@@ -38,7 +38,7 @@ Starting in the 2024-05-01-preview REST API and the Azure portal, which also tar
 
 During indexing, the **AML** skill can connect to the model catalog to generate vectors for the index. At query time, queries can use a vectorizer to connect to the same model to vectorize text strings for a vector query. In this workflow, you should use the **AML** skill and the model catalog vectorizer together so that the same embedding model is used for indexing and queries. For more information, including a list of supported embedding models, see [Use embedding models from Azure AI Foundry model catalog](vector-search-integrated-vectorization-ai-studio.md).
 
-We recommend using the [**Import and vectorize data wizard**](search-get-started-portal-import-vectors.md) to generate a skillset that includes an AML skill for deployed embedding models in Azure AI Foundry. The wizard generates the AML skill definition for inputs, outputs, and mappings, providing an easy way to test a model before writing any code.
+We recommend using the [**Import data (new)** wizard](search-get-started-portal-import-vectors.md) to generate a skillset that includes an AML skill for deployed embedding models in Azure AI Foundry. The wizard generates the AML skill definition for inputs, outputs, and mappings, providing an easy way to test a model before writing any code.
 
 ## Prerequisites
 

@@ -18,7 +18,7 @@ This article contains tips to help you get started with AI enrichment and skills
 
 ## Tip 1: Start simple and start small
 
-Both the [**Import data wizard**](search-get-started-skillset.md) and [**Import and vectorize data wizard**](search-get-started-portal-import-vectors.md) in the Azure portal support AI enrichment. Without writing any code, you can create and examine all of the objects used in an enrichment pipeline: an index, indexer, data source, and skillset.
+Both the [**Import data** wizard](search-get-started-skillset.md) and the [**Import data (new)** wizard](search-get-started-portal-import-vectors.md) in the Azure portal support AI enrichment. Without writing any code, you can create and examine all of the objects used in an enrichment pipeline: an index, indexer, data source, and skillset.
 
 Another way to start simply is by creating a data source with just a handful of documents or rows in a table that are representative of the documents that will be indexed. A small data set is the best way to increase the speed of finding and fixing issues.Run your sample through the end-to-end pipeline and check that the results meet your needs. Once you're satisfied with the results, you're ready to add more files to your data source.
 

@@ -271,7 +271,7 @@ Although skill output can be optionally cached for reuse purposes, it's usually
 
 ## Tips for a first skillset
 
-+ Try the [Import data wizard](search-get-started-portal.md) or [Import and vectorize data wizard](search-get-started-portal-import-vectors.md).
++ Try the [**Import data** wizard](search-get-started-portal.md) or [**Import data (new)** wizard](search-get-started-portal-import-vectors.md).
 
   The wizards automate several steps that can be challenging the first time around. It defines the skillset, index, and indexer, including field mappings and output field mappings. It also defines projections in a knowledge store if you're using one. For some skills, such as OCR or image analysis, the wizard adds utility skills that merge the image and text content that was separated during document cracking.
 

@@ -132,7 +132,7 @@ If skills produce output but the search index is empty, check the field mappings
 
 Select one of the mapping options and expand the details view to review source and target definitions.
 
-+ [**Projection Mappings**](index-projections-concept-intro.md) are found in skillsets that provide integrated vectorization, such as the skills created by the [Import and vectorize data wizard](search-get-started-portal-import-vectors.md). These mappings determine parent-child (chunk) field mappings and whether a secondary index is created for just the chunked content
++ [**Projection Mappings**](index-projections-concept-intro.md) are found in skillsets that provide integrated vectorization, such as the skills created by the [**Import data (new)** wizard](search-get-started-portal-import-vectors.md). These mappings determine parent-child (chunk) field mappings and whether a secondary index is created for just the chunked content
 
 + [**Output Field Mappings**](cognitive-search-output-field-mapping.md) are found in indexers and are used when skillsets invoke built-in or custom skills. These mappings are used to set the data path from a node in the enrichment tree to a field in the search index. For more information about paths, see [enrichment node path syntax](cognitive-search-concept-annotations-syntax.md). 
 

@@ -16,7 +16,7 @@ ms.date: 09/12/2025
 
 The **Azure OpenAI Embedding** skill connects to an embedding model deployed to your [Azure OpenAI](/azure/ai-services/openai/overview) resource or [Azure AI Foundry](/azure/ai-foundry/what-is-azure-ai-foundry) project to generate embeddings during indexing. Your data is processed in the [Geo](https://azure.microsoft.com/explore/global-infrastructure/data-residency/) where your model is deployed.
 
-The [Import and vectorize data wizard](search-get-started-portal-import-vectors.md) in the Azure portal uses the Azure OpenAI Embedding skill to vectorize content. You can run the wizard and review the generated skillset to see how the wizard builds the skill for embedding models.
+The [**Import data (new)** wizard](search-get-started-portal-import-vectors.md) in the Azure portal uses the Azure OpenAI Embedding skill to vectorize content. You can run the wizard and review the generated skillset to see how the wizard builds the skill for embedding models.
 
 > [!NOTE]
 > This skill is bound to Azure OpenAI and is charged at the existing [Azure OpenAI Standard price](https://azure.microsoft.com/pricing/details/cognitive-services/openai-service/#pricing).

@@ -46,7 +46,7 @@ Supported regions vary by modality and how the skill connects to the Document In
 
 | Approach | Requirement |
 |----------|-------------|
-| [Import and vectorize data wizard](search-import-data-portal.md) | Create an Azure AI multi-service resource in one of these regions to get the portal experience: **East US**, **West Europe**, **North Central US**. | 
+| [**Import data (new)** wizard](search-import-data-portal.md) | Create an Azure AI multi-service resource in one of these regions to get the portal experience: **East US**, **West Europe**, **North Central US**. | 
 | Programmatic, using [Microsoft Entra ID authentication (preview)](cognitive-search-attach-cognitive-services.md#bill-through-a-keyless-connection) for billing |  Create Azure AI Search in one of these regions:  **East US**, **West Europe**, **North Central US**, **West US 2**. <br>Create the Azure AI multi-service resource in any region listed in the [Product availability by region](https://azure.microsoft.com/explore/global-infrastructure/products-by-region/table) table.|
 | Programmatic, using a [multi-service resource API key](cognitive-search-attach-cognitive-services.md#bill-through-a-keyless-connection) for billing | Create your Azure AI Search service and AI multi-service resource in the same region: **East US**, **West Europe**, **North Central US**, **West US 2**. |
 

@@ -24,7 +24,7 @@ In this article, learn how to:
 
 ## Prerequisites
 
-+ A search index containing `searchable` vector and nonvector fields. We recommend the [Import and vectorize data wizard](search-import-data-portal.md) to create an index quickly. Otherwise, see [Create an index](search-how-to-create-search-index.md) and [Add vector fields to a search index](vector-search-how-to-create-index.md).
++ A search index containing `searchable` vector and nonvector fields. We recommend the [**Import data (new)** wizard](search-import-data-portal.md) to create an index quickly. Otherwise, see [Create an index](search-how-to-create-search-index.md) and [Add vector fields to a search index](vector-search-how-to-create-index.md).
 
 + (Optional) If you want the [semantic ranker](semantic-search-overview.md), your search service must be Basic tier or higher, with [semantic ranker enabled](semantic-how-to-enable-disable.md).
 

@@ -0,0 +1,155 @@
+---
+manager: nitinme
+author: haileytap
+ms.author: haileytapia
+ms.service: azure-ai-search
+ms.topic: include
+ms.date: 09/16/2025
+---
+
+> [!IMPORTANT]
+> The **Import data (new)** wizard now supports keyword search, which was previously only available in the **Import data** wizard. We recommend the new wizard for an improved search experience. For more information about how we're consolidating the wizards, see [Import data wizards in the Azure portal](../../search-import-data-portal.md).
+
+In this quickstart, you use the **Import data (new)** wizard and sample data about fictitious hotels to create your first search index. The wizard requires no code to create an index, helping you write interesting queries within minutes.
+
+The wizard creates multiple objects on your search service, including a searchable [index](../../search-what-is-an-index.md), an [indexer](../../search-indexer-overview.md), and a data source connection for automated data retrieval. At the end of this quickstart, you review each object.
+
+## Prerequisites
+
++ An Azure account with an active subscription. [Create an account for free](https://azure.microsoft.com/free/).
+
++ An Azure AI Search service. [Create a service](../../search-create-service-portal.md) or [find an existing service](https://portal.azure.com/#blade/HubsExtension/BrowseResourceBlade/resourceType/Microsoft.Search%2FsearchServices) in your current subscription. You can use a free service for this quickstart.
+
++ An [Azure Storage account](/azure/storage/common/storage-account-create). Use Azure Blob Storage or Azure Data Lake Storage Gen2 (storage account with a hierarchical namespace) on a standard performance (general-purpose v2) account. To avoid bandwidth charges, use the same region as Azure AI Search.
+
+### Check for network access
+
+For this quickstart, all of the preceding resources must have public access enabled so that the Azure portal nodes can access them. Otherwise, the wizard fails. After the wizard runs, you can enable firewalls and private endpoints on the integration components for security. For more information, see [Secure connections in the import wizards](../../search-import-data-portal.md#secure-connections).
+
+### Check for space
+
+Many customers start with a free search service, which is limited to three indexes, three indexers, and three data sources. This quickstart creates one of each, so before you begin, make sure you have room for extra objects.
+
+On the **Overview** page, select **Usage** to see how many indexes, indexers, and data sources you currently have.
+
+   :::image type="content" source="../../media/search-get-started-portal/overview-quota-usage.png" alt-text="Screenshot of the Overview page for an Azure AI Search service instance in the Azure portal, showing the number of indexes, indexers, and data sources." lightbox="../../media/search-get-started-portal/overview-quota-usage.png":::
+
+## Prepare sample data
+
+This quickstart uses a JSON document that contains metadata for 50 fictitious hotels, but you can also use your own files.
+
+To prepare the sample data for this quickstart:
+
+1. Sign in to the [Azure portal](https://portal.azure.com/) and select your Azure Storage account.
+
+1. From the left pane, select **Data storage** > **Containers**.
+
+1. Create a container named **hotels-sample**.
+
+1. Upload the [sample JSON document](https://github.com/Azure-Samples/azure-search-sample-data/blob/main/hotels/HotelsData_toAzureBlobs.json) to the container.
+
+## Start the wizard
+
+To start the wizard for this quickstart:
+
+1. Sign in to the [Azure portal](https://portal.azure.com/) and select your search service.
+
+1. On the **Overview** page, select **Import data (new)**.
+
+   :::image type="content" source="../../media/search-import-data-portal/import-data-new-button.png" alt-text="Screenshot that shows how to open the new import wizard in the Azure portal.":::
+
+1. Select your data source: **Azure Blob Storage** or **Azure Data Lake Storage Gen2**.
+
+   :::image type="content" source="../../media/search-get-started-portal-images/select-data-source.png" alt-text="Screenshot of the options for selecting a data source in the wizard." border="true" lightbox="../../media/search-get-started-portal-images/select-data-source.png":::
+
+1. Select **Keyword search**.
+
+   :::image type="content" source="../../media/search-get-started-portal/keyword-search-tile.png" alt-text="Screenshot of the keyword search tile in the Azure portal." border="true" lightbox="../../media/search-get-started-portal/keyword-search-tile.png":::
+
+## Create and load a search index
+
+In this section, you create and load an index in five steps.
+
+### Connect to a data source
+
+Azure AI Search requires a connection to a data source for content ingestion and indexing. In this case, the data source is your Azure Storage account.
+
+To connect to the sample data:
+
+1. On the **Connect to your data** page, select your Azure subscription.
+
+1. Select your storage account, and then select the **hotels-sample** container.
+
+1. Select **JSON array** for the parsing mode.
+
+   :::image type="content" source="../../media/search-get-started-portal/connect-to-your-data.png" alt-text="Screenshot of the Connect to your data page in the Azure portal." lightbox="../../media/search-get-started-portal/connect-to-your-data.png":::
+
+1. Select **Next**.
+
+### Skip configuration for skills
+
+The wizard supports skillset creation and [AI enrichment](../../cognitive-search-concept-intro.md) during indexing, which are beyond the scope of this quickstart. Skip this step by selecting **Next**.
+
+> [!TIP]
+> For a similar walkthrough that focuses on AI enrichment, see [Quickstart: Create a skillset in the Azure portal](../../search-get-started-skillset.md).
+
+### Configure the index
+
+Based on the structure and content of the sample hotel data, the wizard infers a schema for your search index.
+
+To configure the index:
+
+1. For each of the following fields, select **Configure field**, and then set the respective attributes.
+
+   | Fields | Attributes |
+   |-------|------------|
+   | `HotelId` | Key, Retrievable, Filterable, Sortable, Searchable |
+   | `HotelName`, `Category` | Retrievable, Filterable, Sortable, Searchable |
+   | `Description`, `Description_fr` | Retrievable |
+   | `Tags` | Retrievable, Filterable, Searchable |
+   | `ParkingIncluded`, `IsDeleted`, `LastRenovationDate`, `Rating`, `Location` | Retrievable, Filterable, Sortable |
+   | `Address.StreetAddress`, `Rooms.Description`, `Rooms.Description_fr` | Retrievable, Searchable |
+   | `Address.City`, `Address.StateProvince`, `Address.PostalCode`, `Address.Country`, `Rooms.Type`, `Rooms.BedOptions`, `Rooms.Tags` | Retrievable, Filterable, Facetable, Searchable |
+   | `Rooms.BaseRate`, `Rooms.SleepsCount`, `Rooms.SmokingAllowed` | Retrievable, Filterable, Facetable |
+
+   :::image type="content" source="../../media/search-get-started-portal/configure-index.gif" alt-text="GIF that shows how to configure attributes for fields in the index." lightbox="../../media/search-get-started-portal/configure-index.gif":::
+
+1. Delete the `AzureSearch_DocumentKey` field.
+
+1. Select **Next**.
+
+At a minimum, the index requires a name and a collection of fields. The wizard scans for unique string fields and marks one as the document key, which uniquely identifies each document in the index.
+
+Each field has a name, data type, and attributes that control how the field is used in the index. You can enable or disable the following attributes:
+
+| Attribute | Description | Applicable data types |
+|-----------|-------------|------------------------|
+| Retrievable | Fields returned in a query response. | Strings and integers |
+| Filterable | Fields that accept a filter expression. | Integers |
+| Sortable | Fields that accept an orderby expression. | Integers |
+| Facetable | Fields used in a faceted navigation structure. | Integers |
+| Searchable | Fields used in full-text search. Strings are searchable, but numeric and Boolean fields are often marked as not searchable. | Strings |
+
+Attributes affect storage in different ways. For example, filterable fields consume extra storage, while retrievable fields don't. For more information, see [Example demonstrating the storage implications of attributes and suggesters](../../search-what-is-an-index.md#example-demonstrating-the-storage-implications-of-attributes-and-suggesters).
+
+If you want autocomplete or suggested queries, specify language **Analyzers** or **Suggesters**.
+
+### Skip advanced settings
+
+The wizard offers advanced settings for semantic ranking and index scheduling, which are beyond the scope of this quickstart. Skip this step by selecting **Next**.
+
+### Review and create the objects
+
+The last step is to review your configuration and create the index, indexer, and data source on your search service. The indexer automates the process of extracting content from your data source and loading it into the index, enabling keyword search.
+
+To review and create the objects:
+
+1. Change the object name prefix to **hotels-sample**.
+
+1. Review the object configurations.
+
+   :::image type="content" source="../../media/search-get-started-portal/review-and-create.png" alt-text="Screenshot of the object configuration page in the Azure portal." lightbox="../../media/search-get-started-portal/review-and-create.png":::
+
+   AI enrichment, semantic ranker, and indexer scheduling are either disabled or set to their default values because you skipped their wizard steps.
+
+1. Select **Create** to simultaneously create the objects and run the indexer.

@@ -0,0 +1,112 @@
+---
+manager: nitinme
+author: haileytap
+ms.author: haileytapia
+ms.service: azure-ai-search
+ms.topic: include
+ms.date: 09/16/2025
+---
+
+> [!IMPORTANT]
+> The **Import data** wizard will eventually be deprecated. Most of its functionality is available in the **Import data (new)** wizard, which we recommend for most search scenarios. For more information, see [Import data wizards in the Azure portal](../../search-import-data-portal.md).
+
+In this quickstart, you use the **Import data** wizard and a built-in sample of fictitious hotel data to create your first search index. The wizard requires no code to create an index, helping you write interesting queries within minutes.
+
+The wizard creates multiple objects on your search service, including a searchable [index](../../search-what-is-an-index.md), an [indexer](../../search-indexer-overview.md), and a data source connection for automated data retrieval. At the end of this quickstart, you review each object.
+
+## Prerequisites
+
++ An Azure account with an active subscription. [Create an account for free](https://azure.microsoft.com/free/).
+
++ An Azure AI Search service. [Create a service](../../search-create-service-portal.md) or [find an existing service](https://portal.azure.com/#blade/HubsExtension/BrowseResourceBlade/resourceType/Microsoft.Search%2FsearchServices) in your current subscription. You can use a free service for this quickstart.
+
+### Check for network access
+
+For this quickstart, which uses built-in sample data, make sure your search service doesn't have [network access controls](../../service-configure-firewall.md). The Azure portal controller uses a public endpoint to retrieve data and metadata from the Microsoft-hosted data source. For more information, see [Secure connections in the import wizards](../../search-import-data-portal.md#secure-connections).
+
+### Check for space
+
+Many customers start with a free search service, which is limited to three indexes, three indexers, and three data sources. This quickstart creates one of each, so before you begin, make sure you have room for extra objects.
+
+On the **Overview** page, select **Usage** to see how many indexes, indexers, and data sources you currently have.
+
+   :::image type="content" source="../../media/search-get-started-portal/overview-quota-usage.png" alt-text="Screenshot of the Overview page for an Azure AI Search service instance in the Azure portal, showing the number of indexes, indexers, and data sources." lightbox="../../media/search-get-started-portal/overview-quota-usage.png":::
+
+## Start the wizard
+
+To start the wizard for this quickstart:
+
+1. Sign in to the [Azure portal](https://portal.azure.com/) and select your search service.
+
+1. On the **Overview** page, select **Import data**.
+
+   :::image type="content" source="../../media/search-import-data-portal/import-data-button.png" alt-text="Screenshot that shows how to open the Import data wizard in the Azure portal.":::
+
+## Create and load a search index
+
+In this section, you create and load an index in four steps.
+
+### Connect to a data source
+
+The wizard creates a data source connection to sample data that Microsoft hosts on Azure Cosmos DB. The sample data is accessed through a public endpoint, so you don't need an Azure Cosmos DB account or source files for this step.
+
+To connect to the sample data:
+
+1. On the **Connect to your data** page, select the **Data Source** dropdown list, and then select **Samples**.
+
+1. Select **hotels-sample** from the list of built-in samples.
+
+1. Select **Next: Add cognitive skills (Optional)**.
+
+   :::image type="content" source="../../media/search-get-started-portal/import-hotels-sample.png" alt-text="Screenshot that shows how to select the hotels-sample data source in the Import data wizard." lightbox="../../media/search-get-started-portal/import-hotels-sample.png":::
+
+### Skip configuration for skills
+
+The wizard supports skillset creation and [AI enrichment](../../cognitive-search-concept-intro.md) during indexing, which are beyond the scope of this quickstart. Skip this step by selecting **Next: Customize target index**.
+
+> [!TIP]
+> For a similar walkthrough that focuses on AI enrichment, see [Quickstart: Create a skillset in the Azure portal](../../search-get-started-skillset.md).
+
+### Configure the index
+
+Based on the structure and content of the sample hotel data, the wizard infers a schema for your search index.
+
+To configure the index:
+
+1. Accept the system-generated values for the index name (**hotels-sample-index**) and key (**HotelId**).
+
+1. Accept the system-generated values for all field attributes.
+
+1. Select **Next: Create an indexer**.
+
+   :::image type="content" source="../../media/search-get-started-portal/hotels-sample-generated-index.png" alt-text="Screenshot that shows the generated index definition for the hotels-sample data source in the Import data wizard." lightbox="../../media/search-get-started-portal/hotels-sample-generated-index.png":::
+
+At a minimum, the index requires a name and a collection of fields. The wizard scans for unique string fields and marks one as the document key, which uniquely identifies each document in the index.
+
+Each field has a name, data type, and attributes that control how the field is used in the index. You can use the checkboxes to enable or disable the following attributes:
+
+| Attribute | Description | Applicable data types |
+|-----------|-------------|------------------------|
+| Retrievable | Fields returned in a query response. | Strings and integers |
+| Filterable | Fields that accept a filter expression. | Integers |
+| Sortable | Fields that accept an orderby expression. | Integers |
+| Facetable | Fields used in a faceted navigation structure. | Integers |
+| Searchable | Fields used in full-text search. Strings are searchable, but numeric and Boolean fields are often marked as not searchable. | Strings |
+
+Attributes affect storage in different ways. For example, filterable fields consume extra storage, while retrievable fields don't. For more information, see [Example demonstrating the storage implications of attributes and suggesters](../../search-what-is-an-index.md#example-demonstrating-the-storage-implications-of-attributes-and-suggesters).
+
+If you want autocomplete or suggested queries, specify language **Analyzers** or **Suggesters**.
+
+### Configure and run the indexer
+
+The last step is to configure and run the indexer, which automates the process of extracting content from your data source and loading it into your index. This step also creates the data source and index objects on your search service.
+
+To configure and run the indexer:
+
+1. Accept the system-generated value for the indexer name (**hotels-sample-indexer**).
+
+1. Accept the default schedule option to run the indexer once and immediately. The sample data is static, so you can't enable change tracking.
+
+1. Select **Submit** to simultaneously create and run the indexer.
+
+   :::image type="content" source="../../media/search-get-started-portal/hotels-sample-indexer.png" alt-text="Screenshot that shows how to configure the indexer for the hotels-sample data source in the Import data wizard." lightbox="../../media/search-get-started-portal/hotels-sample-indexer.png":::

