Validate requests with Pydantic based on standard Python type hints.

About

flask_typed_routes is a Flask extension designed to effortlessly validate requests with Pydantic based on standard Python type hints.

Documentation: https://rmoralespp.github.io/flask_typed_routes/

Features

• Easy: Easy to use and integrate with Flask applications.
• Standard-based: Based on OpenAPI Specification
• Data validation: Fast data verification based on Pydantic

Requirements

• Python 3.10+
• Pydantic 2.0+
• Flask

Installation

To install flask_typed_routes using pip, run the following command:

pip install flask_typed_routes

Getting Started

This tool allows you to validate request parameters in Flask, similar to how FastAPI handles validation. It supports Path, Query, Header, Cookie, and Body validation.

Example

Create a file items.py with:

import flask
import flask_typed_routes as ftr

app = flask.Flask(__name__)
ftr.FlaskTypedRoutes(app=app)


@app.get("/")
def read_root():
    return flask.jsonify({"Hello": "World"})


@app.get("/items/<user>/")
def read_items(user: str, skip: int = 0, limit: int = 10):
    return flask.jsonify({"user": user, "skip": skip, "limit": limit})

Run the server with:

flask --app items run --debug

Open your browser and go to http://127.0.0.1:5000/items/myuser/?skip=20 You will see the JSON response as:

{
  "limit": 10,
  "skip": 20,
  "user": "myuser"
}

Validation: Open your browser and go to http://127.0.0.1:5000/items/myuser/?skip=abc You will see the JSON response with the error details because the skip parameter is not an integer:

{
  "errors": [
    {
      "input": "abc",
      "loc": [
        "query",
        "skip"
      ],
      "msg": "Input should be a valid integer, unable to parse string as an integer",
      "type": "int_parsing",
      "url": "https://errors.pydantic.dev/2.9/v/int_parsing"
    }
  ]
}

Example Body Validation

You can also use Pydantic models to validate the request body.

Now let's update the items.py file with:

import flask
import flask_typed_routes as ftr
import pydantic


app = flask.Flask(__name__)
ftr.FlaskTypedRoutes(app=app)


class Item(pydantic.BaseModel):
    name: str
    price: float
    description: str = None


@app.get("/")
def read_root():
    return flask.jsonify({"Hello": "World"})


@app.get("/items/<user>/")
def read_items(user: str, skip: int = 0, limit: int = 10):
    return flask.jsonify({"user": user, "skip": skip, "limit": limit})


@app.post('/items/')
def create_item(item: Item):
    return flask.jsonify(item.model_dump())


@app.put('/items/<item_id>/')
def update_item(item_id: int, item: Item):
    return flask.jsonify({'item_id': item_id, **item.model_dump()})

Example Flask Blueprints

Now let's update the items.py file with:

import flask
import flask_typed_routes as ftr

app = flask.Flask(__name__)
ftr.FlaskTypedRoutes(app=app)
orders = flask.Blueprint('orders', __name__)


@orders.get("/orders/<user>/")
def read_orders(user: str, skip: int = 0, limit: int = 10):
    return flask.jsonify({"user": user, "skip": skip, "limit": limit})


app.register_blueprint(orders)

Example Flask Class-Based Views

Now let's update the items.py file with:

import flask
import flask.views
import flask_typed_routes as ftr

app = flask.Flask(__name__)
ftr.FlaskTypedRoutes(app=app)


class UserProducts(flask.views.View):

    def dispatch_request(self, user: str, skip: int = 0, limit: int = 10):
        data = {'user': user, 'skip': skip, 'limit': limit}
        return flask.jsonify(data)


class UserOrders(flask.views.MethodView):

    def get(self, user: str, skip: int = 0, limit: int = 10):
        data = {'user': user, 'skip': skip, 'limit': limit}
        return flask.jsonify(data)


app.add_url_rule('/products/<user>/', view_func=UserProducts.as_view('user_products'))
app.add_url_rule('/orders/<user>/', view_func=UserOrders.as_view('user_orders'))

Interactive API docs

You can generate interactive API docs for your Flask application using OpenAPI schema generated by flask_typed_routes with any OpenAPI UI library. For example, you can use swagger-ui-py to generate the API docs.

pip install swagger-ui-py  # ignore if already installed

import flask
import flask_typed_routes as ftr
import pydantic
import swagger_ui

app = flask.Flask(__name__)
app_ftr = ftr.FlaskTypedRoutes(app=app)


class Item(pydantic.BaseModel):
    name: str
    price: float
    description: str = None


@app.get('/items/<user>/')
def read_items(user: str, skip: int = 0, limit: int = 10):
    data = {'user': user, 'skip': skip, 'limit': limit}
    return flask.jsonify(data)


@app.post('/items/')
def create_item(item: Item):
    return flask.jsonify(item.model_dump())


@app.put('/items/<item_id>/')
def update_item(item_id: int, item: Item):
    return flask.jsonify({'item_id': item_id, **item.model_dump()})


@app.delete('/items/<item_id>/')
def remove_item(item_id: int):
    return flask.jsonify({'item_id': item_id})


swagger_ui.api_doc(app, config=app_ftr.get_openapi_schema(), url_prefix='/docs')

Open your browser and go to http://127.0.0.1:5000/docs/

Create item endpoint:

Read Items endpoint:

Documentation

For more detailed information and usage examples, refer to the project documentation

Development

To contribute to the project, you can run the following commands for testing and documentation:

Running Unit Tests

Install the development dependencies and run the tests:

(env)$ pip install -r requirements-dev.txt  # Skip if already installed
(env)$ python -m pytest tests/
(env)$ python -m pytest --cov # Run tests with coverage

Building the Documentation

To build the documentation locally, use the following commands:

(env)$ pip install -r requirements-doc.txt # Skip if already installed
(env)$ mkdocs serve # Start live-reloading docs server
(env)$ mkdocs build # Build the documentation site

License

This project is licensed under the MIT license.