rasam 0.5.2

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rasam 0.5.2

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rasam
Rasa Improved
Usage
Installation
pip install rasam

Rasa config.yml
importers:
- name: rasam.PlaceholderImporter
fake_data_count: 10 # default value is 1

pipeline:
- name: rasam.RegexEntityExtractor
- name: rasam.URLEntityExtractor

Rasa nlu.yml
PlaceholderImporter
The PlaceholderImporter removes the need to write unnecessary information (eg. name, address, numbers, etc.) and helps focus on writing test data.
Using {} placeholder
nlu:
- intent: tell_name
examples: |
- My name is {name}
- I am {name} and he is {name}

Using @ placeholder
nlu:
- intent: tell_address
examples: |
- I live in @address
- I stay at @address and @address

Mixing {} and @ placeholders
It is possible to mix both {} and @ placeholders but it is recommended to use only one style for consistency.
Available placeholders

any (if you need just any data)
integer
decimal
number
name
first_name
last_name
text
word
paragraph
uri
url
local_uri
email
date
time
month
day
timezone
company
license_plate
address
city
country
user_agent
password
user_name
file_path

Rasam decorators
Rasa relies too heavily on classes to define objects like actions, forms, etc.
Rasam aims to remove these Rasa boilerplates to make writing chatbots easier.
@action decorator
The @action decorator converts function into an Action class.
Here is an example of how we can write custom classes in Rasa:
class ActionHelloWorld(Action):

def name(self) -> Text:
return "action_hello_world"

def run(self, dispatcher: CollectingDispatcher,
tracker: Tracker,
domain: Dict[Text, Any]) -> List[Dict[Text, Any]]:

dispatcher.utter_message(text="Hello World!")

return []

The above code can be simplified using Rasam's @action decorator.
from rasam import action


@action
def action_hello_world(
self: Action, dispatcher: CollectingDispatcher, tracker: Tracker, domain: Dict[Text, Any]
) -> List[Dict[Text, Any]]:
dispatcher.utter_message(text="Hello World!")
return []

Author

Ronie Martinez