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OpenAI API Playground

A production-focused collection of OpenAI API examples built with Python.

This repository demonstrates how to build scalable, maintainable, and production-ready AI systems using modern OpenAI APIs, structured engineering patterns, and real-world architecture practices.

Author

Chaitanya Dasadiya

Repository Goals

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

  • OpenAI APIs
  • AI agents
  • Multi-modal systems
  • Production AI architectures
  • RAG pipelines
  • Realtime systems
  • Fine-tuning
  • Deployment systems
  • AI infrastructure engineering

Repository Structure

The examples are grouped by API area so related scripts stay together:

01_core_platform/
02_responses_api/
03_realtime_apis/
04_audio_apis/
05_vision_apis/
utils/

Folder Guide

  • 01_core_platform/ — production platform foundations such as authentication, API keys, organizations, projects, usage tracking, billing, rate limits, models, tokens, and pricing optimization.
  • 02_responses_api/ — all Responses API examples, including basic responses, structured output, prompting, streaming, function calling, tool calling, multi-turn conversations, and reasoning prompts.
  • 03_realtime_apis/ — realtime API examples covering WebSocket connections, live streaming, voice systems, realtime transcription, interrupt handling, and low-latency architectures.
  • 04_audio_apis/ — audio API examples for transcription, translation, text-to-speech, and voice workflows.
  • 05_vision_apis/ — vision API examples for image understanding, OCR, and vision reasoning.
  • utils/ — shared configuration and OpenAI client utilities used by runnable examples.

Current Implemented Examples

Core Platform

All implemented Core Platform examples live in 01_core_platform/.

  • 01_core_platform/authentication.py
  • 01_core_platform/api_keys.py
  • 01_core_platform/organizations.py
  • 01_core_platform/projects.py
  • 01_core_platform/usage_tracking.py
  • 01_core_platform/billing.py
  • 01_core_platform/rate_limits.py
  • 01_core_platform/models.py
  • 01_core_platform/tokens.py
  • 01_core_platform/pricing_optimization.py

Running Core Platform Examples

Install dependencies:

pip install -r requirements.txt

Set OPENAI_API_KEY in your environment, GitHub Codespaces secret, or local .env file. Optionally set OPENAI_ORG_ID and OPENAI_PROJECT_ID when you need explicit organization or project scoping. Then run examples from the repository root:

python 01_core_platform/authentication.py
python 01_core_platform/api_keys.py
python 01_core_platform/organizations.py
python 01_core_platform/projects.py
python 01_core_platform/usage_tracking.py
python 01_core_platform/billing.py
python 01_core_platform/rate_limits.py
python 01_core_platform/models.py
python 01_core_platform/tokens.py
python 01_core_platform/pricing_optimization.py

Each example also supports running from inside its own folder because it adds the repository root to sys.path before importing shared utilities.

Responses API

All implemented Responses API examples live in 02_responses_api/.

  • 02_responses_api/basic_response.py
  • 02_responses_api/structured_json_output.py
  • 02_responses_api/system_prompting.py
  • 02_responses_api/streaming_responses.py
  • 02_responses_api/function_calling.py
  • 02_responses_api/tool_calling.py
  • 02_responses_api/multi_turn_conversation.py
  • 02_responses_api/reasoning_models.py

Running Responses API Examples

Install dependencies:

pip install -r requirements.txt

Set OPENAI_API_KEY in your environment, GitHub Codespaces secret, or local .env file. Then run examples from the repository root:

python 02_responses_api/basic_response.py
python 02_responses_api/structured_json_output.py
python 02_responses_api/system_prompting.py
python 02_responses_api/streaming_responses.py
python 02_responses_api/function_calling.py
python 02_responses_api/tool_calling.py
python 02_responses_api/multi_turn_conversation.py
python 02_responses_api/reasoning_models.py

Each example also supports running from inside its own folder because it adds the repository root to sys.path before importing shared utilities.

Realtime APIs

All implemented Realtime API examples live in 03_realtime_apis/.

  • 03_realtime_apis/websocket_connections.py
  • 03_realtime_apis/live_streaming.py
  • 03_realtime_apis/realtime_voice.py
  • 03_realtime_apis/realtime_transcription.py
  • 03_realtime_apis/interrupt_handling.py
  • 03_realtime_apis/low_latency_systems.py

Running Realtime API Examples

Install dependencies:

pip install -r requirements.txt

Set OPENAI_API_KEY in your environment, GitHub Codespaces secret, or local .env file. Then run examples from the repository root:

python 03_realtime_apis/websocket_connections.py
python 03_realtime_apis/live_streaming.py
python 03_realtime_apis/realtime_voice.py
python 03_realtime_apis/realtime_transcription.py
python 03_realtime_apis/interrupt_handling.py
python 03_realtime_apis/low_latency_systems.py

The voice and transcription scripts include Codespaces-safe placeholder modes for environments without microphone or speaker devices.

