You signed in with another tab or window. Reload to refresh your session.You signed out in another tab or window. Reload to refresh your session.You switched accounts on another tab or window. Reload to refresh your session.Dismiss alert
|[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). |
28
-
|[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. |
29
-
|[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. |
30
28
|[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.|
31
29
|[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. |
32
30
|[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. |
@@ -35,6 +33,9 @@ Learn about the latest updates to Azure AI Search functionality, docs, and sampl
35
33
|[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. |
36
34
|[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. |
37
35
36
+
<!-- | [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. |
37
+
| [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. | -->
38
+
38
39
## April 2025
39
40
40
41
| Item | Type | Description |
0 commit comments