Merge pull request #276939 from HeidiSteen/heidist-may28

[azure search] Updates to whats new

Author: prmerger-automator[bot]

articles/search/whats-new.md

@@ -7,7 +7,7 @@ author: HeidiSteen
 ms.author: heidist
 ms.service: cognitive-search
 ms.topic: overview
-ms.date: 05/21/2024
+ms.date: 05/31/2024
 ms.custom:
   - references_regions
 ---
@@ -30,10 +30,10 @@ ms.custom:
 | [Azure AI Vision multimodal embeddings skill (preview)](cognitive-search-skill-vision-vectorize.md) | Skill | New skill that's bound to the [multimodal embeddings API of Azure AI Vision](../ai-services/computer-vision/concept-image-retrieval.md). You can generate embeddings for text or images during indexing. This skill is available through the Azure portal and the [2024-05-01-preview REST API](/rest/api/searchservice/operation-groups?view=rest-searchservice-2024-05-01-preview&preserve-view=true).|
 | [Azure AI Vision vectorizer (preview)](vector-search-vectorizer-ai-services-vision.md) | Vectorizer | New vectorizer connects to an Azure AI Vision resource using the [multimodal embeddings API](../ai-services/computer-vision/concept-image-retrieval.md) to generate embeddings at query time. This vectorizer is available through the Azure portal and the [2024-05-01-preview REST API](/rest/api/searchservice/operation-groups?view=rest-searchservice-2024-05-01-preview&preserve-view=true). |
 | [Azure AI Studio model catalog vectorizer (preview)](vector-search-vectorizer-azure-machine-learning-ai-studio-catalog.md) | Vectorizer | New vectorizer connects to an embedding model deployed from the [Azure AI Studio model catalog](../ai-studio/how-to/model-catalog.md). This vectorizer is available through the Azure portal and the [2024-05-01-preview REST API](/rest/api/searchservice/operation-groups?view=rest-searchservice-2024-05-01-preview&preserve-view=true). <br><br>[**How to implement integrated vectorization using models from Azure AI Studio**](vector-search-integrated-vectorization-ai-studio.md).|
-| [AzureOpenAIEmbedding skill (preview) supports more models on Azure OpenAI](cognitive-search-skill-azure-openai-embedding.md) | Skill | Updates to this skill add support for more embedding models on Azure OpenAI. New `dimensions` and `modelName` properties are used for specifying models. Previously, the dimensions limits were fixed at 1,536 dimensions. It's now configurable. This update is available through the Azure portal and the [2024-05-01-preview REST API](/rest/api/searchservice/operation-groups?view=rest-searchservice-2024-05-01-preview&preserve-view=true).|
-| Azure portal updates | Portal | [Import and vectorize data wizard](search-get-started-portal-import-vectors.md) is updated to support [OneLake indexers](search-how-to-index-onelake-files.md) and the [Azure AI Vision multimodal skill](cognitive-search-skill-vision-vectorize.md). [Search explorer](search-explorer.md) now defaults to 2024-05-01-preview and supports the new preview features for vector and hybrid queries. When adding a field, you can now choose a [binary data type](vector-search-how-to-index-binary-data.md). |
-| [2024-05-01-preview Search REST API](/rest/api/searchservice/search-service-api-versions#2024-05-01-preview) | API | New preview version of the Search REST APIs provides new skills and vectorizers, new binary data type, OneLake files indexer, and new query parameters for more relevant results. See [Upgrade REST APIs](search-api-migration.md) if you have existing code written against the 2023-07-01-preview and need to migrate to this version.|
-| Azure SDK beta packages for new features | API | Review the changelogs of the following Azure SDK beta packages for new feature support: [Azure SDK for Python](https://github.com/Azure/azure-sdk-for-python/blob/main/sdk/search/azure-search-documents/CHANGELOG.md), [Azure SDK for .NET](https://github.com/Azure/azure-sdk-for-net/blob/Azure.Search.Documents_11.6.0-beta.4/sdk/search/Azure.Search.Documents/CHANGELOG.md), [Azure SDK for Java](https://github.com/Azure/azure-sdk-for-java/blob/main/sdk/search/azure-search-documents/CHANGELOG.md) |
+| [AzureOpenAIEmbedding skill (preview) supports more models on Azure OpenAI](cognitive-search-skill-azure-openai-embedding.md) | Skill | Now supports text-embedding-3-large and text-embedding-3-small, along with text-embedding-ada-002 from the previous update. New `dimensions` and `modelName` properties make it possible to specify the various embedding models on Azure OpenAI. Previously, the dimensions limits were fixed at 1,536 dimensions, applicable to text-embedding-ada-002 only. The updated skill is available through the Azure portal and the [2024-05-01-preview REST API](/rest/api/searchservice/operation-groups?view=rest-searchservice-2024-05-01-preview&preserve-view=true).|
+| Azure portal updates | Portal | [Import and vectorize data wizard](search-get-started-portal-import-vectors.md) now supports OneLake indexers as a data source. For embeddings, it also supports connections to Azure AI Vision multimodal, Azure AI Studio model catalog, and more embedding models on Azure OpenAI. <br><br>When adding a field to an index, you can choose a [binary data type](vector-search-how-to-index-binary-data.md). <br><br>[Search explorer](search-explorer.md) now defaults to 2024-05-01-preview and supports the new preview features for vector and hybrid queries.  |
+| [2024-05-01-preview](/rest/api/searchservice/search-service-api-versions#2024-05-01-preview) | API | New preview version of the Search REST APIs provides new skills and vectorizers, new binary data type, OneLake files indexer, and new query parameters for more relevant results. See [Upgrade REST APIs](search-api-migration.md) if you have existing code written against the 2023-07-01-preview and need to migrate to this version.|
+| Azure SDK beta packages | API | Review the changelogs of the following Azure SDK beta packages for new feature support: [Azure SDK for Python](https://github.com/Azure/azure-sdk-for-python/blob/main/sdk/search/azure-search-documents/CHANGELOG.md), [Azure SDK for .NET](https://github.com/Azure/azure-sdk-for-net/blob/Azure.Search.Documents_11.6.0-beta.4/sdk/search/Azure.Search.Documents/CHANGELOG.md), [Azure SDK for Java](https://github.com/Azure/azure-sdk-for-java/blob/main/sdk/search/azure-search-documents/CHANGELOG.md) |
 | [Python code samples](https://github.com/Azure/azure-search-vector-samples/blob/main/demo-python/readme.md)  | Samples | New end-to-end samples demonstrate [integration with Cohere Embed v3](https://github.com/Azure/azure-search-vector-samples/blob/main/demo-python/code/community-integration/cohere/azure-search-cohere-embed-v3-sample.ipynb), [integration with OneLake and cloud data platforms on Google and AWS](https://github.com/Azure/azure-search-vector-samples/blob/main/demo-python/code/e2e-demos/azure-ai-search-e2e-build-demo.ipynb), and [integration with Azure AI Vision multimodal APIs](https://github.com/Azure/azure-search-vector-samples/blob/main/demo-python/code/embeddings/multimodal-embeddings/multimodal-embeddings.ipynb). |
 <!-- | Network security perimeter support (preview) | Feature | A network security perimeter is a new service that provides a secure perimeter for communication, and controlled access to resources outside of the perimeter. Azure AI Search is one of the eight Azure services that can run within a network security perimeter. This feature is provided by the [2024-03-01-preview Management REST API](/rest/api/searchmanagement/operation-groups?view=rest-searchmanagement-2024-03-01-preview&preserve-view=true) and the Azure portal. | -->