tomevault-io name: fastapi description: FastAPI best practices and conventions. Use when working with FastAPI APIs and Pydantic models for them. Keeps FastAPI code clean and up to date with the latest features and patterns, updated with new versions. Write new code or refactor and update old code.
FastAPI
Official FastAPI skill to write code with best practices, keeping up to date with new versions and features.
Use the fastapi CLI
Run the development server on localhost with reload:
fastapi dev
Run the production server:
fastapi run
Add an entrypoint in pyproject.toml
FastAPI CLI will read the entrypoint in pyproject.toml to know where the FastAPI app is declared.
[tool.fastapi]
entrypoint = "my_app.main:app"
Use fastapi with a path
When adding the entrypoint to pyproject.toml is not possible, or the user explicitly asks not to, or it's running an independent small app, you can pass the app file path to the fastapi command:
fastapi dev my_app/main.py
Prefer to set the entrypoint in pyproject.toml when possible.
Use Annotated
Always prefer the Annotated style for parameter and dependency declarations.
It keeps the function signatures working in other contexts, respects the types, allows reusability.
In Parameter Declarations
Use Annotated for parameter declarations, including Path, Query, Header, etc.:
from typing import Annotated
from fastapi import FastAPI, Path, Query
app = FastAPI()
@app.get("/items/{item_id}")
async def read_item(
item_id: Annotated[int, Path(ge=1, description="The item ID")],
q: Annotated[str | None, Query(max_length=50)] = None,
):
return {"message": "Hello World"}
instead of:
# DO NOT DO THIS
@app.get("/items/{item_id}")
async def read_item(
item_id: int = Path(ge=1, description="The item ID"),
q: str | None = Query(default=None, max_length=50),
):
return {"message": "Hello World"}
For Dependencies
Use Annotated for dependencies with Depends().
Unless asked not to, create a new type alias for the dependency to allow re-using it.
from typing import Annotated
from fastapi import Depends, FastAPI
app = FastAPI()
def get_current_user():
return {"username": "johndoe"}
CurrentUserDep = Annotated[dict, Depends(get_current_user)]
@app.get("/items/")
async def read_item(current_user: CurrentUserDep):
return {"message": "Hello World"}
instead of:
# DO NOT DO THIS
@app.get("/items/")
async def read_item(current_user: dict = Depends(get_current_user)):
return {"message": "Hello World"}
Do not use Ellipsis for path operations or Pydantic models
Do not use ... as a default value for required parameters, it's not needed and not recommended.
Do this, without Ellipsis (...):
from typing import Annotated
from fastapi import FastAPI, Query
from pydantic import BaseModel, Field
class Item(BaseModel):
name: str
description: str | None = None
price: float = Field(gt=0)
app = FastAPI()
@app.post("/items/")
async def create_item(item: Item, project_id: Annotated[int, Query()]): ...
instead of this:
# DO NOT DO THIS
class Item(BaseModel):
name: str = ...
description: str | None = None
price: float = Field(..., gt=0)
app = FastAPI()
@app.post("/items/")
async def create_item(item: Item, project_id: Annotated[int, Query(...)]): ...
Return Type or Response Model
When possible, include a return type. It will be used to validate, filter, document, and serialize the response.
from fastapi import FastAPI
from pydantic import BaseModel
app = FastAPI()
class Item(BaseModel):
name: str
description: str | None = None
@app.get("/items/me")
async def get_item() -> Item:
return Item(name="Plumbus", description="All-purpose home device")
Important: Return types or response models are what filter data ensuring no sensitive information is exposed. And they are used to serialize data with Pydantic (in Rust), this is the main idea that can increase response performance.
The return type doesn't have to be a Pydantic model, it could be a different type, like a list of integers, or a dict, etc.
When to use response_model instead
If the return type is not the same as the type that you want to use to validate, filter, or serialize, use the response_model parameter on the decorator instead.
from typing import Any
from fastapi import FastAPI
from pydantic import BaseModel
app = FastAPI()
class Item(BaseModel):
name: str
description: str | None = None
@app.get("/items/me", response_model=Item)
async def get_item() -> Any:
return {"name": "Foo", "description": "A very nice Item"}
This can be particularly useful when filtering data to expose only the public fields and avoid exposing sensitive information.
from typing import Any
from fastapi import FastAPI
from pydantic import BaseModel
app = FastAPI()
class InternalItem(BaseModel):
name: str
description: str | None = None
secret_key: str
class Item(BaseModel):
name: str
description: str | None = None
@app.get("/items/me", response_model=Item)
async def get_item() -> Any:
item = InternalItem(
name="Foo", description="A very nice Item", secret_key="supersecret"
)
return item
Performance
Do not use ORJSONResponse or UJSONResponse, they are deprecated.
