Building Customized Chatbots for Document Summarization and Question Answering using Large Language Models using a Framework with OpenAI, Lang chain, and Streamlit

Abstract

This research presents a comprehensive framework for building customized chatbots empowered by large language models (LLMs) to summarize documents and answer user questions. Leveraging technologies such as OpenAI, LangChain, and Streamlit, the framework enables users to combat information overload by efficiently extracting insights from lengthy documents. This study discussed the framework's architecture, implementation, and practical applications, emphasizing its role in enhancing productivity and facilitating information retrieval. Through a step-by-step guide, this research has demonstrated how developers can utilize the framework to create end-to-end document summarization and question-answering applications.