RAGe：一种检索增强生成评估框架
RAGe: A Retrieval-Augmented Generation Evaluation Framework
AI总结：
提出模块化框架RAGe，通过资源遥测和组件推荐，评估检索增强生成应用在准确性、效率和可扩展性之间的权衡，支持领域特定数据集的最佳组件选择。
摘要：
部署大型语言模型（LLM）应用，特别是那些依赖检索增强生成（RAG）的应用，仍然具有挑战性，原因是计算需求高、知识库过时以及需要手动选择最优流水线组件。在这项工作中，我们提出了一个模块化框架，通过关注资源遥测和组件推荐，为基准测试和指导RAG应用的高效开发提供支持，建议针对特定领域数据集的最佳组件。我们的方法利用LLM应用中的核心技术，包括文档分块、向量数据库、嵌入模型和检索器，来评估准确性、效率和可扩展性之间的权衡。通过将检索和生成质量与底层硬件约束直接关联，RAGe帮助研究人员识别最有效、特定领域的RAG设置，以满足其特定操作需求，即使在消费级硬件上也能促进快速原型开发。
英文摘要：
Deploying Large Language Model (LLM) applications, particularly those relying on Retrieval-Augmented Generation (RAG), remains challenging due to high computational demands, outdated knowledge bases, and the need to manually select optimal pipeline components. In this work, we propose a modular framework for benchmarking and guiding the efficient development of RAG applications by focusing on resource telemetry and component recommendation, suggesting the best components for a domain-specific dataset. Our approach leverages core techniques in LLM applications, including document chunking, vector databases, embedding models, and retrievers, to evaluate trade-offs among accuracy, efficiency, and scalability. By directly correlating retrieval and generation quality with underlying hardware constraints, RAGe supports researchers to identify the most effective, domain-specific RAG setups for their specific operational needs, facilitating rapid prototyping even on consumer-grade hardware.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
