A System for Comprehensive Assessment of RAG Frameworks

• URL: http://arxiv.org/abs/2504.07803v1
• Date: Thu, 10 Apr 2025 14:41:34 GMT
• Title: A System for Comprehensive Assessment of RAG Frameworks
• Authors: Mattia Rengo, Senad Beadini, Domenico Alfano, Roberto Abbruzzese,
• Abstract summary: Retrieval Augmented Generation (RAG) has emerged as a standard paradigm for enhancing the factual accuracy and contextual relevance of Large Language Models (LLMs)<n>Existing evaluation frameworks fail to provide a holistic black-box approach to assessing RAG systems.<n>We introduce SCARF, a modular and flexible evaluation framework designed to benchmark deployed RAG applications systematically.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Retrieval Augmented Generation (RAG) has emerged as a standard paradigm for enhancing the factual accuracy and contextual relevance of Large Language Models (LLMs) by integrating retrieval mechanisms. However, existing evaluation frameworks fail to provide a holistic black-box approach to assessing RAG systems, especially in real-world deployment scenarios. To address this gap, we introduce SCARF (System for Comprehensive Assessment of RAG Frameworks), a modular and flexible evaluation framework designed to benchmark deployed RAG applications systematically. SCARF provides an end-to-end, black-box evaluation methodology, enabling a limited-effort comparison across diverse RAG frameworks. Our framework supports multiple deployment configurations and facilitates automated testing across vector databases and LLM serving strategies, producing a detailed performance report. Moreover, SCARF integrates practical considerations such as response coherence, providing a scalable and adaptable solution for researchers and industry professionals evaluating RAG applications. Using the REST APIs interface, we demonstrate how SCARF can be applied to real-world scenarios, showcasing its flexibility in assessing different RAG frameworks and configurations. SCARF is available at GitHub repository.

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