Choosing the right Python package to extract content from source documents

Different source document formats require different Python parsing libraries to extract usable text before chunking and embedding for a RAG pipeline. Choosing the right package depends on the file type (PDF, HTML, DOCX, PPTX, etc.) and the structure of content you need to preserve, such as tables, layout, or metadata.

1 · Learn the must-know

• 1For PDFs, common extraction libraries include PyPDF2/pypdf, pdfplumber, and unstructured, with unstructured and pdfplumber generally better at preserving tables and layout.
• 2For HTML content, BeautifulSoup (bs4) is the standard choice for parsing tags and extracting clean text while stripping markup.
• 3For Microsoft Office formats, python-docx handles Word documents and python-pptx handles PowerPoint files, extracting text and structural elements natively.
• 4The unstructured library is a general-purpose option that can handle multiple formats (PDF, HTML, DOCX, images, etc.) with a consistent API, useful when source data is heterogeneous.
• 5Scanned or image-based PDFs require OCR (e.g., via pytesseract) since text-extraction libraries cannot read non-selectable text.
• 6Always match the extraction library to the need to retain structure (tables, headers) versus simple plain-text extraction, since over-simplifying can degrade downstream RAG answer quality.

2 · Check your understanding