FastDepends

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Documentation: https://lancetnik.github.io/FastDepends/

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FastDepends - FastAPI Dependency Injection system extracted from FastAPI and cleared of all HTTP logic. This is a small library which provides you with the ability to use lovely FastAPI interfaces in your own projects or tools.

Thanks to fastapi and pydantic projects for this great functionality. This package is just a small change of the original FastAPI sources to provide DI functionality in a pure-Python way.

Async and sync modes are both supported.

For why?

This project should be extremely helpful to boost your not-FastAPI applications (even Flask, I know that u like some legacy).

Also the project can be a core of your own framework for anything. Actually, it was build for my another project - 🚀Propan🚀 (and FastStream), check it to see full-featured FastDepends usage example.

Installation

pip install fast-depends

Usage

There is no way to make Dependency Injection easier

You can use this library without any frameworks in both sync and async code.

Async code

import asyncio

from fast_depends import inject, Depends

async def dependency(a: int) -> int:
    return a

@inject
async def main(
    a: int,
    b: int,
    c: int = Depends(dependency)
) -> float:
    return a + b + c

assert asyncio.run(main("1", 2)) == 4.0

Sync code

from fast_depends import inject, Depends

def dependency(a: int) -> int:
    return a

@inject
def main(
    a: int,
    b: int,
    c: int = Depends(dependency)
) -> float:
    return a + b + c

assert main("1", 2) == 4.0

@inject decorator plays multiple roles at the same time:

• resolve Depends classes
• cast types according to Python annotation
• validate incoming parameters using pydantic

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Features

Synchronous code is fully supported in this package: without any async_to_sync, run_sync, syncify or any other tricks.

Also, FastDepends casts functions' return values the same way, it can be very helpful in building your own tools.

These are two main defferences from native FastAPI DI System.

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Dependencies Overriding

Also, FastDepends can be used as a lightweight DI container. Using it, you can easily override basic dependencies with application startup or in tests.

from typing import Annotated

from fast_depends import Depends, dependency_provider, inject

def abc_func() -> int:
    raise NotImplementedError()

def real_func() -> int:
    return 1

@inject
def func(
    dependency: Annotated[int, Depends(abc_func)]
) -> int:
    return dependency

with dependency_provider.scope(abc_func, real_func):
    assert func() == 1

dependency_provider in this case is just a default container already declared in the library. But you can use your own the same way:

from typing import Annotated

from fast_depends import Depends, Provider, inject

provider = Provider()

def abc_func() -> int:
    raise NotImplementedError()

def real_func() -> int:
    return 1

@inject(dependency_overrides_provider=provider)
def func(
    dependency: Annotated[int, Depends(abc_func)]
) -> int:
    return dependency

with provider.scope(abc_func, real_func):
    assert func() == 1

This way you can inherit the basic Provider class and define any extra logic you want!

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Custom Fields

If you wish to write your own FastAPI or another closely by architecture tool, you should define your own custom fields to specify application behavior.

Custom fields can be used to adding something specific to a function arguments (like a BackgroundTask) or parsing incoming objects special way. You able decide by own, why and how you will use these tools.

FastDepends grants you this opportunity a very intuitive and comfortable way.

from fast_depends import inject
from fast_depends.library import CustomField

class Header(CustomField):
    def use(self, /, **kwargs: AnyDict) -> AnyDict:
        kwargs = super().use(**kwargs)
        kwargs[self.param_name] = kwargs["headers"][self.param_name]
        return kwargs

@inject
def my_func(header_field: int = Header()):
    return header_field

assert my_func(
    headers={ "header_field": "1" }
) == 1