Data Analysis and Extraction ChatBot: Leveraging Retrieval-Augmented Generation for Enhanced Document Processing

Abstract In the era of information overload, extracting meaningful insights from unstructured documents remains a significant challenge. This paper introduces a customized chatbot designed to extract and analyze textual data from various documents, utilizing Retrieval-Augmented Generation (RAG) techniques. By integrating frameworks like LlamaIndex and LangChain, and deploying a user-friendly interface via Streamlit, the system demonstrates enhanced capabilities in document comparison and data extraction. The chatbot's architecture, implementation, and potential applications are discussed in detail, highlighting its efficacy in automating data analysis tasks.