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outlines

Generate structured JSON, regex, or Pydantic model outputs from text inputs.

Updated May 3, 2026
Search Tags:#data extraction#pydantic#llm#json schema#structured generation
One-click install
npx skills add https://github.com/eliottbusiness/DeptFlow-Agent --skill outlines-eliottbusiness
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: outlines
Source: https://github.com/eliottbusiness/DeptFlow-Agent/tree/main/profile/skills/mlops/inference/outlines
Command: npx skills add https://github.com/eliottbusiness/DeptFlow-Agent --skill outlines-eliottbusiness

SYSTEM DOCUMENTATION & REQUIREMENTS

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

What problem does it solve?

Outlines helps generate structured text outputs such as JSON, regex matches, or Pydantic models, ensuring validity and efficiency.

Core Features & Use Cases

  • Structured Generation: Generates JSON, regex, or Pydantic model outputs based on provided schemas or models.
  • Type Safety: Ensures output adheres to specified JSON schemas or Pydantic models.
  • Use Case: Ideal for creating APIs, handling complex data formats, or automating data extraction tasks.

Quick Start

Generate a JSON output from a Pydantic model for a given text input using the 'generate.json' function from the Outlines library.

from outlines import generate
from your_model import YourModel

model = generate.json(YourModel, "Your text input")

Frequently Asked Questions about outlines

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

FAQPage Schema
How do I generate structured JSON outputs from LLM text using Pydantic models?▼

You can generate structured JSON outputs by passing a Pydantic model and text input to the generation function. This ensures the LLM output adheres strictly to your specified JSON schema for reliable data extraction.

Can I enforce type safety when extracting data from LLM responses?▼

Yes, type safety is enforced by validating generated text against specified JSON schemas or Pydantic models. This guarantees the LLM output matches your exact data structures without manual parsing errors.

Do I need vllm and transformers to generate regex matches from LLM outputs?▼

Yes, generating regex matches requires the vllm and transformers libraries alongside outlines and pydantic. These dependencies provide the underlying model inference engines needed for structured text generation.

What is the best way to automate data extraction with structured LLM generation?▼

The best way to automate data extraction is using structured generation to produce JSON or Pydantic models directly from unstructured text. This guarantees format validity and type safety for automated data processing tasks.

Does structured generation work for creating APIs from unstructured text inputs?▼

Yes, structured generation works for API creation by transforming unstructured text into valid JSON formats. This ensures the generated API data strictly adheres to your predefined schemas and Pydantic models.

Why does my LLM output fail JSON schema validation during data extraction?▼

LLM output fails JSON schema validation when generated without structured constraints. Using a structured generation approach forces the model to produce text that strictly matches your Pydantic models or JSON schemas.

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