OpenAI Codex Comprehensive Cheatsheet

1. Overview and Introduction to Codex

What is Codex?
- Advanced AI-powered code generation system developed by OpenAI
- Descendant of GPT-3, specifically trained on programming languages and code
- Capable of understanding and generating code across multiple programming languages

2. Access and Authentication Setup

API Authentication

Python

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import openai
openai.api_key = 'your_openai_api_key'  # Set your API key

Authentication Methods

API Key Authentication
OAuth 2.0 (for enterprise solutions)
Environment Variable Configuration

Bash

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export OPENAI_API_KEY='your_api_key'

3. API Endpoints and Usage

Main API Endpoint

Python

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response = openai.Completion.create(
    engine="davinci-codex",
    prompt="Create a Python function to calculate fibonacci sequence",
    max_tokens=150
)

Key API Parameters

engine: Specifies the Codex model (e.g., "davinci-codex")
prompt: Input text describing code generation task
max_tokens: Maximum length of generated code
temperature: Controls randomness (0.0-1.0)
top_p: Nucleus sampling parameter

4. Code Generation Capabilities

Example Scenarios

Function Generation

Python

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# Prompt: "Write a Python function to validate email"
def validate_email(email):
    import re
    pattern = r'^[\w\.-]+@[\w\.-]+\.\w+$'
    return re.match(pattern, email) is not None

Algorithm Implementation

Python

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# Prompt: "Implement binary search in JavaScript"
function binarySearch(arr, target) {
    let left = 0;
    let right = arr.length - 1;

    while (left <= right) {
        const mid = Math.floor((left + right) / 2);
        if (arr[mid] === target) return mid;
        if (arr[mid] < target) left = mid + 1;
        else right = mid - 1;
    }
    return -1;
}

5. Supported Programming Languages

Python
JavaScript
TypeScript
Java
C++
Ruby
Go
Rust
Swift
PHP
SQL
Bash/Shell scripting

6. Prompt Engineering Best Practices

Effective Prompting Techniques

Be specific and detailed
Provide context and constraints
Use clear, concise language
Specify desired output format
Include example inputs/outputs when possible

Bad Prompt

"Write code"

Good Prompt

"Create a Python function to calculate compound interest. 
Parameters: principal amount, annual interest rate, years.
Return the total amount after compound interest."

7. Code Completion and Suggestions

Usage Example

Python

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# Codex can auto-complete partial code
def calculate_area(radius):
    # Codex can suggest the implementation
    return 3.14 * radius ** 2

8. Code Explanation and Documentation

Automatic Documentation Generation

Python

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# Prompt: Generate docstring for a function
def process_data(data: list) -> dict:
    """
    Process a list of data and return summary statistics.
    
    Args:
        data (list): Input list of numeric values
    
    Returns:
        dict: Dictionary containing mean, median, and standard deviation
    """
    # Codex can generate implementation