A community-driven collection of RAG (Retrieval-Augmented Generation) frameworks, projects, and resources. Contribute and explore the evolving RAG ecosystem.
A systematic literature review of Retrieval Augmented Generation (RAG) models, covering architectures, retrieval techniques, real world applications, performance improvements, emerging challenges, and future research directions across healthcare, education, finance, and enterprise AI.