injectipy 0.1.0

A dependency injection library for Python that uses explicit scopes instead of global state

Injectipy

A dependency injection library for Python that uses explicit scopes instead of global state. Provides type-safe dependency resolution with circular dependency detection.

Key Features

• Explicit scopes: Dependencies managed within context managers, no global state
• Type safety: Works with mypy and provides runtime type checking
• Circular dependency detection: Detects dependency cycles at registration time
• Thread safety: Each scope is isolated, safe for concurrent use
• Lazy evaluation: Dependencies resolved only when accessed
• Test isolation: Each test can use its own scope with different dependencies

Installation

pip install injectipy

Quick Start

Basic Usage

from injectipy import inject, Inject, DependencyScope

# Create a dependency scope
scope = DependencyScope()

# Register a simple value
scope.register_value("database_url", "postgresql://localhost/mydb")

# Register a factory function
def create_database_connection(database_url: str = Inject["database_url"]):
    return f"Connected to {database_url}"

scope.register_resolver("db_connection", create_database_connection)

# Use dependency injection in your functions within a scope context
@inject
def get_user(user_id: int, db_connection: str = Inject["db_connection"]):
    return f"User {user_id} from {db_connection}"

# Use the scope as a context manager
with scope:
    user = get_user(123)
    print(user)  # "User 123 from Connected to postgresql://localhost/mydb"

Class-based Injection

from injectipy import inject, Inject, DependencyScope

# Create and configure a scope
scope = DependencyScope()
scope.register_value("db_connection", "PostgreSQL://localhost")
scope.register_value("config", "production_config")
scope.register_value("helper", "UtilityHelper")

class UserService:
    @inject
    def __init__(self, db_connection: str = Inject["db_connection"]):
        self.db = db_connection

    def get_user(self, user_id: int):
        return f"User {user_id} from {self.db}"

    @inject
    @classmethod
    def create_service(cls, config: str = Inject["config"]):
        return cls()

    @inject
    @staticmethod
    def utility_function(helper: str = Inject["helper"]):
        return f"Helper: {helper}"

# Use within scope context for dependency injection
with scope:
    service = UserService()
    print(service.get_user(456))

Factory Functions with Dependencies

from injectipy import inject, Inject, DependencyScope

# Create and configure a scope
scope = DependencyScope()

# Register configuration
scope.register_value("api_key", "secret123")
scope.register_value("base_url", "https://api.example.com")

# Factory function that depends on other registered dependencies
def create_api_client(
    api_key: str = Inject["api_key"],
    base_url: str = Inject["base_url"]
):
    return f"APIClient(key={api_key}, url={base_url})"

# Register the factory
scope.register_resolver("api_client", create_api_client)

# Use in your code within scope context
@inject
def fetch_data(client = Inject["api_client"]):
    return f"Fetching data with {client}"

with scope:
    print(fetch_data())

Singleton Pattern with evaluate_once

from injectipy import DependencyScope
import time

def expensive_resource():
    print("Creating expensive resource...")
    time.sleep(1)  # Simulate expensive operation
    return "ExpensiveResource"

# Create scope and register with evaluate_once=True for singleton behavior
scope = DependencyScope()
scope.register_resolver(
    "expensive_resource",
    expensive_resource,
    evaluate_once=True
)

with scope:
    # First access creates the resource
    resource1 = scope["expensive_resource"]  # Prints "Creating..."
    resource2 = scope["expensive_resource"]  # No print, reuses cached

    assert resource1 is resource2  # Same instance

Advanced Usage

Keyword-Only Parameters

The @inject decorator supports keyword-only parameters:

from injectipy import inject, Inject, DependencyScope

# Create scope and register dependencies
scope = DependencyScope()
scope.register_value("database", "ProductionDB")
scope.register_value("cache", "RedisCache")

