That way, you don't have to read it all first in memory, and you can pass that generator function to the `StreamingResponse`, and return it.

This includes many libraries to interact with cloud storage, video processing, and others.

```{ .python .annotate hl_lines="2  10-12  14" }
{!../../../docs_src/custom_response/tutorial008.py!}
```

Flask is a "microframework", it doesn't include database integrations nor many of the things that come by default in Django.

This simplicity and flexibility allow doing things like using NoSQL databases as the main data storage system.

As it is very simple, it's relatively intuitive to learn, although the documentation gets somewhat technical at some points.

    [
        ("/items", None, 200, {"strange_header": None}),
        ("/items", {"X-Header": "notvalid"}, 200, {"strange_header": None}),
        (
            "/items",
            {"strange_header": "FastAPI test"},
            200,
            {"strange_header": "FastAPI test"},
        ),
        (
            "/items",
            {"strange-header": "Not really underscore"},
            200,

from typing import Union

from fastapi import FastAPI, Header

app = FastAPI()


@app.get("/items/")
async def read_items(
    strange_header: Union[str, None] = Header(default=None, convert_underscores=False),
):

When using Poetry, it would make sense to use **Docker multi-stage builds** because you don't really need to have Poetry and its dependencies installed in the final container image, you **only need** to have the generated `requirements.txt` file to install your project dependencies.

Then in the next (and final) stage you would build the image more or less in the same way as described before.

# (1)
FROM python:3.9 as requirements-stage

# (2)
WORKDIR /tmp

# (3)
RUN pip install poetry

# (4)
COPY ./pyproject.toml ./poetry.lock* /tmp/

# (5)
RUN poetry export -f requirements.txt --output requirements.txt --without-hashes

# (6)
FROM python:3.9

# (7)
WORKDIR /code

# (8)
COPY --from=requirements-stage /tmp/requirements.txt /code/requirements.txt

# (1)
FROM python:3.9 as requirements-stage

# (2)
WORKDIR /tmp

# (3)
RUN pip install poetry

# (4)
COPY ./pyproject.toml ./poetry.lock* /tmp/

# (5)
RUN poetry export -f requirements.txt --output requirements.txt --without-hashes

# (6)
FROM python:3.9

# (7)
WORKDIR /code

# (8)
COPY --from=requirements-stage /tmp/requirements.txt /code/requirements.txt

n'est pas très facile d'utiliser une base de données NoSQL (comme Couchbase, MongoDB, Cassandra, etc.) comme principal moyen de
stockage.

        bool,
        Doc(
            """
            Exclude from the output any fields that start with the name `_sa`.

            This is mainly a hack for compatibility with SQLAlchemy objects, they
            store internal SQLAlchemy-specific state in attributes named with `_sa`,
            and those objects can't (and shouldn't be) serialized to JSON.
            """
        ),
    ] = True,
) -> Any:
    """

```Python hl_lines="22-24"
{!../../../docs_src/extending_openapi/tutorial001.py!}
```

### Cache the OpenAPI schema

You can use the property `.openapi_schema` as a "cache", to store your generated schema.

That way, your application won't have to generate the schema every time a user opens your API docs.

It will be generated only once, and then the same cached schema will be used for the next requests.