@@ -0,0 +1,208 @@
+---
+manager: nitinme
+author: haileytap
+ms.author: haileytapia
+ms.service: azure-ai-search
+ms.topic: include
+ms.date: 09/16/2025
+---
+
+> [!IMPORTANT]
+> The **Import data (new)** wizard now supports keyword search, which was previously only available in the **Import data** wizard. We recommend the new wizard for an improved search experience. For more information about how we're consolidating the wizards, see [Import data wizards in the Azure portal](../../search-import-data-portal.md).
+
+In this quickstart, you learn how a skillset in Azure AI Search adds optical character recognition (OCR), image analysis, language detection, text merging, and entity recognition to generate text-searchable content in an index.
+
+You can run the **Import data (new)** wizard in the Azure portal to apply skills that create and transform textual content during indexing. The input is your raw data, usually blobs in Azure Storage. The output is a searchable index containing AI-generated image text, captions, and entities. You can then query generated content in the Azure portal using [**Search explorer**](../../search-explorer.md).
+
+Before you run the wizard, you create a few resources and upload sample files.
+
+## Prerequisites
+
++ An Azure account with an active subscription. [Create an account for free](https://azure.microsoft.com/free/).
+
++ An Azure AI Search service. [Create a service](../../search-create-service-portal.md) or [find an existing service](https://portal.azure.com/#blade/HubsExtension/BrowseResourceBlade/resourceType/Microsoft.Search%2FsearchServices) in your current subscription. You can use a free service for this quickstart.
+
++ An [Azure Storage account](/azure/storage/common/storage-account-create). Use Azure Blob Storage on a standard performance (general-purpose v2) account. To avoid bandwidth charges, use the same region as Azure AI Search.
+
+> [!NOTE]
+> This quickstart uses [Azure AI services](https://azure.microsoft.com/services/cognitive-services/) for AI enrichment. Because the workload is small, Azure AI services is tapped behind the scenes for free processing up to 20 transactions. Therefore, you don't need to create an Azure AI services multi-service resource.
+
+## Prepare sample data
+
+In this section, you create an Azure Storage container to store sample data consisting of various file types, including images and application files that aren't full-text searchable in their native formats.
+
+To prepare the sample data for this quickstart:
+
+1. Sign in to the [Azure portal](https://portal.azure.com/) and select your Azure Storage account.
+
+1. From the left pane, select **Data storage** > **Containers**.
+
+1. Create a container, and then upload the [sample data](https://github.com/Azure-Samples/azure-search-sample-data/tree/main/ai-enrichment-mixed-media) to the container.
+
+## Run the wizard
+
+To run the wizard:
+
+1. Sign in to the [Azure portal](https://portal.azure.com/) and select your search service.
+
+1. On the **Overview** page, select **Import data (new)**.
+
+   :::image type="content" source="../../media/search-import-data-portal/import-data-new-button.png" alt-text="Screenshot that shows how to open the new import wizard in the Azure portal.":::
+
+1. Select **Azure Blob Storage** for the data source.
+
+   :::image type="content" source="../../media/search-get-started-skillset/choose-data-source.png" alt-text="Screenshot of the Azure Blob Storage data source option in the Azure portal." border="true" lightbox="../../media/search-get-started-skillset/choose-data-source.png":::
+
+1. Select **Keyword search**.
+
+   :::image type="content" source="../../media/search-get-started-portal/keyword-search-tile.png" alt-text="Screenshot of the keyword search tile in the Azure portal." border="true" lightbox="../../media/search-get-started-portal/keyword-search-tile.png":::
+
+### Step 1: Create a data source
+
+Azure AI Search requires a connection to a data source for content ingestion and indexing. In this case, the data source is your Azure Storage account.
+
+To create the data source:
+
+1. On the **Connect to your data** page, select your Azure subscription.
+
+1. Select your storage account, and then select the container you created.
+
+   :::image type="content" source="../../media/search-get-started-skillset/connect-to-your-data.png" alt-text="Screenshot of the Connect to your data page in the Azure portal." border="true" lightbox="../../media/search-get-started-skillset/connect-to-your-data.png":::
+
+1. Select **Next**.
+
+If you get `Error detecting index schema from data source`, the indexer that powers the wizard can't connect to your data source. The data source most likely has security protections. Try the following solutions, and then rerun the wizard.
+
+| Security feature | Solution |
+|--------------------|----------|
+| Resource requires Azure roles, or its access keys are disabled. | [Connect as a trusted service](../../search-indexer-howto-access-trusted-service-exception.md) or [connect using a managed identity](../../search-how-to-managed-identities.md). |
+| Resource is behind an IP firewall. | [Create an inbound rule for Azure AI Search and the Azure portal](../../search-indexer-howto-access-ip-restricted.md). |
+| Resource requires a private endpoint connection. | [Connect over a private endpoint](../../search-indexer-howto-access-private.md). |
+
+### Step 2: Add cognitive skills
+
+The next step is to configure AI enrichment to invoke OCR, image analysis, and entity recognition.
+
+OCR and image analysis are available for blobs in Azure Blob Storage and Azure Data Lake Storage (ADLS) Gen2 and for image content in OneLake. Images can be standalone files or embedded images in a PDF or other files.
+
+To add the skills:
+
+1. Select **Extract entities**, and then select the gear icon.
+
+1. Select and save the following checkboxes:
+
+   + **Persons**
+
+   + **Locations**
+
+   + **Organizations**
+
+   :::image type="content" source="../../media/search-get-started-skillset/extract-entities.png" alt-text="Screenshot of the Extract entities options in the Azure portal." lightbox="../../media/search-get-started-skillset/extract-entities.png":::
+
+1. Select **Extract text from images**, and then select the gear icon.
+
+1. Select and save the following checkboxes:
+
+   + **Generate tags**
+
+   + **Categorize content**
+
+   :::image type="content" source="../../media/search-get-started-skillset/extract-text.png" alt-text="Screenshot of the Extract text from images options in the Azure portal." lightbox="../../media/search-get-started-skillset/extract-text.png":::
+
+1. Leave the **Use a free AI service (limited enrichments)** checkbox selected.
+
+   The sample data consists of 14 files, so the free allotment of 20 transactions on Azure AI services is sufficient.
+
+1. Select **Next**.
+
+### Step 3: Configure the index
+
+An index contains your searchable content. The wizard can usually create the schema by sampling the data source. In this step, you review the generated schema and potentially revise any settings.
+
+For this quickstart, the wizard sets reasonable defaults:  
+
++ Default fields are based on metadata properties of existing blobs and new fields for the enrichment output, such as `persons`, `locations`, and `organizations`. Data types are inferred from metadata and by data sampling.
+
+  :::image type="content" source="../../media/search-get-started-skillset/index-fields-new-wizard.png" alt-text="Screenshot of the index definition page." border="true" lightbox="../../media/search-get-started-skillset/index-fields-new-wizard.png":::
+
++ Default document key is `metadata_storage_path`, which is selected because the field contains unique values.
+
++ Default field attributes are based on the skills you selected. For example, fields created by the Entity Recognition skill (`persons`, `locations`, and `organizations`) are **Retrievable**, **Filterable**, **Facetable**, and **Searchable**. To view and change these attributes, select a field, and then select **Configure field**.
+
+  **Retrievable** fields can be returned in results, while **Searchable** fields support full-text search. Use **Filterable** if you want to use fields in a filter expression.
+  
+  Marking a field as **Retrievable** doesn't mean that the field *must* appear in search results. You can control which fields are returned by using the `select` query parameter.
+
+After you review the index schema, select **Next**.
+
+### Step 4: Skip advanced settings
+
+The wizard offers advanced settings for semantic ranking and index scheduling, which are beyond the scope of this quickstart. Skip this step by selecting **Next**.
+
+### Step 5: Review and create objects
+
+The last step is to review your configuration and create the index, indexer, and data source on your search service. The indexer automates the process of extracting content from your data source, loading the index, and driving skillset execution.
+
+To review and create the objects:
+
+1. Accept the default **Objects name prefix**.
+
+1. Review the object configurations.
+
+   :::image type="content" source="../../media/search-get-started-skillset/review-and-create.png" alt-text="Screenshot of the object configuration page in the Azure portal." border="true" lightbox="../../media/search-get-started-skillset/review-and-create.png":::
+
+   AI enrichment, semantic ranker, and indexer scheduling are either disabled or set to their default values because you skipped their wizard steps.
+
+1. Select **Create** to simultaneously create the objects and run the indexer.
+
+## Monitor status
+
+You can monitor the creation of the indexer in the Azure portal. Skills-based indexing takes longer than text-based indexing, especially OCR and image analysis.
+
+To monitor the progress of the indexer:
+
+1. From the left pane, select **Indexers**.
+
+1. Select your indexer from the list.
+
+1. Select **Success** (or **Failed**) to view execution details.
+
+   :::image type="content" source="../../media/search-get-started-skillset/indexer-notification.png" alt-text="Screenshot of the indexer status page." border="true" lightbox="../../media/search-get-started-skillset/indexer-notification.png":::
+
+  In this quickstart, there are a few warnings, including `Could not execute skill because one or more skill input was invalid.` This warning tells you that a PNG file in the data source doesn't provide a text input to Entity Recognition. It occurs because the upstream OCR skill didn't recognize any text in the image and couldn't provide a text input to the downstream Entity Recognition skill.
+
+  Warnings are common in skillset execution. As you become familiar with how skills iterate over your data, you might begin to notice patterns and learn which warnings are safe to ignore.
+
+## Query in Search explorer
+
+To query your index:
+
+1. From the left pane, select **Indexes**.
+
+1. Select your index from the list. If the index has zero documents or storage, wait for the Azure portal to refresh.
+
+1. On the **Search explorer** tab, enter a search string, such as `satya nadella`.
+
+The search bar accepts keywords, quote-enclosed phrases, and operators. For example: `"Satya Nadella" +"Bill Gates" +"Steve Ballmer"`
+
+Results are returned as verbose JSON, which can be hard to read, especially in large documents. Here are tips for searching in this tool:
+    
+   + Switch to the JSON view to specify parameters that shape results.
+   + Add `select` to limit the fields in results.
+   + Add `count` to show the number of matches.
+   + Use Ctrl-F to search within the JSON for specific properties or terms.
+
+:::image type="content" source="../../media/search-get-started-skillset/search-explorer-new-wizard.png" alt-text="Screenshot of the Search explorer page." border="true" lightbox="../../media/search-get-started-skillset/search-explorer-new-wizard.png":::
+
+Here's some JSON you can paste into the view:
+    
+```json
+{
+"search": "\"Satya Nadella\" +\"Bill Gates\" +\"Steve Ballmer\"",
+"count": true,
+"select": "merged_content, persons"
+}
+```
+
+> [!TIP]
+> Query strings are case sensitive, so if you get an "unknown field" message, check **Fields** or **Index Definition (JSON)** to verify the name and case.
\ No newline at end of file

@@ -0,0 +1,196 @@
+---
+manager: nitinme
+author: haileytap
+ms.author: haileytapia
+ms.service: azure-ai-search
+ms.topic: include
+ms.date: 09/16/2025
+---
+
+> [!IMPORTANT]
+> The **Import data** wizard will eventually be deprecated. Most of its functionality is available in the **Import data (new)** wizard, which we recommend for most search scenarios. For more information, see [Import data wizards in the Azure portal](../../search-import-data-portal.md).
+
+In this quickstart, you learn how a skillset in Azure AI Search adds optical character recognition (OCR), image analysis, language detection, text merging, and entity recognition to generate text-searchable content in an index.
+
+You can run the **Import data** wizard in the Azure portal to apply skills that create and transform textual content during indexing. The input is your raw data, usually blobs in Azure Storage. The output is a searchable index containing AI-generated image text, captions, and entities. You can then query generated content in the Azure portal using [**Search explorer**](../../search-explorer.md).
+
+Before you run the wizard, you create a few resources and upload sample files.
+
+## Prerequisites
+
++ An Azure account with an active subscription. [Create an account for free](https://azure.microsoft.com/free/).
+
++ An Azure AI Search service. [Create a service](../../search-create-service-portal.md) or [find an existing service](https://portal.azure.com/#blade/HubsExtension/BrowseResourceBlade/resourceType/Microsoft.Search%2FsearchServices) in your current subscription. You can use a free service for this quickstart.
+
++ An [Azure Storage account](/azure/storage/common/storage-account-create). Use Azure Blob Storage on a standard performance (general-purpose v2) account. To avoid bandwidth charges, use the same region as Azure AI Search.
+
+> [!NOTE]
+> This quickstart uses [Azure AI services](https://azure.microsoft.com/services/cognitive-services/) for AI enrichment. Because the workload is small, Azure AI services is tapped behind the scenes for free processing up to 20 transactions. Therefore, you don't need to create an Azure AI services multi-service resource.
+
+## Prepare sample data
+
+In this section, you create an Azure Storage container to store sample data consisting of various file types, including images and application files that aren't full-text searchable in their native formats.
+
+To prepare the sample data for this quickstart:
+
+1. Sign in to the [Azure portal](https://portal.azure.com/) and select your Azure Storage account.
+
+1. From the left pane, select **Data storage** > **Containers**.
+
+1. Create a container, and then upload the [sample data](https://github.com/Azure-Samples/azure-search-sample-data/tree/main/ai-enrichment-mixed-media) to the container.
+
+## Run the wizard
+
+To run the wizard:
+
+1. Sign in to the [Azure portal](https://portal.azure.com/) and select your search service.
+
+1. On the **Overview** page, select **Import data**.
+
+   :::image type="content" source="../../media/search-import-data-portal/import-data-button.png" alt-text="Screenshot of the Import data command." border="true" lightbox="../../media/search-import-data-portal/import-data-button.png":::
+
+### Step 1: Create a data source
+
+Azure AI Search requires a connection to a data source for content ingestion and indexing. In this case, the data source is your Azure Storage account.
+
+To create the data source:
+
+1. On the **Connect to your data** page, select the **Data Source** dropdown list, and then select **Azure Blob Storage**.
+
+1. Choose an existing connection string for your storage account, and then select the container you created.
+
+1. Enter a name for the data source.
+
+   :::image type="content" source="../../media/search-get-started-skillset/blob-datasource.png" alt-text="Screenshot of the data source definition page." border="true" lightbox="../../media/search-get-started-skillset/blob-datasource.png":::
+
+1. Select **Next: Add cognitive skills (Optional)**.
+
+If you get `Error detecting index schema from data source`, the indexer that powers the wizard can't connect to your data source. The data source most likely has security protections. Try the following solutions, and then rerun the wizard.
+
+| Security feature | Solution |
+|--------------------|----------|
+| Resource requires Azure roles, or its access keys are disabled. | [Connect as a trusted service](../../search-indexer-howto-access-trusted-service-exception.md) or [connect using a managed identity](../../search-how-to-managed-identities.md). |
+| Resource is behind an IP firewall. | [Create an inbound rule for Azure AI Search and the Azure portal](../../search-indexer-howto-access-ip-restricted.md). |
+| Resource requires a private endpoint connection. | [Connect over a private endpoint](../../search-indexer-howto-access-private.md). |
+
+### Step 2: Add cognitive skills
+
+The next step is to configure AI enrichment to invoke OCR, image analysis, and natural-language processing. 
+
+OCR and image analysis are available for blobs in Azure Blob Storage and Azure Data Lake Storage (ADLS) Gen2 and for image content in OneLake. Images can be standalone files or embedded images in a PDF or other files.
+
+To add the skills:
+
+1. Expand the **Attach Cognitive Services** section.
+
+1. Select **Free (Limited enrichments)** to use a free Azure AI services multi-service resource.
+
+   :::image type="content" source="../../media/search-get-started-skillset/cog-search-attach.png" alt-text="Screenshot of the Attach Azure AI services tab." border="true" lightbox="../../media/search-get-started-skillset/cog-search-attach.png":::
+
+   The sample data consists of 14 files, so the free allotment of 20 transactions on Azure AI services is sufficient.
+
+1. Expand the **Add enrichments** section.
+
+1. Select the **Enable OCR and merge all text into merged_content field** checkbox.
+
+1. Under **Text Cognitive Skills**, select the following checkboxes:
+
+    + **Extract people names**
+    
+    + **Extract organization names**
+    
+    + **Extract location names**
+
+1. Under **Image Cognitive Skills**, select the following checkboxes: 
+
+    + **Generate tags from images**
+    
+    + **Generate captions from images**
+
+   :::image type="content" source="../../media/search-get-started-skillset/skillset.png" alt-text="Screenshot of the skillset definition page." border="true" lightbox="../../media/search-get-started-skillset/skillset.png":::
+
+1. Select **Next: Customer target index**.
+
+### Step 3: Configure the index
+
+An index contains your searchable content. The wizard can usually create the schema by sampling the data source. In this step, you review the generated schema and potentially revise any settings. 
+
+For this quickstart, the wizard sets reasonable defaults:  
+
++ Default fields are based on metadata properties of existing blobs and new fields for the enrichment output, such as `people`, `organizations`, and `locations`. Data types are inferred from metadata and by data sampling.
+
++ Default document key is `metadata_storage_path`, which is selected because the field contains unique values.
+
++ Default attributes are **Retrievable** and **Searchable**. **Retrievable** fields can be returned in results, while **Searchable** fields support full-text search. The wizard assumes you want these fields to be retrievable and searchable because you created them via a skillset. Select **Filterable** if you want to use fields in a filter expression.
+
+  :::image type="content" source="../../media/search-get-started-skillset/index-fields-old-wizard.png" alt-text="Screenshot of the index definition page." border="true" lightbox="../../media/search-get-started-skillset/index-fields-old-wizard.png":::
+
+  Marking a field as **Retrievable** doesn't mean that the field *must* appear in search results. You can control which fields are returned by using the `select` query parameter.
+      
+After you review the index schema, select **Next: Create an indexer**.
+
+### Step 4: Configure the indexer
+
+The indexer drives the indexing process and specifies the data source name, a target index, and frequency of execution. In this step, the wizard creates several objects, including an indexer that you can reset and run repeatedly.
+
+To configure the indexer:
+
+1. On the **Create an indexer** page, accept the default name.
+
+1. Select **Once** for the schedule.
+
+   :::image type="content" source="../../media/search-get-started-skillset/indexer-def.png" alt-text="Screenshot of the indexer definition page." border="true" lightbox="../../media/search-get-started-skillset/indexer-def.png":::
+
+1. Select **Submit** to simultaneously create and run the indexer.
+
+## Monitor status
+
+You can monitor the creation of the indexer in the Azure portal. Skills-based indexing takes longer than text-based indexing, especially OCR and image analysis.
+
+To monitor the progress of the indexer:
+
+1. From the left pane, select **Indexers**.
+
+1. Select your indexer from the list.
+
+1. Select **Success** (or **Failed**) to view execution details.
+
+   :::image type="content" source="../../media/search-get-started-skillset/indexer-notification.png" alt-text="Screenshot of the indexer status page." border="true" lightbox="../../media/search-get-started-skillset/indexer-notification.png":::
+
+  In this quickstart, there are a few warnings, including `Could not execute skill because one or more skill input was invalid.` This warning tells you that a PNG file in the data source doesn't provide a text input to Entity Recognition. It occurs because the upstream OCR skill didn't recognize any text in the image and couldn't provide a text input to the downstream Entity Recognition skill.
+
+  Warnings are common in skillset execution. As you become familiar with how skills iterate over your data, you might begin to notice patterns and learn which warnings are safe to ignore.
+
+## Query in Search explorer
+
+To query your index:
+
+1. From the left pane, select **Indexes**.
+
+1. Select your index from the list. If the index has zero documents or storage, wait for the Azure portal to refresh.
+
+1. On the **Search explorer** tab, enter a search string, such as `satya nadella`.
+
+The search bar accepts keywords, quote-enclosed phrases, and operators. For example: `"Satya Nadella" +"Bill Gates" +"Steve Ballmer"`
+
+Results are returned as verbose JSON, which can be hard to read, especially in large documents. Here are tips for searching in this tool:
+    
+   + Switch to the JSON view to specify parameters that shape results.
+   + Add `select` to limit the fields in results.
+   + Add `count` to show the number of matches.
+   + Use Ctrl-F to search within the JSON for specific properties or terms.
+
+:::image type="content" source="../../media/search-get-started-skillset/search-explorer-old-wizard.png" alt-text="Screenshot of the Search explorer page." border="true" lightbox="../../media/search-get-started-skillset/search-explorer-old-wizard.png":::
+
+Here's some JSON you can paste into the view:
+    
+```json
+{
+"search": "\"Satya Nadella\" +\"Bill Gates\" +\"Steve Ballmer\"",
+"count": true,
+"select": "content, people"
+}
+```
+
+> [!TIP]
+> Query strings are case sensitive, so if you get an "unknown field" message, check **Fields** or **Index Definition (JSON)** to verify the name and case.
\ No newline at end of file

