Python SDK

Introduction

Welcome to the developer documentation for the Kameleoon Python SDK! Our SDK gives you the possibility of running experiments and activating feature flags on your back-end Python server. Integrating our SDK into your web-application is easy, and its footprint (in terms of memory and network usage) is low.

You can refer to the SDK reference to check out all possible features of the SDK. Also make sure you check out our Getting started tutorial which we have prepared to walk you through the installation and implementation.

Latest version of the PHP SDK: 2.1.0 (changelog).

Getting started

This guide is designed to help you integrate our SDK in a few minutes and start running experiments in your Python applications. This tutorial will explain the setup of a simple A/B test to change the number of recommended products based on different variations.

Creating an experiment

First, you must create an experiment in the Kameleoon back-office so that our platform is aware of the new A/B test you're planning to implement on your side. Make sure that server-side type is chosen as shown below:

Upon successful creation of the experiment, you will need to get its ID to use in the SDK as an argument to the trigger_experiment() method.

Installing the SDK

Installing the Python client

pip install kameleoon-client-python

Installing the SDK can be directly achieved through an Python pip package. Our package is hosted on the official pip repository, so you just have to run the following command:

pip install kameleoon-client-python

Additional configuration

You should provide credentials for the Python SDK via a configuration file, which can also be used to customize the SDK behavior. A sample configuration file can be obtained here. We suggest to install this file to the default path of /etc/kameleoon/client-python.yaml, but you can also put it in another location and passing the path as an argument to the KameleoonClient() constructor method. With the current version of the Python SDK, those are the available keys:

• actions_configuration_refresh_interval: this specifies the refresh interval, in minutes, of the configuration for experiments and feature flags (the active experiments and feature flags are fetched from the Kameleoon servers). It means that once you launch an experiment, pause it, or stop it the changes can take (at most) the duration of this interval to be propagated in production to your servers. If not specified, the default interval is 60 minutes.
• visitor_data_maximum_size: this specifies the maximum amount of memory that the hash holding all the visitor data (in particular custom data) can take (in MB). If not specified, the default size is 500MB.
• default_timeout: this specifies the timeout, in milliseconds for network requests of SDK. It is recommended to set the value to 30 seconds or more if you do not have a stable connection. The default value is 15 seconds. Some methods have their own parameters for timeouts, but if you do not specify them explicitly, this value is used.
• environment: an option specifying which feature flag configuration will be used, by default each feature flag is split into production, staging, development. If not specified, will be set to default value of production. More information
• multi_threading: an option of type bool indicating whether threads can be used for network requests. By default, the option is False and everything is executed in one thread to avoid performance issues with GIL if (C)Python interpreter is using. Possible values: True , False.

Alternatively, you can use configuration_object of type KameleoonClientConfiguration as parameter during initializaion. It has the same list of arguments as a config file. configuration_object takes precedence over the configuration file and overwrites the settings from it.

Initializing the Kameleoon client

from kameleoon import KameleoonClient

SITE_CODE = 'a8st4f59bj'

kameleoon_client = KameleoonClient(SITE_CODE)

kameleoon_client = KameleoonClient(SITE_CODE, configuration_path='/etc/kameleoon/client-python.yaml')
# Second version of the Kameleoon::Client, using an asynchronous trigger_experiment() method

kameleoon_client = KameleoonClient(SITE_CODE)

kameleoon_client = KameleoonClient(SITE_CODE, logger=MyLogger)



from kameleoon.client_configuration import KameleoonClientConfiguration

configuration_object = KameleoonClientConfiguration()
kameleoon_client = KameleoonClient(SITE_CODE, configuration_object=configuration_object)

After installing the SDK into your application, configuring the correct credentials (in /etc/kameleoon/client-python.yaml) and setting up a server-side experiment on Kameleoon's back-office, the next step is to create the Kameleoon client in your application code.

The code on the right gives a clear example. A Kameleoon::Client is a singleton object that acts as a bridge between your application and the Kameleoon platform. It includes all the methods and properties you will need to run an experiment.

Using the Kameleoon Python SDK in a Django environment

If you use Django, we recommend you to initialize the Kameleoon client at server start-up, in the apps.py file of your Django application.

When you use python manage.py runserver Django start two processes, one for the actual development server and other to reload your application when the code change.

