instructor

You extract structured data from LLM outputs by defining a Pydantic model that represents your desired schema, then passing it to a validation library that coerces and validates the LLM response into type-safe data structures automatically.

Validating LLM responses and retrying on failures is handled by passing validation errors back to the LLM as informative feedback, allowing the model to correct its output automatically until the Pydantic schema validation succeeds.

Yes, you can stream partial structured data from LLMs in real-time by processing incremental Pydantic model fragments as they arrive, enabling interactive applications and dashboards to update continuously without waiting for the full response.

Structured data extraction works with both OpenAI and Anthropic Claude providers through a consistent, provider-agnostic API, allowing you to switch between different LLM backends while maintaining the same Pydantic validation and streaming workflow.

Yes, you must define a Pydantic model to serve as the expected schema before extracting structured data, as the validation, coercion, retry logic, and streaming all depend on this model to ensure type-safe results from the LLM output.

LLM structured output extraction fails validation on complex schemas when the model's response does not match the Pydantic type constraints, but automatic retries address this by sending validation error feedback back to the LLM to improve subsequent attempts.