This project provides a ready-to-use RAG application that combines semantic search with large language models to generate factually accurate responses grounded in user-provided data. It supports two retrieval paradigms: vector RAG using embedding-based similarity search and graph RAG using knowledge graph traversal for multi-hop reasoning. The application is built on txtai for the retrieval backbone and presents a Streamlit UI for interactive querying.