/fastapi

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.

> **Project note**: This project starts the server via `./run_services.sh` (uvicorn), not `fastapi dev`. The patterns below still apply to all endpoint/model code.

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