instructor

To extract structured data reliably, define a Pydantic model and pass it as the response_model in your LLM client call, enabling automatic validation and retry loops for type-safe results.

Validating structured LLM outputs against a schema is best handled by defining a Pydantic model to enforce precise schemas, enabling automatic validation and retry loops to ensure outputs match your defined structure.

Yes, you can stream partial structured results for real-time processing. This approach allows applications to consume data incrementally as the LLM generates it, rather than waiting for the entire response to complete.

Yes, this structured extraction approach supports multi-provider compatibility, allowing seamless integration with various large language models like OpenAI and Anthropic to power data-driven applications.

Yes, you must define a Pydantic model beforehand. A defined Pydantic model is required to validate and structure outputs from any large language model, serving as the response_model for your client call.

LLM data extraction fails validation when outputs do not match your defined Pydantic schema. You can fix this by enabling automatic retry loops, which prompt the LLM to regenerate outputs until validation succeeds.