Instructor Pydantic Validation

Instructor is a client-side structured extraction and runtime self-healing validation framework for Python; built on Pydantic validators (`@field_validator`), it evaluates deep domain-specific business rules (e.g., date ranges, numeric bounds, inter-field dependencies) against LLM outputs; upon encountering a `ValidationError`, Instructor automatically catches the exception, injects formatted error tracebacks into an error-correction prompt, and executes an automated Self-Healing Retry Loop until validation passes or retry caps are reached.

Strict business logic validation (e.g., `end_date > start_date`), multi-provider structured extraction, and retrofitting legacy models lacking native Structured Outputs.

Grammar-level JSON constraints cannot evaluate complex semantic business rules; Instructor bridges the gap by enforcing dynamic Python validation logic backed by automated iterative error-correction loops.

Detail the self-healing retry loop mechanism in Instructor combining `max_retries` with `ValidationError` prompt injection?

What extraction modes does Instructor support (Tool Calling, JSON Mode, Markdown JSON) and their model compatibility matrix?

How to combine Pydantic `@field_validator` with live database calls to validate field existence and trigger model retries?

How does streaming partial JSON extraction (`create_partial`) parse and yield incomplete Pydantic models in real-time?

Compare server-side CFG masking vs client-side Instructor retries across latency, token costs, and rule expressiveness?