Merge pull request #275987 from HeidiSteen/heidist-features

[azure search] Updates to top tier articles under the Overview node

Author: prmerger-automator[bot]

articles/search/search-api-preview.md

@@ -8,10 +8,9 @@ author: HeidiSteen
 ms.author: heidist
 ms.service: cognitive-search
 ms.custom:
-  - ignite-2023
   - build-2024
 ms.topic: conceptual
-ms.date: 04/04/2024
+ms.date: 05/21/2024
 ---
 
 # Preview features in Azure AI Search
@@ -22,16 +21,19 @@ Preview features are removed from this list if they're retired or transition to
 
 |Feature                          | Category | Description | Availability  |
 |---------|------------------|-------------|---------------|
-|  [**Vector quantization**](vector-search-how-to-configure-compression-storage.md#option-3-configure-scalar-quantization) | Index | Compress vector index size in memory and on disk using built-in scalar quantization. | [Create or Update Index (preview)](/rest/api/searchservice/indexes/create-or-update?view=rest-searchservice-2024-03-01-preview&preserve-view=true) to add a `compressions` section to a vector profile. |
-|  [**Narrow data types**](vector-search-how-to-configure-compression-storage.md#option-1-assign-narrow-data-types-to-vector-fields) | Index | Assign a smaller data type on vector fields, assuming incoming data is of that data type. | [Create or Update Index (preview)](/rest/api/searchservice/indexes/create-or-update?view=rest-searchservice-2024-03-01-preview&preserve-view=true) to specify a vector field definition. |
+|  [**Scalar quantization**](vector-search-how-to-configure-compression-storage.md#option-3-configure-scalar-quantization) | Index | Compress vector index size in memory and on disk using built-in scalar quantization. | [Create or Update Index (preview)](/rest/api/searchservice/indexes/create-or-update?view=rest-searchservice-2024-03-01-preview&preserve-view=true) to add a `compressions` section to a vector profile. |
+|  [**Narrow data types**](vector-search-how-to-configure-compression-storage.md#option-1-assign-narrow-data-types-to-vector-fields) | Index | Assign a smaller data type on vector fields, assuming incoming data is of that data type. | [Create or Update Index (preview)](/rest/api/searchservice/indexes/create-or-update?view=rest-searchservice-2024-03-01-preview&preserve-view=true) to specify a vector field definition. [Binary vector support](vector-search-how-to-index-binary-data.md) is added in 2024-05-01-preview.|
 |  [**stored property**](vector-search-how-to-configure-compression-storage.md#option-2-set-the-stored-property-to-remove-retrievable-storage) | Index | Boolean that reduces storage of vector indexes by *not* storing retrievable vectors. | [Create or Update Index (preview)](/rest/api/searchservice/indexes/create-or-update?view=rest-searchservice-2024-03-01-preview&preserve-view=true) to set `stored` on a vector field. |
 |  [**Vectorizers**](vector-search-integrated-vectorization.md) | Queries | Text-to-vector conversion during query execution. | [Create or Update Index (preview)](/rest/api/searchservice/indexes/create-or-update?view=rest-searchservice-2023-10-01-preview&preserve-view=true) to define a `vectorizer`. [Search POST (preview)](/rest/api/searchservice/documents/search-post?view=rest-searchservice-2023-10-01-preview&preserve-view=true) for `vectorQueries`, 2023-10-01-Preview or later. |
 |  [**Integrated vectorization**](vector-search-integrated-vectorization.md) | Index, skillset | Skills-driven data chunking and embedding during indexing. | [Create or Update Skillset (preview)](/rest/api/searchservice/skillsets/create-or-update?view=rest-searchservice-2023-10-01-preview&preserve-view=true) for AzureOpenAIEmbedding skill and the data chunking properties of the Text Split skill. |
 | [**Import and vectorize data**](search-get-started-portal-import-vectors.md) | Azure portal | A wizard that creates a full indexing pipeline that includes data chunking and vectorization. The wizard creates all of the objects and configuration settings. | Available on all search services, in all regions. |
-| [**AzureOpenAIEmbedding skill**](cognitive-search-skill-azure-openai-embedding.md) | AI enrichment (skills) | A new skill type that calls Azure OpenAI embedding model to generate embeddings during queries and indexing. | [Create or Update Skillset (preview)](/rest/api/searchservice/preview-api/create-or-update-skillset), 2023-10-01-Preview or later. Also available in the portal through the [Import and vectorize data wizard](search-get-started-portal-import-vectors.md). |
