Merge pull request #5277 from HeidiSteen/heidist-freshness

[azure search] Freshness pass, #3

Author: prmerger-automator[bot]

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

@@ -2,7 +2,7 @@
 title: 'Quickstart: Deploy using Terraform'
 description: 'In this article, you create an Azure AI Search service using Terraform.'
 ms.topic: quickstart
-ms.date: 12/10/2024
+ms.date: 05/29/2025
 ms.custom:
   - devx-track-terraform
   - ignite-2023

articles/search/search-how-to-index-sql-server.md

@@ -9,11 +9,11 @@ manager: nitinme
 ms.service: azure-ai-search
 ms.custom:
   - ignite-2023
-ms.topic: conceptual
-ms.date: 12/10/2024
+ms.topic: how-to
+ms.date: 05/29/2025
 ---
 
-# Indexer connections to a SQL Server instance on an Azure virtual machine
+# Configure an indexer connection to a SQL Server instance on an Azure virtual machine
 
 When configuring an [Azure SQL indexer](search-how-to-index-sql-database.md) to extract content from a database on an Azure virtual machine, extra steps are required for secure connections. 
 
@@ -44,11 +44,13 @@ Azure AI Search requires an encrypted channel for all indexer requests over a pu
 
    1. In regedit, browse to this registry key: `HKEY_LOCAL_MACHINE\SOFTWARE\Microsoft\Microsoft SQL Server\[MSSQL13.MSSQLSERVER]\MSSQLServer\SuperSocketNetLib\Certificate`. 
 
-      The `[MSSQL13.MSSQLSERVER]` part varies based on version and instance name. 
+      The `[MSSQL13.MSSQLSERVER]` part varies based on version and instance name.
 
    1. Set the value of the **Certificate** key to the **thumbprint** (without spaces) of the TLS/SSL certificate you imported to the VM.
 
-     There are several ways to get the thumbprint, some better than others. If you copy it from the **Certificates** snap-in in MMC, you might pick up an invisible leading character [as described in this support article](https://support.microsoft.com/kb/2023869/), which results in an error when you attempt a connection. Several workarounds exist for correcting this problem. The easiest is to backspace over and then retype the first character of the thumbprint to remove the leading character in the key value field in regedit. Alternatively, you can use a different tool to copy the thumbprint.
+      For example, copy the hexadecimal characters to text editor, such as Notepad. Delete all spaces from the thumbprint string. If the thumbprint is `c0 d0 f2 70 95 b0 3d 43 17 e2 19 84 10 24 32 8c ef 24 87 79`, then change it to `c0d0f27095b03d4317e219841024328cef248779`.
+
+      There are several ways to get the thumbprint, some better than others. If you copy it from the **Certificates** snap-in in MMC, you might pick up an invisible leading character, which results in an error when you attempt a connection. Several workarounds exist for correcting this problem. The easiest is to backspace over and then retype the first character of the thumbprint to remove the leading character in the key value field in regedit. Alternatively, you can use a different tool to copy the thumbprint. For more information, see [Certificate thumbprint displayed in MMC certificate snap-in has extra invisible unicode character](https://support.microsoft.com/help/2023835/certificate-thumbprint-displayed-in-mmc-certificate-snap-in-has-extra). 
 
 1. Grant permissions to the service account. 
 

articles/search/search-howto-index-azure-data-lake-storage.md

@@ -9,7 +9,7 @@ ms.service: azure-ai-search
 ms.custom:
   - ignite-2023
 ms.topic: how-to
-ms.date: 12/10/2024
+ms.date: 05/29/2025
 ---
 
 # Index data from Azure Data Lake Storage Gen2

articles/search/search-howto-index-mysql.md

@@ -12,15 +12,15 @@ ms.topic: how-to
 ms.custom:
   - kr2b-contr-experiment
   - ignite-2023
-ms.date: 12/10/2024
+ms.date: 05/29/2025
 ---
 
 # Index data from Azure Database for MySQL Flexible Server
 
 > [!IMPORTANT]
-> MySQL support is currently in public preview under [Supplemental Terms of Use](https://azure.microsoft.com/support/legal/preview-supplemental-terms/). You can use 2020-06-30-preview or later to index your content. We recommend the latest preview API. There is currently no portal support.
+> MySQL support is currently in public preview under [Supplemental Terms of Use](https://azure.microsoft.com/support/legal/preview-supplemental-terms/). We recommend the latest preview API. There is currently no portal support.
 
