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Storage Adapters

Storage adapters provide an interface that allows ChatterBot to connect to different storage technologies. Each adapter is optimized for different use cases:

  • Redis Vector Storage: Semantic similarity search using vector embeddings (best for contextual AI responses)
  • SQL Storage: Traditional pattern matching with POS-lemma indexing (best for exact phrase matching)
  • MongoDB Storage: NoSQL document storage with flexible schema
  • Django Storage: Integrated with Django ORM for web applications

The storage adapter that your bot uses can be specified by setting the storage_adapter parameter to the import path of the storage adapter you want to use.

chatbot = ChatBot(
    "My ChatterBot",
    storage_adapter="chatterbot.storage.SQLStorageAdapter"
)

Built-in Storage Adapters

ChatterBot includes multiple storage adapters for different AI and database technologies:

.. toctree::
   :maxdepth: 2

   redis
   mongodb
   sql
   ../django/index

Choosing a Storage Adapter

For Semantic AI Chatbots (Recommended for modern conversational AI):

Note that as of December 2025, the Redis Vector Storage Adapter is still an experimental beta feature.

Use Redis Vector Storage when you need:

  • Context-aware responses based on meaning, not keywords
  • Vector embeddings for semantic similarity search
  • Automatic confidence scoring from cosine similarity
  • Best match for conversational AI and natural language understanding

For Pattern-Based Matching:

Use SQL Storage when you need:

  • Exact phrase or pattern matching
  • POS-lemma bigram indexing
  • Traditional database features (ACID compliance)
  • Lower memory footprint

For Flexibility:

Use MongoDB or Django Storage for schema flexibility and web framework integration.

Common storage adapter attributes

Each storage adapter inherits the following attributes and methods.

.. autoclass:: chatterbot.storage.StorageAdapter
   :members:

Database Migrations

Various frameworks such as Django and SQL Alchemy support functionality that allows revisions to be made to databases programmatically. This makes it possible for updates and revisions to structures in the database to be be applied in consecutive version releases.

The following explains the included migration process for each of the databases that ChatterBot comes with support for.

  • Django: Full schema migrations and data migrations will be included with each release.
  • SQL Alchemy: No migrations are currently provided in releases. If you require migrations between versions Alembic is the recommended solution for generating them.
  • MongoDB: No migrations are provided.
  • Redis: No migrations are provided.

Further Reading

.. toctree::
   :maxdepth: 2

   text-search
   create-a-storage-adapter