This project implements a Retrieval Augmented Generation (RAG) system focused on Formula 1 (F1) racing data. RAG combines the power of large language models with specific domain knowledge retrieval to provide accurate and contextual responses.

• F1 data retrieval and processing through web scraping
• RAG implementation for F1-specific queries
• Information retrieval from F1 historical datasets
• Vector database for efficient similarity search
• Web scraping using LangChain from predefined sources

# Clone the repository
git clone https://github.com/1DeepakSrinivas/f1-rag.git
cd f1-rag

# Install dependencies
npm install

• Next.js
• Vercel AI SDK
• TypeScript
• Databricks Astra
• LangChain
• OpenAI GPT-4 and text-embedding-3-small for text generation

1. Create a .env file in the root directory with the following variables:

ASTRA_DB_NAMESPACE="<your_keyspace_name>" (default - default_keyspace)
ASTRA_DB_COLLECTION="<your_db_collection_name>"
ASTRA_DB_API_ENDPOINT="<your_db_endpoint>"
ASTRA_DB_APPLICATION_TOKEN="<your_db_api_key>"
OPENAI_API_KEY="<your_openai_api_key>"

2. Set up Databricks:
   • Visit https://astra.datastax.com/
   • Create a Databricks workspace
   • Generate a personal access token
   • Note down the cluster HTTP path

# Install dependencies
npm install

# Initialize vector database
npm run seed

# Run development server
npm run dev

# Build for production
npm run build

# Start production server
npm start

The application will be available at http://localhost:3000