provided in one of multiple optional ways (for example, in HTTP Basic
                authentication or in an HTTP Bearer token).
                """
            ),
        ] = True,
    ):
        self.model = HTTPBaseModel(scheme="basic", description=description)
        self.scheme_name = scheme_name or self.__class__.__name__
        self.realm = realm
        self.auto_error = auto_error

## `response_model` Parameter

There are some cases where you need or want to return some data that is not exactly what the type declares.

For example, you could want to **return a dictionary** or a database object, but **declare it as a Pydantic model**. This way the Pydantic model would do all the data documentation, validation, etc. for the object that you returned (e.g. a dictionary or database object).

                provided in one of multiple optional ways (for example, with OAuth2
                or in a cookie).
                """
            ),
        ] = True,
    ):
        self.model = OAuth2Model(
            flows=cast(OAuthFlowsModel, flows), description=description
        )
        self.scheme_name = scheme_name or self.__class__.__name__
        self.auto_error = auto_error

It's relatively tightly coupled with relational databases (like MySQL or PostgreSQL), so, having a NoSQL database (like Couchbase, MongoDB, Cassandra, etc) as the main store engine is not very easy.

It was created to generate the HTML in the backend, not to create APIs used by a modern frontend (like React, Vue.js and Angular) or by other systems (like <abbr title="Internet of Things">IoT</abbr> devices) communicating with it.

                    "strict": strict,
                    "json_schema_extra": current_json_schema_extra,
                }
            )
            kwargs["pattern"] = pattern or regex
        else:
            kwargs["regex"] = pattern or regex
            kwargs.update(**current_json_schema_extra)
        use_kwargs = {k: v for k, v in kwargs.items() if v is not _Unset}

        super().__init__(**use_kwargs)

        {
            "detail": [
                {
                    "type": "model_attributes_type",
                    "loc": ["body"],
                    "msg": "Input should be a valid dictionary or object to extract fields from",
                    "input": "name=Foo&price=50.5",
                }
            ]
        }
    ) | IsDict(
        # TODO: remove when deprecating Pydantic v1
        {

        {
            "detail": [
                {
                    "type": "model_attributes_type",
                    "loc": ["body"],
                    "msg": "Input should be a valid dictionary or object to extract fields from",
                    "input": "name=Foo&price=50.5",
                }
            ]
        }
    ) | IsDict(
        # TODO: remove when deprecating Pydantic v1
        {

### OpenAPI

**FastAPI** generates a "schema" with all your API using the **OpenAPI** standard for defining APIs.

#### "Schema"

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

#### API "schema"

Alternatively, if you don't have a way to provide a command line option like `--root-path` or equivalent, you can set the `root_path` parameter when creating your FastAPI app:

```Python hl_lines="3"
{!../../../docs_src/behind_a_proxy/tutorial002.py!}
```

Passing the `root_path` to `FastAPI` would be the equivalent of passing the `--root-path` command line option to Uvicorn or Hypercorn.

### About `root_path`

    assert response.json() == {"detail": "Incorrect username or password"}


def test_login_incorrect_username():
    response = client.post("/token", data={"username": "foo", "password": "secret"})
    assert response.status_code == 400, response.text
    assert response.json() == {"detail": "Incorrect username or password"}


def test_no_token():
    response = client.get("/users/me")