@@ -12,7 +12,7 @@ In this quickstart, you work with a .NET app to create, populate, and query vect
 In Azure AI Search, a [vector store](../../vector-store.md) has an index schema that defines vector and nonvector fields, a vector search configuration for algorithms that create the embedding space, and settings on vector field definitions that are evaluated at query time. The [Create Index](/rest/api/searchservice/indexes/create-or-update) REST API creates the vector store.
 
 > [!NOTE]
-> This quickstart omits the vectorization step and provides inline embeddings. If you want to add [built-in data chunking and vectorization](../../vector-search-integrated-vectorization.md) over your own content, try the [**Import and vectorize data wizard**](../../search-get-started-portal-import-vectors.md) for an end-to-end walkthrough.
+> This quickstart omits the vectorization step and provides inline embeddings. If you want to add [built-in data chunking and vectorization](../../vector-search-integrated-vectorization.md) over your own content, try the [**Import data (new)** wizard](../../search-get-started-portal-import-vectors.md) for an end-to-end walkthrough.
 
 ## Prerequisites
 

@@ -13,7 +13,7 @@ In this quickstart, you use Java to create, load, and query vectors. The code ex
 In Azure AI Search, a [vector store](../../vector-store.md) has an index schema that defines vector and nonvector fields, a vector search configuration for algorithms that create the embedding space, and settings on vector field definitions that are evaluated at query time. The [Create Index](/rest/api/searchservice/indexes/create-or-update) REST API creates the vector store.
 
 > [!NOTE]
-> This quickstart omits the vectorization step and provides inline embeddings. If you want to add [built-in data chunking and vectorization](../../vector-search-integrated-vectorization.md) over your own content, try the [**Import and vectorize data wizard**](../../search-get-started-portal-import-vectors.md) for an end-to-end walkthrough.
+> This quickstart omits the vectorization step and provides inline embeddings. If you want to add [built-in data chunking and vectorization](../../vector-search-integrated-vectorization.md) over your own content, try the [**Import data (new)** wizard](../../search-get-started-portal-import-vectors.md) for an end-to-end walkthrough.
 
 ## Prerequisites
 

@@ -12,7 +12,7 @@ In this quickstart, you use JavaScript to create, load, and query vectors. The c
 In Azure AI Search, a [vector store](../../vector-store.md) has an index schema that defines vector and nonvector fields, a vector search configuration for algorithms that create the embedding space, and settings on vector field definitions that are evaluated at query time. The [Create Index](/rest/api/searchservice/indexes/create-or-update) REST API creates the vector store.
 
 > [!NOTE]
-> This quickstart omits the vectorization step and provides inline embeddings. If you want to add [built-in data chunking and vectorization](../../vector-search-integrated-vectorization.md) over your own content, try the [**Import and vectorize data wizard**](../../search-get-started-portal-import-vectors.md) for an end-to-end walkthrough.
+> This quickstart omits the vectorization step and provides inline embeddings. If you want to add [built-in data chunking and vectorization](../../vector-search-integrated-vectorization.md) over your own content, try the [**Import data (new)** wizard](../../search-get-started-portal-import-vectors.md) for an end-to-end walkthrough.
 
 ## Prerequisites
 

@@ -12,7 +12,7 @@ In this quickstart, you use a Jupyter notebook to create, load, and query vector
 In Azure AI Search, a [vector store](../../vector-store.md) has an index schema that defines vector and nonvector fields, a vector search configuration for algorithms that create the embedding space, and settings on vector field definitions that are evaluated at query time. The [Create Index](/rest/api/searchservice/indexes/create-or-update) REST API creates the vector store.
 
 > [!NOTE]
-> This quickstart omits the vectorization step and provides inline embeddings. If you want to add [built-in data chunking and vectorization](../../vector-search-integrated-vectorization.md) over your own content, try the [**Import and vectorize data wizard**](../../search-get-started-portal-import-vectors.md) for an end-to-end walkthrough.
+> This quickstart omits the vectorization step and provides inline embeddings. If you want to add [built-in data chunking and vectorization](../../vector-search-integrated-vectorization.md) over your own content, try the [**Import data (new)** wizard](../../search-get-started-portal-import-vectors.md) for an end-to-end walkthrough.
 
 ## Prerequisites
 

@@ -13,7 +13,7 @@ In this quickstart, you use the [Azure AI Search REST APIs](/rest/api/searchserv
 In Azure AI Search, a [vector store](../../vector-store.md) has an index schema that defines vector and nonvector fields, a vector search configuration for algorithms that create the embedding space, and settings on vector field definitions that are evaluated at query time. The [Create Index](/rest/api/searchservice/indexes/create-or-update) REST API creates the vector store.
 
 > [!NOTE]
-> This quickstart omits the vectorization step and provides inline embeddings. If you want to add [built-in data chunking and vectorization](../../vector-search-integrated-vectorization.md) over your own content, try the [**Import and vectorize data wizard**](../../search-get-started-portal-import-vectors.md) for an end-to-end walkthrough.
+> This quickstart omits the vectorization step and provides inline embeddings. If you want to add [built-in data chunking and vectorization](../../vector-search-integrated-vectorization.md) over your own content, try the [**Import data (new)** wizard](../../search-get-started-portal-import-vectors.md) for an end-to-end walkthrough.
 
 ## Prerequisites
 

@@ -12,7 +12,7 @@ In this quickstart, you use TypeScript to create, load, and query vectors. The c
 In Azure AI Search, a [vector store](../../vector-store.md) has an index schema that defines vector and nonvector fields, a vector search configuration for algorithms that create the embedding space, and settings on vector field definitions that are evaluated at query time. The [Create Index](/rest/api/searchservice/indexes/create-or-update) REST API creates the vector store.
 
 > [!NOTE]
-> This quickstart omits the vectorization step and provides inline embeddings. If you want to add [built-in data chunking and vectorization](../../vector-search-integrated-vectorization.md) over your own content, try the [**Import and vectorize data wizard**](../../search-get-started-portal-import-vectors.md) for an end-to-end walkthrough.
+> This quickstart omits the vectorization step and provides inline embeddings. If you want to add [built-in data chunking and vectorization](../../vector-search-integrated-vectorization.md) over your own content, try the [**Import data (new)** wizard](../../search-get-started-portal-import-vectors.md) for an end-to-end walkthrough.
 
 ## Prerequisites
 

@@ -47,7 +47,7 @@ First, you set up sample data in Azure Storage. Next, you run the **Import data*
 
 1. [Find your search service](https://portal.azure.com/#blade/HubsExtension/BrowseResourceBlade/resourceType/Microsoft.Search%2FsearchServices) and on the Overview page, select **Import data** on the command bar to create a knowledge store in four steps.
 
-   :::image type="content" source="media/search-import-data-portal/import-data-cmd.png" alt-text="Screenshot of the Import data command" border="true":::
+   :::image type="content" source="media/search-import-data-portal/import-data-button.png" alt-text="Screenshot of the Import data command" border="true":::
 
 ### Step 1: Create a data source
 

@@ -37,7 +37,7 @@ Multimodal search is ideal for [retrieval-augmented generation (RAG)](retrieval-
 
 ## How multimodal search works in Azure AI Search
 
-To simplify the creation of a multimodal pipeline, Azure AI Search offers the **Import and vectorize data** wizard in the Azure portal. The wizard helps you configure a data source, define extraction and enrichment settings, and generate a multimodal index that contains text, embedded image references, and vector embeddings. For more information, see [Quickstart: Multimodal search in the Azure portal](search-get-started-portal-image-search.md).
+To simplify the creation of a multimodal pipeline, Azure AI Search offers the **Import data (new)** wizard in the Azure portal. The wizard helps you configure a data source, define extraction and enrichment settings, and generate a multimodal index that contains text, embedded image references, and vector embeddings. For more information, see [Quickstart: Multimodal search in the Azure portal](search-get-started-portal-image-search.md).
 
 The wizard follows these steps to create a multimodal pipeline:
 

@@ -32,7 +32,7 @@ Preview features are removed from this list if they're retired or transition to
 | [**Agentic retrieval**](search-agentic-retrieval-concept.md) | Query | Create a conversational search experience powered by large language models (LLMs) and your proprietary content. Agentic retrieval breaks down complex user queries into subqueries, runs the subqueries simultaneously, and either extracts grounding data or synthesizes an answer based on documents indexed in Azure AI Search. To get started, see [Quickstart: Agentic retrieval](search-get-started-agentic-retrieval.md).<p>The pipeline involves one or more [knowledge sources](search-knowledge-source-overview.md) and an associated [knowledge agent](search-agentic-retrieval-how-to-create.md), whose [response payload](search-agentic-retrieval-how-to-retrieve.md) provides full transparency into the query plan and reference data. Knowledge sources currently support [search indexes](search-knowledge-source-how-to-index.md) and [Azure blobs](search-knowledge-source-how-to-blob.md). | [Knowledge Sources (preview)](/rest/api/searchservice/knowledge-sources?view=rest-searchservice-2025-08-01-preview&preserve-view=true), [Knowledge Agents (preview)](/rest/api/searchservice/knowledge-agents?view=rest-searchservice-2025-08-01-preview&preserve-view=true), and [Knowledge Retrieval (preview)](/rest/api/searchservice/knowledge-retrieval?view=rest-searchservice-2025-08-01-preview&preserve-view=true). |
 | [**Multivector support**](vector-search-multi-vector-fields.md) | Indexing | Index multiple child vectors within a single document field. You can now use vector types in nested fields of complex collections, effectively allowing multiple vectors to be associated with a single document.| [Create or Update Index (preview)](/rest/api/searchservice/indexes/create-or-update?view=rest-searchservice-2025-08-01-preview&preserve-view=true). |
 | [**Scoring profiles with semantic ranking**](semantic-how-to-enable-scoring-profiles.md) | Relevance | Semantic ranker adds a new field, `@search.rerankerBoostedScore`, to help you maintain consistent relevance and greater control over final ranking outcomes in your search pipeline. | [Create or Update Index (preview)](/rest/api/searchservice/indexes/create-or-update?view=rest-searchservice-2025-08-01-preview&preserve-view=true). |
-| [**Logic Apps integration in the portal wizard**](search-how-to-index-logic-apps-indexers.md) | Indexing | Create an automated indexing pipeline that retrieves content using a logic app workflow. Use the [Import and vectorize data wizard](search-get-started-portal-import-vectors.md) in the Azure portal to build an indexing pipeline based on Logic Apps. | Image and vectorize data wizard in the Azure portal. |
+| [**Logic Apps integration in the portal wizard**](search-how-to-index-logic-apps-indexers.md) | Indexing | Create an automated indexing pipeline that retrieves content using a logic app workflow. Use the [**Import data (new)** wizard](search-get-started-portal-import-vectors.md) in the Azure portal to build an indexing pipeline based on Logic Apps. | Image and vectorize data wizard in the Azure portal. |
 | [**Document-level access control**](search-document-level-access-overview.md) | Security | Flow document-level permissions from blobs in Azure Data Lake Storage (ADLS) Gen2 to searchable documents in an index. Queries can now filter results based on user identity for selected data sources. | [Create or Update Index (preview)](/rest/api/searchservice/indexes/create-or-update?view=rest-searchservice-2025-08-01-preview&preserve-view=true). |
 | [**GenAI Prompt skill**](cognitive-search-skill-genai-prompt.md) | Skills | A new skill that connects to a large language model (LLM) for information, using a prompt you provide. With this skill, you can populate a searchable field using content from an LLM. A primary use case for this skill is *image verbalization*, using an LLM to describe images and send the description to a searchable field in your index. | [Create or Update Skillset (preview)](/rest/api/searchservice/skillsets/create-or-update?view=rest-searchservice-2025-08-01-preview&preserve-view=true). |
 | [**Document Layout skill**](cognitive-search-skill-document-intelligence-layout.md)| Skills | New parameters are available for this skill if you use the 2025-05-01-preview API version or later. The new parameters support image offset metadata that improves the image search experience. | [Create or Update Skillset (preview)](/rest/api/searchservice/skillsets/create-or-update?view=rest-searchservice-2025-08-01-preview&preserve-view=true). |

@@ -45,7 +45,7 @@ You can start directly in your Storage Account portal page.
 
 1. In the left navigation page under **Data management**, select **Azure AI Search** to select or create a search service. 
 
-1. Follow the steps in the wizard to extract and optionally create searchable content from your blobs. The workflow is the [**Import data** wizard](search-get-started-skillset.md). The workflow creates an indexer, data source, index, and option skillset on your Azure AI Search service.
+1. Use an [import wizard](search-get-started-skillset.md) to extract and optionally create searchable content from your blobs. The workflow creates an indexer, data source, index, and option skillset on your Azure AI Search service.
 
    :::image type="content" source="media/search-blob-storage-integration/blob-blade.png" alt-text="Screenshot of the Azure AI Search wizard in the Azure Storage portal page." border="true":::
 
@@ -73,7 +73,7 @@ Textual content of a document is extracted into a string field named "content".
 
 An *indexer* is a data-source-aware subservice in Azure AI Search, equipped with internal logic for sampling data, reading and retrieving data and metadata, and serializing data from native formats into JSON documents for subsequent import. 
 
-Blobs in Azure Storage are indexed using the [blob indexer](search-howto-indexing-azure-blob-storage.md). You can invoke this indexer by using the **Azure AI Search** command in Azure Storage, the [**Import data** wizards](search-import-data-portal.md), a REST API, or the .NET SDK. In code, you use this indexer by setting the type, and by providing connection information that includes an Azure Storage account along with a blob container. You can subset your blobs by creating a virtual directory, which you can then pass as a parameter, or by filtering on a file type extension.
+Blobs in Azure Storage are indexed using the [blob indexer](search-howto-indexing-azure-blob-storage.md). You can invoke this indexer by using the **Azure AI Search** command in Azure Storage, an [import wizard](search-import-data-portal.md) in the Azure portal, a REST API, or the .NET SDK. In code, you use this indexer by setting the type, and by providing connection information that includes an Azure Storage account along with a blob container. You can subset your blobs by creating a virtual directory, which you can then pass as a parameter, or by filtering on a file type extension.
 
 An indexer ["cracks a document"](search-indexer-overview.md#document-cracking), opening a blob to inspect content. After connecting to the data source, it's the first step in the pipeline. For blob data, this is where PDF, Office docs, and other content types are detected. Document cracking with text extraction is no charge. If your blobs contain image content, images are ignored unless you [add AI enrichment](cognitive-search-concept-intro.md). Standard indexing applies only to text content.
 

@@ -80,7 +80,7 @@ The following table summarizes features by category. There's feature parity in a
 
 | Category                             | Features |
 |-------------------|----------|
-| Tools for prototyping and inspection | [**Add index**](search-what-is-an-index.md) is an index designer in the Azure portal that you can use to create a basic schema consisting of attributed fields and a few other settings. After saving the index, you can populate it using an SDK or the REST API to provide the data. </br></br>[**Import data wizard**](search-import-data-portal.md) creates indexes, indexers, skillsets, and data source definitions. If your data exists in Azure, this wizard can save you significant time and effort, especially for proof-of-concept investigation and exploration. </br></br>[**Import and vectorize data wizard**](search-get-started-portal-import-vectors.md) creates a full indexing pipeline that includes data chunking and vectorization. The wizard creates all of the objects and configuration settings. </br></br>[**Search explorer**](search-explorer.md) is used to test queries and refine scoring profiles.</br></br>[**Create demo app**](search-create-app-portal.md) is used to generate an HTML page that can be used to test the search experience.  </br></br>[**Debug Sessions**](cognitive-search-debug-session.md) is a visual editor that lets you debug a skillset interactively. It shows you dependencies, output, and transformations. |
+| Tools for prototyping and inspection | [**Add index**](search-what-is-an-index.md) is an index designer in the Azure portal that you can use to create a basic schema consisting of attributed fields and a few other settings. After saving the index, you can populate it using an SDK or the REST API to provide the data. </br></br>[**Import data** wizard](search-import-data-portal.md) creates indexes, indexers, skillsets, and data source definitions. If your data exists in Azure, this wizard can save you significant time and effort, especially for proof-of-concept investigation and exploration. </br></br>[**Import data (new)** wizard](search-get-started-portal-import-vectors.md) creates a full indexing pipeline that includes data chunking and vectorization. The wizard creates all of the objects and configuration settings. </br></br>[**Search explorer**](search-explorer.md) is used to test queries and refine scoring profiles.</br></br>[**Create demo app**](search-create-app-portal.md) is used to generate an HTML page that can be used to test the search experience.  </br></br>[**Debug Sessions**](cognitive-search-debug-session.md) is a visual editor that lets you debug a skillset interactively. It shows you dependencies, output, and transformations. |
 | Monitoring and diagnostics | [**Enable monitoring features**](monitor-azure-cognitive-search.md) to go beyond the metrics-at-a-glance that are always visible in the Azure portal. Metrics on queries per second, latency, and throttling are captured and reported in portal pages with no extra configuration required.|
 
 ## Programmability

@@ -26,8 +26,8 @@ To configure and run the indexer, you can use:
 
 + [Search Service preview REST APIs](/rest/api/searchservice), any preview version.
 + An Azure SDK package, any version.
-+ [Import data wizard](search-get-started-portal.md) in the Azure portal.
-+ [Import and vectorize data wizard](search-get-started-portal-import-vectors.md) in the Azure portal.
++ [**Import data** wizard](search-get-started-portal.md) in the Azure portal.
++ [**Import data (new)** wizard](search-get-started-portal-import-vectors.md) in the Azure portal.
 
