Metadata-Version: 2.4
Name: aspy-dependency-injection
Version: 0.0.2
Summary: Dependency injection
Author: Andreu Codina
Author-email: Andreu Codina <30506301+AndreuCodina@users.noreply.github.com>
License-Expression: MIT
License-File: LICENSE
Classifier: Development Status :: 5 - Production/Stable
Classifier: Programming Language :: Python :: 3.14
Classifier: Intended Audience :: Developers
Requires-Dist: fastapi[standard-no-fastapi-cloud-cli]>=0.118.0
Requires-Python: >=3.14
Project-URL: Changelog, https://github.com/AndreuCodina/aspy-dependency-injection/releases
Project-URL: Documentation, https://github.com/AndreuCodina/aspy-dependency-injection
Project-URL: Homepage, https://github.com/AndreuCodina/aspy-dependency-injection
Project-URL: Repository, https://github.com/AndreuCodina/aspy-dependency-injection
Description-Content-Type: text/markdown

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## Features
- **Use it everywhere:** Use dependency injection in web servers, background tasks, console applications, Jupyter notebooks, tests, etc.
- **Lifetimes**: `Singleton` (same instance per application), `Scoped` (same instance per HTTP request scope) and `Transient` (different instance per resolution).
- **FastAPI integration** out of the box, and pluggable to any web framework.
- **Automatic resolution and disposal**: Automatically resolve constructor parameters and manage async and non-async context managers. It's not longer your concern to know how to create or dispose services.
- **Clear design** inspired by one of the most used and battle-tested DI libraries, adding async-native support, important features and good defaults.
- **Centralized configuration**: Register all services in one place using a clean syntax, and without decorators.
- **ty** and **Pyright** strict compliant.

## Installation
```bash
uv add aspy-dependency-injection
```

## Quickstart with FastAPI

Inject services into async endpoints using `Annotated[..., Inject()]`.

```python
class EmailService:
    pass


class UserService:
    def __init__(self, email_service: EmailService) -> None:
        self.email_service = email_service
    
    async def create_user(self) -> None:
        pass


app = FastAPI()

@app.post("/users")
async def create_user(user_service: Annotated[UserService, Inject()]) -> None:
    await user_service.create_user()

services = ServiceCollection()
services.add_transient(EmailService)
services.add_transient(UserService)
services.configure_fastapi(app)
```

## Quickstart without FastAPI

You convert the service collection into a service provider:

```python
class EmailService:
    pass


class UserService:
    def __init__(self, email_service: EmailService) -> None:
        self.email_service = email_service
    
    async def create_user(self) -> None:
        pass

    
services = ServiceCollection()
services.add_transient(EmailService)
services.add_transient(UserService)

async def main() -> None:
    async with services.build_service_provider() as service_provider:
        user_service = await service_provider.get_required_service(UserService)
        await user_service.create_user()


if __name__ == "__main__":
    asyncio.run(main())
```

If you want a scope per operation (e.g., per HTTP request or message from a queue), you can create a scope from the service provider:

```python
async with service_provider.create_scope() as service_scope:
    user_service = await service_scope.get_required_service(UserService)
    await user_service.create_user()
```

## Lifetimes

- `Transient`: A new instance is created every time the service is requested. Examples: Services without state, workflows, repositories, service clients...
- `Singleton`: The same instance is used every time the service is requested. Examples: Settings (`pydantic-settings`), machine learning models, database connection pools, caches.
- `Scoped`: A new instance is created for each new scope, but the same instance is returned within the same scope. Examples: Database clients, unit of work.

## Factories

Sometimes, you need to use a factory function to create a service. For example, you have settings (a connection string, database name, etc.) stored using the package `pydantic-settings` and you want to provide them to a service `DatabaseClient` to access a database.

```python
class ApplicationSettings(BaseSettings):
    database_connection_string: str


class DatabaseClient:
    def __init__(self, connection_string: str) -> None:
        pass
```

In a real `DatabaseClient` implementation, you must use a sync or async context manager, i.e., you instance it with:

```python
async with DatabaseClient(database_connection_string) as client:
    ...
```

And, if you want to re-use it, you create a factory function with yield:

```python
async def create_database_client(application_settings: ApplicationSettings) -> AsyncGenerator[DatabaseClient]:
    async with DatabaseClient(application_settings.database_connection_string) as database_client:
        yield database_client
```

With that factory, you have to provide manually a singleton of `ApplicationSettings`, and to know if `DatabaseClient` implements a sync or async context manager, or neither. Apart from that, if you need a singleton or scoped instance of `DatabaseClient`, it's very complex to manage the disposal of the instance.

Then, why don't just return it? With this package, you just have this:

```python
def inject_database_client(application_settings: ApplicationSettings) -> DatabaseClient:
    return DatabaseClient(
        connection_string=application_settings.database_connection_string
    )

services.add_singleton(ApplicationSettings)
services.add_transient(inject_database_client)
```

## Testing

TBD
