Merge pull request #185081 from HeidiSteen/heidist-fresh

indexer misc edits

Author: PRMerger10

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

@@ -37,14 +37,13 @@ AI enrichment is available in regions where Azure Cognitive Services is also ava
 
 + Australia Southeast
 + China North 2
-+ Norway East
 + Germany West Central
 
 If your search service is located in one of these regions, you will not be able to create and use skillsets, but all other search service functionality is available and fully supported.
 
 ## When to use AI enrichment
 
-You should consider enrichment if your raw content is unstructured text, image content, or content that needs language detection and translation. Applying AI through the built-in cognitive skills can unlock this content, increasing its value and utility in your search and data science apps. 
+You should consider enrichment if your raw content is unstructured text, image content, or content that needs language detection and translation. Applying AI through the built-in cognitive skills can unlock this content for full text search and data science apps.
 
 Additionally, you might consider adding a custom skill if you have open-source, third-party, or first-party code that you'd like to integrate into the pipeline. Classification models that identify salient characteristics of various document types fall into this category, but any package that adds value to your content could be used.
 

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

@@ -8,19 +8,21 @@ author: HeidiSteen
 ms.author: heidist
 ms.service: cognitive-search
 ms.topic: conceptual
-ms.date: 05/14/2021
+ms.date: 01/14/2022
 ---
 
 # Search over Azure Blob Storage content
 
-Searching across the variety of content types stored in Azure Blob Storage can be a difficult problem to solve. In this article, review the basic workflow for extracting content and metadata from blobs and sending it to a search index in Azure Cognitive Search. The resulting index can be queried using full text search.
+Searching across the variety of content types stored in Azure Blob Storage can be a difficult problem to solve, but [Azure Cognitive Search](search-what-is-azure-search.md) provides deep integration at the content layer, extracting and inferring textual information, which can then be queried in a search index.
+
+In this article, review the basic workflow for extracting content and metadata from blobs and sending it to a [search index](search-what-is-an-index.md) in Azure Cognitive Search. The resulting index can be queried using full text search. Optionally, you can send processed blob content to a [knowledge store](knowledge-store-concept-intro.md) for non-search scenarios.
 
 > [!NOTE]
-> Already familiar with the workflow and composition? [How to configure a blob indexer](search-howto-indexing-azure-blob-storage.md) is your next step.
+> Already familiar with the workflow and composition? [Configure a blob indexer](search-howto-indexing-azure-blob-storage.md) is your next step.
 
 ## What it means to add full text search to blob data
 
-Azure Cognitive Search is a search service that supports indexing and query workloads over user-defined indexes that contain your remote searchable content hosted in the cloud. Co-locating your searchable content with the query engine is necessary for performance, returning results at a speed users have come to expect from search queries.
+Azure Cognitive Search is a standalone search service that supports indexing and query workloads over user-defined indexes that contain your remote searchable content hosted in the cloud. Co-locating your searchable content with the query engine is necessary for performance, returning results at a speed users have come to expect from search queries.
 
 Cognitive Search integrates with Azure Blob Storage at the indexing layer, importing your blob content as search documents that are indexed into *inverted indexes* and other query structures that support free-form text queries and filter expressions. Because your blob content is indexed into a search index, you can use the full range of query features in Azure Cognitive Search to find information in your blob content.
 

articles/search/semantic-search-overview.md

@@ -8,31 +8,31 @@ author: HeidiSteen
 ms.author: heidist
 ms.service: cognitive-search
 ms.topic: conceptual
-ms.date: 12/17/2021
+ms.date: 01/13/2022
 ms.custom: references_regions
 ---
 # Semantic search in Azure Cognitive Search
 
 > [!IMPORTANT]
-> Semantic search is in public preview under [supplemental terms of use](https://azure.microsoft.com/support/legal/preview-supplemental-terms/). It's available through the Azure portal, preview REST API, and beta SDKs. These features are billable. For more information about, see [Availability and pricing](semantic-search-overview.md#availability-and-pricing).
+> Semantic search is in public preview under [supplemental terms of use](https://azure.microsoft.com/support/legal/preview-supplemental-terms/). It's available through the Azure portal, preview REST API, and beta SDKs. These features are billable (see [Availability and pricing](semantic-search-overview.md#availability-and-pricing)).
 
-Semantic search is a collection of query-related capabilities that bring semantic relevance and language understanding to search results. This article is a high-level introduction to semantic search all-up, with descriptions of each feature and how they work collectively. The embedded video describes the technology, and the section at the end covers availability and pricing.
+Semantic search is a collection of query-related capabilities that bring semantic relevance and language understanding to search results. This article is a high-level introduction to semantic search. The embedded video describes the technology, and the section at the end covers availability and pricing.
 
 Semantic search is a premium feature. We recommend this article for background, but if you'd rather get started, follow these steps:
 
 > [!div class="checklist"]
 > * [Check regional and service tier requirements](#availability-and-pricing).
 > * [Enable semantic search](#enable-semantic-search) on your search service.
 > * Create or modify queries to [return semantic captions and highlights](semantic-how-to-query-request.md).
-> * Add a few more query properties to also return [semantic answers](semantic-answers.md).
+> * Add a few more query properties to also [return semantic answers](semantic-answers.md).
 
 ## What is semantic search?
 
 Semantic search is a collection of features that improve the quality of search results. When enabled on your search service, it extends the query execution pipeline in two ways. First, it adds secondary ranking over an initial result set, promoting the most semantically relevant results to the top of the list. Second, it extracts and returns captions and answers in the response, which you can render on a search page to improve the user's search experience.
 
 | Feature | Description |
 |---------|-------------|
-| [Semantic re-ranking](semantic-ranking.md) | Uses the context or semantic meaning to compute a new relevance score over existing results. |
+| [Semantic re-ranking](semantic-ranking.md) | Uses the context or semantic meaning of a query to compute a new relevance score over existing results. |
 | [Semantic captions and highlights](semantic-how-to-query-request.md) | Extracts sentences and phrases from a document that best summarize the content, with highlights over key passages for easy scanning. Captions that summarize a result are useful when individual content fields are too dense for the results page. Highlighted text elevates the most relevant terms and phrases so that users can quickly determine why a match was considered relevant. |
 | [Semantic answers](semantic-answers.md) | An optional and additional substructure returned from a semantic query. It provides a direct answer to a query that looks like a question. It requires that a document have text with the characteristics of an answer. |