instructor

Convert unstructured LLM outputs into validated Pydantic models by enforcing strict type safety and customizable cross-field validation rules. This ensures data quality and correctness by transforming free-form chat transcripts into typed, structured records automatically.

Handle data validation failures during structured extraction by utilizing automatic retries. Failed executions are retried with actionable feedback provided back to the LLM, improving accuracy and ensuring the output meets your Pydantic validation rules.

Stream partial LLM responses in real-time while validating structured data by receiving incremental updates as the data is built. This enables responsive UI workflows and provides immediate feedback before the full Pydantic model extraction completes.

Multi-provider LLM usage works with Pydantic structured output and streaming. This allows you to enforce type safety, apply customizable validators, and maintain data quality across different LLM providers without changing your core validation logic.

Use Pydantic models for LLM structured extraction to enforce strict type safety and enable cross-field validation, which parsing raw text lacks. This approach guarantees data correctness by converting free-form responses into reliable, typed records for AI assistants.

You need automatic retries for LLM data validation when structured extraction fails to meet Pydantic rules. Retrying failed executions with actionable feedback corrects the LLM's output format, ensuring your typed data models remain accurate and reliable.