🚀 OpenAI API Playground

A production-focused collection of comprehensive OpenAI API examples built with Python. Learn scalable, maintainable, and production-ready AI systems using modern OpenAI APIs, structured engineering patterns, and real-world architecture best practices.

👨‍💻 About This Repository

This repository is a comprehensive, production-grade reference for OpenAI API engineering. It provides:

✅ 20+ fully implemented examples across all major OpenAI API categories
✅ Production-ready patterns for authentication, error handling, and configuration
✅ Structured examples showing how to build scalable AI systems
✅ Real-world use cases including RAG, agents, realtime systems, and more
✅ Best practices for security, cost optimization, and deployment

Perfect for:

🎓 AI engineering interview preparation
📚 Learning OpenAI API fundamentals
🏗️ Production AI architecture reference
🤖 Building AI SaaS products
🔧 Internal AI tooling and automation

👤 Author

Chaitanya Dasadiya

🔗 GitHub: @cdasadiya
🎯 Focus Areas: AI Engineering, Python Development, OpenAI APIs, Production Systems

📋 Repository Goals

This repository aims to become a complete professional OpenAI engineering reference covering:

Category	Coverage
Core Platform	Authentication, API keys, organizations, projects, usage, billing, rate limits
Text & Reasoning	Responses API, streaming, structured outputs, function calling, tool calling
Realtime Systems	WebSocket, live streaming, voice, transcription, interrupt handling
Audio & Vision	Speech-to-text, translation, TTS, image understanding, OCR
AI Agents	Multi-agent systems, orchestration, memory, MCP integration
Infrastructure	RAG pipelines, embeddings, fine-tuning, deployment, monitoring

📁 Repository Structure

openai-api-playground/
├── 01_core_platform/          # Authentication, billing, rate limits, tokens
├── 02_responses_api/          # Chat, streaming, structured outputs, tool calling
├── 03_realtime_apis/          # WebSocket, voice, low-latency systems
├── 04_audio_apis/             # Transcription, translation, TTS (coming soon)
├── 05_vision_apis/            # Image understanding, OCR (coming soon)
├── utils/                     # Shared OpenAI client and utilities
├── requirements.txt           # Python dependencies
├── .env.example              # Environment template
└── README.md                 # This file

📚 Folder Guide

Folder	Purpose
`01_core_platform/`	Production foundations: authentication, API keys, organizations, projects, usage tracking, billing, rate limits, models, tokens, pricing optimization
`02_responses_api/`	Complete text API examples: basic responses, structured output, streaming, function calling, tool calling, multi-turn conversations, reasoning models
`03_realtime_apis/`	Realtime API examples: WebSocket connections, live streaming, voice systems, transcription, interrupt handling, low-latency architectures
`04_audio_apis/`	Audio API examples: transcription, translation, text-to-speech, voice workflows (⏳ coming soon)
`05_vision_apis/`	Vision API examples: image understanding, OCR, multi-image analysis, reasoning (⏳ coming soon)
`utils/`	Shared configuration, OpenAI client factory, and helper utilities

✅ Implementation Status

🟢 Core Platform

All Core Platform examples are fully implemented in 01_core_platform/:

✓ authentication.py          - Secure authentication validation
✓ api_keys.py                - Safe API key management
✓ organizations.py           - Multi-organization scoping
✓ projects.py                - Project-based isolation
✓ usage_tracking.py          - Token usage analysis
✓ billing.py                 - Billing concepts and tracking
✓ rate_limits.py             - Rate limit handling with exponential backoff
✓ models.py                  - Model discovery and metadata
✓ tokens.py                  - Token estimation and comparison
✓ pricing_optimization.py    - Cost optimization strategies

🟢 Responses API

All Responses API examples are fully implemented in 02_responses_api/:

✓ basic_response.py               - Simple text generation
✓ structured_json_output.py       - Structured outputs with schema
✓ system_prompting.py             - System instructions
✓ streaming_responses.py          - Real-time streaming
✓ function_calling.py             - Function invocation
✓ tool_calling.py                 - Tool orchestration
✓ multi_turn_conversation.py      - Conversational AI
✓ reasoning_models.py             - Advanced reasoning models

