Daily updated RAG System with PostgreSQL Vector Database

A Retrieval-Augmented Generation (RAG) system that automatically processes text documents from Google Drive, stores embeddings in PostgreSQL, and provides a question-answering interface.

Features

• Automatic document processing from Google Drive
• Vector similarity search using pgvector
• Real-time question answering through Gradio interface
• Daily automated checks for new documents via GitHub Actions

Prerequisites

• Supabase account (for PostgreSQL database)
• Google Cloud account with Drive API enabled
• Python 3.10 or higher

To-do

• Develop Gradio app for RAG-enabled chat
• Deploy chat app using Docker and Google Cloud
• Continue adding data