This session explores the best practices for building graph-based Retrieval-Augmented Generation (RAG) systems from multiple documents using Python. Participants will learn how to construct document similarity graphs, integrate graph-based retrieval into RAG models, and fine-tune these systems for efficient document search and generation. With hands-on examples using libraries like NetworkX, FAISS, and HuggingFace, attendees will gain practical experience in building scalable and optimized graph-based retrieval systems. By the end of the session, the Python community will acquire new skills in leveraging graphs for more effective information retrieval and generating relevant outputs, providing valuable insights into cutting-edge AI workflows.