if annotation_is_pydantic_v1(model):
                    raise PydanticV1NotSupportedError(
                        "pydantic.v1 models are no longer supported by FastAPI."
                        f" In responses={{}}, please update {model}."
                    )
                response_field = create_model_field(
                    name=response_name, type_=model, mode="serialization"
                )

import org.apache.maven.api.di.Singleton;
import org.apache.maven.api.model.Build;
import org.apache.maven.api.model.Model;
import org.apache.maven.api.model.Parent;
import org.apache.maven.api.model.Plugin;
import org.apache.maven.api.model.PluginManagement;
import org.apache.maven.api.model.Repository;
import org.apache.maven.api.model.RepositoryPolicy;
import org.apache.maven.api.services.ModelBuilder;

//
//	// CREATE VIEW `user_view` AS SELECT * FROM `users` WHERE age > 20
//	q := DB.Model(&User{}).Where("age > ?", 20)
//	DB.Debug().Migrator().CreateView("user_view", gorm.ViewOption{Query: q})
//
//	// CREATE OR REPLACE VIEW `users_view` AS SELECT * FROM `users` WITH CHECK OPTION
//	q := DB.Model(&User{})

It can't handle nested models very well. So, if the JSON body in the request is a JSON object that has inner fields that in turn are nested JSON objects, it cannot be properly documented and validated.

/// check | Inspired **FastAPI** to

        "//third_party/py/tf_keras/...",
        "//third_party/yggdrasil_decision_forests/...",
        "//waymo/accelerator/...",
        "//waymo/ml/cn/...",
        "//waymo/ml/models/...",
    ],
)

package_group(
    name = "ndarray_tensor_allow_list",
    packages = [
        "//learning/gemini/gemax/...",
        "//third_party/py/courier/...",

This kind of asynchronicity is what made NodeJS popular (even though NodeJS is not parallel) and that's the strength of Go as a programming language.

And that's the same level of performance you get with **FastAPI**.

If your application is **simple**, this will probably **not be a problem**, and you might not need to specify hard memory limits. But if you are **using a lot of memory** (for example with **machine learning** models), you should check how much memory you are consuming and adjust the **number of containers** that runs in **each machine** (and maybe add more machines to your cluster).

        },
        "baz": "simple_include_dict model2 baz",
    }


@app.get(
    "/simple_exclude",
    response_model=Model2,
    response_model_exclude={"ref": {"bar"}},
)
def simple_exclude():
    return Model2(
        ref=Model1(foo="simple_exclude model foo", bar="simple_exclude model bar"),
        baz="simple_exclude model2 baz",
    )


@app.get(
    "/simple_exclude_dict",

from collections.abc import Sequence
from typing import Annotated, Any, Callable, Optional, Union

from annotated_doc import Doc
from fastapi import params
from fastapi._compat import Undefined
from fastapi.openapi.models import Example
from pydantic import AliasChoices, AliasPath
from typing_extensions import Literal, deprecated

_Unset: Any = Undefined


def Path(  # noqa: N802
    default: Annotated[
        Any,
        Doc(

Cuando veas la documentación automática, puedes verificar que el modelo de entrada y el modelo de salida tendrán cada uno su propio JSON Schema:

<img src="/img/tutorial/response-model/image01.png">

Y ambos modelos se utilizarán para la documentación interactiva de la API:

<img src="/img/tutorial/response-model/image02.png">

## Otras Anotaciones de Tipos de Retorno { #other-return-type-annotations }