# RAGnaroX: A Secure, Local-Hosted ChatOps Assistant Using Small Language Models

arXiv: https://arxiv.org/abs/2604.03291 · HTML: https://mlsanity.com/paper/2604.03291/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 14 of its 21 references that are in the map.
- First posted on arXiv 27 Mar 2026.

## Builds on (most cited first, 14 of 14)

- [SQuAD: 100,000+ Questions for Machine Comprehension of Text](https://mlsanity.com/paper/1606.05250/) (2016)
- [Ragas: Automated Evaluation of Retrieval Augmented Generation](https://mlsanity.com/paper/2309.15217/) (2023)
- [MLQA: Evaluating Cross-lingual Extractive Question Answering](https://mlsanity.com/paper/1910.07475/) (2019)
- [MultiHop-RAG: Benchmarking Retrieval-Augmented Generation for Multi-Hop Queries](https://mlsanity.com/paper/2401.15391/) (2024)
- [Blended RAG: Improving RAG (Retriever-Augmented Generation) Accuracy with Semantic Search and Hybrid Query-Based Retrievers](https://mlsanity.com/paper/2404.07220/) (2024)
- [RAG vs. GraphRAG: A Systematic Evaluation and Key Insights](https://mlsanity.com/paper/2502.11371/) (2025)
- [Advancing Multi-Agent Systems Through Model Context Protocol: Architecture, Implementation, and Applications](https://mlsanity.com/paper/2504.21030/) (2025)
- [Mitigating Response Delays in Free-Form Conversations with LLM-powered Intelligent Virtual Agents](https://mlsanity.com/paper/2507.22352/) (2025)
- [Enhancing Retrieval Augmented Generation with Hierarchical Text Segmentation Chunking](https://mlsanity.com/paper/2507.09935/) (2025)
- [MDEval: Evaluating and Enhancing Markdown Awareness in Large Language Models](https://mlsanity.com/paper/2501.15000/) (2025)
- [China and the U.S. produce more impactful AI research when collaborating together](https://mlsanity.com/paper/2304.11123/) (2023)
- [Rust: The Programming Language for Safety and Performance](https://mlsanity.com/paper/2206.05503/) (2022)
- [Trends in Frontier AI Model Count: A Forecast to 2028](https://mlsanity.com/paper/2504.16138/) (2025)
- [Rethinking Hybrid Retrieval: When Small Embeddings and LLM Re-ranking Beat Bigger Models](https://mlsanity.com/paper/2506.00049/) (2025)

## Abstract (arXiv)

This paper introduces RAGnaroX, a resource-efficient ChatOps assistant that operates entirely on commodity hardware. Unlike existing solutions that often rely on external providers such as Azure or OpenAI, RAGnaroX offers a fully auditable, on-premise stack implemented in Rust. Its architecture integrates modular data ingestion, hybrid retrieval, and function calling, enabling flexible yet secure deployment. Our evaluation focuses on the RAG pipeline, with benchmarks conducted on the SQuAD (single-hop QA), MultiHopRAG (multi-hop QA), and MLQA (cross-lingual QA) datasets. Results show that RAGnaroX achieves competitive accuracy while maintaining strong resource efficiency, for example, reaching 0.90 context precision on single-hop questions with an average response time of 2.5 seconds per request. A replication package containing the tool, the demonstration video (https://www.youtube.com/watch? v=cDxfuEbcoM4), and all supporting materials are available at https://github.com/genius-itea/RAGnaroX.git.