Principle:Ggml org Llama cpp JSON Constrained Generation

Knowledge Sources	Ggml_org_Llama_cpp
Domains	Grammar, Structured_Output
Last Updated	2026-02-15 00:00 GMT

Overview

JSON Constrained Generation is the principle of forcing language model output to conform to a specified JSON Schema by converting schemas into formal grammars.

Description

This principle addresses the problem of generating guaranteed-valid structured output from language models. By converting a JSON Schema definition into a GBNF (GGML BNF) grammar, the decoding process can be constrained so that only tokens producing valid JSON according to the schema are considered at each generation step. This includes support for partial JSON parsing, Pydantic model conversion, and integration with external grammar guidance libraries.

Usage

Apply this principle when applications require model output that strictly conforms to a JSON Schema, such as function calling responses, structured data extraction, or API response generation.

Theoretical Basis

The conversion from JSON Schema to grammar works by recursively translating each schema construct (object, array, string, number, enum, oneOf, allOf, etc.) into corresponding grammar production rules in GBNF format. The resulting grammar is then used during token sampling to mask out tokens that would produce invalid output. Partial JSON parsing allows incremental validation of output as it is generated token by token, enabling early detection of constraint violations. The Pydantic integration provides a Python-friendly interface for defining schemas using Python type annotations.

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