RAGe框架可自动推荐最适合特定数据集的RAG组件组合。
RAGe: A Retrieval-Augmented Generation Evaluation Framework
- 通过硬件资源监控与组件对比，智能评估RAG各模块性能。
- 在消费级设备上实现快速原型开发，兼顾准确率与效率。
- 适合需要高效部署RAG应用的研究者与工程师使用。
部署大型语言模型（LLM）应用，尤其是依赖检索增强生成（RAG）的应用，仍面临计算开销大、知识库过时及手动选择最优管道组件等挑战。本文提出一个模块化框架RAGe，用于基准测试和指导RAG应用的高效开发，重点聚焦资源遥测与组件推荐，为特定领域数据集推荐最优组件组合。该方法利用文档分块、向量数据库、嵌入模型和检索器等核心技术，评估准确率、效率与可扩展性之间的权衡。通过直接关联检索与生成质量与底层硬件约束，RAGe支持研究者识别最有效的领域特定RAG配置，即使在消费级硬件上也能实现快速原型设计。
原文摘要 · Abstract (English)
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those relying on Retrieval-Augmented Generation (RAG), remains challenging due to high computational demands, outdated knowledge bases, and the need to manually select optimal pipeline components.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.
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