phantom-dev 0.14.5
Utilities to simplify Phantom app development
Requires Python: >=3.6
Keywords: splunk,phantom

# phantom-dev

Utilities for simplifying the development of Phantom apps

## Author

David Finn: dfinn@splunk.com

## Requirements

* Splunk>Phantom
* Python 3.6 or higher

## Features

* Commands for rapid creation, packaging, and deployment of Phantom apps
* Streamlined Python interface for action definition
* Automatic bundling of dependencies using standard pip requirements files
* Pytest integration with automatic mocking and sys path management for local
unit testing
* Debugging remote execution against local source files over a secure SSH
tunnel
* Remote app log monitoring
* Asynchronous action handler support
* Dedicated log files for connector logging output
* Cross-platform client
* Support for apps not created with `phantom-dev`
* Comprehensive Phantom API mocking with type-hinting
* Models for Phantom data types, including `Containers` and `Artifacts`


## Installation

```sh
pip install phantom-dev
```

## Description

`phantom-dev` is a command-line utility for creating, building, and deploying
Phantom apps.

App packages are built from project directories containing a YAML metadata file
and a connector implemented in Python.
Any other files in the project directory will be packaged and included with
the app.

### App Metadata

The `metadata.yaml` file in the project directory will be used to generate the
app JSON expected by the Phantom platform.
Minor alterations to the JSON structure have been made to assist with
readability and maintainability;
related keys have been grouped under a common prefix key
(e.g. JSON `project-*` keys are defined under the `project` object in the YAML)
and lists of uniquely-identifiable objects have been converted to objects
(e.g. JSON `actions` is now a mapping of action names to action data, rather
than a list of action objects with unique and potentially conflicting names).

For information on the generated app metadata, see the official Phantom
documentation.

### Connector Implementation

The `phantom_dev.action_handler` module greatly simplifies the implementation
of a `phantom.base_connector.BaseConnector` subclass, which is the basis for
Phantom app development.

Action handler methods defined using the 
`phantom_dev.action_handler.ActionHandler` decorator will be used to
dynamically infer action metadata unless overridden in the metadata file.
Action names, parameter names, and parameter types can all be inferred from
the implementation assuming parameters are type-annotated in the code.

The `phantom_dev.action_handler` module will be automatically embedded in the
`dependencies` directory when the app is built, allowing the developer to
make full use of the module without being concerned with managing it as a
dependency.

Edge cases and use of more specialised `BaseConnector` methods should be dealt
with as normal, in accordance with the official Phantom documentation.

### App Versioning

Unless a version is explicitly specified in the app metadata,
[`roboversion`](https://pypi.org/project/roboversion/) will be used to
automatically detect the project version from git repository state.
To set the version, tag the current commit with a version label
(e.g. `v1.2.3`).

If the current commit doesn't have a version label, `roboversion` will create
a unique development version using the most recent version tag.

### Compatibility with Apps Not Created with `phantom-dev`

Because `phantom-dev` relies on specific library features to infer action
metadata from code implementations, metadata inference will **not** work with
a connector that has been implemented without `phantom-dev`.
Full specification of app metadata must therefore be provided in either
`metadata.yaml` or in the old-style `.json` file.

If a `.json` file is used instead of `metadata.yaml`, `phantom-dev` will
**not** autogenerate a new `.json` metadata file.
Certain packaging features, such as autopopulation of dependency metadata,
will therefore be disabled.

Finally, the `debug` command will not work unless the app is configured as a
`debugpy` listener when executed as `__main__`.
While `phantom-dev` apps implement this by default, other apps will have to
implement this behaviour or forgo the use of `debug`.

Aside from these limitations, `phantom-dev` should work normally with
any Phantom app once extracted into a project directory.

## Quickstart

Running `phantom-dev create` will prompt the user for the set of details
required to define a Phantom app.

```sh
$ phantom-dev create "My Special App"
Product Vendor: Special Vendor
Product Name: Special Product
Description: My special little app
Publisher: David Finn
License: Special license
```

A new project directory will be created and populated with a `metadata.yaml`
and a `connector.py`.
The metadata YAML will contain the details provided by the user, and the
connector module will define an example connector implementation.
The metadata will also be populated with action information required by the
example connector implementation.


### Configuring IDE Autocompletion

To enable your IDE to resolve Phantom module methods, configure your
IDE to set its `PYTHONPATH` environment variable to include the location of
`phantom_dev.dummy`.

