The growing demand for efficient and lightweight Retrieval-Augmented Generation (RAG) systems in resource-constrained environments has revealed significant challenges. Existing frameworks depend heavily on Large Language Models (LLMs), resulting in high computational costs and limited scalability for edge devices. Addressing this, researchers from the University of Hong Kong introduce MiniRAG, a novel framework optimized for simplicity […]

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