This paper investigates the enhancement of scientific literature chatbots through retrieval-augmented generation (RAG), with a focus on evaluating vector- and graph-based retrieval systems. The proposed chatbot leverages both structured (graph) and unstructured (vector) databases to access scientific articles and gray literature, enabling efficient triage of sources according to research objectives. To systematically assess performance, we examine two use-case scenarios: retrieval from a single uploaded document and retrieval from a large-scale corpus. Benchmark test sets were generated using a GPT model, with selected outputs annotated for evaluation. The comparative analysis emphasizes retrieval accuracy and response relevance, providing insight into the strengths and limitations of each approach. The findings demonstrate the potential of hybrid RAG systems to improve accessibility to scientific knowledge and to support evidence-based decision making.

翻译：本文研究了通过检索增强生成（RAG）技术增强科学文献聊天机器人的方法，重点评估了基于向量和基于图的检索系统。所提出的聊天机器人利用结构化（图）和非结构化（向量）数据库来访问科学文章和灰色文献，从而能够根据研究目标高效地对文献来源进行分类筛选。为系统评估性能，我们考察了两种应用场景：从单个上传文档中进行检索，以及从大规模语料库中进行检索。基准测试集通过GPT模型生成，并对其选定输出进行标注以供评估。比较分析重点关注检索准确性和响应相关性，从而揭示每种方法的优势与局限。研究结果表明，混合RAG系统在提升科学知识可及性以及支持循证决策方面具有潜力。