  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.
