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richardnguyen0715
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outlines

Generate structured text using JSON schemas, regex patterns, and Pydantic models.

Updated May 11, 2026
Search Tags:#pydantic#python#json schema#structured generation#structured text generation
One-click install
npx skills add https://github.com/richardnguyen0715/keep-it-real --skill outlines-richardnguyen0715
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: outlines
Source: https://github.com/richardnguyen0715/keep-it-real/tree/main/refer-projects/hermes-agent/optional-skills/mlops/inference/outlines
Command: npx skills add https://github.com/richardnguyen0715/keep-it-real --skill outlines-richardnguyen0715

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?

Outlines solves the problem of manually generating valid JSON, XML, or code, by providing a way to generate structured text using JSON schemas, regex patterns, or Pydantic models.

Core Features & Use Cases

  • Guaranteed Valid JSON/XML/Code: Ensures the output is valid against a provided schema.
  • Pydantic Support: Utilizes Pydantic models for type-safe and structured output.
  • Supports Local Models: Can use local models like Transformers, llama.cpp, and vLLM.
  • Structured Generation: Generates text that adheres to grammar and schema constraints.

Quick Start

Generate structured text from a JSON schema with the outlines skill:

outlines generate json -s path/to/schema.yaml

Frequently Asked Questions about outlines

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

FAQPage Schema
How do I generate guaranteed valid JSON from a Pydantic model using a local LLM?▼

To generate guaranteed valid JSON, you provide a Pydantic model or JSON schema to constrain a local model like Transformers or vLLM, ensuring the output strictly adheres to the defined structure.

What is structured text generation and when do I need it for data extraction?▼

Structured text generation forces language models to produce output matching specific grammar, regex patterns, or schemas. You need it for data extraction and classification tasks requiring valid JSON or XML.

Does structured generation work with vLLM and Transformers?▼

Yes, structured generation works with both vLLM and Transformers, allowing local models to enforce schema constraints during text generation directly within Python.

How do I use regex patterns to constrain text generation in Python?▼

To constrain text generation in Python, you apply a regex pattern to the generation function, forcing the model to output text that strictly matches the specified regular expression.

What is the best way to ensure LLM output matches a JSON schema?▼

The best way to ensure LLM output matches a JSON schema is to use constrained generation, which guarantees the output is valid against the provided schema without manual parsing.

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