Showing results for embeddings - Azure SQL Devs’ Corner

Storing and querying text embeddings in a database it might seem challenging, but with the right schema design, it's not only possible, it's powerful. Whether you’re building AI-powered search, semantic filtering, or recommendation features, embeddings, and thus vectors, are now a first-class data type. So how do you model them well inside a databa...

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...

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...