runch-datamodel-code-generator 0.0.2
Datamodel Code Generator
License: MIT
Requires Python: <4.0,>=3.8

# datamodel-code-generator

This code generator creates [pydantic v1 and v2](https://docs.pydantic.dev/) model, [dataclasses.dataclass](https://docs.python.org/3/library/dataclasses.html), [typing.TypedDict](https://docs.python.org/3/library/typing.html#typing.TypedDict) 
and [msgspec.Struct](https://github.com/jcrist/msgspec) from an openapi file and others.

[![PyPI version](https://badge.fury.io/py/datamodel-code-generator.svg)](https://pypi.python.org/pypi/datamodel-code-generator)
[![Conda-forge](https://img.shields.io/conda/v/conda-forge/datamodel-code-generator)](https://anaconda.org/conda-forge/datamodel-code-generator)
[![Downloads](https://pepy.tech/badge/datamodel-code-generator/month)](https://pepy.tech/project/datamodel-code-generator)
[![PyPI - Python Version](https://img.shields.io/pypi/pyversions/datamodel-code-generator)](https://pypi.python.org/pypi/datamodel-code-generator)
[![codecov](https://codecov.io/gh/koxudaxi/datamodel-code-generator/graph/badge.svg?token=plzSSFb9Li)](https://codecov.io/gh/koxudaxi/datamodel-code-generator)
![license](https://img.shields.io/github/license/koxudaxi/datamodel-code-generator.svg)
[![Ruff](https://img.shields.io/endpoint?url=https://raw.githubusercontent.com/astral-sh/ruff/main/assets/badge/v2.json)](https://github.com/astral-sh/ruff)
[![Pydantic v1](https://img.shields.io/endpoint?url=https://raw.githubusercontent.com/pydantic/pydantic/main/docs/badge/v1.json)](https://pydantic.dev)
[![Pydantic v2](https://img.shields.io/endpoint?url=https://raw.githubusercontent.com/pydantic/pydantic/main/docs/badge/v2.json)](https://pydantic.dev)

## Help
See [documentation](https://koxudaxi.github.io/datamodel-code-generator) for more details.

## Quick Installation

To install `datamodel-code-generator`:
```bash
$ pip install datamodel-code-generator
```

## Simple Usage
You can generate models from a local file.
```bash
$ datamodel-codegen --input api.yaml --output model.py
```

<details>
<summary>api.yaml</summary>

```yaml
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
```

</details>

<details>
<summary>model.py</summary>

```python
# 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]
```
</details>

## Supported input types
-  OpenAPI 3 (YAML/JSON, [OpenAPI Data Type](https://github.com/OAI/OpenAPI-Specification/blob/main/versions/3.0.2.md#data-types));
-  JSON Schema ([JSON Schema Core](http://json-schema.org/draft/2019-09/json-schema-validation.html)/[JSON Schema Validation](http://json-schema.org/draft/2019-09/json-schema-validation.html));
-  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](https://graphql.org/learn/schema/));

## Supported output types
- [pydantic](https://docs.pydantic.dev/1.10/).BaseModel;
- [pydantic_v2](https://docs.pydantic.dev/2.0/).BaseModel;
- [dataclasses.dataclass](https://docs.python.org/3/library/dataclasses.html);
- [typing.TypedDict](https://docs.python.org/3/library/typing.html#typing.TypedDict);
- [msgspec.Struct](https://github.com/jcrist/msgspec);
- Custom type from your [jinja2](https://jinja.palletsprojects.com/en/3.1.x/) template;

## Sponsors
<table>
  <tr>
    <td valign="top" align="center">
    <a href="https://github.com/JetBrainsOfficial">
      <img src="https://avatars.githubusercontent.com/u/60931315?s=100&v=4" alt="JetBrains Logo" style="width: 100px;">
      <p>JetBrains</p>
    </a>
    </td>
  <td valign="top" align="center">
    <a href="https://github.com/astral-sh">
      <img src="https://avatars.githubusercontent.com/u/115962839?s=200&v=4" alt="Astral Logo" style="width: 100px;">
      <p>Astral</p>
    </a>
  </td>
  <td valign="top" align="center">
    <a href="https://github.com/DataDog">
      <img src="https://avatars.githubusercontent.com/u/365230?s=200&v=4" alt="Datadog, Inc. Logo" style="width: 100px;">
      <p>Datadog, Inc.</p>
    </a>
  </td>
  </tr>
</table>

