I made a DuckDB extension where you can use @typesafeai 's Jev to do quick classification of rows in any csv/parquet file or duckdb table about 10sec for 1k rows ~ better than using an LLM, way more ergonomic than a classifier game-changing for data analysis!
Cookbook: Pre-Parsed Value Extraction
Regex finds candidate emails, phone numbers, and amounts; Jev picks the requested span so code copies a verbatim value that cannot be invented, with no free-text extraction required.
- Category
- Cookbooks & Demos
- Published by
- TypeSafe AI
- Author
- —
- Added
- 2026-09-20
Tagsofficialcookbookextractionconfidencenormalization
Highlights
- Jev only chooses among regex-found spans, so the returned value is copied verbatim and cannot be invented.
- A receipt email was picked at 0.98, a cell number normalized to +14155550177, and an invoice total read as $1,315.50.
- Amounts parse to Decimal in code, while a $50.00 courtesy credit was flagged at P(credit) 0.99.
- find() over-finds and dedupes while pick() offers the spans as options plus a none hatch.
Watch out
Choice caps at 255 options, and candidates without a regex, such as names, must come from a roster, NER, or an LLM.
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From the community
Posts from builders shipping with Jev right now.
Classifying rows in DuckDB
The launch post
After co-inventing ChatGPT, I kept asking myself: why have superhuman chat models not led to AGI? I’ve spent the last 2 years in stealth building a new way to train models (RLCD), and a new type of frontier AI model that we are releasing today: Jev • 20-200x faster • 40-400x Show more
