ctxinject 0.1.4

Context-based dependency injection for Python

🚀 ctxinject

A powerful, FastAPI-inspired dependency injection library for Python with async support, strong typing, and flexible injection strategies.

✨ Key Features

• 🚀 FastAPI-style dependency injection - Familiar Depends() pattern
• 🏗️ Model field injection - Direct access to model fields and methods in function signatures
• 🔒 Strongly typed - Full type safety with automatic validation
• ⚡ Async/Sync support - Works with both synchronous and asynchronous functions
• 🎯 Multiple injection strategies - By type, name, model fields, or dependencies
• ✅ Automatic validation - Built-in Pydantic integration and custom validators
• 🧪 Test-friendly - Easy dependency overriding for testing
• 🐍 Python 3.8+ - Modern Python support
• 📊 100% test coverage - Production-ready reliability

🚀 Quick Start

Here's a practical HTTP request processing example:

import asyncio
from typing import cast
import requests
from pydantic import BaseModel
from typing_extensions import Annotated, Dict, Mapping, Optional, Protocol

from ctxinject.inject import inject_args
from ctxinject.model import DependsInject, ModelFieldInject


class PreparedRequest(Protocol):
    method: str
    url: str
    headers: Mapping[str, str]
    body: bytes


class BodyModel(BaseModel):
    name: str
    email: str
    age: int


# Async dependency function
async def get_db() -> str:
    await asyncio.sleep(0.1)
    return "postgresql"


# Custom model field injector
class FromRequest(ModelFieldInject):
    def __init__(self, field: Optional[str] = None, **kwargs):
        super().__init__(PreparedRequest, field, **kwargs)


# Function with multiple injection strategies
def process_http(
    url: Annotated[str, FromRequest()],  # Extract from model field
    method: Annotated[str, FromRequest()],  # Extract from model field
    body: Annotated[BodyModel, FromRequest()],  # Extract and validate
    headers: Annotated[Dict[str, str], FromRequest()],  # Extract from model field
    db: str = DependsInject(get_db),  # Async dependency
) -> Mapping[str, str]:
    return {
        "url": url,
        "method": method,
        "body": body.name,  # Pydantic model automatically validated
        "headers": len(headers),
        "db": db,
    }


async def main():
    # Create a prepared request
    req = requests.Request(
        method="POST",
        url="https://api.example.com/user",
        headers={"Content-Type": "application/json"},
        json={"name": "João Silva", "email": "[e-mail]", "age": 30}
    )
    prepared_req = cast(PreparedRequest, req.prepare())
    
    # Inject dependencies
    context = {PreparedRequest: prepared_req}
    injected_func = await inject_args(process_http, context)
    
    # Call with all dependencies resolved
    result = injected_func()
    print(result)  # All dependencies automatically injected!

    def mocked_get_db()->str:
        return 'test'

    injected_func = await inject_args(process_http, context, {get_db: mocked_get_db})
    result = injected_func() # get_db mocked!

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

📦 Installation

pip install ctxinject

For Pydantic validation support:

pip install ctxinject[pydantic]

📖 Usage Guide

1. Basic Dependency Injection

from ctxinject.inject import inject_args
from ctxinject.model import ArgsInjectable

def greet(
    name: str,
    count: int = ArgsInjectable(5)    # Optional with default
):
    return f"Hello {name}! (x{count})"

# Inject by name and type
context = {"name": "Alice"}
injected = await inject_args(greet, context)
result = injected()  # "Hello Alice! (x5)"

2. FastAPI-style Dependencies

from ctxinject.model import DependsInject

def get_database_url() -> str:
    return "postgresql://localhost/mydb"

async def get_user_service() -> UserService:
    service = UserService()
    await service.initialize()
    return service

def process_request(
    db_url: str = DependsInject(get_database_url),
    user_service: UserService = DependsInject(get_user_service)
):
    return f"Processing with {db_url}"

