本地运行的轻量级聊天运维助手，安全高效。
RAGnaroX: A Secure, Local-Hosted ChatOps Assistant Using Small Language Models
- 基于小模型与本地部署，用Rust实现可审计的ChatOps系统
- 单跳问答准确率达0.90，响应速度平均2.5秒/请求
- 适合关注数据安全与低资源部署的开发者与企业
本文提出RAGnaroX，一个可在普通硬件上运行的资源高效聊天运维助手。与依赖Azure或OpenAI等外部服务的方案不同，RAGnaroX采用完全可审计的本地化架构，使用Rust实现。其设计融合模块化数据接入、混合检索与函数调用，支持灵活且安全的部署。评估聚焦RAG流程，在SQuAD（单跳问答）、MultiHopRAG（多跳问答）和MLQA（跨语言问答）数据集上进行测试。结果显示，RAGnaroX在保持强资源效率的同时达到具有竞争力的准确率，例如在单跳问题上实现0.90的上下文精确度，平均响应时间仅为2.5秒/请求。复现代码、演示视频（https://www.youtube.com/watch?v=cDxfuEbcoM4）及所有支持材料均可在https://github.com/genius-itea/RAGnaroX.git获取。
原文摘要 · Abstract (English)
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.
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