Showing category results for AI

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...

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 emb...

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 comb...