Challenges of Using PDF Documents as Input for RAG: Text Flow, Tokenization, and Semantic Coherence
Hello Community,I’m currently facing a complex challenge with using PDF documents as an input source for Retrieval-Augmented Generation (RAG) in AI applications. For our university, we’re developing a database of bachelor’s theses and academic publications to make research content more accessible to students. PDFs, primarily designed for visual presentation and print, contain many format-based elements such as page breaks, line breaks, and recurring headers and footers. While these structures make sense to the human reader, they create significant obstacles in machine processing, particularly for coherent tokenization and the preservation of continuous context.Problematic Aspects of the PDF Format and Their Impact on RAGPage Breaks and Headers/Footers as Disruptors of Context and CoherencePage breaks and recurring headers/footers fragment the original text coherence, which can be especially problematic in the RAG workflow during tokenization and context generation. The model may not always recognize these elements as metadata, potentially binding them to the wrong contexts. This weakens semantic coherence, which is essential for the passage retrieval engine and query-linking in RAG.Line Breaks and Split TokensPDFs often contain “hard breaks” mid-sentence, splitting words with hyphens. For the tokenizer model, these create unexpected “split tokens,” complicating semantic processing and potentially leading to misinterpretations of word meanings. Since RAG relies on extracting relevant passage chunks and injecting them directly into answer generation, split tokens disrupt string coherence, leading to a fragmented output that may be less relevant.Mismatch Between Layout-Based and Semantic StructuresPDFs often use a visual structure—such as columns, page numbers, and other formatting elements—that aids the human reading flow. However, these layout-based structures are a challenge for machine understanding. The tokenizers and passage retrieval components of the RAG pipeline depend on a continuous semantic text flow, which can be interrupted by layout structures like columns or arbitrary page breaks. This inconsistency often causes semantic drift, where the text becomes contextually unanchored within the model.Hypothesis: PDFs as RAG Sources Risk Contextual Losses and Fragmented ContextualizationMy hypothesis is that, due to their layout-centered structure, PDFs may result in significant context loss and semantic distortion when used as input sources for RAG models. A cleaner text source—such as a formatting-free TXT file—could simplify the text flow and tokenization process, thereby optimizing accuracy and precision in context generation within the RAG pipeline. I’m particularly interested in understanding whether the RAG model can compensate for the disrupted structure of PDF input, or if “noise reduction” through preprocessing would yield significantly better results.Questions for the Experts:Is this issue critically relevant? Are these formatting issues genuinely impactful, or can modern RAG and AI architectures, particularly in the areas of context anchoring and semantic decomposition, effectively compensate for such disruptions? Are there internal mechanisms or innovative tokenizers that provide “noise compensation” here?Any similar experiences with PDFs as input sources? Has anyone experimented with PDF extraction in RAG workflows and observed specific effects on tokenization and contextual alignment?Solutions for High-Quality PDF PreprocessingWhat are proven methods or specialized tools for converting PDFs to clean TXT files, free from layout-based disruptions such as page breaks, line breaks, or split tokens? Are there specific workflows or libraries that are particularly suited for this context?I’m looking forward to an in-depth discussion and am eager to hear about your experiences and potential solutions. Thank you all in advance for your expertise and insights!Best regards!
2024-11-04T16:46:49Z
Hi, many of the active users who are familiar with LLM gather more often on Discord than on the forum, so I think you’ll get more information there.I believe the LangChain library is the most commonly used for working with PDFs in LLM. The following Reddit is a good reference.But even with these and other attempts, there is still no definitive solution that is complete within the HF ecosystem, and it would be a very worthwhile endeavor to pursue.https://www.reddit.com/r/LangChain/s/KSZfo1pOJF