Speaker
Description
In an era of rapid technological advancement and data proliferation, the ability to efficiently access and utilise scientific knowledge has become paramount. Here, we present the development of an advanced research assistant chatbot specifically designed to navigate and interpret scientific publications. Our approach uses the Retrieval-Augmented Generation (RAG) architecture, a state-of-the-art framework that combines the strengths of retrieval-based and generative machine learning models to improve information retrieval and response accuracy. The assistant chatbot is designed to assist researchers by providing concise summaries, extracting key information, and answering complex queries related to scientific documents. In addition, we will showcase its integration with Kadi4Mat, an open-source platform designed to efficiently manage research data and provide seamless access to a vast repository of research data. By linking our chatbot to Kadi4Mat, we ensure that users have access to up-to-date, high-quality research data and their own uploaded document set, thus providing a robust tool for scientific inquiry and, consequently, enabling more informed decision-making and accelerating the research process. We will demonstrate the effectiveness of our research assistant through a series of use cases, highlighting its ability to improve research efficiency and collaboration. This presentation aims to demonstrate the potential of combining advanced language models with comprehensive data management platforms to transform the landscape of scientific research support.