AI-powered chatbot with retrieval augmented generation (RAG)

Abstract

Through the years, Artificial Intelligence (AI) Chatbots have undergone significant changes, shifting from their initial rule-based systems, to now, much more sophisticated Natural Language Processing (NLP) models, which are able to handle complicated queries and produce human-like speech. In spite of all these advancements, AI Chatbots are bottlenecked by limitations, for example, in their lack of consistency in output, and their instances of hallucinations. Hence, my objective in this project is to fill in the gap between current AI Chatbot capabilities, and their potential of meeting the demands of niche domain expertise, specifically in a domain relevant to Nanyang Technological University (NTU) – an AI Retrieval Augmented Generation (RAG) Chatbot prototype designed to answer user queries about undergraduate programmes of the College of Computing and Data Science (CCDS) at NTU. The Chatbot helps parents and prospective students with their queries related to the niche domain of CCDS undergraduate admissions, illustrating the ability of the RAG-enhanced Chatbot to augment information retrieval, access that information through the relevant websites, as well as facilitate user engagement through the Chatbot experience. The RAG Chatbot leverages web scraping to extract necessary information from the web, which it then processes with OpenAI’s embedding models to generate machine-readable representations. The Supabase vector database stores these embeddings, facilitating quick similarity searches. When a query is submitted, an independent question is formulated by the bot, which then extracts from Supabase the required context, and merges it with the query to generate an informed and precise output with the help of OpenAI’s language models. The architecture is built with Next.js and React on the frontend, while supported by the Supabase Schema on the backend.