评估检索增强生成在石油和天然气企业文档中的分块策略
Evaluating Chunking Strategies For Retrieval-Augmented Generation in Oil and Gas Enterprise Documents
AI总结：
本文研究了四种分块策略在石油和天然气企业文档中的效果，发现结构感知分块在检索效果和计算成本上表现最佳，但对图表类文档仍有限。
摘要：
检索增强生成（RAG）作为一种解决大型语言模型（LLMs）限制的框架，其效果依赖于文档分块策略。本文通过专有石油和天然气企业文档集，评估了四种分块策略：固定大小滑动窗口、递归、基于断点的语义和结构感知。研究发现，结构感知分块在整体检索效果，尤其是Top-K指标上表现更优，且计算成本更低。然而，所有方法在处理管道和仪器图（P和IDs）时效果有限，凸显了纯文本RAG在视觉和空间编码文档中的核心限制。结论指出，尽管显式结构保留对专业领域至关重要，未来研究必须整合多模态模型以克服当前限制。
英文摘要：
Retrieval-Augmented Generation (RAG) has emerged as a framework to address the constraints of Large Language Models (LLMs). Yet, its effectiveness fundamentally hinges on document chunking - an often-overlooked determinant of its quality. This paper presents an empirical study quantifying performance differences across four chunking strategies: fixed-size sliding window, recursive, breakpoint-based semantic, and structure-aware. We evaluated these methods using a proprietary corpus of oil and gas enterprise documents, including text-heavy manuals, table-heavy specifications, and piping and instrumentation diagrams (P and IDs). Our findings show that structure-aware chunking yields higher overall retrieval effectiveness, particularly in top-K metrics, and incurs significantly lower computational costs than semantic or baseline strategies. Crucially, all four methods demonstrated limited effectiveness on P and IDs, underscoring a core limitation of purely text-based RAG within visually and spatially encoded documents. We conclude that while explicit structure preservation is essential for specialised domains, future work must integrate multimodal models to overcome current limitations.
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