Revolutionizing Retrieval-Augmented Generation with Enhanced PDF Structure Recognition Demiao LIN chatdoc.com Abstract: 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 (chatdoc.com), 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. The paper discusses the challenges and methods of PDF parsing and chunking, highlighting the importance of accurate document structure recognition for RAG systems. It compares two approaches: rule-based (PyPDF) and deep learning-based (ChatDOC PDF Parser). The deep learning-based method, ChatDOC PDF Parser, is shown to be more effective in parsing complex documents, preserving document structure, and providing accurate and complete segments for RAG. Empirical experiments demonstrate that ChatDOC significantly outperforms the baseline in terms of answer quality, particularly in handling tables and complex document structures. The paper also discusses limitations of ChatDOC, such as ranking and token limit issues, and fine segmentation drawbacks. Finally, it outlines the applications of ChatDOC in AI file-reading assistance, emphasizing its ability to handle various file types and provide citation-backed responses. The conclusion highlights the importance of effective PDF parsing in enhancing the quality and relevance of data fed into LLMs, and the potential for further improvements in document parsing methods.Revolutionizing Retrieval-Augmented Generation with Enhanced PDF Structure Recognition Demiao LIN chatdoc.com Abstract: 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 (chatdoc.com), 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. The paper discusses the challenges and methods of PDF parsing and chunking, highlighting the importance of accurate document structure recognition for RAG systems. It compares two approaches: rule-based (PyPDF) and deep learning-based (ChatDOC PDF Parser). The deep learning-based method, ChatDOC PDF Parser, is shown to be more effective in parsing complex documents, preserving document structure, and providing accurate and complete segments for RAG. Empirical experiments demonstrate that ChatDOC significantly outperforms the baseline in terms of answer quality, particularly in handling tables and complex document structures. The paper also discusses limitations of ChatDOC, such as ranking and token limit issues, and fine segmentation drawbacks. Finally, it outlines the applications of ChatDOC in AI file-reading assistance, emphasizing its ability to handle various file types and provide citation-backed responses. The conclusion highlights the importance of effective PDF parsing in enhancing the quality and relevance of data fed into LLMs, and the potential for further improvements in document parsing methods.