Opper

SQL

Natural language to SQL query generation evaluates text-to-query fidelity and schema reasoning. This task is particularly relevant for analytics chat assistants and simplified database interfaces where users need to query data using natural language. Models must understand both the intent behind the question and the structure of the underlying database schema.

SQL – mistral/mistral-tiny-eu Task | Opper AI

Input Tokens

0

Output Tokens

0

Duration

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Cost

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Context

Input

Hi I'm Oscar that enrolled in 2018, I don't remember what courses I completed, can you help me?

Expected output

SELECT DISTINCT c.title FROM users u JOIN enrollments e ON u.user_id = e.user_id JOIN lessons l ON l.course_id = e.course_id JOIN completions cp ON cp.lesson_id = l.lesson_id AND cp.user_id = u.user_id JOIN courses c ON c.course_id = l.course_id WHERE u.name ILIKE '%oscar%' AND DATE_TRUNC('year', e.enrolled_at) = DATE '2018-01-01';

Model output

SELECT enrollments.course_id FROM enrollments JOIN completions ON enrollments.user_id = completions.user_id WHERE enrollments.user_id = (SELECT user_id FROM users WHERE name = 'Oscar') AND enrollments.status = 'completed'