Documentation: https://flama.perdy.io
Flama
Flama aims to bring a layer on top of Starlette to provide an easy to learn and fast to develop approach for building highly performant GraphQL and REST APIs. In the same way of Starlette is, Flama is a perfect option for developing asynchronous and production-ready services.
Among other characteristics it provides the following:
- Generic classes for API resources that provides standard CRUD methods over SQLAlchemy tables.
- Schema system based on Marshmallow that allows to declare the inputs and outputs of endpoints and provides a reliable way of validate data against those schemas.
- Dependency Injection that ease the process of managing parameters needed in endpoints. Flama ASGI objects like
Request,Response,Sessionand so on are defined as components and ready to be injected in your endpoints. - Components as the base of the plugin ecosystem, allowing you to create custom or use those already defined in your endpoints, injected as parameters.
- Auto generated API schema using OpenAPI standard. It uses the schema system of your endpoints to extract all the necessary information to generate your API Schema.
- Auto generated docs providing a Swagger UI or ReDoc endpoint.
- Pagination automatically handled using multiple methods such as limit and offset, page numbers...
Requirements
- Python 3.6+
- Starlette 0.12.0+
- Marshmallow 3.0.0+
Installation
$ pip install flama
Example
from marshmallow import Schema, fields, validate
from flama.applications import Flama
import uvicorn
# Data Schema
class Puppy(Schema):
id = fields.Integer()
name = fields.String()
age = fields.Integer(validate=validate.Range(min=0))
# Database
puppies = [
{"id": 1, "name": "Canna", "age": 6},
{"id": 2, "name": "Sandy", "age": 12},
]
# Application
app = Flama(
components=[], # Without custom components
title="Foo", # API title
version="0.1", # API version
description="Bar", # API description
schema="/schema/", # Path to expose OpenAPI schema
docs="/docs/", # Path to expose Swagger UI docs
redoc="/redoc/", # Path to expose ReDoc docs
)
# Views
@app.route("/", methods=["GET"])
def list_puppies(name: str = None) -> Puppy(many=True):
"""
description:
List the puppies collection. There is an optional query parameter that
specifies a name for filtering the collection based on it.
responses:
200:
description: List puppies.
"""
return [puppy for puppy in puppies if name in (puppy["name"], None)]
@app.route("/", methods=["POST"])
def create_puppy(puppy: Puppy) -> Puppy:
"""
description:
Create a new puppy using data validated from request body and add it
to the collection.
responses:
200:
description: Puppy created successfully.
"""
puppies.append(puppy)
return puppy
if __name__ == '__main__':
uvicorn.run(app, host='0.0.0.0', port=8000)
Dependencies
Following Starlette philosophy Flama reduce the number of hard dependencies to those that are used as the core:
starlette- Flama is a layer on top of it.marshmallow- Flama data schemas and validation.
It does not have any more hard dependencies, but some of them are necessaries to use some features:
pyyaml- Required for API Schema and Docs auto generation.apispec- Required for API Schema and Docs auto generation.python-forge- Required for pagination.sqlalchemy- Required for Generic API resources.databases- Required for Generic API resources.
You can install all of these with pip3 install flama[full].
Credits
That library is heavily inspired by APIStar server in an attempt to bring a good amount of it essence to work with Starlette as the ASGI framework and Marshmallow as the schema system.
Contributing
This project is absolutely open to contributions so if you have a nice idea, create an issue to let the community discuss it.
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