Vector Similarity Search (VSS) is no longer a feature that only lives in AI/ML demos! VSS has many applications, including eCommerce, recommendation engines, chatbots, image & video search, and chemistry. In this talk, we'll learn how to implement VSS in Spring applications using the Redis Stack distribution enhanced search capabilities. We'll learn about creating Vectors, Similarity, and Distance Metrics, Generating Machine Learning Embeddings for your data, and wrapping it up with how to provide enhanced search capabilities in your Spring Boot applications.
Brian Sam-Bodden
Redis
Brian Sam-Bodden is a Senior Developer Advocate at Redis and an author, instructor, speaker, Java Champion and avid open-source contributor who has spent over twenty years crafting software systems. He’s a polyglot programmer that enjoys crafting code in Java, Rust, Elixir, Ruby, and Javascript. He holds dual bachelor’s degrees from Ohio Wesleyan University in computer science and physics. Brian is a frequent speaker at user groups and conferences nationally and abroad and is the author of “Beginning POJOs: Spring, Hibernate, JBoss and Tapestry,” and co-author of the “Enterprise Java Development on a Budget: Leveraging Java Open Source Technologies” and a contributor to O’Reilly’s “97 Things Every Project Manager Should Know”.