You can also start the server without the reload option, and you will see only one process running will only be executed once :

python manage.py runserver --noreload

You can also just check the RUN_MAIN env var in the ready() method itself.

def ready(self): if os.environ.get('RUN_MAIN', None) == 'true': configuration_path = os.path.join(ROOT_DIR, 'path_to_config', 'config.yml') self.kameleoon_client = KameleoonClient(SITE_CODE, configuration_path=configuration_path)

This only applies to local development when you use python manage.py runserver. In a production environment, the code in the ready() function will be executed only one time when the application is initialized.

from django.apps import apps

  my_application = apps.get_app_config('your_app')
  client = my_application.kameleoon_client

You can then access the Kameleoon client in your application.

Triggering an experiment

# If you don't use Django, but you're working in a web environment and want to use the visitor code via a cookie, you must provide custom implementations of the read_cookies() and write_cookies() methods.

visitor_code = kameleoon_client.get_visitor_code(request.COOKIES, 'example.com')
variation_id = 0

try:
    variation_id = kameleoon_client.trigger_experiment(visitor_code, 135471)
except NotAllocated as ex:
    # The user triggered the experiment, but did not activate it.
    # Usually, this happens because the user has been associated
    # with excluded traffic
    variation_id = 0
    client.logger.error(ex)
except NotTargeted as ex:
    # The user did not trigger the experiment, as the associated
    # targeting segment conditions were not fulfilled.
    # He should see the reference variation
    variation_id = 0
    client.logger.error(ex)
except ExperimentConfigurationNotFound as ex:
    # The user will not be counted into the experiment,
    # but should see the reference variation
    variation_id = 0
    client.logger.error(ex)

recommended_products_number = 5
# This is the default / reference number of products to display

if variation_id == 148382:
    # We are changing number of recommended products for this variation to 10
    recommended_products_number = 10
elif variation_id == 187791:
    # We are changing number of recommended products for this variation to 8
    recommended_products_number = 8

# Here you should have code to generate the HTML page back to the client, where recommendedProductsNumber will be used
response = JsonResponse({...})

Running an A/B experiment on your Python application means bucketing your visitors into several groups (one per variation). The SDK takes care of this bucketing (and the associated reporting) automatically.

Triggering an experiment by calling the trigger_experiment() method will register a random variation for a given visitor_code. If this visitor_code is already associated with a variation (most likely a returning visitor that has already been exposed to the experiment previously), then it will return the previous variation assigned for the given experiment.

Implementing variation code

if variation_id == 0
    # This is the default / reference number of products to display
    recommended_products_number = 5
elsif variation_id == 148382
    # We are changing number of recommended products for this variation to 10
    recommended_products_number = 10
elsif variation_id == 187791
    # We are changing number of recommended products for this variation to 8
    recommended_products_number = 8
end
 # Here you should have code to generate the response back to the client, where recommended_products_number will be used.

To execute different code paths depending on the variation assigned to the visitor, you will need the list of all the experiment's variation IDs. You can find these variation IDs (as well as the experiment ID) by opening the experiment in the back-office interface. By convention, the reference (original variation) always has an ID equal to 0.

Once you have the IDs of the different variations, you can implement a different action for each variation, and one of the code paths will be executed, based on the associated variation_id for the current visitor. Generally, this can be done using a simple if / else or switch mechanism. In our example, we just change the number of recommended products with two different variations.

Tracking conversion

visitor_code = kameleoon_client.get_visitor_code(cookies, "example.com")
goal_id = 83023

kameleoon_client.track_conversion(visitor_code, goal_id);

After you are done triggering an experiment, the next step is usually to start tracking conversions. This is done to measure performance characteristics according to the goals that make sense for your business.

For this purpose, use the track_conversion() method of the SDK as shown in the example. You need to pass the visitor_code and goal_id parameters so we can correctly track conversions for this particular visitor.

Obtaining results

Once your implementation is in place on the server side (experiment triggering, variations handling, and conversion tracking), it is time to launch the experiment on the Kameleoon platform. You do this in the same way as for a front-end test. Basic operations such as starting, pausing and stopping the experiment work exactly the same way.

After the experiment is launched, the first results will be available on our standard results page in the back-office after a duration of 30 minutes. This is because (as is the case with front-end testing) visits are considered over after 30 minutes of inactivity. Inactivity in this context means the absence of calls sent to the Kameleoon back-end servers (such calls are made via trigger_experiment(), track_conversion() or flush() methods).

