Integration of Retrieval-Augmented Generation and Multimodal Technologies for Advanced Virtual Research Assistants

Abstract

Researchers now rely on AI-powered IVRAs for a wide range of tasks, including providing instantaneous access to research resources and general academic support. When it comes to complicated, multimodal data and providing personalised, context-sensitive replies, however, current technologies can be inadequate. This study investigates potential solutions to these problems by combining multimodal technology with Retrieval-Augmented Generation (RAG). The RAG assistant can find the right information and put it together in a logical way since it uses generative models in addition to information retrieval. Multimodal technologies, which include image and document processing, allow IVRAs to access and understand data in many formats, which improves their overall functionality. Literature reviews, hypothesis development, data analysis, and academic writing are just a few of the many responsibilities that the suggested method hopes to alleviate for academics.