You can find the code for this demo here →

This project aims to create the most efficient stack for constructing a Retrieval-Augmented Generation (RAG) application, with a retrieval latency of just 15 milliseconds. It utilizes binary quantization for speedy retrieval, combined with Groq's swift inference capabilities. To begin, ensure that Python version 3.11 or higher is installed on your system. Next, install the uv package and establish your Python development environment.

Obtain an API key from Groq and save it in a .env file using the specified format. In your terminal, run a command incorporating your Beam token to register the application. This action will successfully deploy your Streamlit application on Beam's cloud platform. You can access the application directly through the provided link in your web browser. Alternatively, you may choose to run the application locally by executing the given command.

As an added bonus, subscribe to our newsletter to receive a FREE Data Science eBook containing over 150 essential lessons. Stay updated with the latest tutorials, insights, and exclusive resources by subscribing today! The project welcomes contributions from the community. If you have improvements to suggest, please fork the repository and submit a pull request with your proposed changes.