Technical considerations

Kameleoon made an important architectural design decision with its SDK technology, namely that every SDK must comply with a zero latency policy. In practice, this means that any blocking remote server call is banned, as even the fastest remote call would add a 20ms latency to your application. And if for any reason our servers are slower to reply than usual (unfortunately, this can happen), this delay can quickly increase to hundreds of milliseconds, seconds... or even completely block the load of the web page for the end user. We believe that web performance is of paramount importance in today's world and we don't think adding server-side A/B testing or feature flagging capabilities should come at the cost of an increased web page rendering time. For this reason, we guarantee that the use of our SDKs has absolutely no impact on the performance of the host platform.

However, having a zero latency policy does impose some constraints. The main one is that user data about your visitor should be kept locally, and not fetched from a remote server. For instance, if you set a custom data for a given visitor, we must store this somehow in your server / infrastructure, not on our (remote) side.

In the case of the Python SDK, this is implemented via a hash of visitor data (where keys are the visitor_codes) directly on RAM. So if you use Kameleoon::CustomData.new() and then kameleoon_client.add_data() methods, the information will be stored in the application's RAM (the one hosting the SDK, usually an application server). The map is regularly cleaned (old visitors data is erased) so it should usually not grow too big in size, unless you have a very big traffic and use lots of custom data.

Overview

When you update your experiment or feature flag configuration, the SDK can get it in two different ways. The first - polling - consists in retrieving the configuration at regular intervals. The second - streaming - is to retrieve the new configuration immediately.

Polling

This mode of obtaining the configuration is used by default. The SDK will send a request to Cloudflare CDN at regular intervals to retrieve the configuration. If you have not set an interval in the SDK configuration, the configuration in SDK will be refreshed every 60 minutes. It can be configured with actions_configuration_refresh_interval parameter.

Benefits:
• When intervals are moderately spaced, this way of updating configuration consumes nominal memory and network resources.

Streaming

This mode allows the SDK to automatically take into account the new configuration without delay. When turned on, Kameleoon SDK is notified of any changes to the configuration in real-time, thanks to server-sent events (SSE).

Benefits:
• Makes it possible to propagate and apply a new configuration in real-time.
• Reduce network traffic compared to polling at short intervals, as streaming does not require sending periodic requests. It opens the connection once and then keeps it permanently open and ready to receive data.

If you rely on instant data updates (real-time), then streaming is for you. If you wish to activate this mode, please contact us.

Reference

This is a full reference documentation of the Python SDK.

If this is your first time working with the Python SDK, we strongly recommend you go over our Getting started tutorial to integrate the SDK and start experimenting in a few minutes.

kameleoon.KameleoonClient

init

from kameleoon import KameleoonClient

SITE_CODE = 'a8st4f59bj'

kameleoon_client = KameleoonClient(SITE_CODE)

kameleoon_client = KameleoonClient(SITE_CODE, configuration_path='/etc/kameleoon/client-python.yaml')

The starting point for using the SDK is the initialization step. All interactions with the SDK are done through an object named Kameleoon::Client, therefore you need to create this object.

Arguments

Kameleoon::Client

get_visitor_code

visitor_code = kameleoon_client.get_visitor_code(cookies)

visitor_code = kameleoon_client.get_visitor_code(cookies, default_visitor_code)

The get_visitor_code() helper method should be called to obtain the Kameleoon visitor_code for the current visitor. This is especially important when using Kameleoon in a mixed front-end and back-end environment, where user identification consistency must be guaranteed. The implementation logic is described here:

1. First we check if a kameleoonVisitorCode cookie or query parameter associated with the current HTTP request can be found. If so, we will use this as the visitor identifier.

2. If no cookie / parameter is found in the current request, we either randomly generate a new identifier, or use the default_visitor_code argument as identifier if it is passed. This allows our customers to use their own identifiers as visitor codes, should they wish to. This can have the added benefit of matching Kameleoon visitors with their own users without any additional look-ups in a matching table.

3. In any case, the server-side (via HTTP header) kameleoonVisitorCode cookie is set with the value. Then this identifier value is finally returned by the method.

For more information, refer to this article.

