@@ -1,5 +1,10 @@
 {
     "redirections": [
+        {
+            "source_path_from_root": "/articles/search/search-security-network-security-perimiter.md",
+            "redirect_url": "/azure/search/search-security-network-security-perimeter",
+            "redirect_document_id": true
+        },
         {
             "source_path_from_root": "/articles/search/cognitive-search-quickstart-blob.md",
             "redirect_url": "/azure/search/search-get-started-skillset",

@@ -57,7 +57,7 @@ Preview features are removed from this list if they're retired or transition to
 
 |Feature                          | Category | Description | Availability  |
 |---------|------------------|-------------|---------------|
-| [**Network security perimeter**](search-security-network-security-perimiter.md) | Service | Join a search service to a [network security perimeter](/azure/private-link/network-security-perimeter-concepts) to control network access to your search service. | The Azure portal and the [Network Security Perimeter APIs 2024-06-01-preview](/rest/api/searchmanagement/network-security-perimeter-configurations?view=rest-searchmanagement-2024-06-01-preview&preserve-view=true). |
+| [**Network security perimeter**](search-security-network-security-perimeter.md) | Service | Join a search service to a [network security perimeter](/azure/private-link/network-security-perimeter-concepts) to control network access to your search service. | The Azure portal and the [Network Security Perimeter APIs 2024-06-01-preview](/rest/api/searchmanagement/network-security-perimeter-configurations?view=rest-searchmanagement-2024-06-01-preview&preserve-view=true). |
 | [**Search service under a user-assigned managed identity**](search-howto-managed-identities-data-sources.md) | Service | Configures a search service to use a previously created user-assigned managed identity. | [Services - Update](/rest/api/searchmanagement/services/update?view=rest-searchmanagement-2024-06-01-preview&preserve-view=true#identity), 2021-04-01-preview, or the latest preview version. We recommend using the latest preview version. |
 
 ## Preview features in Azure SDKs

@@ -1,39 +1,47 @@
 ---
-title: Structure-aware chunking and vectorization
+title: Chunk and vectorize by document layout
 titleSuffix: Azure AI Search
-description: Chunk text content by paragraph or semantically coherent fragment. You can then apply integrated vectorization to generate embeddings and send the results to a searchable index.
+description: Chunk textual content by headings and semantically coherent fragments, generate embeddings, and send the results to a searchable index.
 author: rawan
 ms.author: rawan
 ms.service: azure-ai-search
 ms.topic: how-to
-ms.date: 11/19/2024
+ms.date: 11/22/2024
 ms.custom:
   - references_regions
   - ignite-2024
 ---
 
-# Structure-aware chunking and vectorization in Azure AI Search
+# Chunk and vectorize by document layout or structure
 
 [!INCLUDE [Feature preview](./includes/previews/preview-generic.md)]
 
-Text data chunking strategies play a key role in optimizing RAG responses and performance. By using the new Document Layout skill that's currently in preview, you can chunk content based on paragraphs or semantically coherent fragments of a sentence representation. These fragments can then be processed independently and recombined as semantic representations without loss of information, interpretation, or semantic relevance. The inherent meaning of the text is used as a guide for the chunking process. 
+Text data chunking strategies play a key role in optimizing RAG responses and performance. By using the new **Document Layout** skill that's currently in preview, you can chunk content based on document structure, capturing headings and chunking the content body based on semantic coherence, such as paragraphs and sentences. Chunks are processed independently. Because LLMs work with multiple chunks, when those chunks are of higher quality and semantically coherent, the overall relevance of the query is improved.
 
-The Document Layout skill uses Markdown syntax (headings and content) to articulate document structure in the search document. The searchable content obtained from your source document is plain text but you can add integrated vectorization to generate embeddings for any field.
+<!-- Text data chunking strategies play a key role in optimizing RAG responses and performance. By using the new **Document Layout** skill that's currently in preview, you can chunk content based on document structure, capturing headings and chunking the content body based on semantic coherence, such as paragraphs and sentences. Chunks are processed independently and recombined as semantic representations. The inherent meaning of the text is used as a guide for the chunking process.  -->
+
+The Document Layout skill calls the [layout model](/azure/ai-services/document-intelligence/prebuilt/layout) in Document Intelligence. The model articulates content structure in JSON using Markdown syntax (headings and content), with fields for headings and content stored in a search index on Azure AI Search. The searchable content produced from the Document Layout skill is plain text but you can apply integrated vectorization to generate embeddings for any field in your source documents, including images.
 
