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Jev Document Classification

Local-first app that extracts document text on the machine and files it into configured category folders using typed Jev decisions for category, confidentiality, prompt-injection risk and subject, with a per-run audit that previews and undoes every move.

Stars
5
Language
TypeScript
License
MIT
Published by
Community
Author
Charlyhno-eng
Last verified
Oct 2026

About this project

JEV Document Classification is a local-first application that files root-level documents into configured category folders. It extracts readable text locally, then uses typesafe-ai/jev through Vercel AI Gateway for typed category, confidentiality, prompt-injection-risk, and subject decisions.

Quoted from the project's README. Read the full README.

Highlights

  • Extraction and profiling are local; only a profile capped at 4,500 characters, built from headings, subject candidates and excerpts, is sent.
  • A category probability below 0.75 goes to `Need review`, unreadable or empty files to `Not processable`, injection risks to their own folder.
  • No file is ever overwritten, and every moved file has View and Undo in the run audit, with a name collision getting a name like `report (1).pdf`.
  • Very short documents are batched into one request, up to 8 documents and 4,500 context characters, but each file moves only after its own decision.

Watch out

It needs a Vercel account and an AI Gateway API key, the 0.75 threshold is fixed in the app, and `Need review`, `Not processable` and `Suspected prompt injection` are reserved folder names. When the provider returns no probability the audit shows `Unavailable` and the document is filed in the category Jev selected anyway, so the review gate is not a guarantee. Runs report duration, input tokens and estimated cost rather than a hard spend cap. Copy reviewed against the repository README on 2026-10-04, not from running it.

Questions about this entry

Short answers drawn from the linked project or page.

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