datamodel-code-generator

This code generator creates pydantic v1 and v2 model, dataclasses.dataclass, typing.TypedDict and msgspec.Struct from an openapi file and others.

Help

See documentation for more details.

Sponsors

Quick Installation

To install datamodel-code-generator:

$ pip install datamodel-code-generator

Simple Usage

You can generate models from a local file.

$ datamodel-codegen --input api.yaml --output model.py

openapi: "3.0.0"
info:
  version: 1.0.0
  title: Swagger Petstore
  license:
    name: MIT
servers:
  - url: http://petstore.swagger.io/v1
paths:
  /pets:
    get:
      summary: List all pets
      operationId: listPets
      tags:
        - pets
      parameters:
        - name: limit
          in: query
          description: How many items to return at one time (max 100)
          required: false
          schema:
            type: integer
            format: int32
      responses:
        '200':
          description: A paged array of pets
          headers:
            x-next:
              description: A link to the next page of responses
              schema:
                type: string
          content:
            application/json:
              schema:
                $ref: "#/components/schemas/Pets"
        default:
          description: unexpected error
          content:
            application/json:
              schema:
                $ref: "#/components/schemas/Error"
                x-amazon-apigateway-integration:
                  uri:
                    Fn::Sub: arn:aws:apigateway:${AWS::Region}:lambda:path/2015-03-31/functions/${PythonVersionFunction.Arn}/invocations
                  passthroughBehavior: when_no_templates
                  httpMethod: POST
                  type: aws_proxy
    post:
      summary: Create a pet
      operationId: createPets
      tags:
        - pets
      responses:
        '201':
          description: Null response
        default:
          description: unexpected error
          content:
            application/json:
              schema:
                $ref: "#/components/schemas/Error"
                x-amazon-apigateway-integration:
                  uri:
                    Fn::Sub: arn:aws:apigateway:${AWS::Region}:lambda:path/2015-03-31/functions/${PythonVersionFunction.Arn}/invocations
                  passthroughBehavior: when_no_templates
                  httpMethod: POST
                  type: aws_proxy
  /pets/{petId}:
    get:
      summary: Info for a specific pet
      operationId: showPetById
      tags:
        - pets
      parameters:
        - name: petId
          in: path
          required: true
          description: The id of the pet to retrieve
          schema:
            type: string
      responses:
        '200':
          description: Expected response to a valid request
          content:
            application/json:
              schema:
                $ref: "#/components/schemas/Pets"
        default:
          description: unexpected error
          content:
            application/json:
              schema:
                $ref: "#/components/schemas/Error"
    x-amazon-apigateway-integration:
      uri:
        Fn::Sub: arn:aws:apigateway:${AWS::Region}:lambda:path/2015-03-31/functions/${PythonVersionFunction.Arn}/invocations
      passthroughBehavior: when_no_templates
      httpMethod: POST
      type: aws_proxy
components:
  schemas:
    Pet:
      required:
        - id
        - name
      properties:
        id:
          type: integer
          format: int64
        name:
          type: string
        tag:
          type: string
    Pets:
      type: array
      items:
        $ref: "#/components/schemas/Pet"
    Error:
      required:
        - code
        - message
      properties:
        code:
          type: integer
          format: int32
        message:
          type: string
    apis:
      type: array
      items:
        type: object
        properties:
          apiKey:
            type: string
            description: To be used as a dataset parameter value
          apiVersionNumber:
            type: string
            description: To be used as a version parameter value
          apiUrl:
            type: string
            format: uri
            description: "The URL describing the dataset's fields"
          apiDocumentationUrl:
            type: string
            format: uri
            description: A URL to the API console for each API

# generated by datamodel-codegen:
#   filename:  api.yaml
#   timestamp: 2020-06-02T05:28:24+00:00

from __future__ import annotations

from typing import List, Optional

from pydantic import AnyUrl, BaseModel, Field


class Pet(BaseModel):
    id: int
    name: str
    tag: Optional[str] = None


class Pets(BaseModel):
    __root__: List[Pet]


class Error(BaseModel):
    code: int
    message: str


class Api(BaseModel):
    apiKey: Optional[str] = Field(
        None, description='To be used as a dataset parameter value'
    )
    apiVersionNumber: Optional[str] = Field(
        None, description='To be used as a version parameter value'
    )
    apiUrl: Optional[AnyUrl] = Field(
        None, description="The URL describing the dataset's fields"
    )
    apiDocumentationUrl: Optional[AnyUrl] = Field(
        None, description='A URL to the API console for each API'
    )


class Apis(BaseModel):
    __root__: List[Api]

Projects that use datamodel-code-generator

These OSS projects use datamodel-code-generator to generate many models. See the following linked projects for real world examples and inspiration.