Arguments

Return value

trigger_experiment

visitor_code = kameleoon_client.get_visitor_code(cookies, "example.com")
experiment_id = 75253
variation_id = 0

try:
    variation_id = kameleoon_client.trigger_experiment(visitor_code, experiment_id)
except NotAllocated as ex:
    # The user triggered the experiment, but did not activate it.
    # Usually, this happens because the user has been associated
    # with excluded traffic
    variation_id = 0
    client.logger.error(ex)
except NotTargeted as ex:
    # The user did not trigger the experiment, as the associated
    # targeting segment conditions were not fulfilled.
    # He should see the reference variation
    variation_id = 0
    client.logger.error(ex)
except ExperimentConfigurationNotFound as ex:
    # The user will not be counted into the experiment,
    # but should see the reference variation
    variation_id = 0
    client.logger.error(ex)

To trigger an experiment, call the trigger_experiment() method of our SDK.

This method takes visitor_code and experiment_id as mandatory arguments to register a variation for a given user.

If such a user has never been associated with any variation, the SDK returns a randomly selected variation. If a user with a given visitor_code is already registered with a variation, it will detect the previously registered variation and return the variation_id.

You have to make sure that proper error handling is set up in your code as shown in the example to the right to catch potential exceptions.

Arguments

Return value

Exceptions Thrown

is_feature_active

visitor_code = kameleoon_client.get_visitor_code(cookies, "example.com")
feature_key = "new_checkout"
has_new_checkout = False

try
    has_new_checkout = kameleoon_client.is_feature_active(visitor_code, feature_key)
except FeatureConfigurationNotFound as ex:
    # The user will not be counted into the experiment,
    # but should see the reference variation
    has_new_checkout = False
except VisitorCodeNotValid as ex:
    # The visitor code which you passed to the method isn't valid and can't be accepted by SDK
    has_new_checkout = False

if has_new_checkout
    # Implement new checkout code here

To check if feature flag is active for a visitor, call the is_feature_active() method of our SDK.

This method takes a visitor_code and feature_key as mandatory arguments to check if the specified feature will be active for a given user.

If such a user has never been associated with this feature flag, the SDK returns a boolean value randomly (true if the user should have this feature or false if not). If a user with a given visitor_code is already registered with this feature flag, it will detect the previous feature flag value.

You have to make sure that proper error handling is set up in your code as shown in the example to the right to catch potential exceptions.

Arguments

Return value

Exceptions Thrown

get_feature_variation_key

visitor_code = kameleoon_client.get_visitor_code(cookies, "example.com")

feature_key = "feature_key"
variation_key = ""

try
    variation_key = kameleoon_client.get_feature_variation_key(visitor_code, feature_key)
    case variation_key
    when 'on'
        # main variation key is selected for visitorCode
    when 'alternative_variation'
        # alternative variation key
    else
        # default variation key
    end
except FeatureConfigurationNotFound as ex:
    # The user will not be counted into the experiment, but should see the reference variation
except VisitorCodeNotValid as ex:
    # The visitor code which you passed to the method isn't valid and can't be accepted by SDK

To get feature variation key, call the get_feature_variation_key method of our SDK.

This method takes a visitor_code and feature_key as mandatory arguments to get variation key for a given user.

If such a user has never been associated with this feature flag, the SDK returns a variation key randomly (according to the feature flag rules). If a user with a given visitor_code is already registered with this feature flag, it will detect the previous variation key value. If the user does not match any of the rules, the default value will be returned, which we can define in your customer's account.

You have to make sure that proper error handling is set up in your code as shown in the example to the right to catch potential exceptions.

Arguments

Return value

Exceptions Thrown

get_feature_variable

visitor_code = kameleoon_client.get_visitor_code(cookies, "example.com")

feature_key = "feature_key"
variable_key = "variable_key"
variation_value = ""

try:
    variable_value = kameleoon_client.get_feature_variable(visitor_code, feature_key, variable_key)
except FeatureConfigurationNotFound as ex:
    # The user will not be counted into the experiment, but should see the reference variation
except FeatureVariableNotFound as ex:
    # Requested variable not defined on Kameleoon's side
except VisitorCodeNotValid as ex:
    # The visitor code which you passed to the method isn't valid and can't be accepted by SDK

# your custom code depending of variable_value, e.g.
if variable_value == "value-1":
    # your custom code if variable == 'value-1'
elif variable_value == "value-2":
    # your custom code if variable == 'value-2'
else:

To get variable of variation key associated with a user, call the get_feature_variable method of our SDK.