 In this article, learn how to:
 
 > [!div class="checklist"]
-> + Use the Document Layout skill to detect sections and output Markdown content
+> + Use the Document Layout skill to recognize document structure
 > + Use the Text Split skill to constrain chunk size to each markdown section
 > + Generate embeddings for each chunk
 > + Use index projections to map embeddings to fields in a search index
 
+For illustration purposes, this article uses the [sample health plan PDFs](https://github.com/Azure-Samples/azure-search-sample-data/tree/main/health-plan) uploaded to Azure Blob Storage and then indexed using the **Import and vectorize data wizard**.
+
 ## Prerequisites
 
-+ [An indexer-based indexing pipeline](search-indexer-overview.md) with an index that accepts the output.
++ [An indexer-based indexing pipeline](search-indexer-overview.md) with an index that accepts the output. The index must have fields for receiving headings and content.
+
 + [A supported data source](search-indexer-overview.md#supported-data-sources) having text content that you want to chunk.
+
 + [A skillset with Document Layout skill](cognitive-search-skill-document-intelligence-layout.md) that splits documents based on paragraph boundaries.
+
 + [An Azure OpenAI Embedding skill](cognitive-search-skill-azure-openai-embedding.md) that generates vector embeddings.
+
 + [An index projection](search-how-to-define-index-projections.md) for one-to-many indexing.
 
 ## Prepare data files
@@ -42,7 +50,7 @@ The raw inputs must be in a [supported data source](search-indexer-overview.md#s
 
 + Supported file formats include: PDF, JPEG, JPG, PNG, BMP, TIFF, DOCX, XLSX, PPTX, HTML.
 
-+ Supported indexers can be any indexer that can handle the supported file formats. These include [Blob indexers](search-howto-indexing-azure-blob-storage.md), [OneLake indexers](search-how-to-index-onelake-files.md), [File indexers](search-file-storage-integration.md).
++ Supported indexers can be any indexer that can handle the supported file formats. These indexers include [Blob indexers](search-howto-indexing-azure-blob-storage.md), [OneLake indexers](search-how-to-index-onelake-files.md), [File indexers](search-file-storage-integration.md).
 
 + Supported regions for this feature include: East US, West US2, West Europe, North Central US. Be sure to [check this list](search-region-support.md#azure-public-regions) for updates on regional availability.
 
@@ -53,11 +61,13 @@ You can use the Azure portal, REST APIs, or an Azure SDK package to [create a da
 
 ## Create an index for one-to-many indexing
 
-Here's an example payload of a single search document designed around chunks. In this example, parent fields are the text_parent_id. Child fields are the vector and nonvector chunks of the markdown section.
+Here's an example payload of a single search document designed around chunks. Whenever you're working with chunks, you need a chunk field and a parent field that identifies the origin of the chunk. In this example, parent fields are the text_parent_id. Child fields are the vector and nonvector chunks of the markdown section.
 
-You can use the Azure portal, REST APIs, or an Azure SDK to [create an index](search-how-to-load-search-index.md).
+The Document Layout skill outputs headings and content. In this example, `header_1` through `header_3` store document headings, as detected by the skill. Other content, such as paragraphs, is stored in `chunk`. The `text_vector` field is a vector representation of the chunk field content.
 
-An index must exist on the search service before you create the skill set or run the indexer.
+You can use the **Import and vectorize data** wizard in the Azure portal, REST APIs, or an Azure SDK to [create an index](search-how-to-load-search-index.md). The following index is very similar to what the wizard creates by default. You might have more fields if you add image vectorization.
+
+If you aren't using the wizard, the index must exist on the search service before you create the skillset or run the indexer.
 
 ```json
 {
@@ -173,11 +183,17 @@ An index must exist on the search service before you create the skill set or run
 }
 ```
 
-## Define skill set for structure-aware chunking and vectorization
+## Define a skillset for structure-aware chunking and vectorization
+
+Because the Document Layout skill is in preview, you must use the [Create Skillset 2024-11-01-preview](/rest/api/searchservice/skillsets/create?view=rest-searchservice-2024-11-01-preview&preserve-view=true) REST API for this step. You can also use the Azure portal.
+
+This section shows an example of a skillset definition that projects individual markdown sections, chunks, and their vector equivalents as fields in the search index. It uses the [Document Layout skill](cognitive-search-skill-document-intelligence-layout.md) to detect headings and populate a content field based on semantically coherent paragraphs and sentences in the source document. It uses the [Text Split skill](cognitive-search-skill-textsplit.md) to split the Markdown content into chunks. It uses the [Azure OpenAI Embedding skill](cognitive-search-skill-azure-openai-embedding.md) to vectorize chunks and any other field for which you want embeddings.
 
