RagRabbit, developed by software architect Marco D’Alia, is an open-source tool designed to simplify the process of Retrieval-Augmented Generation (RAG) by using Postgres as a comprehensive solution for web scraping, vector search, and AI responses. The tool crawls websites to convert pages into Markdown format, generates vector embeddings using the pgVector extension in Postgres, and facilitates AI-powered Q&A by conducting similarity searches within the database. It supports authentication methods such as user/password, GitHub OAuth, or magic links, and ensures secure access to indexed content. By leveraging Neon for its Postgres database, RagRabbit benefits from instant provisioning, serverless scaling, and full-text search capabilities, eliminating the need for a separate vector database. The seamless integration with Vercel allows for a one-click deployment, making it easy for users to implement without additional infrastructure. RagRabbit aims to provide a streamlined, low-cost solution capable of scaling according to demand, with a focus on maintaining simplicity and efficiency in deployment and maintenance.