The exponential growth of scientific literature has resulted in information overload, challenging researchers to effectively synthesize relevant publications. This paper explores the integration of traditional reference management software with advanced computational techniques, including Large Language Models and Retrieval-Augmented Generation. We introduce PyZoBot, an AI-driven platform developed in Python, incorporating Zoteros reference management with OpenAIs sophisticated LLMs. PyZoBot streamlines knowledge extraction and synthesis from extensive human-curated scientific literature databases. It demonstrates proficiency in handling complex natural language queries, integrating data from multiple sources, and meticulously presenting references to uphold research integrity and facilitate further exploration. By leveraging LLMs, RAG, and human expertise through a curated library, PyZoBot offers an effective solution to manage information overload and keep pace with rapid scientific advancements. The development of such AI-enhanced tools promises significant improvements in research efficiency and effectiveness across various disciplines.

翻译：科学文献的指数级增长导致了信息过载，使研究人员难以有效合成相关出版物。本文探讨了传统参考文献管理软件与先进计算技术（包括大语言模型和检索增强生成）的整合。我们介绍了PyZoBot——一个基于Python开发的人工智能驱动平台，该平台将Zotero参考文献管理系统与OpenAI的先进大语言模型相结合。PyZoBot能够从广泛的人工精选科学文献数据库中简化和优化知识抽取与合成过程。该系统在处理复杂自然语言查询、整合多源数据以及严谨呈现参考文献方面表现出色，从而有力维护研究完整性并促进进一步探索。通过结合大语言模型、检索增强生成技术以及基于精选文献库的人类专业知识，PyZoBot为管理信息过载和跟进快速发展的科学进展提供了有效解决方案。此类人工智能增强工具的开发有望显著提升跨学科研究效率与效能。