-Because the Document Layout skill is in preview, you must use the [Create Skillset 2024-11-01-preview](/rest/api/searchservice/skillsets/create?view=rest-searchservice-2024-11-01-preview&preserve-view=true) REST API for this step.
+Besides skills, the skillset includes `indexProjections` and `cognitiveServices`:
 
-Here's an example skill set definition payload to project individual markdown sections chunks and their vector outputs as documents in the search index using the [Document Layout skill](cognitive-search-skill-document-intelligence-layout.md) and [Azure OpenAI Embedding skill](cognitive-search-skill-azure-openai-embedding.md)
++ `indexProjections` are used for indexes containing chunked documents. The projections specify how parent-child content is mapped to fields in a search index for one-to-many indexing. For more information, see [Define an index projection](search-how-to-define-index-projections.md).
+
++ `cognitiveServices` [attaches an Azure AI multi-service account](cognitive-search-attach-cognitive-services.md) for billing purposes (the Document Layout skill is available through [pay-as-you pricing](https://azure.microsoft.com/pricing/details/ai-document-intelligence/)).
 
 ```https
 POST {endpoint}/skillsets?api-version=2024-11-01-preview
@@ -298,7 +314,7 @@ POST {endpoint}/skillsets?api-version=2024-11-01-preview
 
 ```
 
-## Run the indexer
+## Configure and run the indexer
 
 Once you create a data source, index, and skillset, you're ready to [create and run the indexer](search-howto-create-indexers.md#run-the-indexer). This step puts the pipeline into execution.
 
@@ -307,9 +323,13 @@ When using the [Document Layout skill](cognitive-search-skill-document-intellige
 + The `allowSkillsetToReadFileData` parameter should be set to `true`.
 + the `parsingMode` parameter should be set to `default`.
 
-Here's an example payload
+`outputFieldMappings` don't need to be set in this scenario because `indexProjections` handle the source field to search field associations. Index projections handle field associations for the Document Layout skill and also regular chunking with the split skill for imported and vectorized data workloads. Output field mappings are still necessary for transformations or complex data mappings with functions which apply in other cases. However, for n-chunks per document, index projections handle this functionality natively.
+
+Here's an example of an indexer creation request.
+
+```https
+POST {endpoint}/indexers?api-version=2024-11-01-preview
 
