# 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.
[](https://pypi.python.org/pypi/datamodel-code-generator)
[](https://anaconda.org/conda-forge/datamodel-code-generator)
[](https://pepy.tech/project/datamodel-code-generator)
[](https://pypi.python.org/pypi/datamodel-code-generator)
[](https://codecov.io/gh/koxudaxi/datamodel-code-generator)
[](https://github.com/astral-sh/ruff)
[](https://pydantic.dev)
[](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
--no-alias Do not add a field alias. E.g., if --snake-case-field is used along
with a base class, which has an alias_generator
--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 (for example
pydantic: datetime, dataclass: str, ...)
--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
Raw data
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"home_page": "https://github.com/koxudaxi/datamodel-code-generator",
"name": "datamodel-code-generator",
"maintainer": null,
"docs_url": null,
"requires_python": "<4.0,>=3.8",
"maintainer_email": null,
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"author": "Koudai Aono",
"author_email": "koxudaxi@gmail.com",
"download_url": "https://files.pythonhosted.org/packages/0e/d3/4989ef484c41c35f45a16b54ffc25ffb4972d2fefde576a604f1c8c0675d/datamodel_code_generator-0.26.4.tar.gz",
"platform": null,
"description": "# datamodel-code-generator\n\nThis 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) \nand [msgspec.Struct](https://github.com/jcrist/msgspec) from an openapi file and others.\n\n[](https://pypi.python.org/pypi/datamodel-code-generator)\n[](https://anaconda.org/conda-forge/datamodel-code-generator)\n[](https://pepy.tech/project/datamodel-code-generator)\n[](https://pypi.python.org/pypi/datamodel-code-generator)\n[](https://codecov.io/gh/koxudaxi/datamodel-code-generator)\n\n[](https://github.com/astral-sh/ruff)\n[](https://pydantic.dev)\n[](https://pydantic.dev)\n\n## Help\nSee [documentation](https://koxudaxi.github.io/datamodel-code-generator) for more details.\n\n## Quick Installation\n\nTo install `datamodel-code-generator`:\n```bash\n$ pip install datamodel-code-generator\n```\n\n## Simple Usage\nYou can generate models from a local file.\n```bash\n$ datamodel-codegen --input api.yaml --output model.py\n```\n\n<details>\n<summary>api.yaml</summary>\n\n```yaml\nopenapi: \"3.0.0\"\ninfo:\n version: 1.0.0\n title: Swagger Petstore\n license:\n name: MIT\nservers:\n - url: http://petstore.swagger.io/v1\npaths:\n /pets:\n get:\n summary: List all pets\n operationId: listPets\n tags:\n - pets\n parameters:\n - name: limit\n in: query\n description: How many items to return at one time (max 100)\n required: false\n schema:\n type: integer\n format: int32\n responses:\n '200':\n description: A paged array of pets\n headers:\n x-next:\n description: A link to the next page of responses\n schema:\n type: string\n content:\n application/json:\n schema:\n $ref: \"#/components/schemas/Pets\"\n default:\n description: unexpected error\n content:\n application/json:\n schema:\n $ref: \"#/components/schemas/Error\"\n x-amazon-apigateway-integration:\n uri:\n Fn::Sub: arn:aws:apigateway:${AWS::Region}:lambda:path/2015-03-31/functions/${PythonVersionFunction.Arn}/invocations\n passthroughBehavior: when_no_templates\n httpMethod: POST\n type: aws_proxy\n post:\n summary: Create a pet\n operationId: createPets\n tags:\n - pets\n responses:\n '201':\n description: Null response\n default:\n description: unexpected error\n content:\n application/json:\n schema:\n $ref: \"#/components/schemas/Error\"\n x-amazon-apigateway-integration:\n uri:\n Fn::Sub: arn:aws:apigateway:${AWS::Region}:lambda:path/2015-03-31/functions/${PythonVersionFunction.Arn}/invocations\n passthroughBehavior: when_no_templates\n httpMethod: POST\n type: aws_proxy\n /pets/{petId}:\n get:\n summary: Info for a specific pet\n operationId: showPetById\n tags:\n - pets\n parameters:\n - name: petId\n in: path\n required: true\n