XRAG: Examining the Core - Benchmarking Foundational Components in Advanced Retrieval-Augmented Generation

Retrieval-augmented generation (RAG) synergizes the retrieval of pertinent database with the generative capabilities of Large Language Models (LLMs), ensuring that the generated output is contextually relevant but also accurate and current. We introduce XRAG, an open-source, modular codebase that facilitates exhaustive evaluation of the performance of foundational components of advanced RAG modules. These components are categorized into four core phases: pre-retrieval, retrieval, post-retrieval, and generation. We systematically analyze them across reconfigured datasets, providing a comprehensive benchmark for their effectiveness. These components can be combined in different ways through an orchestrator, of which there are five types: sequential, conditional, iterative, parallel, and hybrid. Our work thoroughly evaluates the performance of advanced core components in RAG systems, offering actionable insights for optimizing RAG architectures in dataset-driven AI applications.