### Return a Response Directly

The most common case would be [returning a Response directly as explained later in the advanced docs](../advanced/response-directly.md){.internal-link target=_blank}.

```Python hl_lines="8  10-11"
{!> ../../../docs_src/response_model/tutorial003_02.py!}
```

from typing import Optional

from fastapi.exceptions import HTTPException
from fastapi.openapi.models import HTTPBase as HTTPBaseModel
from fastapi.openapi.models import HTTPBearer as HTTPBearerModel
from fastapi.security.base import SecurityBase
from fastapi.security.utils import get_authorization_scheme_param
from pydantic import BaseModel
from starlette.requests import Request
from starlette.status import HTTP_401_UNAUTHORIZED, HTTP_403_FORBIDDEN

from sqlalchemy.ext.declarative import declarative_base
from sqlalchemy.orm import sessionmaker

SQLALCHEMY_DATABASE_URL = "sqlite:///./sql_app.db"
# SQLALCHEMY_DATABASE_URL = "postgresql://user:password@postgresserver/db"

engine = create_engine(
    SQLALCHEMY_DATABASE_URL, connect_args={"check_same_thread": False}
)
SessionLocal = sessionmaker(autocommit=False, autoflush=False, bind=engine)

!!! tip
    **GraphQL** solves some very specific use cases.

    It has **advantages** and **disadvantages** when compared to common **web APIs**.

    Make sure you evaluate if the **benefits** for your use case compensate the **drawbacks**. 🤓

## GraphQL Libraries

Here are some of the **GraphQL** libraries that have **ASGI** support. You could use them with **FastAPI**:

You can declare the type of a path parameter in the function, using standard Python type annotations:

```Python hl_lines="7"
{!../../../docs_src/path_params/tutorial002.py!}
```

In this case, `item_id` is declared to be an `int`.

!!! check
    This will give you editor support inside of your function, with error checks, completion, etc.

## Proxy with a stripped path prefix

Having a proxy with a stripped path prefix, in this case, means that you could declare a path at `/app` in your code, but then, you add a layer on top (the proxy) that would put your **FastAPI** application under a path like `/api/v1`.

In this case, the original path `/app` would actually be served at `/api/v1/app`.

Even though all your code is written assuming there's just `/app`.

In some cases, you might still have to use Pydantic's version of `dataclasses`. For example, if you have errors with the automatically generated API documentation.

In that case, you can simply swap the standard `dataclasses` with `pydantic.dataclasses`, which is a drop-in replacement:

```{ .python .annotate hl_lines="1  5  8-11  14-17  23-25  28" }
{!../../../docs_src/dataclasses/tutorial003.py!}
```

!!! tip
    The next sections are **not necessarily "advanced"**.

    And it's possible that for your use case, the solution is in one of them.

## Read the Tutorial first

The next sections assume you already read the main [Tutorial - User Guide: Security](../../tutorial/security/index.md){.internal-link target=_blank}.

#### "Schema"

A "schema" is a definition or description of something. Not the code that implements it, but just an abstract description.

#### API "schema"

In this case, <a href="https://github.com/OAI/OpenAPI-Specification" class="external-link" target="_blank">OpenAPI</a> is a specification that dictates how to define a schema of your API.

# Génération de projets - Modèle

Vous pouvez utiliser un générateur de projet pour commencer, qui réalisera pour vous la mise en place de bases côté architecture globale, sécurité, base de données et premières routes d'API.

Un générateur de projet fera toujours une mise en place très subjective que vous devriez modifier et adapter suivant vos besoins, mais cela reste un bon point de départ pour vos projets.

## Full Stack FastAPI PostgreSQL