A Python client for working with the FocusLab API.
Currently this client only implements trigger recording but eventually it will provide support for the full API.
First you need to create an instance of the FocusLabClient:
from brockman.client import FocusLabClient
client = FocusLabClient(api_key='da9dcaa0364541498d4ae6b4865cf395')The only required parameter for the client is api_key. You can get your API
key from your FocusLab Dashboard. Optionally you can also pass an
endpoint parameter but you probably shouldn't do this unless you've been
told to do so by somebody from FocusLab.
You're now ready to start making calls to the FocusLab API.
To record a trigger, all you need to do is call the record_trigger method
on a client instance:
from brockman.client import FocusLabClient
client = FocusLabClient(api_key='da9dcaa0364541498d4ae6b4865cf395')
client.record_trigger(
actor_id=user.id,
action='viewed',
obj='blog post',
identities={'email': user.email, 'user_id': user.id},
attributes={'plan': user.plan},
variables={'tags': post.tags, 'title': post.title}
)The parameters to record_trigger are described below:
actor_id
: A required string that acts as a session token. If you are using this
client in combination with the javascript tracking code, you can use
the fl_actor_id cookie value as the user's actor_id. Otherwise any
string that will be relatively consistent for a user can be used (e.g. a
user id).
action
: A required string that describes the event that the trigger is
representing (e.g. viewed, downloaded, streamed, etc).
obj
: A required string that describes the thing that the event being
represented by the trigger is happening to (e.g. artist, blog post,
product).
identities
: An optional dictionary of identities for the user. Any identities
provided will be returned when you download a group from FocusLab.
Additionally they are used to connect together activity streams from the
user. Dictionary values can be strings, integers, floats, or lists.
Possible identities include email addresses, user ids, and facebook ids.
attributes
: An optional dictionary of attributes for the user. Attributes are pieces
of data about a user that you'd like to filter against, but should not be
used for connecting activity streams. Dictionary values can be strings,
integers, floats, or lists. Possible attributes include plan type, user
level, or date joined.
variables
: An optional dictionary of variables for the trigger. Variables are pieces
of data about the event the trigger is representing. Dictionary values
can be strings, integers, floats, or lists. Possible variables include
genres, tags, categories, titles, product code, or author.
Brockman includes some optional pieces that make it a little easier to work within a Django environment.
The Django Client is a version of the FocusLabClient class that automatically
pulls configuration info in from Django's settings module.
To use the Django Client just set FL_API_KEY in your settings module and
then use the brockman.django.client.DjangoFocusLabClient class instead of
brockman.client.FocusLabClient.
Celery is a popular framework for handling delayed/async tasks. By using
the task provided at brockman.django.celery.tasks.record_trigger instead of
the client directly, you can record a trigger to FocusLab without introducing
any extra latency or errors to your own application. Below is an example of
using the record_trigger task.
from brockman.django.celery.tasks import record_trigger
record_trigger.delay(
actor_id=user.id,
action='viewed',
obj='blog post',
identities={'email': user.email, 'user_id': user.id},
attributes={'plan': user.plan},
variables={'tags': post.tags, 'title': post.title}
)As you can see the method signature is the same as on the client.
If you have any questions or run into any problems please feel free to email us at help@focuslab.io or to use the chat box at http://focuslab.io.