---
title: Generative AI Tables
sidebarTitle: AI Tables
---

MindsDB empowers organizations to harness the power of AI by abstracting AI models as Generative AI Tables. These tables are capable of learning from the input data and generating predictions from the underlying model upon being queried. This abstraction makes AI highly accessible, enabling development teams to use their existing SQL skills to build applications powered by AI.

<Tip>
MindsDB integrates with numerous AI frameworks. [Learn more here](/integrations/ai-overview).
</Tip>

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  <img src="https://docs.google.com/drawings/d/e/2PACX-1vQDXTuCWl8IxTEO-2ntjN17B5XtCtJDJ_d_PDCeX0ch0GBzSJfuJmefGuM_FEyGOwlgrxnNSzmLaYGO/pub?w=951&h=460"/>
</p>

## What are Generative AI Tables?

Generative AI is a subfield of artificial intelligence that trains AI models to create new content, such as realistic text, forecasts, images, and more, by learning patterns from existing data.

MindsDB revolutionizes machine learning within enterprise databases by introducing the concept of **Generative AI tables**. These essentially abstract AI models as virtual AI tables, capable of producing output when given certain input.

## How to Use Generative AI Tables

AI tables, introduced by MindsDB, abstract AI models as virtual tables so you can simply query AI models for predictions.

With MindsDB, you can join multiple AI tables (that abstract AI models) with multiple data tables (that provide input to the models) to get all predictions at once.

Let's look at some examples.

### Deploy AI Models as AI Tables

You can deploy an AI model as a virtual AI table using the `CREATE MODEL` statement.

Here we create a model that classifies sentiment of customer reviews as instructed in the prompt template message. The required input is the review and output is the sentiment predicted by the model.

```sql
CREATE MODEL sentiment_classifier_model
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}}.":';
```

Next we create a model that generates responses to the reviews. The required input includes review, product name, and sold product quantity, and output is the response generated by the model.

```sql
CREATE MODEL response_generator_model
PREDICT response
USING
    engine = 'openai_engine',
    model_name = 'gpt-4',
    prompt_template = 'briefly respond to the customer review: {{review}}, added by a customer after buying {{product_name}} in quantity {{quantity}}';
```

<Info>
Follow [this doc page](/integrations/ai-engines/openai) to configure the OpenAI engine in MindsDB.
</Info>

Now let's look at the data tables that we'll use to provide input data to the AI tables.

### Prepare Input Data

The `amazon_reviews` table stores the following columns:

```sql
+----------------------------+-----------------------------+------------------------+-------------+
| created_at                 | product_name                | review                 | customer_id |
+----------------------------+-----------------------------+------------------------+-------------+
| 2023-10-03 16:30:00.000000 | Power Adapter               | It is a great product. | 1           |
| 2023-10-03 16:31:00.000000 | Bluetooth and Wi-Fi Speaker | It is ok.              | 2           |
| 2023-10-03 16:32:00.000000 | Kindle eReader              | It doesn’t work.       | 3           |
+----------------------------+-----------------------------+------------------------+-------------+
```

It provides sufficient input data for the `sentiment_classifier_model`, but not for the `response_generator_model`.

The `products_sold` table stores the following columns:

```sql
+----------------------------+-----------------------------+-------------+----------+
| sale_date                  | product_name                | customer_id | quantity |
+----------------------------+-----------------------------+-------------+----------+
| 2023-10-03 16:30:00.000000 | Power Adapter               | 1           | 20       |
| 2023-10-03 16:31:00.000000 | Bluetooth and Wi-Fi Speaker | 2           | 5        |
| 2023-10-03 16:32:00.000000 | Kindle eReader              | 3           | 10       |
+----------------------------+-----------------------------+-------------+----------+
```

The `response_generator_model` requires the two tables to be joined to provide it with sufficient input data.

### Make Predictions

You can query the AI tables directly or join AI tables with data tables to get the predictions.

