Creating a Natural Language to SQL Application with OpenAI’s GPT-3 and Its Applications Across Industries

In today’s data-driven age, we frequently need to analyze vast amounts of structured data in databases for insights. This usually involves SQL (Structured Query Language), the predominant way to communicate with databases, but it requires significant knowledge and experience in SQL, making it less accessible for many non-technical people. Natural Language Processing (NLP) techniques, powered by large language models like OpenAI’s GPT-3, can help remove this hurdle by developing a natural language SQL application. In this technical blog, we will create a natural language to SQL applications and discuss how it relates to different industries.

Read our blog to explore how Prompt Engineering connects natural language with SQL.

The Technology Stack

Our application is developed using Python and uses OpenAI’s text-Davinci-003, a version of the GPT-3 model, specifically tuned to generate human-resembling text and responses. It is the core of our application and allows us to interpret and convert natural language queries into SQL commands.

Building the Natural Language to SQL Application

Step 1: Setting Up the Environment
The first task of developing our application is to set up the Python environment. This can be accomplished using virtual environments and package managers such as pip. Important libraries and packages we will be using include OpenAI’s Python package, Pandas for data manipulation, and SQL Alchemy for database interaction.

# Example of setting up the environment
virtualenv nlp_to_sql
source nlp_to_sql/bin/activate
pip install openai pandas sqlalchemy

Step 2: API Integration
We need to integrate OpenAI’s GPT-3 model into our application. To do this, you’ll need an API key from OpenAI. Once you have the key, you can use the OpenAI Python library to make API calls. Here’s an example of sending a natural language query to the model:

import openai
openai.api_key = “your_api_key_here”
response = openai.Completion.create(
engine=”text-davinci-003″,
prompt=”Translate the following English sentence to SQL: ‘Show me all the customers who made a purchase in the last month.'”,
max_tokens=50)
sql_query = response.choices[0].text.strip()

Step 3: Executing SQL Queries
With the translated SQL query in hand, we can now execute it against our database. This step will vary depending on the database you’re using. You’ll need to establish a connection, create a cursor, and execute the SQL query. Here’s an example using SQLite:

import sqlite3
# Connect to the SQLite database
conn = sqlite3.connect(‘my_database.db’)
cursor = conn.cursor()
# Execute the SQL query
cursor.execute(sql_query)
# Fetch and display the results
results = cursor.fetchall()
for row in results:
print(row)
# Close the database connection
conn.close()

Applications of Natural Language to SQL Across Industries

Now let’s explore its applications in various industries:

• Healthcare
  Without needing in-depth SQL knowledge, healthcare professionals can use this tool to extract patient data, generate reports, and conduct analytics. Physicians and nurses can now access queries such as “Show me the patient records with blood pressure above 140.”
• Finance
  By posing queries such as “What is the total revenue for Q2 2023?” financial analysts can rapidly obtain financial data, compute metrics, and produce custom reports. This simplifies the reporting and analysis of financial data.
• Retail
  Retailers can help improve inventory management by asking questions about product sales, inventory levels, and customer preferences in natural language. For example, the question “Which products are running low in inventory?” can simply map to SQL; there is no difference.
• Education
  Educators and administrators can access, analyze, and visualize student performance and enrollment information easily. The question, “What are the average test scores for each grade level?” can now be presented without requiring any knowledge of SQL.
• Customer Support
  Customer support teams can access customer data and generate insights by asking questions like “Retrieve all customer complaints from the last week.” This helps in providing more personalized support and resolving issues efficiently.