RAGs is a Streamlit application that allows users to create and customize Retrieval-Augmented Generation (RAG) pipelines over their data using natural language, eliminating the need for coding. Users can set up a RAG pipeline by describing their task, configuring settings, and interacting with the RAG agent. The app accommodates both technical and non-technical users by providing a user-friendly interface for parameter adjustments, such as top-k retrieval and summarization options, while also allowing deeper customization for those with more technical expertise. RAGs features an interactive chatbot interface for querying data, utilizing top-k vector search and summarization tools to generate responses. The setup process involves cloning the project, installing required packages, and launching the app, with the option to modify configurations to tailor the agent's behavior. RAGs represents a step toward building large language model applications powered by natural language, with ongoing improvements and community support available through its GitHub repository.