Subhangi Sati

University of Petroleum and Energy Studies

Papers

Total Citations

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About

Subhangi Sati is a rising researcher at the intersection of natural language processing and conversational AI, with a focus on making large language models more accessible for real-world document interaction. Her most cited work, "PDF-Based Chatbot Development Using LLAMA2 and LangChain: Training and Deployment for Document Interaction" (2024), introduces a practical framework for building intelligent chatbots capable of navigating and extracting information from complex PDF documents. By leveraging the LLAMA2 model alongside LangChain, Sati demonstrates how to train and deploy a system that allows users to query massive textual corpora conversationally—a significant contribution to streamlining information retrieval in academic, legal, and business settings. Though early in her career, with her paper already garnering citations, Sati’s work underscores the growing demand for efficient, user-friendly document interaction tools. Her research not only advances the capabilities of chatbots but also provides a replicable blueprint for developers and researchers seeking to harness open-source models for specialized tasks. Sati’s contributions signal a promising trajectory in making AI-driven document analysis more practical and impactful.