ChatterBot – gunthercox

ChatterBot

ChatterBot is a machine-learning based conversational dialog engine built in
Python which makes it possible to generate responses based on collections of
known conversations. The language independent design of ChatterBot allows it
to be trained to speak any language.

<img src="https://img.shields.io/pypi/v/chatterbot.svg" alt="Package Version" class="ra0-md-img" loading="lazy" />
<img src="https://img.shields.io/badge/python-3.12-blue.svg" alt="Python 3.12" class="ra0-md-img" loading="lazy" />
<img src="https://img.shields.io/coveralls/gunthercox/ChatterBot.svg" alt="Coverage Status" class="ra0-md-img" loading="lazy" />
<img src="https://img.shields.io/badge/🦋%20Bluesky-1185fe" alt="Follow on Bluesky" class="ra0-md-img" loading="lazy" />
<img src="https://badges.gitter.im/chatterbot/Lobby.svg" alt="Join the chat at https://gitter.im/chatterbot/Lobby" class="ra0-md-img" loading="lazy" />
<!-- <img src="https://codeclimate.com/github/gunthercox/ChatterBot/badges/gpa.svg" alt="Code Climate" class="ra0-md-img" loading="lazy" /> -->

An example of typical input would be something like this:

> user:* Good morning! How are you doing?
> bot:* I am doing very well, thank you for asking.
> user:* You're welcome.
> bot:* Do you like hats?

How it works

An untrained instance of ChatterBot starts off with no knowledge of how to communicate. Each time a user enters a statement, the library saves the text that they entered and the text that the statement was in response to. As ChatterBot receives more input the number of responses that it can reply to, and the accuracy of each response in relation to the input statement increases. The program selects the closest matching response by searching for the closest matching known statement that matches the input, it then returns the most likely response to that statement based on how frequently each response is issued by the people the bot communicates with.

Documentation

View the documentation
for ChatterBot.

Installation

This package can be installed from PyPi by running:

pip install chatterbot

Basic Usage

from chatterbot import ChatBot
from chatterbot.trainers import ChatterBotCorpusTrainer

chatbot = ChatBot('Ron Obvious')

# Create a new trainer for the chatbot
trainer = ChatterBotCorpusTrainer(chatbot)

# Train the chatbot based on the english corpus
trainer.train("chatterbot.corpus.english")

# Get a response to an input statement
chatbot.get_response("Hello, how are you today?")

Training data

ChatterBot comes with a data utility module that can be used to train chat bots.
At the moment there is training data for over a dozen languages in this module.
Contributions of additional training data or training data
in other languages would be greatly appreciated. Take a look at the data files
in the chatterbot-corpus
package if you are interested in contributing.

from chatterbot.trainers import ChatterBotCorpusTrainer

# Create a new trainer for the chatbot
trainer = ChatterBotCorpusTrainer(chatbot)

# Train based on the english corpus
trainer.train("chatterbot.corpus.english")

# Train based on english greetings corpus
trainer.train("chatterbot.corpus.english.greetings")

# Train based on the english conversations corpus
trainer.train("chatterbot.corpus.english.conversations")

Corpus contributions are welcome! Please make a pull request.*

Examples

For examples, see the examples
section of the documentation.

History

See release notes for changes https://github.com/gunthercox/ChatterBot/releases

Contributing

Contributions are welcomed, to help ensure a smooth process please start with the contributing guidelines in our documentation:
https://docs.chatterbot.us/contributing/

Sponsors

ChatterBot is sponsored by:

<p>
<a href="https://www.testmuai.com/?utm_source=chatterbot&utm_medium=sponsor" target="_blank">
<img src="docs/_static/testmu-ai-white-logo.png" style="vertical-align: middle;" width="250" height="80" />
</a>
</p>

License

ChatterBot is licensed under the BSD 3-clause license.