How Braintrust turns customer requests into code with Codex

Learn how to use OpenAI's Codex technology to convert natural language customer requests into working Python code through a hands-on tutorial.

Introduction

In this tutorial, you'll learn how to use OpenAI's Codex technology to convert natural language requests into working code. Codex is a powerful tool that translates human language into programming code, making it easier for developers and non-developers alike to create software solutions. This tutorial will guide you through setting up your environment and using Codex to transform simple customer requests into functional code snippets.

What You'll Build

You'll create a simple Python program that takes a natural language description of a task and generates the corresponding Python code using OpenAI's Codex API. This will demonstrate how to turn customer requirements into working code automatically.

Prerequisites

Before starting this tutorial, you'll need:

An OpenAI API key (get one at platform.openai.com)
Python 3.6 or higher installed on your computer
Basic understanding of Python programming concepts
Access to a code editor or IDE

Step-by-Step Instructions

Step 1: Set Up Your Development Environment

First, create a new directory for your project and navigate to it:

mkdir codex-demo
 cd codex-demo

This creates a dedicated space for our Codex project and moves into it.

Step 2: Install Required Python Packages

Install the OpenAI Python library using pip:

pip install openai

This library provides an easy way to interact with OpenAI's APIs, including Codex.

Step 3: Create Your API Key Environment Variable

Set up your API key as an environment variable to keep it secure:

export OPENAI_API_KEY='your-api-key-here'

Replace 'your-api-key-here' with your actual OpenAI API key. This approach keeps your API key secure and prevents it from being accidentally shared in your code.

Step 4: Create Your Main Python Script

Create a new file called codex_demo.py and add the following code:

import openai
import os

# Initialize the OpenAI API client
openai.api_key = os.getenv('OPENAI_API_KEY')

def generate_code_from_prompt(prompt):
    """Generate Python code from a natural language prompt"""
    response = openai.Completion.create(
        engine="code-davinci-002",  # This is the Codex engine
        prompt=prompt,
        max_tokens=150,
        temperature=0.3
    )
    return response.choices[0].text.strip()

# Example usage
if __name__ == "__main__":
    # Sample customer request
    customer_request = "Create a Python function that calculates the factorial of a number"
    
    print("Customer Request:", customer_request)
    print("\nGenerated Code:")
    
    code = generate_code_from_prompt(customer_request)
    print(code)

This script sets up the basic structure for using Codex. The generate_code_from_prompt function takes a natural language description and uses the Codex engine to generate Python code.

Step 5: Run Your First Code Generation

Execute your script to see Codex in action:

python codex_demo.py

You should see output showing your customer request and the generated Python code. The Codex engine will have translated your natural language into functional Python code.

Step 6: Enhance Your Code Generator

Let's improve our script to handle multiple requests and format the output better:

import openai
import os

# Initialize the OpenAI API client
openai.api_key = os.getenv('OPENAI_API_KEY')

def generate_code_from_prompt(prompt):
    """Generate Python code from a natural language prompt"""
    response = openai.Completion.create(
        engine="code-davinci-002",
        prompt=prompt,
        max_tokens=200,
        temperature=0.3,
        stop="\n\n"
    )
    return response.choices[0].text.strip()

# List of customer requests
requests = [
    "Create a Python function that calculates the factorial of a number",
    "Write a Python script that sorts a list of numbers in ascending order",
    "Develop a Python class that represents a simple bank account with deposit and withdraw methods"
]

# Generate code for each request
for i, request in enumerate(requests, 1):
    print(f"\n=== Request {i} ===")
    print(f"Customer Request: {request}")
    print("\nGenerated Code:")
    
    code = generate_code_from_prompt(request)
    print(code)
    
    # Save to file
    with open(f"generated_code_{i}.py", "w") as f:
        f.write(code)
    
    print(f"\nCode saved to generated_code_{i}.py")

This enhanced version processes multiple requests and saves each generated code snippet to a separate file for easy reference.

Step 7: Test with Different Types of Requests

Try running your script with different types of customer requests to see how Codex handles various scenarios:

"Create a Python function that finds the maximum value in a list of numbers"
"Write a Python program that reads data from a CSV file and prints the first 5 rows"
"Develop a Python script that calculates the average of numbers in a list"

Experiment with different request styles to understand how Codex interprets natural language.

Step 8: Understanding the Codex Engine Parameters

Let's modify our script to better understand how different parameters affect code generation:

import openai
import os

openai.api_key = os.getenv('OPENAI_API_KEY')

def generate_code_with_parameters(prompt, temperature=0.3, max_tokens=150):
    """Generate code with customizable parameters"""
    response = openai.Completion.create(
        engine="code-davinci-002",
        prompt=prompt,
        max_tokens=max_tokens,
        temperature=temperature,
        stop="\n\n"
    )
    return response.choices[0].text.strip()

# Example with different temperature settings
prompt = "Create a Python function that checks if a number is prime"

print("=== Temperature = 0.3 (Balanced) ===")
result1 = generate_code_with_parameters(prompt, temperature=0.3)
print(result1)

print("\n=== Temperature = 0.8 (Creative) ===")
result2 = generate_code_with_parameters(prompt, temperature=0.8)
print(result2)

print("\n=== Temperature = 0.1 (Conservative) ===")
result3 = generate_code_with_parameters(prompt, temperature=0.1)
print(result3)

The temperature parameter controls how creative or deterministic the code generation is. Lower values produce more predictable results, while higher values generate more varied outputs.

Summary

In this tutorial, you've learned how to use OpenAI's Codex technology to convert natural language customer requests into working Python code. You've set up your development environment, created a basic code generator, and experimented with different parameters to understand how Codex works.

The key concepts covered include:

Setting up your OpenAI API key securely
Using the Codex engine (code-davinci-002) for code generation
Understanding how to structure prompts for better results
Adjusting parameters like temperature to control code creativity

This foundation allows you to start integrating Codex into your development workflow, helping you quickly prototype solutions from customer requirements. Remember that while Codex is powerful, it's still important to review and test generated code before using it in production environments.