- tutorial/query-param-models.md
    - tutorial/body-multiple-params.md
    - tutorial/body-fields.md
    - tutorial/body-nested-models.md
    - tutorial/schema-extra-example.md
    - tutorial/extra-data-types.md
    - tutorial/cookie-params.md
    - tutorial/header-params.md
    - tutorial/cookie-param-models.md
    - tutorial/header-param-models.md
    - tutorial/response-model.md

      <plugin>
        <groupId>org.codehaus.modello</groupId>
        <artifactId>modello-maven-plugin</artifactId>
        <configuration>
          <version>4.2.0</version>
          <velocityBasedir>${project.basedir}/../../src/mdo</velocityBasedir>
          <models>
            <model>src/main/mdo/maven.mdo</model>
          </models>
          <templates>
            <template>model.vm</template>
          </templates>

### Model for Input { #model-for-input }

If you use this model as an input like here:

{* ../../docs_src/separate_openapi_schemas/tutorial001_py310.py ln[1:15] hl[14] *}

...then the `description` field will **not be required**. Because it has a default value of `None`.

### Input Model in Docs { #input-model-in-docs }

We'll fix these things by adding a few **extra models**. Here's where SQLModel will shine. ✨

### Create Multiple Models { #create-multiple-models }

In **SQLModel**, any model class that has `table=True` is a **table model**.

And any model class that doesn't have `table=True` is a **data model**, these ones are actually just Pydantic models (with a couple of small extra features). 🤓

...viendra surement plus tard, suivant le temps que j'ai.  😅 🎉

## Modèles d'apprentissage automatique avec spaCy et FastAPI

GitHub : <a href="https://github.com/microsoft/cookiecutter-spacy-fastapi" class="external-link" target="_blank">https://github.com/microsoft/cookiecutter-spacy-fastapi</a>

## Modèles d'apprentissage automatique avec spaCy et FastAPI - Fonctionnalités

* Intégration d'un modèle NER **spaCy**.

Por ejemplo, para declarar otro response con un código de estado `404` y un modelo Pydantic `Message`, puedes escribir:

{* ../../docs_src/additional_responses/tutorial001.py hl[18,22] *}

/// note | Nota

Ten en cuenta que debes devolver el `JSONResponse` directamente.

///

/// info | Información

La clave `model` no es parte de OpenAPI.

    *   `Model.fit_generator`, `Model.evaluate_generator`,
        `Model.predict_generator`, `Model.train_on_batch`,
        `Model.test_on_batch`, and `Model.predict_on_batch` methods now respect
        the `run_eagerly` property, and will correctly run using `tf.function`
        by default. Note that `Model.fit_generator`, `Model.evaluate_generator`,

It improves editor support for Pydantic models, with:

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

///

## Use the model { #use-the-model }

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

{* ../../docs_src/body/tutorial002_py310.py *}

/// info

## Use Case { #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.

package gorm

import "time"

// Model a basic GoLang struct which includes the following fields: ID, CreatedAt, UpdatedAt, DeletedAt
// It may be embedded into your model or you may build your own model without it
//
//	type User struct {
//	  gorm.Model
//	}
type Model struct {
	ID        uint `gorm:"primarykey"`
	CreatedAt time.Time
	UpdatedAt time.Time
	DeletedAt DeletedAt `gorm:"index"`