-| [**Azure AI Services Vision Vectorize skill**](cognitive-search-skill-vision-vectorize.md) | AI enrichment (skills) | A new skill type that calls Azure AI Services Vision Vectorize API to generate embeddings for text or images during indexing. | [Create or Update Skillset (preview)](/rest/api/searchservice/preview-api/create-or-update-skillset), 2024-05-01-Preview or later. |
-| [**Text Split skill**](cognitive-search-skill-textsplit.md) | AI enrichment (skills) | Text Split has two new chunking-related properties in preview: `maximumPagesToTake`, `pageOverlapLength`. | [Create or Update Skillset (preview)](/rest/api/searchservice/preview-api/create-or-update-skillset), 2023-10-01-Preview or later. Also available in the portal through the [Import and vectorize data wizard](search-get-started-portal-import-vectors.md). |
-| [**Index projections**](index-projections-concept-intro.md) | AI enrichment (skills) | A component of a skillset definition that defines the shape of a secondary index, supporting a one-to-many index pattern, where content from an enrichment pipeline can target multiple indexes.| [Create or Update Skillset (preview)](/rest/api/searchservice/preview-api/create-or-update-skillset), 2023-10-01-Preview or later. Also available in the portal through the [Import and vectorize data wizard](search-get-started-portal-import-vectors.md). |
+| [**AzureOpenAIEmbedding skill**](cognitive-search-skill-azure-openai-embedding.md) | Applied AI (skills) | A new skill type that calls Azure OpenAI embedding model to generate embeddings during queries and indexing. | [Create or Update Skillset (preview)](/rest/api/searchservice/preview-api/create-or-update-skillset), 2023-10-01-Preview or later. Also available in the portal through the [Import and vectorize data wizard](search-get-started-portal-import-vectors.md). |
+| [**Azure AI Vision multimodal embedding skill**](cognitive-search-skill-vision-vectorize.md) | Applied AI (skills) | A new skill type that calls Azure AI Vision multimodal API to generate embeddings for text or images during indexing. | [Create or Update Skillset (preview)](/rest/api/searchservice/skillsets/create-or-update?view=rest-searchservice-2024-05-01-preview&preserve-view=true), 2024-05-01-Preview or later. |
+| [**Text Split skill**](cognitive-search-skill-textsplit.md) | Applied AI (skills) | Text Split has two new chunking-related properties in preview: `maximumPagesToTake`, `pageOverlapLength`. | [Create or Update Skillset (preview)](/rest/api/searchservice/preview-api/create-or-update-skillset), 2023-10-01-Preview or later. Also available in the portal through the [Import and vectorize data wizard](search-get-started-portal-import-vectors.md). |
+| [**Azure Machine Learning (AML) skill**](cognitive-search-aml-skill.md) | Applied AI (skills) | A new skill type to integrate an inferencing endpoint from Azure Machine Learning. | [Create or Update Skillset (preview)](/rest/api/searchservice/preview-api/create-or-update-skillset), 2019-05-06-preview or later. Using 2024-05-01-preview, you can use this skill to connect to a model in the Azure AI Studio model catalog. It's also available in the portal, in skillset design, assuming Azure AI Search and Azure Machine Learning services are deployed in the same subscription. |
+| [**Incremental enrichment**](cognitive-search-incremental-indexing-conceptual.md) | Applied AI (skills) | Adds caching to an enrichment pipeline, allowing you to reuse existing output if a targeted modification, such as an update to a skillset or another object, doesn't change the content. Caching applies only to enriched documents produced by a skillset.| [Create or Update Indexer (preview)](/rest/api/searchservice/preview-api/create-or-update-indexer), API versions 2021-04-30-Preview, 2020-06-30-Preview, or 2019-05-06-Preview. |
+| [**Index projections**](index-projections-concept-intro.md) | Applied AI (skills) | A component of a skillset definition that defines the shape of a secondary index, supporting a one-to-many index pattern, where content from an enrichment pipeline can target multiple indexes.| [Create or Update Skillset (preview)](/rest/api/searchservice/preview-api/create-or-update-skillset), 2023-10-01-Preview or later. Also available in the portal through the [Import and vectorize data wizard](search-get-started-portal-import-vectors.md). |
