Merge pull request #244476 from HeidiSteen/heidist-docs

[azure search] Pablo feedback on docs, part 1

Author: prmerger-automator[bot]

articles/search/search-features-list.md

@@ -8,7 +8,7 @@ author: HeidiSteen
 ms.author: heidist
 ms.service: cognitive-search
 ms.topic: conceptual
-ms.date: 06/29/2023
+ms.date: 07/10/2023
 ---
 # Features of Azure Cognitive Search
 
@@ -23,7 +23,7 @@ The following table summarizes features by category. For more information about
 
 | Category                             | Features |
 |-------------------|----------|
-| Data sources | Search indexes can accept text from any source, provided it's submitted as a JSON document. <br/><br/> [**Indexers**](search-indexer-overview.md) are a feature that automates data import from supported data sources to extract searchable content in primary data stores. Indexers handle JSON serialization for you and most support some form of change and deletion detection. You can connect to a [variety of data sources](search-data-sources-gallery.md), including [Azure SQL Database](search-howto-connecting-azure-sql-database-to-azure-search-using-indexers.md), [Azure Cosmos DB](search-howto-index-cosmosdb.md), or [Azure Blob storage](search-howto-indexing-azure-blob-storage.md). |
+| Data sources | Search indexes can accept text from any source, provided it's submitted as a JSON document. <br/><br/>At the field level, you can also [index vectors](vector-search-how-to-create-index.md). Vector fields can co-exist with nonvector fields in the same document.<br/><br/> [**Indexers**](search-indexer-overview.md) are a feature that automates data import from supported data sources to extract searchable content in primary data stores. Indexers handle JSON serialization for you and most support some form of change and deletion detection. You can connect to a [variety of data sources](search-data-sources-gallery.md), including [Azure SQL Database](search-howto-connecting-azure-sql-database-to-azure-search-using-indexers.md), [Azure Cosmos DB](search-howto-index-cosmosdb.md), or [Azure Blob storage](search-howto-indexing-azure-blob-storage.md). |
 | Hierarchical and nested data structures | [**Complex types**](search-howto-complex-data-types.md) and collections allow you to model virtually any type of JSON structure within a search index. One-to-many and many-to-many cardinality can be expressed natively through collections, complex types, and collections of complex types.|
 | Linguistic analysis | Analyzers are components used for text processing during indexing and search operations. By default, you can use the general-purpose Standard Lucene analyzer, or override the default with a language analyzer, a custom analyzer that you configure, or another predefined analyzer that produces tokens in the format you require. <br/><br/>[**Language analyzers**](index-add-language-analyzers.md) from Lucene or Microsoft are used to intelligently handle language-specific linguistics including verb tenses, gender, irregular plural nouns (for example, 'mouse' vs. 'mice'), word de-compounding, word-breaking (for languages with no spaces), and more. <br/><br/>[**Custom lexical analyzers**](index-add-custom-analyzers.md) are used for complex query forms such as phonetic matching and regular expressions.<br/><br/> |
 
@@ -40,6 +40,7 @@ The following table summarizes features by category. For more information about
 | Category&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;  | Features |
 |-------------------|----------|
 |Free-form text search | [**Full-text search**](search-lucene-query-architecture.md) is a primary use case for most search-based apps. Queries can be formulated using a supported syntax. <br/><br/>[**Simple query syntax**](query-simple-syntax.md) provides logical operators, phrase search operators, suffix operators, precedence operators. <br/><br/>[**Full Lucene query syntax**](query-lucene-syntax.md) includes all operations in simple syntax, with extensions for fuzzy search, proximity search, term boosting, and regular expressions.|
+|Vector queries| [**Vector search (preview)**](vector-search-overview.md) adds [query support for vector data](vector-search-how-to-query.md).  |
 | Relevance | [**Simple scoring**](index-add-scoring-profiles.md) is a key benefit of Azure Cognitive Search. Scoring profiles are used to model relevance as a function of values in the documents themselves. For example, you might want newer products or discounted products to appear higher in the search results. You can also build scoring profiles using tags for personalized scoring based on customer search preferences you've tracked and stored separately. <br/><br/>[**Semantic search (preview)**](semantic-search-overview.md) is premium feature that reranks results based on semantic relevance to the query. Depending on your content and scenario, it can significantly improve search relevance with almost minimal configuration or effort. |
 | Geospatial search | [**Geospatial functions**](search-query-odata-geo-spatial-functions.md) filter over and match on geographic coordinates. You can [match on distance](search-query-simple-examples.md#example-6-geospatial-search) or by inclusion in a polygon shape. |
 | Filters and facets | [**Faceted navigation**](search-faceted-navigation.md) is enabled through a single query parameter. Azure Cognitive Search returns a faceted navigation structure you can use as the code behind a categories list, for self-directed filtering (for example, to filter catalog items by price-range or brand). <br/><br/> [**Filters**](query-odata-filter-orderby-syntax.md) can be used to incorporate faceted navigation into your application's UI, enhance query formulation, and filter based on user- or developer-specified criteria. Create filters using the OData syntax. |