## 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](https://github.com/airbytehq/airbyte)
  - *[Generate Python, Java/Kotlin, and Typescript protocol models](https://github.com/airbytehq/airbyte-protocol/tree/main/protocol-models/bin)*
- [apache/iceberg](https://github.com/apache/iceberg)
  - *[Generate Python code](https://github.com/apache/iceberg/blob/d2e1094ee0cc6239d43f63ba5114272f59d605d2/open-api/README.md?plain=1#L39)* 
    *[`make generate`](https://github.com/apache/iceberg/blob/d2e1094ee0cc6239d43f63ba5114272f59d605d2/open-api/Makefile#L24-L34)*
- [argoproj-labs/hera](https://github.com/argoproj-labs/hera)
  - *[`Makefile`](https://github.com/argoproj-labs/hera/blob/c8cbf0c7a676de57469ca3d6aeacde7a5e84f8b7/Makefile#L53-L62)*
- [awslabs/aws-lambda-powertools-python](https://github.com/awslabs/aws-lambda-powertools-python)
  - *Recommended for [advanced-use-cases](https://awslabs.github.io/aws-lambda-powertools-python/2.6.0/utilities/parser/#advanced-use-cases) in the official documentation*
- [DataDog/integrations-core](https://github.com/DataDog/integrations-core)
  - *[Config models](https://github.com/DataDog/integrations-core/blob/master/docs/developer/meta/config-models.md)*
- [hashintel/hash](https://github.com/hashintel/hash)
  - *[`codegen.sh`](https://github.com/hashintel/hash/blob/9762b1a1937e14f6b387677e4c7fe4a5f3d4a1e1/libs/%40local/hash-graph-client/python/scripts/codegen.sh#L21-L39)*
- [IBM/compliance-trestle](https://github.com/IBM/compliance-trestle)
  - *[Building the models from the OSCAL schemas.](https://github.com/IBM/compliance-trestle/blob/develop/docs/contributing/website.md#building-the-models-from-the-oscal-schemas)*
- [Netflix/consoleme](https://github.com/Netflix/consoleme)
  - *[How do I generate models from the Swagger specification?](https://github.com/Netflix/consoleme/blob/master/docs/gitbook/faq.md#how-do-i-generate-models-from-the-swagger-specification)*
- [Nike-Inc/brickflow](https://github.com/Nike-Inc/brickflow)
  - *[Code generate tools](https://github.com/Nike-Inc/brickflow/blob/e3245bf638588867b831820a6675ada76b2010bf/tools/README.md?plain=1#L8)[`./tools/gen-bundle.sh`](https://github.com/Nike-Inc/brickflow/blob/e3245bf638588867b831820a6675ada76b2010bf/tools/gen-bundle.sh#L15-L22)*
- [open-metadata/OpenMetadata](https://github.com/open-metadata/OpenMetadata)
  - *[Makefile](https://github.com/open-metadata/OpenMetadata/blob/main/Makefile)*
- [PostHog/posthog](https://github.com/PostHog/posthog)
  - *[Generate models via `npm run`](https://github.com/PostHog/posthog/blob/e1a55b9cb38d01225224bebf8f0c1e28faa22399/package.json#L41)* 
- [SeldonIO/MLServer](https://github.com/SeldonIO/MLServer)
  - *[generate-types.sh](https://github.com/SeldonIO/MLServer/blob/master/hack/generate-types.sh)*

## Installation

To install `datamodel-code-generator`:
```bash
$ pip install datamodel-code-generator
```

### `http` extra option
If you want to resolve `$ref` for remote files then you should specify `http` extra option.
```bash
$ 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.
```bash
$ pip install 'datamodel-code-generator[graphql]'
```

### Docker Image
The docker image is in [Docker Hub](https://hub.docker.com/r/koxudaxi/datamodel-code-generator)
```bash
$ docker pull koxudaxi/datamodel-code-generator
```

## Advanced Uses
You can generate models from a URL.
```bash
$ datamodel-codegen --url https://<INPUT FILE URL> --output model.py
```
This method needs the [http extra option](#http-extra-option)


## All Command Options

The `datamodel-codegen` command:

<!-- start command help -->
```bash
usage: 
  datamodel-codegen [options]

Generate Python data models from schema definitions or structured data

Options:
  --additional-imports ADDITIONAL_IMPORTS
                        Custom imports for output (delimited list input). For example
                        "datetime.date,datetime.datetime"
  --custom-formatters CUSTOM_FORMATTERS
                        List of modules with custom formatter (delimited list input).
  --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
  --http-query-parameters HTTP_QUERY_PARAMETERS [HTTP_QUERY_PARAMETERS ...]
                        Set query parameters in HTTP requests to the remote host. (example:
                        "ref=branch")
  --input INPUT         Input file/directory (default: stdin)
  --input-file-type {auto,openapi,jsonschema,json,yaml,dict,csv,graphql}
                        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}
                        Output model type (default: pydantic.BaseModel)
  --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
  --union-mode {smart,left_to_right}
                        Union mode for only pydantic v2 field
  --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 merged into 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
  --keyword-only        Defined models as keyword only (for example
                        dataclass(kw_only=True)).
  --output-datetime-class {datetime,AwareDatetime,NaiveDatetime}
                        Choose Datetime class between AwareDatetime, NaiveDatetime or
                        datetime. Each output model has its default mapping, and only
                        pydantic, dataclass, and msgspec support this override"
  --reuse-model         Reuse models on the field when a module has the model with the same
                        content
  --target-python-version {3.6,3.7,3.8,3.9,3.10,3.11,3.12}
                        target python version (default: 3.8)
  --treat-dot-as-module
                        treat dotted module names as modules
  --use-exact-imports   import exact types instead of modules, for example: "from .foo
                        import Bar" instead of "from . import foo" with "foo.Bar"
  --use-pendulum        use pendulum instead of datetime
  --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-formatters-kwargs CUSTOM_FORMATTERS_KWARGS
                        A file with kwargs for custom formatters.
  --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)

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
```
<!-- end command help -->

## Related projects
### fastapi-code-generator
This code generator creates [FastAPI](https://github.com/tiangolo/fastapi) app from an openapi file.

[https://github.com/koxudaxi/fastapi-code-generator](https://github.com/koxudaxi/fastapi-code-generator)

### pydantic-pycharm-plugin
[A JetBrains PyCharm plugin](https://plugins.jetbrains.com/plugin/12861-pydantic) for [`pydantic`](https://github.com/samuelcolvin/pydantic).

[https://github.com/koxudaxi/pydantic-pycharm-plugin](https://github.com/koxudaxi/pydantic-pycharm-plugin)

## PyPi

[https://pypi.org/project/datamodel-code-generator](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