Merge pull request #3489 from HeidiSteen/heidist-march

[azure search] March freshness pass

Author: denrea

articles/search/cognitive-search-predefined-skills.md

@@ -10,14 +10,14 @@ ms.custom:
   - build-2024
   - ignite-2024
 ms.topic: concept-article
-ms.date: 09/19/2024
+ms.date: 03/11/2025
 ---
 
 # Skills for extra processing during indexing (Azure AI Search)
 
 This article describes the skills in Azure AI Search that you can include in a [skillset](cognitive-search-working-with-skillsets.md) to access external processing. 
 
-A *skill* provides an atomic operation that transforms content in some way. Often, it's an operation that recognizes or extracts text, but it can also be a utility skill that reshapes the enrichments that are already created. Typically, the output is text-based so that it can be used in [full text search](search-lucene-query-architecture.md) or vectors used in [vector search](vector-search-overview.md).
+A *skill* is an atomic operation that transforms content in some way. Often, it's an operation that recognizes or extracts text, but it can also be a utility skill that reshapes the enrichments that are already created. Typically, the output is either text-based so that it can be used in [full text search](search-lucene-query-architecture.md), or vectors used in [vector search](vector-search-overview.md).
 
 Skills are organized into categories:
 

articles/search/hybrid-search-how-to-query.md

@@ -9,7 +9,7 @@ ms.service: azure-ai-search
 ms.custom:
   - ignite-2023
 ms.topic: how-to
-ms.date: 10/01/2024
+ms.date: 03/11/2025
 ---
 
 # Create a hybrid query in Azure AI Search
@@ -19,19 +19,13 @@ ms.date: 10/01/2024
 In this article, learn how to:
 
 + Set up a basic request
-+ Formulate hybrid queries with more parameters and filters
++ Add parameters and filters
 + Improve relevance using semantic ranking or vector weights
 + Optimize query behaviors by controlling text and vector inputs
 
 > [!NOTE]
 > New in [**2024-09-01-preview**](/rest/api/searchservice/documents/search-post?view=rest-searchservice-2024-09-01-preview&preserve-view=true) is the ability to target filters to just the vector subqueries in a hybrid request. This gives you more precision over how filters are applied. For more information, see [targeting filters to vector subqueries](#hybrid-search-with-filters-targeting-vector-subqueries-preview) in this article.
 
-<!-- To improve relevance in a hybrid query, use these parameters:
-
-+ [vector.queries.weight](vector-search-how-to-query.md#vector-weighting) lets you set the relative weight of the vector query. This feature is particularly useful in complex queries where two or more distinct result sets need to be combined, as is the case for hybrid search. This feature is generally available.
-
-+ [hybridsearch.maxTextRecallSize and countAndFacetMode (preview)](#set-maxtextrecallsize-and-countandfacetmode) give you more control over text inputs into a hybrid query. This feature requires a preview API version.
- -->
 ## Prerequisites
 
 + A search index containing `searchable` vector and nonvector fields. We recommend the [Import and vectorize data wizard](search-import-data-portal.md) to create an index quickly. Otherwise, see [Create an index](search-how-to-create-search-index.md) and [Add vector fields to a search index](vector-search-how-to-create-index.md).

articles/search/hybrid-search-ranking.md

@@ -9,12 +9,12 @@ ms.service: azure-ai-search
 ms.custom:
   - ignite-2023
 ms.topic: conceptual
-ms.date: 10/01/2024
+ms.date: 03/11/2025
 ---
 
