instructor

Validate structured data extracted from LLM outputs by enforcing a defined structure and types using Pydantic models. This ensures correctness through type-safe validation and triggers automatic retries with feedback when validation fails.

Yes, you can stream partial LLM outputs for real-time dashboards. The streaming feature allows you to process partial results incrementally as they are generated, enabling real-time updates for analytics and automation workflows.

Yes, this approach works with multiple LLM providers for data extraction. It offers flexible provider support and reusable response models, allowing you to maintain consistent classification, extraction, and multi-entity parsing across different setups.

Handle validation failures when parsing unstructured LLM replies by using automatic retries. When validation fails, the system provides feedback to the LLM and retries the extraction, ensuring high-quality structured results.

Enforce type-safety on LLM generated data by applying Pydantic-based validation to the extracted responses. This approach guarantees strong type-safety by converting unstructured replies into validated structured data models.