KET-RAG (Knowledge-Enhanced Text Retrieval Augmented Generation) is a powerful retrieval-augmented generation framework enhanced with knowledge graph technology. It achieves efficient knowledge retrieval and generation through a multi-granularity indexing framework, such as a knowledge graph skeleton and a text-keyword bipartite graph. This framework significantly improves retrieval and generation quality while reducing indexing costs, making it well-suited for large-scale RAG applications. Developed in Python, KET-RAG supports flexible configuration and extension, catering to the needs of developers and researchers seeking efficient knowledge retrieval and generation.