SmartRGPT: An NLP-Based Conversational Research Support System Designed Using Open- Source RAG and LLM

Abstract

This case study research aims to focus on designing a conversational Research Support System (RSS) named SmartRGPT using open-source Retrieval Augmented Generation (RAG) and a Large Language Model (LLM) to enhance and address the challenges faced by researchers while using the existing Research Support Services called SmartRLibrary (initiated by and applied in B C Roy Memorial Library, alternatively, IIM Calcutta Library). It also addresses the limitations of traditional keyword-based search and the hallucination issues of standalone LLM. The prototype has been designed using several open-source software components, including the RAG pipeline, LangChain, the ChromaDB vector database, and the Llama-3 (70-billion-parameter model). A curated set of over 250 datasets was collected, preprocessed, and ingested using Wget (WarcGPT framework) for preparing the knowledge base. The prototype was tested and evaluated using real-world queries. Based on internal review and initial observations of the authors on the generated responses, in the majority of tested cases, the findings demonstrate that the proposed system generated accurate, context‑aware responses without hallucinations. It has responded to short and long-range queries based on its ingested knowledge bases, citing the sources as references. The findings further indicate that the proposed system has the potential to provide 24/7 personalised research assistance, reduce repetitive library workload, and enable the library to provide more advanced services if applied after rigorous evaluation in larger populations. Its cost-effective open-source architecture also offers libraries with limited budgets an independent and customisable alternative to vendor-dependent solutions, thereby contributing to the advancement of the Library and Information Science (LIS) domain.