The dummy file path can be found by running `phantom-dev --dummy_path`.
The path will typically resemble
`<PYTHON_ENV>/lib/python<VERSION>/site-packages/phantom_dev/dummy`,
where `PYTHON_ENV` is your python environment and `<VERSION>` is your Python
version.

#### Example: VSCode

Find the path to the dummy module:
```
$ phantom-dev --dummy_path
/home/USER/my_app/.venv/lib/python3.8/site-packages/phantom_dev/dummy
```

To add this to the PYTHONPATH for VSCode, place the following entry in the
`.env` file in the workspace directory (create the file if it doesn't exist):

```
PYTHONPATH=/home/USER/my_app/.venv/lib/python3.8/site-packages/phantom_dev/dummy
```


### Extending the App

The following defines the implementation of a new action called "echo message":

```python
from phantom_dev.action_handler import ActionHandler, SmartConnector
...
class Connector(SmartConnector, main=True):
	...
	@ActionHandler
	def echo_message(self, message: str):
		"""
		Echo a message

		:param message: The message to be echoed
		"""
		yield {'response': message}
	...

	@echo_message.summary
	def summarise_echo(self, results):
		result, = results
		return {'response': result['response']}
...
```

If possible, the action description will be inferred from the method docstring-
in this case, "Echo a message".
Similarly, parameter descriptions will also be inferred if specified in the
docstring with appropriate docstring syntax (currently, Sphinx syntax is
confirmed to work).
In this case, the description for the paramter `message` will be "The message
to be echoed".

The action summary data for the `echo message` action is created by decorating
the summary method with `@echo_message.summary`.
Action summary methods take the action results as an iterable, and create
the appropriate summary data.

Thanks to `SmartConnector`, all methods of the `BaseConnector` class are
available for use in the action handler logic implementation.

Although the app will successfully install and run given the above metadata,
it's still missing output fields and `contains` information.
Unless specfied in `metadata.yaml`, `phantom-dev` (and therefore Phantom) has
no way of knowing that the output data should contain a `response` field
with a `data_type` of `string`.
If we want to add `contains` information, such as setting a `contains`
value of `['text']` for the message and response, we also need to specify
these somewhere.

These *can* be specified as normal using `metadata.yaml`, but this can also
be done directly using appropriate `ActionHandler` parameters and methods:

```python
from phantom_dev.action_handler import ActionHandler, contains, SmartConnector
...

@contains('text')
class Text(str):
	"""A string-based type which contains `['text']`"""

...

class Connector(SmartConnector, main=True):
	...
	@ActionHandler(action_type='generic', data_contains={'response': Text})
	def echo_message(self, message: Text):
		"""
		Echo a message

		:param message: The message to be echoed
		"""
		yield {'response': message}

	@echo_message.summary_contains({'response': Text})
	def summarise_echo(self, results):
		result, = results
		return {'response': result['response']}
	...
...
```

Several things are happening here.
First, a new type called `Text` inherits from `str` and is decorated with the
`@contains` decorator, mapping it to a `contains` value of `['text']`.
This type is used instead of `str` to annotate the `message` parameter of the
action, which allows `phantom-dev` to infer both the `data_type` and the
`contains` value for the parameter.

Next, the `data_contains` parameter is being used to instantiate the method
constructor, and a mapping of `'response'` to `Text` is being specified as the
argument.
This allows `phantom-dev` to infer that each data result has a field named
`'response'`, and that its `data_type` and `contains` information should
be inferred from the `Text` type the same way it was for the `message`
parameter.
Note that we can also set the action `type` metadata with use of another
decorator parameter, `action_type`.

Finally, the same thing is being done with the `echo_message.summary_contains`
decorator (used instead of `summary`), though in this case it's the action
summary fields rather than the results data fields that are being described.

Use of these decorators is preferred to `metadata.yaml` specification, as they
keep metadata definitions colocated with the data they describe.
They also encourage code reuse though the use of common `contains`-mapped
type definitions, shareable between both parameters and output fields.

#### Parameters with specific allowed values

To define a set of allowed values for a parameter, define a subclass of `Enum`
with a concrete type that maps to a `data_type`, and assign those values to
members of that class.

Specify this class as the parameter annotation to allow `phantom-dev` to infer
the `value_list` metadata for this parameter.