 ## Prerequisites
 

@@ -13,7 +13,7 @@ ms.custom:
 
 # Quickstart: Search for multimodal content in the Azure portal
 
-In this quickstart, you use the **Import and vectorize data** wizard in the Azure portal to get started with [multimodal search](multimodal-search-overview.md). The wizard simplifies the process of extracting, chunking, vectorizing, and loading both text and images into a searchable index.
+In this quickstart, you use the **Import data (new)** wizard in the Azure portal to get started with [multimodal search](multimodal-search-overview.md). The wizard simplifies the process of extracting, chunking, vectorizing, and loading both text and images into a searchable index.
 
 Unlike [Quickstart: Vector search in the Azure portal](search-get-started-portal-import-vectors.md), which processes simple text-containing images, this quickstart supports advanced image processing for multimodal RAG scenarios.
 
@@ -179,9 +179,9 @@ To start the wizard for multimodal search:
 
 1. Sign in to the [Azure portal](https://portal.azure.com/) and select your Azure AI Search service.
 
-1. On the **Overview** page, select **Import and vectorize data**.
+1. On the **Overview** page, select **Import data (new)**.
 
-   :::image type="content" source="media/search-get-started-portal-import-vectors/command-bar.png" alt-text="Screenshot of the command to open the wizard for importing and vectorizing data.":::
+   :::image type="content" source="media/search-import-data-portal/import-data-new-button.png" alt-text="Screenshot of the command to open the wizard for importing and vectorizing data.":::
 
 1. Select your data source: **Azure Blob Storage** or **Azure Data Lake Storage Gen2**.
 
@@ -460,7 +460,7 @@ This quickstart uses billable Azure resources. If you no longer need the resourc
 
 ## Next steps
 
-This quickstart introduced you to the **Import and vectorize data** wizard, which creates all of the necessary objects for multimodal search. To explore each step in detail, see the following tutorials:
+This quickstart introduced you to the **Import data (new)** wizard, which creates all of the necessary objects for multimodal search. To explore each step in detail, see the following tutorials:
 
 + [Tutorial: Verbalize images using generative AI](tutorial-document-extraction-image-verbalization.md)
 + [Tutorial: Verbalize images from a structured document layout](tutorial-document-layout-image-verbalization.md)

@@ -14,7 +14,7 @@ ms.date: 09/12/2025
 
 # Quickstart: Vectorize text in the Azure portal
 
-In this quickstart, you use the **Import and vectorize data** wizard in the Azure portal to get started with [integrated vectorization](vector-search-integrated-vectorization.md). The wizard chunks your content and calls an embedding model to vectorize the chunks at indexing and query time.
+In this quickstart, you use the **Import data (new)** wizard in the Azure portal to get started with [integrated vectorization](vector-search-integrated-vectorization.md). The wizard chunks your content and calls an embedding model to vectorize the chunks at indexing and query time.
 
 This quickstart uses text-based PDFs from the [azure-search-sample-data](https://github.com/Azure-Samples/azure-search-sample-data/tree/main/sustainable-ai-pdf) repo. However, you can use images and still complete this quickstart.
 
@@ -32,11 +32,11 @@ This quickstart uses text-based PDFs from the [azure-search-sample-data](https:/
 
 ### Supported data sources
 
-The **Import and vectorize data wizard** [supports a wide range of Azure data sources](search-import-data-portal.md#supported-data-sources-and-scenarios). However, this quickstart only covers the data sources that work with whole files, which are described in the following table.
+The wizard [supports a wide range of Azure data sources](search-import-data-portal.md#supported-data-sources-and-scenarios). However, this quickstart only covers the data sources that work with whole files, which are described in the following table.
 
 | Supported data source | Description |
 |--|--|
-| [Azure Blob Storage](search-howto-indexing-azure-blob-storage.md) | This data source works with blobs and tables. You must use a standard performance (general-purpose v2) account. Access tiers can be hot, cool, or cold. |
+| [Azure Blob Storage](/azure/storage/common/storage-account-create) | This data source works with blobs and tables. You must use a standard performance (general-purpose v2) account. Access tiers can be hot, cool, or cold. |
 | [Azure Data Lake Storage (ADLS) Gen2](/azure/storage/blobs/create-data-lake-storage-account) | This is an Azure Storage account with a hierarchical namespace enabled. To confirm that you have Data Lake Storage, check the **Properties** tab on the **Overview** page.<br><br> :::image type="content" source="media/search-get-started-portal-import-vectors/data-lake-storage.png" alt-text="Screenshot of an Azure Data Lake Storage account in the Azure portal." border="true" lightbox="media/search-get-started-portal-import-vectors/data-lake-storage.png"::: |
 | [OneLake](search-how-to-index-onelake-files.md) | This data source is currently in preview. For information about limitations and supported shortcuts, see [OneLake indexing](search-how-to-index-onelake-files.md). |
 
@@ -60,7 +60,7 @@ For integrated vectorization, you must use one of the following embedding models
 
 ### Public endpoint requirements
 
-For the purposes of this quickstart, all of the preceding resources must have public access enabled so that the Azure portal nodes can access them. Otherwise, the wizard fails. After the wizard runs, you can enable firewalls and private endpoints on the integration components for security. For more information, see [Secure connections in the import wizards](search-import-data-portal.md#secure-connections).
+For this quickstart, all of the preceding resources must have public access enabled so that the Azure portal nodes can access them. Otherwise, the wizard fails. After the wizard runs, you can enable firewalls and private endpoints on the integration components for security. For more information, see [Secure connections in the import wizards](search-import-data-portal.md#secure-connections).
 
 If private endpoints are already present and you can't disable them, the alternative option is to run the respective end-to-end flow from a script or program on a virtual machine. The virtual machine must be on the same virtual network as the private endpoint. Here's a [Python code sample](https://github.com/Azure/azure-search-vector-samples/tree/main/demo-python/code/integrated-vectorization) for integrated vectorization. The same [GitHub repo](https://github.com/Azure/azure-search-vector-samples/tree/main) has samples in other programming languages.
 
@@ -252,9 +252,9 @@ To start the wizard for vector search:
 
 1. Sign in to the [Azure portal](https://portal.azure.com/) and select your Azure AI Search service.
 
-1. On the **Overview** page, select **Import and vectorize data**.
+1. On the **Overview** page, select **Import data (new)**.
 
-   :::image type="content" source="media/search-get-started-portal-import-vectors/command-bar.png" alt-text="Screenshot of the command to open the wizard for importing and vectorizing data.":::
+   :::image type="content" source="media/search-import-data-portal/import-data-new-button.png" alt-text="Screenshot of the command to open the wizard for importing and vectorizing data.":::
 
 1. Select your data source: **Azure Blob Storage**, **ADLS Gen2**, or **OneLake**.
 
@@ -576,4 +576,4 @@ This quickstart uses billable Azure resources. If you no longer need the resourc
 
 ## Next step
 
-This quickstart introduced you to the **Import and vectorize data wizard**, which creates all of the necessary objects for integrated vectorization. To explore each step in detail, see [Set up integrated vectorization in Azure AI Search](search-how-to-integrated-vectorization.md).
+This quickstart introduced you to the **Import data (new)** wizard, which creates all of the necessary objects for integrated vectorization. To explore each step in detail, see [Set up integrated vectorization in Azure AI Search](search-how-to-integrated-vectorization.md).

@@ -1,167 +1,57 @@
 ---
-title: "Quickstart: Keyword Search in the Azure Portal"
+title: "Quickstart: Keyword Search in the Azure portal"
 titleSuffix: Azure AI Search
-description: Learn how to create, load, and query your first search index using the Import Data wizard in the Azure portal. This quickstart uses a fictitious hotel dataset for sample data.
+description: Learn how to create, load, and query your first search index using an import wizard in the Azure portal. This quickstart uses a fictitious hotel dataset for sample data.
 manager: nitinme
 author: haileytap
 ms.author: haileytapia
 ms.service: azure-ai-search
 ms.topic: quickstart
-ms.date: 03/04/2025
+ms.date: 09/16/2025
 ms.custom:
   - mode-ui
   - ignite-2023
   - ignite-2024
+zone_pivot_groups: azure-portal-wizards
 ---
 
 # Quickstart: Create a search index in the Azure portal
 
-In this quickstart, you create your first Azure AI Search index using the [**Import data** wizard](search-import-data-portal.md) and a built-in sample of fictitious hotel data hosted by Microsoft. The wizard requires no code to create an index, helping you write interesting queries within minutes.
+::: zone pivot="import-data-new"
+[!INCLUDE [Import data (new) instructions](includes/quickstarts/search-get-started-portal-new-wizard.md)]
+::: zone-end
 
-The wizard creates multiple objects on your search service, including a [searchable index](search-what-is-an-index.md), an [indexer](search-indexer-overview.md), and a data source connection for automated data retrieval. At the end of this quickstart, we review each object.
-
-> [!NOTE]
-> The **Import data** wizard includes options for OCR, text translation, and other AI enrichments that aren't covered in this quickstart. For a similar walkthrough that focuses on applied AI, see [Quickstart: Create a skillset in the Azure portal](search-get-started-skillset.md).
-
-## Prerequisites
-
-+ An Azure account with an active subscription. [Create an account for free](https://azure.microsoft.com/free/).
-
-+ An Azure AI Search service. [Create a service](search-create-service-portal.md) or [find an existing service](https://portal.azure.com/#blade/HubsExtension/BrowseResourceBlade/resourceType/Microsoft.Search%2FsearchServices) in your current subscription. You can use a free service for this quickstart.
-
-+ Familiarity with the wizard. See [Import data wizards in the Azure portal](search-import-data-portal.md).
-
-### Check for network access
-
-For this quickstart, which uses built-in sample data, make sure your search service doesn't have [network access controls](service-configure-firewall.md). The Azure portal controller uses a public endpoint to retrieve data and metadata from the Microsoft-hosted data source. For more information, see [Secure connections in the import wizards](search-import-data-portal.md#secure-connections).
-
-### Check for space
-
-Many customers start with a free search service, which is limited to three indexes, three indexers, and three data sources. This quickstart creates one of each, so before you begin, make sure you have room for extra objects.
-
-On the **Overview** tab, select **Usage** to see how many indexes, indexers, and data sources you currently have.
-
-   :::image type="content" source="media/search-get-started-portal/overview-quota-usage.png" alt-text="Screenshot of the Overview page for an Azure AI Search service instance in the Azure portal, showing the number of indexes, indexers, and data sources." lightbox="media/search-get-started-portal/overview-quota-usage.png":::
-
-## Start the wizard
-
-1. Sign in to the [Azure portal](https://portal.azure.com/).
-
-1. Go to your search service.
-
-1. On the **Overview** tab, select **Import data** to start the wizard.
-
-   :::image type="content" source="media/search-import-data-portal/import-data-cmd.png" alt-text="Screenshot that shows how to open the Import data wizard in the Azure portal.":::
-
-## Create and load a search index
-
-In this section, you create and load an index in four steps:
-
-1. [Connect to a data source](#connect-to-a-data-source)
-1. [Skip configuration for cognitive skills](#skip-configuration-for-cognitive-skills)
-1. [Configure the index](#configure-the-index)
-1. [Configure and run the indexer](#configure-and-run-the-indexer)
-
-### Connect to a data source
-
-The wizard creates a data source connection to sample data that Microsoft hosts on Azure Cosmos DB. The sample data is accessed through a public endpoint, so you don't need an Azure Cosmos DB account or source files for this step.
-
-To connect to the sample data:
-
-1. On **Connect to your data**, expand the **Data Source** dropdown list and select **Samples**.
-
-1. Select **hotels-sample** from the list of built-in samples.
-
-1. Select **Next: Add cognitive skills (Optional)** to continue.
-
-   :::image type="content" source="media/search-get-started-portal/import-hotels-sample.png" alt-text="Screenshot that shows how to select the hotels-sample data source in the Import data wizard.":::
-
-### Skip configuration for cognitive skills
-
-Although the wizard supports skillset creation and [AI enrichment](cognitive-search-concept-intro.md) during indexing, cognitive skills are beyond the scope of this quickstart.
-
-To skip this step in the wizard:
-
-1. On **Add cognitive skills**, ignore the AI enrichment configuration options.
-
-1. Select **Next: Customize target index** to continue.
-
-   :::image type="content" source="media/search-get-started-portal/skip-cognitive-skills.png" alt-text="Screenshot that shows how to Skip to the Customize target index tab in the Import data wizard.":::
-
-> [!TIP]
-> To get started with AI enrichment, see [Quickstart: Create a skillset in the Azure portal](search-get-started-skillset.md).
-
-### Configure the index
-
-The wizard infers a schema for the hotels-sample index. To configure the index:
-
-1. Accept the system-generated values for the **Index name** (_hotels-sample-index_) and **Key** (_HotelId_).
-
-1. Accept the system-generated values for all field attributes.
-
-1. Select **Next: Create an indexer** to continue.
-
-   :::image type="content" source="media/search-get-started-portal/hotels-sample-generated-index.png" alt-text="Screenshot that shows the generated index definition for the hotels-sample data source in the Import data wizard.":::
-
-At a minimum, the search index requires a name and a collection of fields. The wizard scans for unique string fields and marks one as the document key, which uniquely identifies each document in the index.
-
-Each field has a name, a data type, and attributes that control how the field is used in the index. Use the checkboxes to enable or disable the following attributes:
-
-| Attribute | Description | Applicable data types |
-|-----------|-------------|------------------------|
-| Retrievable | Fields returned in a query response. | Strings and integers |
-| Filterable | Fields that accept a filter expression. | Integers |
-| Sortable | Fields that accept an orderby expression. | Integers |
-| Facetable | Fields used in a faceted navigation structure. | Integers |
-| Searchable | Fields used in full text search. Strings are searchable, but numeric and Boolean fields are often marked as not searchable. | Strings |
-
-Attributes affect storage in different ways. For example, filterable fields consume extra storage, while retrievable fields don't. For more information, see [Example demonstrating the storage implications of attributes and suggesters](search-what-is-an-index.md#example-demonstrating-the-storage-implications-of-attributes-and-suggesters).
-
-If you want autocomplete or suggested queries, specify language **Analyzers** or **Suggesters**.
-
-### Configure and run the indexer
-
-Finally, you configure and run the indexer, which defines an executable process. The data source and index are also created in this step.
-
-To configure and run the indexer:
-
-1. Accept the system-generated value for the **Indexer name** (_hotels-sample-indexer_).
-
-1. For this quickstart, use the default option to run the indexer immediately and only once. The sample data is static, so you can't enable change tracking.
-
-1. Select **Submit** to simultaneously create and run the indexer.
-
-   :::image type="content" source="media/search-get-started-portal/hotels-sample-indexer.png" alt-text="Screenshot that shows how to configure the indexer for the hotels-sample data source in the Import data wizard.":::
+::: zone pivot="import-data"
+[!INCLUDE [Import data instructions](includes/quickstarts/search-get-started-portal-old-wizard.md)]
+::: zone-end
 
 ## Monitor indexer progress
 
-You can monitor the creation of the indexer and index in the Azure portal. The **Overview** tab provides links to the resources created in your search service.
+You can monitor the creation of the indexer and index in the Azure portal. The **Overview** page provides links to the objects created on your search service.
 
 To monitor the progress of the indexer:
 
-1. Go to your search service in the [Azure portal](https://portal.azure.com/).
-
 1. From the left pane, select **Indexers**.
 
-   :::image type="content" source="media/search-get-started-portal/indexers-status.png" alt-text="Screenshot that shows the creation of the indexer in progress in the Azure portal.":::
+1. Find **hotels-sample-indexer** in the list.
+
+   :::image type="content" source="media/search-get-started-portal/indexers-status.png" alt-text="Screenshot that shows the creation of the indexer in progress in the Azure portal." lightbox="media/search-get-started-portal/indexers-status.png":::
 
    It can take a few minutes for the results to update. You should see the newly created indexer with a status of **In progress** or **Success**. The list also shows the number of documents indexed.
 
 ## Check search index results
 
-1. Go to your search service in the [Azure portal](https://portal.azure.com/).
-
 1. From the left pane, select **Indexes**.
 
 1. Select **hotels-sample-index**. If the index has zero documents or storage, wait for the Azure portal to refresh.
 
-   :::image type="content" source="media/search-get-started-portal/indexes-list.png" alt-text="Screenshot of the Indexes list on the Azure AI Search service dashboard in the Azure portal.":::
+   :::image type="content" source="media/search-get-started-portal/indexes-list.png" alt-text="Screenshot of the Indexes list on the Azure AI Search service dashboard in the Azure portal." lightbox="media/search-get-started-portal/indexes-list.png":::
 
 1. Select the **Fields** tab to view the index schema.
 
 1. Check which fields are **Filterable** or **Sortable** so that you know what queries to write.
 
-   :::image type="content" source="media/search-get-started-portal/index-schema-definition.png" alt-text="Screenshot that shows the schema definition for an index in the Azure AI Search service in the Azure portal.":::
+   :::image type="content" source="media/search-get-started-portal/index-schema-definition.png" alt-text="Screenshot that shows the schema definition for an index in the Azure AI Search service in the Azure portal." lightbox="media/search-get-started-portal/index-schema-definition.png":::
 
 ## Add or change fields
 
@@ -173,28 +63,30 @@ Review the index definition options to understand what you can and can't edit du
 
 ## Query with Search explorer
 
-You now have a search index that can be queried using [**Search explorer**](search-explorer.md), which sends REST calls that conform to the [Search POST REST API](/rest/api/searchservice/documents/search-post?view=rest-searchservice-2024-05-01-preview&preserve-view=true). This tool supports [simple query syntax](/rest/api/searchservice/simple-query-syntax-in-azure-search) and [full Lucene query syntax](/rest/api/searchservice/lucene-query-syntax-in-azure-search).
+You now have a search index that can be queried using [**Search explorer**](search-explorer.md), which sends REST calls that conform to [Documents - Search Post (REST API)](/rest/api/searchservice/documents/search-post?view=rest-searchservice-2024-05-01-preview&preserve-view=true). This tool supports [simple query syntax](/rest/api/searchservice/simple-query-syntax-in-azure-search) and [full Lucene query syntax](/rest/api/searchservice/lucene-query-syntax-in-azure-search).
 
 To query your search index:
 
 1. On the **Search explorer** tab, enter text to search on.
 
-   :::image type="content" source="media/search-get-started-portal/search-explorer-query-string.png" alt-text="Screenshot that shows how to enter and run a query in the  Search Explorer tool.":::
+   :::image type="content" source="media/search-get-started-portal/search-explorer-query-string.png" alt-text="Screenshot that shows how to enter and run a query in the  Search Explorer tool." lightbox="media/search-get-started-portal/search-explorer-query-string.png":::
 
 1. To jump to nonvisible areas of the output, use the mini map.
 
-   :::image type="content" source="media/search-get-started-portal/search-explorer-query-results.png" alt-text="Screenshot that shows long results for a query in the Search Explorer tool and the mini-map.":::
+   :::image type="content" source="media/search-get-started-portal/search-explorer-query-results.png" alt-text="Screenshot that shows long results for a query in the Search Explorer tool and the mini-map." lightbox="media/search-get-started-portal/search-explorer-query-results.png":::
 
 1. To specify syntax, switch to the JSON view.
 
-   :::image type="content" source="media/search-get-started-portal/search-explorer-change-view.png" alt-text="Screenshot of the JSON view selector.":::
+   :::image type="content" source="media/search-get-started-portal/search-explorer-change-view.png" alt-text="Screenshot of the JSON view selector." lightbox="media/search-get-started-portal/search-explorer-change-view.png":::
 
 ## Example queries for hotels-sample index
 
-The following examples assume the JSON view and the 2024-05-01-preview REST API version.
+The following examples assume the JSON view and 2024-05-01-preview REST API version.
 
