GeezerVison

The World Wide Web, restored to 800 pixels of glory.

GeezerVison is showing docs.chatterbot.us — About ChatterBot — ChatterBot 1.2.15 documentation

About ChatterBot¶

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.

Modern AI Capabilities (2025):

Semantic Vector Search: Advanced context understanding using vector embeddings and Redis vector database
Large Language Model (LLM) Integration: Experimental support for Ollama and OpenAI models
Storage-Aware Architecture: Automatic optimization based on storage backend capabilities
Multi-Language Support: Language-independent design with spaCy integration

For more details about the ideas and concepts behind ChatterBot see the 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?

Originally, ChatterBot was created as a part of the codebase for the humanoid robot Salvius. As the project grew, the chatterbot library was released as a separate open-source project.

Language Independence¶

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.

Note

Starting in version 1.2.0 ChatterBot has started to implement some features that are language specific. This change is being made to improve the quality of responses that ChatterBot can generate.

How ChatterBot Works¶

ChatterBot is a Python library designed to make it easy to create software that can engage in conversation.

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 chooses a response from the selection of known responses to that statement.

April 2025

The dialog processing flow will be slightly different when using large language models (LLMs). See the LLM Roadmap for more details.

Process flow diagram¶

Contents:¶

Report an Issue¶

Please direct all bug reports and feature requests to the project’s issue tracker on GitHub.

Indices and tables¶

Sponsors¶

ChatterBot is sponsored by:

If you, or your organization is interested in sponsoring the ChatterBot project, you can do so directly via GitHub Sponsors as well as reach out directly via [email protected] for more information and to discuss sponsorship opportunities.

Ask a question under the chatterbot tag	Report an issue on GitHub
Subscribe to updates via @chatterbot.us on Bluesky

You are visitor 01402503 — GeezerVison does not store the pages you look up.
Best viewed with Netscape Navigator 4.7 or Internet Explorer 6 at 800×600. No cookies required.