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Text extraction with Jev: field extraction, PII detection and exact quotes, built on TypeSafe's Jev.
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- Last pushed
- Updated Sep 2026
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CommunityJev Demos & Experiments
Jeveryword
Zero-dependency extraction library that numbers the words in your text, offers those numbers to Jev as choices, and maps the picks back to exact substrings with offsets.
- Stars
- 5
- Language
- JavaScript
- License
- MIT
- Published by
- Community
- Author
- jkrup
- Last verified
- Oct 2026
About this project
Text extraction with Jev. Field extraction, PII detection and exact quotes, built on TypeSafe's Jev. Live demo · Try it · Field extraction · Labelling words · Custom questions · Coding agents · Hosted API.
Quoted from the project's README. Read the full README.
Works forJev for Classification
Used inJev for Software & DevTools
- Languages
- JavaScript
Highlights
- Returns extracted, missing, or ambiguous results with the lowest decision probability for confirmation filtering.
- Handles up to 16 fields and long inputs per call, with sentence-first search and shrink-and-retry on limits.
- Labels every word with scores so a UI threshold slider can re-merge without asking the model again.
- Runs on Node, workers, Deno, and Bun with tokenize, extractSpans, and classifyChunks building on one core.
Watch out
Experimental and checked only on a small set of synthetic messages with no benchmark; try your own text in the demo first. 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.
Numbering turns generation into selection. Jev picks token numbers for starts and ends, and the library converts them back to slices where text.slice(start, end) equals the value, with start, end, and probability attached.
Write the field as an id plus a plain-English description naming whose value you mean and which one wins when several appear. That description is the whole prompt, so corrections like the new number need to be explicit.
It asks one short question per word, up to dozens per request, and mergeChunks keeps words above your threshold. Moving the slider re-runs the merge locally without new model calls, but the label set must include a none option.
Empty text returns every field as missing without a call, absent values return missing rather than guesses, and close boundaries trigger a small follow-up showing the rival spans as text. Long text is searched sentence-first, never cut off.
npm install jeveryword and set TYPESAFE_API_KEY; import extractSpans for named fields, classifyChunks for per-word labels, or tokenize for anything else, and it has no dependencies and runs on Node 20+, workers, Deno and Bun.
Up to 16 fields and 20,000 characters per call; ten fields from a short message take one request, a few hundred milliseconds and about $0.0004 at Jev's list price, and empty text returns every field missing without calling the model.
Yes. npx skills add jkrup/jeveryword installs a skill for Claude Code, Cursor, Codex and the other agents the skills CLI supports, after which you describe the extraction you want in plain language.
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