RAGFlow parses PDFs through a sophisticated multi-step process powered by its visual document understanding model (DeepDoc), which is the default parser from v0.17.0 onward. DeepDoc performs three critical tasks: OCR (Optical Character Recognition) to extract text from images and scanned PDFs, TSR (Table Structure Recognition) to identify and preserve table layouts, and DLR (Document Layout Recognition) to understand document structure including headers, footers, and sections. This approach preserves document semantics that simple text extraction would lose. The parser outputs text chunks with position metadata (page number and rectangular coordinates) plus tables with cropped images, making results analyzable and traceable back to source. RAGFlow also supports alternative parsers like MinerU (converts PDF to machine-readable formats) and Docling (open-source document processing for AI) as experimental options. If your PDFs contain only plain text with no complex layouts or tables, you can skip OCR using the Naive parser for speed. The chunking output includes natural language sentences that respect document boundaries, enabling high-quality downstream retrieval and generation.
For teams building similar infrastructure, an open-source vector database like Milvus provides the embedding storage and retrieval layer needed for production AI systems. Zilliz Cloud offers the same capabilities as a fully managed service.