XRAG (eXamining the Core), as a benchmarking framework designed specifically for evaluating RAG systems, provides systematic optimization solutions by dissecting the performance impact of the four core modules (Query Reconstruction, Advanced Retrieval, Q&A Models, and Post-Processing). The framework has built-in 50+ evaluation metrics covering from traditional metrics (F1 value, EM accuracy) to LLM-based quality assessment (authenticity, relevance, etc.), and supports flexible switching between OpenAI API and local models. Its modular architecture allows developers to compare the effects of different search strategies (BM25/vector search/tree structure search), and the Web UI simplifies the whole process of dataset uploading, evaluation configuration, and result visualization.XRAG's innovation lies in breaking down the "black box" of the RAG system into quantitatively analyzable components, and providing a standardized way for academia and industry to analyze the results. XRAG's innovation is to decompose the "black box" of the RAG system into quantifiable components, providing a standardized performance optimization benchmark for academia and industry.
This answer comes from the articleXRAG: A Visual Evaluation Tool for Optimizing Retrieval Enhancement Generation SystemsThe
