Merge pull request #260049 from HeidiSteen/heidist-fix

[azure search] Catching up on PR feedback

Author: prmerger-automator[bot]

articles/search/search-api-migration.md

@@ -19,10 +19,10 @@ Use this article to migrate data plane calls to newer *stable* versions of the [
 
 + [**2023-11-01**](/rest/api/searchservice/search-service-api-versions#2023-11-01) is the most recent stable version. Semantic ranking and vector search support are generally available in this version.
 
-+ [**2023-10-01-preview**](/rest/api/searchservice/search-service-api-versions#2023-10-01-preview) is the most recent preview version. [Integrated data chunking and vectorization](vector-search-integrated-vectorization.md) using the [Text Split](cognitive-search-skill-textsplit.md) skill and [Azure OpenAI Embedding](cognitive-search-skill-azure-openai-embedding.md) skill are introduced in this version. There's no migration guidance for preview API versions, but you can review [code samples](https://github.com/Azure/azure-search-vector-samples) and [walkthroughs](vector-search-how-to-configure-vectorizer.md) for guidance.
++ [**2023-10-01-preview**](/rest/api/searchservice/search-service-api-versions#2023-10-01-preview) is the most recent preview version. [Integrated data chunking and vectorization](vector-search-integrated-vectorization.md) using the [Text Split](cognitive-search-skill-textsplit.md) skill and [Azure OpenAI Embedding](cognitive-search-skill-azure-openai-embedding.md) skill are introduced in this version. *There's no migration guidance for preview API versions*, but you can review [code samples](https://github.com/Azure/azure-search-vector-samples) and [walkthroughs](vector-search-how-to-configure-vectorizer.md) for help with new features.
 
 > [!NOTE]
-> API reference docs are now versioned. To get the right information, open a reference page and then apply the version-specific filter located above the table of contents.
+> API reference docs are now versioned. To get the right content, open a reference page and then apply the version-specific filter located above the table of contents.
 
 <a name="UpgradeSteps"></a>
 
@@ -49,7 +49,7 @@ This version has breaking changes and behavioral differences for semantic rankin
 If you added vector support using 2023-10-01-preview, there are no breaking changes, but there's one behavior difference: the `vectorFilterMode` default changed from postfilter to prefilter for [filter expressions](vector-search-filters.md). The default is  prefilter for indexes created after 2023-10-01. Indexes created before that date only support postfilter, regardless of how you set the filter mode. 
 
 > [!TIP]
-> Azure portal supports a one-click upgrade path for 2023-07-01-preview indexes. The portal detects that version and provides a **Migrate** button. Before selecting **Migrate**, select **Edit JSON** to review the updated schema first. You should find a schema that conforms to the changes described in this section. Portal migration only handles indexes with one vector field. Indexes with more fields require manual migration.
+> Azure portal supports a one-click upgrade path for 2023-07-01-preview indexes. The portal detects 2023-07-01-preview indexes and provides a **Migrate** button. Before selecting **Migrate**, select **Edit JSON** to review the updated schema first. You should find a schema that conforms to the changes described in this section. Portal migration only handles indexes with one vector search algorithm configuration, creating a default profile that maps to the algorithm. Indexes with multiple configurations require manual migration.
 
 Here are the steps for migrating from 2023-07-01-preview:
 
@@ -104,7 +104,7 @@ Here are the steps for migrating from 2023-07-01-preview:
       }
     ```
 
-1. Modify vector field definitions, replacing `vectorSearchConfiguration` with `vectorSearchProfile`. Other vector field properties remain unchanged. For example, they can't be filterable, sortable, or facetable, nor use analyzers or normalizers or synonym maps.
+1. Modify vector field definitions, replacing `vectorSearchConfiguration` with `vectorSearchProfile`. Make sure the profile name resolves to a new vector profile definition, and not the algorithm configuration name. Other vector field properties remain unchanged. For example, they can't be filterable, sortable, or facetable, nor use analyzers or normalizers or synonym maps.
 