🟢 Realtime APIs

All Realtime API examples are fully implemented in 03_realtime_apis/:

✓ websocket_connections.py    - WebSocket fundamentals
✓ live_streaming.py           - Audio stream handling
✓ realtime_voice.py           - Voice interaction systems
✓ realtime_transcription.py   - Real-time speech-to-text
✓ interrupt_handling.py       - User interruption patterns
✓ low_latency_systems.py      - Optimized latency architecture

🟡 Audio APIs (Coming Soon)

⏳ Speech-to-text
⏳ Transcription
⏳ Translation
⏳ Text-to-speech
⏳ Voice synthesis
⏳ Audio generation

🟡 Vision APIs (Coming Soon)

⏳ Image understanding
⏳ OCR
⏳ Multi-image analysis
⏳ Vision reasoning

🚀 Quick Start

Prerequisites

Python 3.12 or higher
OpenAI API key from platform.openai.com
Git and basic terminal knowledge

1. Clone the Repository

git clone https://github.com/cdasadiya/openai-api-playground.git
cd openai-api-playground

2. Choose Your Setup Method

Option A: Local Development (Recommended)

# Create virtual environment
python3 -m venv .venv
source .venv/bin/activate          # macOS/Linux
# or
.venv\Scripts\activate             # Windows

# Install dependencies
pip install -r requirements.txt

# Create .env file
echo "OPENAI_API_KEY=sk-proj-your_api_key_here" > .env

# Run an example
python 02_responses_api/basic_response.py

Option B: GitHub Codespaces (No setup required)

Click Code → Codespaces → Create codespace on main
Add GitHub Codespaces secret:
- Settings → Secrets and variables → Codespaces
- Name: OPENAI_API_KEY
- Value: Your OpenAI API key
- Grant access to openai-api-playground
Run: python 02_responses_api/basic_response.py

3. Run Your First Example

# Run a simple Responses API example
python 02_responses_api/basic_response.py

# Or run a Core Platform example
python 01_core_platform/authentication.py

🔐 API Key Setup Guide

Local Development with `.env`

# 1. Create .env file in repository root
OPENAI_API_KEY=sk-proj-your_api_key_here

# Optional: Set organization and project scope
OPENAI_ORG_ID=org-...
OPENAI_PROJECT_ID=proj-...

GitHub Codespaces with Secrets

Settings → Codespaces → Secrets → New repository secret
Name: OPENAI_API_KEY
Value: sk-proj-your_api_key_here
Repository access: openai-api-playground

🔒 Security Best Practices

✅ DO:

✔️ Store API keys in .env files locally
✔️ Use GitHub Codespaces secrets for cloud development
✔️ Use environment variables in production
✔️ Add .env to .gitignore
✔️ Rotate compromised keys immediately

❌ DON'T:

❌ Commit API keys to GitHub
❌ Hardcode keys in Python files
❌ Push .env files to repositories
❌ Expose keys in screenshots/logs
❌ Store secrets in public repositories

Recommended `.gitignore`

# Environment
.env
.env.*
!.env.example

# Python
__pycache__/
*.py[cod]
*$py.class
*.so
.Python

# Virtual Environment
.venv/
venv/
ENV/

# IDE
.vscode/
.idea/
*.swp
*.swo

📊 Complete API Coverage Checklist

✅ Implemented

Core Platform — 10/10 topics covered
Responses API — 8/8 topics covered
Realtime APIs — 6/6 topics covered

🟡 Planned

🏗️ Tech Stack

Layer	Technologies
Language	Python 3.12+
AI Platform	OpenAI API (latest)
Environment	Python Venv, Python-dotenv
Patterns	Async/await, Streaming, Structured outputs
Focus	Production-ready, secure, scalable

💡 Engineering Principles

This repository follows production-grade standards:

🔐 Secure Architecture — Centralized client, environment-based configuration
📦 Structured Outputs — Type-safe, schema-validated responses
🔄 Error Handling — Graceful failures, exponential backoff retry logic
📊 Observability — Logging, debugging support, usage tracking
⚡ Performance — Streaming support, async operations, caching patterns
🚀 Scalability — Reusable utilities, modular design
🛡️ Security — Secret management, no hardcoded keys
🐳 Compatibility — Codespaces-ready, Docker-compatible

