Merge pull request #4734 from HeidiSteen/heidist-rb-rag

[azure search] Update the What's New table for build

Author: prmerger-automator[bot]

articles/search/whats-new.md

@@ -24,10 +24,18 @@ Learn about the latest updates to Azure AI Search functionality, docs, and sampl
 
 | Item                         | Type |  Description |
 |-----------------------------|------|--------------|
-| [Agentic retrieval (preview)](search-agentic-retrieval-concept.md) | Query | Create a conversational search experience powered by large language models (LLMs) and your proprietary data. Agentic retrieval breaks down complex user queries into subqueries, runs the subqueries in parallel, and extracts grounding data from documents indexed in Azure AI Search. The output is intended for integration with custom chat solutions. A new [knowledge agent](search-agentic-retrieval-how-to-create.md) is introduced in this preview. Its [response payload](search-agentic-retrieval-how-to-retrieve.md) is designed for downstream agent and chat model consumption, with full transparency of the query plan and reference data. To get started, see [Quickstart: Agentic retrieval](search-get-started-agentic-retrieval.md). |
-| [Logic Apps integration (preview)](search-how-to-index-logic-apps-indexers.md) | Indexing | Create an automated indexing pipeline that retrieves content using a Logic Apps workflow. Use the [Quickstart wizard](search-get-started-portal-import-vectors.md) in the Azure portal to build the indexing pipeline.|
+| [Agentic retrieval (preview)](search-agentic-retrieval-concept.md) | Query | Create a conversational search experience powered by large language models (LLMs) and your proprietary data. Agentic retrieval breaks down complex user queries into subqueries, runs the subqueries in parallel, and extracts grounding data from documents indexed in Azure AI Search. The output is intended for agents and custom chat solutions. A new [knowledge agent](search-agentic-retrieval-how-to-create.md) object is introduced in this preview. Its [response payload](search-agentic-retrieval-how-to-retrieve.md) is designed for downstream agent and chat model consumption, with full transparency of the query plan and reference data. To get started, see [Quickstart: Agentic retrieval](search-get-started-agentic-retrieval.md). |
+| [Multivector support (preview)](vector-search-multi-vector-fields.md) | Indexing | Index multiple child vectors within a single document field. You can now use vector types in nested fields of complex collections, effectively allowing multiple vectors to be associated with a single document.|
+| [Scoring profiles with semantic ranking (preview)​](semantic-how-to-enable-scoring-profiles.md) | Relevance | Semantic ranker adds a new field, `@search.rerankerBoostedScore`, to help you maintain consistent relevance and greater control over final ranking outcomes in your search pipeline. |
+| [Logic Apps integration (preview)](search-how-to-index-logic-apps-indexers.md) | Indexing | Create an automated indexing pipeline that retrieves content using a logic app workflow. Use the [Import and vectorize data wizard](search-get-started-portal-import-vectors.md) in the Azure portal to build an indexing pipeline based on Logic Apps. |
+| [Gen AI prompt skill (preview)](cognitive-search-skill-genai-prompt.md) | Skills | A new skill that connects to a large language model (LLM) for information, using a prompt you provide. With this skill, you can populate a searchable field using content from an LLM. A primary use case for this skill is *image verbalization*, using an LLM to describe images and send the description to a searchable field in your index. |
+| Import and vectorize data wizard enhancements | Portal | This wizard provides two paths for creating and populating vector indexes: Retrieval Augmented Generation (RAG) and multimodal support. Logic apps integration is through the RAG path. |
+| [Index "description" support (preview)](/rest/api/searchservice/indexes/create-or-update?view=rest-searchservice-2025-05-01-preview&preserve-view=true#request-body) | REST | The latest preview API adds a description to an index. A description is useful in agentic solutions, where the agent reads the description to decide whether to run a query or move on to another index. |
 | [2025-05-01-preview](/rest/api/searchservice/operation-groups?view=rest-searchservice-2025-05-01-preview&preserve-view=true) | REST | New data plane preview REST API version providing programmatic access to the preview features announced in this release. |
 
+<!-- | [Document-level access control (preview)](search-document-level-access-overview.md) | Security | Flow document-level permissions from blobs in Azure Data Lake Storage (ADLS) Gen2 to searchable documents in an index. Queries can now filter results based on user identity for selected data sources. |
+| [Multimodal search (preview)](multimodal-search-overview.md) | Indexing, Query | Ingest, understand, and retrieve documents that contain text and images, enabling you to perform searches that combine various modalities, such as querying with text to find information embedded in relevant complex images. | -->
+
 ## April 2025
 
 | Item&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; | Type |  Description |