🚀 PDFs Break RAG Pipelines 🚀
Do you have problems with PDF parsing?
💥 Most PDF parsers weren’t designed for LLMs. The parsing tool you choose determines 90% of your RAG pipeline’s accuracy.
📌 “If the data isn’t parsed properly, your RAG system will never retrieve accurate answers. Garbage in = garbage out.”
Have you met these problems?
📝 Scrambled Reading Order
Multi-column layouts read left-to-right across the page, mixing content from different columns. Your LLM receives jumbled text that makes no sense.
📝 Lost Table Structure
Tables become walls of unformatted text. Row and column relationships disappear, making financial data and specifications unusable.
📝 No Source Coordinates
No way to cite where information came from or highlight the original PDF location. Users can’t verify your AI’s answers.
📝 Privacy & Cost Trade-offs
Cloud APIs leak sensitive data (HIPAA/SOC2 violations). Commercial services charge $0.01–0.10 per page at scale.
Why Bounding Boxes Matter for RAG
When your LLM answers a question, bounding boxes let you:
- Highlight the exact source location in the PDF
- Build citation links with page and position references
- Verify extraction accuracy by visual comparison
For more info https://opendataloader.org/ or be part of our community https://github.com/opendataloader-project/opendataloader-pdf
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