  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.
