Showing results for AI - Azure SQL Devs’ Corner

OpenAI recently introduced a powerful new feature for developers: structured output using JSON Schema via the parameter. This makes it possible to request responses from a GPT-4o model that strictly match a given schema—no free-text, no guesswork. If you're working with Azure SQL, this is a game-changer. Combined with the stored procedure and SQ...

In today's data-driven world, delivering precise and contextually relevant search results is critical. SQL Server and Azure SQL Database now enable this through Hybrid Search—a technique that combines traditional full-text search with modern vector similarity search. This allows developers to build intelligent, AI-powered search experiences directl...

For decades, SQL Server has been a cornerstone of data management, evolving to meet the needs of modern developers. What began as a traditional relational engine is now a cloud-enabled, AI-integrated platform designed for building real-world applications. SQL Server continues to meet developers where they are—offering tools, services, and capabilit...

Vector databases like Qdrant and Milvus are specifically designed to efficiently store, manage, and retrieve embeddings. However, many applications already use relational databases like SQL Server or SQL Azure. In such cases, installing and managing another database can be challenging, especially since these vector databases may not offer all t...

Security is a significant topic today, and the ability to access a service requiring authentication without using an API key, password, or secret is a common request from those concerned about the security of a solution, which includes all of us. In today's digital landscape, cybersecurity threats are increasingly sophisticated and frequent, mak...

Welcome to an exciting, new workshop where we blend the power of AI with the versatility of GraphQL and SQL databases in Microsoft Fabric. This guide will walk you through creating a set of GraphQL RAG (Retrieval-Augmented Generation) application APIs, leveraging relational data and Azure OpenAI. What You’ll Learn You’ll dive into querying and ma...

In the previous article of this series, it was discussed how embeddings can be quickly created from data already in Azure SQL. This is a useful starting point, but since data in a database changes frequently, a common question arises: “How can the vectors be kept updated whenever there is a change to the content from which they have been generated?...

Embeddings and vectors are becoming common terms not only for engineers involved in AI-related activities but also for those using databases. Some common points of discussion that frequently arise among users familiar with vectors and embeddings include: Let’s tackle each one of these questions one by one starting from the very...

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...

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 fo...