When PyMuPDF Can’t See the Table: Parse PDFs for RAG with Azure Layout

Parsing PDFs for Retrieval-Augmented Generation (RAG) systems with the PyMuPDF library presents significant challenges, as it often fails to preserve table structures and cannot read text within images or on scanned pages. These limitations result in incomplete or poorly structured data, which degrades the performance of downstream AI models. As an alternative, Azure Document Intelligence's `prebuilt-layout` model offers a more robust solution by using advanced OCR and layout analysis. This service correctly identifies structured tables, extracts text from images and scanned documents, and uses explicit role labels for elements like headings and captions. By producing a richer and more accurate representation of the document's content, this approach enables the creation of more effective and reliable enterprise RAG systems.