Natural language to SQL

import os, json, sqlite3
from openai import OpenAI

client = OpenAI(
    base_url="https://api.opper.ai/v3/compat",
    api_key=os.environ["OPPER_API_KEY"],
)

# A sample database
db = sqlite3.connect(":memory:")
db.executescript("""
CREATE TABLE employees (id INTEGER, name TEXT, department TEXT, salary INTEGER, hire_date TEXT);
INSERT INTO employees VALUES
 (1,'Alice','Engineering',145000,'2021-03-01'),
 (2,'Bjorn','Engineering',132000,'2022-07-15'),
 (3,'Chloe','Sales',98000,'2020-01-10'),
 (4,'Dan','Engineering',151000,'2019-11-20'),
 (5,'Eva','Sales',105000,'2023-02-05');
""")

def schema(con):
    rows = con.execute("SELECT sql FROM sqlite_master WHERE type='table'").fetchall()
    return "\n".join(r[0] for r in rows)

QUERY_SCHEMA = {
    "type": "object",
    "properties": {
        "reasoning": {"type": "string", "description": "Brief reasoning about how to answer."},
        "sql": {"type": "string", "description": "A single valid SQLite query."},
    },
    "required": ["reasoning", "sql"],
}

def to_sql(question, db_schema):
    r = client.chat.completions.create(
        model="openai/gpt-5-mini",
        messages=[
            {"role": "system", "content": "Given a SQLite schema and a question, write one SQLite query that answers it. Return only the query in `sql`."},
            {"role": "user", "content": f"Schema:\n{db_schema}\n\nQuestion: {question}"},
        ],
        response_format={"type": "json_schema", "json_schema": {"name": "query", "schema": QUERY_SCHEMA}},
    )
    return json.loads(r.choices[0].message.content)

question = "Who are the two highest-paid people in Engineering? Show name and salary."
out = to_sql(question, schema(db))

print("SQL:", out["sql"])
print("Rows:", db.execute(out["sql"]).fetchall())

import Database from "better-sqlite3";
import OpenAI from "openai";

const client = new OpenAI({
  baseURL: "https://api.opper.ai/v3/compat",
  apiKey: process.env.OPPER_API_KEY!,
});

// A sample database
const db = new Database(":memory:");
db.exec(`
CREATE TABLE employees (id INTEGER, name TEXT, department TEXT, salary INTEGER, hire_date TEXT);
INSERT INTO employees VALUES
 (1,'Alice','Engineering',145000,'2021-03-01'),
 (2,'Bjorn','Engineering',132000,'2022-07-15'),
 (3,'Chloe','Sales',98000,'2020-01-10'),
 (4,'Dan','Engineering',151000,'2019-11-20'),
 (5,'Eva','Sales',105000,'2023-02-05');
`);

const schema = () =>
  db.prepare("SELECT sql FROM sqlite_master WHERE type='table'").all().map((r: any) => r.sql).join("\n");

const question = "Who are the two highest-paid people in Engineering? Show name and salary.";
const r = await client.chat.completions.create({
  model: "openai/gpt-5-mini",
  messages: [
    { role: "system", content: "Given a SQLite schema and a question, write one SQLite query that answers it. Return only the query in `sql`." },
    { role: "user", content: `Schema:\n${schema()}\n\nQuestion: ${question}` },
  ],
  response_format: {
    type: "json_schema",
    json_schema: {
      name: "query",
      schema: {
        type: "object",
        properties: { reasoning: { type: "string" }, sql: { type: "string" } },
        required: ["reasoning", "sql"],
      },
    },
  },
});

const out = JSON.parse(r.choices[0].message.content!);
console.log("SQL:", out.sql);
console.log("Rows:", db.prepare(out.sql).all());

pip install openai
export OPPER_API_KEY="your-api-key"
python app.py

npm install openai better-sqlite3
export OPPER_API_KEY="your-api-key"
npx tsx app.ts