Exploratory project to extract information from an SQLite database with natural language queries. Users can ask questions of the database and the questions are converted into a SQL query by an LLM. The query is then executed on the database and results are returned to the LLM. Finally,the LLM uses the query results to answer the original question with natural language.
Prompt engineering and normalization strategies inspired by Chang, Shuaichen, and Eric Fosler-Lussier. "How to Prompt LLMs for Text-to-SQL: A Study in Zero-shot, Single-domain, and Cross-domain Settings." arXiv preprint arXiv:2305.11853 (2023).
To query the database:
python3 run.py "your natural language query"
Question: "Which plan makes us the most money each month?"
Generated query:
SELECT plan_name, COUNT(*) * monthly_price AS monthly_revenue FROM user JOIN plan ON user.plan_id = plan.plan_id GROUP BY plan_name ORDER BY monthly_revenue DESC LIMIT 1;
Friendly anwer: ""The plan that makes us the most money each month is the 'Enterprise' plan."
This database represents the backend of a machine learning application that allows users to create projects, upload data to their projects, and fit machine learning models to the data.
run.py will automatically create and populate a sample database if one does not already exist.
The sample database schema is:
