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arXiv · 2605.02967

AutoRAGTuner: A Declarative Framework for Automatic Optimization of RAG Pipelines

Xintan Zeng·Yongchao Liu·Yice Luo·Jiajun Zhen
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Abstract

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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BibTeXRIS

Xintan Zeng, Yongchao Liu, Yice Luo, Jiajun Zhen. 2026-05-03. AutoRAGTuner: A Declarative Framework for Automatic Optimization of RAG Pipelines. https://arxiv.org/abs/2605.02967

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