Skip to content

Dependency Injection

Dependency Injection (DI) is a powerful design pattern used to achieve Inversion of Control (IoC) between classes and their dependencies. Within the context of our voice assistant, Dependency Injection facilitates the provision of specific objects or values to command functions. This ensures that these functions can readily access external resources or other system components.

This guide provides an overview of the Dependency Injection implementation, how to utilize it in your voice assistant, and some native dependencies.

Response Handler

There are two response handlers: AsyncResponseHandler and ResponseHandler. They oversee the processing of responses, asynchronously and synchronously, respectively. To employ them, simply include the required type (class) annotation as an argument within the function declaration. The argument's name isn't significant for this dependency.'hello')
async def hello(handler: AsyncResponseHandler) -> Response: 
    await handler.respond(Response(text = 'Hi'))

In the showcased example, the AsyncResponseHandler is automatically injected into the foo command function upon its invocation.


The inject_dependency method serves to integrate specific dependencies into a function. This method determines the function's dependencies and subsequently calls it. Contrary to the response handler, this dependency is identified by the argument's name.

async def foo(handler: AsyncResponseHandler) -> Response: 
    return Response(text = 'foo!')'bar')
async def bar(inject_dependencies): 
    return await inject_dependencies(foo)()

Here, the foo dependency is injected and executed within the bar command function.

Accessing DIContainer in a Command

The CommandsContext class initializes with a dependency_manager of the DependencyManager type. This manager undertakes the role of identifying and injecting the requisite dependencies for command functions.

To tap into the DIContainer inside a command, simply declare the needed dependency as a command function parameter. The DependencyManager will resolve this parameter and supply the appropriate object or value.

For more advanced access, you can extract the container as a dependency of type DIContainer, as demonstrated:'baz')
async def baz(di_container: DIContainer): 

This is feasible because the default DI container internally registers itself as a dependency:

default_dependency_manager.add_dependency(None, DependencyManager, default_dependency_manager)

Adding Custom Dependency

You can incorporate custom dependencies using the add_dependency method of the default shared instance of DependencyManager.


from stark.general.dependencies import default_dependency_manager
default_dependency_manager.add_dependency("custom_name", CustomType, custom_value)

In this instance, a new dependency named custom_name, of CustomType, with the value custom_value is appended. If the name is set to None, you can later choose any name for the function argument; the dependency will be discerned solely by type (like ResponseHandler and AsyncResponseHandler). Conversely, setting the type to None allows the dependency to be detected purely by the argument name (like inject_dependencies).

Creating a Custom Container

To employ a custom container for Dependency Injection in lieu of the default one, instantiate a new DependencyManager and input your custom dependencies. This tailored container can subsequently be utilized during the CommandsContext initialization.


custom_dependency_manager = DependencyManager()

context = CommandsContext(..., dependency_manager=custom_dependency_manager)

It's worth noting that the CommandsContext always registers several native dependencies upon initialization:

self.dependency_manager.add_dependency(None, AsyncResponseHandler, self)
self.dependency_manager.add_dependency(None, ResponseHandler, SyncResponseHandler(self))
self.dependency_manager.add_dependency('inject_dependencies', None, self.inject_dependencies)

However, other native dependencies will be absent in the custom container unless you manually incorporate them.

The adaptability provided by the Dependency Injection framework ensures your command functions remain modular, simplifying testing. As you further develop your voice assistant, utilize this system to adeptly handle your dependencies.

Last update: 2023-09-21
Created: 2023-09-21