+|  [**OneLake files indexer**](search-how-to-index-onelake-files.md) | Indexer data source | New data source for extracting searchable data and metadata data from a [lakehouse](/fabric/onelake/create-lakehouse-onelake) on top of [OneLake](/fabric/onelake/onelake-overview) | [Create or Update Data Source (preview)](/rest/api/searchservice/data-sources/create-or-update?view=rest-searchservice-2024-05-01-preview&preserve-view=true), 2024-05-01-preview or later. |
 |  [**Azure Files indexer**](search-file-storage-integration.md) | Indexer data source | New data source for indexer-based indexing from [Azure Files](https://azure.microsoft.com/services/storage/files/) | [Create or Update Data Source (preview)](/rest/api/searchservice/preview-api/create-or-update-data-source), 2021-04-30-Preview or later. |
 | [**SharePoint Online indexer**](search-howto-index-sharepoint-online.md) | Indexer data source | New data source for indexer-based indexing of SharePoint content. | [Sign up](https://aka.ms/azure-cognitive-search/indexer-preview) to enable the feature. Use [Create or Update Data Source (preview)](/rest/api/searchservice/preview-api/create-or-update-data-source), 2020-06-30-Preview or later, or the Azure portal. |
 |  [**MySQL indexer**](search-howto-index-mysql.md) | Indexer data source | New data source for indexer-based indexing of Azure MySQL data sources.| [Sign up](https://aka.ms/azure-cognitive-search/indexer-preview) to enable the feature. Use [Create or Update Data Source (preview)](/rest/api/searchservice/preview-api/create-or-update-data-source), 2020-06-30-Preview or later, [.NET SDK 11.2.1](/dotnet/api/azure.search.documents.indexes.models.searchindexerdatasourcetype.mysql), and Azure portal. |
@@ -42,8 +44,6 @@ Preview features are removed from this list if they're retired or transition to
 | [**speller**](speller-how-to-add.md) | Query | Optional spelling correction on query term inputs for simple, full, and semantic queries. | [Search Documents (preview)](/rest/api/searchservice/preview-api/search-documents), 2020-06-30-Preview or later, and Search Explorer (portal). |
 | [**Normalizers**](search-normalizers.md) | Query |  Normalizers provide simple text preprocessing: consistent casing, accent removal, and ASCII folding, without invoking the full text analysis chain.| [Search Documents (preview)](/rest/api/searchservice/preview-api/search-documents), 2020-06-30-Preview or later.|
 | [**featuresMode parameter**](/rest/api/searchservice/preview-api/search-documents#query-parameters) | Relevance (scoring) | Relevance score expansion to include details: per field similarity score, per field term frequency, and per field number of unique tokens matched. You can consume these data points in [custom scoring solutions](https://github.com/Azure-Samples/search-ranking-tutorial). | [Search Documents (preview)](/rest/api/searchservice/preview-api/search-documents), 2019-05-06-Preview or later.|
-| [**Azure Machine Learning (AML) skill**](cognitive-search-aml-skill.md) | AI enrichment (skills) | A new skill type to integrate an inferencing endpoint from Azure Machine Learning. | [Create or Update Skillset (preview)](/rest/api/searchservice/preview-api/create-or-update-skillset), 2019-05-06-Preview or later. Also available in the portal, in skillset design, assuming Azure AI Search and Azure Machine Learning services are deployed in the same subscription. |
-| [**Incremental enrichment**](cognitive-search-incremental-indexing-conceptual.md) | AI enrichment (skills) | Adds caching to an enrichment pipeline, allowing you to reuse existing output if a targeted modification, such as an update to a skillset or another object, doesn't change the content. Caching applies only to enriched documents produced by a skillset.| [Create or Update Indexer (preview)](/rest/api/searchservice/preview-api/create-or-update-indexer), API versions 2021-04-30-Preview, 2020-06-30-Preview, or 2019-05-06-Preview. |
 | [**moreLikeThis**](search-more-like-this.md) | Query | Finds documents that are relevant to a specific document. This feature has been in earlier previews. | [Search Documents (preview)](/rest/api/searchservice/preview-api/search-documents) calls, in all supported API versions: 2023-10-10-Preview, 2023-07-01-Preview, 2021-04-30-Preview, 2020-06-30-Preview, 2019-05-06-Preview, 2016-09-01-Preview, 2017-11-11-Preview. |
 
