</ul>
        <script>
        var ws = null;
            function connect(event) {
                var itemId = document.getElementById("itemId")
                var token = document.getElementById("token")
                ws = new WebSocket("ws://localhost:8000/items/" + itemId.value + "/ws?token=" + token.value);
                ws.onmessage = function(event) {

     * the item's current state.
     * </p>
     *
     * @param dictId the dictionary ID to store the character mapping item in
     * @param charMappingItem the character mapping item to store
     */
    public void store(final String dictId, final CharMappingItem charMappingItem) {
        getCharMappingFile(dictId).ifPresent(file -> {
            if (charMappingItem.getId() == 0) {

@app.get("/items/validlist", response_model=list[Item])
def get_validlist():
    return [
        Item(aliased_name="foo"),
        Item(aliased_name="bar", price=1.0),
        Item(aliased_name="baz", price=2.0, owner_ids=[1, 2, 3]),
    ]


@app.get("/items/validdict", response_model=dict[str, Item])
def get_validdict():
    return {
        "k1": Item(aliased_name="foo"),
        "k2": Item(aliased_name="bar", price=1.0),

app = FastAPI()


@dataclass
class Item:
    name: str
    date: datetime
    price: Optional[float] = None
    owner_ids: Optional[list[int]] = None


@app.get("/items/valid", response_model=Item)
def get_valid():
    return {"name": "valid", "date": datetime(2021, 7, 26), "price": 1.0}


@app.get("/items/object", response_model=Item)
def get_object():
    return Item(

{* ../../docs_src/pydantic_v1_in_v2/tutorial003_an_py310.py hl[2:3,6,12,21:22] *}

В примере выше модель входных данных — это модель Pydantic v1, а модель выходных данных (указанная в `response_model=ItemV2`) — это модель Pydantic v2.

### Параметры Pydantic v1 { #pydantic-v1-parameters }

        List<SuggestItem> items = defaultContentsParser.parseQueryLog(queryLog, supportedFields, tagFieldNames, roleFieldName,
                createDefaultReadingConverter(), createDefaultNormalizer());
        SuggestItem item = items.get(0);
        assertEquals("検索エンジン", item.getText());
        assertEquals(SuggestItem.Kind.QUERY, item.getKinds()[0]);
        assertEquals(1, item.getQueryFreq());
    }

    @Test

app = FastAPI()

items = {"foo": {"name": "Fighters", "size": 6}, "bar": {"name": "Tenders", "size": 3}}


@app.put("/items/{item_id}")
async def upsert_item(
    item_id: str,
    name: Annotated[str | None, Body()] = None,
    size: Annotated[int | None, Body()] = None,
):
    if item_id in items:
        item = items[item_id]
        item["name"] = name
        item["size"] = size

            "price": 3,
            "tax": 10.5,
            "tags": ["tag1", "tag2"],
        },
    )
    assert response.json() == {
        "name": "Fooz",
        "description": "Item description",
        "price": 3,
        "tax": 10.5,
        "tags": ["tag1", "tag2"],
    }


def test_patch_name(client: TestClient):
    response = client.patch(
        "/items/bar",

                                "application/json": {
                                    "schema": {
                                        "items": {"$ref": "#/components/schemas/Item"},
                                        "type": "array",
                                        "title": "Response Read Items Items  Get",
                                    }
                                }
                            },
                        }

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"])
async def read_items():