 > [!TIP]
-> The JSON view supports intellisense for parameter name completion. Place your cursor inside the JSON view and type a space character to see a list of all query parameters. You can also type a letter, like "s," to see only the query parameters that begin with that letter. Intellisense doesn't exclude invalid parameters, so use your best judgment.
+> The JSON view supports intellisense for parameter name completion. Place your cursor inside the JSON view and enter a space character to see a list of all query parameters. You can also enter a letter, like `s`, to see only the query parameters that begin with that letter.
+> 
+> Intellisense doesn't exclude invalid parameters, so use your best judgment.
 
 ### Filter examples
 
@@ -249,7 +141,7 @@ The default syntax is [simple syntax](query-simple-syntax.md), but if you want f
 
 Misspelled query terms, like `seatle` instead of `Seattle`, don't return matches in a typical search. The `queryType=full` parameter invokes the full Lucene query parser, which supports the tilde (`~`) operand. When you use these parameters, the query performs a fuzzy search for the specified keyword and matches on terms that are similar but not an exact match.
 
-Take a minute to try these example queries on your index. To learn more about queries, see [Querying in Azure AI Search](search-query-overview.md).
+Take a minute to try these example queries on your index. For more information, see [Querying in Azure AI Search](search-query-overview.md).
 
 ## Clean up resources
 

@@ -1,7 +1,7 @@
 ---
-title: "Quickstart: Create a Skillset in the Azure Portal"
+title: "Quickstart: Create a Skillset in the Azure portal"
 titleSuffix: Azure AI Search
-description: Learn how to use the Import Data wizard to generate searchable text from images and unstructured documents. Skills in this quickstart include optical character recognition (OCR), image analysis, and natural language processing.
+description: Learn how to use an import wizard to generate searchable text from images and unstructured documents. Skills in this quickstart include optical character recognition (OCR), image analysis, and natural-language processing.
 manager: nitinme
 author: haileytap
 ms.author: haileytapia
@@ -10,189 +10,43 @@ ms.update-cycle: 180-days
 ms.custom:
   - ignite-2023
 ms.topic: quickstart
-ms.date: 03/04/2025
+ms.date: 09/16/2025
+zone_pivot_groups: azure-portal-wizards
 ---
 
 # Quickstart: Create a skillset in the Azure portal
 
-In this quickstart, you learn how a skillset in Azure AI Search adds optical character recognition (OCR), image analysis, language detection, text translation, and entity recognition to generate text-searchable content in a search index.
+::: zone pivot="import-data-new"
+[!INCLUDE [Import data (new) instructions](includes/quickstarts/search-get-started-skillset-new-wizard.md)]
+::: zone-end
 
-You can run the **Import data** wizard in the Azure portal to apply skills that create and transform textual content during indexing. The input is your raw data, usually blobs in Azure Storage. The output is a searchable index containing AI-generated image text, captions, and entities. You can query generated content in the Azure portal using [**Search explorer**](search-explorer.md).
-
-To prepare, you create a few resources and upload sample files before running the wizard.
-
-## Prerequisites
-
-+ An Azure account with an active subscription. [Create an account for free](https://azure.microsoft.com/free/).
-
-+ An Azure AI Search service. [Create a service](search-create-service-portal.md) or [find an existing service](https://portal.azure.com/#view/Microsoft_Azure_ProjectOxford/CognitiveServicesHub/~/CognitiveSearch) in your current subscription. For this quickstart, you can use a free service.
-
-+ An Azure Storage account with Azure Blob Storage.
-
-> [!NOTE]
-> This quickstart uses [Azure AI services](https://azure.microsoft.com/services/cognitive-services/) for AI transformations. Because the workload is so small, Azure AI services is tapped behind the scenes for free processing for up to 20 transactions. You can complete this exercise without having to create an Azure AI services multi-service resource.
-
-## Set up your data
-
-In the following steps, set up a blob container in Azure Storage to store heterogeneous content files.
-
-1. [Download sample data](https://github.com/Azure-Samples/azure-search-sample-data/tree/main/ai-enrichment-mixed-media) consisting of a small file set of different types. 
-
-1. Sign in to the [Azure portal](https://portal.azure.com/) with your Azure account.
-
-1. [Create an Azure Storage account](/azure/storage/common/storage-account-create?tabs=azure-portal) or [find an existing account](https://portal.azure.com/#blade/HubsExtension/BrowseResourceBlade/resourceType/Microsoft.Storage%2storageAccounts/). 
-
-   + Choose the same region as Azure AI Search to avoid bandwidth charges. 
-
-   + Choose the StorageV2 (general purpose V2).
-
-1. In Azure portal, open your Azure Storage page and create a container. You can use the default access level. 
-
-1. In Container, select **Upload** to upload the sample files. Notice that you have a wide range of content types, including images and application files that aren't full text searchable in their native formats.
-
-   :::image type="content" source="media/search-get-started-skillset/sample-data.png" alt-text="Screenshot of source files in Azure Blob Storage." border="false":::
-
-You're now ready to move on the Import data wizard.
-
-## Run the Import data wizard
-
-1. Sign in to the [Azure portal](https://portal.azure.com/) with your Azure account.
-
-1. [Find your search service](https://portal.azure.com/#blade/HubsExtension/BrowseResourceBlade/resourceType/Microsoft.Search%2FsearchServices). On the Overview page, select **Import data** on the command bar to create searchable content in four steps.
-
-   :::image type="content" source="media/search-import-data-portal/import-data-cmd.png" alt-text="Screenshot of the Import data command." border="true":::
-
-### Step 1: Create a data source
-
-1. In **Connect to your data**, choose **Azure Blob Storage**.
-
-1. Choose an existing connection to the storage account and select the container you created. Give the data source a name, and use default values for the rest. 
-
-   :::image type="content" source="media/search-get-started-skillset/blob-datasource.png" alt-text="Screenshot of the data source definition page." border="true":::
-
-    Continue to the next page.
-
-If you get *Error detecting index schema from data source*, the indexer that powers the wizard can't connect to your data source. Most likely, the data source has security protections. Try the following solutions and then rerun the wizard.
-
-| Security feature | Solution |
-|--------------------|----------|
-| Resource requires Azure roles, or its access keys are disabled | [Connect as a trusted service](search-indexer-howto-access-trusted-service-exception.md) or [connect using a managed identity](search-how-to-managed-identities.md) |
-| Resource is behind an IP firewall | [Create an inbound rule for Search and for the Azure portal](search-indexer-howto-access-ip-restricted.md) |
-| Resource requires a private endpoint connection | [Connect over a private endpoint](search-indexer-howto-access-private.md) |
-
-### Step 2: Add cognitive skills
-
-Next, configure AI enrichment to invoke OCR, image analysis, and natural language processing. 
-
-OCR and image analysis are available for blobs in Azure Blob Storage and Azure Data Lake Storage (ADLS) Gen2, and for image content in OneLake. Images can be standalone files or embedded images in a PDF or other files.
-
-1. For this quickstart, we're using the **Free** Azure AI services resource. The sample data consists of 14 files, so the free allotment of 20 transactions on Azure AI services is sufficient for this quickstart. 
-
-   :::image type="content" source="media/search-get-started-skillset/cog-search-attach.png" alt-text="Screenshot of the Attach Azure AI services tab." border="true":::
-
-1. Expand **Add enrichments** and make six selections. 
-
-   Enable OCR to add image analysis skills to wizard page.
-
-   Choose entity recognition (people, organizations, locations) and image analysis skills (tags, captions).
-
-   :::image type="content" source="media/search-get-started-skillset/skillset.png" alt-text="Screenshot of the skillset definition page." border="true":::
-
-   Continue to the next page.
-
-### Step 3: Configure the index
-
-An index contains your searchable content and the **Import data** wizard can usually create the schema by sampling the data source. In this step, review the generated schema and potentially revise any settings. 
-
-For this quickstart, the wizard does a good job setting reasonable defaults:  
-
-+ Default fields are based on metadata properties of existing blobs, plus the new fields for the enrichment output (for example, `people`, `organizations`, `locations`). Data types are inferred from metadata and by data sampling.
-
-+ Default document key is *metadata_storage_path* (selected because the field contains unique values).
-
-+ Default attributes are **Retrievable** and **Searchable**. **Searchable** allows full text search a field. **Retrievable** means field values can be returned in results. The wizard assumes you want these fields to be retrievable and searchable because you created them via a skillset. Select **Filterable** if you want to use fields in a filter expression.
-
-  :::image type="content" source="media/search-get-started-skillset/index-fields.png" alt-text="Screenshot of the index definition page." border="true":::
-
-Marking a field as **Retrievable** doesn't mean that the field *must* be present in the search results. You can control search results composition by using the **select** query parameter to specify which fields to include.
-  
-Continue to the next page.
-
-### Step 4: Configure the indexer
-
-The indexer drives the indexing process. It specifies the data source name, a target index, and frequency of execution. The **Import data** wizard creates several objects, including an indexer that you can reset and run repeatedly.
-
-1. In the **Indexer** page, accept the default name and select **Once**. 
-
-   :::image type="content" source="media/search-get-started-skillset/indexer-def.png" alt-text="Screenshot of the indexer definition page." border="true":::
-
-1. Select **Submit** to create and simultaneously run the indexer.
-
-## Monitor status
-
-Select **Indexers** from the left pane to monitor status, and then select the indexer. Skills-based indexing takes longer than text-based indexing, especially OCR and image analysis.
-
-  :::image type="content" source="media/search-get-started-skillset/indexer-notification.png" alt-text="Screenshot of the indexer status page." border="true":::
-
-To view details about execution status, select **Success** (or **Failed**) to view execution details.
-
-In this demo, there are a few warnings: *"Could not execute skill because one or more skill input was invalid."* It tells you that a PNG file in the data source doesn't provide a text input to Entity Recognition. This warning occurs because the upstream OCR skill didn't recognize any text in the image, and thus couldn't provide a text input to the downstream Entity Recognition skill.
-
-Warnings are common in skillset execution. As you become familiar with how skills iterate over your data, you might begin to notice patterns and learn which warnings are safe to ignore.
-
-## Query in Search explorer
-
-After an index is created, use **Search explorer** to return results.
-
-1. On the left, select **Indexes** and then select the index. **Search explorer** is on the first tab.
-
-1. Enter a search string to query the index, such as `satya nadella`. The search bar accepts keywords, quote-enclosed phrases, and operators: `"Satya Nadella" +"Bill Gates" +"Steve Ballmer"`
-
-Results are returned as verbose JSON, which can be hard to read, especially in large documents. Some tips for searching in this tool include the following techniques:
-
-+ Switch to JSON view to specify parameters that shape results.
-+ Add `select` to limit the fields in results.
-+ Add `count` to show the number of matches.
-+ Use CTRL-F to search within the JSON for specific properties or terms.
-
-  :::image type="content" source="media/search-get-started-skillset/search-explorer.png" alt-text="Screenshot of the Search explorer page." border="true":::
-
-Here's some JSON you can paste into the view:
-
-  ```json
-  {
-  "search": "\"Satya Nadella\" +\"Bill Gates\" +\"Steve Ballmer\"",
-  "count": true,
-  "select": "content, people"
-  }
-  ```
-
-> [!TIP]
-> Query strings are case-sensitive so if you get an "unknown field" message, check **Fields** or **Index Definition (JSON)** to verify name and case.
+::: zone pivot="import-data"
+[!INCLUDE [Import data instructions](includes/quickstarts/search-get-started-skillset-old-wizard.md)]
+::: zone-end
 
 ## Takeaways
 
-You've now created your first skillset and learned the basic steps of skills-based indexing.
+You've created your first skillset and learned the basic steps of skills-based indexing.
 
-Some key concepts that we hope you picked up include the dependencies. A skillset is bound to an indexer, and indexers are Azure and source-specific. Although this quickstart uses Azure Blob Storage, other Azure data sources are possible. For more information, see [Indexers in Azure AI Search](search-indexer-overview.md). 
+Some key concepts that we hope you picked up include the dependencies. A skillset is bound to an indexer, and indexers are Azure and source-specific. Although this quickstart uses Azure Blob Storage, other Azure data sources are available. For more information, see [Indexers in Azure AI Search](search-indexer-overview.md). 
 
-Another important concept is that skills operate over content types, and when working with heterogeneous content, some inputs are skipped. Also, large files or fields might exceed the indexer limits of your service tier. It's normal to see warnings when these events occur. 
+Another important concept is that skills operate over content types, and when you use heterogeneous content, some inputs are skipped. Also, large files or fields might exceed the indexer limits of your service tier. It's normal to see warnings when these events occur. 
 
-Output is routed to a search index, and there's a mapping between name-value pairs created during indexing and individual fields in your index. Internally, the wizard sets up [an enrichment tree](cognitive-search-concept-annotations-syntax.md) and defines a [skillset](cognitive-search-defining-skillset.md), establishing the order of operations and general flow. These steps are hidden in the wizard, but when you start writing code, these concepts become important.
+The output is routed to a search index, and there's a mapping between name-value pairs created during indexing and individual fields in your index. Internally, the wizard sets up [an enrichment tree](cognitive-search-concept-annotations-syntax.md) and defines a [skillset](cognitive-search-defining-skillset.md), establishing the order of operations and general flow. These steps are hidden in the wizard, but when you start writing code, these concepts become important.
 
-Finally, you learned that you can verify content by querying the index. In the end, what Azure AI Search provides is a searchable index, which you can query using either the [simple](/rest/api/searchservice/simple-query-syntax-in-azure-search) or [fully extended query syntax](/rest/api/searchservice/lucene-query-syntax-in-azure-search). An index containing enriched fields is like any other. You can incorporate standard or [custom analyzers](search-analyzers.md), [scoring profiles](/rest/api/searchservice/add-scoring-profiles-to-a-search-index), [synonyms](search-synonyms.md), [faceted navigation](search-faceted-navigation.md), geo-search, or any other Azure AI Search feature.
+Finally, you learned that you can verify content by querying the index. Ultimately, Azure AI Search provides a searchable index that you can query using either [simple](/rest/api/searchservice/simple-query-syntax-in-azure-search) or [fully extended query syntax](/rest/api/searchservice/lucene-query-syntax-in-azure-search). An index containing enriched fields is like any other. You can incorporate standard or [custom analyzers](search-analyzers.md), [scoring profiles](/rest/api/searchservice/add-scoring-profiles-to-a-search-index), [synonyms](search-synonyms.md), [faceted navigation](search-faceted-navigation.md), geo-search, and other Azure AI Search features.
 
 ## Clean up resources
 
 When you're working in your own subscription, it's a good idea at the end of a project to identify whether you still need the resources you created. Resources left running can cost you money. You can delete resources individually or delete the resource group to delete the entire set of resources.
 
-You can find and manage resources in the Azure portal, using the **All resources** or **Resource groups** link in the left-navigation pane.
+You can find and manage resources in the Azure portal by selecting **All resources** or **Resource groups** from the left pane.
 
 If you used a free service, remember that you're limited to three indexes, indexers, and data sources. You can delete individual items in the Azure portal to stay under the limit. 
 
 ## Next step
 
-You can create skillsets using the Azure portal, .NET SDK, or REST API. To further your knowledge, try the REST API by using a REST client and more sample data:
+You can use the Azure portal, REST APIs, or an Azure SDK to create skillsets. Try the REST APIs by using a REST client and more sample data:
 
 > [!div class="nextstepaction"]
 > [Tutorial: Use skillsets to generate searchable content in Azure AI Search](tutorial-skillset.md)

@@ -163,11 +163,11 @@ When you're ready to create an indexer on a remote search service, you need a se
 
 ### [**Azure portal**](#tab/portal)
 
-1. Sign in to the [Azure portal](https://portal.azure.com), then find your search service.
+1. Sign in to the [Azure portal](https://portal.azure.com) and select your search service.
 
-1. On the search service **Overview** page, choose from two options:
+1. Choose from the following options:
 
-   + [**Import data** wizard](search-import-data-portal.md): The wizard is unique in that it creates all of the required elements. Other approaches require a predefined data source and index.
+   + [**Import wizards**](search-import-data-portal.md): The wizards are unique in that they create all of the required elements. Other approaches require a predefined data source and index.
 
        :::image type="content" source="media/search-how-to-create-indexers/portal-indexer-client.png" alt-text="Screenshot that shows the Import data wizard." border="true":::
 

@@ -96,7 +96,7 @@ Setting a field as searchable, filterable, sortable, or facetable has an effect
 
 If a field isn't set to be searchable, filterable, sortable, or facetable, the field can't be referenced in any query expression. This is desirable for fields that aren't used in queries, but are needed in search results.
 
-The REST APIs have default attribution based on [data types](/rest/api/searchservice/supported-data-types), which is also used by the [Import wizards](search-import-data-portal.md) in the Azure portal. The Azure SDKs don't have defaults, but they have field subclasses that incorporate properties and behaviors, such as [SearchableField](/dotnet/api/azure.search.documents.indexes.models.searchablefield) for strings and [SimpleField](/dotnet/api/azure.search.documents.indexes.models.simplefield) for primitives.
+The REST APIs have default attribution based on [data types](/rest/api/searchservice/supported-data-types), which is also used by the [import wizards](search-import-data-portal.md) in the Azure portal. The Azure SDKs don't have defaults, but they have field subclasses that incorporate properties and behaviors, such as [SearchableField](/dotnet/api/azure.search.documents.indexes.models.searchablefield) for strings and [SimpleField](/dotnet/api/azure.search.documents.indexes.models.simplefield) for primitives.
 
 Default field attributions for the REST APIs are summarized in the following table.
 
@@ -113,7 +113,7 @@ Default field attributions for the REST APIs are summarized in the following tab
 
 String fields can also be optionally associated with [analyzers](search-analyzers.md) and [synonym maps](search-synonyms.md). Fields of type `Edm.String` that are filterable, sortable, or facetable can be at most 32 kilobytes in length. This is because values of such fields are treated as a single search term, and the maximum length of a term in Azure AI Search is 32 kilobytes. If you need to store more text than this in a single string field, you should explicitly set filterable, sortable, and facetable to `false` in your index definition.
 
-Vector fields must be associated with [dimensions and vector profiles](vector-search-how-to-create-index.md). Retrievable is true by default if you add the vector field using the [Import and vectorize data wizard](search-get-started-portal-import-vectors.md) in the Azure portal. If you use the REST API, it's false.
+Vector fields must be associated with [dimensions and vector profiles](vector-search-how-to-create-index.md). Retrievable is true by default if you add the vector field using the [**Import data (new)** wizard](search-get-started-portal-import-vectors.md) in the Azure portal. If you use the REST API, it's false.
 
 Field attributes are described in the following table.
 
@@ -154,7 +154,7 @@ Index design through the Azure portal enforces requirements and schema rules for
 
    The wizard is an end-to-end workflow that creates an indexer, a data source, and a finished index. It also loads the data. If this is more than what you want, use **Add index** instead.
 
-The following screenshot highlights where **Add index**, **Import data**, and **Import and vectorize data wizard** appear on the command bar.
+The following screenshot highlights where the **Add index**, **Import data**, and **Import data (new)** wizards appear on the command bar.
 
