| Name | datamodel-code-generator JSON |
| Version |
0.33.0
JSON |
| download |
| home_page | None |
| Summary | Datamodel Code Generator |
| upload_time | 2025-08-14 13:50:36 |
| maintainer | None |
| docs_url | None |
| author | None |
| requires_python | >=3.9 |
| license | None |
| keywords |
|
| VCS |
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| bugtrack_url |
|
| requirements |
No requirements were recorded.
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| Travis-CI |
No Travis.
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No coveralls.
|
# 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*
- [cloudcoil/cloudcoil](https://github.com/cloudcoil/cloudcoil)
- *[Cloudcoil - Model generation](https://github.com/cloudcoil/cloudcoil#%EF%B8%8F-model-generation)
- [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).
--formatters {black,isort,ruff-check,ruff-format} [{black,isort,ruff-check,ruff-format} ...]
Formatters for output (default: [black, isort])
--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 Deprecated: Allow passing extra fields. This flag is deprecated. Use
`--extra-fields=allow` instead.
--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
--extra-fields {allow,ignore,forbid}
Set the generated models to allow, forbid, or ignore extra fields.
--frozen-dataclasses Generate frozen dataclasses (dataclass(frozen=True)). Only applies
to dataclass output.
--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, ...)
--parent-scoped-naming
Set name of models defined inline from the parent model
--reuse-model Reuse models on the field when a module has the model with the same
content
--target-python-version {3.9,3.10,3.11,3.12,3.13}
target python version
--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 for output models. Input is supposed to be a
json/yaml file. For OpenAPI and Jsonschema the keys are the spec
path of the object, or the name of the object if you want to apply
the template data to multiple objects with the same name. If you are
using another input file type (e.g. GraphQL), the key is the name of
the object. The value is a dictionary of the template data to add.
--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:
--include-path-parameters
Include path parameters in generated parameter models in addition to
query parameters (Only OpenAPI)
--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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"maintainer": null,
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"maintainer_email": null,
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"author_email": "Koudai Aono <koxudaxi@gmail.com>",
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"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- [cloudcoil/cloudcoil](https://github.com/cloudcoil/cloudcoil)\n - *[Cloudcoil - Model generation](https://github.com/cloudcoil/cloudcoil#%EF%B8%8F-model-generation)\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 --formatters {black,isort,ruff-check,ruff-format} [{black,isort,ruff-check,ruff-format} ...]\n Formatters for output (default: [black, isort])\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 Deprecated: Allow passing extra fields. This flag is deprecated. Use\n `--extra-fields=allow` instead.\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 --extra-fields {allow,ignore,forbid}\n Set the generated models to allow, forbid, or ignore extra fields.\n --frozen-dataclasses Generate frozen dataclasses (dataclass(frozen=True)). Only applies\n to dataclass output.\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 --parent-scoped-naming\n Set name of models defined inline from the parent model\n --reuse-model Reuse models on the field when a module has the model with the same\n content\n --target-python-version {3.9,3.10,3.11,3.12,3.13}\n target python version\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 for output models. Input is supposed to be a\n json/yaml file. For OpenAPI and Jsonschema the keys are the spec\n path of the object, or the name of the object if you want to apply\n the template data to multiple objects with the same name. If you are\n using another input file type (e.g. GraphQL), the key is the name of\n the object. The value is a dictionary of the template data to add.\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 --include-path-parameters\n Include path parameters in generated parameter models in addition to\n query parameters (Only OpenAPI)\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",
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