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Extract Structured Data From Any Document Information Extract Api Is Live

Source
upstage.ai
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Upstage
Date

Context

Upstage's Information Extract API turns document contents into fields an application can use. The developer supplies a schema defining the desired structure and data types, and the service returns JSON organized around that schema. The launch post describes extracting information from layout-heavy documents without training a custom model or building a template for each layout.

The schema gives the extraction a concrete destination: named fields with specified types and nesting. In the post's rent-roll example, the intended output separates rent, deposits, and other charges into structured records, reducing the work needed to map document content into an application's data model.

Terms in this piece · Glossary
  • fine-tuning — Taking a trained model and training it a bit more on your own examples so it gets better at one specific job.
  • token — The chunk of text a model reads and writes in — roughly three-quarters of a word — and the unit AI usage is billed in.
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