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. 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.

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 and the accuracy of each response in relation to the input statement increase.

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.

Process flow diagram

Contents:

• Installation
  • Installing from PyPi
  • Optional dependencies
  • Installing from GitHub
  • Installing from source
    • Checking the version of ChatterBot that you have installed
    • Upgrading ChatterBot to the latest version
      • Upgrading to Newer Releases
• Quick Start Guide
  • Create a new chat bot
  • Training your ChatBot
  • Get a response
• ChatterBot Tutorial
  • Getting help
  • Installing ChatterBot
  • Creating your first chat bot
    • Setting the storage adapter
    • Specifying logic adapters
    • Getting a response from your chat bot
    • Training your chat bot
• Examples
  • Simple Example
  • Terminal Example
  • Using MongoDB
  • Time and Mathematics Example
  • Using SQL Adapter
  • Read only mode
  • More Examples
• Training
  • Setting the training class
  • Training classes
    • Training via list data
      • ListTrainer()
    • Training with corpus data
      • ChatterBotCorpusTrainer()
      • Specifying corpus scope
    • Training with the Ubuntu dialog corpus
      • UbuntuCorpusTrainer()
  • Creating a new training class
• Preprocessors
  • Preprocessor functions
    • clean_whitespace()
    • unescape_html()
    • convert_to_ascii()
  • Creating new preprocessors
• Logic Adapters
  • How logic adapters select a response
    • Response selection methods
      • get_first_response()
      • get_most_frequent_response()
      • get_random_response()
      • Use your own response selection method
    • Setting the response selection method
    • Response selection in logic adapters
    • Selecting a response from multiple logic adapters
      • Response selection
  • Creating a new logic adapter
    • Example logic adapter
    • Directory structure
    • Responding to specific input
    • Interacting with services
    • Providing extra arguments
  • Common logic adapter attributes
    • LogicAdapter
      • LogicAdapter.can_process()
      • LogicAdapter.class_name
      • LogicAdapter.get_default_response()
      • LogicAdapter.process()
  • Best Match Adapter
    • BestMatch()
    • How it works
    • Setting parameters
  • Time Logic Adapter
    • TimeLogicAdapter()
  • Mathematical Evaluation Adapter
    • MathematicalEvaluation()
  • Specific Response Adapter
    • SpecificResponseAdapter()
    • Specific response example
    • Low confidence response example
• Storage Adapters
  • Text Search
    • Text Search Example
    • Bigram Text Index
  • Creating a new storage adapter
  • Common storage adapter attributes
    • StorageAdapter
      • StorageAdapter.AdapterMethodNotImplementedError
      • StorageAdapter.EmptyDatabaseException
      • StorageAdapter.count()
      • StorageAdapter.create()
      • StorageAdapter.create_many()
      • StorageAdapter.drop()
      • StorageAdapter.filter()
      • StorageAdapter.get_model()
      • StorageAdapter.get_object()
      • StorageAdapter.get_random()
      • StorageAdapter.remove()
      • StorageAdapter.update()
  • SQL Storage Adapter
    • SQLStorageAdapter
      • SQLStorageAdapter.count()
      • SQLStorageAdapter.create()
      • SQLStorageAdapter.create_database()
      • SQLStorageAdapter.create_many()
      • SQLStorageAdapter.drop()
      • SQLStorageAdapter.filter()
      • SQLStorageAdapter.get_random()
      • SQLStorageAdapter.get_statement_model()
      • SQLStorageAdapter.get_tag_model()
      • SQLStorageAdapter.remove()
      • SQLStorageAdapter.update()
  • MongoDB Storage Adapter
    • MongoDatabaseAdapter
      • MongoDatabaseAdapter.count()
      • MongoDatabaseAdapter.create()
      • MongoDatabaseAdapter.create_many()
      • MongoDatabaseAdapter.drop()
      • MongoDatabaseAdapter.filter()
      • MongoDatabaseAdapter.get_random()
      • MongoDatabaseAdapter.get_statement_model()
      • MongoDatabaseAdapter.mongo_to_object()
      • MongoDatabaseAdapter.remove()
      • MongoDatabaseAdapter.update()
  • Database Migrations
• Filters
  • Setting filters
    • get_recent_repeated_responses()
• ChatterBot
  • ChatBot
    • ChatBot.ChatBotException
    • ChatBot.generate_response()
    • ChatBot.get_latest_response()
    • ChatBot.get_response()
    • ChatBot.learn_response()
  • Example chat bot parameters
  • Example expanded chat bot parameters
  • Enable logging
  • Using a custom logger
  • Adapters
    • Adapters types
    • Accessing the ChatBot instance
• Conversations
  • Conversation scope
  • Conversation example
    • Statements
      • Statement
    • Statement-response relationship
• Comparisons
  • Statement comparison
    • JaccardSimilarity
      • JaccardSimilarity.compare()
    • LevenshteinDistance
      • LevenshteinDistance.compare()
    • SpacySimilarity
      • SpacySimilarity.compare()
    • Use your own comparison function
      • Setting the comparison method
• Utility Methods
  • Module imports
    • import_module()
  • Class initialization
    • initialize_class()
  • ChatBot response time
    • get_response_time()
  • Parsing datetime information
    • datetime_parsing()
• ChatterBot Corpus
  • Corpus language availability
  • Exporting your chat bot’s database as a training corpus
• Django Integration
  • Install packages
    • Installed Apps
    • Migrations
    • MongoDB and Django
  • Django App Development
    • Chatterbot Django Settings
      • Additional Django settings
    • ChatterBot Django Views
      • Example API Views
    • Webservices
      • WSGI
      • Hosting static files
    • Django Tutorials
      • Django
      • Django REST Framework
• Frequently Asked Questions
  • Python String Encoding
    • Does ChatterBot handle non-ascii characters?
    • How do I fix Python encoding errors?
      • Unicode header
      • Unicode escape characters
      • Import unicode literals from future
  • How do I deploy my chat bot to the web?
  • What kinds of machine learning does ChatterBot use?
    • 1. Search algorithms
    • 2. Classification algorithms
• Command line tools
  • Get the installed ChatterBot version
• Development
  • Contributing to ChatterBot
    • Setting Up a Development Environment
    • Reporting a Bug
    • Requesting New Features
    • Contributing Documentation
    • Contributing Code
  • Releasing ChatterBot
    • Versioning
    • Release Process
  • Release Notes
  • Unit Testing
    • ChatterBot tests
    • Django integration tests
    • Django example app tests
    • Benchmark tests
    • Running tests
  • Packaging your code for ChatterBot
    • Package directory structure
      • Register on PyPI
      • Generate packages
      • Upload packages
      • Install your package locally
      • Using your package
      • Testing your code
  • Suggested Development Tools
    • Text Editors
      • Visual Studio Code
    • Database Clients
      • Sqlectron
      • Nosqlclient
• Glossary

Report an Issue

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

Indices and tables

• Index

• Module Index

• Search Page

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