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Pydantic based API support for Flask

Project description

Flask Pydantic API

A wrapper for flask methods allowing them to use Pydantic argment and response types.


  1. Use pydantic models for request data validation (post bodies and query strings) as well as for formatting responses
  2. Type annotation driven on the view function instead of the decorator.
  3. OpenAPI schema generation and documentation
  4. Smart response fields and expansions using pydantic-enhanced-serializer.
  5. Fold path parameters into input Pydantic models
  6. File Uploads into Pydantic model fields
  7. Async views


$ pip install flask-pydantic-api

With support for pydantic-enhanced-serializer:

$ pip install flask-pydantic-api[serializer]

Basic Usage

    from flask import Flask
    from flask_pydantic_api import pydantic_api
    from pydantic import BaseModel

    app = Flask("my_app")

    class RequestBody(BaseModel):
        field1: str
        field2: Optional[int]

    class ResponseBody(BaseModel):
        response_field1: str

    # GET with query string field1=...&field2=..., responding with json RequestBody
        name="Go get something",        # Name of path operation in OpenAPI schema
        tags=["MyTag"],                 # OpenAPI tags
    def do_work(body: RequestBody) -> ResponseBody:
        return ResponseBody(....)

    # POST with body"/api/something_else")
        name="Go do something",        # Name of path operation in OpenAPI schema
        tags=["MyTag"],                # OpenAPI tags
    def do_work_post(body: RequestBody) -> ResponseBody:
        return ResponseBody(....)


This library will generate the openapi.json schema to go with your usage of @pydantic_api. An example view is provided to serve it using RapiDoc, but you can use any other openapi viewer you wish.

    from flask_pydantic_api import apidocs_views

    app = Flask("my_app")

    # GET /apidocs will render the rapidoc viewer
    # GET /apidocs/openapi.json will render the OpenAPI schema
    app.register_blueprint(apidocs_views.blueprint, url_prefix="/apidocs")

Note that you may wish to customize your schema results more than this module provides. In that case:

    from flask_pydantic_api.openapi import get_openapi_schema

    def get_openapi_schema() -> str:
        # param Info: from openapi_schema_pydantic
        # returns: openapi_schema_pydantic.OpenAPI
        my_schema = get_openapi_schema(info)

        # customize my_schema as wanted...

        return make_response(
                my_schema.json(by_alias=True, exclude_none=True, indent=2),
                {"content-type": "application/json"},

Configuration and Parameters

@pydantic_api accepts the following parameters:

  • name: str - Name for this operation that will be used in the OpenAPI schema
  • Tags: List[str] - Tags that will be used for this operation in the OpenAPI schema
  • success_status_code: int = 200 - HTTP Status code that will be used on successful response
  • merge_path_parameters: bool = False - See Path Parameter Folding
  • request_fields_name: str = "fields" - If using pydantic-enhanced-serialzer this is the name of the request parameter that controls the fieldsets returned. See Using the Enhanced Serializer.
  • maximum_expansion_depth: int = 5 - If using pydantic-enhanced-serialzer this controls how deep expansions can go. See Using the Enhanced Serializer.
  • openapi_schema_extra: Optional[Dict[str, Any]] - Optional extra data to add to the openapi schema. Will be merged with automatically generated schema data at paths.<path>.<method>.

Flask configuration:

  • FLASK_PYDANTIC_API_RENDER_ERRORS: bool = True. If true, pydantic validation errors will be rendered to json and returned as a normal response. If false, pydantic errors will yield a standard ValidationError exception.
  • FLASK_PYDANTIC_API_ERROR_STATUS_CODE: int = 400. If FLASK_PYDANTIC_API_RENDER_ERRORS is true, this is the HTTP status code that will be returned.

Path Parameter Folding

For paths that include parameters, you can request that the path parameters be moved into the pydantic object for the request body. In this case you will no longer need the parameter as an argument to your view function.

