By inheriting from `str` the API docs will be able to know that the values must be of type `string` and will be able to render correctly.

Then create class attributes with fixed values, which will be the available valid values:

```Python hl_lines="1  6-9"
{!../../../docs_src/path_params/tutorial005.py!}
```

!!! info

# `APIRouter` class

Here's the reference information for the `APIRouter` class, with all its parameters, attributes and methods.

You can import the `APIRouter` class directly from `fastapi`:

```python
from fastapi import APIRouter
```

::: fastapi.APIRouter
    options:
        members:
            - websocket
            - include_router
            - get
            - put
            - post
            - delete

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"
{!../../../docs_src/nosql_databases/tutorial001.py!}
```

!!! note

# `FastAPI` class

Here's the reference information for the `FastAPI` class, with all its parameters, attributes and methods.

You can import the `FastAPI` class directly from `fastapi`:

```python
from fastapi import FastAPI
```

::: fastapi.FastAPI
    options:
        members:
            - openapi_version
            - webhooks
            - state
            - dependency_overrides
            - openapi

#### Data "schema"

The term "schema" might also refer to the shape of some data, like a JSON content.

In that case, it would mean the JSON attributes, and data types they have, etc.

#### OpenAPI and JSON Schema

## Multiple body parameters

In the previous example, the *path operations* would expect a JSON body with the attributes of an `Item`, like:

```JSON
{
    "name": "Foo",
    "description": "The pretender",
    "price": 42.0,
    "tax": 3.2
}
```

```Python hl_lines="4  7-8  18-19"
{!../../../docs_src/sql_databases/sql_app/models.py!}
```

The `__tablename__` attribute tells SQLAlchemy the name of the table to use in the database for each of these models.

### Create model attributes/columns

Now create all the model (class) attributes.

Each of these attributes represents a column in its corresponding database table.

We use `Column` from SQLAlchemy as the default value.

!!! check "Inspired **FastAPI** to"
    Define extra validations for data types using the "default" value of model attributes. This improves editor support, and it was not available in Pydantic before.

The same way, this database wouldn't receive a Pydantic model (an object with attributes), only a `dict`.

You can use `jsonable_encoder` for that.

It receives an object, like a Pydantic model, and returns a JSON compatible version:

=== "Python 3.10+"

    ```Python hl_lines="4  21"

!!! note
    Notice that the input model is still validated.

    So, if you want to receive partial updates that can omit all the attributes, you need to have a model with all the attributes marked as optional (with default values or `None`).