outlines

Generate structured text outputs from unstructured input using Outlines.

Updated May 25, 2026

Search Tags:#pydantic #code generation #json #xml #structured generation

Ready to run

npx skills add https://github.com/webdevtodayjason/subctl-rust --skill outlines-webdevtodayjason

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: outlines
Source: https://github.com/webdevtodayjason/subctl-rust/tree/main/skills/outlines
Command: npx skills add https://github.com/webdevtodayjason/subctl-rust --skill outlines-webdevtodayjason

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill requires outlines, transformers, vllm, pydantic, and includes scripts (resource) and references (resource) and assets (resource) components.

What problem does it solve?

This Skill ensures the creation of valid JSON/XML/code during text generation, offering type-safe outputs and efficient inference speeds with Outlines - dottxt.ai's structured generation library.

Core Features & Use Cases

Guaranteed Valid Outputs: Ensures that generated JSON/XML/code conforms to predefined schemas.
Type-Safe Outputs: Utilizes Pydantic models for structured data generation.
Support for Local Models: Integrates with popular local models like Transformers, llama.cpp, and vLLM.
Use Case: Ideal for developers needing to generate structured outputs from free-form text, such as converting user input into structured product information or extracting data from textual descriptions.

Quick Start

Generate structured information from the following text: "Extract user: John Doe, 30 years old, [email protected]".

Frequently Asked Questions about outlines

High-intent search queries and answers about installing and using this skill.

FAQPage Schema

How do I generate valid JSON from unstructured text using a local model?▼

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.

Can I use Pydantic models to enforce structured text 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.

Does structured generation work with vLLM and Transformers backends?▼

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.

What is the best way to ensure LLM outputs match a specific JSON schema?▼

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.

How do I extract structured data from free-form textual descriptions?▼

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.

Why do my generated JSON outputs fail schema validation during inference?▼

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.