outlines

To ensure LLM outputs match a JSON schema or Pydantic models, you apply structured text generation constraints. This skill enforces schema validation directly during generation to guarantee valid JSON outputs for data extraction and classification tasks.

Yes, you can use structured generation with local models like vLLM and llama.cpp. This skill supports text generation constraints across local frameworks including Transformers and llama.cpp, alongside API-based models like OpenAI.

The best way to extract structured data from unstructured text is by using structured generators. This skill provides choice, JSON, regex, integer, float, and custom generators to precisely extract specific data formats from raw text inputs.

You generate valid JSON from user input without parsing errors by enforcing schema constraints during the generation process. This skill translates Pydantic models to JSON schemas and applies strict validation to prevent invalid outputs.

You do not strictly need Pydantic to generate regex-constrained text outputs, but it is required for JSON schema integration. This skill uses Pydantic for model translation while also offering independent regex, integer, and float generators.