-In this article, learn how to configure an [**indexer**](search-indexer-overview.md) that imports content from Azure Database for MySQL and makes it searchable in Azure AI Search. Inputs to the indexer are your row, in a single table or view. Output is a search index with searchable content in individual fields.
+In this article, learn how to configure an [**indexer**](search-indexer-overview.md) that imports content from Azure Database for MySQL and makes it searchable in Azure AI Search. Inputs to the indexer are rows from a single table or view. Output is a search index with searchable content in individual fields.
 
 This article supplements [**Create an indexer**](search-howto-create-indexers.md) with information that's specific to indexing from Azure Database for MySQL Flexible Server. It uses the REST APIs to demonstrate a three-part workflow common to all indexers: create a data source, create an index, create an indexer. Data extraction occurs when you submit the Create Indexer request.
 
@@ -50,7 +50,7 @@ As noted, there’s no portal support for indexer creation, but a MySQL indexer
 
 The data source definition specifies the data to index, credentials, and policies for identifying changes in the data. The data source is defined as an independent resource so that it can be used by multiple indexers.
 
-[Create or Update Data Source](/rest/api/searchservice/data-sources/create?view=rest-searchservice-2024-05-01-preview&preserve-view=true) specifies the definition. Be sure to use a preview REST API when creating the data source.
+[Create or Update Data Source](/rest/api/searchservice/data-sources/create?view=rest-searchservice-2025-05-01-preview&preserve-view=true) specifies the definition. Be sure to use a preview REST API when creating the data source.
 
 ```http
 {   
@@ -88,7 +88,7 @@ The data source definition specifies the data to index, credentials, and policie
 
 ## Create an index
 
-[Create or Update Index](/rest/api/searchservice/indexes/create?view=rest-searchservice-2024-05-01-preview&preserve-view=true) specifies the index schema:
+[Create or Update Index](/rest/api/searchservice/indexes/create?view=rest-searchservice-2025-05-01-preview&preserve-view=true) specifies the index schema:
 
 ```http
 {
@@ -128,7 +128,7 @@ The following table maps the MySQL database to Azure AI Search equivalents. For
 
 Once the index and data source have been created, you're ready to create the indexer. Indexer configuration specifies the inputs, parameters, and properties controlling run time behaviors.
 
-[Create or update an indexer](/rest/api/searchservice/indexers/create-or-update?view=rest-searchservice-2024-05-01-preview&preserve-view=true) by giving it a name and referencing the data source and target index:
+[Create or update an indexer](/rest/api/searchservice/indexers/create-or-update?view=rest-searchservice-2025-05-01-preview&preserve-view=true) by giving it a name and referencing the data source and target index:
 
 ```http
 {
@@ -157,10 +157,10 @@ Once the index and data source have been created, you're ready to create the ind
 
 ## Check indexer status
 
-Send a [Get Indexer Status](/rest/api/searchservice/indexers/get-status?view=rest-searchservice-2024-05-01-preview&preserve-view=true) request to monitor indexer execution:
+Send a [Get Indexer Status](/rest/api/searchservice/indexers/get-status?view=rest-searchservice-2025-05-01-preview&preserve-view=true) request to monitor indexer execution:
 