This method takes a visitor_code, feature_key and variable_key as mandatory arguments to get a variable of variation key for a given user.

If such a user has never been associated with this feature flag, the SDK returns a variable value of variation key randomly (according to the feature flag rules). If a user with a given visitor_code is already registered with this feature flag, it will detect the variable value for previous associated variation. If the user does not match any of the rules, the variable of default value will be returned.

You have to make sure that proper error handling is set up in your code as shown in the example to the right to catch potential exceptions.

Arguments

Return value

Exceptions Thrown

get_variation_associated_data

require "json"

visitor_code = kameleoon_client.get_visitor_code(cookies, "example.com")
experiment_id = 75253

try:
    variation_id = kameleoon_client.trigger_experiment(visitor_code, experiment_id)
    json_object = kameleoon_client.get_variation_associated_data(variation_id) # Return a json encoded string
    first_name = json_object["firstName"]
except VariationConfigurationNotFound as ex:
    # The variation is not yet activated on Kameleoon's side, ie the associated experiment is not online

To retrieve JSON data associated with a variation, call the get_variation_associated_data() method of our SDK. The JSON data usually represents some metadata of the variation, and can be configured on our web application interface or via our Automation API.

This method takes the variation_id as a parameter and will return the data as a Hash instance. It will throw an exception (VariationConfigurationNotFound) if the variation ID is wrong or corresponds to an experiment that is not yet online.

Arguments

Return value

Exceptions Thrown

obtain_feature_variable

feature_key = "myFeature"
variable_key = "myVariable"

try:
    data = kameleoon_client.obtain_feature_variable(feature_key, variable_key)
except FeatureConfigurationNotFound as ex:
    # The feature is not yet activated on Kameleoon's side
except FeatureVariableNotFound as ex:
    # Request variable not defined on Kameleoon's side

To retrieve a feature variable, call the obtain_feature_variable() method of our SDK. A feature variable can be changed easily via our web application.

This method takes two input parameters: feature_key and variable_key. It will return the data with the expected type, as defined on the web interface. It will throw an exception (FeatureConfigurationNotFound) if the requested feature has not been found in the internal configuration of the SDK.

Arguments

Return value

Exceptions Thrown

get_feature_all_variables

feature_key = "myFeature"

try
    data = kameleoon_client.get_feature_all_variables(feature_key)
except FeatureConfigurationNotFound as ex:
    # The feature is not yet activated on Kameleoon's side

To retrieve the all feature variables, call the get_feature_all_variables method of our SDK. A feature variable can be changed easily via our web application.

This method takes one input parameter: feature_key. It will return the data with the Dict[str,Any] type, as defined on the web interface. It will throw an exception (FeatureConfigurationNotFound) if the requested feature has not been found in the internal configuration of the SDK.

Arguments

Return value

Exceptions Thrown

track_conversion

require "kameleoon"
require "kameleoon/data"

visitor_code = kameleoon_client.get_visitor_code(cookies, "example.com")
goal_id = 83023

kameleoon_client.add_data(visitor_code, Kameleoon::Browser.new(Kameleoon::BrowserType::CHROME))
kameleoon_client.add_data(
    visitor_code,
    Kameleoon::PageView.new("https://url.com", "title", [3]),
    Kameleoon::Interest.new(2)
)
kameleoon_client.add_data(visitor_code, Kameleoon::Conversion.new(32, 10, false))
kameleoon_client.track_conversion(visitor_code, goal_id)

To track conversion, use the track_conversion() method. This method requires visitor_code and goal_id to track conversion on this particular goal. In addition, this method also accepts revenue as a third optional argument to track revenue. The visitor_code is usually identical to the one that was used when triggering the experiment.

The track_conversion() method doesn't return any value. This method is non-blocking as the server call is made asynchronously if multi_threading is True

Arguments

add_data

require "kameleoon"
require "kameleoon/data"

visitor_code = kameleoon_client.get_visitor_code(cookies, "example.com")

kameleoon_client.add_data(visitor_code, Kameleoon::Browser.new(Kameleoon::BrowserType::CHROME))
kameleoon_client.add_data(
    visitor_code,
    Kameleoon::PageView.new("https://url.com", "title", [3]),
    Kameleoon::Interest.new(0)
)
kameleoon_client.add_data(visitor_code, Kameleoon::Conversion.new(32, 10, false))

To associate various data with the current user, we can use the add_data() method. This method requires the visitor_code as a first parameter, and then accepts several additional parameters. These additional parameters represent the various Data Types allowed in Kameleoon.

