outlines

To generate structured JSON outputs conforming to a Pydantic model, use constrained token sampling to force the LLM to produce schema-valid results. This approach ensures deterministic, production-safe generation without post-hoc repair loops for unstructured data extraction.

Yes, you can use vLLM and llama.cpp to enforce regex patterns during generation. Backend-aware configuration supports constrained token sampling across these local and production stacks, ensuring outputs match specific regex patterns deterministically.

The best way to stop language models from returning invalid JSON schemas is applying constrained token sampling with schema validation. This removes fragile trial-and-error by forcing the model to generate valid structured outputs that match your defined schemas.

Yes, structured generation works with Transformers for extracting data from invoices and resumes. It supports backend-aware configuration for local stacks, turning unstructured documents into reliable structured records using JSON schemas, enums, and Pydantic models.

Limitations of using API-backed models for schema-constrained generation include restricted support compared to local stacks like Transformers and vLLM. Full deterministic, production-safe structured generation requires backend-aware configuration and constrained token sampling, which may be limited on API endpoints.