@@ -11,7 +11,7 @@ ms.custom:
   - references_regions
   - ignite-2024
 ms.topic: reference
-ms.date: 04/07/2025
+ms.date: 04/30/2025
 ---
 
 # Document Layout skill
@@ -26,7 +26,7 @@ The **Document Layout** skill calls the [Document Intelligence Public preview ve
 
 Supported regions varies by modality:
 
-+ In code, your skillset can call Document Intelligence through an Azure AI multi-service resource in any region that provides both AI enrichment and Document Intelligence. See [Product availability by region](https://azure.microsoft.com/explore/global-infrastructure/products-by-region/table) to find regions that provide both *AI enrichment* in Azure AI Search and *Document Intelligence* under Azure AI services.
++ When you're using AI services keys [to attach your multi-service resource to your skillset](cognitive-search-attach-cognitive-services.md#bill-through-a-resource-key) via the REST API, both your Azure AI Search service and AI multi-service resource must be in the same region. This is only possible in the Azure regions of **East US**, **West Europe**, **North Central US**, **West US 2**. But if you're using a managed identity for [billing through a keyless connection](cognitive-search-attach-cognitive-services.md#bill-through-a-keyless-connection), your Azure AI Search service must be in one of the following regions: **East US**, **West Europe**, **North Central US**, **West US 2**. On the other hand, you can use AI Document Intelligence through an Azure AI multi-service resource in any region where this service is available. See [Product availability by region](https://azure.microsoft.com/explore/global-infrastructure/products-by-region/table).
 
 + In the [Import and vectorize data](search-import-data-portal.md) wizard in the Azure portal, you can enable document layout detection in the data source connection step. Document layout detection in the portal is available in the following Azure regions: **East US**, **West Europe**, **North Central US**. Create an Azure AI multi-service resource in one of these three regions to get the portal experience.
 

@@ -8,7 +8,7 @@ author: HeidiSteen
 ms.author: heidist
 ms.service: azure-ai-search
 ms.topic: conceptual
-ms.date: 04/14/2025
+ms.date: 04/30/2025
 ms.custom:
   - references_regions
   - build-2024
@@ -241,6 +241,7 @@ Indexing APIs:
 
 Query APIs:
 
++ Maximum 10 fields in a vector query
 + Maximum 32 fields in $orderby clause.
 + Maximum 100,000 characters in a search clause.
 + Maximum number of clauses in search is 3,000.

@@ -9,7 +9,7 @@ ms.service: azure-ai-search
 ms.custom:
   - build-2024
 ms.topic: how-to
-ms.date: 03/11/2025
+ms.date: 04/30/2025
 ---
 
 # Create a vector query in Azure AI Search
@@ -98,8 +98,11 @@ This section shows you the basic structure of a vector query. You can use the Az
 + `vectorQueries` is the construct for vector search.
 + `vectorQueries.kind` set to `vector` for a vector array, or set to `text` if the input is a string and [you have a vectorizer](#query-with-integrated-vectorization).
 + `vectorQueries.vector` is query (a vector representation of text or an image).
++ `vectorQueries.exhaustive` (optional) invokes exhaustive KNN at query time, even if the field is indexed for HNSW.
++ `vectorQueries.fields` (optional) targets specific fields for query execution (up to 10 per query).
 + `vectorQueries.weight` (optional) specifies the relative weight of each vector query included in search operations (see [Vector weighting](#vector-weighting)).
-+ `exhaustive` (optional) invokes exhaustive KNN at query time, even if the field is indexed for HNSW.
++ `vectorQueries.k` is the number of matches to return.
+
 
 In the following example, the vector is a representation of this string: "what Azure services support full text search". The query targets the `contentVector` field. The query returns `k` results. The actual vector has 1536 embeddings, so it's trimmed in this example for readability.
 
@@ -257,7 +260,9 @@ If you do want vector fields in the result, here's an example of the response st
 
 ## Multiple vector fields
 
-You can set the "vectorQueries.fields" property to multiple vector fields. The vector query executes against each vector field that you provide in the `fields` list. When querying multiple vector fields, make sure each one contains embeddings from the same embedding model, and that the query is also generated from the same embedding model.
+You can set the "vectorQueries.fields" property to multiple vector fields. The vector query executes against each vector field that you provide in the `fields` list. You can specify up to 10 fields.
+
+When querying multiple vector fields, make sure each one contains embeddings from the same embedding model, and that the query is also generated from the same embedding model.
 
 ```http
 POST https://{{search-service-name}}.search.windows.net/indexes/{{index-name}}/docs/search?api-version=2024-07-01