• airbytehq/airbyte
  • code-generator/Dockerfile
• apache/iceberg
  • Generate Python code make generate
• argoproj-labs/hera
  • Makefile
• awslabs/aws-lambda-powertools-python
  • Recommended for advanced-use-cases in the official documentation
• DataDog/integrations-core
  • Config models
• hashintel/hash
  • codegen.sh
• IBM/compliance-trestle
  • Building the models from the OSCAL schemas.
• Netflix/consoleme
  • How do I generate models from the Swagger specification?
• Nike-Inc/brickflow
  • Code generate tools./tools/gen-bundle.sh
• open-metadata/OpenMetadata
  • Makefile
• PostHog/posthog
  • Generate models via npm run
• SeldonIO/MLServer
  • generate-types.sh

Supported input types

• OpenAPI 3 (YAML/JSON, OpenAPI Data Type);
• JSON Schema (JSON Schema Core/JSON Schema Validation);
• JSON/YAML/CSV Data (it will be converted to JSON Schema);
• Python dictionary (it will be converted to JSON Schema);
• GraphQL schema (GraphQL Schemas and Types);

Supported output types

• pydantic.BaseModel;
• pydantic_v2.BaseModel;
• dataclasses.dataclass;
• typing.TypedDict;
• msgspec.Struct;
• Custom type from your jinja2 template;

Installation

To install datamodel-code-generator:

$ pip install datamodel-code-generator

http extra option

If you want to resolve $ref for remote files then you should specify http extra option.

$ pip install 'datamodel-code-generator[http]'

graphql extra option

If you want to generate data model from a GraphQL schema then you should specify graphql extra option.

$ pip install 'datamodel-code-generator[graphql]'

Docker Image

The docker image is in Docker Hub

$ docker pull koxudaxi/datamodel-code-generator

Advanced Uses

You can generate models from a URL.

$ datamodel-codegen --url https://<INPUT FILE URL> --output model.py

This method needs the http extra option

All Command Options

The datamodel-codegen command:

usage:
  datamodel-codegen [options]

Generate Python data models from schema definitions or structured data

Options:
  --http-headers HTTP_HEADER [HTTP_HEADER ...]
                        Set headers in HTTP requests to the remote host.
                        (example: "Authorization: Basic dXNlcjpwYXNz")
  --http-ignore-tls     Disable verification of the remote host's TLS
                        certificate
  --input INPUT         Input file/directory (default: stdin)
  --input-file-type {auto,openapi,jsonschema,json,yaml,dict,csv}
                        Input file type (default: auto)
  --output OUTPUT       Output file (default: stdout)
  --output-model-type {pydantic.BaseModel,pydantic_v2.BaseModel,dataclasses.dataclass,typing.TypedDict,msgspec.Struct}
  --url URL             Input file URL. `--input` is ignored when `--url` is
                        used

Typing customization:
  --base-class BASE_CLASS
                        Base Class (default: pydantic.BaseModel)
  --enum-field-as-literal {all,one}
                        Parse enum field as literal. all: all enum field type
                        are Literal. one: field type is Literal when an enum
                        has only one possible value
  --field-constraints   Use field constraints and not con* annotations
  --set-default-enum-member
                        Set enum members as default values for enum field
  --strict-types {str,bytes,int,float,bool} [{str,bytes,int,float,bool} ...]
                        Use strict types
  --use-annotated       Use typing.Annotated for Field(). Also, `--field-
                        constraints` option will be enabled.
  --use-generic-container-types
                        Use generic container types for type hinting
                        (typing.Sequence, typing.Mapping). If `--use-standard-
                        collections` option is set, then import from
                        collections.abc instead of typing
  --use-non-positive-negative-number-constrained-types
                        Use the Non{Positive,Negative}{FloatInt} types instead
                        of the corresponding con* constrained types.
  --use-one-literal-as-default
                        Use one literal as default value for one literal field
  --use-standard-collections
                        Use standard collections for type hinting (list, dict)
  --use-subclass-enum   Define Enum class as subclass with field type when
                        enum has type (int, float, bytes, str)
  --use-union-operator  Use | operator for Union type (PEP 604).
  --use-unique-items-as-set
                        define field type as `set` when the field attribute
                        has `uniqueItems`

