A lightning walkthrough taking an existing site and turning it into searchable content using OpenAI embeddings. We'll use react server components to create an extremely snappy UX while using server-side cosine similarity functions to determine the best search results.

Join me on the RAG (Retrieval Augmented Generation) road. In this talk we start from the basics, we will go through embeddings and vector databases. Then finally i'll show you a great use case for a RAG application that has a React front end and utilizes the capabilities of generative AI.

Learn about MongoDB Atlas’s new Vector Search offering and how developers can use this to do semantic searches on top of their application data. We’ll then go through a demo where we show the experience of embedding/vectorizing a document, inserting it into the cluster, and finally querying that data to find semantically similar data to our questions.

It's pretty common to evaluate new changes with a vibe check but that makes it difficult to really know what's up with your system. We'll talk a bit about how to build more complex apps.