swift-extract
An open-source Swift library that helps developers turn PDFs, photos, and text into typed structured data with an LLM.
Tool overview
Adoption verdict: swift-extract is worth a small proof-of-concept for Swift document extraction, but the available evidence is too thin to recommend it for critical production workflows. Its apparent value is connecting unstructured document inputs with typed Swift outputs, potentially reducing hand-written OCR glue, regex parsing, and field-mapping code.
The GitHub project title and the accompanying X post describe support for PDFs, photos, and text, using any LLM to produce typed structured data, with positioning similar to Instructor for Swift. The more accurate analogy is a typed LLM extraction layer inside a Swift application—not a standalone OCR engine, document-management system, or database. The supplied evidence does not show concrete schemas, failure handling, image preprocessing, test coverage, or reliability measurements.
The practical entry cost includes Swift integration, defining the target data model, selecting and configuring an LLM provider, and handling incomplete outputs, poor document quality, and validation failures. No official pricing, API rates, or resource estimates are provided.