Do you need to search your data based on meaning instead of matching by keywords or phrases? Do you need to match the data in a language different from the query term?

Semantic search, or search based on the meaning, analyzes the context and intent behind the query term to provide relevant results. Using Vector embeddings, the data structure behind semantic search, you can supercharge your search to include text, images, and other types of data. With vector databases, you can store and index vector embeddings and provide similarity search over these embeddings.

In this session, we will explore the building blocks of semantic search and explore vector embeddings and similarity metrics. You will learn how to generate embeddings with large language models using OpenAI. I will also explain how to store, index, and query vector embeddings using the vector data type in Azure SQL Database or with Microsoft SQL Server 2025. Finally, you will see how to optimize the queries with the DiskANN vector index.

Join me for a demo-rich session and learn how to implement semantic search with Azure SQL Database.