ROGRAG
ROGRAG is a graph‑RAG framework that improves large language model accuracy on specialized domains through a two‑stage retrieval process combining dense similarity search with logic‑form reasoning. It builds knowledge graphs incrementally, supports fuzzy matching and structured reasoning, and achieves about a fifteen percent increase in score on the SeedBench benchmark compared to baseline methods. The system can be deployed via Docker or from source, offers an API compatible with previous versions for integration with chat, web or voice interfaces, and includes multi‑database support. Performance results show higher accuracy, F1 and ROUGE scores than vanilla LLM, LangChain, BM25 and recent RAG approaches.