Merge branch 'main' into release-fabric-mirroring-pupr

Author: v-alje

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

@@ -3,8 +3,8 @@ title: Intelligent applications
 description: "Use AI options such as OpenAI and vectors to build intelligent applications with Azure SQL Database."
 author: damauri
 ms.author: damauri
-ms.reviewer: damauri, randolphwest, josephsack, mathoma
-ms.date: 3/13/2024
+ms.reviewer: damauri, josephsack, randolphwest, mathoma
+ms.date: 03/13/2024
 ms.service: sql-database
 ms.topic: conceptual
 ---
@@ -31,7 +31,7 @@ The following links provide sample code of various Azure SQL Database options to
 | **[Vectors](#vectors)** | Learn how to store and query vectors in Azure SQL Database. |
 | **[Azure AI Search](#azure-ai-search)** | Use Azure SQL 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 for Azure SQL Database (preview)](../copilot/copilot-azure-sql-overview.md)** | Manage and develop with intuitive assistance and natural language to SQL processing. |
+| **[Copilot for Azure SQL Database](#copilot-for-azure-sql-database-preview)** | Learn about the set of AI-assisted experiences designed to streamline the design, operation, optimization, and health of Azure SQL Database-driven applications. |
 
 ## Key concepts
 
@@ -153,18 +153,28 @@ Implement RAG-patterns with Azure SQL Database and Azure AI Search. You can run
 
 ## Intelligent applications
 
-Azure SQL Database can be used to build intelligent applications that include AI features, such as recommenders, and Retrieval Augmented Generation (RAG) as the following diagram demonstrates:
+Azure SQL Database can be used to build intelligent applications that include AI features, such as recommenders, and Retrieval Augmented Generation (RAG) as the following diagram demonstrates: 
 
 :::image type="content" source="media/ai-artificial-intelligence-intelligent-applications/session-recommender-architecture.png" alt-text="Diagram of different AI features to build intelligent applications with Azure SQL Database.":::
 
-For an end-to-end sample to build a recommender using session abstract as a sample dataset, see [How I built a session recommender in 1 hour using Open AI](https://devblogs.microsoft.com/azure-sql/how-i-built-a-session-recommender-in-1-hour-using-open-ai/).
+For an end-to-end sample to build a AI-enabled application using sessions abstract as a sample dataset, see:
+
+- [How I built a session recommender in 1 hour using Open AI](https://devblogs.microsoft.com/azure-sql/how-i-built-a-session-recommender-in-1-hour-using-open-ai/).
+- [Using Retrieval Augmented Generation to build a conference session assistant](https://github.com/Azure-Samples/azure-sql-db-session-recommender-v2)
 
 ### LangChain integration
 
 LangChain is a well-known framework for developing applications powered by language models.
 
 For an example that shows how LangChain can be used to create a Chatbot on your own data, see [Building your own DB Copilot for Azure SQL with Azure OpenAI GPT-4](https://devblogs.microsoft.com/azure-sql/building-your-own-db-copilot-for-azure-sql-with-azure-openai-gpt-4/).
 
+### Semantic Kernel integration
+
+[Semantic Kernel is an open-source SDK](https://learn.microsoft.com/semantic-kernel/overview/) that lets you easily build agents that can call your existing code. As a highly extensible SDK, you can use Semantic Kernel with models from OpenAI, Azure OpenAI, Hugging Face, and more! By combining your existing C#, Python, and Java code with these models, you can build agents that answer questions and automate processes.
+
+- [Semantic Kernel & Kernel Memory - SQL Connector](https://github.com/kbeaugrand/SemanticKernel.Connectors.Memory.SqlServer) - Provides a connection to a SQL database for the Semantic Kernel for the memories.
+- [OpenAI Embeddings Sample](https://github.com/marcominerva/OpenAIEmbeddingSample): An example that shows how to use Semantic Kernel and Kernel Memory to work with embeddings in a .NET application using SQL Server as Vector Database.
+
 ## Copilot for Azure SQL Database (preview)
 
 [Copilot in Azure SQL Database (preview)](../copilot/copilot-azure-sql-overview.md) is a set of AI-assisted experiences designed to streamline the design, operation, optimization, and health of Azure SQL Database-driven applications. Copilot can improve productivity by offering natural language to SQL conversion and self-help for database administration.
@@ -176,7 +186,6 @@ Copilot provides relevant answers to user questions, simplifying database manage
 
 > [!NOTE]
 > Copilot in Azure SQL Database is currently in a limited public preview for a limited number of early adopters. To sign up for this program, visit [Request Access to Copilot in Azure SQL Database: Limited Access Public Preview](https://aka.ms/sqlcopilot-signup).
-
 The limited public preview of Copilot for Azure SQL Database includes two Azure portal experiences:
 
 | Portal location | Experiences |
@@ -190,6 +199,6 @@ For more information, see [Frequently asked questions about Copilot in Microsoft
 
 - [Create and deploy an Azure OpenAI Service resource](/azure/ai-services/openai/how-to/create-resource?pivots=web-portal)
 - [Embeddings models](/azure/ai-services/openai/concepts/models#embeddings-models)
+- [SQL AI Samples and Examples](https://aka.ms/sqlaisamples)
 - [Frequently asked questions about Copilot in Azure SQL Database (preview)](../copilot/copilot-azure-sql-faq.yml)
 - [Responsible AI FAQ for Microsoft Copilot for Azure (preview)](/azure/copilot/responsible-ai-faq)
-- [SQL AI Samples and Examples](https://aka.ms/sqlaisamples)

azure-sql/database/logins-create-manage.md

@@ -44,7 +44,7 @@ When a user attempts to connect to a database, they provide a user account and a
   With this authentication method, the user submits a user account name and associated password to establish a connection. This password is stored in the `master` database for user accounts linked to a login or stored in the database containing the user accounts *not* linked to a login.
 
   > [!NOTE]
-  > Azure SQL Database only enforces [password complexity](/sql/relational-databases/security/password-policy#password-complexity) for [password policy](/sql/relational-databases/security/password-policy). For password policy in Azure SQL Managed Instance, see [Azure SQL Managed Instance frequently asked questions (FAQ)](../managed-instance/frequently-asked-questions-faq.yml#password-policy-).
+  > Azure SQL Database only enforces [password complexity](/sql/relational-databases/security/password-policy#password-complexity) for [password policy](/sql/relational-databases/security/password-policy). For password policy in Azure SQL Managed Instance, see [Azure SQL Managed Instance frequently asked questions (FAQ)](../managed-instance/frequently-asked-questions-faq.yml#password-policy).
   
 - [Microsoft Entra authentication](authentication-aad-overview.md)