 # Relevance scoring in hybrid search using Reciprocal Rank Fusion (RRF)
 
-Reciprocal Rank Fusion (RRF) is an algorithm that evaluates the search scores from multiple, previously ranked results to produce a unified result set. In Azure AI Search, RRF is used whenever there are two or more queries that execute in parallel. Each query produces a ranked result set, and RRF is used to merge and homogenize the rankings into a single result set, returned in the query response. Examples of scenarios where RRF is always used include [*hybrid search*](hybrid-search-overview.md) and multiple vector queries executing concurrently. 
+Reciprocal Rank Fusion (RRF) is an algorithm that evaluates the search scores from multiple, previously ranked results to produce a unified result set. In Azure AI Search, RRF is used whenever there are two or more queries that execute in parallel. Each query produces a ranked result set, and RRF merges and homogenizes the rankings into a single result set for the query response. Examples of scenarios where RRF is always used include [*hybrid search*](hybrid-search-overview.md) and multiple vector queries executing concurrently. 
 
 RRF is based on the concept of *reciprocal rank*, which is the inverse of the rank of the first relevant document in a list of search results. The goal of the technique is to take into account the position of the items in the original rankings, and give higher importance to items that are ranked higher in multiple lists. This can help improve the overall quality and reliability of the final ranking, making it more useful for the task of fusing multiple ordered search results.
 

articles/search/search-capacity-planning.md

@@ -11,7 +11,7 @@ ms.custom:
   - ignite-2023
   - ignite-2024
 ms.topic: conceptual
-ms.date: 10/02/2024
+ms.date: 03/11/2025
 ---
 
 # Estimate and manage capacity of a search service

articles/search/search-filters.md

@@ -8,7 +8,7 @@ author: HeidiSteen
 ms.author: heidist
 ms.service: azure-ai-search
 ms.topic: concept-article
-ms.date: 09/19/2024
+ms.date: 03/11/2025
 ms.custom:
   - devx-track-csharp
   - ignite-2023

articles/search/search-how-to-index-csv-blobs.md

@@ -11,7 +11,7 @@ ms.service: azure-ai-search
 ms.custom:
   - ignite-2023
 ms.topic: how-to
-ms.date: 10/23/2024
+ms.date: 03/11/2025
 ---
 
 # Index CSV blobs and files using delimitedText parsing mode

articles/search/search-howto-schedule-indexers.md

@@ -9,7 +9,7 @@ ms.service: azure-ai-search
 ms.custom:
   - ignite-2023
 ms.topic: how-to
-ms.date: 10/02/2024
+ms.date: 03/11/2025
 ---
 
 # Schedule an indexer in Azure AI Search

articles/search/search-indexer-tutorial.md

@@ -8,7 +8,7 @@ author: HeidiSteen
 ms.author: heidist
 ms.service: azure-ai-search
 ms.topic: tutorial
-ms.date: 09/23/2024
+ms.date: 03/11/2025
 ms.custom:
   - devx-track-csharp
   - devx-track-dotnet

articles/search/search-what-is-data-import.md

@@ -10,12 +10,12 @@ ms.service: azure-ai-search
 ms.custom:
   - ignite-2023
 ms.topic: concept-article
-ms.date: 09/17/2024
+ms.date: 03/11/2025
 ---
 
 # Data import in Azure AI Search
 
-In Azure AI Search, queries execute over user-owned content that's loaded into a [search index](search-what-is-an-index.md). This article describes the two basic workflows for populating an index: *push* your data into the index programmatically, or *pull* in the data using a [search indexer](search-indexer-overview.md).
+In Azure AI Search, queries execute over your content that's loaded into a [search index](search-what-is-an-index.md). This article describes the two basic workflows for populating an index: *push* your data into the index programmatically, or *pull* in the data using a [search indexer](search-indexer-overview.md).
 
 Both approaches load documents from an external data source. Although you can create an empty index, it's not queryable until you add the content.
 

articles/search/tutorial-rag-build-solution-maximize-relevance.md

@@ -10,7 +10,7 @@ ms.service: azure-ai-search
 ms.custom:
   - ignite-2024
 ms.topic: tutorial
-ms.date: 10/05/2024
+ms.date: 03/11/2025
 ---
 
 # Tutorial: Maximize relevance (RAG in Azure AI Search)