@inject
def process_data(data: str, *, db=Inject["database"], cache=Inject["cache"], debug=False):
    return f"Processing {data} with {db}, {cache}, debug={debug}"

with scope:
    # Keyword-only parameters work seamlessly
    result = process_data("user_data")
    print(result)  # "Processing user_data with ProductionDB, RedisCache, debug=False"

    # Override specific parameters
    result = process_data("user_data", cache="MemoryCache", debug=True)
    print(result)  # "Processing user_data with ProductionDB, MemoryCache, debug=True"

Decorator Compatibility

The @inject decorator works with other Python decorators. Order matters:

from injectipy import inject, Inject, DependencyScope

# Create scope and register dependencies
scope = DependencyScope()
scope.register_value("logger", "ProductionLogger")

class APIService:
    # ✅ Recommended order: @inject comes after @classmethod/@staticmethod
    @inject
    @classmethod
    def create_from_config(cls, logger=Inject["logger"]):
        return cls(logger)

    @inject
    @staticmethod
    def validate_data(data, logger=Inject["logger"]):
        print(f"Validating with {logger}")
        return True

# Works with other decorators too
def timer_decorator(func):
    def wrapper(*args, **kwargs):
        result = func(*args, **kwargs)
        return f"timed({result})"
    return wrapper

@timer_decorator
@inject
def process_data(data, logger=Inject["logger"]):
    return f"Processed {data} with {logger}"

with scope:
    result = process_data("user_data")
    print(result)  # "timed(Processed user_data with ProductionLogger)"

Decorator ordering rules:

• @inject comes after @classmethod or @staticmethod
• @inject comes after other decorators (@contextmanager, @lru_cache, @property)
• Apply @inject last (closest to the function definition)

# Correct order
@classmethod
@inject
def create_service(cls, dep=Inject["service"]): ...

@lru_cache(maxsize=128)
@inject
def cached_func(dep=Inject["service"]): ...

Type Safety

Works with mypy for static type checking:

from typing import Protocol
from injectipy import inject, Inject, DependencyScope

class DatabaseProtocol(Protocol):
    def query(self, sql: str) -> list: ...

class PostgreSQLDatabase:
    def query(self, sql: str) -> list:
        return ["result1", "result2"]

# Create scope and register with type hints
scope = DependencyScope()
scope.register_value("database", PostgreSQLDatabase())

@inject
def get_users(db: DatabaseProtocol = Inject["database"]) -> list:
    return db.query("SELECT * FROM users")

with scope:
    # mypy will verify types correctly
    users: list = get_users()

Scope Isolation and Nesting

You can create multiple isolated scopes and even nest them:

from injectipy import DependencyScope, inject, Inject

# Create separate scopes for different contexts
production_scope = DependencyScope()
production_scope.register_value("config", {"env": "production"})
production_scope.register_value("db_url", "postgresql://prod-server/db")

test_scope = DependencyScope()
test_scope.register_value("config", {"env": "test"})
test_scope.register_value("db_url", "sqlite:///:memory:")

@inject
def get_environment(config: dict = Inject["config"]):
    return config["env"]

# Use different scopes for different contexts
with production_scope:
    print(get_environment())  # "production"

with test_scope:
    print(get_environment())  # "test"

# Scopes can also be nested - inner scope takes precedence
with production_scope:
    with test_scope:
        print(get_environment())  # "test" (inner scope wins)

Error Handling

The library raises clear error messages for common issues:

from injectipy import inject, Inject, DependencyScope
from injectipy import DependencyNotFoundError, CircularDependencyError, DuplicateRegistrationError

# Missing dependency
@inject
def missing_dep(value: str = Inject["nonexistent"]):
    return value

try:
    missing_dep()
except DependencyNotFoundError as e:
    print(e)  # "Dependency 'nonexistent' not found"

# Circular dependency (detected at registration)
def service_a(b = Inject["service_b"]):
    return f"A: {b}"

def service_b(a = Inject["service_a"]):
    return f"B: {a}"

scope = DependencyScope()
scope.register_resolver("service_a", service_a)

try:
    scope.register_resolver("service_b", service_b)
except CircularDependencyError as e:
    print(e)  # "Circular dependency detected"

