DEVELOPING THE APP
Part 1 — Developing the code using Python, Gradio, GROQ & LlamaIndex
This is the first of a two-part series of articles. In this part (Part 1) I’ll show you how to develop a useful Web app that can upload and read many different types of file e.g. PDF, TXT, DOCX etc … We’ll then use AI and RAG to analyse the files and answer questions on them.
In part 2, I’ll show you how you can use Hugging Face Spaces to deploy your app to the web so the rest of the world can marvel at your greatness.
PS. If you want a sneak peek at the deployed app on Hugging Face Spaces, click on this
link
Undoubtedly, one of the largest growth areas to come out of AI and large Language Models is the field of Retrieval Augmented Generation (RAG). RAG is a fine-tuning method, where you provide an LLM with specific information it didn’t have access to in its training data.
If you’ve never heard of RAG before, don’t worry, it’s not that complicated. A typical RAG pipeline entails reading in one or more (usually PDF format) documents, but they can be CSV, TXT, or whatever. Splitting these document(s) into smaller chunks of text, encoding each token (kinda like an…