MindsDB provides the CREATE CHATBOT statement that lets you customize your chatbot with an AI model and a data source of your choice. Follow this tutorial to learn build a chatbot with a Text2SQL skill.

The CREATE CHATBOT statement requires the following components:

  1. Chat app: A connection to a chat app, such as Slack or MS Teams.

  2. AI agent: An AI agent that comes with an AI model trained with the provided training data. Learn more about AI agents here.

Learn more about chatbots here.

Let’s go over getting all the components ready.

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Chatbot Components

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Chat App

Use the CREATE DATABASE statement to connect the chat app to MindsDB.

If you want to use Slack, follow this link to setup a Slack app, generate required tokens, and connect it to MindsDB.

If you want to use MS Teams, follow this link to generate required tokens and connect it to MindsDB.

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AI Agent

Start by creating and deploying the model.

If you haven’t created a LangChain engine, use the CREATE ML_ENGINE statement, as explained here.

CREATE MODEL my_model
PREDICT answer
USING
    engine = 'langchain',
    input_column = 'question',
    openai_api_key = 'your-model-api-key', -- choose one of OpenAI (openai_api_key) or Anthropic (anthropic_api_key)
    model_name='gpt-4', -- optional model name from OpenAI or Anthropic
    mode = 'conversational',
    user_column = 'question' ,
    assistant_column = 'answer',
    max_tokens=100,
    temperature=0,
    verbose=True,
    prompt_template='Answer the user input in a helpful way';

Here is the command to check its status:

DESCRIBE my_model;

The status should read complete before proceeding.

Next step is to create one or more skills for an AI agent. Here we create a Text2SQL skill.

CREATE SKILL text_to_sql_skill
USING
    type = 'text2sql',
    database = 'example_db',   -- this is a data source that must be connected to MindsDB with CREATE DATABASE statement
    tables = ['sales_data'],   -- this table comes from the connected example_db data source
    description = "Sales data that includes stores, sold products, and other sale details";

This skill enables a model to answer questions about data from the sales_data table.

Now let’s create an AI agent using the above model and skill.

CREATE AGENT support_agent
USING
model = 'my_model',                  -- this was created with CREATE MODEL
skills = ['text_to_sql_skill'];    -- this was created with CREATE SKILL

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Create Chatbot

Once all the components are ready, let’s proceed to creating the chatbot.

CREATE CHATBOT my_chatbot
USING
   database = 'chat_app',     -- this parameters stores a connection to a chat app, like Slack or MS Teams
   agent = 'support_agent',   -- this parameter stores an agent name, which was create with CREATE AGENT
   is_running = true;            -- this parameter is optional and set to true by default, meaning that the chatbot is running

The database parameter stores connection to a chat app. And the agent parameter stores an AI agent created by passing a model and training data.

You can query all chatbot using this query:

SELECT * FROM chatbots;

Now you can go to Slack or MS Teams and chat with the chatbot created with MindsDB.

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