  • Use the merge_path_parameters argument to @pydantic_api to control this.
  • For this to work, a field of the same name must exist in the request body model
    # Normally...
    class RequestBodyNormal(BaseModel):
        field1: str"/path/<path_param1>/whatever")
    def do_work(path_param1: str, body: RequestBody) -> Response:
        path_param1 = "whatever was in path"
    # With merging:
    class RequestBodyNormal(BaseModel):
        path_param1: str    # path_param1 is now here INSTEAD of the do_work signature
        field1: str"/path/<path_param1>/whatever")
    def do_work(body: RequestBody) -> Response:
        body.path_param1  # use this instead of the function arg

Response Object Flexibility

When returning from an api view, you will typically instantiate a populated response model and return that.

You can also return a dict, which will be cast into the response model.

You can also return any other object that Flask can handle.

    class MyResponseModel(BaseModel):
        field1: str
        field2: int

    # returning a model instance
    def do_work() -> MyResponseModel:
        model = MyResponseModel(field1="foo", field2=1234)
        return model

    # Returning a dict that is expected to be compliant with MyResponseModel:
    #   To make mypy happy, you need to indicate a dict return, but for the
    #   OpenAPI schema to work, you also need to specify the model.  Make
    #   both happy with a Union return type.
    # NOTE: if the dict fails validation with MyResponseModel, the result
    # will be a 500 server error
    def do_work() -> Union[dict, MyResponseModel]:
        return {
            "field1": "foo",
            "field2": 1234,

    # Return something that isn't a dict or a model.
    # What you get here depends on how Flask supports what you are returning.
    # If it isn't a dict or a model, @pydantic_api will just pass it through.
    def do_work() -> SomthingElse:
        return SomethingElse()

Error Handling

By default, errors on pydantic validations of inputs will return a 400 HTTP status code with a json response body that encodes the pydantic errors in its native format (loc, msg, etc). You can return a status code other than 400 by setting the flask config FLASK_PYDANTIC_API_ERROR_STATUS_CODE.

If you want to handle the error differently (for example to customize the data structure of the errors), you can turn off the automatic error handling by settings the flask config FLASK_PYDANTIC_API_RENDER_ERRORS to False.

When error handling is turned off, pydantic validation errors will throw the pydantic.ValidationError exception. You will need to handle that exception or else the server response will be a 500 server error. See Flask Registering Error Handlers.

Response Validation Errors:

If pydantic validation fails on your response object, the error will never be serialized and returned in the response. This is because the client user cannot easily distinguish between the error happening on input or on your response. Response validation errors will throw an exception and yield a 500 server error.

Using the Enhanced Serializer

This module supports pydantic-enhanced-serializer. It will use it automatically if installed.

The argument parameter used to select fields and expansions is fields. This can be customized with the request_fields_name parameter of @pydantic_api. You do not need to specify the fields parameter in your function arguments or request body model.

The fields parameter may be in the query string or in the post body. It can be a list of strings or a string of field names separated by commas.

The maxium expansion depth defaults to 5 and can be controlled with the maximum_expansion_depth parameter of @pydantic_api


    class MyResponse(BaseModel):
        field1: str
        field2: str

        class Config:
            fieldsets = [
                default: ["field2"],

    def get_something() -> MyResponse:
        return MyResponse(field1="value1", field2="value2")
    curl http://localhost:8080/something?fields=field1,field2
    curl http://localhost:8080/something?fields=field1&fields=field2

    curl -X POST \
        -H'Content-Type: application/json' \
        -d '{"fields": ["field1", "field2"]} \

See Pydantic Enhanced Serializer for more information.

File Uploads

File uploading with multipart/form-data content into pydantic request models is supported and the usual required and type checks will be done.

Multiple files can be uploaded in the same request so long as each has a distinct field name.

    from pydantic import BaseModel
    from pydantic_api import UploadedFile, pydantic_api

    class MyRequest(BaseModel):
        photo: UploadedFile
        caption: str"/upload-photo"
    def upload_photo(body: MyRequest) -> MyResponse:
        binary_file_data =  # is werkzeug.datastructures.FileStorage object
        file_name =

    curl -F photo=@some_file.jpg -F caption="A great picture!" http://localhsot:8080/upload-photo


This project is licensed under the terms of the MIT license.

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