instructor

You can extract structured data from LLM responses by applying Pydantic-based schemas to enforce type safety. This skill automatically validates the LLM output, triggers retries on parsing errors to guide the model, and ensures consistent formats across multiple providers.

The best way to handle validation errors during structured data extraction is to use automatic retries. When extraction or parsing fails, the system retries the request, providing descriptive error messages to guide the LLM toward generating valid, schema-compliant output.

Yes, you can stream partial structured data from LLM outputs for real-time processing. Streaming support delivers incremental results as the model generates data, allowing downstream applications to process nested models and optional fields progressively.

Pydantic validation works across multiple LLM providers like OpenAI and Anthropic to ensure consistent structured data extraction. By enforcing predictable schemas, it allows you to extract complex nested models and multi-entity data regardless of the underlying provider.

To validate complex nested models and optional fields in LLM outputs, define strict Pydantic schemas for your extraction task. The system enforces these schemas during data extraction, ensuring type safety and robust error handling for multi-entity scenarios.