     **Before (2023-07-01-preview)**:
 
@@ -220,7 +220,7 @@ Existing code written against earlier API versions will break on api-version=202
 
 ### Behavior changes
 
-* [BM25 ranking algorithm](index-ranking-similarity.md) replaces the previous ranking algorithm with newer technology. New services use this algorithm automatically. For existing services, you must set parameters to use the new algorithm.
+* [BM25 ranking algorithm](index-ranking-similarity.md) replaces the previous ranking algorithm with newer technology. Services created after 2019 use this algorithm automatically. For older services, you must set parameters to use the new algorithm.  
 
 * Ordered results for null values have changed in this version, with null values appearing first if the sort is `asc` and last if the sort is `desc`. If you wrote code to handle how null values are sorted, be aware of this change.
 

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

@@ -1,7 +1,7 @@
 ---
 title: Introduction to Azure AI Search
 titleSuffix: Azure AI Search
-description: Azure AI Search is a fully managed cloud search service from Microsoft. Learn about use cases, the development workflow, comparisons to other Microsoft search products, and how to get started.
+description: Azure AI Search is an AI-powered information retrieval platform, helps developers build rich search experiences and generative AI apps that combine large language models with enterprise data.
 
 manager: nitinme
 author: HeidiSteen
@@ -21,9 +21,9 @@ Azure AI Search ([formerly known as "Azure Cognitive Search"](whats-new.md#new-s
 
 Information retrieval is foundational to any app that surfaces text and vectors. Common scenarios include catalog or document search, data exploration, and increasingly chat-style copilot apps over proprietary grounding data. When you create a search service, you work with the following capabilities:
 
-+ A search engine for [full text](search-lucene-query-architecture.md) and [vector search](vector-search-overview.md) over a search index 
++ A search engine for [vector search](vector-search-overview.md) and [full text](search-lucene-query-architecture.md) and [hybrid search](hybrid-search-overview.md) over a search index 
 + Rich indexing with [integrated data chunking and vectorization (preview)](vector-search-integrated-vectorization.md), [lexical analysis](search-analyzers.md) for text, and [optional AI enrichment](cognitive-search-concept-intro.md) for content extraction and transformation
-+ Rich query syntax for [vector queries](vector-search-how-to-query.md), text search, [hybrid search](hybrid-search-overview.md), fuzzy search, autocomplete, geo-search and others
++ Rich query syntax for [vector queries](vector-search-how-to-query.md), text search, [hybrid queries](hybrid-search-how-to-query.md), fuzzy search, autocomplete, geo-search and others
 + Azure scale, security, and reach
 + Azure integration at the data layer, machine learning layer, Azure AI services and Azure OpenAI
 
@@ -54,9 +54,9 @@ On the search service itself, the two primary workloads are *indexing* and *quer
 
 Azure AI Search is well suited for the following application scenarios:
 
-+ Search over your vector and text content. You own or control what's searchable.
++ Use it for traditional full text search and next-generation vector similarity search. Back your generative AI apps with information retrieval that leverages the strength of keyword and similarity search. Use both modalities to retrieve the most relevant results.
 
-+ Consolidate heterogeneous content into a user-defined and populated search index composed of vectors and text. 
++ Consolidate heterogeneous content into a user-defined and populated search index composed of vectors and text. You own and control what's searchable.
 
 + [Integrate data chunking and vectorization](vector-search-integrated-vectorization.md) for generative AI and RAG apps.
 
@@ -110,6 +110,9 @@ Customers often ask how Azure AI Search compares with other search-related solut
 
 Key strengths include:
 
++ Store, index, and search vector embeddings for sentences, images, audio, graphs, and more. 
++ Find information that’s semantically similar to search queries, even if the search terms aren’t exact matches. 
++ Use hybrid search for the best of keyword and vector search.
 + Relevance tuning through semantic ranking and scoring profiles.
 + Data integration (crawlers) at the indexing layer.
 + Azure AI integration for transformations that make content text and vector searchable.