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Description

Triggers enable users to define event-based actions. For example, if a table is updated, then run a query to update predictions.
Currently, you can create triggers on the following data sources:

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Syntax

Here is the syntax for creating a trigger: 
CREATE TRIGGER trigger_name
ON integration_name.table_name
[COLUMNS column_name1, column_name2, ...]
(
    sql_code
)
By creating a trigger on a data source, every time this data source is updated or new data is inserted, the sql_code provided in the statement will be executed. You can create a trigger either on a table… 
CREATE TRIGGER trigger_name
ON integration_name.table_name
(
    sql_code
)
…or on one or more columns of a table. 
CREATE TRIGGER trigger_name
ON integration_name.table_name
COLUMNS column_name1, column_name2
(
    sql_code
)
Here is how to list all triggers: 
SHOW TRIGGERS;

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Example

Firstly, connect Slack to MindsDB following this instruction and connect the Slack app to a channel. 
CREATE DATABASE mindsdb_slack
WITH
  ENGINE = 'slack',
  PARAMETERS = {
      "token": "xoxb-...",
      "app_token": "xapp-..."
    };
Create a model that will be used to answer chat questions every time new messages arrive. Here we use the OpenAI engine, but you can use any other LLM. 
CREATE MODEL chatbot_model
PREDICT answer
USING
    engine = 'openai_engine',
    prompt_template = 'answer the question: {{text}}';
Here is how to generate answers to Slack messages using the model: 
SELECT s.text AS question, m.answer
FROM chatbot_model m
JOIN mindsdb_slack.messages s
WHERE s.channel_id = 'slack-bot-channel-id' 
AND s.user != 'U07J30KPAUF'
AND s.created_at > LAST;
Let’s analyze this query:
  • We select the question from the Slack connection and the answer generated by the model.
  • We join the model with the messages table.
  • In the WHERE clause:
    • We provide the channel name where the app/bot is integrated.
    • We exclude the messages sent by the app/bot. You can find the user ID of the app/bot by querying the mindsdb_slack.users table.
    • We use the LAST keyword to ensure that the model generates answers only to the newly sent messages.
Finally, create a trigger that will insert an answer generated by the model every time when new messages are sent to the channel. 
CREATE TRIGGER slack_trigger
ON mindsdb_slack.messages
(
    INSERT INTO mindsdb_slack.messages (channel_id, text)
        SELECT 'slack-bot-channel-id' AS channel_id, answer AS text
        FROM chatbot_model m
        JOIN TABLE_DELTA s
        WHERE s.user != 'slack-bot-id' # this is to prevent the bot from replying to its own messages
        AND s.channel_id = 'slack-bot-channel-id'
);
Let’s analyze this statement:
  • We create a trigger named slack_trigger.
  • The trigger is created on the mindsdb_slack.messages table. Therefore, every time when data is added or updated, the trigger will execute its code.
  • We provide the code to be executed by the trigger every time the triggering event takes place.
    • We insert an answer generated by the model into the messages table.
    • The TABLE_DELTA stands for the table on which the trigger has been created.
    • We exclude the messages sent by the app/bot. You can find the user ID of the app/bot by querying the mindsdb_slack.users table.