final readthrough, minor edits

Author: HeidiSteen

articles/search/search-how-to-create-search-index.md

@@ -14,17 +14,15 @@ ms.date: 07/01/2024
 
 # Create an index in Azure AI Search
 
-In Azure AI Search, query requests target the searchable text in a [**search index**](search-what-is-an-index.md). 
-
-In this article, learn the steps for defining a schema for the index and pushing it to a search service. Creating an index establishes the physical data structures on your search service. Once the index exists, [**load the index**](search-what-is-data-import.md) as a separate task. 
+In this article, learn the steps for defining a schema for a [**search index**](search-what-is-an-index.md) and pushing it to a search service. Creating an index establishes the physical data structures on your search service. Once the index exists, [**load the index**](search-what-is-data-import.md) as a separate task. 
 
 ## Prerequisites
 
-+ Write permissions as a [**Search Service Contributor**](search-security-rbac.md). If you're using key-based authentication provide an [admin API key](search-security-api-keys.md) on the request.
++ Write permissions as a [**Search Service Contributor**](search-security-rbac.md) or an [admin API key](search-security-api-keys.md) for key-based authentication.
 
 + An understanding of the data you want to index. A search index is based on external content that you want to make searchable. Searchable content is stored as fields in an index. You should have a clear idea of which source fields you want to make searchable, retrievable, filterable, facetable, and sortable (see the [schema checklist](#schema-checklist) for guidance).
 
-  You must also have a unique field in source data that can be used as the [document key (or ID)](#document-keys) in the index.
++ You must also have a unique field in source data that can be used as the [document key (or ID)](#document-keys) in the index.
 
 + A stable index location. Moving an existing index to a different search service isn't supported out-of-the-box. Revisit application requirements and make sure that your existing search service (capacity and location), are sufficient for your needs.
 
@@ -50,7 +48,11 @@ Use this checklist to assist the design decisions for your search index.
 
 1. Identify a [document key](#document-keys). A document key is an index requirement. It's a single string field and it's populated from a source data field that contains unique values. For example, if you're indexing from Blob Storage, the metadata storage path is often used as the document key because it uniquely identifies each blob in the container.
 
-1. Identify the fields in your data source that contribute searchable content in the index. Searchable nonvector content includes short or long strings that are queried using the full text search engine. If the content is verbose (small phrases or bigger chunks), experiment with different analyzers to see how the text is tokenized. Searchable vector content can be images or text (in any language) that exists as a mathematical representation. You can use narrow data types or vector compression to make vector fields smaller.
+1. Identify the fields in your data source that contribute searchable content in the index.
+
+   Searchable nonvector content includes short or long strings that are queried using the full text search engine. If the content is verbose (small phrases or bigger chunks), experiment with different analyzers to see how the text is tokenized. 
+
+   Searchable vector content can be images or text (in any language) that exists as a mathematical representation. You can use narrow data types or vector compression to make vector fields smaller.
 
    [Field attribute assignments](search-what-is-an-index.md#index-attributes) determine both search behaviors and the physical representation of your index on the search service. Determining how fields should be specified is an iterative process for many customers. To speed up iterations, start with sample data so that you can drop and rebuild easily.
 

articles/search/search-how-to-load-search-index.md

@@ -34,19 +34,20 @@ 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 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 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.
 
 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.Storage%2storageAccounts/).
 
 1. On the Overview page, select **Import data** or **Import and vectorize data** on the command bar to create and populate a search index. 
 
-   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 (preview)](search-get-started-portal-import-vectors.md).
-
    :::image type="content" source="media/search-import-data-portal/import-data-cmd.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 (preview)](search-get-started-portal-import-vectors.md).
+
 1. After the wizard is finished, use [Search Explorer](search-explorer.md) to check for results.
 
-If indexers are already defined, you can [reset and run an indexer](search-howto-run-reset-indexers.md) from the Azure portal, which is useful if you're adding fields incrementally. Reset forces the indexer to start over, picking up all fields from all source documents.
+> [!TIP]
+> The import wizards create and run indexers. If indexers are already defined, you can [reset and run an indexer](search-howto-run-reset-indexers.md) from the Azure portal, which is useful if you're adding fields incrementally. Reset forces the indexer to start over, picking up all fields from all source documents.
 
 ## Use the REST APIs
 
@@ -90,6 +91,8 @@ If indexers are already defined, you can [reset and run an indexer](search-howto
    | mergeOrUpload | Behaves like merge if the document exists, and upload if the document is new. This is the most common action for incremental updates. |
    | upload | Similar to an "upsert" where the document is inserted if it's new, and updated or replaced if it exists. If the document is missing values that the index requires, the document field's value is set to null. |
 
+1. Send the request.
+
 1. [Look up the documents](/rest/api/searchservice/documents/get) you just added as a validation step:
 
     ```http
@@ -160,8 +163,6 @@ Code samples include:
 
 ---
 
-Internally during indexing, each vector field is populated with embeddings in an internal vector index, and each nonvector field's inverted index is populated with all of the unique, tokenized words from each document. Each field is associated with a document key that determines the logical structure of the document. For example, when indexing a hotels data set, an inverted index created for a City field might contain terms for Seattle, Portland, and so forth. Documents that include Seattle or Portland in the City field would have their document ID listed alongside the term. On any [Documents - Index](/rest/api/searchservice/documents) operation, the terms and document ID list are updated accordingly. For more information about inverted indexes, see [Full text search in Azure AI Search](search-lucene-query-architecture.md).
-
 ## See also
 
 + [Search indexes overview](search-what-is-an-index.md)