 :::image type="content" source="media/search-what-is-an-index/add-index.png" alt-text="Screenshot of the options to add an index." border="true":::
 

@@ -305,7 +305,7 @@ If a parent document is completely deleted from the datasource, the correspondin
 
 ### Projected key value
 
-To ensure data integrity for updated and deleted content, data refresh in one-to-many indexing relies on a *projected key value* on the "many" side. If you're using integrated vectorization or the [Import and vectorize data wizard](search-import-data-portal.md), the projected key value is the `parent_id` field in a chunked or "many" side of the index.
+To ensure data integrity for updated and deleted content, data refresh in one-to-many indexing relies on a *projected key value* on the "many" side. If you're using integrated vectorization or the [**Import data (new)** wizard](search-import-data-portal.md), the projected key value is the `parent_id` field in a chunked or "many" side of the index.
 
 A projected key value is a unique identifier that the indexer generates for each document. It ensures uniqueness and allows for change and deletion tracking to work correctly. This key contains the following segments:
 

@@ -17,9 +17,9 @@ ms.custom:
 
 [!INCLUDE [Feature preview](./includes/previews/preview-generic.md)]
 
-Support for Azure Logic Apps integration is now in public preview, available in the Azure portal [Import and vectorize data wizard](search-get-started-portal-import-vectors.md) only. In Azure AI Search, a logic app workflow is used for indexing and vectorization, and it's equivalent to an indexer and data source in Azure AI Search. 
+Support for Azure Logic Apps integration is currently in public preview and only available through the [**Import data (new)** wizard](search-get-started-portal-import-vectors.md) in the Azure portal. In Azure AI Search, a logic app workflow is used for indexing and vectorization, and it's equivalent to an indexer and data source in Azure AI Search. 
 
-You can create a workflow in Azure AI Search using the Import and vectorize data wizard, and then manage the workflow in Azure Logic Apps alongside your other workflows. Behind the scenes, the wizard follows a workflow template that pulls in (ingests) content from a source for indexing in AI Search. The connectors used in this scenario are prebuilt and already exist in Azure Logic Apps, so the workflow template just provides details for those connectors to create connections to the data source, AI Search, and other items to complete the ingestion workflow. 
+You can create a workflow in Azure AI Search using the **Import data (new)** wizard, and then manage the workflow in Azure Logic Apps alongside your other workflows. Behind the scenes, the wizard follows a workflow template that pulls in (ingests) content from a source for indexing in AI Search. The connectors used in this scenario are prebuilt and already exist in Azure Logic Apps, so the workflow template just provides details for those connectors to create connections to the data source, AI Search, and other items to complete the ingestion workflow. 
 
 > [!NOTE]
 > A logic app workflow is a billable resource. For more information, see [Azure Logic Apps pricing](/azure/logic-apps/logic-apps-pricing).
@@ -33,7 +33,7 @@ Azure Logic Apps integration in Azure AI Search adds support for:
 + Scheduled or on-demand indexing
 + Change detection of new and existing documents
 
-Import and vectorize data wizard inputs include:
+The **Import data (new)** wizard inputs include:
 
 + A supported data source
 + A supported text embedding model
@@ -77,7 +77,8 @@ End-to-end functionality is available in the following regions, which provide th
 
 ### Supported models
 
-The logic app path through the **Import and vectorize data** wizard supports a selection of embedding models.
+The logic app path through the **Import data (new)** wizard supports a selection of embedding models.
+
 Deploy one of the following [embedding models](/azure/ai-services/openai/concepts/models#embeddings) on Azure OpenAI for your end-to-end workflow.
 
 + text-embedding-3-small
@@ -109,11 +110,11 @@ Currently, the public preview has these limitations:
 
 Follow these steps to create a logic app workflow for indexing content in Azure AI Search.
 
-1. Start the Import and vectorize data wizard in the Azure portal.
+1. Start the **Import data (new)** wizard in the Azure portal.
 
 1. Choose a [supported Azure Logic Apps connector](#supported-connectors).
 
-   :::image type="content" source="media/logic-apps-connectors/choose-data-source.png" alt-text="Screenshot of the chosen data source page in the Import and vectorize data wizard." lightbox="media/logic-apps-connectors/choose-data-source.png" :::
+   :::image type="content" source="media/logic-apps-connectors/choose-data-source.png" alt-text="Screenshot of the chosen data source page in the Import data (new) wizard." lightbox="media/logic-apps-connectors/choose-data-source.png" :::
 
 1. In **Connect to your data**, provide a name prefix used for the search index and workflow. Having a common name helps you manage them together.
 

@@ -23,8 +23,8 @@ To configure and run the indexer, you can use:
 
 + [2024-05-01-preview REST API](/rest/api/searchservice/data-sources/create-or-update?view=rest-searchservice-2024-05-01-preview&tabs=HTTP&preserve-view=true) or a newer preview REST API.
 + An Azure SDK beta package that provides the feature.
-+ [Import data wizard](search-get-started-portal.md) in the Azure portal.
-+ [Import and vectorize data wizard](search-get-started-portal-import-vectors.md) in the Azure portal.
++ [**Import data** wizard](search-get-started-portal.md) in the Azure portal.
++ [**Import data (new)** wizard](search-get-started-portal-import-vectors.md) in the Azure portal.
 
 This article uses the REST APIs to illustrate each step.
   
@@ -454,6 +454,6 @@ There are five indexer properties that control the indexer's response when error
 
 ## Next steps
 
-Review how the [Import and vectorize data wizard](search-get-started-portal-import-vectors.md) works and try it out for this indexer. You can use [integrated vectorization](vector-search-integrated-vectorization.md) to chunk and create embeddings for vector or hybrid search using a default schema.
+Review how the [**Import data (new)** wizard](search-get-started-portal-import-vectors.md) works and try it out for this indexer. You can use [integrated vectorization](vector-search-integrated-vectorization.md) to chunk and create embeddings for vector or hybrid search using a default schema.
 
 <!-- + Check out [this Python demo](add a link to demo location) that shows how to set this up using code. -->

@@ -136,13 +136,17 @@ In this step, specify the data source, index, and indexer.
 
    :::image type="content" source="media/search-how-to-index-sql-database/search-data-source.png" alt-text="Screenshot of the data source creation page in the Azure portal.":::
 
-1. Start the **Import data** wizard to create the index and indexer.
+1. Use an [import wizard](search-import-data-portal.md) to create the index and indexer.
 
-   1. On the Overview page, select **Import data**.
-   1. Select the data source you just created, and select **Next**.
-   1. Skip the **Add cognitive skills (Optional)** page.
-   1. On **Customize target index**, name the index, set the key to your primary key in the table, and then group select *Retrievable* and *Searchable* for all fields, and optionally add *Filterable* and *Sortable* for short strings or numeric values.
-   1. On **Create an indexer**, name the indexer and select **Submit**.
+   1. On the **Overview** page, select **Import data** or **Import data (new)**.
+
+   1. Select the data source you just created.
+
+   1. Skip the step for adding AI enrichments.
+
+   1. Name the index, set the key to your primary key in the table, attribute all fields as **Retrievable** and **Searchable**, and optionally add **Filterable** and **Sortable** for short strings or numeric values.
+
+   1. Name the indexer and finish the wizard to create the necessary objects.
 
 ### [**REST**](#tab/test-sql)
 
@@ -363,7 +367,7 @@ For Azure SQL indexers, there are two change detection policies:
 
 + "HighWaterMarkChangeDetectionPolicy" (works for views)
 
-### SQL Integrated Change Tracking Policy
+### SQL integrated change tracking policy
 
 We recommend using "SqlIntegratedChangeTrackingPolicy" for its efficiency and its ability to identify deleted rows.
 
@@ -401,7 +405,7 @@ When using SQL integrated change tracking policy, don't specify a separate data
 
 <a name="HighWaterMarkPolicy"></a>
 
-### High Water Mark Change Detection policy
+### High water mark change detection policy
 
 This change detection policy relies on a "high water mark" column in your table or view that captures the version or time when a row was last updated. If you're using a view, you must use a high water mark policy. 
 
