Retrieval-Augmented Generation (RAG) improves the factual accuracy of Large Language Models by grounding responses in external documents. However, most existing systems rely on dense em-beddings, vector databases, and GPU-based computation, making them unsuitable for low-resource environments. This paper presents a lightweight RAG system designed specifically for CPU-only environments. The system integrates PDF text extraction and Optical Character Recognition (OCR) using PyMuPDF and Tesseract, followed by a keyword-based retrieval mechanism. The retrieved context is then passed to a language model API for response generation. Experimental evaluation demonstrates that the system achieves an accuracy of 83.3% with an average response time of approximately 2.2 seconds. The results highlight that efficient document intelligence systems can be developed without heavy computational requirements
Halnor et al. (Thu,) studied this question.