For example, for a `rule` string parameter with allowed values `'allow'` and
`'block'`:

```python
@contains('rule')
class Rule(str, Enum):
	ALLOW = 'allow'
	BLOCK = 'block'

...
class Connector(SmartConnector, main=True):
	@ActionHandler(...)
	def set_rule(self, rule: Rule, ...): ...
```

### Dependencies

Any package specified in `requirements-whl.txt` in the app project directory
will be automatically downloaded as a wheel and packaged with the app.
`requirements-whl.txt` should be a normal `pip` requirements file.

For example, if the app requires the `roboversion` package, a
`requirements-whl.txt` could be created with the following content:

```
roboversion>=2

```

When the app is built, the `roboversion` wheel will be automatically downloaded
and included in the package `wheels` directory, and the autogenerated app
JSON will specify its location for Phantom installation.

If there isn't a compatible wheel for the Phantom platform,
`requirements-sdist.txt` can be used instead of `requirements-whl.txt`.
This will download the package as a source distribution instead of as a wheel.
Unlike `requirements-whl.txt`, subdependencies will not be automatically
installed for packages in `requirements-sdist.txt`, as sdist installation is
intended to be used as a backup for when a wheel is unavailable.

Finally, `requirements-pypi.txt` can be used to specify dependencies that won't
be packaged with the app, but that will be downloaded and installed by the
Phantom platform itself.


### Testing

`phantom-dev` can automatically manage `pytest` execution, including adding
the app directory to the Python path and mocking imports of the `phantom`
module, using the `phantom-dev test` command:

```sh
$ phantom-dev test <app_directory> [<pytest arguments>...]
```

Apps created using `phantom-dev create` will be initialised with a `tests`
directory containing a basic example of a `pytest` script.

If working from the app directory, `pytest` will automatically locate and run
the tests when it is invoked:

```sh
$ phantom-dev test
```

`pytest` also accepts a test directory location:

```sh
$ phantom-dev test my/special/test/location
```

If working from a directory other than the app directory, the app directory can
also be specified:

```sh
$ phantom-dev test --app-directory my_special_app
```

Positional arguments will be passed to `pytest`, allowing us to specify test
locations as well:

```sh
$ phantom-dev test --app-directory my_special_app my/special/test/location
```

As `phantom-dev test` is a wrapper around `pytest` invocation, it supports
all of `pytest`'s features and behaviour.
To avoid capturing optional parameters before passing them to `pytest`, use the
pseudo-argument `--`:

```sh
$ phantom-dev test --app-directory my_special_app -- --show-locals my/special/test/location
```

Refer to the `pytest` documentation for details.


### Deploying the App

Once the app is ready to install, assuming a Phantom server location of
`phantom.example.com`:

```sh
$ phantom-dev push my_special_app/ root@phantom.example.com
```

Note: The Phantom server must be a known host; SSH to it first to confirm
credentials and connectivity.

The user will be prompted for the SSH password. Once supplied, the app will be
automatically packaged, sent to the Phantom server, and installed.

The SSH password can also be provided as part of the command:

```sh
$ phantom-dev push my_special_app/ root:PASSWORD@phantom.example.com
```

If certificate authentication is used, an empty password can be also be
specified:

```sh
$ phantom-dev push my_special_app/ root:@phantom.example.com
```

### Remote Debugging

`phantom-dev` streamlines the debugging process by automating the creation of
test action data and configuring a debugger connection through a secure SSH
tunnel.
To open a remote debugging session:

```sh
$ phantom-dev debug my_special_app/ root:@phantom.example.com "dummy action"
```

This command will:

1. Generate a test action JSON file on the remote Phantom host
2. Open an SSH tunnel to the remote Phantom host, forwarding the local
port to the remote debugging port
3. Wait for a debugger connection, then run the connector script with the
correct Python path using the generated action JSON

A local debugging client (e.g. Visual Studio Code) will then be able to connect
and step through action execution.

#### Visual Studio Code Debugging Configuration

Visual Studio Code debugging is configured through entries in `launch.json`.

