Creating a new logic adapter¶
You can write your own logic adapters by creating a new class that
inherits from LogicAdapter and overrides the necessary
methods established in the LogicAdapter base class.
Example logic adapter¶
from chatterbot.logic import LogicAdapter
class MyLogicAdapter(LogicAdapter):
def __init__(self, chatbot, **kwargs):
super().__init__(chatbot, **kwargs)
def can_process(self, statement):
return True
def process(self, input_statement, additional_response_selection_parameters):
import random
# Randomly select a confidence between 0 and 1
confidence = random.uniform(0, 1)
# For this example, we will just return the input as output
selected_statement = input_statement
selected_statement.confidence = confidence
return selected_statement
Directory structure¶
If you create your own logic adapter you will need to have it in a separate file from your chat bot. Your directory setup should look something like the following:
project_directory
├── cool_chatbot.py
└── cool_adapter.py
Then assuming that you have a class named MyLogicAdapter in your cool_adapter.py file,
you should be able to specify the following when you initialize your chat bot.
ChatBot(
# ...
logic_adapters=[
{
'import_path': 'cool_adapter.MyLogicAdapter'
}
]
)
Responding to specific input¶
If you want a particular logic adapter to only respond to a unique type of
input, the best way to do this is by overriding the can_process
method on your own logic adapter.
Here is a simple example. Let’s say that we only want this logic adapter to generate a response when the input statement starts with “Hey Mike”. This way, statements such as “Hey Mike, what time is it?” will be processed, but statements such as “Do you know what time it is?” will not be processed.
def can_process(self, statement):
if statement.text.startswith('Hey Mike'):
return True
else:
return False
Interacting with services¶
In some cases, it is useful to have a logic adapter that can interact with an external service or api in order to complete its task. Here is an example that demonstrates how this could be done. For this example we will use a fictitious API endpoint that returns the current temperature.
def can_process(self, statement):
"""
Return true if the input statement contains
'what' and 'is' and 'temperature'.
"""
words = ['what', 'is', 'temperature']
if all(x in statement.text.split() for x in words):
return True
else:
return False
def process(self, input_statement, additional_response_selection_parameters):
from chatterbot.conversation import Statement
import requests
# Make a request to the temperature API
response = requests.get('https://api.temperature.com/current?units=celsius')
data = response.json()
# Let's base the confidence value on if the request was successful
if response.status_code == 200:
confidence = 1
else:
confidence = 0
temperature = data.get('temperature', 'unavailable')
response_statement = Statement(text='The current temperature is {}'.format(temperature))
response_statement.confidence = confidence
return response_statement
Providing extra arguments¶
All key word arguments that have been set in your ChatBot class’s constructor
will also be passed to the __init__ method of each logic adapter.
This allows you to access these variables if you need to use them in your logic adapter.
(An API key might be an example of a parameter you would want to access here.)
You can override the __init__ method on your logic adapter to store additional
information passed to it by the ChatBot class.
class MyLogicAdapter(LogicAdapter):
def __init__(self, chatbot, **kwargs):
super().__init__(chatbot, **kwargs)
self.api_key = kwargs.get('secret_key')
The secret_key variable would then be passed to the ChatBot class as shown below.
chatbot = ChatBot(
# ...
secret_key='************************'
)
