* Because pydantic data structures are just instances of classes you define; auto-completion, linting, mypy and your intuition should all work properly with your validated data.
* Validate **complex structures**:
    * Use of hierarchical Pydantic models, Python `typing`’s `List` and `Dict`, etc.
    * And validators allow complex data schemas to be clearly and easily defined, checked and documented as JSON Schema.

  {% endfor %}

# To simplify the automations section, some calculations are placed under unique YAML keys defined here.
# Read the "|" not as "or", but as a "pipe", taking the output of the previous command and passing it to the next command.

# TODO: It would be very nice to avoid having this list somehow, but it needs to be defined in YAML and not in JS for the looping template to work

# `UploadFile` class

You can define *path operation function* parameters to be of the type `UploadFile` to receive files from the request.

You can import it directly from `fastapi`:

```python
from fastapi import UploadFile
```

::: fastapi.UploadFile
    options:
        members:
            - file
            - filename
            - size
            - headers
            - content_type
            - read

# this PR, this automations will have its `if` checked to run.  In a way, the `on` conditions
# function as an implicit first `if` for every automation in the file.

# You can define multiple automations in a .cm file, but each automation name should be unique
# within the file.  We keep each automation (or very closely related group of automations) in
# its own file.

on:
  - pr_created
  - commit

* API usage monitoring systems
* response data injection systems
* etc.

## Simple and Powerful

Although the hierarchical dependency injection system is very simple to define and use, it's still very powerful.

You can define dependencies that in turn can define dependencies themselves.

# Request Files

You can define files to be uploaded by the client using `File`.

!!! info
    To receive uploaded files, first install <a href="https://github.com/Kludex/python-multipart" class="external-link" target="_blank">`python-multipart`</a>.

    E.g. `pip install python-multipart`.

    This is because uploaded files are sent as "form data".

## Import `File`

Import `File` and `UploadFile` from `fastapi`:

=== "Python 3.9+"

So, you could add additional data to the automatically generated schema.

For example, you could decide to read and validate the request with your own code, without using the automatic features of FastAPI with Pydantic, but you could still want to define the request in the OpenAPI schema.

You could do that with `openapi_extra`:

```Python hl_lines="20-37  39-40"

#### "*Schema*"

Um "*schema*" é uma definição ou descrição de algo. Não o código que o implementa, mas apenas uma descrição abstrata.

#### API "*schema*"

Nesse caso, <a href="https://github.com/OAI/OpenAPI-Specification" class="external-link" target="_blank">OpenAPI</a> é uma especificação que determina como definir um *schema* da sua API.

But it was created before there existed Python type hints. So, to define every <abbr title="the definition of how data should be formed">schema</abbr> you need to use specific utils and classes provided by Marshmallow.

!!! check "Inspired **FastAPI** to"
    Use code to define "schemas" that provide data types and validation, automatically.

 ---
 Hervé Boutemy
 ---
 2013-08-02
 ---

Legacy Artifact Handlers Reference

  Maven 3 artifact handlers (see {{{../maven-artifact/apidocs/org/apache/maven/artifact/handler/ArtifactHandler.html} API}})
  define for each {{{../maven-model/maven.html#class_dependency}dependency type}} information on the artifact
  (classifier, extension, language) and how to manage it as dependency (add to classpath, include dependencies).