-```json
 {
   "name": "my_indexer",
   "dataSourceName": "my_blob_datasource",
@@ -333,6 +353,8 @@ Here's an example payload
 }
 ```
 
+When you send the request to the search service, the indexer runs.
+
 ## Verify results
 
 You can query your search index after processing concludes to test your solution.
@@ -344,16 +366,41 @@ For Search Explorer, you can copy just the JSON and paste it into the JSON view
 ```http
 POST /indexes/[index name]/docs/search?api-version=[api-version]
 {
-    "search": "*",
-    "select": "metadata_storage_path, markdown_section, vector"
+  "search": "copay for in-network providers",
+  "count": true,
+  "searchMode": "all",
+  "vectorQueries": [
+    {
+      "kind": "text",
+      "text": "*",
+      "fields": "text_vector,image_vector"
+    }
+  ],
+  "queryType": "semantic",
+  "semanticConfiguration": "healthplan-doc-layout-test-semantic-configuration",
+  "captions": "extractive",
+  "answers": "extractive|count-3",
+  "queryLanguage": "en-us",
+  "select": "header_1, header_2, header_3"
 }
 ```
 
+If you used the health plan PDFs to test this skill, Search Explorer results for the example query should look similar to the results in the following screenshot. 
+
++ The query is a [hybrid query](hybrid-search-how-to-query.md) over text and vectors, so you see a `@search.rerankerScore` and results are ranked by that score. `searchMode=all` means that *all* query terms must be considered for a match (the default is *any*).
+
++ The query uses semantic ranking, so you see `captions` (it also has `answers`, but those aren't shown in the screenshot). The results are the most semantically relevant to the query input, as determined by the [semantic ranker](semantic-search-overview.md).
+
++ The `select` statement (not shown in the screenshot) specifies the header fields that the Document Layout skill detects and populates. You can add more fields to the select clause to inspect the content of chunks, title, or any other human readable field.
+
+:::image type="content" source="media/search-how-to-semantic-chunking/query-results-doc-layout.png" lightbox="media/search-how-to-semantic-chunking/query-results-doc-layout.png" alt-text="Screenshot of hybrid query results that include doc layout skill output fields.":::
+
 ## See also
 
 + [Create or update a skill set](cognitive-search-defining-skillset.md).
 + [Create a data source](search-howto-indexing-azure-blob-storage.md)
 + [Define an index projection](search-how-to-define-index-projections.md)
++ [Attach an Azure AI multi-service account](cognitive-search-attach-cognitive-services.md)
 + [Document Layout skill](cognitive-search-skill-document-intelligence-layout.md)
 + [Text Split skill](cognitive-search-skill-textsplit.md)
 + [Azure OpenAI Embedding skill](cognitive-search-skill-azure-openai-embedding.md)

@@ -11,17 +11,17 @@ ms.custom:
   - ignite-2023
   - build-2024
 ms.topic: how-to
-ms.date: 09/11/2024
+ms.date: 11/22/2024
 ---
 
 # Configure a search service to connect using a managed identity in Azure AI Search
 
 > [!IMPORTANT]
-> User-assigned managed identity assignment is in public preview under [Supplemental Terms of Use](https://azure.microsoft.com/support/legal/preview-supplemental-terms/). The [Management preview REST API](/rest/api/searchmanagement/services/update?view=rest-searchmanagement-2024-03-01-preview&preserve-view=true#identity) provides user-assigned managed identity assignment for Azure AI Search. Support for a system-assigned managed identity is generally available.
+> User-assigned managed identity assignment is in public preview under [Supplemental Terms of Use](https://azure.microsoft.com/support/legal/preview-supplemental-terms/). The [Management preview REST API](/rest/api/searchmanagement/services/update?view=rest-searchmanagement-2024-03-01-preview&preserve-view=true#identity) provides user-assigned managed identity assignment for Azure AI Search. Support for a *system-assigned* managed identity is generally available.
 
 You can use Microsoft Entra ID and role assignments for outbound connections from Azure AI Search to resources providing data, applied AI, or vectorization during indexing or queries. 
 
-To use roles on an outbound connection, first configure your search service to use either a [system-assigned or user-assigned managed identity](/azure/active-directory/managed-identities-azure-resources/overview) as the security principle for your search service in a Microsoft Entra tenant. Once you have a managed identity, you can assign roles for authorized access. Managed identities and role assignments eliminate the need for passing secrets and credentials in a connection string or code.
+To use roles on an outbound connection, first configure your search service to use either a [system-assigned or user-assigned managed identity](/azure/active-directory/managed-identities-azure-resources/overview) as the security principal for your search service in a Microsoft Entra tenant. Once you have a managed identity, you can assign roles for authorized access. Managed identities and role assignments eliminate the need for passing secrets and credentials in a connection string or code.
 
 ## Prerequisites
 
@@ -201,7 +201,6 @@ You can use a preview Management REST API instead of the portal to assign a user
 
    + "userAssignedIdentities" includes the details of the user assigned managed identity. This identity [must already exist](/azure/active-directory/managed-identities-azure-resources/how-manage-user-assigned-managed-identities) before you can specify it in the Update Service request.
   
-
 ---
 
 ## Assign a role
@@ -295,7 +294,11 @@ A debug session runs in the portal and takes a connection string when you start
 
 [**Custom skill:**](cognitive-search-custom-skill-interface.md)
 
-A custom skill targets the endpoint of an Azure function or app hosting custom code. The endpoint is specified in the [custom skill definition](cognitive-search-custom-skill-web-api.md). The presence of the "authResourceId" tells the search service to connect using a managed identity, passing the application ID of the target function or app in the property.
+A [custom skill](cognitive-search-custom-skill-web-api.md) targets the endpoint of an Azure function or app hosting custom code. 
+
++ `uri` is the endpoint of the function or app. 
+
++ `authResourceId` tells the search service to connect using a managed identity, passing the application ID of the target function or app in the property.
 
