chat-sql is a cli, which converts queries written in natural language to SQL queries via ChatGPT. It connects a PostgreSQL database, to execute the queries directly. The schema of the all tables of the database is sent to ChatGPT in order to provide better results.

Setup

Make sure you have a Scala 3 compiler and sbt installed. Then you can clone the git repository.

To use the ChatGPT api you have to create an API key on the OpenAI Platform. You also need to buy tokens or use the tokens you get for free the first three months after you created your OpenAI account.

To configure chat-sql, create a config file chatSql.conf in the git directory with the following content:

database = "jdbc:postgresql://localhost:5432/database_name?user=username&password=password"
apiKey = "your api key"

Replace the values accordingly.

Usage

Start the program with sbt run and enter your query into the prompt.

If you do not get the expected result you can try to adjust ChatGPT's temperature. The temperature parameter in GPT-based models affects the randomness of generated text, including SQL queries. A higher temperature leads to more diverse and creative queries, but they may be less accurate or syntactically correct. A lower temperature produces more focused and deterministic queries, adhering closely to standard syntax. Choosing the temperature depends on the desired trade-off between query accuracy and creativity. Lower values prioritize correctness, while higher values introduce more randomness and exploration. Experimentation with different temperature values helps find the right balance for generating SQL queries from natural language.

Example output

First I asked the program, to create the following tables and fill it with example data.

Table: Customers

Column Name	Data Type
customer_id	integer
name	varchar
email	varchar
address	varchar
city	varchar
state	varchar
country	varchar

Table: Orders

Column Name	Data Type
order_id	integer
customer_id	integer
order_date	date
total_amount	decimal
status	varchar

$ create the follwing tables and fill it with 10 example customers and 20 example orders: Table: Customers Columns: customer_id (integer) primary key, name (varchar), email (varchar), address (varchar), city (varchar), state (varchar), country (varchar) Table: Orders Columns: order_id (integer) primary key, customer_id (integer), order_date (date), total_amount (decimal), status (varchar)
CREATE TABLE Customers CREATE TABLE Customers (
  customer_id INTEGER PRIMARY KEY,
  name VARCHAR,
  email VARCHAR,
  address VARCHAR,
  city VARCHAR,
  state VARCHAR,
  country VARCHAR
);

CREATE TABLE Orders (
  order_id INTEGER PRIMARY KEY,
  customer_id INTEGER,
  order_date DATE,
  total_amount DECIMAL,
  status VARCHAR
);

INSERT INTO Customers (customer_id, name, email, address, city, state, country)
VALUES (1, 'John Doe', 'johndoe@example.com', '123 Main St', 'New York', 'NY', 'USA'),
       (2, 'Jane Smith', 'janesmith@example.com', '456 Elm St', 'Los Angeles', 'CA', 'USA'),
       (3, 'Robert Johnson', 'robertjohnson@example.com', '789 Oak St', 'Chicago', 'IL', 'USA'),
       (4, 'Emily Davis', 'emilydavis@example.com', '321 Pine St', 'Houston', 'TX', 'USA'),
       (5, 'Michael Wilson', 'michaelwilson@example.com', '654 Cedar St', 'Miami', 'FL', 'USA'),
       (6, 'Jessica Brown', 'jessicabrown@example.com', '987 Birch St', 'San Francisco', 'CA', 'USA'),
       (7, 'David Anderson', 'davidanderson@example.com', '159 Maple St', 'Seattle', 'WA', 'USA'),
       (8, 'Jennifer Martinez', 'jennifermartinez@example.com', '753 Spruce St', 'Boston', 'MA', 'USA'),
       (9, 'Daniel Thompson', 'danielthompson@example.com', '852 Walnut St', 'Phoenix', 'AZ', 'USA'),
       (10, 'Sarah Garcia', 'sarahgarcia@example.com', '369 Ash St', 'Denver', 'CO', 'USA');

INSERT INTO Orders (order_id, customer_id, order_date, total_amount, status)
VALUES (1, 1, '2021-01-01', 100.00, 'Completed'),
       (2, 1, '2021-02-15', 50.00, 'Pending'),
       (3, 2, '2021-03-10', 200.00, 'Completed'),
       (4, 3, '2021-04-05', 75.00, 'Completed'),
       (5, 4, '2021-05-20', 120.00, 'Pending'),
       (6, 5, '2021-06-12', 80.00, 'Completed'),
       (7, 6, '2021-07-18', 150.00, 'Pending'),
       (8, 7, '2021-08-09', 90.00, 'Completed'),
       (9, 8, '2021-09-25', 180.00, 'Pending'),
       (10, 9, '2021-10-30', 60.00, 'Completed'),
       (11, 10, '2021-11-22', 135.00, 'Pending'),
       (12, 2, '2021-12-15', 95.00, 'Completed'),
       (13, 3, '2022-01-02', 70.00, 'Completed'),
       (14, 4, '2022-02-25', 160.00, 'Pending'),
       (15, 5, '2022-03-18', 110.00, 'Completed'),
       (16, 6, '2022-04-14', 45.00, 'Completed'),
       (17, 7, '2022-05-06', 75.00, 'Pending'),
       (18, 8, '2022-06-29', 140.00, 'Completed'),
       (19, 9, '2022-07-23', 105.00, 'Pending'),
       (20, 10, '2022-08-12', 65.00, 'Completed');

This took a second attempt, the first time, it did not generate the example data correctly because it did not write all examples out and used "..." instead.