description: The id of the pet to retrieve\n schema:\n type: string\n responses:\n '200':\n description: Expected response to a valid request\n content:\n application/json:\n schema:\n $ref: \"#/components/schemas/Pets\"\n default:\n description: unexpected error\n content:\n application/json:\n schema:\n $ref: \"#/components/schemas/Error\"\n x-amazon-apigateway-integration:\n uri:\n Fn::Sub: arn:aws:apigateway:${AWS::Region}:lambda:path/2015-03-31/functions/${PythonVersionFunction.Arn}/invocations\n passthroughBehavior: when_no_templates\n httpMethod: POST\n type: aws_proxy\ncomponents:\n schemas:\n Pet:\n required:\n - id\n - name\n properties:\n id:\n type: integer\n format: int64\n name:\n type: string\n tag:\n type: string\n Pets:\n type: array\n items:\n $ref: \"#/components/schemas/Pet\"\n Error:\n required:\n - code\n - message\n properties:\n code:\n type: integer\n format: int32\n message:\n type: string\n apis:\n type: array\n items:\n type: object\n properties:\n apiKey:\n type: string\n description: To be used as a dataset parameter value\n apiVersionNumber:\n type: string\n description: To be used as a version parameter value\n apiUrl:\n type: string\n format: uri\n description: \"The URL describing the dataset's fields\"\n apiDocumentationUrl:\n type: string\n format: uri\n description: A URL to the API console for each API\n```\n\n</details>\n\n<details>\n<summary>model.py</summary>\n\n```python\n# generated by datamodel-codegen:\n# filename: api.yaml\n# timestamp: 2020-06-02T05:28:24+00:00\n\nfrom __future__ import annotations\n\nfrom typing import List, Optional\n\nfrom pydantic import AnyUrl, BaseModel, Field\n\n\nclass Pet(BaseModel):\n id: int\n name: str\n tag: Optional[str] = None\n\n\nclass Pets(BaseModel):\n __root__: List[Pet]\n\n\nclass Error(BaseModel):\n code: int\n message: str\n\n\nclass Api(BaseModel):\n apiKey: Optional[str] = Field(\n None, description='To be used as a dataset parameter value'\n )\n apiVersionNumber: Optional[str] = Field(\n None, description='To be used as a version parameter value'\n )\n apiUrl: Optional[AnyUrl] = Field(\n None, description=\"The URL describing the dataset's fields\"\n )\n apiDocumentationUrl: Optional[AnyUrl] = Field(\n None, description='A URL to the API console for each API'\n )\n\n\nclass Apis(BaseModel):\n __root__: List[Api]\n```\n</details>\n\n## Supported input types\n- OpenAPI 3 (YAML/JSON, [OpenAPI Data Type](https://github.com/OAI/OpenAPI-Specification/blob/main/versions/3.0.2.md#data-types));\n- 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));\n- JSON/YAML/CSV Data (it will be converted to JSON Schema);\n- Python dictionary (it will be converted to JSON Schema);\n- GraphQL schema ([GraphQL Schemas and Types](https://graphql.org/learn/schema/));\n\n## Supported output types\n- [pydantic](https://docs.pydantic.dev/1.10/).BaseModel;\n- [pydantic_v2](https://docs.pydantic.dev/2.0/).BaseModel;\n- [dataclasses.dataclass](https://docs.python.org/3/library/dataclasses.html);\n- [typing.TypedDict](https://docs.python.org/3/library/typing.html#typing.TypedDict);\n- [msgspec.Struct](https://github.com/jcrist/msgspec);\n- Custom type from your [jinja2](https://jinja.palletsprojects.com/en/3.1.x/) template;\n\n## Sponsors\n<table>\n <tr>\n <td valign=\"top\" align=\"center\">\n <a href=\"https://github.com/JetBrainsOfficial\">\n <img src=\"https://avatars.githubusercontent.com/u/60931315?s=100&v=4\" alt=\"JetBrains Logo\" style=\"width: 100px;\">\n <p>JetBrains</p>\n </a>\n </td>\n <td valign=\"top\" align=\"center\">\n <a href=\"https://github.com/astral-sh\">\n <img src=\"https://avatars.githubusercontent.com/u/115962839?s=200&v=4\" alt=\"Astral Logo\" style=\"width: 100px;\">\n <p>Astral</p>\n </a>\n </td>\n <td valign=\"top\" align=\"center\">\n <a href=\"https://github.com/DataDog\">\n <img src=\"https://avatars.githubusercontent.com/u/365230?s=200&v=4\" alt=\"Datadog, Inc. Logo\" style=\"width: 100px;\">\n <p>Datadog, Inc.