AutoRAGTuner自动优化RAG系统，大幅减少人工调参和代码修改。
AutoRAGTuner: A Declarative Framework for Automatic Optimization of RAG Pipelines
- 用声明式配置语言解耦RAG流程，支持模块化构建
- 引入双向指针的域元素模型统一异构数据
- 自适应贝叶斯优化实现端到端超参数调优，减少95%代码变更
检索增强生成（RAG）虽能提升大模型性能，但其效果高度依赖复杂架构设计与超参数配置，现有方法依赖低效的人工调参。本文提出AutoRAGTuner，一个声明式、配置驱动的自动化RAG全生命周期框架，涵盖构建、执行、评估与优化。该框架采用模块化架构，通过组件注册机制解耦各阶段。为统一异构数据，提出域元素模型（DEM），将对象表示为带双向指针的原子元素，支持节点、边与超边。此外，集成自适应贝叶斯优化引擎，实现端到端超参数调优。实验表明，该框架在多种RAG架构（从基础到图结构）中均显著优于默认基线。尤其在工程开销方面，其声明式配置语言使架构调整的代码变动减少高达95%。整体上，AutoRAGTuner为构建可演化、可复用的RAG系统提供了系统化优化基础。
原文摘要 · Abstract (English)
Retrieval-Augmented Generation (RAG) enhances LLMs, but performance is highly sensitive to complex architecture designs and hyper-parameter configurations, which currently rely on inefficient manual tuning. We present AutoRAGTuner, a declarative, configuration-driven framework that automates the RAG life cycle: construction, execution,evaluation, and optimization. AutoRAGTuner employs a modular architecture to decouple pipeline stages through a component registration mechanism. To unify heterogeneous data, we introduce the Domain-Element Model (DEM), representing objects as atomic elements with bidirectional pointers to support nodes, edges, and hyperedges. Furthermore, AutoRAGTuner integrates an adaptive Bayesian optimization engine for end-to-end hyper-parameter tuning. Experimental results demonstrate AutoRAGTuner's architectural generality: across diverse RAG pipelines, ranging from vanilla to graph-based, the framework consistently outperforms default baselines. Notably, AutoRAGTuner significantly mitigates engineering overhead, where its declarative configuration language enables a up to 95\% reduction in code churn for architectural adjustments. Overall, AutoRAGTuner provides a systematically optimizable foundation for building evolvable and reusable RAG systems.
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