---

name: core-chatbot description: "Use ChatterBot core ChatBot, Statement, preprocessing, tagging, search, comparison, response selection, and CLI/version workflows." disable-model-invocation: true metadata: disco-role: operating license: BSD 3-Clause

Core ChatBot

Use this sub-skill when a task asks how to instantiate a ChatterBot bot, call get_response, control conversation IDs, understand Statement fields, debug default response behavior, use preprocessors/taggers/search, or run the small python -m chatterbot CLI.

Quick route

1. For installation or spaCy model errors, read the root installation notes and cross-cutting troubleshooting.
2. For constructor and method signatures, read references/api-reference.md.
3. For basic response lifecycle examples, conversation IDs, read_only, preprocessors, and taggers, read references/workflows.md.
4. For missing input, missing language models, repeated responses, bad adapter configs, and confidence surprises, read references/troubleshooting.md.
5. Run or adapt scripts/core_chat_smoke.py for a deterministic in-memory ChatBot smoke test.

Core objects

The central object is:

from chatterbot import ChatBot
bot = ChatBot("Example Bot", database_uri=None)
response = bot.get_response("Hello")

get_response accepts a string, a Statement, a dict containing text, or text= as a keyword. It returns a Statement with a text value and a confidence assigned by the selected logic adapter.

Use database_uri=None for in-memory SQL smoke tests and read_only=True when a bot should not learn from user input during inference.

Important behavior

• A ChatBot has one storage adapter and a list of logic adapters.
• The default storage adapter is chatterbot.storage.SQLStorageAdapter.
• The default logic adapter list is ['chatterbot.logic.BestMatch'].
• ChatBot creates a default conversation ID so repeated calls can track prior responses unless a caller passes conversation=.
• If read_only is false, get_response saves both the input statement and learned response relationship.
• Statement carries text, search fields, conversation label, persona, tags, in_response_to, timestamps, and confidence.
• Default SQL matching uses PosLemmaTagger, which normally needs a spaCy model such as en_core_web_sm.

CLI

ChatterBot exposes only simple module CLI checks:

python -m chatterbot --version
python -m chatterbot --help

Use these for package identity, not for training or serving.

Boundaries

• For trainers and data formats, route to training.
• For built-in/custom logic adapter configuration, route to logic-adapters.
• For SQL/Mongo/Redis storage details, route to storage-adapters.
• For Django projects, route to django-integration.