 ```json
 {
@@ -312,7 +315,9 @@ A custom skill targets the endpoint of an Azure function or app hosting custom c
 
 [**Azure OpenAI embedding skill**](cognitive-search-skill-azure-openai-embedding.md) and [**Azure OpenAI vectorizer:**](vector-search-how-to-configure-vectorizer.md)
 
- An Azure OpenAI embedding skill and vectorizer in AI Search target the endpoint of an Azure OpenAI service hosting an embedding model. The endpoint is specified in the [Azure OpenAI embedding skill definition](cognitive-search-skill-azure-openai-embedding.md) and/or in the [Azure OpenAI vectorizer definition](vector-search-how-to-configure-vectorizer.md). The system-managed identity is used if configured and if the "apikey" and "authIdentity" are empty. The "authIdentity" property is used for user-assigned managed identity only.
+ An Azure OpenAI embedding skill and vectorizer in AI Search target the endpoint of an Azure OpenAI service hosting an embedding model. The endpoint is specified in the [Azure OpenAI embedding skill definition](cognitive-search-skill-azure-openai-embedding.md) and/or in the [Azure OpenAI vectorizer definition](vector-search-how-to-configure-vectorizer.md). 
+
+The system-managed identity is used automatically if `"apikey"` and `"authIdentity"` are empty, as demonstrated in the following example. The `"authIdentity"` property is used for user-assigned managed identity only.
 
 **System-managed identity example:**
  
@@ -337,6 +342,8 @@ A custom skill targets the endpoint of an Azure function or app hosting custom c
 }
 ```
 
+Here's a [vectorizer example](vector-search-how-to-configure-vectorizer.md) configured for a system-assigned managed identity. A vectorizer is specified in a search index.
+
 ```json
  "vectorizers": [
     {
@@ -353,6 +360,8 @@ A custom skill targets the endpoint of an Azure function or app hosting custom c
 
 **User-assigned managed identity example:**
 
+A user-assigned managed identity is used if `"apiKey"` is empty and a valid `"authIdentity"` is provided.
+
 ```json
 {
   "@odata.type": "#Microsoft.Skills.Text.AzureOpenAIEmbeddingSkill",
@@ -378,6 +387,8 @@ A custom skill targets the endpoint of an Azure function or app hosting custom c
 }
 ```
 