To use the SSH tunnel provided by the `phantom-dev debug` command, create an
entry configured to connect to `localhost` on the specified debugger port:

```json
		...
		{
			"name": "Remote Debuggable Phantom App",
			"type": "python",
			"request": "attach",
			"connect": {
				"host": "localhost",
				"port": 8869
			},
			"pathMappings": [
				{
					"localRoot": "${workspaceFolder}/my_special_app/connector.py",
					"remoteRoot": "/opt/phantom/apps/myspecialapp_<SOME UUID>/connector.py"
				}
			]
		}
		...
```

The `pathMappings` entry should be configured to map the connector module to
its location in the installed app on the remote Phantom host.
The installed app folder will be under `<PHANTOM_HOME>/apps`.

Once configured, a debugging session can be run against the remote action
execution with full debugger functionality.

### App Documentation

Aside from the action and parameter descriptions which can be inferred from
connector method docstrings, Phantom supports adding more detailed
documentation to apps using a `readme.html` file.

Apps created with `phantom-dev` will be created with a default `readme.html`,
which can be updated with arbitrary HTML content.


### Other commands

For information on the other `phantom-dev` subcommands including `package` and
`deploy`, run:

```sh
$ phantom-dev --help
$ phantom-dev <subcommand> --help
```

## Details

In the above example, use of the `ActionHandler`
(or`ActionHandler.data_contains`) decorator wraps the decorated `echo_message`
method in the logic required for error handling and results reporting.
The `param` dictionary is automatically unpacked as keyword arguments to
handler method, allowing for quick and explicit argument validation and
intuitive access to action parameters.
`param` contains the parameters described in the app JSON.

Handler methods such as `echo_message` are expected to return iterables of
results data.
The items from this iterable are added as data objects to the `ActionResult`.
Implementing handler methods as generators is highly convenient, as this allows
custom logic to be run any time before or after data is yielded, but methods
can also be implemented as normal functions that return iterable objects.

The `HandlerMixin` superclass provided by `SmartConnector` automatically
delegates incoming actions to the correct method based on the action
identifier.

`SmartConnector` also wraps the functionality of a the `main_connector`
decorator. `main_connector` simply calls the class's `main` method if
the class is defined in the `__main__` module, reproducing the testing
functionality provided by autogenerated app wizard code.

### Signaling Failure

Failure is signaled through raising exceptions.
If the handler executes without raising an exception, the action is treated as
a success.

To implement an `echo fail` action that does the same thing as `echo message`,
but always fails after producing results:

```python
...
@contains('text')
class Text(str):
	"""A string-based type which contains `['text']`"""

...

class Connector(SmartConnector, main=True):
	...
	@ActionHandler(action_type='generic', data_contains={'response': Text})
	def echo_message(self, message: Text):
		"""
		Echo a message

		:param message: The message to be echoed
		"""
		yield {'response': message}

	@echo_message.summary_contains({'response': Text})
	def summarise_echo(self, results):
		result, = results
		return {'response': result['response']}
	...

	@ActionHandler(action_type='generic', data_contains={'response': Text})
	def echo_fail(self, **param):
		"""
		Echo the message as normal, then fail
		"""
		# Demonstration of re-packing param; this will be the same as the
		# original param dictionary, which we can then unpack for the call
		# to echo_message.
		# Unfortunately, this will require manual specification of more
		# parameter metadata.
		yield from self.echo_message(**param)
		raise RuntimeError('Failed on purpose')

	# The same summary method can be decorated multiple times for different
	# handlers to duplicate functionality
	@echo_fail.summary_contains({'response': Text})
	@echo_message.summary_contains({'response': Text})
	def summarise_echo(self, results):
		result, = results
		return {'response': result['response']}

```

In the example, parameter packing with `**param` was used instead of describing
and annotating the parameters for `echo fail`.
This is possible but not recommended, because now the user must manually
specify more parameter information in `metadata.yaml`:

```yaml
...
actions:
	...
	echo_fail:
		parameters:
			message:
				data_type: string
				description: The message to be echoed
				required: true
				contains:
					- text
	...
...
```

