Mining Mate: A Chat Bot for Navigating Mining Regulations Using LLM Models

This work introduces an innovative system tailored for automated document processing and advanced question-answering. The dataset creation code employs PyMuPDF and NL TK, orchestrating a systematic extraction of textual information from PDF files. This methodical process extends to content tokenization, where the text is segmented into structured sentences, culminating in the creation of a Pandas data Frame. The resultant dataset is then stored in the Parquet format, ensuring both efficient retrieval and in-depth analysis capabilities. The chatbot component of the system integrates ChainLit and Haystack libraries, incorporating a BM25 retriever for precise document retrieval and a generative language model for nuanced natural language interactions. Proficient in handling user queries, the chatbot adeptly retrieves relevant documents from the meticulously curated dataset and generates responses marked by conciseness and contextual accuracy. This paper not only delineates the architecture and methodologies employed in the system but also explores the diverse applications of this integrated approach. Representing a significant advancement in intelligent document-based inquiries, this system showcases versatility across a spectrum of fields, underlining its potential impact in research, customer support, and various domains relying on automated information retrieval and interpretation.