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dependency-injector 4.49.1

pip install dependency-injector

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Dependency injection framework for Python

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  • License: BSD License (Copyright (c) 2024, Roman Mogylatov)
  • Author: Roman Mogylatov
  • Maintainer: Roman Mogylatov
  • Tags Dependency injection , DI , Inversion of Control , IoC , Factory , Singleton , Design patterns , Flask
  • Requires: Python >=3.8
  • Provides-Extra: yaml , pydantic , pydantic2 , flask , aiohttp

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What is Dependency Injector?

Dependency Injector is a dependency injection framework for Python.

It helps implement the dependency injection principle.

Key features of the Dependency Injector:

  • Providers. Provides Factory, Singleton, Callable, Coroutine, Object, List, Dict, Configuration, Resource, Dependency, and Selector providers that help assemble your objects. See Providers.

  • Overriding. Can override any provider by another provider on the fly. This helps in testing and configuring dev/stage environment to replace API clients with stubs etc. See Provider overriding.

  • Configuration. Reads configuration from yaml, ini, and json files, pydantic settings, environment variables, and dictionaries. See Configuration provider.

  • Resources. Helps with initialization and configuring of logging, event loop, thread or process pool, etc. Can be used for per-function execution scope in tandem with wiring. See Resource provider.

  • Containers. Provides declarative and dynamic containers. See Containers.

  • Wiring. Injects dependencies into functions and methods. Helps integrate with other frameworks: Django, Flask, Aiohttp, Sanic, FastAPI, etc. See Wiring.

  • Asynchronous. Supports asynchronous injections. See Asynchronous injections.

  • Typing. Provides typing stubs, mypy-friendly. See Typing and mypy.

  • Performance. Fast. Written in Cython.

  • Maturity. Mature and production-ready. Well-tested, documented, and supported.

fromdependency_injectorimport containers, providersfromdependency_injector.wiringimport Provide, injectclassContainer(containers.DeclarativeContainer): config = providers.Configuration() api_client = providers.Singleton( ApiClient, api_key=config.api_key, timeout=config.timeout, ) service = providers.Factory( Service, api_client=api_client, )@injectdefmain(service: Service = Provide[Container.service]) -> None: ...if __name__ == "__main__": container = Container() container.config.api_key.from_env("API_KEY", required=True) container.config.timeout.from_env("TIMEOUT", as_=int, default=5) container.wire(modules=[__name__]) main() # <-- dependency is injected automatically with container.api_client.override(mock.Mock()): main() # <-- overridden dependency is injected automatically

When you call the main() function the Service dependency is assembled and injected automatically.

When you do testing, you call the container.api_client.override() method to replace the real API client with a mock. When you call main(), the mock is injected.

You can override any provider with another provider.

It also helps you in a re-configuring project for different environments: replace an API client with a stub on the dev or stage.

With the Dependency Injector, object assembling is consolidated in a container. Dependency injections are defined explicitly. This makes it easier to understand and change how an application works.

👁 https://raw.githubusercontent.com/wiki/ets-labs/python-dependency-injector/img/di-readme.svg

Visit the docs to know more about the Dependency injection and inversion of control in Python.

Installation

The package is available on the PyPi:

pip install dependency-injector

Documentation

The documentation is available here.

Examples

Choose one of the following:

Tutorials

Choose one of the following:

Concept

The framework stands on the PEP20 (The Zen of Python) principle:

Explicitisbetterthanimplicit

You need to specify how to assemble and where to inject the dependencies explicitly.

The power of the framework is in its simplicity. Dependency Injector is a simple tool for the powerful concept.

Frequently asked questions

What is dependency injection?

  • dependency injection is a principle that decreases coupling and increases cohesion

Why should I do the dependency injection?

  • your code becomes more flexible, testable, and clear 😎

How do I start applying the dependency injection?

  • you start writing the code following the dependency injection principle

  • you register all of your application components and their dependencies in the container

  • when you need a component, you specify where to inject it or get it from the container

What price do I pay and what do I get?

  • you need to explicitly specify the dependencies

  • it will be extra work in the beginning

  • it will payoff as project grows

Have a question?
Found a bug?
Want to help?

  • ⭐️ Star the Dependency Injector on the Github

  • 🆕 Start a new project with the Dependency Injector

  • 💬 Tell your friend about the Dependency Injector

Want to contribute?

  • 🔀 Fork the project

  • ⬅️ Open a pull request to the develop branch

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  • License: BSD License (Copyright (c) 2024, Roman Mogylatov)
  • Author: Roman Mogylatov
  • Maintainer: Roman Mogylatov
  • Tags Dependency injection , DI , Inversion of Control , IoC , Factory , Singleton , Design patterns , Flask
  • Requires: Python >=3.8
  • Provides-Extra: yaml , pydantic , pydantic2 , flask , aiohttp

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