RAG based Chatbot using LLMs

Abstract

Historically, Artificial Intelligence (AI) was used to understand and recommend information. Now, Generative AI can also help us create new content. Generative AI builds on existing technologies, like Large Language Models (LLMs) which are trained on large amounts of text and learn to predict the next word in a sentence. Generative AI can not only create new text, but also images, videos, or audio. This project focuses on the implementation of a chatbot based the concepts of Generative AI and Large Language Models which can answer any query regarding the content provided in the PDFs. The primary technologies utilized include Python libraries like LangChain, PyTorch for model training, and Hugging Face’s Transformers library for accessing pre-trained models like Llama2, GPT- 3.5 (Generative Pre-trained Transformer) architectures. The re- sponses are generated using the Retrieval Augmented Generation (RAG) approach. The project aims to develop a chatbot which can generate the sensible responses from the data in the form of PDF files. The project demonstrates the capabilities and applications of advanced Natural Language Processing (NLP) techniques in creating conversational agents that can be deployed across various platforms in the corporation, to enhance user interaction and support automated tasks. Index Terms—Generative AI, Artificial Intelligence, Natural Language Processing, Large Language Model, Llama2, Tran- formers, Document Loaders, Retrieval Augmented Generation, Vector Database, Langchain, Chainlit