from fastapi import FastAPI
from pydantic import BaseModel

app = FastAPI()


class Item(BaseModel):
    name: str
    description: Union[str, None] = None
    price: float
    tax: Union[float, None] = None
    tags: List[str] = []


@app.post("/items/", response_model=Item)
async def create_item(item: Item) -> Any:
    return item


@app.get("/items/", response_model=List[Item])

     */
    @Override
    public void setLastModified(final Long lastModified) {
        this.lastModified = lastModified;
    }

    @Override
    public float getWeight() {
        return weight;
    }

    @Override
    public void setWeight(float weight) {
        this.weight = weight;
    }

    @Override
    public String toString() {

# Extra Data Types

Up to now, you have been using common data types, like:

* `int`
* `float`
* `str`
* `bool`

But you can also use more complex data types.

And you will still have the same features as seen up to now:

* Great editor support.
* Data conversion from incoming requests.
* Data conversion for response data.
* Data validation.
* Automatic annotation and documentation.

## Other data types

from contextlib import asynccontextmanager

from fastapi import FastAPI


def fake_answer_to_everything_ml_model(x: float):
    return x * 42


ml_models = {}


@asynccontextmanager
async def lifespan(app: FastAPI):
    # Load the ML model
    ml_models["answer_to_everything"] = fake_answer_to_everything_ml_model
    yield
    # Clean up the ML models and release the resources
    ml_models.clear()

from typing import Annotated

from fastapi import Body, FastAPI
from pydantic import BaseModel

app = FastAPI()


class Item(BaseModel):
    name: str
    description: str | None = None
    price: float
    tax: float | None = None


@app.put("/items/{item_id}")
async def update_item(
    *,
    item_id: int,
    item: Annotated[
        Item,
        Body(
            examples=[
                {

from typing import Set, Union

from fastapi import FastAPI
from pydantic import BaseModel

app = FastAPI()


class Item(BaseModel):
    name: str
    description: Union[str, None] = None
    price: float
    tax: Union[float, None] = None
    tags: Set[str] = set()


@app.post("/items/", response_model=Item, tags=["items"])
async def create_item(item: Item):
    return item


@app.get("/items/", tags=["items"])

app = FastAPI()


class Item(BaseModel):
    name: str = Field(examples=["Foo"])
    description: Union[str, None] = Field(default=None, examples=["A very nice Item"])
    price: float = Field(examples=[35.4])
    tax: Union[float, None] = Field(default=None, examples=[3.2])


@app.put("/items/{item_id}")
async def update_item(item_id: int, item: Item):
    results = {"item_id": item_id, "item": item}

from pydantic import BaseModel

app = FastAPI()


class Image(BaseModel):
    url: str
    name: str


class Item(BaseModel):
    name: str
    description: Union[str, None] = None
    price: float
    tax: Union[float, None] = None
    tags: Set[str] = set()
    image: Union[Image, None] = None


@app.put("/items/{item_id}")
async def update_item(item_id: int, item: Item):

from dataclasses import dataclass, field
from typing import List, Union

from fastapi import FastAPI


@dataclass
class Item:
    name: str
    price: float
    tags: List[str] = field(default_factory=list)
    description: Union[str, None] = None
    tax: Union[float, None] = None


app = FastAPI()


@app.get("/items/next", response_model=Item)
async def read_next_item():
    return {

from fastapi import FastAPI
from pydantic import BaseModel

app = FastAPI()


class Item(BaseModel):
    name: str
    description: str | None = None
    price: float
    tax: float | None = None
    tags: set[str] = set()


@app.post("/items/", response_model=Item, tags=["items"])
async def create_item(item: Item):
    return item


@app.get("/items/", tags=["items"])
async def read_items():