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azure-sql/database/ai-artificial-intelligence-intelligent-applications.md

@@ -4,7 +4,7 @@ description: "Use AI options such as OpenAI and vectors to build intelligent app
 author: WilliamDAssafMSFT
 ms.author: wiassaf
 ms.reviewer: damauri, josephsack, randolphwest, mathoma
-ms.date: 04/18/2025
+ms.date: 05/06/2025
 ms.service: azure-sql-database
 ms.topic: conceptual
 ms.collection:
@@ -39,7 +39,7 @@ The following links provide sample code of various options to build intelligent
 | AI Option | Description |
 | --- | --- |
 | **[Azure OpenAI](#azure-openai)** | Generate embeddings for RAG and integrate with any model supported by Azure OpenAI. |
-| **[Vectors](#vectors)** | Learn how to store and query vectors in the database. |
+| **[Vectors](#vectors)** | Learn how to store vectors and use vector functions in the database. |
 | **[Azure AI Search](#azure-ai-search)** | Use your database together with Azure AI Search to train LLM on your data. |
 | **[Intelligent applications](#intelligent-applications)** | Learn how to create an end-to-end solution using a common pattern that can be replicated in any scenario. |
 | **[Copilot skills in Azure SQL Database](#microsoft-copilot-skills-in-azure-sql-database)** | Learn about the set of AI-assisted experiences designed to streamline the design, operation, optimization, and health of Azure SQL Database-driven applications. |
@@ -86,7 +86,9 @@ In Azure OpenAI, input text provided to the API is turned into tokens (tokenized
 
 ### Vectors
 
-Vectors are ordered arrays of numbers (typically floats) that can represent information about some data. For example, an image can be represented as a vector of pixel values, or a string of text can be represented as a vector or ASCII values. The process to turn data into a vector is called *vectorization*. For more information, see [Vectors](#vectors-1).
+Vectors are ordered arrays of numbers (typically floats) that can represent information about some data. For example, an image can be represented as a vector of pixel values, or a string of text can be represented as a vector or ASCII values. The process to turn data into a vector is called *vectorization*. For more information, see [Vector examples](#vector-examples).
+
+Working with vector data is easier with the introduction of the [vector data type](/sql/t-sql/data-types/vector-data-type?view=azuresqldb-current&preserve-view=true) and [vector functions](/sql/t-sql/functions/vector-functions-transact-sql?view=azuresqldb-current&preserve-view=true). 
 
 ### Embeddings
 
@@ -104,7 +106,7 @@ Vectors in Azure SQL Database can be efficiently stored and queried, as describe
 
 ## Azure OpenAI
 
-Embedding is the process of representing the real world as data. Text, images, or sounds can be converted into embeddings. Azure OpenAI models are able to transform real-world information into embeddings. The models are available as REST endpoints and thus can be easily consumed from Azure SQL Database using the [`sp_invoke_external_rest_endpoint`](/sql/relational-databases/system-stored-procedures/sp-invoke-external-rest-endpoint-transact-sql) system stored procedure:
+Embedding is the process of representing the real world as data. Text, images, or sounds can be converted into embeddings. Azure OpenAI models are able to transform real-world information into embeddings. The models are available as REST endpoints and thus can be easily consumed from Azure SQL Database using the [`sp_invoke_external_rest_endpoint`](/sql/relational-databases/system-stored-procedures/sp-invoke-external-rest-endpoint-transact-sql?view=azuresqldb-current&preserve-view=true) system stored procedure:
 
 ```sql
 DECLARE @retval INT, @response NVARCHAR(MAX);
@@ -132,11 +134,9 @@ For additional examples on using SQL Database and OpenAI, see the following arti
 - [Generate images with Azure OpenAI Service (DALL-E) and Azure SQL Database](https://devblogs.microsoft.com/azure-sql/generate-images-with-openai-and-azure-sql/)
 - [Using OpenAI REST Endpoints with Azure SQL Database](https://devblogs.microsoft.com/azure-sql/using-openai-rest-endpoints-with-azure-sql-database/)
 
-## Vectors
-
-In November 2024, the new **vector** data type was introduced in Azure SQL Database.
+## Vector examples
 
-The dedicated **vector** type allows for efficient and optimized storing of vector data, and comes with a set of functions to help developers streamline vector and similarity search implementation. Calculating distance between two vectors can be done in one line of code using the new `VECTOR_DISTANCE` function. For more information on the [**vector** data type](/sql/t-sql/data-types/vector-data-type) and related functions, see [Overview of vectors in the SQL Database Engine](/sql/relational-databases/vectors/vectors-sql-server).
+The dedicated **vector** data type allows for efficient and optimized storing of vector data, and comes with a set of functions to help developers streamline vector and similarity search implementation. Calculating distance between two vectors can be done in one line of code using the new `VECTOR_DISTANCE` function. For more information and examples, see [Vector search and vector indexes in the SQL Database Engine](/sql/relational-databases/vectors/vectors-sql-server?view=azuresqldb-current&preserve-view=true).
 
 For example:
 
@@ -156,7 +156,6 @@ ORDER BY
     VECTOR_DISTANCE('cosine', @my_reference_vector, embedding)
 ```
 
-
 ## Azure AI Search
 
 Implement RAG-patterns with Azure SQL Database and Azure AI Search. You can run supported chat models on data stored in Azure SQL Database, without having to train or fine-tune models, thanks to the integration of Azure AI Search with Azure OpenAI and Azure SQL Database. Running models on your data enables you to chat on top of, and analyze, your data with greater accuracy and speed.

