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To generate valid JSON from unstructured text, you use structured text generation with predefined JSON schemas or Pydantic models. This ensures outputs are type-safe, validated, and match the exact structure automatically.

Yes, you can use Pydantic models for structured text generation with vLLM. The framework supports multiple backends, including vLLM, Transformers, and llama.cpp, to enforce type-safe and validated structured outputs directly.

The best way to constrain LLM outputs to match a specific regex pattern is through structured text generation. By applying regex text matching during generation, the model produces text strictly adhering to your specified regular expressions.

Yes, structured text generation works with Transformers and llama.cpp backends. It supports various models and backends, allowing you to generate JSON or XML using predefined schemas, regex patterns, or Pydantic models across these environments.

You need structured text generation when parsing unstructured text into structured formats like JSON, ensuring accuracy and type safety. Standard prompt engineering cannot guarantee valid schemas or regex matching, whereas this method enforces strict structural validation.