Imagine building with LEGOs, but for AI. That's the core idea behind Modular RAG, a revolutionary approach to building retrieval-augmented generation (RAG) systems. Traditional RAG systems, which enhance large language models (LLMs) by connecting them to external databases, often struggle with complex tasks. They follow a rigid 'retrieve-then-generate' structure, limiting their flexibility. Modular RAG tackles this by breaking down RAG systems into interchangeable blocks, or modules, like LEGO bricks. These modules handle specific functions like indexing, retrieving, processing, and generating text. By mixing and matching these modules, developers can create custom RAG systems optimized for various tasks. This LEGO-like design offers unparalleled flexibility. Need to integrate a knowledge graph? Just add a KG module. Facing issues with long documents? Swap in a compression module. This adaptability extends to how the modules interact. Modular RAG introduces 'flows' that determine the order of operations. These flows can be linear, conditional, branching, or even looping, enabling complex interactions between modules. This modularity makes complex systems easier to manage and debug—simply swap out or adjust individual modules. Modular RAG also fuels innovation. Researchers can develop new modules or operators, expanding the system's capabilities and tailoring it to emerging needs. This approach not only makes current RAG systems more flexible but also creates a framework for future advancements in AI, allowing systems to adapt to complex, evolving demands.