Reverted and added screenshots of portal wizards

Author: haileytap

articles/search/media/search-explorer/search-explorer-cmd2.png

@@ -6,14 +6,14 @@ author: haileytap
 ms.author: haileytapia
 ms.service: azure-ai-search
 ms.topic: quickstart
-ms.date: 05/09/2025
+ms.date: 05/12/2025
 ms.custom:
   - references_regions
 ---
 
 # 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. Multimodality refers to the ability to process and query over multiple types of data, such as text and images.
+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). Multimodality refers to the ability to process and query over multiple types of data, such as text and images.
 
 The sample data consists of a multimodal PDF in the [azure-search-sample-data](https://github.com/Azure-Samples/azure-search-sample-data/tree/main/sustainable-ai-pdf) repo, but you can use different files and still follow this quickstart.
 
@@ -71,14 +71,16 @@ To start the wizard for multimodal search:
 
 1. Sign in to the [Azure portal](https://portal.azure.com/) and go to your Azure AI Search service.
 
-1. On the **Overview** page, select **Import and vectorize data wizard**.
+1. On the **Overview** page, select **Import and vectorize data**.
 
-   :::image type="content" source="media/search-get-started-portal-import-vectors/command-bar-quickstart-wizard.png" alt-text="Screenshot of the command to open the wizard for importing and vectorizing data.":::
+   :::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.":::
 
 1. Select your data source: **Azure Blob Storage** or **Azure Data Lake Storage Gen2**.
 
 1. Select the **Multimodal RAG** tile.
 
+   :::image type="content" source="media/search-get-started-portal-images/wizard-scenarios-multimodal-rag.png" alt-text="Screenshot of the Multimodal RAG tile in the wizard." border="true" lightbox="media/search-get-started-portal-images/wizard-scenarios-multimodal-rag.png":::
+
 ## Connect to your data
 
 Azure AI Search requires a connection to the data source that contains the sample data. In this case, the data source is an Azure Storage account.
@@ -248,4 +250,4 @@ 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** that creates all of the necessary objects for multimodal search. If you want to explore each step in detail, try an [integrated vectorization sample](https://github.com/Azure/azure-search-vector-samples/blob/main/demo-python/code/integrated-vectorization/azure-search-integrated-vectorization-sample.ipynb).
+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 [Tutorial: Index mixed content using multimodal embeddings and the Document Extraction skill](tutorial-multimodal-indexing-with-embedding-and-doc-extractio.md).

articles/search/media/search-get-started-portal-images/wizard-scenarios-multimodal-rag.png

@@ -9,7 +9,7 @@ ms.custom:
   - build-2024
   - ignite-2024
 ms.topic: quickstart
-ms.date: 05/08/2025
+ms.date: 05/12/2025
 ---
 
 # Quickstart: Vectorize text in the Azure portal
@@ -259,9 +259,9 @@ For the model catalog, you should have an [Azure AI Foundry project](/azure/ai-f
 
 1. Sign in to the [Azure portal](https://portal.azure.com/) and go to your Azure AI Search service.
 
-1. On the **Overview** page, select **Import and vectorize data wizard**.
+1. On the **Overview** page, select **Import and vectorize data**.
 
-   :::image type="content" source="media/search-get-started-portal-import-vectors/command-bar-quickstart-wizard.png" alt-text="Screenshot of the command to open the wizard for importing and vectorizing data.":::
+   :::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.":::
 
 1. Select your data source:
 
@@ -273,6 +273,8 @@ For the model catalog, you should have an [Azure AI Foundry project](/azure/ai-f
 
 1. Select the **RAG** tile.
 
+   :::image type="content" source="media/search-get-started-portal-import-vectors/wizard-scenarios-rag.png" alt-text="Screenshot of the RAG tile in the wizard." border="true" lightbox="media/search-get-started-portal-import-vectors/wizard-scenarios-rag.png":::
+
 ## Connect to your data
 
 The next step is to connect to a data source to use for the search index.

articles/search/media/search-get-started-portal-import-vectors/command-bar-quickstart-wizard.png

@@ -9,7 +9,7 @@ ms.service: azure-ai-search
 ms.custom:
   - ignite-2024
 ms.topic: concept-article
-ms.date: 05/08/2025
+ms.date: 05/12/2025
 customer intent: As a developer, I want to use wizards for index creation so that I can query the content quickly.
 ---
 
@@ -23,12 +23,12 @@ Azure AI Search has two wizards that automate indexing and object creation so th
 
 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.
 
-This article isn't a step by step. For help with using the wizard with sample data see:
+This isn't a step-by-step article. To use the wizard with sample data, see:
 
 + [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: Image search (vectors)](search-get-started-portal-image-search.md)
 
 ## Supported data sources and scenarios
 
@@ -268,4 +268,4 @@ The best way to understand the benefits and limitations of the wizard is to step
 + [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: Image search (vectors)](search-get-started-portal-image-search.md)

articles/search/media/search-get-started-portal-import-vectors/wizard-scenarios-rag.png

@@ -145,9 +145,10 @@ Here are some of the key benefits of the integrated vectorization:
 
 + Projecting chunked content to secondary indexes. Secondary indexes are created as you would any search index (a schema with fields and other constructs), but they're populated in tandem with a primary index by an indexer. Content from each source document flows to fields in primary and secondary indexes during the same indexing run. 
 
-  Secondary indexes are intended for question and answer or chat style apps. The secondary index contains granular information for more specific matches, but the parent index has more information and can often produce a more complete answer. When a match is found in the secondary index, the query returns the parent document from the primary index. For example, assuming a large PDF as a source document, the primary index might have basic information (title, date, author, description), while a secondary index has chunks of searchable content. 
+  Secondary indexes are intended for question and answer or chat style apps. The secondary index contains granular information for more specific matches, but the parent index has more information and can often produce a more complete answer. When a match is found in the secondary index, the query returns the parent document from the primary index. For example, assuming a large PDF as a source document, the primary index might have basic information (title, date, author, description), while a secondary index has chunks of searchable content.
 
 ## Next steps
 
++ [Set up integrated vectorization](search-how-to-integrated-vectorization.md)
 + [Configure a vectorizer in a search index](vector-search-how-to-configure-vectorizer.md)
 + [Configure index projections in a skillset](index-projections-concept-intro.md)