assert response.status_code == 422, response.text
    assert response.json() == IsDict(
        {
            "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",
                }

    assert response.status_code == 422, response.text
    assert response.json() == IsDict(
        {
            "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",
                }

* `tags: List[str] = []` has a default of an empty list: `[]`.

but you might want to omit them from the result if they were not actually stored.

For example, if you have models with many optional attributes in a NoSQL database, but you don't want to send very long JSON responses full of default values.

### Use the `response_model_exclude_unset` parameter

# `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

# `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

```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.

!!! 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`).

## Pydantic models

<a href="https://docs.pydantic.dev/" class="external-link" target="_blank">Pydantic</a> is a Python library to perform data validation.

You declare the "shape" of the data as classes with attributes.

And each attribute has a type.

Then you create an instance of that class with some values and it will validate the values, convert them to the appropriate type (if that's the case) and give you an object with all the data.