---
title: Create a Trigger
sidebarTitle: Create a Trigger
---

## Description

Triggers enable users to define event-based actions. For example, if a table is updated, then run a query to update predictions.

<Info>
Currently, you can create triggers on the following data sources:

- [MongoDB](/integrations/data-integrations/mongodb) (available for MongoDB Atlas Database),
- [Slack](/integrations/app-integrations/slack),
- [Solace](https://github.com/mindsdb/mindsdb/tree/main/mindsdb/integrations/handlers/solace_handler),
- [PostgreSQL](/integrations/data-integrations/postgresql) (requires write access).
</Info>

## Syntax

Here is the syntax for creating a trigger:

```sql
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...

```sql
CREATE TRIGGER trigger_name
ON integration_name.table_name
(
    sql_code
) 
```

...or on one or more columns of a table.

```sql
CREATE TRIGGER trigger_name
ON integration_name.table_name
COLUMNS column_name1, column_name2
(
    sql_code
) 
```

Here is how to list all triggers:

```sql
SHOW TRIGGERS;
```

## Example

Firstly, connect Slack to MindsDB following [this instruction](/integrations/app-integrations/slack#set-up-a-slack-app-and-generate-tokens) and connect the Slack app to a channel.

```sql
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](/integrations/ai-engines/openai), but you can use any [other LLM](/integrations/ai-overview#large-language-models).

```sql
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:

```sql
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.

```sql
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.