Field customization:
  --capitalise-enum-members, --capitalize-enum-members
                        Capitalize field names on enum
  --empty-enum-field-name EMPTY_ENUM_FIELD_NAME
                        Set field name when enum value is empty (default: `_`)
  --field-extra-keys FIELD_EXTRA_KEYS [FIELD_EXTRA_KEYS ...]
                        Add extra keys to field parameters
  --field-extra-keys-without-x-prefix FIELD_EXTRA_KEYS_WITHOUT_X_PREFIX [FIELD_EXTRA_KEYS_WITHOUT_X_PREFIX ...]
                        Add extra keys with `x-` prefix to field parameters.
                        The extra keys are stripped of the `x-` prefix.
  --field-include-all-keys
                        Add all keys to field parameters
  --force-optional      Force optional for required fields
  --original-field-name-delimiter ORIGINAL_FIELD_NAME_DELIMITER
                        Set delimiter to convert to snake case. This option
                        only can be used with --snake-case-field (default: `_`
                        )
  --remove-special-field-name-prefix
                        Remove field name prefix if it has a special meaning
                        e.g. underscores
  --snake-case-field    Change camel-case field name to snake-case
  --special-field-name-prefix SPECIAL_FIELD_NAME_PREFIX
                        Set field name prefix when first character can't be
                        used as Python field name (default: `field`)
  --strip-default-none  Strip default None on fields
  --use-default         Use default value even if a field is required
  --use-default-kwarg   Use `default=` instead of a positional argument for
                        Fields that have default values.
  --use-field-description
                        Use schema description to populate field docstring

Model customization:
  --allow-extra-fields  Allow to pass extra fields, if this flag is not
                        passed, extra fields are forbidden.
  --allow-population-by-field-name
                        Allow population by field name
  --class-name CLASS_NAME
                        Set class name of root model
  --collapse-root-models
                        Models generated with a root-type field will be
                        mergedinto the models using that root-type model
  --disable-appending-item-suffix
                        Disable appending `Item` suffix to model name in an
                        array
  --disable-timestamp   Disable timestamp on file headers
  --enable-faux-immutability
                        Enable faux immutability
  --enable-version-header
                        Enable package version on file headers
  --keep-model-order    Keep generated models' order
  --reuse-model         Re-use models on the field when a module has the model
                        with the same content
  --target-python-version {3.6,3.7,3.8,3.9,3.10,3.11}
                        target python version (default: 3.7)
  --use-schema-description
                        Use schema description to populate class docstring
  --use-title-as-name   use titles as class names of models

Template customization:
  --aliases ALIASES     Alias mapping file
  --custom-file-header CUSTOM_FILE_HEADER
                        Custom file header
  --custom-file-header-path CUSTOM_FILE_HEADER_PATH
                        Custom file header file path
  --custom-template-dir CUSTOM_TEMPLATE_DIR
                        Custom template directory
  --encoding ENCODING   The encoding of input and output (default: UTF-8)
  --extra-template-data EXTRA_TEMPLATE_DATA
                        Extra template data
  --use-double-quotes   Model generated with double quotes. Single quotes or
                        your black config skip_string_normalization value will
                        be used without this option.
  --wrap-string-literal
                        Wrap string literal by using black `experimental-
                        string-processing` option (require black 20.8b0 or
                        later)
  --additional-imports  Custom imports for output (delimited list input).
                        For example "datetime.date,datetime.datetime"
  --custom-formatters   List of modules with custom formatter (delimited list input).
  --custom-formatters-kwargs A file with kwargs for custom formatters.

OpenAPI-only options:
  --openapi-scopes {schemas,paths,tags,parameters} [{schemas,paths,tags,parameters} ...]
                        Scopes of OpenAPI model generation (default: schemas)
  --strict-nullable     Treat default field as a non-nullable field (Only
                        OpenAPI)
  --use-operation-id-as-name
                        use operation id of OpenAPI as class names of models
  --validation          Deprecated: Enable validation (Only OpenAPI). this
                        option is deprecated. it will be removed in future
                        releases

General options:
  --debug               show debug message (require "debug". `$ pip install 'datamodel-code-generator[debug]'`)
  --disable-warnings    disable warnings
  --no-color            disable colorized output
  --version             show version
  -h, --help            show this help message and exit

Related projects

fastapi-code-generator

This code generator creates FastAPI app from an openapi file.

https://github.com/koxudaxi/fastapi-code-generator

pydantic-pycharm-plugin

A JetBrains PyCharm plugin for pydantic.

https://github.com/koxudaxi/pydantic-pycharm-plugin

PyPi

https://pypi.org/project/datamodel-code-generator

Contributing

See docs/development-contributing.md for how to get started!

License

datamodel-code-generator is released under the MIT License. http://www.opensource.org/licenses/mit-license