Merge pull request #4741 from HeidiSteen/heidist-rb-rag

BULK EDIT quickstart wizard reversion + small TOC update

Author: JillGrant615

articles/search/cognitive-search-aml-skill.md

@@ -37,7 +37,7 @@ Starting in 2024-05-01-preview REST API and in the Azure portal (which also targ
 
 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, the **AML** skill and the model catalog vectorizer should be used together so that you're using the same embedding model for both indexing and queries. See [Use embedding models from Azure AI Foundry model catalog](vector-search-integrated-vectorization-ai-studio.md) for details and for a list of the [supported embedding models](vector-search-integrated-vectorization-ai-studio.md#supported-embedding-models).
 
-We recommend using the [**Quickstart wizard**](search-get-started-portal-import-vectors.md) to generate a skillset that includes an AML skill for deployed embedding models on Azure AI Foundry. AML skill definition for inputs, outputs, and mappings are generated by the wizard, which gives you an easy way to test a model before writing any code.
+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 on Azure AI Foundry. AML skill definition for inputs, outputs, and mappings are generated by the wizard, which gives you an easy way to test a model before writing any code.
 
 ## Prerequisites
 

articles/search/cognitive-search-concept-troubleshooting.md

@@ -17,7 +17,7 @@ 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 [**Quickstart 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 [**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.
 
 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.
 

articles/search/cognitive-search-defining-skillset.md

@@ -269,7 +269,7 @@ 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 [Quickstart wizard](search-get-started-portal-import-vectors.md).
++ Try the [Import data wizard](search-get-started-portal.md) or [Import and vectorize data 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.
 

articles/search/cognitive-search-how-to-debug-skillset.md

@@ -131,7 +131,7 @@ 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 [Quickstart 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 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
 
 + [**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). 
 

articles/search/cognitive-search-skill-azure-openai-embedding.md

@@ -22,7 +22,7 @@ Your Azure OpenAI Service must have an associated [custom subdomain](/azure/ai-s
 
 Azure OpenAI Service resources (with access to embedding models) that were created in Azure AI Foundry portal aren't supported. Only the Azure OpenAI Service resources created in the Azure portal are compatible with the **Azure OpenAI Embedding** skill integration.
 
-The [Quickstart 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 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.
 
 > [!NOTE]
 > This skill is bound to Azure OpenAI and is charged at the existing [Azure OpenAI pay-as-you go price](https://azure.microsoft.com/pricing/details/cognitive-services/openai-service/#pricing).

articles/search/cognitive-search-skill-document-intelligence-layout.md

@@ -28,7 +28,7 @@ Supported regions vary by modality:
 
 + When you're using AI services keys [to attach your multi-service resource to your skillset](cognitive-search-attach-cognitive-services.md#bill-through-a-resource-key) via the REST API, both your Azure AI Search service and AI multi-service resource must be in the same region. This is only possible in the Azure regions of **East US**, **West Europe**, **North Central US**, **West US 2**. But if you're using a managed identity for [billing through a keyless connection](cognitive-search-attach-cognitive-services.md#bill-through-a-keyless-connection), your Azure AI Search service must be in one of the following regions: **East US**, **West Europe**, **North Central US**, **West US 2**. On the other hand, you can use AI Document Intelligence through an Azure AI multi-service resource in any region where this service is available. See [Product availability by region](https://azure.microsoft.com/explore/global-infrastructure/products-by-region/table).
 
-+ In the [Quickstart wizard](search-import-data-portal.md) in the Azure portal, you can enable document layout detection in the data source connection step. Document layout detection in the portal is available in the following Azure regions: **East US**, **West Europe**, **North Central US**. Create an Azure AI multi-service resource in one of these three regions to get the portal experience.
++ In the [Import and vectorize data wizard](search-import-data-portal.md) in the Azure portal, you can enable document layout detection in the data source connection step. Document layout detection in the portal is available in the following Azure regions: **East US**, **West Europe**, **North Central US**. Create an Azure AI multi-service resource in one of these three regions to get the portal experience.
 
 Supported file formats include:
 

articles/search/hybrid-search-how-to-query.md

@@ -28,7 +28,7 @@ In this article, learn how to:
 
 ## Prerequisites
 
-+ A search index containing `searchable` vector and nonvector fields. We recommend the [Quickstart 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 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).
 
 + (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).
 

articles/search/search-features-list.md

@@ -73,7 +73,7 @@ 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/>[**Quickstart 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 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. |
 | 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

articles/search/search-file-storage-integration.md

@@ -25,7 +25,7 @@ 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.
-+ [Quickstart wizard](search-get-started-portal-import-vectors.md) in the Azure portal.
++ [Import and vectorize data wizard](search-get-started-portal-import-vectors.md) in the Azure portal.
 
 ## Prerequisites
 

articles/search/search-get-started-portal-image-search.md

@@ -13,7 +13,7 @@ ms.custom:
 
 # Quickstart: Search for multimodal content in the Azure portal
 
-In this quickstart, you use the **Quickstart 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. 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.
 
@@ -71,7 +71,7 @@ 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 **Quickstart wizard**.
+1. On the **Overview** page, select **Import and vectorize data wizard**.
 
    :::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.":::
 
@@ -212,7 +212,7 @@ When the wizard completes the configuration, it creates the following objects:
 
 ## Check results
 
-Search Explorer accepts text, images, and vectors as query inputs. For images, Search Explorer vectorizes the image and sends the vector as a query input to the search engine. Image vectorization assumes that your index has a vectorizer definition, which the **Quickstart wizard** creates based on your embedding model inputs.
+Search Explorer accepts text, images, and vectors as query inputs. For images, Search Explorer vectorizes the image and sends the vector as a query input to the search engine. Image vectorization assumes that your index has a vectorizer definition, which the **Import and vectorize data wizard** creates based on your embedding model inputs.
 
 The following steps assume that you're searching for images. For the other two query types, see [Quickstart: Keyword search](search-get-started-portal.md#query-with-search-explorer) and [Quickstart: Vector search](search-get-started-portal-import-vectors.md#check-results).
 
@@ -248,4 +248,4 @@ This quickstart uses billable Azure resources. If you no longer need the resourc
 
 ## Next step
 
-This quickstart introduced you to the **Quickstart 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** 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).