📁 Base Options¶

📋 Options¶

--encoding¶

Specify character encoding for input and output files.

The --encoding flag sets the character encoding used when reading the schema file and writing the generated Python code. This is useful for schemas containing non-ASCII characters (e.g., Japanese, Chinese). Default is UTF-8, which is the standard encoding for JSON and most modern text files.

datamodel-codegen --input schema.json --encoding utf-8 # (1)!

{
  "$schema": "http://json-schema.org/draft-07/schema#",
  "title": "日本語Model",
  "description": "モデルの説明文",
  "type": "object",
  "properties": {
    "名前": {
      "type": "string",
      "description": "ユーザー名"
    },
    "年齢": {
      "type": "integer"
    }
  }
}

# generated by datamodel-codegen:
#   filename:  encoding_test.json
#   timestamp: 2019-07-26T00:00:00+00:00

from __future__ import annotations

from pydantic import BaseModel, Field


class 日本語Model(BaseModel):
    名前: str | None = Field(None, description='ユーザー名')
    年齢: int | None = None

--input¶

Specify the input schema file path.

The --input flag specifies the path to the schema file (JSON Schema, OpenAPI, GraphQL, etc.). Multiple input files can be specified to merge schemas. Required unless using --url to fetch schema from a URL.

datamodel-codegen --input schema.json --input pet_simple.json --output output.py # (1)!

{
  "$schema": "http://json-schema.org/draft-07/schema#",
  "title": "Pet",
  "type": "object",
  "properties": {
    "id": {
      "type": "integer"
    },
    "name": {
      "type": "string"
    },
    "tag": {
      "type": "string"
    }
  }
}

# generated by datamodel-codegen:
#   filename:  pet_simple.json
#   timestamp: 2019-07-26T00:00:00+00:00

from __future__ import annotations

from pydantic import BaseModel


class Pet(BaseModel):
    id: int | None = None
    name: str | None = None
    tag: str | None = None

--input-file-type¶

Specify the input file type for code generation.

The --input-file-type flag explicitly sets the input format.

Important distinction:

• Use jsonschema, openapi, or graphql for schema definition files
• Use json, yaml, or csv for raw sample data to automatically infer a schema

For example, if you have a JSON Schema written in YAML format, use --input-file-type jsonschema, not --input-file-type yaml. The yaml type treats the file as raw data and infers a schema from it.

datamodel-codegen --input schema.json --input-file-type json # (1)!

{
  "Pet": {
    "name": "dog",
    "age": 2
  }
}

# generated by datamodel-codegen:
#   filename:  pet.json
#   timestamp: 2019-07-26T00:00:00+00:00

from __future__ import annotations

from pydantic import BaseModel


class Pet(BaseModel):
    name: str
    age: int


class Model(BaseModel):
    Pet: Pet

Pet:
  name: cat
  age: 3

# generated by datamodel-codegen:
#   filename:  pet.yaml
#   timestamp: 2019-07-26T00:00:00+00:00

from __future__ import annotations

from pydantic import BaseModel


class Pet(BaseModel):
    name: str
    age: int


class Model(BaseModel):
    Pet: Pet

--output¶

Specify the destination path for generated Python code.

The --output flag specifies where to write the generated Python code. It can be either a file path (single-file output) or a directory path (multi-file output for modular schemas). If omitted, the generated code is written to stdout.

datamodel-codegen --input schema.json --input pet_simple.json --output output.py # (1)!

{
  "$schema": "http://json-schema.org/draft-07/schema#",
  "title": "Pet",
  "type": "object",
  "properties": {
    "id": {
      "type": "integer"
    },
    "name": {
      "type": "string"
    },
    "tag": {
      "type": "string"
    }
  }
}

# generated by datamodel-codegen:
#   filename:  pet_simple.json
#   timestamp: 2019-07-26T00:00:00+00:00

from __future__ import annotations

from pydantic import BaseModel


class Pet(BaseModel):
    id: int | None = None
    name: str | None = None
    tag: str | None = None

--url¶

Fetch schema from URL with custom HTTP headers.

The --url flag specifies a remote URL to fetch the schema from instead of a local file. The --http-headers flag adds custom HTTP headers to the request, useful for authentication (e.g., Bearer tokens) or custom API requirements. Format: HeaderName:HeaderValue.

datamodel-codegen --input schema.json --url https://api.example.com/schema.json --http-headers "Authorization:Bearer token" # (1)!

{
  "$schema": "http://json-schema.org/draft-07/schema#",
  "title": "Pet",
  "type": "object",
  "properties": {
    "id": {
      "type": "integer"
    },
    "name": {
      "type": "string"
    },
    "tag": {
      "type": "string"
    }
  }
}

# generated by datamodel-codegen:
#   filename:  https://api.example.com/schema.json
#   timestamp: 2019-07-26T00:00:00+00:00

from __future__ import annotations

from pydantic import BaseModel


class Pet(BaseModel):
    id: int | None = None
    name: str | None = None
    tag: str | None = None