</p>\n </a>\n </td>\n </tr>\n</table>\n\n## Projects that use datamodel-code-generator\n \nThese OSS projects use datamodel-code-generator to generate many models. \nSee the following linked projects for real world examples and inspiration.\n\n- [airbytehq/airbyte](https://github.com/airbytehq/airbyte)\n - *[Generate Python, Java/Kotlin, and Typescript protocol models](https://github.com/airbytehq/airbyte-protocol/tree/main/protocol-models/bin)*\n- [apache/iceberg](https://github.com/apache/iceberg)\n - *[Generate Python code](https://github.com/apache/iceberg/blob/d2e1094ee0cc6239d43f63ba5114272f59d605d2/open-api/README.md?plain=1#L39)* \n *[`make generate`](https://github.com/apache/iceberg/blob/d2e1094ee0cc6239d43f63ba5114272f59d605d2/open-api/Makefile#L24-L34)*\n- [argoproj-labs/hera](https://github.com/argoproj-labs/hera)\n - *[`Makefile`](https://github.com/argoproj-labs/hera/blob/c8cbf0c7a676de57469ca3d6aeacde7a5e84f8b7/Makefile#L53-L62)*\n- [awslabs/aws-lambda-powertools-python](https://github.com/awslabs/aws-lambda-powertools-python)\n - *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*\n- [DataDog/integrations-core](https://github.com/DataDog/integrations-core)\n - *[Config models](https://github.com/DataDog/integrations-core/blob/master/docs/developer/meta/config-models.md)*\n- [hashintel/hash](https://github.com/hashintel/hash)\n - *[`codegen.sh`](https://github.com/hashintel/hash/blob/9762b1a1937e14f6b387677e4c7fe4a5f3d4a1e1/libs/%40local/hash-graph-client/python/scripts/codegen.sh#L21-L39)*\n- [IBM/compliance-trestle](https://github.com/IBM/compliance-trestle)\n - *[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)*\n- [Netflix/consoleme](https://github.com/Netflix/consoleme)\n - *[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)*\n- [Nike-Inc/brickflow](https://github.com/Nike-Inc/brickflow)\n - *[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)*\n- [open-metadata/OpenMetadata](https://github.com/open-metadata/OpenMetadata)\n - *[Makefile](https://github.com/open-metadata/OpenMetadata/blob/main/Makefile)*\n- [PostHog/posthog](https://github.com/PostHog/posthog)\n - *[Generate models via `npm run`](https://github.com/PostHog/posthog/blob/e1a55b9cb38d01225224bebf8f0c1e28faa22399/package.json#L41)* \n- [SeldonIO/MLServer](https://github.com/SeldonIO/MLServer)\n - *[generate-types.sh](https://github.com/SeldonIO/MLServer/blob/master/hack/generate-types.sh)*\n\n## Installation\n\nTo install `datamodel-code-generator`:\n```bash\n$ pip install datamodel-code-generator\n```\n\n### `http` extra option\nIf you want to resolve `$ref` for remote files then you should specify `http` extra option.\n```bash\n$ pip install 'datamodel-code-generator[http]'\n```\n\n### `graphql` extra option\n\nIf you want to generate data model from a GraphQL schema then you should specify `graphql` extra option.\n```bash\n$ pip install 'datamodel-code-generator[graphql]'\n```\n\n### Docker Image\nThe docker image is in [Docker Hub](https://hub.docker.com/r/koxudaxi/datamodel-code-generator)\n```bash\n$ docker pull koxudaxi/datamodel-code-generator\n```\n\n## Advanced Uses\nYou can generate models from a URL.\n```bash\n$ datamodel-codegen --url https://<INPUT FILE URL> --output model.py\n```\nThis method needs the [http extra option](#http-extra-option)\n\n\n## All Command Options\n\nThe `datamodel-codegen` command:\n\n<!-- start command help -->\n```bash\nusage: \n datamodel-codegen [options]\n\nGenerate Python data models from schema definitions or structured data\n\nOptions:\n --additional-imports ADDITIONAL_IMPORTS\n Custom imports for output (delimited list input). For example\n \"datetime.date,datetime.datetime\"\n --custom-formatters CUSTOM_FORMATTERS\n List of modules with custom formatter (delimited list input).\n --http-headers HTTP_HEADER [HTTP_HEADER ...]