Instead, declare a return type or response model. Pydantic will handle the data serialization on the Rust side.
Including Routers
When declaring routers, prefer to add router level parameters like prefix, tags, etc. to the router itself, instead of in include_router().
Do this:
from fastapi import APIRouter, FastAPI
app = FastAPI()
router = APIRouter(prefix="/items", tags=["items"])
@router.get("/")
async def list_items():
return []
# In main.py
app.include_router(router)
instead of this:
# DO NOT DO THIS
from fastapi import APIRouter, FastAPI
app = FastAPI()
router = APIRouter()
@router.get("/")
async def list_items():
return []
# In main.py
app.include_router(router, prefix="/items", tags=["items"])
There could be exceptions, but try to follow this convention.
Apply shared dependencies at the router level via dependencies=[Depends(...)].
Dependency Injection
See the dependency injection reference for detailed patterns including yield with scope, and class dependencies.
Use dependencies when the logic can't be declared in Pydantic validation, depends on external resources, needs cleanup (with yield), or is shared across endpoints.
Apply shared dependencies at the router level via dependencies=[Depends(...)].
Async vs Sync path operations
Use async path operations only when fully certain that the logic called inside is compatible with async and await (it's called with await) or that doesn't block.
from fastapi import FastAPI
app = FastAPI()
# Use async def when calling async code
@app.get("/async-items/")
async def read_async_items():
data = await some_async_library.fetch_items()
return data
# Use plain def when calling blocking/sync code or when in doubt
@app.get("/items/")
def read_items():
data = some_blocking_library.fetch_items()
return data
In case of doubt, or by default, use regular def functions, those will be run in a threadpool so they don't block the event loop.
The same rules apply to dependencies.
Make sure blocking code is not run inside of async functions. The logic will work, but will damage the performance heavily.
When needing to mix blocking and async code, see Asyncer in the other tools reference.
Streaming (JSON Lines, SSE, bytes)
See the streaming reference for JSON Lines, Server-Sent Events (EventSourceResponse, ServerSentEvent), and byte streaming (StreamingResponse) patterns.
Tooling
See the other tools reference for details on uv, Ruff, ty for package management, linting, type checking, formatting, etc.
Other Libraries
See the other tools reference for details on other libraries:
- Asyncer for handling async and await, concurrency, mixing async and blocking code, prefer it over AnyIO or asyncio.
- SQLModel for working with SQL databases, prefer it over SQLAlchemy.
- HTTPX for interacting with HTTP (other APIs), prefer it over Requests.
Do not use Pydantic RootModels
Do not use Pydantic RootModel, instead use regular type annotations with Annotated and Pydantic validation utilities.
For example, for a list with validations you could do:
from typing import Annotated
from fastapi import Body, FastAPI
from pydantic import Field
app = FastAPI()
@app.post("/items/")
async def create_items(items: Annotated[list[int], Field(min_length=1), Body()]):
return items
instead of:
# DO NOT DO THIS
from typing import Annotated
from fastapi import FastAPI
from pydantic import Field, RootModel
app = FastAPI()
class ItemList(RootModel[Annotated[list[int], Field(min_length=1)]]):
pass
@app.post("/items/")
async def create_items(items: ItemList):
return items
FastAPI supports these type annotations and will create a Pydantic TypeAdapter for them, so that types can work as normally and there's no need for the custom logic and types in RootModels.
Use one HTTP operation per function
Don't mix HTTP operations in a single function, having one function per HTTP operation helps separate concerns and organize the code.
Do this:
from fastapi import FastAPI
from pydantic import BaseModel
app = FastAPI()
class Item(BaseModel):
name: str
@app.get("/items/")
async def list_items():
return []
@app.post("/items/")
async def create_item(item: Item):
return item
instead of this:
# DO NOT DO THIS
from fastapi import FastAPI, Request
from pydantic import BaseModel
app = FastAPI()
class Item(BaseModel):
name: str
@app.api_route("/items/", methods=["GET", "POST"])
async def handle_items(request: Request):
if request.method == "GET":
return []
Source: MeNiceGuy/home-health-startup-risk-intelligence — distributed by TomeVault.