There are two ways you can provide input to the models:

1. If you query the AI table directly, you can provide input data in the `WHERE` clause, like this:

    ```sql
    SELECT review, sentiment
    FROM sentiment_classifier_model
    WHERE review = 'I like it';
    ```

2. You can provide input data to AI tables from the joined data tables, like this:

    ```sql
    SELECT inp.product_name,
          inp.review,
          m1.sentiment,
          m2.response
    FROM data_integration_conn.amazon_reviews2 AS inp
    JOIN data_integration_conn.products_sold AS inp2
    ON inp.customer_id = inp2.customer_id
    JOIN sentiment_classifier_model AS m1
    JOIN response_generator_model AS m2;
    ```

    The `sentiment_classifier_model` requires a parameter named `review`, so the data table should contain a column named `review`, which is picked up by the model.

    Note that, when joining data tables, you must provide the `ON` clause condition, which is implemented implicitly when joining the AI tables.

Moreover, you can combine these two options and provide the input data to the AI tables partially from the data tables and partially from the `WHERE` clause, like this:

```sql
SELECT inp.product_name,
       inp.review,
       m1.sentiment,
       m2.response
FROM data_integration_conn.amazon_reviews2 AS inp
JOIN sentiment_classifier_model AS m1
JOIN response_generator_model AS m2
WHERE m2.quantity = 5;
```

Here the `sentiment_classifier_model` takes input data from the `amazon_review` table, while the `response_generator_model` takes input data from the `amazon_reviews` table and from the `WHERE` clause.

Furthermore, you can make use of subqueries to provide input data to the models via the `WHERE` clause, like this:

```sql
SELECT inp.product_name,
       inp.review,
       m1.sentiment,
       m2.response
FROM data_integration_conn.amazon_reviews2 AS inp
JOIN sentiment_classifier_model AS m1
JOIN response_generator_model AS m2
WHERE m2.quantity = (SELECT quantity
                     FROM data_integration_conn.products_sold
                     WHERE customer_id = 2);
```

## Difference between AI Tables and Standard Tables

To understand the difference, let's go over a simpler example. Here we will see how traditional database tables are designed to give you a deterministic response given some input, and how Generative AI Tables are designed to generate an approximate response given some input.

Let’s consider the following `income_table` table that stores the `income` and `debt` values.

```sql
SELECT income, debt
FROM income_table;
```

On execution, we get:

```sql
+------+-----+
|income|debt |
+------+-----+
|60000 |20000|
|80000 |25100|
|100000|30040|
|120000|36010|
+------+-----+
```

A simple visualization of the data present in the `income_table` table is as follows:

![Income vs Debt](/assets/sql/income_vs_debt.png)

Querying the income table to get the `debt` value for a particular `income` value results in the following:

```sql
SELECT income, debt
FROM income_table
WHERE income = 80000;
```

On execution, we get:

```sql
+------+-----+
|income|debt |
+------+-----+
|80000 |25100|
+------+-----+
```

And here is what we get:

![Income vs Debt chart](/assets/sql/income_vs_debt_known_value.png)

But what happens when querying the table for an `income` value that is not
present there?

```sql
SELECT income, debt
FROM income_table
WHERE income = 90000;
```

On execution, we get:

```sql
Empty set (0.00 sec)
```

When the `WHERE` clause condition is not fulfilled for any of the rows, no value is returned.

![Income vs Debt query](/assets/sql/income_vs_debt_unknown_value.png)

When a table doesn’t have an exact match, the query returns an empty set or null value. This is where the AI Tables come into play!

Let’s create a `debt_model` model that allows us to approximate the `debt` value for any `income` value. We train the `debt_model` model using the data from the `income_table` table.

```sql
CREATE MODEL mindsdb.debt_model
FROM income_table
PREDICT debt;
```

On execution, we get:

```sql
Query OK, 0 rows affected (x.xxx sec)
```

MindsDB provides the [`CREATE MODEL`](/sql/create/model/) statement. On execution of this statement, the predictive model works in the background, automatically creating a vector representation of the data that can be visualized as follows:

![Income vs Debt model](/assets/sql/income_vs_debt_predictor.png)

Let’s now look for the `debt` value of some random `income` value. To get the approximated `debt` value, we query the `mindsdb.debt_model` model instead of the `income_table` table.

```sql
SELECT income, debt
FROM mindsdb.debt_model
WHERE income = 90000;
```

On execution, we get:

```sql
+------+-----+
|income|debt |
+------+-----+
|90000 |27820|
+------+-----+
```

And here is how it looks:

![Income vs Debt model](/assets/sql/income_vs_debt_prediction.png)