 ```http
-GET https://myservice.search.windows.net/indexers/myindexer/status?api-version=2024-05-01-preview
+GET https://myservice.search.windows.net/indexers/myindexer/status?api-version=2025-05-01-preview
   Content-Type: application/json  
   api-key: [admin key]
 ```

articles/search/search-indexer-troubleshooting.md

@@ -9,7 +9,7 @@ ms.service: azure-ai-search
 ms.custom:
   - ignite-2023
 ms.topic: conceptual
-ms.date: 12/10/2024
+ms.date: 05/29/2025
 ---
 
 # Indexer troubleshooting guidance for Azure AI Search

articles/search/search-modeling-multitenant-saas-applications.md

@@ -8,7 +8,7 @@ ms.service: azure-ai-search
 ms.custom:
   - ignite-2023
 ms.topic: conceptual
-ms.date: 12/10/2024
+ms.date: 05/29/2025
 ---
 
 # Design patterns for multitenant SaaS applications and Azure AI Search

articles/search/search-normalizers.md

@@ -9,13 +9,13 @@ ms.service: azure-ai-search
 ms.custom:
   - ignite-2023
 ms.topic: how-to
-ms.date: 12/10/2024
+ms.date: 05/19/2025
 ---
 
 # Text normalization for case-insensitive filtering, faceting and sorting
 
 > [!IMPORTANT] 
-> This feature is in public preview under [Supplemental Terms of Use](https://azure.microsoft.com/support/legal/preview-supplemental-terms/). The [preview REST API](/rest/api/searchservice/index-preview) supports this feature.
+> This feature is in public preview under [Supplemental Terms of Use](https://azure.microsoft.com/support/legal/preview-supplemental-terms/). We recommend the latest [preview REST API version](/rest/api/searchservice/search-service-api-versions) for this feature.
 
 In Azure AI Search, a *normalizer* is a component that pre-processes text for keyword matching over fields marked as "filterable", "facetable", or "sortable". In contrast with full text "searchable" fields that are paired with [text analyzers](search-analyzers.md), content that's created for filter-facet-sort operations doesn't undergo analysis or tokenization. Omission of text analysis can produce unexpected results when casing and character differences show up, which is why you need a normalizer to homogenize variations in your content.
 

articles/search/search-pagination-page-layout.md

@@ -10,12 +10,12 @@ ms.service: azure-ai-search
 ms.custom:
   - ignite-2023
 ms.topic: how-to
-ms.date: 12/09/2024
+ms.date: 05/29/2025
 ---
 
 # Shape search results or modify search results composition in Azure AI Search
 
-This article explains search results composition and how to shape search results to fit your scenarios. Search results are returned in a query response. The shape of a response is determined by parameters in the query itself. These parameters include:
+This article explains search results composition and how to shape full text search results to fit your scenarios. Search results are returned in a query response. The shape of a response is determined by parameters in the query itself. These parameters include:
 
 + Number of matches found in the index (`count`)
 + Number of matches returned in the response (50 by default, configurable through `top`) or per page (`skip` and `top`)

articles/search/search-query-overview.md

@@ -8,7 +8,7 @@ author: HeidiSteen
 ms.author: heidist
 ms.service: azure-ai-search
 ms.topic: conceptual
-ms.date: 12/10/2024
+ms.date: 05/29/2025
 ---
 
 # Querying in Azure AI Search
@@ -21,12 +21,13 @@ Azure AI Search supports query constructs for a broad range of scenarios, from f
 
 | Query form | Searchable content | Description |
 |------------|--------------------|-------------|
-| [full text search](search-lucene-query-architecture.md) | Inverted indexes of tokenized terms. | Full text queries iterate over inverted indexes that are structured for fast scans, where a match can be found in potentially any field, within any number of search documents. Text is analyzed and tokenized for full text search.|
+| [Full text search](search-lucene-query-architecture.md) | Inverted indexes of tokenized terms. | Full text queries iterate over inverted indexes that are structured for fast scans, where a match can be found in potentially any field, within any number of search documents. Text is analyzed and tokenized for full text search.|
 | [Vector search](vector-search-overview.md) | Vector indexes of generated embeddings. | Vector queries iterate over vector fields in a search index. |
 | [Hybrid search](hybrid-search-overview.md) | All of the above, in a single search index. | Combines text search and vector search in a single query request. Text search works on plain text content in "searchable" and "filterable" fields. Vector search works on content in vector fields. |
+| [Agentic retrieval (preview)](search-agentic-retrieval-concept.md) | All of the above, in a single search index. | This is an alternative retrieval path on Azure AI Search that leverages large language models for query planning. The response is designed for agent consumption, where the agent rather than search app client code coordinates the response delivered to the user. |
 | Others | Plain text and human-readable content.| Raw content, extracted verbatim from source documents, supporting filters and pattern matching queries like geo-spatial search, fuzzy search, and fielded search. |
 
-This article brings focus to the last category: queries that work on plain text and human-readable content, extracted intact from original source, used for filters and other specialized query forms.
+The remainder of this article brings focus to the last category: classic queries that work on plain text and human-readable content, extracted intact from original source, used for filters and other specialized query forms. If you're creating a traditional search application that isn't using AI, this section explains the query methods that you can implement in your client code.
 
 ## Autocomplete and suggested queries
 

articles/search/search-query-understand-collection-filters.md

@@ -9,7 +9,7 @@ ms.service: azure-ai-search
 ms.custom:
   - ignite-2023
 ms.topic: conceptual
-ms.date: 12/10/2024
+ms.date: 05/29/2025
 ---
 
 # Understand how OData collection filters work in Azure AI Search