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Author: HeidiSteen

articles/search/whats-new.md

@@ -31,7 +31,7 @@ ms.custom:
 | [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 | 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) supports OneLake indexers, Azure AI Vision multimodal, Azure AI Studio model catalog, and more embedding models on Azure OpenAI. When adding a field, 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.  |
+| 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). |