datamodel-code-generator

This code generator creates pydantic model from an openapi file.

license

This project is an experimental phase.

Supported file formats

OpenAPI 3 (yaml/json)
JsonSchema

Implemented list

OpenAPI 3 and JsonSchema

DataType

string (include patter/minLength/maxLenght)
number (include maximum/exclusiveMaximum/minimum/exclusiveMinimum/multipleOf/le/ge)
integer (include maximum/exclusiveMaximum/minimum/exclusiveMinimum/multipleOf/le/ge)
boolean
array
object

String Format

date
datetime
password
email
uuid (uuid1/uuid2/uuid3/uuid4/uuid5)
ipv4
ipv6

Other schema

enum
allOf (as Multiple inheritance)
anyOf (as Union)
$ref (exclude URL Reference)

Installation

To install datamodel-code-generator:

$ pip install datamodel-code-generator

Usage

The datamodel-codegen command:

usage: datamodel-codegen [-h] [--input INPUT] [--output OUTPUT]
                         [--base-class BASE_CLASS]
                         [--custom-template-dir CUSTOM_TEMPLATE_DIR]
                         [--extra-template-data EXTRA_TEMPLATE_DATA]
                         [--target-python-version {3.6,3.7}] [--debug]
                         [--version]

optional arguments:
  -h, --help            show this help message and exit
  --input INPUT         Open API YAML file (default: stdin)
  --input-file-type {auto,openapi,jsonschema}
  --output OUTPUT       Output file (default: stdout)
  --base-class BASE_CLASS
                        Base Class (default: pydantic.BaseModel)
  --custom-template-dir CUSTOM_TEMPLATE_DIR
                        Custom Template Directory
  --extra-template-data EXTRA_TEMPLATE_DATA
                        Extra Template Data
  --target-python-version {3.6,3.7}
                        target python version (default: 3.7)
  --debug               show debug message
  --version             show version

Formatting

Code generated by datamodel-codegen will be passed through isort and black to produce consistent, well-formatted results. Settings for these tools can be specified in pyproject.toml (located in the output directory, or in some parent of the output directory).

Example pyproject.toml:

[tool.black]
string-normalization = true
line-length = 100

[tool.isort]
multi_line_output = 3
include_trailing_comma = true
force_grid_wrap = 0
use_parentheses = true
line_length = 100
known_first_party = "kelvin"

See the Black Project for more information.

Example

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

api.yaml


```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
```

model.py:

# generated by datamodel-codegen:
#   filename:  api.yaml
#   timestamp: 2019-09-26T01:04:25+00:00

from __future__ import annotations

from typing import List, Optional

from pydantic import BaseModel, UrlStr


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] = None
    apiVersionNumber: Optional[str] = None
    apiUrl: Optional[UrlStr] = None
    apiDocumentationUrl: Optional[UrlStr] = None


class apis(BaseModel):
    __root__: List[api]

Development

Install the package in editable mode:

$ git clone git@github.com:koxudaxi/datamodel-code-generator.git
$ pip install -e datamodel-code-generator

Contribute

We are waiting for your contributions to datamodel-code-generator.

How to contribute

## 1. Clone your fork repository
$ git clone git@github.com:<your username>/datamodel-code-generator.git
$ cd datamodel-code-generator

## 2. Create `venv` with python3.7 (also you should do with python3.6)
$ python3.7 -m venv venv37
$ source venv37/bin/activate  

## 3. Install dependencies
$ python3 -m pip install ".[all]" 

## 4. Create new branch and rewrite code.
$ git checkout -b new-branch

## 5. Run unittest (you should pass all test and coverage should be 100%)
$ ./scripts/test.sh

## 6. Format code
$ ./scripts/format.sh

## 7. Check lint (mypy)
$ ./scripts/lint.sh

## 8. Commit and Push...

PyPi

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

Source Code

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

Documentation

https://koxudaxi.github.io/datamodel-code-generator

License

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

Metadata

Release files for datamodel-code-generator 0.3.2

For a detailed explanation of source distributions (sdists) and built distributions (wheels), please see the package formats documentation.

Source distribution (sdist)

Source distribution for datamodel-code-generator 0.3.2
File	Size	Uploaded
datamodel-code-generator-0.3.2.tar.gz	37.9 kB	Feb 5, 2020	Details

Built distribution (wheel)

Table of built distributions (wheels) for datamodel-code-generator 0.3.2
File	Interpreter	ABI	Platform	Reset
datamodel_code_generator-0.3.2-py3-none-any.whl 54.8 kB Feb 5, 2020	Python 3	none	any	Details

Total release size: 92.6 kB

Release files / datamodel-code-generator-0.3.2.tar.gz

Download URL	datamodel-code-generator-0.3.2.tar.gz
Size	37.9 kB
Tags	Source
SHA-256 checksum How to use checksums	`96233b5aace610563ed13c868446a3c01c627bfb16091fd465d801b155fe7079`
BLAKE2b-256 checksum How to use checksums	`14a700acc344134e065476e579489f9b4e181fe28b5bc83bf1809ebe45091ad2`
Upload date	Feb 5, 2020
Uploaded using Trusted Publishing? What is trusted publishing?	No
Uploaded via	`twine/3.1.1 pkginfo/1.5.0.1 requests/2.22.0 setuptools/45.1.0 requests-toolbelt/0.9.1 tqdm/4.42.1 CPython/3.7.1`

Release files / datamodel_code_generator-0.3.2-py3-none-any.whl

Download URL	datamodel_code_generator-0.3.2-py3-none-any.whl
Size	54.8 kB
Tags	Python 3
SHA-256 checksum How to use checksums	`5fbef3f606d1e5bf062cd71bab243ac354b48a4176f3fbddc7e1d06fff26c5bc`
BLAKE2b-256 checksum How to use checksums	`9eaf02115e76b73c317f00ab61d90103c68bc0a4bf9fb85b099895ead8d238b4`
Upload date	Feb 5, 2020
Uploaded using Trusted Publishing? What is trusted publishing?	No
Uploaded via	`twine/3.1.1 pkginfo/1.5.0.1 requests/2.22.0 setuptools/45.1.0 requests-toolbelt/0.9.1 tqdm/4.42.1 CPython/3.7.1`