An Approach to Intelligent Information Extraction and Utilization from Diverse Documents

In this study, an innovative tool called DOCUFYme is introduced, designed to transform how interactions with and insights from various documents are handled. Leveraging advanced NLP methodologies and the Retrieval-Augmented Generation (RAG) framework, DOCUFYme facilitates efficient and precise information extraction across different fields such as engineering, science, medicine, and agriculture. Unlike conventional NLP systems that depend on predefined rules, this system employs existing metrics (like BLEU) and Large Language Models (LLMs) to achieve a deeper semantic understanding. Additionally, the architecture supports seamless integration of new features and domain-specific modules, enhancing the adaptability and relevance across different document types. The effectiveness of DOCUFYme is validated through multiple case studies and assessments based on real-world applications, including research, corporate knowledge management, healthcare, and education. This positions DOCUFYme as a pivotal tool for intelligent information extraction, bridging knowledge gaps and facilitating access to global knowledge.