Building graph-based RAG application for accurate, complete, and explainable AI
Muhammad Arif Wicaksana
RAG (retrieval-augmented generation) is a technique for enhancing the accuracy and reliability of LLMs with facts fetched from external sources. However, the naive RAG approach that solely relies on vector databases falls short for complex use cases. Representing the knowledge and facts into a structured & connected data (“knowledge graph”) can help improve the results as the facts are explicitly decoded into a graph structure, resulting in accurate and deterministic answers. In this session, I will discuss graph-based RAG and demonstrate how to easily build a graph-based RAG application using Python by leveraging tools and libraries freely available.