\n Set headers in HTTP requests to the remote host. (example:\n \"Authorization: Basic dXNlcjpwYXNz\")\n --http-ignore-tls Disable verification of the remote host''s TLS certificate\n --http-query-parameters HTTP_QUERY_PARAMETERS [HTTP_QUERY_PARAMETERS ...]\n Set query parameters in HTTP requests to the remote host. (example:\n \"ref=branch\")\n --input INPUT Input file/directory (default: stdin)\n --input-file-type {auto,openapi,jsonschema,json,yaml,dict,csv,graphql}\n Input file type (default: auto)\n --output OUTPUT Output file (default: stdout)\n --output-model-type {pydantic.BaseModel,pydantic_v2.BaseModel,dataclasses.dataclass,typing.TypedDict,msgspec.Struct}\n Output model type (default: pydantic.BaseModel)\n --url URL Input file URL. `--input` is ignored when `--url` is used\n\nTyping customization:\n --base-class BASE_CLASS\n Base Class (default: pydantic.BaseModel)\n --enum-field-as-literal {all,one}\n Parse enum field as literal. all: all enum field type are Literal.\n one: field type is Literal when an enum has only one possible value\n --field-constraints Use field constraints and not con* annotations\n --set-default-enum-member\n Set enum members as default values for enum field\n --strict-types {str,bytes,int,float,bool} [{str,bytes,int,float,bool} ...]\n Use strict types\n --use-annotated Use typing.Annotated for Field(). Also, `--field-constraints` option\n will be enabled.\n --use-generic-container-types\n Use generic container types for type hinting (typing.Sequence,\n typing.Mapping). If `--use-standard-collections` option is set, then\n import from collections.abc instead of typing\n --use-non-positive-negative-number-constrained-types\n Use the Non{Positive,Negative}{FloatInt} types instead of the\n corresponding con* constrained types.\n --use-one-literal-as-default\n Use one literal as default value for one literal field\n --use-standard-collections\n Use standard collections for type hinting (list, dict)\n --use-subclass-enum Define Enum class as subclass with field type when enum has type\n (int, float, bytes, str)\n --use-union-operator Use | operator for Union type (PEP 604).\n --use-unique-items-as-set\n define field type as `set` when the field attribute has\n `uniqueItems`\n\nField customization:\n --capitalise-enum-members, --capitalize-enum-members\n Capitalize field names on enum\n --empty-enum-field-name EMPTY_ENUM_FIELD_NAME\n Set field name when enum value is empty (default: `_`)\n --field-extra-keys FIELD_EXTRA_KEYS [FIELD_EXTRA_KEYS ...]\n Add extra keys to field parameters\n --field-extra-keys-without-x-prefix FIELD_EXTRA_KEYS_WITHOUT_X_PREFIX [FIELD_EXTRA_KEYS_WITHOUT_X_PREFIX ...]\n Add extra keys with `x-` prefix to field parameters. The extra keys\n are stripped of the `x-` prefix.\n --field-include-all-keys\n Add all keys to field parameters\n --force-optional Force optional for required fields\n --no-alias Do not add a field alias. E.g., if --snake-case-field is used along\n with a base class, which has an alias_generator\n --original-field-name-delimiter ORIGINAL_FIELD_NAME_DELIMITER\n Set delimiter to convert to snake case. This option only can be used\n with --snake-case-field (default: `_` )\n --remove-special-field-name-prefix\n Remove field name prefix if it has a special meaning e.g.\n underscores\n --snake-case-field Change camel-case field name to snake-case\n --special-field-name-prefix SPECIAL_FIELD_NAME_PREFIX\n Set field name prefix when first character can''t be used as Python\n field name (default: `field`)\n --strip-default-none Strip default None on fields\n --union-mode {smart,left_to_right}\n Union mode for only pydantic v2 field\n --use-default Use default value even if a field is required\n --use-default-kwarg Use `default=` instead of a positional argument for Fields that have\n default values.\n --use-field-description\n Use schema description to populate field docstring\n\nModel customization:\n --allow-extra-fields Allow to pass extra fields, if this flag is not passed, extra fields\n are forbidden.