How to build Text to SQL chat app to communicate with your database.

This snippet uses DuckDB and a CSV file as the database.

Prerequisites

This snippet requires the following CSV file. Please place it in the project root directory.

Download it here

Code

Python

import os
import morph
from morph import MorphGlobalContext
from morph_lib.stream import create_chunk
import json
from openai import OpenAI
from morph_lib.database import execute_sql

@morph.func
def chat_text_to_sql(context: MorphGlobalContext):
    client = OpenAI(api_key=os.getenv("OPENAI_API_KEY"))
    prompt = context.vars["prompt"]
    messages = [{
        "role": "system",
        "content": f"""Please execute SQL queries on a table named ./example.csv in DuckDB with the following schema:
city: text - Names of cities in the United States
state: text - Names of states in the United States
population: int - Population

This table contains aggregated population data for the United States.
"""
    }]
    messages.append({"role": "user", "content": prompt})

    response = client.chat.completions.create(
        model="gpt-4o",
        messages=messages,
        functions=[
            {
                "name": "generate_sql",
                "description": "Generate SQL queries based on user prompt",
                "parameters": {
                    "type": "object",
                    "properties": {
                        "sql": {
                            "type": "string",
                            "description": "The SQL query generated based on the prompt"
                        },
                    },
                    "required": ["sql"]
                }
            }
        ]
    )

    response_json = json.loads(response.choices[0].message.function_call.arguments)
    sql = response_json['sql']
    yield create_chunk(f"""
## SQL Query
{sql}
""")
    data = execute_sql(sql, "DUCKDB")
    data_md = data.to_markdown(index=False)
    messages = [{
        "role": "system",
        "content": f"""Please answer in markdown format.
You can use the following data:
{data_md}
"""
    },{
        "role": "user",
        "content": prompt
    }]
    response = client.chat.completions.create(
        model="gpt-4o",
        messages=messages,
        stream=True,
    )

    for chunk in response:
        yield create_chunk(chunk.choices[0].delta.content)

    return response

MDX

export const title = "Text to SQL app"

# Text to SQL app

<LLM postData="chat_text_to_sql" />

Result