🤖 AI EngineeringID:
instructor-pydantic-validationInstructor Pydantic Validation
Instructor 运行时重试自愈
🎯Core Definition
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.
💡Use Cases
Strict business logic validation (e.g., `end_date > start_date`), multi-provider structured extraction, and retrofitting legacy models lacking native Structured Outputs.
⚡Key Problems Solved
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.
🎯5 High-Frequency Exam Points
1
Detail the self-healing retry loop mechanism in Instructor combining `max_retries` with `ValidationError` prompt injection?
2
What extraction modes does Instructor support (Tool Calling, JSON Mode, Markdown JSON) and their model compatibility matrix?
3
How to combine Pydantic `@field_validator` with live database calls to validate field existence and trigger model retries?
4
How does streaming partial JSON extraction (`create_partial`) parse and yield incomplete Pydantic models in real-time?
5
Compare server-side CFG masking vs client-side Instructor retries across latency, token costs, and rule expressiveness?
📖 In-depth Guide:📄 prompt-engineering-and-guardrails →
Updated 2026-08-14🎯
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