AI APIs Integration 🔌

Production-ready integrations with major AI services and APIs. Demonstrating efficient orchestration and implementation of various AI services in real-world applications.

Features • Installation • Quick Start • Documentation • Contributing

📑 Table of Contents

• Features
• Project Structure
• Prerequisites
• Installation
• Quick Start
• Documentation
  • Integrations
  • Performance
  • Cost Optimization
• Contributing
• Versioning
• Authors
• Citation
• License
• Acknowledgments

✨ Features

• Multi-service AI orchestration
• Intelligent fallback strategies
• Rate limiting and caching
• Cost optimization techniques
• Production monitoring tools

📁 Project Structure

graph TD
    A[ai-apis-integration] --> B[services]
    A --> C[orchestration]
    A --> D[optimization]
    A --> E[monitoring]
    B --> F[openai]
    B --> G[anthropic]
    B --> H[google]
    C --> I[routing]
    C --> J[fallback]
    D --> K[caching]
    D --> L[cost]
    E --> M[metrics]
    E --> N[alerts]

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Click to expand full directory structure

ai-apis-integration/
├── services/          # Service integrations
│   ├── openai/       # OpenAI integration
│   ├── anthropic/    # Anthropic integration
│   └── google/       # Google AI integration
├── orchestration/     # Service orchestration
│   ├── routing/      # Request routing
│   └── fallback/     # Fallback strategies
├── optimization/      # Optimization tools
├── monitoring/        # Monitoring systems
├── tests/            # Unit tests
└── README.md         # Documentation

🔧 Prerequisites

• Python 3.8+
• