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Generate structured data

Guardrails AI is effective for generating structured data across from a variety of LLMs. This guide contains the following:

  1. General instructions on generating structured data from Guardrails using Pydantic or Markup (i.e. RAIL), and
  2. Examples to generate structured data using Pydantic or Markup.

Syntax for generating structured data​

There are two ways to generate structured data with Guardrails AI: using Pydantic or Markup (i.e. RAIL).

  1. Pydantic: In order to generate structured data with Pydantic models, create a Pydantic model with the desired fields and types, then create a Guard object that uses the Pydantic model to generate structured data, and finally call the LLM of your choice with the guard object to generate structured data.
  2. RAIL: In order to generate structured data with RAIL specs, create a RAIL spec with the desired fields and types, then create a Guard object that uses the RAIL spec to generate structured data, and finally call the LLM of your choice with the guard object to generate structured data.

Below is the syntax for generating structured data with Guardrails AI using Pydantic or Markup (i.e. RAIL).

In order to generate structured data, first create a Pydantic model with the desired fields and types.

from pydantic import BaseModel

class Person(BaseModel):
    name: str
    age: int
    is_employed: bool

Then, create a Guard object that uses the Pydantic model to generate structured data.

from guardrails import Guard

guard = Guard.from_pydantic(Person)

Finally, call the LLM of your choice with the guard object to generate structured data.

import openai

res = guard(
    openai.chat.completion.create,
    model="gpt-3.5-turbo",
)

Generate a JSON object with simple types​

{
  "name": "John Doe",
  "age": 30,
  "is_employed": true
}

Generate a dictionary of nested types​

{
  "name": "John Doe",
  "age": 30,
  "is_employed": true,
  "address": {
    "street": "123 Main St",
    "city": "Anytown",
    "zip": "12345"
  }
}

Generate a list of types​

[
  {
    "name": "John Doe",
    "age": 30,
    "is_employed": true
  },
  {
    "name": "Jane Smith",
    "age": 25,
    "is_employed": false
  }
]