\n --allow-population-by-field-name\n Allow population by field name\n --class-name CLASS_NAME\n Set class name of root model\n --collapse-root-models\n Models generated with a root-type field will be merged into the\n models using that root-type model\n --disable-appending-item-suffix\n Disable appending `Item` suffix to model name in an array\n --disable-timestamp Disable timestamp on file headers\n --enable-faux-immutability\n Enable faux immutability\n --enable-version-header\n Enable package version on file headers\n --keep-model-order Keep generated models'' order\n --keyword-only Defined models as keyword only (for example\n dataclass(kw_only=True)).\n --output-datetime-class {datetime,AwareDatetime,NaiveDatetime}\n Choose Datetime class between AwareDatetime, NaiveDatetime or\n datetime. Each output model has its default mapping (for example\n pydantic: datetime, dataclass: str, ...)\n --reuse-model Reuse models on the field when a module has the model with the same\n content\n --target-python-version {3.6,3.7,3.8,3.9,3.10,3.11,3.12}\n target python version (default: 3.8)\n --treat-dot-as-module\n treat dotted module names as modules\n --use-exact-imports import exact types instead of modules, for example: \"from .foo\n import Bar\" instead of \"from . import foo\" with \"foo.Bar\"\n --use-pendulum use pendulum instead of datetime\n --use-schema-description\n Use schema description to populate class docstring\n --use-title-as-name use titles as class names of models\n\nTemplate customization:\n --aliases ALIASES Alias mapping file\n --custom-file-header CUSTOM_FILE_HEADER\n Custom file header\n --custom-file-header-path CUSTOM_FILE_HEADER_PATH\n Custom file header file path\n --custom-formatters-kwargs CUSTOM_FORMATTERS_KWARGS\n A file with kwargs for custom formatters.\n --custom-template-dir CUSTOM_TEMPLATE_DIR\n Custom template directory\n --encoding ENCODING The encoding of input and output (default: utf-8)\n --extra-template-data EXTRA_TEMPLATE_DATA\n Extra template data\n --use-double-quotes Model generated with double quotes. Single quotes or your black\n config skip_string_normalization value will be used without this\n option.\n --wrap-string-literal\n Wrap string literal by using black `experimental-string-processing`\n option (require black 20.8b0 or later)\n\nOpenAPI-only options:\n --openapi-scopes {schemas,paths,tags,parameters} [{schemas,paths,tags,parameters} ...]\n Scopes of OpenAPI model generation (default: schemas)\n --strict-nullable Treat default field as a non-nullable field (Only OpenAPI)\n --use-operation-id-as-name\n use operation id of OpenAPI as class names of models\n --validation Deprecated: Enable validation (Only OpenAPI). this option is\n deprecated. it will be removed in future releases\n\nGeneral options:\n --debug show debug message (require \"debug\". `$ pip install ''datamodel-code-\n generator[debug]''`)\n --disable-warnings disable warnings\n --no-color disable colorized output\n --version show version\n -h, --help show this help message and exit\n```\n<!-- end command help -->\n\n## Related projects\n### fastapi-code-generator\nThis code generator creates [FastAPI](https://github.com/tiangolo/fastapi) app from an openapi file.\n\n[https://github.com/koxudaxi/fastapi-code-generator](https://github.com/koxudaxi/fastapi-code-generator)\n\n### pydantic-pycharm-plugin\n[A JetBrains PyCharm plugin](https://plugins.jetbrains.com/plugin/12861-pydantic) for [`pydantic`](https://github.com/samuelcolvin/pydantic).\n\n[https://github.com/koxudaxi/pydantic-pycharm-plugin](https://github.com/koxudaxi/pydantic-pycharm-plugin)\n\n## PyPi\n\n[https://pypi.org/project/datamodel-code-generator](https://pypi.org/project/datamodel-code-generator)\n\n## Contributing\n\nSee `docs/development-contributing.md` for how to get started!\n\n## License\n\ndatamodel-code-generator is released under the MIT License. http://www.opensource.org/licenses/mit-license\n\n",
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