fastapi

仓库: teachingai/full-stack-skills
安装量: 36
排名: #22530

安装

npx skills add https://github.com/fastapi/fastapi --skill fastapi

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 [ ]

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