Merge pull request #92539 from HeidiSteen/heidist-master

Azure Search: AI blob doc, Tessa's feedback

Author: PRMerger13

articles/search/knowledge-store-create-rest.md

@@ -25,7 +25,7 @@ Create the following services:
 
 - Create an [Azure storage account](https://docs.microsoft.com/azure/storage/common/storage-quickstart-create-account) to store the sample data and the knowledge store. Your storage account must use the same location (such as US-West) for your Azure Search service. The value for **Account kind** must be **StorageV2 (general purpose V2)** (default) or **Storage (general purpose V1)**.
 
-- Recommended: Get the [Postman desktop app](https://www.getpostman.com/) for sending requests to Azure Search. You can use the REST API with any tool that's capable of working with HTTP requests and responses. Postman is a good choice for exploring REST APIs. We use Postman in this article. Also, the [source code](https://github.com/Azure-Samples/azure-search-postman-samples/blob/master/knowledge-store/KnowledgeStore.postman_collection.json) for this article includes a Postman collection of requests. 
+- Recommended: Get the [Postman desktop app](https://www.getpostman.com/) for sending requests to Azure Search. You can use the REST API with any tool that's capable of working with HTTP requests and responses. Postman is a good choice for exploring REST APIs. We use Postman in this article. Also, the [source code](https://github.com/Azure-Samples/azure-search-postman-samples/tree/master/knowledge-store) for this article includes a Postman collection of requests. 
 
 ## Store the data
 

articles/search/search-blob-ai-integration.md

@@ -1,5 +1,5 @@
 ---
-title: Use AI to understand Blob data
+title: Use AI to understand Blob storage data
 titleSuffix: Azure Search
 description: Add semantic, natural language processing and image analysis to Azure blobs using an AI enrichment pipeline in Azure Search.
 
@@ -11,7 +11,7 @@ ms.topic: conceptual
 ms.date: 10/09/2019
 ---
 
-# Use AI to understand Blob data
+# Use AI to understand Blob storage data
 
 Data in Azure Blob storage is often a variety of unstructured content such as images, long text, PDFs, and Office documents. By using the AI capabilities in Azure Search, you can understand and extract valuable information from blobs in a variety of ways. Examples of applying AI to blob content include:
 
@@ -108,7 +108,8 @@ An enriched document at the end of the pipeline differs from its original input
 
 There’s a lot more you can do with AI enrichment to get the most out of your data in Azure Storage, including combining Cognitive Services in different ways, and authoring custom skills for cases where there’s no existing Cognitive Service for the scenario. You can learn more by following the links below.
 
-> [!div class="nextstepaction"]
-> [AI enrichment overview](cognitive-search-concept-intro.md) 
-> [Create a skillset](cognitive-search-defining-skillset.md)
-> [Map nodes in an annotation tree](cognitive-search-output-field-mapping.md)
++ [Upload, download, and list blobs with the Azure portal (Azure Blob storage)](https://docs.microsoft.com/azure/storage/blobs/storage-quickstart-blobs-portal)
++ [Set up a blob indexer (Azure Search)](search-howto-indexing-azure-blob-storage.md) 
++ [AI enrichment overview (Azure Search)](cognitive-search-concept-intro.md) 
++ [Create a skillset (Azure Search)](cognitive-search-defining-skillset.md)
++ [Map nodes in an annotation tree (Azure Search)](cognitive-search-output-field-mapping.md)

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

@@ -66,5 +66,5 @@ A more permanent solution is to gather query inputs and present the response as
 
 ## Next steps
 
-> [!div class="nextstepaction"]
-> [Set up a blob indexer](search-howto-indexing-azure-blob-storage.md) 
++ [Upload, download, and list blobs with the Azure portal (Azure Blob storage)](https://docs.microsoft.com/azure/storage/blobs/storage-quickstart-blobs-portal)
++ [Set up a blob indexer (Azure Search)](search-howto-indexing-azure-blob-storage.md)