document-parsers

You can extract text and tables from PDF documents for RAG pipelines using tools like PyPDF2 and PDFPlumber, which handle diverse structures and prepare content for chunking. This approach supports advanced table extraction alongside standard text processing.

Parsing unstructured data from DOCX and HTML files is best handled by combining python-docx, BeautifulSoup, and the Unstructured library to extract content. This multi-format approach structures locked data for analysis.

Yes, you can use LlamaParse for AI-powered document extraction on complex PDFs to achieve superior parsing accuracy. This integration handles intricate document structures that traditional extraction methods often struggle to process.

Yes, this document parsing approach supports OCR capabilities for scanned files by utilizing pytesseract and pdf2image. These dependencies work together to extract text from image-based PDFs where standard text layers are unavailable.

You can chunk extracted document content for a vector database using built-in document chunking tools designed for RAG readiness. This process segments parsed text and tables appropriately for embedding generation.

Standard PDF text extraction fails on complex layouts because basic tools cannot interpret multi-column structures, embedded tables, and mixed media. AI-powered options like LlamaParse or the Unstructured library resolve these intricate structures.