KAG (Knowledge Augmented Generation) is a specialized framework for domain knowledge services, aimed at leveraging the strengths of knowledge graphs and vector retrieval to mutually enhance large language models and knowledge graphs. It addresses the significant gaps in vector similarity and knowledge reasoning relevance, as well as sensitivity to knowledge logic that are typical issues with Retrieval Augmentation Generation (RAG) techniques. KAG significantly outperforms methods like NaiveRAG and HippoRAG in multi-hop QA tasks; for instance, it achieves a 19.6% relative improvement in F1 scores on hotpotQA and a 33.5% increase on 2wiki. KAG has been successfully applied in two specialized knowledge Q&A tasks at Ant Group, including government and health Q&A, notably improving professionalism compared to RAG methods.