PDFs, Office Files, and Document Understanding
Document AI often combines file parsing, OCR, layout extraction, chunking, and model reasoning. Preserve page numbers, section headings, tables, and source offsets where possible so answers can cite evidence. OCR and layout extraction can introduce errors, particularly with scans, columns, handwriting, or complex tables.
Apply upload limits, malware scanning where appropriate, content-type validation, access controls, and retention rules. Treat file contents as untrusted instructions and do not let embedded document text override application policy. For large files, use asynchronous ingestion with progress and retry states.
Document ingestion pipeline
File -> validation / malware scan -> parser / OCR
-> normalized text + page metadata
-> chunks + embeddings -> retrieval index
-> question-time retrieval -> cited answer💡 This pipeline separates expensive file processing from interactive question answering. Keep parser version and page provenance to debug incorrect extraction or citations.
Key Takeaways
- Preserve layout and provenance when citations matter.
- Treat document contents as untrusted data.
- Use asynchronous processing for expensive large-file ingestion.
Knowledge Check
Why preserve page or section provenance during document ingestion?