With the rapid development of Large Language Models (LLMs),
Retrieval-Augmented Generation (RAG) has become a predominant method in the
field of professional knowledge-based question answering. Presently, major
foundation model companies have opened up Embedding and Chat API interfaces,
and frameworks like LangChain have already integrated the RAG process. It
appears that the key models and steps in RAG have been resolved, leading to the
question: are professional knowledge QA systems now approaching perfection?
This article discovers that current primary methods depend on the premise of
accessing high-quality text corpora. However, since professional documents are
mainly stored in PDFs, the low accuracy of PDF parsing significantly impacts
the effectiveness of professional knowledge-based QA. We conducted an empirical
RAG experiment across hundreds of questions from the corresponding real-world
professional documents. The results show that, ChatDOC, a RAG system equipped
with a panoptic and pinpoint PDF parser, retrieves more accurate and complete
segments, and thus better answers. Empirical experiments show that ChatDOC is
superior to baseline on nearly 47% of questions, ties for 38% of cases, and
falls short on only 15% of cases. It shows that we may revolutionize RAG with
enhanced PDF structure recognition.

当前研究发现，基于高质量文本语料的专业知识问答系统的有效性受到 PDF 解析准确度的限制。一项实证实验表明，配备全景式和精准的 PDF 解析器的 RAG 系统 ChatDOC 能够检索更准确、更完整的片段，并给出更好的答案。实证实验显示，在接近 47% 的问题上，ChatDOC 优于基线系统，在 38% 的情况下相当，仅在 15% 的情况下表现不及。这表明通过提高 PDF 结构识别技术，我们可能革新 RAG。