Extraction ✦ 85outlineCalibrate on your documents

候选片段抽取 + 代码规范化

先找候选,Choice 点选,代码原样规范化。

Who: Back-office / document ops. Steps: Regex/roster finds candidates → Choice picks span (+ classify currency/country) → code normalizes verbatim (email lower, E.164, Decimal). Expected effect: no invented digits outside the candidate set. Dedup: catalog card; Hands-on extract-and-validate keeps the recipe.

1

复制提示词(保留英文原文效果最好)。

2

在代码里实现——调用 System One / Jev,由你的程序组合答案。

3

回答它的设置问题——大多数提示词会先采访你,然后进入固定节奏。

提示词

提示词
Audience: back-office / document ops.
Steps:
1) Regex/roster builds candidate spans.
2) Choice picks span (+ optional currency/country class).
3) Code normalizes verbatim (lowercase email, E.164, Decimal) — never invent digits outside candidates.
Expected effect: lower hallucinated values.
Cross-link: Hands-on [extract and validate](/guides/extract-and-validate/).

需要授权:typesafe-sdkparsers

它是怎么搭起来的

以下说明为英文原文(来自社区作者),提示词本身建议保留英文。

Sources: pre_parsed_value_extraction cookbook · Hands-on extract-and-validate.

Pipeline: find candidates → pick → normalize in code. Cross-link Hands-on extract-and-validate.

Sources (wave-2 deepen)

为什么它好用

Jev selects among known candidates; deterministic code owns the final string.

本案例收集自公开的 Jev 社区,版权归原作者所有。提示词属于参考资料:运行前请先审阅内容, 并且不要让 bot 超出你实际授予的权限行事。