azure-sql/database/authentication-microsoft-entra-create-users-with-nonunique-names.md

@@ -5,7 +5,7 @@ description: Learn how to mitigate naming conflicts for Microsoft Entra logins a
 author: tameikal-msft
 ms.author: talawren
 ms.reviewer: vanto, mathoma, jaszymas
-ms.date: 11/20/2024
+ms.date: 04/30/2025
 ms.service: azure-sql
 ms.subservice: security
 ms.topic: conceptual
@@ -46,7 +46,7 @@ The *duplicate display name* error occurs because Microsoft Entra ID allows dupl
 > [!NOTE]
 > Most nonunique display names in Microsoft Entra ID are related to service principals, though occasionally group names can also be nonunique. Microsoft Entra user principal names are unique, as two users can't have the same user principal. However, an app registration (service principal) can be created with a display name that is the same as a user principal name.
 >
-> If the service principal display name isn't a duplicate, the default `CREATE LOGIN` or `CREATE USER` statement should be used. The `WITH OBJECT_ID` extension is a troubleshooting repair item implemented for use with nonunique service principals. Using it with a unique service principal isn't recommended. Using the `WITH OBJECT_ID` extension for a service principal without adding a suffix will run successfully, but it will not be obvious which service principal the login or user was created for. It's recommended to create an alias using a suffix to uniquely identify the service principal. The `WITH OBJECT_ID` extension isn't supported for SQL Server.
+> If the service principal display name isn't a duplicate, the default `CREATE LOGIN` or `CREATE USER` statement should be used. The `WITH OBJECT_ID` extension is a troubleshooting repair item implemented for use with nonunique service principals. Using it with a unique service principal isn't recommended. Using the `WITH OBJECT_ID` extension for a service principal without adding a suffix will run successfully, but it will not be obvious which service principal the login or user was created for. It's recommended to create an alias using a suffix to uniquely identify the service principal. The `WITH OBJECT_ID` extension is supported for SQL Server 2025 and later versions.
 
 ## T-SQL create login/user syntax for nonunique display names
 

azure-sql/database/doc-changes-updates-release-notes-whats-new-archive.md

@@ -32,7 +32,7 @@ Return to [What's new in Azure SQL Database?](doc-changes-updates-release-notes-
 | **Vector data type (preview)** | November | Working with vector data is now easier in Azure SQL Database with the introduction of a new [vector data type](/sql/t-sql/data-types/vector-data-type) and [functions](/sql/t-sql/functions/vector-functions-transact-sql). This feature is now in preview. For more information, see [Intelligent applications with Azure SQL Database](ai-artificial-intelligence-intelligent-applications.md#vectors). |
 | **Hyperscale single database increased maximum size** | November | The maximum single database size in Azure SQL Database Hyperscale has been increased from 100 TB to 128 TB. For more information, see [Blog: November 2024 Hyperscale enhancements](https://aka.ms/AAslnql). |
 | **Hyperscale increased log generation rate (preview)** | November | The transaction log generation rate in Azure SQL Database Hyperscale single databases is set to increase from 100 MiB/s to 150 MiB/s. The increased log generation rate is available as an opt-in preview feature. This feature is now in preview. For more information and to opt in to 150 MiB/s, see [Blog: November 2024 Hyperscale enhancements](https://aka.ms/AAslnql). |
-| **Hyperscale continuous priming (preview)** | November | [Continuous priming](service-tier-hyperscale.md#buffer-pool-resilient-buffer-pool-extension-and-continuous-priming) is an innovative new feature is designed to optimize Hyperscale performance during failovers by priming secondary compute replicas. Continuous priming is currently in a gated preview. This feature is now in preview. For more information and to opt in to continuous priming, see [Blog: November 2024 Hyperscale enhancements](https://aka.ms/AAslnql). |
+| **Hyperscale continuous priming (preview)** | November | [Continuous priming](service-tier-hyperscale.md#continuous-priming) is an innovative new feature is designed to optimize Hyperscale performance during failovers by priming secondary compute replicas. Continuous priming is currently in a gated preview. This feature is now in preview. For more information and to opt in to continuous priming, see [Blog: November 2024 Hyperscale enhancements](https://aka.ms/AAslnql). |
 | **Network Security Perimeter (preview)** | November | [Network Security Perimeter](network-security-perimeter.md) secures both inbound and outbound network traffic between Azure SQL Database and other Platform as a Service (PaaS) resources, and attempts made to communicate with Azure resources that aren't inside the perimeter are blocked. This feature is now in preview. |
 | **OBJECT_ID T-SQL syntax (GA)** | November | The [CREATE USER](/sql/t-sql/statements/create-user-transact-sql) Transact-SQL (T-SQL) syntax has been extended to include `WITH OBJECT_ID` to support creating Microsoft Entra logins and users in Azure SQL Database that have nonunique names. This feature is now generally available. For more information, see [Microsoft Entra nonunique name support](authentication-microsoft-entra-create-users-with-nonunique-names.md). |
 | **Lower auto-pause delay for serverless** | October | Reduce costs by lowering the [autopause delay for serverless compute in Azure SQL Database](https://aka.ms/AAs7lpz). For more information, see [serverless compute tier](serverless-tier-overview.md). |