Processing documents for LLM training or AI pipelines often means dealing with thousands of files in various formats.
After encountering this challenge repeatedly in my work, I developed Monkt - a tool that helps transform documents and URLs into structured formats like JSON or Markdown.
The common challenges
- Maintaining format consistency across different document types
- Preserving structural elements (headers, tables, relationships)
- Scaling the conversion process efficiently
Best practices for document processing
- Preserve semantic structure: Maintain document hierarchy, relationships between headers, sections, and lists.
- Handle mixed content: Process both text and non-text elements consistently, including images and tables.
- Implement quality validation: Use automated checks and schemas to catch structural errors.
- Design for scale: Utilize batch operations, parallel processing, and caching mechanisms.
A modern approach
Rather than combining multiple Python libraries (pdf2text, docx, BeautifulSoup, markitdown), modern document processing should focus on:
- Automated format handling
- Consistent structure preservation
- Flexible output formats (Markdown/JSON)
- Efficient caching for improved performance
The quality of your document conversion directly impacts both model training efficiency and inference accuracy.
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