vector index for performing [Retrieval Augmented Generation (RAG)](../concepts/retrieval-augmented-generation.md) in the Azure AI Foundry portal.
+In this article, you learn how to create and use a vector index for performing [retrieval-augmented generation (RAG)](../concepts/retrieval-augmented-generation.md) in the Azure AI Foundry portal.
 
 A vector index isn't required for RAG, but a vector query can match on semantically similar content, which is useful for RAG workloads.
 
 ## Prerequisites
 
 You must have:
-- An Azure AI Foundry project
+- An Azure AI Foundry resource
 - An [Azure AI Search resource](/azure/search/search-create-service-portal)
 - You should have content in a supported format that provides sufficient information for a chat experience. It can be an existing index on Azure AI Search, or create a new index using content files in Azure Blob Storage, your local system, or data in Azure AI Foundry.
 
 ## Create an index from the Chat playground
 
 [!INCLUDE [tip-left-pane](../includes/tip-left-pane.md)]
 
-1. Sign in to [Azure AI Foundry](https://ai.azure.com/?cid=learnDocs).
-1. Go to your project or [create a new project](../how-to/create-projects.md) in Azure AI Foundry portal.
-1. From the menu on the left, select **Playgrounds**. Select the **Chat Playground**.
+1. Sign in to the [Azure AI Foundry portal](https://ai.azure.com/?cid=learnDocs).
+
+1. Go to your project or [create a new project](../how-to/create-projects.md) in your Azure AI Foundry resource.
+
+1. From the sidebar menu, select **Playgrounds**. Select **Try the Chat playground**.
 
     :::image type="content" source="../media/index-retrieve/project-left-menu.png" alt-text="Screenshot of Project Left Menu." lightbox="../media/index-retrieve/project-left-menu.png":::
 
-1. Select a deployed chat completion model. If you have not done so already, deploy a model by selecting **Create new deployment**.
+1. Select a deployed chat completion model. If you don't have one, deploy a model by selecting **Create new deployment**, then choose a model.
 
    :::image type="content" source="../media/index-retrieve/create-deployment.png" alt-text="Screenshot of create a deployment button." lightbox="../media/index-retrieve/create-deployment.png":::
-   
-1. Scroll to the bottom of the model window. Select **+ Add a new data source**
+
+1. Scroll to the bottom of the model window. Select **+ Add a new data source**.
 
    :::image type="content" source="../media/index-retrieve/add-your-data.png" alt-text="Screenshot of the add your data section." lightbox="../media/index-retrieve/add-your-data.png":::
-   
+
 1. Choose your **Source data**. You can choose source data from a list of your recent data sources, a storage URL on the cloud, or upload files and folders from the local machine. You can also add a connection to another data source such as Azure Blob Storage.
 
    If you don't have sample data, you can [download these PDFs](https://github.com/Azure-Samples/azure-search-sample-data/tree/main/health-plan) to your local system, and then upload them as your source data.
 
     :::image type="content" source="../media/index-retrieve/select-source-data.png" alt-text="Screenshot of select source data." lightbox="../media/index-retrieve/select-source-data.png":::
 
-1. Select **Next** after choosing source data
-1. Choose the **Index Storage** - the location where you want your index to be stored in the **Index configuration** tab. 
+1. Select **Next** after choosing source data.
+
+1. Choose the **Index storage**, the location where you want your index to be stored in the **Index configuration** tab.
+
 1. If you already have an Azure AI Search resource, you can browse the list of search service resources for your subscription and then select **Connect** for the one you want to use. If you're connecting with API keys, confirm your search service [uses API keys](/azure/search/search-security-api-keys).
 
     :::image type="content" source="../media/index-retrieve/index-storage.png" alt-text="Screenshot of select index store." lightbox="../media/index-retrieve/index-storage.png":::
 
-    1. If you don't have an existing resource, choose **Create a new Azure AI Search resource**. Select **Next**.
+    If you don't have an existing resource, choose **Create a new Azure AI Search resource**. Select **Next**.
 
 1. Select the Azure OpenAI connection you want to use. Select **Next**.
-    
-1. Enter a name you want to use for your vector index. Select **Next**.
-1. Review the details you entered and select **Create**
+
+1. Review the details you entered and select **Create vector index**.
+
 1. You're taken to the index details page where you can see the status of your index creation.
 
 ## Use an index in prompt flow
 
-1. Sign in to [Azure AI Foundry](https://ai.azure.com/?cid=learnDocs) and select your project. 
-1. From the collapsible left menu, select **Prompt flow** from the **Build and customize** section.
+1. Under **Build and customize** in the sidebar menu, select **Prompt flow**.
+
 1. Open an existing prompt flow or select **+ Create** to create a new flow.
-1. On the top menu of the flow designer, select **More tools**, and then select ***Index Lookup***.
+
+1. Select **Create** in the **Chat flow** tile, then select **Create** again.
+
+1. Select **Start compute session**, and wait a few minutes for the compute session to begin.
+
+1. Select **More tools**, and then select **Index Lookup**.
 
     :::image type="content" source="../media/index-retrieve/index-lookup-tool.png" alt-text="Screenshot of Vector index Lookup from More Tools." lightbox="../media/index-retrieve/index-lookup-tool.png":::
 
-1. Provide a name for your Index Lookup Tool and select **Add**.
+1. Provide a node name for your Index Lookup Tool and select **Add**.
+
 1. Select the **mlindex_content** value box, and select your index from the value section. After completing this step, enter the queries and **query_types** to be performed against the index.
 
    :::image type="content" source="../media/index-retrieve/configure-index-lookup-tool.png" alt-text="Screenshot of the prompt flow node to configure index lookup." lightbox="../media/index-retrieve/configure-index-lookup-tool.png":::
 
 
-
 ## Related content
 
-- [Learn more about RAG](../concepts/retrieval-augmented-generation.md)
+- [Retrieval augmented generation and indexes](../concepts/retrieval-augmented-generation.md)
 - [Build and consume an index using code](../tutorials/copilot-sdk-create-resources.md)

articles/ai-foundry/media/index-retrieve/index-lookup-tool.png

@@ -5,7 +5,7 @@ items:
     href: ../../concepts/retrieval-augmented-generation.md
   - name: Use Azure AI Search
     href: /azure/search/search-what-is-azure-search?context=/azure/ai-foundry/context/context
-  - name: Build and consume vector indexes (Portal)
+  - name: Build and consume vector indexes (portal)
     href: ../../how-to/index-add.md
   - name: Build a RAG solution using Azure AI Search
     href: /azure/search/tutorial-rag-build-solution?context=/azure/ai-foundry/context/context