While most scientists aren't at the scale of black hole imaging research teams that analyze Petabytes of data every day, you can easily fall into a situation where your laptop doesn't have quite enough power to do the analytics you need.

In this hands-on tutorial, you will learn the fundamentals of analyzing massive datasets with real-world examples on actual powerful machines on a cloud provided by the presenter – starting from how the data is stored and read, to how it is processed and visualized.

As we descend from the peak of the hype cycle around Large Language Models (LLMs), chat-based document interrogation systems have emerged as a high value practical use case. The ability to ask natural language questions and get relevant answers from a large corpus of documents has the potential to fundamentally transform organizations and make institutional knowledge accessible.

Retrieval-augmented generation (RAG) is a technique to make foundational LLMs more powerful and accurate, and a leading way to implement a personal or company-level chat-based document interrogation system. In this talk, we’ll understand RAG by creating a personal chat application. We’ll use a new OSS project called Ragna that provides a friendly Python and REST API, designed for this particular case. We’ll also demonstrate a web application that leverages the REST API built with Panel–a powerful OSS Python application development framework.

By the end of this talk, you will have an understanding of the fundamental components that form a RAG model as well as exposure to open source tools that can help you or your organization explore and build on your own applications.