Complete OpenAI API Topics Checklist

Core Platform

  • Authentication
  • API keys
  • Organizations
  • Projects
  • Usage tracking
  • Billing
  • Rate limits
  • Models
  • Tokens
  • Pricing optimization

Text & Reasoning APIs

  • Responses API
  • Streaming
  • Structured outputs
  • JSON schema outputs
  • Function calling
  • Tool calling
  • Multi-turn conversations
  • Reasoning models
  • Chat Completions

Realtime APIs

  • WebSocket connections
  • Live streaming
  • Realtime voice
  • Realtime transcription
  • Interrupt handling
  • Low-latency systems

Audio APIs

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

Vision APIs

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

Image APIs

  • Image generation
  • Image editing
  • Variations
  • Inpainting
  • Style transfer

Embeddings & Search

  • Embeddings
  • Semantic search
  • Similarity search
  • RAG pipelines
  • Vector databases

Fine-Tuning

  • Dataset preparation
  • Training jobs
  • Hyperparameters
  • Evaluation
  • Deployment

Assistants & Agents

  • Agents SDK
  • Tool orchestration
  • Memory
  • Sessions
  • MCP
  • Multi-agent systems

File & Data APIs

  • Files API
  • Uploads
  • Batch processing
  • Vector stores

Safety & Moderation

  • Moderation API
  • Safety systems
  • Guardrails
  • Prompt injection prevention

Production Engineering

  • Scaling
  • Monitoring
  • Observability
  • Logging
  • Retry systems
  • Queue systems
  • Caching
  • Load balancing
  • Cost optimization

AI Architecture

  • RAG systems
  • AI workflows
  • Agent systems
  • Multi-modal systems
  • Hybrid AI architectures
  • Autonomous systems

Deployment

  • Docker
  • Kubernetes
  • Serverless
  • Edge deployment
  • CI/CD
  • Cloud deployment

Ecosystem Integrations

  • LangChain
  • LlamaIndex
  • Pinecone
  • Weaviate
  • Supabase
  • Vercel AI SDK

Advanced Systems

  • Long-context systems
  • AI memory systems
  • Planning systems
  • Reflection loops
  • Self-improving agents
  • Tool ecosystems
  • Computer-use agents
  • Browser agents
  • Coding agents

Tech Stack

Category Tools
Language Python 3.12+
AI Platform OpenAI API
Environment Virtualenv / dotenv
Patterns Async IO, Structured Outputs, Streaming
Focus Production-ready AI engineering

Quick Start

Clone the Repository

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

OpenAI API Key Setup Guide

This repository supports:

  1. Local machine development
  2. GitHub Codespaces development

Method 1: Local Machine Setup

Create Virtual Environment

python3 -m venv .venv
source .venv/bin/activate

Windows:

.venv\Scripts\activate

Install Dependencies

pip install -r requirements.txt

Create .env File

OPENAI_API_KEY=sk-proj-your_api_key_here

Run the Project

python 02_responses_api/basic_response.py

Method 2: GitHub Codespaces Setup

Create Codespaces Secret

Go to:

GitHub → Settings → Codespaces → Secrets

Create:

Name: OPENAI_API_KEY
Value: sk-proj-your_api_key_here

Grant access to:

openai-api-playground

Verify API Key

echo $OPENAI_API_KEY

Run the Project

python 02_responses_api/basic_response.py

API Key Security Best Practices

Never Do These

❌ Never commit API keys to GitHub

❌ Never hardcode API keys inside Python files

❌ Never push .env files

❌ Never expose API keys in screenshots

❌ Never store secrets in public repositories

Correct Secure Approach

✅ Use .env locally

✅ Use GitHub Secrets for Codespaces and Actions

✅ Add .env to .gitignore

✅ Rotate compromised keys immediately

✅ Validate AI outputs before execution

Recommended .gitignore

.env
.env.*
__pycache__/
*.pyc
.venv/
venv/

Engineering Principles

This repository follows production-focused engineering standards:

  • Centralized OpenAI client architecture
  • Environment-based configuration
  • Structured outputs
  • Reusable utilities
  • Debugging support
  • Error handling
  • Secure secret management
  • Codespaces compatibility
  • Production-safe patterns

Recommended Learning Path

  1. Responses API
  2. Structured Outputs
  3. Streaming
  4. Function Calling
  5. Embeddings
  6. RAG
  7. Agents
  8. Realtime APIs
  9. Fine-Tuning
  10. Production AI Systems

Use Cases

This repository can be used for:

  • AI engineering interview preparation
  • OpenAI API learning
  • Production AI architecture references
  • AI SaaS development
  • Internal AI tooling
  • Rapid AI prototyping
  • Agent system experimentation
  • Multi-modal AI systems

Contributing

Contributions are welcome.

Suggested contribution areas:

  • New OpenAI API examples
  • RAG systems
  • Realtime applications
  • AI agent orchestration
  • CI/CD automation
  • Deployment examples
  • Performance optimization
  • Production monitoring

License

This repository is licensed under the MIT License. See the LICENSE file for details.

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OpenAI API Playground with Python

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