F1-AI: Retrieval-Augmented Generation (RAG) Application

Overview

F1-AI is a Retrieval-Augmented Generation (RAG) application that leverages OpenAI's GPT-4 model and a vector database to provide context-aware answers to questions about Formula 1 racing. This project demonstrates how to build a RAG application using TypeScript, OpenAI, DataStax Astra DB, and Playwright.

Prerequisites

• Node.js
• OpenAI API Key
• DataStax Astra DB

Installation

1. Clone the repository:

git clone https://github.com/IAmTomShaw/f1-rag-ai.git

2. Install the dependencies:

cd f1-rag-ai
npm install

Configuration

You'll need to paste your OpenAI API key and DataStax Astra DB credentials into the relevant files, or create a .env file in the root directory with the following environment variables:

OPENAI_API_KEY=your-openai-api-key
ASTRA_DB_ID=your-astra-db-id
ASTRA_DB_REGION=your-astra-db-region
ASTRA_DB_USERNAME=your-astra-db-username
ASTRA_DB_PASSWORD=your-astra-db-password

You'll then need to make sure that these environment variables are referenced in your code and loaded correctly.

Usage

You can modify the list of urls that I am scraping in the src/ingest.ts file. You can then run the following command to scrape the data:

npm run ingest

This will scrape the data from the urls and store it in the Astra DB.

You can then run the following command to test the RAG application using the query defined in the src/answer.ts file:

npm run answer

License

This project is licensed under the MIT License - see the LICENSE file for details.

Credit

This project was created by Tom Shaw