Organizations store huge amounts of knowledge in PDFs: policies, manuals, research papers, and reports. Turning PDFs into an AI knowledge base means extracting clean text and structure, chunking intelligently, and indexing for semantic search and RAG.
Native PDFs contain selectable text—parsers can extract words and often layout. Scanned PDFs need OCR first; OCR quality affects downstream retrieval. Always validate a sample of pages after ingestion.
Tables, multi-column layouts, and footnotes confuse naive extractors. Prefer parsers that recover reading order and table structure so numbers and headers stay with the right rows. See Document AI for how WeKnora approaches document understanding.
Split PDF content into chunks sized for embeddings and LLM context. Use heading-aware splits when the PDF has an outline. Details: RAG chunking best practices.
Store filename, page number, and section titles with each chunk so chat-with-documents UIs can show trustworthy citations.
Plan for re-ingestion when PDFs change. Version documents and invalidate or replace affected chunks to avoid stale answers—especially in enterprise settings.
WeKnora supports PDF among other formats and connects parsing to vector indexing and Q&A. Start with Getting started or the API for programmatic uploads.