DocuMind: An Enterprise-Grade Contextual Information Retrieval System Using Retrieval-Augmented Generation and Java Spring Boot

ABSTRACT DocuMind AI is an advanced document-based question answering system developed using Retrieval-Augmented Generation (RAG) to address challenges in extracting meaningful information from unstructured data. Organizations often struggle to extract useful information from large volumes of unstructured documents. Traditional keyword-based search methods often fail to capture semantic context, resulting in irrelevant outputs. To overcome this limitation, the proposed system integrates semantic search with large language models (LLMs) to generate accurate and context-aware responses. The system utilizes vector embeddings to represent document content and retrieve relevant information efficiently. It is implemented using a Java Spring Boot backend integrated with LangChain4j, which manages document ingestion, text chunking, embedding generation, and retrieval processes. Users interact with the system through a React.js-based interface, allowing them to upload documents and query in natural language. The system supports multiple document formats such as PDF and DOCX, ensuring flexibility in real-world applications. Additionally, JWTbased authentication is implemented to provide secure access to the system. The modular and scalable architecture makes DocuMind suitable for enterprise-level deployment, bridging the gap between traditional information retrieval systems and modern AI-driven solutions. Keywords: Retrieval-Augmented Generation, Natural Language Processing, Large Language Models, Vector Database, Java Spring Boot, LangChain4j, PostgreSQL pgvector, Cosine Similarity.