articles/search/search-get-started-vector.md

@@ -446,6 +446,8 @@ api-key: {{admin-api-key}}
 
 ### Semantic hybrid search
 
+In Cognitive Search, semantic search and vector search are separate features, but you can use them together as described in this example. Semantic search adds language representation models that rerank search results based on query intent. This feature is optional and billable for the transactions against the language models.
+
 Assuming that you've [enabled semantic search](semantic-search-overview.md#enable-semantic-search) and your index definition includes a [semantic configuration](semantic-how-to-query-request.md), you can formulate a query that includes vector search, plus keyword search with semantic ranking, caption, answers, and spell check. 
 
 ```http

articles/search/search-what-is-azure-search.md

@@ -8,7 +8,7 @@ author: HeidiSteen
 ms.author: heidist
 ms.service: cognitive-search
 ms.topic: overview
-ms.date: 07/22/2022
+ms.date: 07/10/2023
 ms.custom: contperf-fy21q1, ignite-2022, build-2023, build-2023-dataai
 ---
 # What's Azure Cognitive Search?
@@ -17,9 +17,9 @@ Azure Cognitive Search ([formerly known as "Azure Search"](whats-new.md#new-serv
 
 Search is foundational to any app that surfaces text to users, where common scenarios include catalog or document search, online retail apps, or data exploration over proprietary content. When you create a search service, you'll work with the following capabilities:
 
-+ A search engine for full text search over a search index containing user-owned content
++ A search engine for full text and [vector search](vector-search-overview.md) over a search index containing user-owned content
 + Rich indexing, with [lexical analysis](search-analyzers.md) and [optional AI enrichment](cognitive-search-concept-intro.md) for content extraction and transformation
-+ Rich query syntax for text search, fuzzy search, autocomplete, geo-search and more
++ Rich query syntax for [vector queries](vector-search-how-to-query.md), text search, fuzzy search, autocomplete, geo-search and more
 + Programmability through REST APIs and client libraries in Azure SDKs
 + Azure integration at the data layer, machine learning layer, and AI (Cognitive Services)
 
@@ -38,7 +38,7 @@ Across the Azure platform, Cognitive Search can integrate with other Azure servi
 
 On the search service itself, the two primary workloads are *indexing* and *querying*. 
 
-+ [**Indexing**](search-what-is-an-index.md) is an intake process that loads content into your search service and makes it searchable. Internally, inbound text is processed into tokens and stored in inverted indexes for fast scans. You can upload JSON documents, or use an indexer to serialize your data into JSON.
++ [**Indexing**](search-what-is-an-index.md) is an intake process that loads content into your search service and makes it searchable. Internally, inbound text is processed into tokens and store in inverted indexes, and inbound vectors are stored in vector indexes. You can upload JSON documents, or use an indexer to serialize your data into JSON.
 
   [AI enrichment](cognitive-search-concept-intro.md) through [cognitive skills](cognitive-search-working-with-skillsets.md) is an extension of indexing. If your content needs image or language analysis before it can be indexed, AI enrichment can extract text embedded in application files, translate text, and also infer text and structure from non-text files by analyzing the content.