MindsDB provides the CREATE CHATBOT statement that is used under the hood by the Chatbot Interface. But, if you want to customize your chatbot to learn from the underlying database or use a different model, check out this tutorial.

The CREATE CHATBOT statement requires three componenets:

  1. The AI model in the conversational mode.

Currently, we support the langchain and llamaindex engines to create models for chatbots.

  1. The connection to the chat app, like Slack or Rocket Chat.

  2. The training data source used to train the chatbot with your private data.

Let’s go over getting all the components ready.

Chatbot Components

AI Model

Here is the command to create and deploy the model:

CREATE MODEL conversational_langchain_model
PREDICT answer
  engine = 'langchain',
  mode = 'conversational',
  prompt = 'answer users questions as a helpful assistant',
  user_column = 'question',
  assistant_column = 'answer';

Here is the command to check its status:

DESCRIBE conversational_langchain_model;

The status should read complete before proceeding.

Chat App

Here is the command to connect your Slack app to MindsDB:

CREATE DATABASE chatbot_slack
  ENGINE = 'slack',
      "token": "xoxb-...",
      "app_token": "xapp-..."

Follow this link to learn how to setup Slack app and generate tokens.

Training Data

Use the CREATE DATABASE statement to connect your database that stores training data.

Here is an example for the MySQL database:

CREATE DATABASE mysql_demo_db
WITH ENGINE = "mysql",
    "user": "user",
    "password": "MindsDBUser123!",
    "host": "db-demo-data.cwoyhfn6bzs0.us-east-1.rds.amazonaws.com",
    "port": "3306",
    "database": "public"

Create Chatbot

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

   model = 'conversational_langchain_model',
   database = 'chatbot_slack',
   backoffice_db = 'mysql_demo_db';

The model parameter defines the underlying model for the chatbot. The database parameter stores connection to a chat app. And the backoffice_db parameter stores connection to a data source that contains training data.

You can query all chatbot using this query:

SELECT * FROM chatbots;

Now you can go to Slack and chat with the app directly.