Examples

The following examples are available to help you get started with ChatterBot.

Note

Before you run any example, you will need to install ChatterBot on your system. See the Setup guide for instructions.

Simple Example

from chatterbot import ChatBot
from chatterbot.trainers import ListTrainer

# Create a new chat bot named Charlie
chatbot = ChatBot('Charlie')

trainer = ListTrainer(chatbot)

trainer.train([
    "Hi, can I help you?",
    "Sure, I'd like to book a flight to Iceland.",
    "Your flight has been booked."
])

# Get a response to the input text 'I would like to book a flight.'
response = chatbot.get_response('I would like to book a flight.')

print(response)

Terminal Example

This example program shows how to create a simple terminal client that allows you to communicate with your chat bot by typing into your terminal.

from chatterbot import ChatBot


# Uncomment the following lines to enable verbose logging
# import logging
# logging.basicConfig(level=logging.INFO)

# Create a new instance of a ChatBot
bot = ChatBot(
    'Terminal',
    storage_adapter='chatterbot.storage.SQLStorageAdapter',
    logic_adapters=[
        'chatterbot.logic.MathematicalEvaluation',
        'chatterbot.logic.TimeLogicAdapter',
        'chatterbot.logic.BestMatch'
    ],
    database_uri='sqlite:///database.sqlite3'
)

print('Type something to begin...')

# The following loop will execute each time the user enters input
while True:
    try:
        user_input = input()

        bot_response = bot.get_response(user_input)

        print(bot_response)

    # Press ctrl-c or ctrl-d on the keyboard to exit
    except (KeyboardInterrupt, EOFError, SystemExit):
        break

Using MongoDB

Before you can use ChatterBot’s built in adapter for MongoDB, you will need to install MongoDB. Make sure MongoDB is running in your environment before you execute your program. To tell ChatterBot to use this adapter, you will need to set the storage_adapter parameter.

storage_adapter="chatterbot.storage.MongoDatabaseAdapter"

from chatterbot import ChatBot

# Uncomment the following lines to enable verbose logging
# import logging
# logging.basicConfig(level=logging.INFO)

# Create a new ChatBot instance
bot = ChatBot(
    'Terminal',
    storage_adapter='chatterbot.storage.MongoDatabaseAdapter',
    logic_adapters=[
        'chatterbot.logic.BestMatch'
    ],
    database_uri='mongodb://localhost:27017/chatterbot-database'
)

print('Type something to begin...')

while True:
    try:
        user_input = input()

        bot_response = bot.get_response(user_input)

        print(bot_response)

    # Press ctrl-c or ctrl-d on the keyboard to exit
    except (KeyboardInterrupt, EOFError, SystemExit):
        break

Time and Mathematics Example

ChatterBot has natural language evaluation capabilities that allow it to process and evaluate mathematical and time-based inputs.

from chatterbot import ChatBot


bot = ChatBot(
    'Math & Time Bot',
    logic_adapters=[
        'chatterbot.logic.MathematicalEvaluation',
        'chatterbot.logic.TimeLogicAdapter'
    ]
)

# Print an example of getting one math based response
response = bot.get_response('What is 4 + 9?')
print(response)

# Print an example of getting one time based response
response = bot.get_response('What time is it?')
print(response)

Using SQL Adapter

ChatterBot data can be saved and retrieved from SQL databases.

from chatterbot import ChatBot

# Uncomment the following lines to enable verbose logging
# import logging
# logging.basicConfig(level=logging.INFO)

# Create a new instance of a ChatBot
bot = ChatBot(
    'SQLMemoryTerminal',
    storage_adapter='chatterbot.storage.SQLStorageAdapter',
    database_uri=None,
    logic_adapters=[
        'chatterbot.logic.MathematicalEvaluation',
        'chatterbot.logic.TimeLogicAdapter',
        'chatterbot.logic.BestMatch'
    ]
)

# Get a few responses from the bot

bot.get_response('What time is it?')

bot.get_response('What is 7 plus 7?')

Read only mode

Your chat bot will learn based on each new input statement it receives. If you want to disable this learning feature after your bot has been trained, you can set read_only=True as a parameter when initializing the bot.

chatbot = ChatBot("Johnny Five", read_only=True)

More Examples

Even more examples can be found in the examples directory on GitHub: https://github.com/gunthercox/ChatterBot/tree/master/examples