Intelligent Document Processing Platform Using Retrieval-Augmented Generation (RAG)

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The rapid growth of digital documents across enterprises, educational institutions, healthcare organizations, and research environments has created significant challenges in information retrieval and knowledge management. Traditional keyword-based search systems often fail to capture the semantic meaning and contextual relationships present in large collections of unstructured documents, resulting in inefficient information access and reduced productivity. This study presents an Intelligent Document Processing Platform using Retrieval-Augmented Generation (RAG) to enable accurate, context-aware, and reliable document intelligence. The proposed system integrates document ingestion, text extraction, preprocessing, chunking, embedding generation, vector-based semantic retrieval, and Large Language Models (LLMs) within a unified framework. Uploaded documents are transformed into vector embeddings and stored in a vector database, enabling efficient similarity-based retrieval. When a user submits a natural language query, the system retrieves the most relevant document segments and augments them as contextual knowledge for response generation. This retrieval-grounded approach reduces hallucinations, improves factual accuracy, and enhances the relevance of generated answers. The platform is implemented using Python, Streamlit, LangChain, embedding models, and vector databases to provide an interactive and scalable solution for document-centric knowledge discovery. Experimental evaluation demonstrates improved retrieval effectiveness, faster access to relevant information, and enhanced user experience compared with conventional document search approaches. The proposed system offers a practical and scalable framework for intelligent document understanding, semantic search, and AI-assisted question answering in modern knowledge management environments.