### Actions with no results

`test connectivity` is an example of an action which produces no results.
The handler method needs only to return an empty iterable, which is easily
accomplished by returning an empty collection rather than implementing a
generator:

```python
...
@contains('text')
class Text(str):
	"""A string-based type which contains `['text']`"""

...

class Connector(SmartConnector, main=True):
	...
	@ActionHandler(action_type='generic', data_contains={'response': Text})
	def echo_message(self, message: Text):
		"""
		Echo a message

		:param message: The message to be echoed
		"""
		yield {'response': message}

	@echo_message.summary_contains({'response': Text})
	def summarise_echo(self, results):
		result, = results
		return {'response': result['response']}
	...

	@ActionHandler(action_type='generic', data_contains={'response': Text})
	def echo_fail(self, **param):
		"""
		Echo the message as normal, then fail
		"""
		# Demonstration of re-packing param; this will be the same as the
		# original param dictionary, which we can then unpack for the call
		# to echo_message.
		# Unfortunately, this will require manual specification of more
		# parameter metadata.
		yield from self.echo_message(**param)
		raise RuntimeError('Failed on purpose')

	# The same summary method can be decorated multiple times for different
	# handlers to duplicate functionality
	@echo_fail.summary_contains({'response': Text})
	@echo_message.summary_contains({'response': Text})
	def summarise_echo(self, results):
		result, = results
		return {'response': result['response']}

	@ActionHandler(action_type='generic')
	def test_connectivity(self):
		"""
		Check `echo message` and `echo fail`
		"""
		# The test connectivity action is a special case that does not
		# receive a param dictionary at all, so there are no arguments to
		# unpack
		test_value = 'SOME TEST MESSAGE'
		results = []
		try:
			for result in self.echo_fail(test_value):
				results.append(result)
		except RuntimeError:
			pass
		else:
			raise RuntimeError('echo fail failed to fail')

		message, = results
		if message != test_value:
			raise ValueError('echo fail failed to echo')

		return []

```

It would also be possible to achieve this with a `return` statement before a
`yield` statement in a generator, or by failing before any results are yielded.


### Action Context

The hidden phantom action `context` parameter can be accessed as the `context`
member of the connector object during action execution.
This data contains entries for `artifact_id`, `guid`, and `parent_action_run`;
see 'Understanding Datapaths' in the offical phantom documentation for details.


### Logging

The `logger` member of the connector object is a standard Python
`logging.Logger` object that can be used in the normal way.

All log messages of `ERROR` and above will be logged to 
`<syslog>/phantom/spawn.log`.
`<syslog>` will typically be `/var/log` on privileged installations and
`<PHANTOM_HOME>/var/log` on unprivileged installations.

If the system is configured for `DEBUG` logging, all messages of `DEBUG`
and above will also be logged.
Additionally, all log messages of level `INFO` and above will be reported
using `BaseConnector.save_progress` regardless of Phantom debug configuration.

The connector will also keep logfiles in the app's state directory containing
logging records created by calls to the connector's `logger`.
This allows convenient consumption of connector logging without having to
filter through the content of `spawn.log`.
The logfile can be found at
`<PHANTOM_HOME>/local_data/app_states/<APP_ID>/logs/connector.log`.


### Vault Files

The `get_vault_path` method can be used to retrieve a `pathlib.Path` object
from a vault ID:

```python
vault_path = connector.get_vault_path('<VAULT_ID>')
with vault_path.open() as vault_file:
	...
```

This is implemented to work on both the Phantom 4.10 and 4.8 Vault interfaces.


### Persistent State

The `state` connector property can be used to access and save persistent state.
The property will automatically load the peristent state `dict` from the
filesystem on the first access, and save the current state to the filesystem at
the end of action handling if such access has been performed.
This property is intended to replace the variety of methods available on the
base connector for loading, accessing, and saving state data.

More sophisticated state management can be performed using the `open_state`
method.
Providing a subpath of the app's state directory to the method will allow
IO to the specified file.
If no subpath is specified, the default state file containing the state dict
will be opened.


### Asynchronous Actions

Scheduling actions to run simultaneously can be configured with the optional
`lock` parameter given to the `ActionHandler` decorator.

Within a single action, asynchronous coroutine execution can be configured by
implementing the action handler as an `async` coroutine method.

#### Asynchronous Action Scheduling

By default, actions are scheduled synchronously with regard to the asset
running the action; i.e. only one action will be running on a given asset at
at time.

This behaviour can be altered by specifying a value for `ActionHandler`'s
`lock` parameter.
If set to `None`, the action will be scheduled asynchronously with regard to
all other actions.
If given a `str` value, the action will be scheduled synchronously with regard
to all other actions using the same `lock` value.

A timeout (in seconds) for lock acquision can be set by specifying a value for
`ActionHandler`'s `lock_timeout` parameter.

