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FSM-based constraints enforce structured generation by restricting the model's token choices to only those that produce valid JSON or XML, guaranteeing that outputs conform strictly to your specified schema or Pydantic models.

You can use Pydantic models for constrained generation by passing them to the system, which uses the schema to guide local backends like Transformers, vLLM, and llama.cpp to produce validated, structurally sound outputs automatically.

Yes, llama.cpp and vLLM are supported local backends for offline use, allowing you to generate validated JSON schemas with strict data shapes while maintaining privacy and processing speed without relying on external APIs.

FSM-based constrained generation is the best way to enforce strict data shapes for production data extraction, as it integrates directly with JSON Schema and Pydantic to provide auditable, safe, and structurally valid outputs.

Yes, constrained generation works for API spec generation and form processing by applying CFG workflows that restrict local model outputs to strictly conform to required structured formats and validation rules.