scaffold-extraction

To scaffold a complete extraction pipeline, you provide your target domain and field definitions to generate Pydantic models, example scripts, and tests automatically. This automates the setup of structured data extraction projects.

Supported extraction methods include one-shot, stepwise, raw schema, and data-driven extraction. These options allow you to configure provider behavior and adapt to diverse data generation requirements.

Yes, you can scaffold pipelines for medical records, product reviews, or any new domain. You specify the target fields like 'product_name' or 'rating', and the pipeline generates the required Pydantic models and tests.

Automating Pydantic model creation is best achieved by defining your target domain fields upfront. The scaffolding process then generates the models, example scripts, and tests needed to validate your structured data extraction logic.

No, you do not need to manually write unit tests. The scaffolding process automatically creates tests alongside Pydantic models and example scripts to validate your structured data extraction pipeline immediately.

Using a scaffolding approach for structured data extraction automates the creation of Pydantic models and tests, reducing manual setup errors. It streamlines development across diverse domains by generating a ready-to-use pipeline.