Fastapi Templates
Create production-ready FastAPI projects with async patterns, dependency injection, and comprehensive error handling. Use when building new FastAPI applications or setting up backend API projects.
Score breakdown
Estimated from the available content and source signals.
Model compatibility
Inferred fit is not the same as a recorded hands-on test.
Overview
FastAPI Project Templates
Production-ready FastAPI project structures with async patterns, dependency injection, middleware, and best practices for building high-performance APIs.
When to Use This Skill
- Starting new FastAPI projects from scratch
- Implementing async REST APIs with Python
- Building high-performance web services and microservices
- Creating async applications with PostgreSQL, MongoDB
- Setting up API projects with proper structure and testing
Core Concepts
1. Project Structure
Recommended Layout:
app/
├── api/ # API routes
│ ├── v1/
│ │ ├── endpoints/
│ │ │ ├── users.py
│ │ │ ├── auth.py
│ │ │ └── items.py
│ │ └── router.py
│ └── dependencies.py # Shared dependencies
├── core/ # Core configuration
│ ├── config.py
│ ├── security.py
│ └── database.py
├── models/ # Database models
│ ├── user.py
│ └── item.py
├── schemas/ # Pydantic schemas
│ ├── user.py
│ └── item.py
├── services/ # Business logic
│ ├── user_service.py
│ └── auth_service.py
├── repositories/ # Data access
│ ├── user_repository.py
│ └── item_repository.py
└── main.py # Application entry
2. Dependency Injection
FastAPI's built-in DI system using Depends:
- Database session management
- Authentication/authorization
- Shared business logic
- Configuration injection
3. Async Patterns
Proper async/await usage:
- Async route handlers
- Async database operations
- Async background tasks
- Async middleware
Detailed worked examples and patterns
Detailed sections (starting with ## Implementation Patterns) live in references/details.md. Read that file when the navigation summary above is insufficient.
Testing
# tests/conftest.py
import pytest
import asyncio
from httpx import AsyncClient
from sqlalchemy.ext.asyncio import create_async_engine, AsyncSession
from sqlalchemy.orm import sessionmaker
from app.main import app
from app.core.database import get_db, Base
TEST_DATABASE_URL = "sqlite+aiosqlite:///:memory:"
@pytest.fixture(scope="session")
def event_loop():
loop = asyncio.get_event_loop_policy().new_event_loop()
yield loop
loop.close()
@pytest.fixture
async def db_session():
engine = create_async_engine(TEST_DATABASE_URL, echo=True)
async with engine.begin() as conn:
await conn.run_sync(Base.metadata.create_all)
AsyncSessionLocal = sessionmaker(
engine, class_=AsyncSession, expire_on_commit=False
)
async with AsyncSessionLocal() as session:
yield session
@pytest.fixture
async def client(db_session):
async def override_get_db():
yield db_session
app.dependency_overrides[get_db] = override_get_db
async with AsyncClient(app=app, base_url="http://test") as client:
yield client
# tests/test_users.py
import pytest
@pytest.mark.asyncio
async def test_create_user(client):
response = await client.post(
"/api/v1/users/",
json={
"email": "[email protected]",
"password": "testpass123",
"name": "Test User"
}
)
assert response.status_code == 201
data = response.json()
assert data["email"] == "[email protected]"
assert "id" in data
Best for
- Starting new FastAPI projects from scratch
- Implementing async REST APIs with Python
- Building high-performance web services and microservices
- Creating async applications with PostgreSQL, MongoDB
- Setting up API projects with proper structure and testing
Tips and best practices
- Review the source instructions and adapt inputs before running the workflow.
What This Skill Can Do
AI-generated examples showing real capabilities
Was this skill useful?
Be the first to share a result.