outlines

To generate valid JSON from unstructured text using a local model, you can use the Outlines library to constrain outputs to predefined JSON schemas or Pydantic models, ensuring type-safe structured generation.

You can use Pydantic models for structured text generation by defining your desired data schema, allowing the generation process to produce type-safe outputs that conform strictly to your Pydantic model definitions.

Structured generation works with vLLM and Transformers backends, as the Outlines library integrates directly with these local model frameworks to provide efficient inference speeds and constrained text outputs.

The best way to ensure LLM outputs match a specific JSON schema is to apply structured generation techniques that constrain the decoding process, guaranteeing the generated text conforms exactly to your predefined JSON schema.

To extract structured data from free-form textual descriptions, apply structured generation to parse unstructured input text into valid JSON or XML formats based on a predefined schema or regex pattern.

Generated JSON outputs fail schema validation during inference because standard LLMs do not guarantee format adherence, a problem solved by using structured generation to constrain the output token selection process.