PyZoBot: A Platform for Conversational Information Extraction and Synthesis from Curated Zotero Reference Libraries through Advanced Retrieval-Augmented Generation.
A Python implementation of a RAG architecture for querying Zotero reference management libraries
Suad Alshammari, Lama Basalelah, Walaa Abu Rukbah, Ali Alsuhibani, Dayanjan S. Wijesinghe
The exponential growth of scientific literature has resulted in information overload, presenting significant challenges for researchers attempting to navigate and effectively synthesize relevant information from a vast array of publications. In this paper, we explore the potential of merging traditional reference management software with advanced computational techniques, specifically Large Language Models (LLMs) and Retrieval-Augmented Generation (RAG), to address these challenges. We introduce PyZoBot, an AI-driven platform developed using Python that incorporates Zotero’s reference management capabilities alongside OpenAI’s sophisticated LLMs. PyZoBot is designed to streamline the extraction and synthesis of knowledge from extensive human curated scientific literature databases. Our work showcases PyZoBot’s proficiency in handling complex natural language queries, integrating and synthesizing data from multiple sources, and meticulously presenting references to uphold research integrity and facilitate further exploration. By harnessing the combined power of LLMs, RAG, and the expertise of human researchers through a curated library of pertinent scientific literature, PyZoBot offers an effective solution to manage the deluge of information and keep pace with rapid scientific advancements. The development and implementation of such AI-enhanced tools promise to significantly improve the efficiency and effectiveness of research processes across various disciplines.
Reference Management Software, Large Language Models (LLMs), Information Overload, Literature Review, Artificial Intelligence, Retrieval-Augmented Generation (RAG).
Copyright 2024, Suad Alshammari, Lama Basalelah, Walaa Abu Rukbah, Ali Alsuhibani, Dayanjan Wijesinghe, Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with the License. You may obtain a copy of the License at http://www.apache.org/licenses/LICENSE-2.0
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