I also wanted to add some more orders, so each customer does not have the same amount of orders.

$ insert 15 different example orders and write out for all 15 orders. only use customer_id 1 to 10 and order_id above 20
INSERT INTO orders (order_id, customer_id, order_date, total_amount, status) VALUES
(21, 1, '2021-01-01', 100.00, 'Delivered'),
(22, 2, '2021-01-02', 150.00, 'Delivered'),
(23, 3, '2021-01-03', 200.00, 'Delivered'),
(24, 4, '2021-01-04', 250.00, 'Delivered'),
(25, 5, '2021-01-05', 300.00, 'Delivered'),
(26, 6, '2021-01-06', 350.00, 'Delivered'),
(27, 7, '2021-01-07', 400.00, 'Delivered'),
(28, 8, '2021-01-08', 450.00, 'Delivered'),
(29, 9, '2021-01-09', 500.00, 'Delivered'),
(30, 10, '2021-01-10', 550.00, 'Delivered'),
(31, 1, '2021-01-11', 600.00, 'Delivered'),
(32, 2, '2021-01-12', 650.00, 'Delivered'),
(33, 3, '2021-01-13', 700.00, 'Delivered'),
(34, 4, '2021-01-14', 750.00, 'Delivered'),
(35, 5, '2021-01-15', 800.00, 'Delivered');

I had to be a bit more specific here with the IDs because only the schema is provided to ChatGPT and therefore can not know which IDs it can use. It would be interesting to input the complete table into GPT, but for large tables it will be to much data. It also used 'Delivered' as status instead of 'Completed'. However, it does not matter much for the follow-up questions.

Then I let ChatGPT themself come up with the following tasks:

Write a query to retrieve the total number of customers in the database.

SELECT COUNT(*) AS total_customers FROM customers;
total_customers: 10

Write a query to calculate the total revenue generated from all orders.

SELECT SUM(total_amount) AS total_revenue FROM orders;
total_revenue: 8855.00

Write a query to find the top 5 customers who have placed the highest total amount of orders.

SELECT c.customer_id, c.name, SUM(o.total_amount) as total_order_amount
FROM customers c
JOIN orders o ON c.customer_id = o.customer_id
GROUP BY c.customer_id, c.name
ORDER BY total_order_amount DESC
LIMIT 5;
customer_id: 5  name: Michael Wilson    total_order_amount: 1290.00
customer_id: 4  name: Emily Davis       total_order_amount: 1280.00
customer_id: 2  name: Jane Smith        total_order_amount: 1095.00
customer_id: 3  name: Robert Johnson    total_order_amount: 1045.00
customer_id: 1  name: John Doe  total_order_amount: 850.00`

As you can see the answer is not correct as it sums the money spend rather than the amount of orders. I guess it is because the attribute is named total_amount. But it worked with a different temperature of 1.5:

SELECT c.name, COUNT(o.order_id) AS total_orders
FROM customers AS c
JOIN orders AS o ON c.customer_id = o.customer_id
GROUP BY c.name
ORDER BY total_orders DESC
LIMIT 5;

Write a query to find the average order amount for each customer.

SELECT customers.customer_id, AVG(orders.total_amount) AS average_order_amount
FROM customers
LEFT JOIN orders ON customers.customer_id = orders.customer_id
GROUP BY customers.customer_id;
Execute the query? [Y/n]:
customer_id: 5  average_order_amount: 322.5000000000000000
customer_id: 4  average_order_amount: 320.0000000000000000
customer_id: 10 average_order_amount: 250.0000000000000000
customer_id: 6  average_order_amount: 181.6666666666666667
customer_id: 2  average_order_amount: 273.7500000000000000
customer_id: 7  average_order_amount: 188.3333333333333333
customer_id: 1  average_order_amount: 212.5000000000000000
customer_id: 8  average_order_amount: 256.6666666666666667
customer_id: 9  average_order_amount: 221.6666666666666667
customer_id: 3  average_order_amount: 261.2500000000000000

Write a query to find the customers who have not placed any orders.

I first added costumers for that.

SELECT customer_id, name, email, address, city, state, country
FROM customers
WHERE customer_id NOT IN (SELECT customer_id FROM orders)
customer_id: 11 name: Funny Name 1      email: funny1@example.com       address: 123 Funny Address      city: Funny City        state: Funny State country: Funny Country
customer_id: 12 name: Funny Name 2      email: funny2@example.com       address: 456 Funny Address      city: Funny City        state: Funny State country: Funny Country
customer_id: 13 name: Funny Name 3      email: funny3@example.com       address: 789 Funny Address      city: Funny City        state: Funny State country: Funny Country

Write a query to retrieve the number of orders placed in each country.

SELECT country, COUNT(*) as order_count
FROM customers c
JOIN orders o ON c.customer_id = o.customer_id
GROUP BY country;
country: USA    order_count: 35