thinking-injection 0.0.1

DI framework

thinking-injection

Part of thinking family.

Pythonic DI for AI, as funny as it sounds.

Requires python 3.12. Is properly typed.

Better docs pending. For now, see "long story short" and an example

Long story short

• put your code in a root package (let's call it root) with any number and depth of subpackages
• mark the types that should be picked up by framework with @discover
  • from thinking_injection.discovery import discover
• mark your protocols and ABCs with @interface
  • from thinking_injection.interfaces import interface
• make the classes that should be managed extend Injectable
  • in Java you'd call these "beans", but I wanted to avoid that kind of copycating
  • from thinking_injection.injectable import Injectable
• implement inject_requirements(self, ...) and make sure to annotate the arguments
  • class X(Injectable): def inject_requirements(self, a: A, b: list[B], c: C | None: ...
  • in that case, instance of X will be created for you, and that method will be called with auto-created instances of A, list of all known instances of B and instance of C if there is one
  • A is a SIMPLE dependency, B - a COLLECTIVE one, C - an optional one
  • there is a notion of primary implementation, which works as you'd expect it (the type itself for non-interface types, the subclass if there is only one subclass, but you can also force one, e.g. with decorator)
    • see 'Implementations.build(...)'
    • from thinking_injection.discovery import PrimaryImplementation - use @PrimaryImplementation(Supertype) on the subtype
  • simple dependencies will use the primary implementation
  • optional dependencies can be phrased any way you like: C | None, Optional[C] or Union[C, None], etc
    • if there is no primary implementation, None will be injected, even if there are multiple implementations
  • collective dependencies can only be phrased as lists of supertype (interface or not)
  • framework will only call that method for you with proper args, you should store them in self on your own - or act on them in any way you'd like
• make a ContextScope using of factory method:
  • from thinking_injection.scope import ContextScope
  • ContextScope.of(*from_package("root"))
  • more readable factory methods pending
  • generally, it's ContextScope.of(Type1, Type2, ...)
  • you can also define which implementations should be primary with defaults and forced kw-only args

    • todo document this

  • there is also from_module("root.some.submodule") function, as well as types(Type1, Type2, ...)
  • see thinking_injection.typeset

The entrypoint to your program should boil down to:

if __name__=="__main__":
    scope = ContextScope.of(...)
    ctx = BasicContext.build(scope)
    with ctx.lifecycle():
        instance = ctx.instance(SomeType)
        instance.foo(...)

An example

There is an example app in this repo. It's short, but showcases most of the basic features.

See the ./calculator directory.

Local development

There are 2 useful scripts in this repo. They both work based on ./thinking-dependencies.txt file, which lists other thinking projects used by this one.

• install_develop.sh
  • will install all the thinking dependencies from their develop branches
• ./install_local_thinking.sh
  • aligned to my personal layout of home directory
  • assumes you clone repositories to ~/repos
  • expects all the thinking dependencies to be cloned there
  • will create venv for you if missing or use an existing one
  • will remove currently installed thinking dependencies
  • and will install them again from the cloned repos
  • useful when you're making complimentary changes across repos