+Here's a [vectorizer example](vector-search-how-to-configure-vectorizer.md) configured for a user-assigned managed identity. A vectorizer is specified in a search index.
+
 ```json
  "vectorizers": [
     {
@@ -398,11 +409,11 @@ A custom skill targets the endpoint of an Azure function or app hosting custom c
 
 ## Check for firewall access
 
-If your Azure resource is behind a firewall, make sure there's an inbound rule that admits requests from your search service. 
+If your Azure resource is behind a firewall, make sure there's an inbound rule that admits requests from your search service and from the Azure portal.
 
 + For same-region connections to Azure Blob Storage or Azure Data Lake Storage Gen2, use a system managed identity and the [trusted service exception](search-indexer-howto-access-trusted-service-exception.md). Optionally, you can configure a [resource instance rule](/azure/storage/common/storage-network-security#grant-access-from-azure-resource-instances) to admit requests.
 
-+ For all other resources and connections, [configure an IP firewall rule](search-indexer-howto-access-ip-restricted.md) that admits requests from Search. See [Indexer access to content protected by Azure network security features](search-indexer-securing-resources.md) for details.
++ For all other resources and connections, [configure an IP firewall rule](search-indexer-howto-access-ip-restricted.md) that admits requests from Azure AI Search. See [Indexer access to content protected by Azure network security features](search-indexer-securing-resources.md) for details.
 
 ## See also
 

@@ -45,7 +45,7 @@ You can add a search service to a network security perimeter in the Azure portal
 
 Azure Network Security Perimeter allows administrators to define a logical network isolation boundary for PaaS resources (for example, Azure Storage and Azure SQL Database) that are deployed outside virtual networks. It restricts communication to resources within the perimeter, and it allows non-perimeter public traffic through inbound and outbound access rules.
 
-You can add Azure AI Search to a network security perimiter so that all indexing and query requests occur within the security boundary.
+You can add Azure AI Search to a network security perimeter so that all indexing and query requests occur within the security boundary.
 
 1. In the Azure portal, create or find the network security perimeter service for your subscription.
 
@@ -269,7 +269,7 @@ In order to test your connection through network security perimeter, you need ac
 
 ## View and manage network security perimeter configuration
 
-You can use the [Network Security Perimiter Configuration REST APIs](/rest/api/searchmanagement/network-security-perimeter-configurations?view=rest-searchmanagement-2024-06-01-preview&preserve-view=true) to review and reconcile perimeter configurations.
+You can use the [Network Security Perimeter Configuration REST APIs](/rest/api/searchmanagement/network-security-perimeter-configurations?view=rest-searchmanagement-2024-06-01-preview&preserve-view=true) to review and reconcile perimeter configurations.
 
 Be sure to use preview API version `2024-06-01-preview`. [Learn how to call the Management REST APIs](search-manage-rest.md).
 

@@ -321,8 +321,6 @@ items:
         href: cognitive-search-concept-image-scenarios.md
       - name: Cache (incremental) enrichment
         href: search-howto-incremental-index.md
-      - name: Structure-aware chunking and vectorization
-        href: search-how-to-semantic-chunking.md        
       - name: Design tips
         href: cognitive-search-concept-troubleshooting.md
       - name: Custom skills
@@ -339,6 +337,8 @@ items:
       href: vector-search-how-to-create-index.md
     - name: Chunk documents
       href: vector-search-how-to-chunk-documents.md
+    - name: Chunk and vectorize by document layout
+      href: search-how-to-semantic-chunking.md   
     - name: Generate embeddings
       href: vector-search-how-to-generate-embeddings.md
     - name: Use embedding models from Azure AI Studio
@@ -469,7 +469,7 @@ items:
       - name: Connect through a firewall
         href: search-indexer-howto-access-ip-restricted.md
       - name: Connect using Network Security Perimeter
-        href: search-security-network-security-perimiter.md
+        href: search-security-network-security-perimeter.md
       - name: Connect as a trusted service
         href: search-indexer-howto-access-trusted-service-exception.md
       - name: Connect through a shared private link

@@ -24,7 +24,7 @@ ms.custom:
 
 | Item                         | Type |  Description |
 |-----------------------------|------|--------------|
-| [**Network security perimeter**](search-security-network-security-perimiter.md) | Security | Join a search service to a [network security perimeter](/azure/private-link/network-security-perimeter-concepts) to control network access to your search service. The Azure portal and the Management REST APIs in the [2024-06-01-preview](/rest/api/searchmanagement/network-security-perimeter-configurations?view=rest-searchmanagement-2024-06-01-preview&preserve-view=true) can be used to view and reconcile network security perimeter configurations. |
+| [**Network security perimeter**](search-security-network-security-perimeter.md) | Security | Join a search service to a [network security perimeter](/azure/private-link/network-security-perimeter-concepts) to control network access to your search service. The Azure portal and the Management REST APIs in the [2024-06-01-preview](/rest/api/searchmanagement/network-security-perimeter-configurations?view=rest-searchmanagement-2024-06-01-preview&preserve-view=true) can be used to view and reconcile network security perimeter configurations. |
 | [**Shared private link support for Azure AI service connections**](search-indexer-howto-access-private.md) | Security  | Connections to Azure AI for built-in skills processing can now be private using a shared private link on the connection. |
 | [**Rescoring options for compressed vectors**](/azure/search/vector-search-how-to-quantization?tabs=2024-11-01-preview%2Cquery-2024-07-01#add-compressions-to-a-search-index) | Relevance | You can set options to rescore with original vectors instead of compressed vectors. Applies to HNSW and exhaustive KNN vector algorithms, using binary and scalar compression. Available in the [Create or Update Index (2024-11-01-preview)](/rest/api/searchservice/indexes/create-or-update?view=rest-searchservice-2024-09-01-preview&preserve-view=true), the Azure portal, and in the Azure SDK beta packages that provide this feature. |
 | [**Store fewer vector instances**](vector-search-how-to-storage-options.md) | vector search | In vector compression scenarios, you can omit storage of full precision vectors if you don't need them for rescoring. Available in the [Create or Update Index (2024-11-01-preview)](/rest/api/searchservice/indexes/create-or-update?view=rest-searchservice-2024-09-01-preview&preserve-view=true), the Azure portal, and in the Azure SDK beta packages that provide this feature. |