Azure SQL Devs’ Corner
Voices from the Azure SQL PM Team, focusing on development and developers
Featured posts
Announcing General Availability of Native Vector Type & Functions in Azure SQL
We are happy to announce that Native vector support in Azure SQL Database and Azure SQL Managed Instance is moving to General Availability this summer. Deploym...
Latest posts
Improve the “R” in RAG and embrace Agentic RAG in Azure SQL
The RAG (Retrieval Augmented Generation) pattern, which is commonly discussed today, is based on the foundational idea that the retrieval part is done using vector search. This ensures that all the most relevant information available to answer the given question is returned and then fed to an LLM to generate the final answer. While vector search is great for its specific use case - semantic search, which is approximate by nature - it fails when it comes to searching for precise questions, like "show me the latest 10 code samples" as that request can - and should - be answered precisely without the need for vec...
Extending Regular Expressions (Regex) Support on Azure SQL Managed Instance (MI)
We are happy to announce the Private Preview of Regular Expressions (Regex) support on Azure SQL Managed Instance (MI). This new feature brings powerful text processing capabilities to your SQL queries, enabling you to perform complex pattern matching and data manipulation with ease. Regex support in Azure SQL The Regex feature in Azure SQL follows the POSIX standard and is compatible with the standard regex syntax and supports a variety of regex functions, such as REGEXP_LIKE, REGEXP_COUNT, REGEXP_INSTR, REGEXP_REPLACE, and REGEXP_SUBSTR. The feature also supports case sensitivity, character classes, quantifi...
SQL Server Native Vector Search for .NET Developers
It seems that the majority of developers naturally believe this field of software development belongs to mathematics and Python developers, who originally started building the first solutions. However, .NET and C# provide a great foundation for building almost any kind of application on any platform in a highly professional way. In this post, we will show, step by step, how to build GenAI solutions in C#/.NET that leverage SQL Server's native vector search capabilities. Introduction In the space of Generative AI, large language models offer different types of functionalities, often categorized as Complet...
Building a RAG-Based Smart Memory Application with Azure SQL Database
Project Mission The way people work and manage information is changing rapidly in our digital age. More and more people are struggling to keep track of all the online resources they use daily. They need a better way to save, organize, and retrieve important information from websites, articles, and other online sources. This is especially true for knowledge workers, researchers, and students who often need to quickly access and synthesize information from many different places. Project Goal The primary goal of our project is to develop My Smart Memory, a full-stack web application that enables users to save U...
Embedding models and dimensions: optimizing the performance to resource-usage ratio
Since the release of vector preview, we've been working with many customers that are building AI solution on Azure SQL and SQL Server and one of the most common questions is how to support high-dimensional data, for example more than 2000 dimensions per vector. In fact, at the moment, the vector type supports "only" up to 1998 dimensions for an embedding. One of the impressions that such limitation may give, is that you cannot use the latest and greatest embedding model offered by OpenAI, and also available in Azure, which is the text-3-embedding-large model, as it returns 3072 dimensions. Well, that's not the...
Azure SQL ❤️ Python!
I recently presented at Python Day 2024 on Langchain integration. I created a slide deck that I believe can be useful beyond just the session, so I wanted to share it here for everyone's benefit. The deck covers the most common topics for a Python developer: The slide deck, which I created using the Sli.dev SDK, so that slide themselves are available online in a very easy to access format, is available here: The session covers a lot more than what is in the deck. It was recorded and is available for everyone to view: https://www.youtube.com/watch?v=_Tfej--_4-Q&a...
Introducing the enhanced MSSQL Extension for Visual Studio Code
The MSSQL extension for Visual Studio Code is designed to support developers in building applications that use Azure SQL (including Azure SQL Database, Azure SQL Managed Instance, and SQL Server on Azure VMs), SQL Database in Fabric (Preview) or SQL Server as backend databases. With a comprehensive suite of features for connecting to databases, designing and managing database schemas, exploring database objects, executing queries, and visualizing query plans, this extension transforms the SQL development experience within VS Code. The latest enhancements to the MSSQL extension for Visual Studio Code are specific...
Announcing LangChain integration for your SQL-based AI applications
In today's data-driven world, the ability to seamlessly integrate various technologies is crucial for efficient data management and analysis. We’re excited to announce LangChain integration with Azure SQL Database and SQL database in Microsoft Fabric! LangChain, a powerful tool for building solutions with language models, can be effectively combined with these services to build AI-ready applications. What’s New? Native Vector Support: SQL Database and SQL database in Fabric now support native vector search capabilities. This new capability brings the power of vector search operations directly to your SQL...
LangChain Integration for Vector Support for SQL-based AI applications
LangChain Integration for Vector Support for Azure SQL and SQL database in Microsoft Fabric Microsoft SQL now supports native vector search capabilities in Azure SQL and SQL database in Microsoft Fabric. We also released the langchain-sqlserver package, enabling the management of SQL Server as a Vectorstore in LangChain. In this step-by-step tutorial, we will show you how to add generative AI features to your own applications with just a few lines of code using Azure SQL DB, LangChain, and LLMs. Our Example Dataset The Harry Potter series, written by J.K. Rowling, is a globally beloved collection of seven book...