# Response Model - Return Type

You can declare the type used for the response by annotating the *path operation function* **return type**.

You can use **type annotations** the same way you would for input data in function **parameters**, you can use Pydantic models, lists, dictionaries, scalar values like integers, booleans, etc.

=== "Python 3.10+"

    ```Python hl_lines="16  21"
    {!> ../../../docs_src/response_model/tutorial001_01_py310.py!}
    ```

                        "type": "string"
                    },
                    "type": {
                        "title": "Error Type",
                        "type": "string"
                    }
                }
            },
            "HTTPValidationError": {
                "title": "HTTPValidationError",
                "type": "object",
                "properties": {

```JSON
{"item_id":"foo"}
```

## Path parameters with types

You can declare the type of a path parameter in the function, using standard Python type annotations:

```Python hl_lines="7"
{!../../../docs_src/path_params/tutorial002.py!}
```

In this case, `item_id` is declared to be an `int`.

!!! check

7. Here the `response_model` is using a type annotation of a list of `Author` dataclasses.

    Again, you can combine `dataclasses` with standard type annotations.

8. Notice that this *path operation function* uses regular `def` instead of `async def`.

    As always, in FastAPI you can combine `def` and `async def` as needed.

It had automatic data validation, data serialization and OpenAPI schema generation based on the same type hints in several places.

vous verrez comme réponse :

```JSON
{"item_id":"foo"}
```

## Paramètres de chemin typés

Vous pouvez déclarer le type d'un paramètre de chemin dans la fonction, en utilisant les annotations de type Python :


```Python hl_lines="7"
{!../../../docs_src/path_params/tutorial002.py!}
```

Ici, `item_id` est déclaré comme `int`.

!!! check "vérifier"

</blockquote>

## Recherche

                "item_id"
            ],
            "msg": "value is not a valid integer",
            "type": "type_error.integer"
        }
    ]
}

```

被替换为了以下文本格式的错误信息：

```
1 validation error
path -> item_id
  value is not a valid integer (type=type_error.integer)

```

### `RequestValidationError` vs `ValidationError`

!!! warning "警告"

                "item_id"
            ],
            "msg": "value is not a valid integer",
            "type": "type_error.integer"
        }
    ]
}
```

👆 🔜 🤚 ✍ ⏬, ⏮️:

```
1 validation error
path -> item_id
  value is not a valid integer (type=type_error.integer)
```

#### `RequestValidationError` 🆚 `ValidationError`

!!! warning

    ```Python hl_lines="31"
    {!> ../../../docs_src/security/tutorial002.py!}
    ```

Notice that we declare the type of `current_user` as the Pydantic model `User`.

This will help us inside of the function with all the completion and type checks.

!!! tip
    You might remember that request bodies are also declared with Pydantic models.