# Duplicate registration
scope = DependencyScope()
scope.register_value("config", "prod")

try:
    scope.register_value("config", "dev")
except DuplicateRegistrationError as e:
    print(e)  # "Key 'config' already registered"

Testing

Use separate scopes for test isolation:

import pytest
from injectipy import DependencyScope, inject, Inject

@pytest.fixture
def test_scope():
    """Provide a clean scope for each test"""
    return DependencyScope()

def test_dependency_injection(test_scope):
    test_scope.register_value("test_value", "hello")

    @inject
    def test_function(value: str = Inject["test_value"]):
        return value

    with test_scope:
        assert test_function() == "hello"

def test_isolation(test_scope):
    # Each test gets a fresh scope, so dependencies are isolated
    test_scope.register_value("isolated_value", "test_specific")

    @inject
    def isolated_function(value: str = Inject["isolated_value"]):
        return value

    with test_scope:
        assert isolated_function() == "test_specific"

def test_scoped_mocking(test_scope):
    # Easy to mock dependencies per test
    test_scope.register_value("database", "MockDatabase")
    test_scope.register_value("cache", "MockCache")

    @inject
    def service_function(db=Inject["database"], cache=Inject["cache"]):
        return f"Using {db} and {cache}"

    with test_scope:
        result = service_function()
        assert result == "Using MockDatabase and MockCache"

Thread Safety

Scopes are thread-safe and can be shared between threads:

import threading
from injectipy import DependencyScope, inject, Inject

# Create a shared scope
shared_scope = DependencyScope()
shared_scope.register_value("shared_resource", "ThreadSafeResource")

@inject
def worker_function(resource: str = Inject["shared_resource"]):
    return f"Worker using {resource}"

def worker():
    with shared_scope:  # Each thread uses the same scope safely
        print(worker_function())

# Safe to use across multiple threads
threads = []
for i in range(10):
    thread = threading.Thread(target=worker)
    threads.append(thread)
    thread.start()

for thread in threads:
    thread.join()

API Reference

Core Components

@inject decorator

Decorates functions/methods to enable automatic dependency injection within active scopes.

Inject[key]

Type-safe dependency marker for function parameters.

DependencyScope

Context manager for managing dependency lifecycles and isolation.

DependencyScope Methods

register_value(key, value)

Register a static value as a dependency. Returns self for method chaining.

register_resolver(key, resolver, *, evaluate_once=False)

Register a factory function as a dependency. Returns self for method chaining.

• evaluate_once=True: Cache the result after first evaluation (singleton pattern)

[key] (getitem)

Resolve and return a dependency by key. Only works within active scope context.

contains(key)

Check if a dependency key is registered in this scope.

is_active()

Check if this scope is currently active (within a with block).

Context Manager Protocol

• __enter__(): Activate the scope
• __exit__(): Deactivate the scope and clean up

Development

See CONTRIBUTING.md for development setup and contribution guidelines.

Development Setup

# Clone the repository
git clone https://github.com/Wimonder/injectipy.git
cd injectipy

# Install dependencies
poetry install

# Run tests
poetry run pytest

# Run type checking
poetry run mypy injectipy/

# Run linting
poetry run ruff check .

Testing

# Run all tests
poetry run pytest

# Run with coverage
poetry run pytest --cov=injectipy

# Run specific test files
poetry run pytest tests/test_core_inject.py
poetry run pytest tests/test_scope_functionality.py

License

This project is licensed under the MIT License - see the LICENSE file for details.

Changelog

Version 0.1.0 (2024-01-20)

• Initial release of Injectipy dependency injection library
• Core features: @inject decorator, Inject[key] markers, DependencyScope context managers
• Advanced capabilities: Thread safety, circular dependency detection, lazy evaluation
• Modern Python: Python 3.11+ support with native union types
• Developer experience: Type safety with mypy, comprehensive testing

See CHANGELOG.md for complete details.