Agentic document extraction: decompose the PDF, then reason over the parts
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Announcing: Agentic Document Extraction! PDF files represent information visually - via layout, charts, graphs, etc. - and are more than just text. Unlike traditional OCR and most PDF-to-text approaches, which focus on extracting the text, an agentic approach lets us break a document down into components and reason about them, resulting in more accurate extraction of the underlying meaning for RAG and other applications. Watch the video for details.
Context
Extracting a PDF's words can leave out meaning carried by its layout, charts, and other visual components. In his Agentic Document Extraction announcement, Andrew Ng describes breaking a document into components and reasoning about them to recover that meaning. The intended use is to supply better information to applications that retrieve documents to answer questions. The supported contribution is this extraction approach; the post's accuracy claim comes without a stated test or measured comparison.
- RAG — Retrieval-augmented generation — fetching relevant documents first and pasting them into the model's context so it answers from your data instead of memory.
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