The project builds knowledge graphs from raw text by extracting entities and relations, then storing them as graph structures. These knowledge graphs serve as retrievers for LLM-based systems, enabling Graph Retrieval Augmented Generation (GRAG). This approach enhances document Q&A compared to traditional vector-database-based RAG by leveraging graph relationships and structural information.