from inline_snapshot import snapshot
from pydantic import BaseModel

app = FastAPI()


class ItemGroup(BaseModel):
    items: list[tuple[str, str]]


class Coordinate(BaseModel):
    x: float
    y: float


@app.post("/model-with-tuple/")
def post_model_with_tuple(item_group: ItemGroup):
    return item_group


@app.post("/tuple-of-models/")

* Conversão de dados
* Validação de dados
* Documentação automática

## Tipos especiais e validação { #special-types-and-validation }

Além dos tipos singulares normais como `str`, `int`, `float`, etc. Você também pode usar tipos singulares mais complexos que herdam de `str`.

  @CanIgnoreReturnValue
  PrimitiveSink putInt(int i);

  /** Puts a long into this sink. */
  @CanIgnoreReturnValue
  PrimitiveSink putLong(long l);

  /** Puts a float into this sink. */
  @CanIgnoreReturnValue
  PrimitiveSink putFloat(float f);

  /** Puts a double into this sink. */
  @CanIgnoreReturnValue
  PrimitiveSink putDouble(double d);

  /** Puts a boolean into this sink. */
  @CanIgnoreReturnValue

from pydantic.v1 import BaseModel


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

def get_items(item_a: str, item_b: int, item_c: float, item_d: bool, item_e: bytes):

from fastapi import FastAPI

app = FastAPI()


@app.get("/keyword-weights/", response_model=dict[str, float])
async def read_keyword_weights():

from fastapi import FastAPI
from pydantic import BaseModel

app = FastAPI(strict_content_type=False)


class Item(BaseModel):
    name: str
    price: float


@app.post("/items/")
async def create_item(item: Item):

      "suggestWord": {
        "type": "keyword"
      },
      "reading": {
        "type": "keyword"
      },
      "permissions": {
        "type": "keyword"
      },
      "boost": {
        "type": "float"
      },
      "createdBy": {
        "type": "keyword"
      },
      "createdTime": {
        "type": "long"
      },
      "updatedBy": {
        "type": "keyword"
      },
      "updatedTime": {

     * @throws InvalidQueryException if the query is not a FuzzyQuery
     */
    @Override
    public QueryBuilder execute(final QueryContext context, final Query query, final float boost) {
        if (query instanceof final FuzzyQuery fuzzyQuery) {
            if (logger.isDebugEnabled()) {
                logger.debug("FuzzyQuery: query={}, boost={}", query, boost);
            }

    add(primToWrap, wrapToPrim, byte.class, Byte.class);
    add(primToWrap, wrapToPrim, char.class, Character.class);
    add(primToWrap, wrapToPrim, double.class, Double.class);
    add(primToWrap, wrapToPrim, float.class, Float.class);
    add(primToWrap, wrapToPrim, int.class, Integer.class);
    add(primToWrap, wrapToPrim, long.class, Long.class);
    add(primToWrap, wrapToPrim, short.class, Short.class);