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Learn about the various concepts and components involved in creating effective and user-friendly chatbots using ChatBotKit. From backstories and models to bots and integrations, this comprehensive guide has everything you need to know.

When using ChatBotKit, it is imperative to have a deep understanding of the various concepts involved, such as the utilization of natural language processing to comprehend user input, the development of conversational flows to seamlessly guide the user through the chatbot experience, and the integration of external APIs to enhance functionality. By taking these concepts into account, you can confidently create a chatbot that is not only highly effective but also user-friendly.


The backstory includes information about the chatbot's experiences, skills, and personality traits. By understanding the backstory, you can understand the chatbot's perspective and what it is trying to convey. It is a vital component in ChatBotKit as it helps the chatbot to understand the context of the user's message.

For more information on backstories, please visit the Backstories documentation.


Models are the building blocks of chatbots. They are the algorithms that the chatbot uses to understand the user's message and to generate a response. ChatBotKit supports various machine learning models.

For more information on models, please visit the Models documentation.


ChatBotKit Stores are an abstract storage class that is used to retrieve information. They are a fundamental component that enables the efficient and organized storage and retrieval of data. Stores are exclusively used with Datasets. Upon creating the datasets, the user can select the appropriate store to be used to store the data.

For more information on stores, please visit the Stores documentation.


Bots are the actual chatbot applications that you create using ChatBotKit. You can create many bots using ChatBotKit, each with its unique configuration settings.

For more information on bots, please visit the Bots documentation.


A dataset is a structured collection of data that can be used to provide additional context and information to a chatbot. It is a way for chatbots to access relevant data and use it to generate responses based on user input. ChatBotKit supports various dataset formats, including PDF, DOCX, CSV, JSON, and text files.

For more information on datasets, please visit the Datasets documentation.


Skillsets are a set of skills that the chatbot can use to respond to user messages. Each skillset comprises of one or more abilities. You can create different skillsets for different bots.

For more information on skillsets, please visit the Skillsets documentation.


Conversations are the interactions between the user and the chatbot. ChatBotKit allows you to manage conversations between the user and the chatbot effectively.

For more information on conversations, please visit the Conversations documentation.


Integrations are the different channels or platforms where you can deploy your chatbot. ChatBotKit supports a wide range of integrations, including Slack, Discord, Notion and many more.

For more information on integrations, please visit the Integrations documentation.