📚 Recommended Learning Path

Follow this progression to master OpenAI APIs:

Start Here → 01_core_platform/authentication.py — Validate setup
Basics → 02_responses_api/basic_response.py — Text generation
Structured → 02_responses_api/structured_json_output.py — Typed outputs
Real-time → 02_responses_api/streaming_responses.py — Live responses
Tools → 02_responses_api/function_calling.py — API integration
Advanced → 02_responses_api/tool_calling.py — Tool orchestration
Conversation → 02_responses_api/multi_turn_conversation.py — Memory
Reasoning → 02_responses_api/reasoning_models.py — Complex tasks
Voice → 03_realtime_apis/realtime_voice.py — Voice systems
Production → Scale to production with monitoring and optimization

💼 Use Cases

This repository can accelerate development for:

🎓 Interview Prep — AI engineering interview preparation and problem solving
📖 Learning — Understanding OpenAI API capabilities and best practices
🏛️ Architecture — Reference implementations for production AI systems
🚀 SaaS — Building AI-powered SaaS products and platforms
🔧 Internal Tools — Creating internal AI tooling and automation
🎨 Prototyping — Rapid AI prototyping and experimentation
🤖 Agents — Multi-agent systems and orchestration
🎬 Multimodal — Combining text, voice, vision, and audio

🤝 Contributing

Contributions are welcome and appreciated! We're looking for help in these areas:

✨ New Examples — Additional OpenAI API examples
🏗️ Architecture — RAG systems, workflow patterns, agent orchestration
🔄 Realtime — Voice, video, and low-latency applications
🤖 Agents — Multi-agent systems, tool orchestration
🚀 Deployment — CI/CD, Docker, Kubernetes, serverless
⚡ Performance — Optimization, benchmarking, caching
🛡️ Production — Monitoring, logging, observability
📚 Documentation — Improving guides and examples

See CONTRIBUTING.md for guidelines.

📄 License

This repository is licensed under the MIT License.

See LICENSE file for complete details.

🔗 Quick Links

Made with ❤️ by Chaitanya Dasadiya

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🚀 OpenAI API Playground

👨‍💻 About This Repository

👤 Author

📋 Repository Goals

📁 Repository Structure

📚 Folder Guide

✅ Implementation Status

🟢 Core Platform

🟢 Responses API

🟢 Realtime APIs

🟡 Audio APIs (Coming Soon)

🟡 Vision APIs (Coming Soon)

🚀 Quick Start

Prerequisites

1. Clone the Repository

2. Choose Your Setup Method

Option A: Local Development (Recommended)

Option B: GitHub Codespaces (No setup required)

3. Run Your First Example

🔐 API Key Setup Guide

Local Development with `.env`

GitHub Codespaces with Secrets

🔒 Security Best Practices

Recommended `.gitignore`

📊 Complete API Coverage Checklist

✅ Implemented

🟡 Planned

🏗️ Tech Stack

💡 Engineering Principles

📚 Recommended Learning Path

💼 Use Cases

🤝 Contributing

📄 License

🔗 Quick Links

FilesExpand file tree

README.md

Latest commit

History

README.md

File metadata and controls

🚀 OpenAI API Playground

👨‍💻 About This Repository

👤 Author

📋 Repository Goals

📁 Repository Structure

📚 Folder Guide

✅ Implementation Status

🟢 Core Platform

🟢 Responses API

🟢 Realtime APIs

🟡 Audio APIs (Coming Soon)

🟡 Vision APIs (Coming Soon)

🚀 Quick Start

Prerequisites

1. Clone the Repository

2. Choose Your Setup Method

Option A: Local Development (Recommended)

Option B: GitHub Codespaces (No setup required)

3. Run Your First Example

🔐 API Key Setup Guide

Local Development with .env

GitHub Codespaces with Secrets

🔒 Security Best Practices

Recommended .gitignore

📊 Complete API Coverage Checklist

✅ Implemented

🟡 Planned

🏗️ Tech Stack

💡 Engineering Principles

📚 Recommended Learning Path

💼 Use Cases

🤝 Contributing

📄 License

🔗 Quick Links

Local Development with `.env`

Recommended `.gitignore`