Azure SQL Devs’ Corner
Voices from the Azure SQL PM Team, focusing on development and developers
Featured posts
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 a...
Latest posts
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...
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...
Build AI apps faster and easier with SQL database in Fabric – now Public Preview!
The SQL Server and Azure SQL team has embarked on a mission to make building AI apps faster and easier than ever, announcing the Public Preview of SQL database in Microsoft Fabric. This new simple, autonomous and secure, and optimized for AI service will help you in the era of AI, where 1 billion new apps are estimated to be built in the next 5 years, and 87% of leaders believe AI will give their organizations a competitive edge. With operational databases coming to Fabric, Fabric is evolving from an analytics platform to a data platform and the data layer of the AI and Copilot stack. SQL database in Fabric ...
RAG with SQL Vector Store: A Low-Code/No-Code Approach using Azure Logic Apps
Data is at the heart of every AI application, and efficient data ingestion is critical for success. With over 1,400 enterprise connectors, Logic Apps offers unmatched access to a diverse range of systems, applications, and databases, whether hosted in the cloud or on-premises. These connectors give businesses the flexibility to keep their data where it resides while seamlessly powering AI experiences. By leveraging Azure Logic Apps' native capabilities, organizations can implement the Retrieval-Augmented Generation (RAG) pattern, enabling straightforward ingestion and retrieval of data from multiple sources to...
Exciting Announcement: Public Preview of Native Vector Support in Azure SQL Database!
Public Preview of Native Vector Support in Azure SQL Database We are excited to share that the dedicated vector data type in Azure SQL Database, which was previously available through the Early Adopter Preview, is now transitioning to Public Preview! As of today, the Vector data type and its associated functions are accessible to everyone, automatically and seamlessly. You can start using them immediately in any Azure SQL Database you currently have or will create in the future. Azure SQL MI will move to Public Preview in the next months: stay tuned for more details! SQL Database as a Vector Store: With the ...
Soccer Analytics Copilot with Azure SQL and OpenAI
The Football (aka Soccer in US 😀) Analisys Copilot provides an intuitive interface for users to interact with complex football data without needing advanced technical skills. By utilizing natural language processing, users can ask questions and retrieve detailed insights from vast datasets, including competitions, matches, teams, players, and events. This makes data exploration accessible to analysts, coaches, and fans who may not be familiar with coding or database queries, enabling them to gain valuable information through simple conversations with the chatbot. Through this interactive platform, users ca...
The ultimate chatbot?
RAG - Retrieval Augmented Generation - is by far one of the most common patterns today as it enables the creation of chatbots that can chat on your own data, as I described in my previous "Retrieval Augmented Generation with Azure SQL" blog post. As soon as you start to use it excitement grows, as it provides a sort of a magical experience. Experience that unfortunately stops the moment you try to ask it question that requires the ability to extract data from a structured source, like a database, as RAG is great when dealing with unstructured data, like text. By integrating the RAG pattern alongside NL2SQL, wh...