## Use Case

Let's start with an example **use case** and then see how to solve it with this.

Let's imagine that you have some **machine learning models** that you want to use to handle requests. 🤖

The same models are shared among requests, so, it's not one model per request, or one per user or something similar.

///

## Importez le `BaseModel` de Pydantic

Commencez par importer la classe `BaseModel` du module `pydantic` :

{* ../../docs_src/body/tutorial001.py hl[4] *}

## Créez votre modèle de données

Déclarez ensuite votre modèle de données en tant que classe qui hérite de `BaseModel`.

Utilisez les types Python standard pour tous les attributs :

Let's see how that works and how to change it if you need to do that.

## Pydantic Models for Input and Output

Let's say you have a Pydantic model with default values, like this one:

{* ../../docs_src/separate_openapi_schemas/tutorial001_py310.py ln[1:7] hl[7] *}

### Model for Input

It improves editor support for Pydantic models, with:

* auto-completion
* type checks
* refactoring
* searching
* inspections

///

## Use the model

Inside of the function, you can access all the attributes of the model object directly:

{!> ../../docs_src/body/tutorial002_py310.py!}

## Request body + path parameters

    hashed_password = user_dict["hashed_password"],
)
```

/// info | Informação

Para uma explicação mais completa de `**user_dict`, verifique [a documentação para **Extra Models**](../extra-models.md#about-user_indict){.internal-link target=_blank}.

///

## Retorne o token

A resposta do endpoint `token` deve ser um objeto JSON.

Vamos corrigir essas coisas adicionando alguns **modelos extras**. Aqui é onde o SQLModel vai brilhar. ✨

### Criar Múltiplos Modelos

No **SQLModel**, qualquer classe de modelo que tenha `table=True` é um **modelo de tabela**.

E qualquer classe de modelo que não tenha `table=True` é um **modelo de dados**, esses são na verdade apenas modelos Pydantic (com alguns recursos extras pequenos). 🤓

## Import `Field`

First, you have to import it:

{* ../../docs_src/body_fields/tutorial001_an_py310.py hl[4] *}


/// warning

Notice that `Field` is imported directly from `pydantic`, not from `fastapi` as are all the rest (`Query`, `Path`, `Body`, etc).

///

## Declare model attributes

If you want to secure your API, there are several better things you can do, for example:

* Make sure you have well defined Pydantic models for your request bodies and responses.
* Configure any required permissions and roles using dependencies.
* Never store plaintext passwords, only password hashes.

import jetbrains.buildServer.configs.kotlin.BuildStep
import jetbrains.buildServer.configs.kotlin.buildSteps.script
import model.CIBuildModel
import model.Stage

class CheckTeamCityKotlinDSL(model: CIBuildModel, stage: Stage) : OsAwareBaseGradleBuildType(
    os = Os.LINUX, stage = stage, init = {
        id("${model.projectId}_CheckTeamCityKotlinDSL")
        name = "CheckTeamCityKotlinDSL"
        description = "Check Kotlin DSL in .teamcity/"

{* ../../docs_src/first_steps/tutorial001.py hl[8] *}

Możesz zwrócić `dict`, `list`, pojedynczą wartość jako `str`, `int`, itp.

Możesz również zwrócić modele Pydantic (więcej o tym później).

Istnieje wiele innych obiektów i modeli, które zostaną automatycznie skonwertowane do formatu JSON (w tym ORM itp.). Spróbuj użyć swoich ulubionych, jest bardzo prawdopodobne, że są już obsługiwane.

## Podsumowanie