| title | RAGU: A Multi-Step GraphRAG Engine with a Compact Domain-Adapted LLM |
|---|---|
| authors | Mikhail Komarov, Ivan Bondarenko, Stanislav Shtuka, Oleg Sedukhin, Roman Shuvalov, Yana Dementyeva, Matvey Solovyov, Nikolay O. Nikitin |
| date | 2026-07-13 |
| venue | arXiv (preprint, not peer reviewed) |
| id | arXiv:2607.11683 |
| affiliation | Novosibirsk State University |
| upvotes | 148 (Hugging Face Daily Papers, 2026-11-01) |
| links | abs pdf hf code |
| abstract | Graph retrieval-augmented generation (GraphRAG) enhances large language models with structured knowledge, yet existing systems construct knowledge graphs in a single extraction pass, producing noisy entities and brittle retrieval. RAGU, an open-source modular GraphRAG engine, addresses this by separating extraction from consolidation: entities and relations pass through two-stage typed extraction, DBSCAN-backed deduplication, LLM summarization, and Leiden community detection. A key insight motivates a compact extractor: the skills an in-pipeline LLM needs - comprehension, extraction, reasoning over context - are language skills that grow only weakly with model size, unlike factual world knowledge. Accordingly, we train Meno-Lite-0.1, a 7B model optimized for language skills, which outperforms Qwen2.5-32B on knowledge-graph construction (+12.5% relative harmonic mean) and matches it on English GraphRAG tasks. On GraphRAG-Bench (Medical), RAGU retrieves the most complete context at every factoid level (evidence recall up to 0.84 vs. leq0.76) and overtakes HippoRAG2 on synthesis tasks; on multi-hop factoid QA, the apparent HippoRAG2 advantage is shown to be largely an answer-format artifact. RAGU is installable via pip install graph_ragu, runs on a single GPU, and is released under MIT. The source code is publicly available at https://github.com/RaguTeam/RAGU, and the Meno-Lite-0.1 model can be obtained from https://huggingface.co/bond005/meno-lite-0.1. |
cite
@misc{komarov2026ragu,
title={RAGU: A Multi-Step GraphRAG Engine with a Compact Domain-Adapted LLM},
author={Mikhail Komarov and Ivan Bondarenko and Stanislav Shtuka and Oleg Sedukhin and Roman Shuvalov and Yana Dementyeva and Matvey Solovyov and Nikolay O. Nikitin},
year={2026},
eprint={2607.11683},
archivePrefix={arXiv},
url={https://arxiv.org/abs/2607.11683}
}