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MasteringOpenAI - Code Prompt Library

Code Prompt Library

Boost your development workflow with AI-powered code generation, debugging, and optimization prompts. From simple scripts to complex architectures.

89 Code Prompts Available
All 89
Python 24
JavaScript 18
API 15
Debugging 12
Web Dev 20
Python • API
(31)

OpenAI API Integration with Python

Write a Python function that connects to the OpenAI API, handles errors, and processes the response. Include rate limiting and retry logic.
Python
import openai import time from datetime import datetime class OpenAIClient: def __init__(self, api_key): openai.api_key = api_key self.rate_limit_delay = 1 # seconds between requests self.max_retries = 3 def call_api(self, prompt, model="gpt-3.5-turbo"): for attempt in range(self.max_retries): try: # Add rate limiting time.sleep(self.rate_limit_delay) response = openai.ChatCompletion.create( model=model, messages=[{"role": "user", "content": prompt}], max_tokens=500 ) return { "success": True, "content": response.choices[0].message.content, "model": response.model, "tokens_used": response.usage.total_tokens } except openai.error.RateLimitError as e: if attempt < self.max_retries - 1: wait_time = 2 ** attempt # Exponential backoff print(f"Rate limit hit. Waiting {wait_time} seconds...") time.sleep(wait_time) else: return {"success": False, "error": "Rate limit exceeded after retries"} except Exception as e: return {"success": False, "error": str(e)} return {"success": False, "error": "Max retries exceeded"} # Usage example client = OpenAIClient("YOUR_API_KEY") result = client.call_api("Explain quantum computing in simple terms") if result["success"]: print(result["content"]) else: print(f"Error: {result['error']}")
Example Input:
Prompt: "Explain quantum computing in simple terms"
Model: GPT-3.5-Turbo or GPT-4
Max Retries: 3 with exponential backoff
JavaScript • Web Dev
(27)

AI Chat Interface Component

Create a React component for an AI chat interface with message history, typing indicators, and markdown rendering.
JavaScript
import React, { useState, useEffect } from 'react'; import ReactMarkdown from 'react-markdown'; import './ChatInterface.css'; const ChatInterface = ({ messages = [], isTyping = false, onSendMessage }) => { const [inputValue, setInputValue] = useState(''); const handleSubmit = (e) => { e.preventDefault(); if (inputValue.trim()) { onSendMessage(inputValue); setInputValue(''); } }; return ( <div className="chat-container"> <div className="message-history"> {messages.map((msg, idx) => ( <div key={idx} className={`message-bubble ${msg.role}`}> <div className="message-role"> {msg.role === 'user' ? 'You' : 'Assistant'} </div> <div className="message-content"> <ReactMarkdown>{msg.content}</ReactMarkdown> </div> <div className="message-time"> {msg.timestamp} </div> </div> ))} {isTyping && ( <div className="typing-indicator"> <div className="typing-dots"> <span></span><span></span><span></span> </div> AI is thinking... </div> )} </div> <form onSubmit={handleSubmit} className="message-input-form"> <textarea value={inputValue} onChange={(e) => setInputValue(e.target.value)} placeholder="Type your message here..." rows="3" /> <button type="submit" disabled={!inputValue.trim()}> Send </button> </form> </div> ); }; export default ChatInterface;
Example Usage:
Messages Array: [{role: 'user', content: 'Hello AI!', timestamp: '10:30 AM'}]
Features: Markdown rendering, typing indicator, message history
Debugging • Python
(19)

Debug Python Code with AI

Analyze this Python code, identify bugs, suggest fixes, and explain the reasoning behind each change.
Python
from langchain_openai import ChatOpenAI import openai # Buggy code to analyze and fix: openai.api_key = "YOUR_API_KEY" chat_history = [ "What is your name?", "How are you today?", ] # Initialize the chat model chat_openai = ChatOpenAI(model_name="gpt-3.5-turbo") # This line has incorrect method name and parameter format response = chat_openai.chatCompletion( # Should be: chat_openai() or chat_openai.predict() model="gpt-3.5-turbo", messages=chat_history, ) print(response) print(response.choices[0].message.content) # Issues to fix: # 1. Incorrect method call - should use __call__() or predict() # 2. chat_history format is wrong - should be list of dictionaries # 3. No error handling # 4. Incorrect attribute access for response
Common Issues to Fix:
Method Name: chatCompletion → predict() or __call__()
Message Format: Strings → Dictionaries with role/content
Response Access: response.choices[0].message.content → response.content
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