Chunking information is a key step in Retrieval Augmented Generation (RAG).
Current research primarily centers on paragraph-level chunking. This approach
treats all texts as equal and neglects the information contained in the
structure of documents. We propose an expanded approach to chunk documents by
moving beyond mere paragraph-level chunking to chunk primary by structural
element components of documents. Dissecting documents into these constituent
elements creates a new way to chunk documents that yields the best chunk size
without tuning. We introduce a novel framework that evaluates how chunking
based on element types annotated by document understanding models contributes
to the overall context and accuracy of the information retrieved. We also
demonstrate how this approach impacts RAG assisted Question & Answer task
performance. Our research includes a comprehensive analysis of various element
types, their role in effective information retrieval, and the impact they have
on the quality of RAG outputs. Findings support that element type based
chunking largely improve RAG results on financial reporting. Through this
research, we are also able to answer how to uncover highly accurate RAG.

通过使用基于文档结构的元素类型作为切块依据，我们提出了一种扩展的文档切块方法，以获得最佳切块大小，进而改善 Retrieval Augmented Generation (RAG) 的性能。该研究还深入分析了不同元素类型在信息检索中的作用，并讨论了它们对 RAG 结果质量的影响。