  Retrieval-augmented Generation (RAG) has markedly enhanced the capabilities
of Large Language Models (LLMs) in tackling knowledge-intensive tasks. The
increasing demands of application scenarios have driven the evolution of RAG,
leading to the integration of advanced retrievers, LLMs and other complementary
technologies, which in turn has amplified the intricacy of RAG systems.
However, the rapid advancements are outpacing the foundational RAG paradigm,
with many methods struggling to be unified under the process of
"retrieve-then-generate". In this context, this paper examines the limitations
of the existing RAG paradigm and introduces the modular RAG framework. By
decomposing complex RAG systems into independent modules and specialized
operators, it facilitates a highly reconfigurable framework. Modular RAG
transcends the traditional linear architecture, embracing a more advanced
design that integrates routing, scheduling, and fusion mechanisms. Drawing on
extensive research, this paper further identifies prevalent RAG
patterns-linear, conditional, branching, and looping-and offers a comprehensive
analysis of their respective implementation nuances. Modular RAG presents
innovative opportunities for the conceptualization and deployment of RAG
systems. Finally, the paper explores the potential emergence of new operators
and paradigms, establishing a solid theoretical foundation and a practical
roadmap for the continued evolution and practical deployment of RAG
technologies.
