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Updates to add a vector index in Foundry portal
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articles/ai-studio/how-to/index-add.md

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- build-2024
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ms.topic: how-to
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ms.date: 12/11/2024
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ms.date: 01/31/2025
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ms.reviewer: estraight
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ms.author: ssalgado
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author: ssalgadodev
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[!INCLUDE [feature-preview](../includes/feature-preview.md)]
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In this article, you learn how to create and use a vector index for performing [Retrieval Augmented Generation (RAG)](../concepts/retrieval-augmented-generation.md).
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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.
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A vector index isn't required for RAG, but a vector query can match on semantically similar content, which is useful for RAG workloads.
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## Prerequisites
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You must have:
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- An Azure AI Foundry project
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- An Azure AI Search resource
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- An [Azure AI Search resource](/azure/search/search-create-service-portal)
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- 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.
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## Create an index from the Chat playground
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1. Sign in to [Azure AI Foundry](https://ai.azure.com).
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1. Go to your project or [create a new project](../how-to/create-projects.md) in Azure AI Foundry portal.
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1. From the menu on the left, select **Playgrounds**.
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1. From the menu on the left, select **Playgrounds**. Select the **Chat Playground**.
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:::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":::
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1. Select a deployed model. If you have not done so already, deploy a model by selecting **Create new deployment**.
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1. Select a deployed chat completion model. If you have not done so already, deploy a model by selecting **Create new deployment**.
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:::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":::
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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.
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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.
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:::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":::
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1. Select **Next** after choosing source data
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1. Choose the **Index Storage** - the location where you want your index to be stored in the **Index configuration** tab.
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1. If you already have an Azure AI Search resource, you can choose that from the dropdown.
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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).
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:::image type="content" source="../media/index-retrieve/index-storage.png" alt-text="Screenshot of select index store." lightbox="../media/index-retrieve/index-storage.png":::
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1. Review the details you entered and select **Create**
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1. You're taken to the index details page where you can see the status of your index creation.
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## Use an index in prompt flow
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1. Sign in to [Azure AI Foundry](https://ai.azure.com) and select your project.

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