key: ${{ runner.os }}-python-${{ env.pythonLocation }}-pydantic-v2-${{ hashFiles('pyproject.toml', 'requirements-tests.txt', 'requirements-docs-tests.txt') }}-test-v08
      - name: Install Dependencies
        if: steps.cache.outputs.cache-hit != 'true'
        run: pip install -r requirements-tests.txt
      - name: Install Pydantic v2
        run: pip install "pydantic>=2.0.2,<3.0.0"
      - name: Lint
        run: bash scripts/lint.sh

  test:

from fastapi import FastAPI
from pydantic import BaseModel


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


app = FastAPI(separate_input_output_schemas=False)


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


@app.get("/items/")
def read_items() -> list[Item]:
    return [
        Item(
            name="Portal Gun",

## Descripción avanzada desde el docstring

Puedes limitar las líneas usadas desde el docstring de una *operación de path* para OpenAPI.

Agregar un `\f` (un carácter de "form feed" escapado) hace que **FastAPI** trunque el output utilizada para OpenAPI en ese punto.

No será mostrado en la documentación, pero otras herramientas (como Sphinx) serán capaces de usar el resto.

```Python hl_lines="19 20 21 22 23 24 25 26 27 28 29"

### Notice the `response_model=List[Note]`

It uses `typing.List`.

That documents (and validates, serializes, filters) the output data, as a `list` of `Note`s.

## Create notes

Create the *path operation function* to create notes:

```Python hl_lines="61-65"
{!../../../docs_src/async_sql_databases/tutorial001.py!}
```

You can set them as follows:

```Python hl_lines="3-16  19-32"
{!../../../docs_src/metadata/tutorial001.py!}
```

!!! tip
    You can write Markdown in the `description` field and it will be rendered in the output.

With this configuration, the automatic API docs would look like:

<img src="/img/tutorial/metadata/image01.png">

## License identifier

!!! tip "Tipp"
    Beachten Sie, dass wir in diesem Fall eine Standard-Python-Funktion `open()` verwenden, die mit einer Datei interagiert.

    Es handelt sich also um I/O (Input/Output), welches „Warten“ erfordert, bis Dinge auf die Festplatte geschrieben werden.

    Aber `open()` verwendet nicht `async` und `await`.

!!! tip "Dica"
    Perceba que nesse caso nós estamos usando a função padrão do Python `open()` que interage com um arquivo.

    Então, isso envolve I/O (input/output), que exige "esperar" que coisas sejam escritas em disco.

    Mas `open()` não usa `async` e `await`.

    Então, nós declaramos uma função de manipulação de evento com o padrão `def` ao invés de `async def`.

    ```

With this, even if you receive a request with duplicate data, it will be converted to a set of unique items.

And whenever you output that data, even if the source had duplicates, it will be output as a set of unique items.

And it will be annotated / documented accordingly too.

## Nested Models

Each attribute of a Pydantic model has a type.

```JSON  hl_lines="7"
{
  "name": "frontend-app",
  "version": "1.0.0",
  "description": "",
  "main": "index.js",
  "scripts": {
    "generate-client": "openapi-ts --input http://localhost:8000/openapi.json --output ./src/client --client axios"
  },
  "author": "",
  "license": "",
  "devDependencies": {
    "@hey-api/openapi-ts": "^0.27.38",
    "typescript": "^4.6.2"
  }
}
```

Continuing with the previous example, it will be common to have more than one related model.

This is especially the case for user models, because:

* The **input model** needs to be able to have a password.
* The **output model** should not have a password.
* The **database model** would probably need to have a hashed password.

!!! danger
    Never store user's plaintext passwords. Always store a "secure hash" that you can then verify.