Quickstart¶
Get from zero to your first AI-generated SQL query in under 2 minutes.
Step 1: Start the Server¶
Expected output:
INFO: Will watch for changes in these directories: ['.']
INFO: Uvicorn running on http://127.0.0.1:8000 (Press CTRL+C to quit)
INFO: Started reloader process [29872]
INFO: Application startup complete.
Step 2: Ask a Question¶
Send your first natural language query using curl:
Step 3: Inspect the Response¶
{
"sql": "SELECT Sector, AVG(AmountGranted) AS avg_loan\nFROM msmeloans\nGROUP BY Sector\nORDER BY avg_loan DESC;",
"data": [
{ "Sector": "Agriculture", "avg_loan": 5750000.0 },
{ "Sector": "Trade and Commerce", "avg_loan": 3200000.0 },
{ "Sector": "Manufacturing", "avg_loan": 2800000.0 }
],
"plotly_code": "import plotly.express as px\nfig = px.bar(df, x='Sector', y='avg_loan', title='Average Loan by Sector')\nfig.show()"
}
Step 4: Explore with Swagger UI¶
Open the interactive API documentation in your browser:
🔗 http://127.0.0.1:8000/api/docs
URL structure
The MkDocs documentation site is served at / (root).
The Swagger UI lives at /api/docs and ReDoc at /api/redoc.
Sample Questions to Try¶
| Question | What it tests |
|---|---|
Which sector has the highest average predicted default probability? |
Aggregation + ordering |
Show the top 10 borrowers with the highest predicted default risk. |
TOP-N query |
What percentage of loans are classified as high-risk (probability > 0.5)? |
Filtered percentage |
Which states have the largest number of startup borrowers? |
Boolean column filtering |
Show the average loan-to-turnover ratio by number of employees bucket. |
Ratio + grouping |