@@ -484,7 +488,7 @@ You can also disable the `ORDER BY [High Water Mark Column]` clause. However, th
     }
 ```
 
-### Soft Delete Column Deletion Detection policy
+### Soft delete column deletion detection policy
 
 When rows are deleted from the source table, you probably want to delete those rows from the search index as well. If you use the SQL integrated change tracking policy, this is taken care of for you. However, the high water mark change tracking policy doesn’t help you with deleted rows. What to do?
 

@@ -36,7 +36,7 @@ Integrated vectorization works with [all supported data sources](search-indexer-
 
 | Supported data source | Description |
 |--|--|
-| [Azure Blob Storage](search-howto-indexing-azure-blob-storage.md) | This data source works with blobs and tables. You must use a standard performance (general-purpose v2) account. Access tiers can be hot, cool, or cold. |
+| [Azure Blob Storage](/azure/storage/common/storage-account-create) | This data source works with blobs and tables. You must use a standard performance (general-purpose v2) account. Access tiers can be hot, cool, or cold. |
 | [Azure Data Lake Storage (ADLS) Gen2](/azure/storage/blobs/create-data-lake-storage-account) | This is an Azure Storage account with a hierarchical namespace enabled. To confirm that you have Data Lake Storage, check the **Properties** tab on the **Overview** page.<br><br> :::image type="content" source="media/search-how-to-integrated-vectorization/data-lake-storage-account.png" alt-text="Screenshot of an Azure Data Lake Storage account in the Azure portal." border="true" lightbox="media/search-how-to-integrated-vectorization/data-lake-storage-account.png"::: |
 <!--| [OneLake](search-how-to-index-onelake-files.md) | This data source is currently in preview. For information about limitations and supported shortcuts, see [OneLake indexing](search-how-to-index-onelake-files.md). |-->
 

@@ -33,13 +33,13 @@ For more information, see [Data import strategies](search-what-is-data-import.md
 
 ## Use the Azure portal
 
-In the Azure portal, use the [import wizards](search-import-data-portal.md) to create and load indexes in a seamless workflow. If you want to load an existing index, choose an alternative approach.
+In the Azure portal, use an [import wizard](search-import-data-portal.md) to create and load indexes in a seamless workflow. If you want to load an existing index, choose an alternative approach.
 
 1. Sign in to the [Azure portal](https://portal.azure.com/) with your Azure account and [find your search service](https://portal.azure.com/#blade/HubsExtension/BrowseResourceBlade/resourceType/Microsoft.Search%2FsearchServices).
 
-1. On the **Overview** page, select **Import data** or **Import and vectorize data wizard** on the command bar to create and populate a search index.
+1. On the **Overview** page, select **Import data** or **Import data (new)** on the command bar to create and populate a search index.
 
-   :::image type="content" source="media/search-import-data-portal/import-data-cmd.png" alt-text="Screenshot of the Import data command" border="true":::
+   :::image type="content" source="media/search-import-data-portal/import-wizards.png" alt-text="Screenshot of the Import data command." border="true":::
 
     You can follow these links to review the workflow: [Quickstart: Create an Azure AI Search index](search-get-started-portal.md) and [Quickstart: Integrated vectorization](search-get-started-portal-import-vectors.md).
 

@@ -30,7 +30,7 @@ In this article, learn how to:
 > + Generate embeddings for each chunk
 > + Use index projections to map embeddings to fields in a search index
 
-For illustration purposes, this article uses the [sample health plan PDFs](https://github.com/Azure-Samples/azure-search-sample-data/tree/main/health-plan) uploaded to Azure Blob Storage and then indexed using the **Import and vectorize data wizard**.
+For illustration purposes, this article uses the [sample health plan PDFs](https://github.com/Azure-Samples/azure-search-sample-data/tree/main/health-plan) uploaded to Azure Blob Storage and then indexed using the **Import data (new)** wizard.
 
 ## Prerequisites
 
@@ -59,15 +59,15 @@ The raw inputs must be in a [supported data source](search-indexer-overview.md#s
 You can use the Azure portal, REST APIs, or an Azure SDK package to [create a data source](search-howto-indexing-azure-blob-storage.md).
 
 > [!TIP]
-> Upload the [health plan PDF](https://github.com/Azure-Samples/azure-search-sample-data/tree/main/health-plan) sample files to your supported data source to try out the Document Layout skill and structure-aware chunking on your own search service. The [Import and vectorize data wizard](search-get-started-portal-import-vectors.md) is an easy code-free approach for trying out this skill. Be sure to select the **default parsing mode** to use structure-aware chunking. Otherwise, the [Markdown parsing mode](search-how-to-index-markdown-blobs.md) is used.
+> Upload the [health plan PDF](https://github.com/Azure-Samples/azure-search-sample-data/tree/main/health-plan) sample files to your supported data source to try out the Document Layout skill and structure-aware chunking on your own search service. The [**Import data (new)** wizard](search-get-started-portal-import-vectors.md) is an easy code-free approach for trying out this skill. Be sure to select the **default parsing mode** to use structure-aware chunking. Otherwise, the [Markdown parsing mode](search-how-to-index-markdown-blobs.md) is used.
 
 ## Create an index for one-to-many indexing
 
 Here's an example payload of a single search document designed around chunks. Whenever you're working with chunks, you need a chunk field and a parent field that identifies the origin of the chunk. In this example, parent fields are the text_parent_id. Child fields are the vector and nonvector chunks of the markdown section.
 
 The Document Layout skill outputs headings and content. In this example, `header_1` through `header_3` store document headings, as detected by the skill. Other content, such as paragraphs, is stored in `chunk`. The `text_vector` field is a vector representation of the chunk field content.
 
-You can use the **Import and vectorize data wizard** in the Azure portal, REST APIs, or an Azure SDK to [create an index](search-how-to-load-search-index.md). The following index is very similar to what the wizard creates by default. You might have more fields if you add image vectorization.
+You can use the **Import data (new)** wizard in the Azure portal, REST APIs, or an Azure SDK to [create an index](search-how-to-load-search-index.md). The following index is very similar to what the wizard creates by default. You might have more fields if you add image vectorization.
 
 If you aren't using the wizard, the index must exist on the search service before you create the skillset or run the indexer.
 

@@ -22,7 +22,7 @@ Complex fields represent either a single object in the document, or an array of
 
 Azure AI Search natively supports complex types and collections. These types allow you to model almost any JSON structure in an Azure AI Search index. In previous versions of Azure AI Search APIs, only flattened row sets could be imported. In the newest version, your index can now more closely correspond to source data. In other words, if your source data has complex types, your index can have complex types also.
 
-To get started, we recommend the [Hotels data set](https://github.com/Azure-Samples/azure-search-sample-data/tree/main/hotels), which you can load in the **Import data** wizard in the Azure portal. The wizard detects complex types in the source and suggests an index schema based on the detected structures.
+To get started, we recommend the [hotels data set](https://github.com/Azure-Samples/azure-search-sample-data/tree/main/hotels), which you can load using an [import wizard](search-get-started-portal.md) in the Azure portal. The wizard detects complex types in the source and suggests an index schema based on the detected structures.
 
 > [!NOTE]
 > Support for complex types became generally available starting in `api-version=2019-05-06`. 
@@ -369,9 +369,7 @@ If you implement the workaround, be sure to test extentively.
 
 ## Next steps
 
-Try the [Hotels data set](https://github.com/Azure-Samples/azure-search-sample-data/tree/master/hotels) in the **Import data** wizard. You need the Azure Cosmos DB connection information provided in the readme to access the data.
-
-With that information in hand, your first step in the wizard is to create a new Azure Cosmos DB data source. Further on in the wizard, when you get to the target index page, you see an index with complex types. Create and load this index, and then execute queries to understand the new structure.
+Use an import wizard with sample data to guide you through creating, loading, and querying an index.
 
 > [!div class="nextstepaction"]
-> [Quickstart: portal wizard for import, indexing, and queries](search-get-started-portal.md)
+> [Quickstart: Create a search index in the Azure portal](search-get-started-portal.md)

@@ -67,7 +67,7 @@ The Description field provides the most verbose content. You should target this
 
 ## Use the Azure portal
 
-You can use either the **Import data** wizard or **Import and vectorize data wizard** to automate indexing from an SQL database table or view. The data source configuration is similar for both wizards.
+You can use either the **Import data** wizard or the **Import data (new)** wizard to automate indexing from an SQL database table or view. The data source configuration is similar for both wizards.
 
 1. [Start the wizard](search-import-data-portal.md#starting-the-wizards).
 
@@ -81,17 +81,17 @@ You can use either the **Import data** wizard or **Import and vectorize data wiz
 
    If you [configure Azure AI Search to use a managed identity](search-how-to-managed-identities.md), and you create a [role assignment on Cosmos DB](/azure/cosmos-db/how-to-setup-rbac#built-in-role-definitions) that grants **Cosmos DB Account Reader** and **Cosmos DB Built-in Data Reader** permissions to the identity, your indexer can connect to Cosmos DB using Microsoft Entra ID and roles.
 
-1. For the **Import and vectorize data wizard**, you can specify options for change and deletion tracking.
+1. For the **Import data (new)** wizard, you can specify options for change and deletion tracking.
 
    [Change detection](#incremental-indexing-and-custom-queries) is supported by default through a `_ts` field (timestamp). If you upload content using the approach described in [Try with sample data](#try-with-sample-data), the collection is created with a `_ts` field.
 
    [Deletion detection](#indexing-deleted-documents) requires that you have a preexisting top-level field in the collection that can be used as a soft-delete flag. It should be a Boolean field (you could name it IsDeleted). Specify `true` as the soft-deleted value. In the search index, add a corresponding search field called *IsDeleted* set to retrievable and filterable.
 
 1. Continue with the remaining steps to complete the wizard:
 
-   + [Import data wizard](search-get-started-portal.md)
+   + [**Import data** wizard](search-get-started-portal.md)
 
-   + [Import and vectorize data wizard](search-get-started-portal-import-vectors.md)
+   + [**Import data (new)** wizard](search-get-started-portal-import-vectors.md)
 
 ## Use the REST APIs
 

@@ -78,7 +78,7 @@ api-key: [admin key]
 
 ### json example (single hotel JSON files)
 
-The [hotel JSON document data set](https://github.com/Azure-Samples/azure-search-sample-data/tree/main/hotels/hotel-json-documents) on GitHub is helpful for testing JSON parsing, where each blob represents a structured JSON file. You can upload the data files to Blob Storage and use the [**Import data** wizard](search-get-started-portal.md) to quickly evaluate how this content is parsed into individual search documents. 
+The [hotel JSON document data set](https://github.com/Azure-Samples/azure-search-sample-data/tree/main/hotels/hotel-json-documents) on GitHub is helpful for testing JSON parsing, where each blob represents a structured JSON file. You can upload the data files to Blob Storage and use an [import wizard](search-get-started-portal.md) to quickly evaluate how this content is parsed into individual search documents. 
 
 The data set consists of five blobs, each containing a hotel document with an address collection and a rooms collection. The blob indexer detects both collections and reflects the structure of the input documents in the index schema.
 
@@ -113,7 +113,7 @@ api-key: [admin key]
 
 ### jsonArrays example
 
-The [New York Philharmonic JSON data set](https://github.com/Azure-Samples/azure-search-sample-data/tree/main/ny-philharmonic) on GitHub is helpful for testing JSON array parsing. You can upload the data files to Blob storage and use the [**Import data** wizard](search-get-started-portal.md) to quickly evaluate how this content is parsed into individual search documents. 
+The [New York Philharmonic JSON data set](https://github.com/Azure-Samples/azure-search-sample-data/tree/main/ny-philharmonic) on GitHub is helpful for testing JSON array parsing. You can upload the data files to Blob storage and use an [import wizard](search-get-started-portal.md) to quickly evaluate how this content is parsed into individual search documents. 
 
 The data set consists of eight blobs, each containing a JSON array of entities, for a total of 100 entities. The entities vary as to which fields are populated, but the end result is one search document per entity, from all arrays, in all blobs.
 

@@ -23,8 +23,8 @@ To configure and run the indexer, you can use:
 
 + [Search Service REST API](/rest/api/searchservice), any version.
 + An Azure SDK package, any version.
-+ [Import data wizard](search-get-started-portal.md) in the Azure portal.
-+ [Import and vectorize data wizard](search-get-started-portal-import-vectors.md) in the Azure portal.
++ [**Import data** wizard](search-get-started-portal.md) in the Azure portal.
++ [**Import data (new)** wizard](search-get-started-portal-import-vectors.md) in the Azure portal.
 
 This article uses the REST APIs to illustrate each step.
 

@@ -54,7 +54,7 @@ The Description field provides the most verbose content. You should target this
 
 ## Use the Azure portal
 
-You can use either the **Import data** wizard or **Import and vectorize data wizard** to automate indexing from an SQL database table or view. The data source configuration is similar for both wizards.
+You can use either the **Import data** wizard or the **Import data (new)** wizard to automate indexing from an SQL database table or view. The data source configuration is similar for both wizards.
 
 1. [Start the wizard](search-import-data-portal.md#starting-the-wizards).
 
@@ -68,15 +68,15 @@ You can use either the **Import data** wizard or **Import and vectorize data wiz
 
    If you [configure Azure AI Search to use a managed identity](search-how-to-managed-identities.md), and you create a role assignment on Azure Storage that grants **Reader and Data Access** permissions to the identity, your indexer can connect to table storage using Microsoft Entra ID and roles.
 
-1. For the **Import and vectorize data wizard**, you can specify options for deletion detection.
+1. For the **Import data (new)** wizard, you can specify options for deletion detection.
 
    Deletion detection requires that you have a preexisting field in the table that can be used as a soft-delete flag. It should be a Boolean field (you could name it IsDeleted). Specify `true` as the soft-delete value. In the search index, add a corresponding search field called *IsDeleted* set to retrievable and filterable.
 
 1. Continue with the remaining steps to complete the wizard:
 
-   + [Import data wizard](search-get-started-portal.md)
+   + [**Import data** wizard](search-get-started-portal.md)
 
-   + [Import and vectorize data wizard](search-get-started-portal-import-vectors.md)
+   + [**Import data (new)** wizard](search-get-started-portal-import-vectors.md)
 
 ## Use the REST APIs
 

@@ -1,11 +1,11 @@
 ---
-title: Import Wizards in the Azure Portal
+title: Import Wizards in the Azure portal
 titleSuffix: Azure AI Search
-description: Learn about the import wizards in the Azure portal used to create and load an index, and optionally invoke applied AI for vectorization, natural language processing, language translation, OCR, and image analysis.
+description: Learn about the Azure portal wizards that create and load an index and optionally invoke applied AI for vectorization, natural-language processing, language translation, OCR, and image analysis.
 author: HeidiSteen
 ms.author: heidist
 manager: nitinme
-ms.date: 05/12/2025
+ms.date: 09/16/2025
 ms.service: azure-ai-search
 ms.topic: concept-article
 ms.custom:
@@ -16,257 +16,296 @@ ms.custom:
 
 # Import data wizards in the Azure portal
 
-Azure AI Search has two wizards that automate indexing and object creation so that you can begin querying immediately. If you're new to Azure AI Search, these wizards are one of the most powerful features at your disposal. With minimal effort, you can create an indexing or enrichment pipeline that exercises most of the functionality of Azure AI Search.
+> [!IMPORTANT]
+> We're consolidating the Azure AI Search wizards. Key changes include:
+>
+> + The **Import and vectorize data** wizard is now called **Import data (new)**.
+> + The **Import data** workflow is now available in **Import data (new)**.
+>
+> The **Import data** wizard will eventually be deprecated. For now, you can still use this wizard, but we recommend the new wizard for an improved search experience that uses the latest frameworks.
+>
+> The wizards don't have identical keyword search workflows. Certain skills and capabilities are only available in the old wizard. For more information about their similarities and differences, continue reading this article.
 
-+ **Import data wizard** supports nonvector workflows. You can extract text and numbers from raw documents. You can also configure applied AI and built-in skills that infer structure and generate text searchable content from image files and unstructured data.
+Azure AI Search has two wizards that automate indexing, enrichment, and object creation for various search scenarios:
 
-+ **Import and vectorize data wizard** adds chunking and vectorization. You must specify an existing deployment of an embedding model, but the wizard makes the connection, formulates the request, and handles the response. It generates vector content from text or image content.
++ The **Import data** wizard supports keyword (nonvector) search. You can extract text and numbers from raw documents. You can also configure applied AI and built-in skills to infer structure and generate searchable text from image files and unstructured data.
 
-If you're using the wizard for proof-of-concept testing, this article explains the internal workings of the wizards so that you can use them more effectively.
++ The **Import data (new)** wizard supports keyword search, RAG, and multimodal RAG. For keyword search, it modernizes the **Import data** workflow but lacks some functionality, such as automatic metadata field creation. For RAG and multimodal RAG, it connects to your embedding model deployment, sends requests, and generates vectors from text or images.
 
-This isn't a step-by-step article. To use the wizard with sample data, see:
+Despite their differences, the wizards follow similar workflows for content ingestion and indexing. The following table summarizes their capabilities.
 
-+ [Quickstart: Create a search index](search-get-started-portal.md)
-+ [Quickstart: Create a text translation and entity skillset](search-get-started-skillset.md)
-+ [Quickstart: Create a vector index](search-get-started-portal-import-vectors.md)
-+ [Quickstart: Image search (vectors)](search-get-started-portal-image-search.md)
+| Capability | Import data wizard | Import data (new) wizard |
+|--|--|--|
+| Index creation | ✅ | ✅ |
+| Indexer pipeline creation | ✅ | ✅ |
+| Azure Logic Apps connectors | ❌ | ✅ |
+| Sample data | ✅ | ❌ |
+| Skills-based enrichment | ✅ | ✅ |
+| Vector and multimodal support | ❌ | ✅ |
+| Semantic ranking support | ❌ | ✅ |
+| Knowledge store support | ✅ | ❌ |
+
+This article explains how the wizards work to help you with proof-of-concept testing. For step-by-step instructions using sample data, see [Try the wizards](#try-the-wizards).
 
 ## Supported data sources and scenarios
 
-The wizards support most of the data sources supported by indexers.
+This section describes the available options in each wizard.
+
+### Data sources
+
+The wizards support the following data sources, most of which use [built-in indexers](search-indexer-overview.md#supported-data-sources). Exceptions are noted in the table's footnotes.
 
-| Data | Import data wizard | Import and vectorize data wizard |
-|------|--------------------|----------------------------------|
-| [ADLS Gen2](search-howto-index-azure-data-lake-storage.md)  | ✅ | ✅ |
+| Data source | Import data wizard | Import data (new) wizard |
+|--|--|--|
+| [ADLS Gen2](search-howto-index-azure-data-lake-storage.md) | ✅ | ✅ |
 | [Azure Blob Storage](search-howto-indexing-azure-blob-storage.md) | ✅ | ✅ |
-| [Azure File Storage](search-file-storage-integration.md)  | ❌ | ❌ |
-| [Azure Table Storage](search-howto-indexing-azure-tables.md)  | ✅ | ✅ |
-| [Azure SQL database and managed instance](search-how-to-index-sql-database.md)  | ✅ | ✅ |
+| [Azure File Storage](search-how-to-index-logic-apps-indexers.md#supported-connectors) | ❌ | ✅ <sup>1, 2</sup> |
+| [Azure Queues](search-how-to-index-logic-apps-indexers.md#supported-connectors) | ❌ | ✅ <sup>1</sup> |
+| [Azure Table Storage](search-howto-indexing-azure-tables.md) | ✅ | ✅ |
+| [Azure SQL Database and Managed Instance](search-how-to-index-sql-database.md) | ✅ | ✅ |
 | [Cosmos DB for NoSQL](search-howto-index-cosmosdb.md) | ✅ | ✅ |
 | [Cosmos DB for MongoDB](search-howto-index-cosmosdb-mongodb.md) | ✅ | ✅ |
 | [Cosmos DB for Apache Gremlin](search-howto-index-cosmosdb-gremlin.md) | ✅ | ✅ |
 | [MySQL](search-howto-index-mysql.md)  | ❌ | ❌ |
-| [OneLake](search-how-to-index-onelake-files.md)  | ✅ | ✅ |
-| [SharePoint Online](search-howto-index-sharepoint-online.md)  | ❌ | ❌ |
-| [SQL Server on virtual machines](search-how-to-index-sql-server.md)  | ✅ | ✅ |
+| [OneDrive](search-how-to-index-logic-apps-indexers.md#supported-connectors) | ❌ | ✅ <sup>1</sup> |
+| [OneDrive for Business](search-how-to-index-logic-apps-indexers.md#supported-connectors) | ❌ | ✅ <sup>1</sup> |
+| [OneLake](search-how-to-index-onelake-files.md) | ✅ | ✅ |
+| [Service Bus](search-how-to-index-logic-apps-indexers.md#supported-connectors) | ❌ | ✅ <sup>1</sup> |
+| [SharePoint Online](search-how-to-index-logic-apps-indexers.md#supported-connectors) | ❌ | ✅ <sup>1, 2</sup> |
+| [SQL Server on virtual machines](search-how-to-index-sql-server.md) | ✅ | ✅ |
+
+<sup>1</sup> This data source uses an [Azure Logic Apps connector (preview)](search-how-to-index-logic-apps-indexers.md#supported-connectors) instead of a built-in indexer.
+
+<sup>2</sup> Instead of using a Logic Apps connector, you can use the Search Service REST APIs to programmatically index data from [Azure File Storage](search-file-storage-integration.md) or [SharePoint Online](search-howto-index-sharepoint-online.md).
 
 ### Sample data
 
-Microsoft hosts sample data so that you can omit a data source configuration step on a wizard workflow.
+Microsoft hosts the following sample data so that you can skip the wizard step for data source configuration.
 
-| Sample data | Import data wizard | Import and vectorize data wizard |
-|-------------|--------------------|----------------------------------|
-| hotels      | ✅                 | ❌                              |
-| real estate | ✅                 | ❌                              |
+| Sample data | Import data wizard | Import data (new) wizard |
+|--|--|--|
+| Hotels      | ✅ | ❌ |
+| Real estate | ✅ | ❌ |
 
 ### Skills
 
-This section lists the skills that might appear in a skillset generated by a wizard. Wizards generate a skillset and output field mappings based on options you select. After the skillset is created, you can modify its JSON definition to add more skills.
-
-Here are some points to keep in mind about the skills in the following list:
+Each wizard generates a skillset and outputs field mappings based on options you select. After the skillset is created, you can modify its JSON definition to add or remove skills.
 
-+ OCR and image analysis options are available for blobs in Azure Storage and files in OneLake, assuming the default parsing mode. Images are either an image content type (such as PNG or JPG) or an embedded image in an application file (such as PDF).
-+ Shaper is added if you configure a knowledge store.
-+ Text Split and Text Merge are added for data chunking if you choose an embedding model. They are added for other non-embedding skills if the source field granularity is set to pages or sentences.
+The following skills might appear in a wizard-generated skillset.
 
-| Skills | Import data wizard | Import and vectorize data wizard |
-|------|--------------------|----------------------------------|
-| [AI Vision multimodal](cognitive-search-skill-vision-vectorize.md)  | ❌ | ✅ |
-| [Azure OpenAI embedding](cognitive-search-skill-azure-openai-embedding.md)  | ❌ | ✅ |
-| [Azure Machine Learning (Azure AI Foundry model catalog)](cognitive-search-aml-skill.md)  | ❌ | ✅ |
-| [Document layout](cognitive-search-skill-document-intelligence-layout.md)  | ❌ | ✅ |
-| [Entity recognition](cognitive-search-skill-entity-recognition-v3.md)  | ✅ | ❌ |
-| [Image analysis (applies to blobs, default parsing, whole file indexing](cognitive-search-skill-image-analysis.md)  | ✅ | ❌ |
-| [Keyword extraction](cognitive-search-skill-keyphrases.md)  | ✅ | ❌ |
-| [Language detection](cognitive-search-skill-language-detection.md)  | ✅ | ❌ |
+| Skill | Import data wizard | Import data (new) wizard |
+|--|--|--|
+| [Azure AI Vision multimodal](cognitive-search-skill-vision-vectorize.md)  | ❌ | ✅ <sup>1</sup> |
+| [Azure OpenAI embedding](cognitive-search-skill-azure-openai-embedding.md)  | ❌ | ✅ <sup>1</sup> |
+| [Azure Machine Learning (Azure AI Foundry model catalog)](cognitive-search-aml-skill.md)  | ❌ | ✅ <sup>1</sup> |
+| [Document layout](cognitive-search-skill-document-intelligence-layout.md)  | ❌ | ✅ <sup>1</sup> |
+| [Entity recognition](cognitive-search-skill-entity-recognition-v3.md)  | ✅ | ✅ |
+| [Image analysis](cognitive-search-skill-image-analysis.md) <sup>2</sup> | ✅ | ✅ |
+| [Key phrase extraction](cognitive-search-skill-keyphrases.md)  | ✅ | ✅ |
+| [Language detection](cognitive-search-skill-language-detection.md)  | ✅ | ✅ |
 | [Text translation](cognitive-search-skill-text-translation.md)  | ✅ | ❌ |
-| [OCR (applies to blobs, default parsing, whole file indexing)](cognitive-search-skill-ocr.md)  | ✅ | ✅ |
+| [OCR](cognitive-search-skill-ocr.md) <sup>2</sup> | ✅ | ✅ |
 | [PII detection](cognitive-search-skill-pii-detection.md)  | ✅ | ❌ |
 | [Sentiment analysis](cognitive-search-skill-sentiment.md)  | ✅ | ❌ |
-| [Shaper (applies to knowledge store)](cognitive-search-skill-shaper.md)  | ✅ | ❌ |
-| [Text Split](cognitive-search-skill-textsplit.md)  | ✅ | ✅ |
-| [Text Merge](cognitive-search-skill-textmerger.md)  | ✅ | ✅ |
+| [Shaper](cognitive-search-skill-shaper.md) <sup>3</sup> | ✅ | ❌ |
+| [Text Split](cognitive-search-skill-textsplit.md) <sup>4</sup> | ✅ | ✅ |
+| [Text Merge](cognitive-search-skill-textmerger.md) <sup>4</sup> | ✅ | ✅ |
 
-### Knowledge store
+<sup>1</sup> This skill is available for RAG and multimodal RAG workflows only. Keyword search isn't supported.
 
-You can [generate a knowledge store](knowledge-store-create-portal.md) for secondary storage of enriched (skills-generated) content. You might want a knowledge store for information retrieval workflows that don't require a search engine.
+<sup>2</sup> This skill is available for Azure Storage blobs and OneLake files, assuming the default parsing mode. Images can be an image content type (such as PNG or JPG) or an embedded image in an application file (such as PDF).
 
-| Knowledge store | Import data wizard | Import and vectorize data wizard |
-|-----------------|--------------------|----------------------------------|
-| storage         | ✅                 | ❌                              |
+<sup>3</sup> This skill is added when you configure a knowledge store.
 
-## What the wizards create
+<sup>4</sup> This skill is added for data chunking when you choose an embedding model. For nonembedding skills, it's added when you set the source field granularity to pages or sentences.
 
-The import wizards create the objects described in the following table. After the objects are created, you can review their JSON definitions in the Azure portal or call them from code.
+### Semantic ranker
 
-To view these objects after the wizard runs:
+You can [configure semantic ranking](semantic-how-to-configure.md) to improve the relevance of search results.
 
-1. [Sign in to the Azure portal](https://portal.azure.com) and [find your search service](https://portal.azure.com/#blade/HubsExtension/BrowseResourceBlade/resourceType/Microsoft.Search%2FsearchServices).
+| Capability | Import data wizard | Import data (new) wizard |
+|--|--|--|
+| Semantic ranker | ❌ | ✅ |
 
-1. Select **Search management** on the menu to find pages for indexes, indexers, data sources, and skillsets.
+### Knowledge store
+
+You can [generate a knowledge store](knowledge-store-create-portal.md) for secondary storage of enriched (skills-generated) content. A knowledge store is useful for information retrieval workflows that don't require a search engine.
+
