```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.

```

## ⚙️ 🎏 🔗 💗 🕰

🚥 1️⃣ 👆 🔗 📣 💗 🕰 🎏 *➡ 🛠️*, 🖼, 💗 🔗 ✔️ ⚠ 🎧-🔗, **FastAPI** 🔜 💭 🤙 👈 🎧-🔗 🕴 🕐 📍 📨.

&amp; ⚫️ 🔜 🖊 📨 💲 <abbr title="A utility/system to store computed/generated values, to re-use them instead of computing them again.">"💾"</abbr> &amp; 🚶‍♀️ ⚫️ 🌐 "⚓️" 👈 💪 ⚫️ 👈 🎯 📨, ↩️ 🤙 🔗 💗 🕰 🎏 📨.

Doing that in separated functions that don't share logic or variables together is more difficult as you would need to store values in global variables or similar tricks.

Because of that, it's now recommended to instead use the `lifespan` as explained above.

## Technical Details

Just a technical detail for the curious nerds. 🤓

    return items[item_id]


@app.patch("/items/{item_id}", response_model=Item)
async def update_item(item_id: str, item: Item):
    stored_item_data = items[item_id]
    stored_item_model = Item(**stored_item_data)
    update_data = item.dict(exclude_unset=True)
    updated_item = stored_item_model.copy(update=update_data)
    items[item_id] = jsonable_encoder(updated_item)

### Project file structure

Let's say your project file structure looks like this:

```
.
├── app
│   ├── __init__.py
│   ├── main.py
```

Now create a directory to store those static files.

Your new file structure could look like this:

```
.
├── app
│   ├── __init__.py
│   ├── main.py
└── static/
```

### Download the files

```

We will use this model in our *path operation function*, so, we don't include in it the `hashed_password`.

### `UserInDB` model

Now, let's create a `UserInDB` model.

This will have the data that is actually stored in the database.

We don't create it as a subclass of Pydantic's `BaseModel` but as a subclass of our own `User`, because it will have all the attributes in `User` plus a couple more:

```Python hl_lines="31-33"

        * So, the user will have to log in again at some point later.
        * And if the token is stolen, the risk is less. It is not like a permanent key that will work forever (in most of the cases).
* The frontend stores that token temporarily somewhere.
* The user clicks in the frontend to go to another section of the frontend web app.
* The frontend needs to fetch some more data from the API.

    ```

### Использование параметра `update` в Pydantic

Теперь можно создать копию существующей модели, используя `.copy()`, и передать параметр `update` с `dict`, содержащим данные для обновления.

Например, `stored_item_model.copy(update=update_data)`:

=== "Python 3.10+"

    ```Python hl_lines="33"
    {!> ../../../docs_src/body_updates/tutorial002_py310.py!}
    ```

=== "Python 3.9+"

    ```Python hl_lines="35"

    * Query parameters.
    * Cookies, etc.

Plus all the security features from Starlette (including **session cookies**).

All built as reusable tools and components that are easy to integrate with your systems, data stores, relational and NoSQL databases, etc.

### Dependency Injection

    return items[item_id]


@app.patch("/items/{item_id}", response_model=Item)
async def update_item(item_id: str, item: Item):
    stored_item_data = items[item_id]
    stored_item_model = Item(**stored_item_data)
    update_data = item.dict(exclude_unset=True)
    updated_item = stored_item_model.copy(update=update_data)
    items[item_id] = jsonable_encoder(updated_item)