instructor

To validate structured LLM outputs using Pydantic, define a Pydantic model representing your desired schema and pass it to the client. The system automatically extracts the data, validates it against your model, and retries with contextual error feedback if validation fails.

Yes, you can stream partial LLM outputs for real-time processing. The system provides a streaming interface that delivers progressive outputs, allowing you to handle partial results and update UI elements continuously as the LLM generates the response.

The best way to extract structured data across multiple LLM providers is to use a unified client that integrates with various APIs. This approach uses Pydantic schemas for validation and supports automatic retries, ensuring consistent structured data extraction regardless of the underlying provider.

Yes, Pydantic validation works with automatic retries for LLM extraction. When the LLM response fails to conform to your predefined Pydantic schema, the system automatically retries the request, passing contextual error feedback back to the model to improve subsequent extraction quality.

You need Pydantic schemas for structured LLM outputs to ensure the extracted data conforms to a predefined format. This validation mechanism guarantees data integrity, prevents downstream parsing errors, and enables the automatic retry logic when the LLM response does not match the expected structure.