Skip to content

📋 Generate from JSON Schema

Generate Pydantic models from JSON Schema definitions. See Supported Data Types for supported JSON Schema features.

🚀 Quick Start

datamodel-codegen --input person.json --input-file-type jsonschema --output model.py

📝 Example

person.json 

{
  "$schema": "http://json-schema.org/draft-07/schema#",
  "title": "Person",
  "type": "object",
  "properties": {
    "firstName": {
      "type": "string",
      "description": "The person's first name."
    },
    "lastName": {
      "type": "string",
      "description": "The person's last name."
    },
    "age": {
      "description": "Age in years which must be equal to or greater than zero.",
      "type": "integer",
      "minimum": 0
    },
    "friends": {
      "type": "array"
    },
    "comment": {
      "type": "null"
    }
  }
}

✨ Generated model.py 

# generated by datamodel-codegen:
#   filename:  person.json
#   timestamp: 2020-04-27T16:12:27+00:00

from __future__ import annotations

from typing import Any

from pydantic import BaseModel, Field, conint


class Person(BaseModel):
    firstName: str | None = Field(None, description="The person's first name.")
    lastName: str | None = Field(None, description="The person's last name.")
    age: conint(ge=0) | None = Field(
        None, description='Age in years which must be equal to or greater than zero.'
    )
    friends: list | None = None
    comment: Any | None = None

Tuple validation

JSON Schema's prefixItems syntax lets you describe heterogeneous arrays.

When:

  • prefixItems is present
  • no items are specified
  • minItems/maxItems match the number of prefix entries

datamodel-code-generator emits precise tuple annotations.

Example

{
  "$defs": {
    "Span": {
      "type": "object",
      "properties": {
        "value": { "type": "integer" }
      },
      "required": ["value"]
    }
  },
  "title": "defaults",
  "type": "object",
  "properties": {
    "a": {
      "type": "array",
      "prefixItems": [
        { "$ref": "#/$defs/Span" },
        { "type": "string" }
      ],
      "minItems": 2,
      "maxItems": 2
    }
  },
  "required": ["a"]
}

from pydantic import BaseModel


class Span(BaseModel):
    value: int


class Defaults(BaseModel):
    a: tuple[Span, str]

📖 See Also