instructor

To extract structured data from LLM outputs, define a target Pydantic model and pass a prompt to the client. It enforces type-safe JSON parsing and validates the LLM response against your schema.

When structured data extraction fails validation, the skill automatically retries the failed extraction. It provides clear error feedback to the LLM to ensure type-safe parsing on subsequent attempts.

Yes, you can stream partial structured data results for real-time processing. This feature allows you to handle large outputs incrementally as the LLM generates validated data.

Yes, the tool supports multi-provider compatibility with a consistent API. This allows you to build robust data extraction pipelines across different LLM providers without changing validation logic.

The best way to build data extraction pipelines is using a client that converts free-form text into validated Pydantic data models. This enforces type safety and handles validation failures with automatic retries.