SciPy 2024

As we descend from the peak of the hype cycle around Large Language Models (LLMs), chat-based document inquiry systems have emerged as a high-value practical use case. Retrieval-Augmented Generation (RAG) is a technique to share relevant context and external information (retrieved from vector storage) to LLMs, thus making them more powerful and accurate.

In this hands-on tutorial, we’ll dive into RAG by creating a personal chat app that accurately answers questions about your selected documents. We’ll use a new OSS project called Ragna that provides a friendly Python and REST API, designed for this particular case. We’ll test the effectiveness of different LLMs and vector databases, including an offline LLM (i.e., local LLM) running on GPUs on the cloud-machines provided to you. We'll then develop a web application that leverages the REST API, built with Panel–a powerful OSS Python application development framework.

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 public cloud provided by the presenters – starting from how the data is stored and read, to how it is processed and visualized.