 ## Preview features in Azure SDKs

articles/search/search-faq-frequently-asked-questions.yml

@@ -8,10 +8,8 @@ metadata:
   author: HeidiSteen
   ms.author: heidist
   ms.service: cognitive-search
-  ms.custom:
-    - ignite-2023
   ms.topic: faq
-  ms.date: 02/21/2024
+  ms.date: 05/21/2024
 title: Azure AI Search Frequently Asked Questions
 summary:  Find answers to commonly asked questions about Azure AI Search.
 
@@ -36,7 +34,9 @@ sections:
       - question: |
           What languages are supported?
         answer: |
-          The default analyzer used for tokenization is standard Lucene and it is language agnostic. Otherwise, language support is expressed through [language analyzers](index-add-language-analyzers.md#supported-language-analyzers) that apply linguistic rules to inbound (indexing) and outbound (queries) content. Some features, such as [speller](speller-how-to-add.md#supported-languages), are limited to a subset of languages.
+          For vectors, the embedding models you use determines the linguistic experience. 
+          
+          For nonvector strings and numbers, the default analyzer used for tokenization is standard Lucene and it is language agnostic. Otherwise, language support is expressed through [language analyzers](index-add-language-analyzers.md#supported-language-analyzers) that apply linguistic rules to inbound (indexing) and outbound (queries) content. Some features, such as [speller](speller-how-to-add.md#supported-languages), are limited to a subset of languages.
 
       - question: |
           How do I integrate search into my solution?
@@ -106,16 +106,16 @@ sections:
       - question: |
           How does vector search work in Azure AI Search?
         answer: |
-          With standalone vector search, you first use an embedding model to transform content into a vector representation within an embedding space. You can then provide these vectors in a document payload to the search index for indexing. To serve search requests, you use the same DNN from indexing to transform the search query into a vector representation, and vector search finds the most similar vectors and return the corresponding documents.
+          With standalone vector search, you first use an embedding model to transform content into a vector representation within an embedding space. You can then provide these vectors in a document payload to the search index for indexing. To serve search requests, you use the same deep neural network (DNN) from indexing to transform the search query into a vector representation, and vector search finds the most similar vectors and return the corresponding documents.
           
-          In Azure AI Search, you can index vector data as fields in documents alongside textual and other types of content. The data type for a vector field is `Collection(Edm.Single)`. 
+          In Azure AI Search, you can index vector data as fields in documents alongside textual and other types of content. There are [multiple data types](/rest/api/searchservice/supported-data-types#edm-data-types-for-vector-fields) for vector fields. 
           
           Vector queries can be issued standalone or in combination with other query types, including term queries and filters in the same search request.
 
       - question: |
           Can Azure AI Search vectorize my content or queries?
         answer: |
-          Built-in integrated vectorization is now in public preview. 
+          [Built-in integrated vectorization](vector-search-integrated-vectorization.md) is now in public preview. 
 
       - question: |
           Does my search service support vector search?
@@ -141,7 +141,7 @@ sections:
           
           * Add a "vectorSearch" section to the index schema specifying the configuration used by vector search fields, including the parameters of the Approximate Nearest Neighbor algorithm used, like HNSW.
           
-          * Use [**2023-11-01**](/rest/api/searchservice) or an Azure SDK to create or update the index, load documents, and issue queries.
+          * Use [**2023-11-01**](/rest/api/searchservice) or later, oor an Azure SDK to create or update the index, load documents, and issue queries.
 
   - name: Queries
     questions:
@@ -184,12 +184,14 @@ sections:
       - question: |
           Does Azure AI Search send customer data to other services for processing?
         answer: |
-          Yes, if you use the built-in skills based on Azure AI services, the indexer sends requests to Azure AI services over the internal network. If you add a custom skill, the indexer sends content to the URI provided in the custom skill over the public network.
+          Yes, if you use the built-in skills based on Azure AI services, the indexer sends requests to Azure AI services over the internal network. If you add a custom skill, the indexer sends content to the URI provided in the custom skill over the public network unless you configure a [shared private link](search-indexer-howto-access-private.md).
+
+          When you configure indexing and queries for text-to-vector or image-to-image conversions, indexers and vectorizors send requests to models on Azure OpenAI, Azure AI Vision multimodal API, or to the model catalog in Azure AI Studio.
 
       - question: |
           Can I control access to search results based on user identity?
         answer: |
-          Not exactly. Typically, users who are authorized to run your application are also authorized to see all search results. Azure AI Search doesn't have built-in support for row-level or document-level permissions, but you can implement [security filters](./search-security-trimming-for-azure-search.md) as a workaround. 
+          Not exactly. Typically, users who are authorized to run your application are also authorized to see all search results. Azure AI Search doesn't have built-in support for row-level or document-level permissions, but you can implement [security filters](./search-security-trimming-for-azure-search.md) as a workaround. For steps and script, see [Get started with the Python enterprise chat sample using RAG](/azure/developer/python/get-started-app-chat-template).
 
       - question: |
           Can I control access to operations based on user identity?