KAG is a graph-augmented retrieval augmented generation system and knowledge graph engine. It functions as a framework that integrates large language models with graph retrieval and numerical calculation to resolve natural language queries.
The system creates unified knowledge representations by aligning unstructured data and expert rules through semantic mapping. It maintains mutual indexing between graph structures and original text blocks to ensure that reasoning processes remain linked to verifiable source data.
The project provides capabilities for semantic information integration, graph-based data retrieval, and hybrid logical reasoning. It employs a pipeline that combines semantic graph search with numerical calculations and symbolic logic.
