Automate notifications about incoming customer reviews

This tutorial presents how to chain OpenAI models within MindsDB to analyze text sentiment and generate responses, which will be sent in the form of Slack notifications.

Data setup

Connect your database to MindsDB.

CREATE DATABASE local_postgres
WITH ENGINE = 'postgres',
PARAMETERS = {
    "host": "4.tcp.eu.ngrok.io",
    "port": 12888,
    "database": "postgres",
    "user": "postgres",
    "password": "password"
    };

Query the input data table.

SELECT *
FROM local_postgres.demo.amazon_reviews;

+----------------------------+-----------------------------+------------------------+
| created_at                 | product_name                | review                 |
+----------------------------+-----------------------------+------------------------+
| 2023-11-08 17:23:21.028485 | Power Adapter               | It is a great product. |
| 2023-11-08 17:23:21.028485 | Bluetooth and Wi-Fi Speaker | It is ok.              |
| 2023-11-08 17:23:21.028485 | Kindle eReader              | It doesn’t work.       |
+----------------------------+-----------------------------+------------------------+

Model 1 setup

Configure an AI engine, providing the OpenAI API key.

CREATE ML_ENGINE openai_engine
FROM openai
USING
    openai_api_key = 'sk-xxx';

Deploy a model using this AI engine.

CREATE MODEL sentiment_classifier
PREDICT sentiment
USING
    engine = 'openai_engine',
    model_name = 'gpt-4',
    prompt_template = 'describe the sentiment of the reviews
    strictly as "positive", "neutral", or "negative".
    "I love the product":positive
    "It is a scam":negative
    "{{review}}.":';

Check its status.

DESCRIBE sentiment_classifier;

Predictions from Model 1

You can make a single predictions, providing input to the mode in the WHERE clause.

SELECT review, sentiment
FROM sentiment_classifier
WHERE review = 'It is ok.';

Or, make batch predictions, joining the data table with the model.

SELECT input.review, output.sentiment
FROM local_postgres.demo.amazon_reviews AS input
JOIN sentiment_classifier AS output;

Automation

The alert system will send notification to Slack. Here is how to connect Slack to MindsDB.

CREATE DATABASE customer_reviews_slack_app
WITH
ENGINE = 'slack',
PARAMETERS =  {
    "token": "xoxb-xxx" 
};

Send a test message to test the connection.

INSERT INTO customer_reviews_slack_app.channels (channel, text)
VALUES("customer-reviews", "Testing Slack connection");

Create a job to send notification every time a negative review is received.

CREATE JOB customer_reviews_notifications (

    INSERT INTO customer_reviews_slack_app.channels (channel, text)
        SELECT "customer-reviews" as channel,
                concat('Product: ', input.product_name, chr(10), 'Received negative review at: ', input.created_at, chr(10), 'Review: ', input.review) as text
        FROM local_postgres.demo.amazon_reviews AS input
        JOIN sentiment_classifier AS output
        WHERE input.created_at > LAST
        AND output.sentiment = 'negative'
)
EVERY 1 minute;

These commands are used to monitor the job.

SELECT * FROM jobs WHERE name = 'customer_reviews_notifications';

SELECT * FROM jobs_history WHERE name = 'customer_reviews_notifications';

Use this command to disable the job.

DROP JOB customer_reviews_notifications;

Model 2 setup

Deploy a model using the AI engine created earlier.

CREATE MODEL response_model
PREDICT response
USING
    engine = 'openai_engine',
    model_name = 'gpt-4',
    prompt_template = 'briefly respond to the customer review: {{review}}';

Check its status.

DESCRIBE response_model;

Predictions from Model 2

You can make a single predictions, providing input to the mode in the WHERE clause.

SELECT review, response
FROM response_model
WHERE review = 'It is ok.';

Or, make batch predictions, joining the data table with the model.

SELECT input.review, output.response
FROM local_postgres.demo.amazon_reviews AS input
JOIN response_model AS output;

Automation and Chaining Models 1 & 2

Create a job to send notification, including a sample response, every time a positive review is received.

CREATE JOB customer_reviews_and_reponses_notifications (

    INSERT INTO customer_reviews_slack_app.channels (channel, text)
        SELECT "customer-reviews" as channel,
                concat('---------', chr(10),
                       'Product: ', input.product_name, chr(10), 
                       'Received ', input.sentiment, ' review at: ', input.created_at, chr(10), 
                       'Review: ', input.review, chr(10), 
                       'Sample response: ', output.response) as text
        FROM (SELECT inp.created_at AS created_at, inp.product_name AS product_name, inp.review AS review, outp.sentiment AS sentiment
              FROM local_postgres.demo.amazon_reviews AS inp
              JOIN sentiment_classifier AS outp
              WHERE inp.created_at > LAST) AS input --'2023-10-03 16:50:00' AND inp.created_at > "{{PREVIOUS_START_DATETIME}}"
        JOIN response_model AS output
        WHERE input.sentiment = 'positive';
)
EVERY 1 minute;

These commands are used to monitor the job.

SELECT * FROM jobs WHERE name = 'customer_reviews_and_reponses_notifications';

SELECT * FROM jobs_history WHERE name = 'customer_reviews_and_reponses_notifications';

Use this command to disable the job.

DROP JOB customer_reviews_and_reponses_notifications;