ChatterBot is a Python library that makes it easy to generate automated responses to a user's input. ChatterBot uses a selection of machine learning algorithms to produce different types of responses. This makes it easy for developers to create chat bots and automate conversations with users. For more details about the ideas and concepts behind ChatterBot see the :ref:`process flow diagram <process_flow_diagram>`.
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?
The language independent design of ChatterBot allows it to be trained to speak any language. Additionally, the machine-learning nature of ChatterBot allows an agent instance to improve it's own knowledge of possible responses as it interacts with humans and other sources of informative data.
How ChatterBot Works
ChatterBot is a Python library designed to make it easy to create software that can engage in conversation.
An :term:`untrained instance` of ChatterBot starts off with no knowledge of how to communicate. Each time a user enters a :term:`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 and the accuracy of each response in relation to the input statement increase.
The program selects the closest matching :term:`response` by searching for the closest matching known statement that matches the input, it then chooses a response from the selection of known responses to that statement.
Process flow diagram
.. toctree:: :maxdepth: 4 setup quickstart tutorial examples training preprocessors logic/index storage/index filters chatterbot conversations comparisons utils corpus django/index faq commands development glossary
Report an Issue
Please direct all bug reports and feature requests to the project's issue tracker on GitHub.