# Dependencies resolved automatically
injected = await inject_args(process_request, {})
result = injected()

3. Model Field Injection

from ctxinject.model import ModelFieldInject

class Config:
    database_url: str = "sqlite:///app.db"
    debug: bool = True
    
    def get_secret_key(self) -> str:
        return "super-secret-key"

def initialize_app(
    db_url: str = ModelFieldInject(Config, "database_url"),
    debug: bool = ModelFieldInject(Config, "debug"),
    secret: str = ModelFieldInject(Config, "get_secret_key")  # Method call
):
    return f"App: {db_url}, debug={debug}, secret={secret}"

config = Config()
context = {Config: config}
injected = await inject_args(initialize_app, context)
result = injected()

4. Validation and Type Conversion

from typing_extensions import Annotated
from ctxinject.model import ArgsInjectable

def validate_positive(value: int, **kwargs) -> int:
    if value <= 0:
        raise ValueError("Must be positive")
    return value

def process_data(
    count: Annotated[int, ArgsInjectable(1, validate_positive)],
    email: str = ArgsInjectable(...),  # Automatic email validation if Pydantic available
):
    return f"Processing {count} items for {email}"

context = {"count": 5, "email": "[e-mail]"}
injected = await inject_args(process_data, context)
result = injected()

5. Partial Injection (Mixed Arguments)

def process_user_data(
    user_id: str,  # Not injected - will remain as parameter
    db_url: str = DependsInject(get_database_url),
    config: Config = ModelFieldInject(Config)
):
    return f"Processing user {user_id} with {db_url}"

# Only some arguments are injected
context = {Config: config_instance}
injected = await inject_args(process_user_data, context, allow_incomplete=True)

# user_id still needs to be provided
result = injected("user123")  # "Processing user user123 with postgresql://..."

6. Function Signature Validation

Validate function signatures at bootstrap time to catch injection issues early. Unlike runtime errors, func_signature_check() returns all validation errors at once, giving you a complete overview of what needs to be fixed.

from ctxinject.sigcheck import func_signature_check

def validate_at_startup():
    # Check if function can be fully injected at bootstrap time
    errors = func_signature_check(process_request, modeltype=[Config])
    
    if errors:
        print("Function cannot be fully injected:")
        for error in errors:
            print(f"  - {error}")
    else:
        print("✅ Function is ready for injection!")

# Run validation before your app starts
validate_at_startup()

7. Testing with Overrides

# Original dependency
async def get_real_service() -> str:
    return "production-service"

def business_logic(service: str = DependsInject(get_real_service)):
    return f"Using {service}"

# Test with mock
async def get_mock_service() -> str:
    return "mock-service"

# Override for testing
injected = await inject_args(
    business_logic, 
    context={},
    overrides={get_real_service: get_mock_service}
)
result = injected()  # "Using mock-service"

🎯 Injection Strategies

Strategy	Description	Example
By Name	Match parameter name to context key	{"param_name": value}
By Type	Match parameter type to context type	{MyClass: instance}
Model Field	Extract field/method from model instance	ModelFieldInject(Config, "field")
Dependency	Call function to resolve value	DependsInject(get_value)
Default	Use default value from injectable	ArgsInjectable(42)

🔧 Advanced Features

Async Optimization

• Concurrent resolution of async dependencies
• Fast isinstance() checks for sync/async separation
• Optimal performance with minimal overhead

Type Safety

• Full type checking with mypy support
• Runtime type validation
• Generic type support

Extensible Validation

• Built-in Pydantic integration
• Custom validator functions
• Constraint validation (min/max, patterns, etc.)

🏗️ Architecture

ctxinject uses a resolver-based architecture:

1. Analysis Phase: Function signature is analyzed to identify injectable parameters
2. Mapping Phase: Parameters are mapped to appropriate resolvers based on injection strategy
3. Resolution Phase: Resolvers are executed (sync immediately, async concurrently)
4. Injection Phase: Resolved values are injected into the function

This design ensures optimal performance and flexibility.

🤝 Contributing

Contributions are welcome! Please check out our contributing guidelines and make sure all tests pass:

pytest --cov=ctxinject --cov-report=html

📄 License

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

🔗 Related Projects

• FastAPI - The inspiration for the dependency injection pattern
• Pydantic - Validation and serialization library

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ctxinject - Powerful dependency injection for modern Python applications!