Note that the add_data() method doesn't return any value and doesn't interact with the Kameleoon back-end servers by itself. Instead, all declared data is saved for further sending via the flush() method described in the next paragraph. This reduces the number of server calls made, as data is usually grouped into a single server call triggered by the execution of flush().

Arguments

flush

require "kameleoon"
require "kameleoon/data"

visitor_code = kameleoon_client.get_visitor_code(cookies, "example.com")

kameleoon_client.add_data(visitor_code, Kameleoon::Browser.new(Kameleoon::BrowserType::CHROME))
kameleoon_client.add_data(
    visitor_code,
    Kameleoon::PageView.new("https://url.com", "title", [3]),
    Kameleoon::Interest.new(0)
)
kameleoon_client.add_data(visitor_code, Kameleoon::Conversion.new(32, 10, false))

kameleoon_client.flush(visitor_code)

Data associated with the current user via add_data() method is not immediately sent to the server. It is stored and accumulated until it is sent automatically by the trigger_experiment() or track_conversion() methods, or manually by the flush() method. This allows the developer to control exactly when the data is flushed to our servers. For instance, if you call the add_data() method a dozen times, it would be a waste of ressources to send data to the server after each add_data() invocation. Just call flush() once at the end.

The flush() method doesn't return any value. This method is non-blocking as the server call is made asynchronously if multi_threading is True

Arguments

get_experiment_list

all_experiment_list = kameleoon_client.get_experiment_list()

Returns a list of experiment IDs currently available for the SDK

Return value

get_experiment_list_for_visitor

active_experiments_for_visitor = kameleoon_client.get_experiment_list_for_visitor(visitor_code) # only_allocated == True

all_experiments_for_visitor = kameleoon_client.get_experiment_list_for_visitor(visitor_code, only_allocated=False)

Returns a list of experiment IDs currently available for the SDK or a list of experiment IDs currently available for specific visitor_code according to the only_allocated option. This method can takes two input parameters: visitor_code and only_allocated. If only_allocated parameter is True result contains only allocated experiments, otherwise it contains all targeted experiments for specific visitor_code.

Arguments

Return value

get_feature_list

all_feature_list = kameleoon_client.get_feature_list()

Returns a list of feature flag keys currently available for the SDK

Return value

get_active_feature_list_for_visitor

active_feature_flag_for_visitor = kameleoonClient.get_active_feature_list_for_visitor(visitor_code)

This method takes only input parameters: visitorCode. Result contains only active feature flags for a given visitor.

Arguments

Return value

retrieve_data_from_remote_source

    kameleoon_client.retrieve_data_from_remote_source('test') # 2 seconds default timeout
    kameleoon_client.retrieve_data_from_remote_source('test', 1)

The retrieve_data_from_remote_source() method allows you to retrieve data (according to a key passed as argument) for specified siteCode (specified in Kameleoon::ClientFactory.create) stored on a remote Kameleoon server. Usually data will be stored on our remote servers via the use of our Data API. This method, along with the availability of our highly scalable servers for this purpose, provides a convenient way to quickly store massive amounts of data that can be later retrieved for each of your visitors / users.

Arguments

Return value

on_update_configuration

kameleoon_client.on_update_configuration(
    // configuration was updated
)

The on_update_configuration() method allows you to handle the event when configuration has updated data. It takes one input parameter: callable handler. The handler that will be called when the configuration is updated using a real-time configuration event.

Arguments

Kameleoon::Data

Browser

kameleoon_client.add_data(visitor_code, Kameleoon::Browser.new(Kameleoon::BrowserType::CHROME))

PageView

kameleoon_client.add_data(visitor_code, Kameleoon::PageView.new("https://url.com", "title", [3]))

Conversion

kameleoon_client.add_data(visitor_code, Kameleoon::Conversion.new(32, 10, false))

CustomData

kameleoon_client.add_data(visitor_code, Kameleoon::CustomData.new(1, "some custom value"))

Device

kameleoon_client.add_data(visitor_code, Device(DeviceType.DESKTOP))

UserAgent

kameleoon_client.add_data(visitor_code, UserAgent('TestUserAgent'))