# `Request` class

You can declare a parameter in a *path operation function* or dependency to be of type `Request` and then you can access the raw request object directly, without any validation, etc.

You can import it directly from `fastapi`:

```python
from fastapi import Request
```

!!! tip

        "components": {
            "schemas": {
                "ValidationError": {
                    "title": "ValidationError",
                    "required": ["loc", "msg", "type"],
                    "type": "object",
                    "properties": {
                        "loc": {
                            "title": "Location",
                            "type": "array",
                            "items": {

        "components": {
            "schemas": {
                "ValidationError": {
                    "title": "ValidationError",
                    "required": ["loc", "msg", "type"],
                    "type": "object",
                    "properties": {
                        "loc": {
                            "title": "Location",
                            "type": "array",
                            "items": {

                                    "schema": {
                                        "title": "Response Read Keyword Weights Keyword Weights  Get",
                                        "type": "object",
                                        "additionalProperties": {"type": "number"},
                                    }
                                }
                            },
                        }

* 따라서 이미지, 동영상, 큰 이진코드와 같은 대용량 파일들을 많은 메모리를 소모하지 않고 처리하기에 적합합니다.
* 업로드 된 파일의 메타데이터를 얻을 수 있습니다.
* <a href="https://docs.python.org/3/glossary.html#term-file-like-object" class="external-link" target="_blank">file-like</a>  `async` 인터페이스를 갖고 있습니다.

            },
        },
        "components": {
            "schemas": {
                "HTTPValidationError": {
                    "title": "HTTPValidationError",
                    "type": "object",
                    "properties": {
                        "detail": {
                            "title": "Detail",
                            "type": "array",

For example, you could want to **return a dictionary** or a database object, but **declare it as a Pydantic model**. This way the Pydantic model would do all the data documentation, validation, etc. for the object that you returned (e.g. a dictionary or database object).

                        "length": {"type": "integer", "title": "Length"},
                        "area": {"type": "integer", "title": "Area", "readOnly": True},
                    },
                    "type": "object",
                    "required": ["width", "length", "area"],
                    "title": "Rectangle",
                }
            }
        },

is data "<abbr title="also called marshalling, conversion">serialization</abbr>" which is taking data from the code (Python) and converting it into something that can be sent through the network. For example, converting an object containing data from a database into a JSON object. Converting `datetime` objects into strings, etc.

Another big feature needed by APIs is data validation, making sure that the data is valid, given certain parameters. For example, that some field is an `int`, and...

                    "required": ["alias"],
                    "type": "object",
                    "properties": {"alias": {"title": "Alias", "type": "string"}},
                },
                "ModelNoAlias": {
                    "title": "ModelNoAlias",
                    "required": ["name"],
                    "type": "object",
                    "properties": {"name": {"title": "Name", "type": "string"}},