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SQL sorcerer
Transform everyday language into SQL queries.
System
Transform the following natural language requests into valid SQL queries. Assume a database with the following tables and columns exists:
Customers:
- customer_id (INT, PRIMARY KEY)
- first_name (VARCHAR)
- last_name (VARCHAR)
- email (VARCHAR)
- phone (VARCHAR)
- address (VARCHAR)
- city (VARCHAR)
- state (VARCHAR)
- zip_code (VARCHAR)
Products:
- product_id (INT, PRIMARY KEY)
- product_name (VARCHAR)
- description (TEXT)
- category (VARCHAR)
- price (DECIMAL)
- stock_quantity (INT)
Orders:
- order_id (INT, PRIMARY KEY)
- customer_id (INT, FOREIGN KEY REFERENCES Customers)
- order_date (DATE)
- total_amount (DECIMAL)
- status (VARCHAR)
Order_Items:
- order_item_id (INT, PRIMARY KEY)
- order_id (INT, FOREIGN KEY REFERENCES Orders)
- product_id (INT, FOREIGN KEY REFERENCES Products)
- quantity (INT)
- price (DECIMAL)
Reviews:
- review_id (INT, PRIMARY KEY)
- product_id (INT, FOREIGN KEY REFERENCES Products)
- customer_id (INT, FOREIGN KEY REFERENCES Customers)
- rating (INT)
- comment (TEXT)
- review_date (DATE)
Employees:
- employee_id (INT, PRIMARY KEY)
- first_name (VARCHAR)
- last_name (VARCHAR)
- email (VARCHAR)
- phone (VARCHAR)
- hire_date (DATE)
- job_title (VARCHAR)
- department (VARCHAR)
- salary (DECIMAL)
Provide the SQL query that would retrieve the data based on the natural language request.
Customers:
- customer_id (INT, PRIMARY KEY)
- first_name (VARCHAR)
- last_name (VARCHAR)
- email (VARCHAR)
- phone (VARCHAR)
- address (VARCHAR)
- city (VARCHAR)
- state (VARCHAR)
- zip_code (VARCHAR)
Products:
- product_id (INT, PRIMARY KEY)
- product_name (VARCHAR)
- description (TEXT)
- category (VARCHAR)
- price (DECIMAL)
- stock_quantity (INT)
Orders:
- order_id (INT, PRIMARY KEY)
- customer_id (INT, FOREIGN KEY REFERENCES Customers)
- order_date (DATE)
- total_amount (DECIMAL)
- status (VARCHAR)
Order_Items:
- order_item_id (INT, PRIMARY KEY)
- order_id (INT, FOREIGN KEY REFERENCES Orders)
- product_id (INT, FOREIGN KEY REFERENCES Products)
- quantity (INT)
- price (DECIMAL)
Reviews:
- review_id (INT, PRIMARY KEY)
- product_id (INT, FOREIGN KEY REFERENCES Products)
- customer_id (INT, FOREIGN KEY REFERENCES Customers)
- rating (INT)
- comment (TEXT)
- review_date (DATE)
Employees:
- employee_id (INT, PRIMARY KEY)
- first_name (VARCHAR)
- last_name (VARCHAR)
- email (VARCHAR)
- phone (VARCHAR)
- hire_date (DATE)
- job_title (VARCHAR)
- department (VARCHAR)
- salary (DECIMAL)
Provide the SQL query that would retrieve the data based on the natural language request.
user
Get the list of customers who have placed orders but have not provided any reviews, along with the total amount they have spent on orders.
To get the list of customers who have placed orders but have not provided any reviews, along with the total amount they have spent on orders, you can use the following SQL query:
sqlSELECT c.customer_id, c.first_name, c.last_name, SUM(o.total_amount) AS total_spent FROM Customers c LEFT JOIN Orders o ON c.customer_id = o.customer_id LEFT JOIN Reviews r ON c.customer_id = r.customer_id WHERE o.order_id IS NOT NULL AND r.review_id IS NULL GROUP BY c.customer_id, c.first_name, c.last_name;
Here's how the query works:
- The
FROM
clause starts with theCustomers
table. - The
LEFT JOIN
clause is used to join theOrders
table with theCustomers
table based on thecustomer_id
column. This will include all customers, even those who have not placed any orders. - Another
LEFT JOIN
clause is used to join theReviews
table with the combined result from the previous join. This will include all customers, even those who have not provided any reviews. - The
WHERE
clause filters the result to include only those customers who have placed orders (o.order_id IS NOT NULL
) but have not provided any reviews (r.review_id IS NULL
). - The
GROUP BY
clause groups the rows bycustomer_id
,first_name
, andlast_name
. - The
SUM(o.total_amount)
calculates the total amount spent by each customer on their orders, and theAS total_spent
aliases the calculated column.
The query will return the customer_id
, first_name
, last_name
, and total_spent
columns for the customers who meet the specified criteria.
API request
pythonimport anthropic client = anthropic.Anthropic( # defaults to os.environ.get("ANTHROPIC_API_KEY") api_key="my_api_key", ) message = client.messages.create( model="claude-3-opus-20240229", max_tokens=2000, temperature=0, system="Transform the following natural language requests into valid SQL queries. Assume a database with the following tables and columns exists:\n\nCustomers:\n- customer_id (INT, PRIMARY KEY)\n- first_name (VARCHAR)\n- last_name (VARCHAR)\n- email (VARCHAR)\n- phone (VARCHAR)\n- address (VARCHAR)\n- city (VARCHAR)\n- state (VARCHAR)\n- zip_code (VARCHAR)\n\nProducts:\n- product_id (INT, PRIMARY KEY)\n- product_name (VARCHAR)\n- description (TEXT)\n- category (VARCHAR)\n- price (DECIMAL)\n- stock_quantity (INT)\n\nOrders:\n- order_id (INT, PRIMARY KEY)\n- customer_id (INT, FOREIGN KEY REFERENCES Customers)\n- order_date (DATE)\n- total_amount (DECIMAL)\n- status (VARCHAR)\n\nOrder_Items:\n- order_item_id (INT, PRIMARY KEY)\n- order_id (INT, FOREIGN KEY REFERENCES Orders)\n- product_id (INT, FOREIGN KEY REFERENCES Products)\n- quantity (INT)\n- price (DECIMAL)\n\nReviews:\n- review_id (INT, PRIMARY KEY)\n- product_id (INT, FOREIGN KEY REFERENCES Products)\n- customer_id (INT, FOREIGN KEY REFERENCES Customers)\n- rating (INT)\n- comment (TEXT)\n- review_date (DATE)\n\nEmployees:\n- employee_id (INT, PRIMARY KEY)\n- first_name (VARCHAR)\n- last_name (VARCHAR)\n- email (VARCHAR)\n- phone (VARCHAR)\n- hire_date (DATE)\n- job_title (VARCHAR)\n- department (VARCHAR)\n- salary (DECIMAL)\n\nProvide the SQL query that would retrieve the data based on the natural language request.", messages=[{"role":"user","content":"Get the list of customers who have placed orders but have not provided any reviews, along with the total amount they have spent on orders."}] ) print(message.content)