Principle:Sgl project Sglang Structured Output Validation
| Knowledge Sources | |
|---|---|
| Domains | NLP, Data_Validation, Structured_Generation |
| Last Updated | 2026-02-10 00:00 GMT |
Overview
A post-generation validation pattern that parses constrained LLM output into typed Python objects using JSON parsing and Pydantic model validation.
Description
After constrained generation produces structured text, the output needs to be parsed into usable Python data structures. For JSON output, this involves json.loads() to convert the string to a dict, optionally followed by Pydantic.model_validate_json() for type-safe validation with automatic coercion. While constrained decoding guarantees syntactic validity (valid JSON), Pydantic validation adds semantic checks (correct types, value ranges, required fields).
Usage
Apply output validation after every constrained JSON generation to convert raw text into typed Python objects. Use json.loads for simple cases and Pydantic.model_validate_json when you need full type checking and validation.
Theoretical Basis
Output validation follows a two-layer approach:
- Syntactic guarantee (from constrained decoding): Output is valid JSON
- Semantic validation (from Pydantic): Values have correct types and satisfy constraints
This separation means:
- Constrained decoding handles the grammar — brackets, commas, quotes
- Pydantic handles the semantics — type coercion, value validation, defaults