Document Pipelines for AI
A document pipeline extracts text from source files (PDFs, wikis, tickets) before it ever reaches chunking or embedding.
Prerequisites
Overview
Real documents are messy — PDFs with multi-column layouts, wikis with embedded tables, tickets with quoted email threads. A document pipeline’s job is turning that mess into clean, structured text before chunking ever sees it.
Where It Fits
Raw File (PDF, HTML, …)
Text Extraction
Cleaning / Normalization
Clean Text
Key Points
- Format-specific extraction
- PDFs, HTML, and Office documents each need different extraction logic to reliably recover readable text and structure.
- Noise removal
- Headers, footers, boilerplate, and navigation text usually need to be stripped before chunking, or they’ll pollute retrieval.
- Change detection
- A pipeline needs to know when a source document has changed, so it can re-process only what’s new rather than the entire corpus.
Interview Question
Why can’t you just chunk a PDF directly without a separate extraction step?
PDFs encode visual layout, not clean text — multi-column pages, headers/footers, and tables often extract as jumbled or duplicated text if handled naively. A dedicated extraction step recovers readable, ordered text and strips boilerplate before chunking, or retrieval quality suffers from noisy chunks.
Explain It in 30 Seconds
A document pipeline extracts and cleans text from real-world file formats — PDFs, wikis, tickets — before chunking ever runs, since raw extraction is often noisy or out of order without that step.