Merge pull request #3790 from MicrosoftDocs/main

3/27/2025 PM Publish

Author: Taojunshen

articles/ai-services/content-understanding/media/quickstarts/cu-landing-page.png

@@ -6,7 +6,7 @@ author: laujan
 manager: nitinme
 ms.service: azure-ai-document-intelligence
 ms.topic: conceptual
-ms.date: 03/17/2025
+ms.date: 03/27/2025
 ms.author: lajanuar
 ---
 
@@ -120,6 +120,11 @@ The layout model extracts structural elements from your documents. To follow are
 * [**Figures**](#figures)
 * [**Sections**](#sections)
 
+Run the sample layout document analysis within [Document Intelligence Studio](https://documentintelligence.ai.azure.com/studio), then navigate to the results tab to access the full JSON output.
+
+   :::image type="content" source="../media/studio/json-output-tab.png" alt-text="Screenshot of results JSON output tab in the Document Intelligence Studio.":::
+
+
 ### Pages
 
 The pages collection is a list of pages within the document. Each page is represented sequentially within the document and includes the orientation angle indicating if the page is rotated and the width and height (dimensions in pixels). The page units in the model output are computed as shown:

articles/ai-services/document-intelligence/media/studio/json-output-tab.png

@@ -6,14 +6,14 @@ manager: nitinme
 ms.service: azure-ai-openai
 ms.custom: devx-track-python
 ms.topic: how-to
-ms.date: 03/26/2025
+ms.date: 03/27/2025
 author: mrbullwinkle
 ms.author: mbullwin
 recommendations: false
 ---
 # Learn how to generate embeddings with Azure OpenAI
 
-An embedding is a special format of data representation that can be easily utilized by machine learning models and algorithms. The embedding is an information dense representation of the semantic meaning of a piece of text. Each embedding is a vector of floating point numbers, such that the distance between two embeddings in the vector space is correlated with semantic similarity between two inputs in the original format. For example, if two texts are similar, then their vector representations should also be similar. Embeddings power vector similarity search in Azure Databases such as [Azure Cosmos DB for MongoDB vCore](/azure/cosmos-db/mongodb/vcore/vector-search) , [Azure SQL Database](/azure/azure-sql/database/ai-artificial-intelligence-intelligent-applications?view=azuresql&preserve-view=true#vector-search) or [Azure Database for PostgreSQL - Flexible Server](/azure/postgresql/flexible-server/how-to-use-pgvector).
+An embedding is a special format of data representation that can be easily utilized by machine learning models and algorithms. The embedding is an information dense representation of the semantic meaning of a piece of text. Each embedding is a vector of floating point numbers, such that the distance between two embeddings in the vector space is correlated with semantic similarity between two inputs in the original format. For example, if two texts are similar, then their vector representations should also be similar. Embeddings power vector similarity search in Azure Databases such as [Azure Cosmos DB for NoSQL](/azure/cosmos-db/nosql/vector-search), [Azure Cosmos DB for MongoDB vCore](/azure/cosmos-db/mongodb/vcore/vector-search), [Azure SQL Database](/azure/azure-sql/database/ai-artificial-intelligence-intelligent-applications?view=azuresql&preserve-view=true#vector-search) or [Azure Database for PostgreSQL - Flexible Server](/azure/postgresql/flexible-server/how-to-use-pgvector).
 
 ## How to get embeddings
 

articles/ai-services/document-intelligence/prebuilt/layout.md

@@ -7,7 +7,7 @@ manager: nitinme
 
 ms.service: azure-ai-translator
 ms.topic: conceptual
-ms.date: 01/27/2025
+ms.date: 03/27/2025
 ms.author: lajanuar
 ---
 
@@ -50,3 +50,10 @@ No. Don't include SAS token-appended URLs. Managed identities eliminate the need
 #### Which PDF format renders the best results?
 
 PDF documents generated from digital file formats (also known as "native" PDFs) provide optimal output. Scanned PDFs are images of printed documents scanned into an electronic format. Translating scanned PDF files can result in loss of the original formatting, layout, and style, and affect the quality of the translation.
+
+#### Can I learn more about single document synchronous translation?
+
+Yes.
+
+* For more information, *see* [Synchronous document translation](overview.md#synchronous-translation)
+* To view `synchronous document translation` sample code in our GitHub repository, *see* [.NET/C#](https://github.com/Azure/azure-sdk-for-net/blob/main/sdk/translation/Azure.AI.Translation.Document/samples/Sample5_SynchronousTranslation.md); [Python](https://github.com/Azure/azure-sdk-for-python/blob/azure-ai-translation-document_1.0.0/sdk/translation/azure-ai-translation-document/samples/sample_single_document_translation.py); [REST API](quickstarts/rest-api.md#synchronously-translate-a-single-document-post)

articles/ai-services/openai/how-to/embeddings.md

@@ -7,7 +7,7 @@ manager: nitinme
 
 ms.service: azure-ai-translator
 ms.topic: conceptual
-ms.date: 01/29/2025
+ms.date: 03/26/2025
 ms.author: lajanuar
 ---
 

articles/ai-services/translator/document-translation/faq.md

@@ -74,14 +74,14 @@ $cosmosdb_acc_name = <cosmos db account name>
 $resource_group = <resource group name>
 $subsciption = <subscription ID>
 $system_assigned_principal = <Object (principal) ID for the search service's system/user assigned identity>
-$readOnlyRoleDefinitionId = "00000000-0000-0000-0000-00000000000"
-$scope=$(az cosmosdb show --name $cosmosdbname --resource-group $resourcegroup --query id --output tsv)
+$readOnlyRoleDefinitionId = "00000000-0000-0000-0000-000000000001"
+$scope=$(az cosmosdb show --name $cosmosdb_acc_name --resource-group $resource_group --query id --output tsv)
 ```
 
 Define a role assignment for the system-assigned identity:
 
 ```azurecli
-az cosmosdb sql role assignment create --account-name $cosmosdbname --resource-group $resourcegroup --role-definition-id $readOnlyRoleDefinitionId --principal-id $sys_principal --scope $scope
+az cosmosdb sql role assignment create --account-name $cosmosdb_acc_name --resource-group $resource_group --role-definition-id $readOnlyRoleDefinitionId --principal-id $system_assigned_principal --scope $scope
 ```
 
 For more information, see [Use data plane role-based access control with Azure Cosmos DB for NoSQL](/azure/cosmos-db/nosql/security/how-to-grant-data-plane-role-based-access)

articles/ai-services/translator/faq.md

@@ -49,7 +49,7 @@ You're setting up two clients, so you need endpoints and permissions on both res
 # Set endpoints and API keys for Azure services
 AZURE_SEARCH_SERVICE: str = "PUT YOUR SEARCH SERVICE ENDPOINT HERE"
 # AZURE_SEARCH_KEY: str = "DELETE IF USING ROLES, OTHERWISE PUT YOUR SEARCH SERVICE ADMIN KEY HERE"
-AZURE_OPENAI_ACCOUNT: str = "PUR YOUR AZURE OPENAI ENDPOINT HERE"
+AZURE_OPENAI_ACCOUNT: str = "PUT YOUR AZURE OPENAI ENDPOINT HERE"
 # AZURE_OPENAI_KEY: str = "DELETE IF USING ROLES, OTHERWISE PUT YOUR AZURE OPENAI KEY HERE"
 ```