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

Abstract

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.

Introduction

The text presents an Intelligent Document Processing Platform using Retrieval-Augmented Generation (RAG) to improve document search, understanding, and question answering.

• Problem: The growing volume of digital documents makes traditional keyword-based search inefficient. Such systems often fail to understand the semantic meaning, context, and intent behind user queries, forcing users to manually search through documents.
• Proposed solution: The system combines Natural Language Processing, semantic search, vector embeddings, hybrid retrieval, and Large Language Models (LLMs) through a RAG architecture.
• Document processing: Users upload PDF, DOCX, or text files through a Streamlit interface. The system extracts and cleans the text, divides it into meaningful chunks, and converts these chunks into vector embeddings.
• Vector database: The embeddings are stored in a vector database, allowing relevant document sections to be retrieved based on semantic similarity rather than only exact keyword matches.
• Query processing: A user's natural-language query is converted into an embedding and compared with document embeddings using cosine similarity. The most relevant Top-K chunks are selected.
• Hybrid retrieval: The system combines semantic vector search with keyword-based matching, improving retrieval when queries contain domain-specific terms or exact keywords.
• RAG response generation: Retrieved document content is supplied to the LLM along with the user's query. The LLM generates a response grounded in the uploaded documents, reducing dependence on its pre-trained knowledge and helping minimize unsupported answers.
• Experimental results: The reported system achieved over 90% retrieval accuracy, response relevance, and user satisfaction. Its optimization score improved from 0.3288 at Episode 01 to 0.9700 at Episode 100.
• Advantages: Compared with traditional keyword search, the proposed platform provides semantic understanding, context-aware answers, source references, faster retrieval, and an interactive user experience.

Conclusion

The proposed Intelligent Document Processing Platform Using Retrieval-Augmented Generation (RAG) provides an effective approach for intelligent document understanding and context-aware question answering by integrating document ingestion, text extraction, preprocessing, chunking, embedding generation, vector storage, hybrid retrieval, and Large Language Model (LLM)-based response generation into a unified framework. The Streamlit-based interface enables users to upload documents and interact with the system through natural-language queries, while the retrieval mechanism identifies relevant document content using semantic similarity and keyword-assisted retrieval. The retrieved information is supplied as contextual input to the LLM, enabling the system to generate responses that are grounded in the uploaded document content. The experimental evaluation considered retrieval accuracy, response relevance, response time, context precision, and source attribution accuracy, demonstrating the effectiveness of the proposed approach for document-based question answering. The reported optimization results show an improvement from an Initial System Competence of 0.3288 in Episode 01 to a Converged Optimal Strategy of 0.9700 by Episode 100, while the reported retrieval accuracy, response relevance, and user satisfaction exceeded 90%. The proposed platform therefore provides an integrated and scalable solution for reducing manual document searching, improving information accessibility, and supporting efficient knowledge extraction from large document collections

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Copyright

Copyright © 2026 Joshi Priya , Dr. M. Arathi. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.