This talk will begin by explaining the transition from traditional lexical search to vector-based similarity search, and how OpenSearch combines both approaches in one complete package.

The session introduces fundamental concepts of vector databases, including how they store and process embedded meanings of various data types (text, images, and audio) using k-nearest neighbors (k-NN) functionality.

We'll explore practical applications such as visual search, semantic search, and recommendation engines, with emphasis on real-world use cases. This is your opportunity to learn how OpenSearch can serve as a knowledge base for AI systems, particularly in applications like retrieval augmented generation (RAG) with large language models.