+| Capability | Import data wizard | Import data (new) wizard |
+|--|--|--|
+| Knowledge store | ✅ | ❌ |
+
+## What the wizards create
+
+The following table lists the objects created by the wizards. After the objects are created, you can review their JSON definitions in the Azure portal or call them from code.
 
 | Object | Description |
-|--------|-------------|
-| [Indexer](/rest/api/searchservice/indexers/create)  | A configuration object specifying a data source, target index, an optional skillset, optional schedule, and optional configuration settings for error handing and base-64 encoding. |
-| [Data Source](/rest/api/searchservice/data-sources/create)  | Persists connection information to a [supported data source](search-indexer-overview.md#supported-data-sources) on Azure. A data source object is used exclusively with indexers. |
-| [Index](/rest/api/searchservice/indexes/create) | Physical data structure used for full text search and other queries. |
-| [Skillset](/rest/api/searchservice/skillsets/create) | Optional. A complete set of instructions for manipulating, transforming, and shaping content, including analyzing and extracting information from image files. Skillsets are also used for integrated vectorization. Unless the volume of work fall under the limit of 20 transactions per indexer per day, the skillset must include a reference to an Azure AI services multi-service resource that provides enrichment. For integrated vectorization, you can use either Azure AI Vision or an embedding model in the Azure AI Foundry model catalog. | 
-| [Knowledge store](knowledge-store-concept-intro.md) | Optional. Available only in the **Import data** wizard. Stores enriched skillset output from in tables and blobs in Azure Storage for independent analysis or downstream processing in nonsearch scenarios. |
+|--|--|
+| [Indexer](/rest/api/searchservice/indexers/create) | Configuration object that specifies a data source, target index, optional skillset, optional schedule, and optional configuration settings for error handling and base-64 encoding. |
+| [Data source](/rest/api/searchservice/data-sources/create)  | Persists connection information to a [supported data source](search-indexer-overview.md#supported-data-sources) on Azure. A data source object is used exclusively with indexers. |
+| [Index](/rest/api/searchservice/indexes/create) | Physical data structure for full-text search, vector search, and other queries. |
+| [Skillset](/rest/api/searchservice/skillsets/create) | (Optional) Complete set of instructions for manipulating, transforming, and shaping content, including analyzing and extracting information from image files. Skillsets are also used for integrated vectorization. If the volume of work exceeds 20 transactions per indexer per day, the skillset must include a reference to an Azure AI services multi-service resource that provides enrichment. For integrated vectorization, you can use either Azure AI Vision or an embedding model in the Azure AI Foundry model catalog. |
+| [Knowledge store](knowledge-store-concept-intro.md) | (Optional) Stores enriched skillset output from tables and blobs in Azure Storage for independent analysis or downstream processing in nonsearch scenarios. Available only in the **Import data** wizard. |
+
+To view these objects after the wizards run:
+
+1. Sign in to the [Azure portal](https://portal.azure.com) and select your search service.
+1. From the left pane, select **Search management** to find pages for indexes, indexers, data sources, and skillsets.
 
 ## Benefits
 
-Before writing any code, you can use the wizards for prototyping and proof-of-concept testing. The wizards connect to external data sources, sample the data to create an initial index, and then import and optionally vectorize the data as JSON documents into an index on Azure AI Search. 
+Before you write any code, you can use the wizards for prototyping and proof-of-concept testing. The wizards connect to external data sources, sample the data to create an initial index, and then import and optionally vectorize the data as JSON documents into an index on Azure AI Search.
 
-If you're evaluating skillsets, the wizard handles output field mappings and adds helper functions to create usable objects. Text split is added if you specify a parsing mode. Text merge is added if you chose image analysis so that the wizard can reunite text descriptions with image content. Shaper skills are added to support valid projections if you chose the knowledge store option. All of the above tasks come with a learning curve. If you're new to enrichment, the ability to have these steps handled for you allows you to measure the value of a skill without having to invest much time and effort.
+If you're evaluating skillsets, the wizards handle output field mappings and add helper functions to create usable objects. [Text Split](cognitive-search-skill-textsplit.md) is added when you specify a parsing mode. [Text Merge](cognitive-search-skill-textmerger.md) is added when you choose image analysis so that the wizards can reunite text descriptions with image content. [Shaper](cognitive-search-skill-shaper.md) is added to support valid projections when you choose the knowledge store option. All of these tasks come with a learning curve. If you're new to enrichment, having these steps handled for you allows you to measure the value of a skill without investing much time and effort.
 
-Sampling is the process by which an index schema is inferred, and it has some limitations. When the data source is created, the wizard picks a random sample of documents to decide what columns are part of the data source. Not all files are read, as this could potentially take hours for very large data sources. Given a selection of documents, source metadata, such as field name or type, is used to create a fields collection in an index schema. Depending on the complexity of source data, you might need to edit the initial schema for accuracy, or extend it for completeness. You can make your changes inline on the index definition page.
+Sampling is the process by which an index schema is inferred, which has some limitations. When the data source is created, the wizards pick a random sample of documents to decide what columns are part of the data source. Not all files are read, as doing so could potentially take hours for large data sources. Given a selection of documents, source metadata (such as field name or type) is used to create a fields collection in an index schema. Based on the complexity of the source data, you might need to edit the initial schema for accuracy or extend it for completeness. You can make your changes inline on the index definition page.
 
-Overall, the advantages of using the wizard are clear: as long as requirements are met, you can create a queryable index within minutes. Some of the complexities of indexing, such as serializing data as JSON documents, are handled by the wizards.
+Overall, the advantages of the wizards are clear: as long as requirements are met, you can create a queryable index within minutes. The wizards handle some of the complexities of indexing, such as serializing data as JSON documents.
 
 ## Limitations
 
-The import wizards aren't without limitations. Constraints are summarized as follows:
+The wizards have the following limitations:
 
-+ The wizards don't support iteration or reuse. Each pass through the wizard creates a new index, skillset, and indexer configuration. Only data sources can be persisted and reused within the wizard. To edit or refine other objects, either delete the objects and start over, or use the REST APIs or .NET SDK to modify the structures.
++ The wizards don't support iteration or reuse. Each pass through the wizards creates an index, skillset, and indexer configuration. You can reuse data sources only in the **Import data** wizard. After you finish the wizards, you can edit the created objects by using other portal tools, the REST APIs, or the Azure SDKs.
 
 + Source content must reside in a [supported data source](search-indexer-overview.md#supported-data-sources).
 
-+ Sampling is over a subset of source data. For large data sources, it's possible for the wizard to miss fields. You might need to extend the schema, or correct the inferred data types, if sampling is insufficient.
++ Sampling occurs over a subset of source data. For large data sources, it's possible for the wizards to miss fields. If sampling is insufficient, you might need to extend the schema or correct the inferred data types.
 
-+ AI enrichment, as exposed in the Azure portal, is limited to a subset of built-in skills. 
++ [AI enrichment](cognitive-search-concept-intro.md), as exposed in the Azure portal, is limited to a subset of built-in skills.
 
-+ A [knowledge store](knowledge-store-concept-intro.md), which can be created by the **Import data** wizard, is limited to a few default projections and uses a default naming convention. If you want to customize names or projections, you'll need to create the knowledge store through REST API or the SDKs.
++ A [knowledge store](knowledge-store-concept-intro.md), which is only available through the **Import data** wizard, is limited to a few default projections and uses a default naming convention. To customize projections and names, you must create the knowledge store through the REST APIs or Azure SDKs.
 
 ## Secure connections
 
-The import wizards make outbound connections using the Azure portal controller and public endpoints. You can't use the wizards if Azure resources are accessed over a private connection or through a shared private link.
+The wizards use the Azure portal controller and public endpoints to make outbound connections. You can't use the wizards if Azure resources are accessed over a private connection or through a shared private link.
 
 You can use the wizards over restricted public connections, but not all functionality is available.
 
 + On a search service, importing the built-in sample data requires a public endpoint and no firewall rules.
 
-  Sample data is hosted by Microsoft on specific Azure resources. The Azure portal controller connects to those resources over a public endpoint. If you put your search service behind a firewall, you get this error when attempting to retrieve the builtin sample data: `Import configuration failed, error creating Data Source`, followed by `"An error has occured."`.
+  Microsoft hosts the sample data on specific Azure resources. The Azure portal controller connects to these resources over a public endpoint. If your search service is behind a firewall, you get the following error when you attempt to retrieve the sample data: `Import configuration failed, error creating Data Source`, followed by `"An error has occured."`.
 
 + On supported Azure data sources protected by firewalls, you can retrieve data if you have the right firewall rules in place.
 
   The Azure resource must admit network requests from the IP address of the device used on the connection. You should also list Azure AI Search as a trusted service on the resource's network configuration. For example, in Azure Storage, you can list `Microsoft.Search/searchServices` as a trusted service.
 
-+ On connections to an Azure AI services multi-service account that you provide, or on connections to embedding models deployed in [Azure AI Foundry portal](https://ai.azure.com/?cid=learnDocs) or Azure OpenAI, public internet access must be enabled unless your search service meets the creation date, tier, and region requirements for private connections. For more information about these requirements, see [Make outbound connections through a shared private link](search-indexer-howto-access-private.md).
++ On connections to an Azure AI services multi-service account that you provide, or on connections to embedding models deployed in the [Azure AI Foundry portal](https://ai.azure.com/?cid=learnDocs) or Azure OpenAI, public internet access must be enabled unless your search service meets the creation date, tier, and region requirements for private connections. For more information, see [Make outbound connections through a shared private link](search-indexer-howto-access-private.md).
 
-  Connections to Azure AI services multi-service are for [billing purposes](cognitive-search-attach-cognitive-services.md). Billing occurs when API calls exceed the free transaction count (20 per indexer run) for built-in skills called by the **Import data** wizard or integrated vectorization in the **Import and vectorize data wizard**.
+  Connections to Azure AI services multi-service accounts are for [billing purposes](cognitive-search-attach-cognitive-services.md). You're billed when API calls for built-in skills (in the **Import data** wizard or the keyword search workflow in the **Import data (new)** wizard) and integrated vectorization (in the **Import data (new)** wizard) exceed the free transaction count (20 per indexer run).
 
   If Azure AI Search can't connect:
 
-  + In the **Import and vectorize data wizard**, the error is `"Access denied due to Virtual Network/Firewall rules."`
+  + In the **Import data (new)** wizard, the error is `"Access denied due to Virtual Network/Firewall rules."`.
 
   + In the **Import data** wizard, there's no error, but the skillset won't be created.
 
 If firewall settings prevent your wizard workflows from succeeding, consider scripted or programmatic approaches instead.
 
 ## Workflow
 
-The wizard is organized into four main steps:
+Both wizards follow a similar high-level workflow:
 
 1. Connect to a supported Azure data source.
 
-1. Create an index schema, inferred by sampling source data.
+1. (Optional) Add skills to extract or generate content and structure.
 
-1. Optionally, it adds skills to extract or generate content and structure. Inputs for creating a knowledge store are collected in this step.
+1. Create an index schema, inferred by sampling source data.
 
-1. Run the wizard to create objects, optionally vectorize data, load data into an index, set a schedule and other configuration options.
+1. Run the wizard to create objects, optionally vectorize data, load data into an index, set a schedule, and configure other options.
 
-The workflow is a pipeline, so it's one way. You can't use the wizard to edit any of the objects that were created, but you can use other portal tools, such as the index or indexer designer or the JSON editors, for allowed updates.
+The workflow is a one-way pipeline. You can't use the wizard to edit any of the objects that were created, but you can use other portal tools, such as the index designer, indexer designer, or JSON editors, to make allowed updates.
 
 ### Starting the wizards
 
-Here's how you start the wizards.
+To start the wizards:
 
-1. In the [Azure portal](https://portal.azure.com), open the search service page from the dashboard or [find your service](https://portal.azure.com/#blade/HubsExtension/BrowseResourceBlade/resourceType/Microsoft.Search%2FsearchServices) in the service list.
+1. Sign in to the [Azure portal](https://portal.azure.com) and select your search service.
 
-1. On the **Overview** page, select **Import data** or **Import and vectorize data wizard**.
+1. On the **Overview** page, select **Import data** or **Import data (new)**.
 
-    :::image type="content" source="media/search-import-data-portal/import-data-cmd.png" alt-text="Screenshot of the import wizard options." border="true":::
+    :::image type="content" source="media/search-import-data-portal/import-wizards.png" alt-text="Screenshot of the import wizard options." border="true":::
 
-    The wizards open fully expanded in the browser window so that you have more room to work.
+    The wizards open fully expanded in the browser window, giving you more room to work.
 
-1. If you selected **Import data**, you can select the **Samples** option to index a Microsoft-hosted dataset from a supported data source.
+1. If you selected **Import data**, you can select **Samples** to index a Microsoft-hosted dataset from a supported data source.
 
     :::image type="content" source="media/search-what-is-an-index/add-index-import-samples.png" alt-text="Screenshot of the import data page with the samples option selected." border="true":::
 
-1. Follow the remaining steps in the wizard to create the index and indexer.
+1. Follow the remaining steps to create the index, indexer, and other applicable objects.
 
-You can also launch **Import data** from other Azure services, including Azure Cosmos DB, Azure SQL Database, SQL Managed Instance, and Azure Blob Storage. Look for **Add Azure AI Search** in the left-navigation pane on the service overview page.
+You can also launch **Import data** from other Azure services, including Azure Cosmos DB, Azure SQL Database, SQL Managed Instance, and Azure Blob Storage. Look for **Add Azure AI Search** in the left pane on the service overview page.
 
 <a name="data-source-inputs"></a>
 
 ### Data source configuration in the wizard
 
-The wizards connect to an external [supported data source](search-indexer-overview.md#supported-data-sources) using the internal logic provided by Azure AI Search indexers, which are equipped to sample the source, read metadata, crack documents to read content and structure, and serialize contents as JSON for subsequent import to Azure AI Search.
+The wizards connect to an external [supported data source](search-indexer-overview.md#supported-data-sources) using the internal logic provided by indexers, which are equipped to sample the source, read metadata, crack documents to read content and structure, and serialize contents as JSON for subsequent import to Azure AI Search.
 
-You can paste in a connection to a supported data source in a different subscription or region, but the **Choose an existing connection** picker is scoped to the active subscription.
+In the **Import data** wizard, you can paste a connection to a supported data source in a different subscription or region, but the **Choose an existing connection** picker is scoped to the active subscription.
 
 :::image type="content" source="media/search-import-data-portal/choose-connection-same-subscription.png" alt-text="Screenshot of the Connect to your data tab." border="true":::
 
-Not all preview data sources are guaranteed to be available in the wizard. Because each data source has the potential for introducing other changes downstream, a preview data source will only be added to the data sources list if it fully supports all of the experiences in the wizard, such as skillset definition and index schema inference.
+Not all preview data sources are guaranteed to be available in the wizards. Because each data source has the potential to introduce changes downstream, a preview data source is only added when it fully supports all of the wizard's experiences, such as skillset definition and index schema inference.
 
-You can only import from a single table, database view, or equivalent data structure, however the structure can include hierarchical or nested substructures. For more information, see [How to model complex types](search-howto-complex-data-types.md).
+You can only import from a single table, database view, or equivalent data structure. However, the structure can include hierarchical or nested substructures. For more information, see [How to model complex types](search-howto-complex-data-types.md).
 
 ### Skillset configuration in the wizard
 
-Skillset configuration occurs after the data source definition because the type of data source informs the availability of certain built-in skills. In particular, if you're indexing files from Blob storage, your choice of parsing mode of those files determine whether sentiment analysis is available.
+Skillset configuration occurs after the data source definition because the type of data source informs the availability of certain built-in skills. For example, if you're indexing files from Azure Blob Storage, the parsing mode you choose for those files determines whether sentiment analysis is available.
 
-The wizard adds the skills you choose. It also adds other skills that are necessary for achieving a successful outcome. For example, if you specify a knowledge store, the wizard adds a Shaper skill to support projections (or physical data structures).
+The wizards add not only skills you choose but also skills that are necessary for a successful outcome. For example, if you specify a knowledge store in the **Import data** wizard, the wizard adds a Shaper skill to support projections or physical data structures.
 
-Skillsets are optional and there's a button at the bottom of the page to skip ahead if you don't want AI enrichment.
+Skillsets are optional, and there's a button at the bottom of the page to skip ahead if you don't want AI enrichment.
 
 <a name="index-definition"></a>
 
 ### Index schema configuration in the wizard
 
-The wizards sample your data source to detect the fields and field type. Depending on the data source, it might also offer fields for indexing metadata.
+The wizards sample your data source to detect the fields and field types. Depending on the data source, they might also offer fields for indexing metadata.
 
 Because sampling is an imprecise exercise, review the index for the following considerations:
 
-1. Is the field list accurate? If your data source contains fields that weren't picked up in sampling, you can manually add any new fields that sampling missed, and remove any that don't add value to a search experience or that won't be used in a [filter expression](search-query-odata-filter.md) or [scoring profile](index-add-scoring-profiles.md).
+1. Is the field list accurate? If your data source contains fields that weren't picked up in sampling, you can manually add the missed fields. You can also remove fields that don't add value to the search experience or won't be used in a [filter expression](search-query-odata-filter.md) or [scoring profile](index-add-scoring-profiles.md).
 
-1. Is the data type appropriate for the incoming data? Azure AI Search supports the [entity data model (EDM) data types](/rest/api/searchservice/supported-data-types). For Azure SQL data, there's [mapping chart](search-how-to-index-sql-database.md#TypeMapping) that lays out equivalent values. For more background, see [Field mappings and transformations](search-indexer-field-mappings.md).
+1. Is the data type appropriate for the incoming data? Azure AI Search supports the [entity data model (EDM) data types](/rest/api/searchservice/supported-data-types). For Azure SQL data, there's a [mapping chart](search-how-to-index-sql-database.md#TypeMapping) that lays out equivalent values. For more information, see [Field mappings and transformations](search-indexer-field-mappings.md).
 
-1. Do you have one field that can serve as the *key*? This field must be Edm.string and it must uniquely identify a document. For relational data, it might be mapped to a primary key. For blobs, it might be the `metadata-storage-path`. If field values include spaces or dashes, you must set the **Base-64 Encode Key** option in the **Create an Indexer** step, under **Advanced options**, to suppress the validation check for these characters.
+1. Do you have one field that can serve as the *key*? This field must be an Edm.String that uniquely identifies a document. For relational data, it might be mapped to a primary key. For blobs, it might be the `metadata-storage-path`. If field values include spaces or dashes, you must set the **Base-64 Encode Key** option in the **Create an indexer** step, under **Advanced options**, to suppress the validation check for these characters.
 
 1. Set attributes to determine how that field is used in an index.
 
-   Take your time with this step because attributes determine the physical expression of fields in the index. If you want to change attributes later, even programmatically, you'll almost always need to drop and rebuild the index. Core attributes like **Searchable** and **Retrievable** have a [negligible effect on storage](search-what-is-an-index.md#index-size). Enabling filters and using suggesters increase storage requirements.
+   Take your time with this step because attributes determine the physical expression of fields in the index. If you want to change attributes later, even programmatically, you almost always need to drop and rebuild the index. Core attributes like **Searchable** and **Retrievable** have a [negligible effect on storage](search-what-is-an-index.md#index-size). Enabling filters and using suggesters increase storage requirements.
 
-   + **Searchable** enables full-text search. Every field used in free form queries or in query expressions must have this attribute. Inverted indexes are created for each field that you mark as **Searchable**.
+   + **Searchable** enables full-text search. Every field used in free-form queries or in query expressions must have this attribute. Inverted indexes are created for each field that you mark as **Searchable**.
 
    + **Retrievable** returns the field in search results. Every field that provides content to search results must have this attribute. Setting this field doesn't appreciably affect index size.
 
-   + **Filterable** allows the field to be referenced in filter expressions. Every field used in a **$filter**  expression must have this attribute. Filter expressions are for exact matches. Because text strings remain intact, more storage is required to accommodate the verbatim content.
+   + **Filterable** allows the field to be referenced in filter expressions. Every field used in a **$filter** expression must have this attribute. Filter expressions are for exact matches. Because text strings remain intact, more storage is required to accommodate the verbatim content.
 
    + **Facetable** enables the field for faceted navigation. Only fields also marked as **Filterable** can be marked as **Facetable**.
 
    + **Sortable** allows the field to be used in a sort. Every field used in an **$Orderby** expression must have this attribute.
 
-1. Do you need [lexical analysis](search-lucene-query-architecture.md#stage-2-lexical-analysis)? For Edm.string fields that are **Searchable**, you can set an **Analyzer** if you want language-enhanced indexing and querying.
+1. Do you need [lexical analysis](search-lucene-query-architecture.md#stage-2-lexical-analysis)? For Edm.String fields that are **Searchable**, you can set an **Analyzer** if you want language-enhanced indexing and querying.
 
-   The default is *Standard Lucene* but you could choose *Microsoft English* if you wanted to use Microsoft's analyzer for advanced lexical processing, such as resolving irregular noun and verb forms. Only language analyzers can be specified in the Azure portal. If you use a custom analyzer or a non-language analyzer like Keyword, Pattern, and so forth, you must create it programmatically. For more information about analyzers, see [Add language analyzers](search-language-support.md).
+   The default is *Standard Lucene*, but you can choose *Microsoft English* if you wanted to use Microsoft's analyzer for advanced lexical processing, such as resolving irregular noun and verb forms. Only language analyzers can be specified in the Azure portal. If you want to use a custom analyzer or non-language analyzer, such as Keyword or Pattern, you must create it programmatically. For more information, see [Add language analyzers](search-language-support.md).
 
-1. Do you need typeahead functionality in the form of autocomplete or suggested results? Select the **Suggester** the checkbox to enable [typeahead query suggestions and autocomplete](index-add-suggesters.md) on selected fields. Suggesters add to the number of tokenized terms in your index, and thus consume more storage.
+1. Do you need typeahead functionality in the form of autocomplete or suggested results? Select the **Suggester** checkbox to enable [typeahead query suggestions and autocomplete](index-add-suggesters.md) on selected fields. Suggesters add to the number of tokenized terms in your index and thus consume more storage.
 
 ### Indexer configuration in the wizard
 
-The last page of the wizard collects user inputs for indexer configuration. You can [specify a schedule](search-howto-schedule-indexers.md) and set other options that will vary by the data source type.
+The last page of the wizard collects user inputs for indexer configuration. You can [specify a schedule](search-howto-schedule-indexers.md) and set other options that vary by the data source type.
 
-Internally, the wizard also sets up the following definitions, which aren't visible in the indexer until after it's created:
+Internally, the wizard sets up the following definitions, which aren't visible in the indexer until after it's created.
 
-+ [field mappings](search-indexer-field-mappings.md) between the data source and index
-+ [output field mappings](cognitive-search-output-field-mapping.md) between skill output and an index
++ [Field mappings](search-indexer-field-mappings.md) between the data source and index.
++ [Output field mappings](cognitive-search-output-field-mapping.md) between the skill output and an index.
 
 ## Try the wizards
 
-The best way to understand the benefits and limitations of the wizard is to step through it. Here are some quickstarts that are based on the wizard.
+The best way to understand the benefits and limitations of the wizards is to step through them. The following quickstarts are based on the wizards.
 
 + [Quickstart: Create a search index](search-get-started-portal.md)
 + [Quickstart: Create a text translation and entity skillset](search-get-started-skillset.md)
 + [Quickstart: Create a vector index](search-get-started-portal-import-vectors.md)
-+ [Quickstart: Image search (vectors)](search-get-started-portal-image-search.md)
++ [Quickstart: Create a multimodal index](search-get-started-portal-image-search.md)