##### Lock Data Paths

Action parameter or configuration values can be specified as data paths for the
`lock` value, by setting the value to `parameter.<name>` or
`configuration.<name>` respectively.
If a datapath for a parameter or configuration value is specified, its value at
the time of action execution will be used as the lock value.
This allows configurable groups of actions which will be scheduled
synchronously with regard to each other, but asynchronously with regard to all
other actions.

#### Asynchronous Coroutine Execution

If a handler method is reused by other handler methods, an asynchronous
implementation will allow the method to be run concurrently with other
coroutines.
Additionally, implementing a handler as an asynchronous coroutine function
will cause the connector to automatically handle the event loop, meaning that
`await` statements can be made in the function implementation without
manual event loop configuration.

Consider:

```python
@ActionHandler
def slow_action(self):
	"""
	Two long IO operations
	"""
	expensive_call()
	expensive_call()

@ActionHandler
def even_slower_action(self):
	"""
	Two slow actions (4 long IO operations)
	"""
	self.slow_action()
	self.slow_action()
```

Executing a single `even_slower_action` action will cause `expensive_call` to
be invoked 4 times in sequence.

Assuming a significant portion of `expensive_call`'s execution time is waiting
for IO operations, an asynchronous implementation called `async_call` will
allow other coroutines to run while it waits.
Assuming access to `async_call`, we can refactor our actions using asynchronous
coroutine functions:

```python
@ActionHandler
async def efficient_action(self):
	"""
	Two long IO operations run concurrently
	"""
	await asyncio.gather(async_call(), async_call())

@ActionHandler
async def even_more_efficient_action(self):
	"""
	Two efficient actions run concurrently
	(4 long IO operations run concurrently)
	"""
	await asyncio.gather(self.efficient_action(), self.efficient_action())
```

Under normal circumstances, `even_slower_action` will take almost 4 times as
long to run as `even_more_efficient_action` while producing the same results.
This efficiency will scale over the number of actions that can be run
concurrently.


### Data Ingestion

Implementing an `on_poll` `ActionHandler` method allows the app to ingest
containers based on arbitrary logic according to a configured schedule.

The default connector includes a reference implementation for `on_poll`.
The following example outlines ingestion of a single container with a single
artifact:

```python
...
	@ActionHandler(action_type='ingest', read_only=True)
	def on_poll(
			self,
			start_time: int,
			end_time: int,
			container_count: int,
			artifact_count: int,
			container_id: str = None,
	):
		"""
		Callback action for the on_poll ingest functionality.

		:param container_id: Container IDs to limit the ingestion to.
		:param start_time:
			Start of time range, in epoch time (milliseconds)

			If not specified, the default is past 10 days
		:param end_time:
			End of time range, in epoch time (milliseconds)

			If not specified, the default is now"
		:param container_count:
			Maximum number of container records to query for.
		:param artifact_count: Maximum number of artifact records to query for.
		"""
		container = Container.from_fields(name='example container', ...)
		container.add_artifact(name='example artifact', ...)
		return self.save_all_containers([container])
...
```

### Rendering Output

Action output rendering can be customised by adding a `render` section to the
action entry in the `metadata.yaml`, which takes the same data as specified in
the official documentation.
If using a custom view, the `ActionHandler.custom_view` decorator can be used
to automatically populate the `render` section of the action metadata.

#### Custom Views

Adding a custom view requires registering a custom view function and creating
a custom view HTML template.

To register a custom view function, the `ActionHandler.custom_view` decorator
can be used on the function.
Alternatively, a custom view can be manually specified by adding a `render`
section to the action entry in the `metadata.yaml`.

Normally, the return value of the function is a string that contains the path
to the HTML template used to render the view.
If the decorator is used to register the function and the function doesn't
return a value, the template location will be inferred to be
`views/<function_name>.html`.

Refer to the Phantom documentation for more information on using custom views.

## App Logo

The app logo can be customised by including an icon file in the app directory
with the name `logo.<extension>`.
`logo_light.<extension>` and `logo_dark.<extension>` can also be used to
differentiate between light-mode and dark-mode logos.
Arbitrary filenames and paths can also be used; specify a path for `logo` in
the app metadata, or paths for `light` and `dark` under `logo` to configure
the respecitve themes.

### Default Logo

<div>Icons made by <a href="https://www.freepik.com" title="Freepik">Freepik</a> from <a href="https://www.flaticon.com/" title="Flaticon">www.flaticon.com</a></div>