Leveraging Large Language Models for Personalized Document Summarization and Question Answering: An Architecture for Stoner-Friendly Chatbots

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This is an unedited manuscript accepted for publication and provided as an Article in Press for early access at the author’s request. The article will undergo copyediting, typesetting, and galley proof review before final publication. Please be aware that errors may be identified during production that could affect the content. All legal disclaimers of the journal apply.

Year : 2025 | Volume : 12 | 03 | Page : –
    By

    Niket Singh,

  • Raj Singh,

  1. Research Scholar, MCA Thakur Institute of Management Studies, Career Development & Research (TIMSCDR) Mumbai, Maharashtra, India
  2. Research Scholar, MCA Thakur Institute of Management Studies, Career Development & Research (TIMSCDR) Mumbai, Maharashtra, India

Abstract

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This paper presents a detailed framework for developing personalized chatbots that utilize large language models (LLMs) to process and extract information from extensive documents while effectively responding to user inquiries. The proposed system is designed to mitigate information overload by employing advanced natural language processing techniques, leveraging technologies such as OpenAI, LangChain, and Streamlit. By integrating these tools, the framework enhances knowledge retrieval, simplifies document comprehension, and improves overall productivity. The study delves into the architecture, implementation, and practical applications of the framework, demonstrating its ability to streamline access to critical information. Furthermore, it explores how developers and researchers can leverage this system to create end-to-end solutions for document summarization and automated question-answering. Through a structured and step-by-step approach, this research provides valuable insights into constructing intelligent chatbot systems capable of efficiently managing vast amounts of textual data. Ultimately, the proposed framework contributes to the advancement of AI-driven knowledge management and human- computer interaction.

Keywords: Large Language Models, Langchain, ChatBots, Streamlit, OpenAI

How to cite this article:
Niket Singh, Raj Singh. Leveraging Large Language Models for Personalized Document Summarization and Question Answering: An Architecture for Stoner-Friendly Chatbots. Journal of Artificial Intelligence Research & Advances. 2025; 12(03):-.
How to cite this URL:
Niket Singh, Raj Singh. Leveraging Large Language Models for Personalized Document Summarization and Question Answering: An Architecture for Stoner-Friendly Chatbots. Journal of Artificial Intelligence Research & Advances. 2025; 12(03):-. Available from: https://journals.stmjournals.com/joaira/article=2025/view=0


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Ahead of Print Subscription Original Research
Volume 12
03
Received 06/03/2025
Accepted 06/04/2025
Published 08/07/2025
Publication Time 124 Days
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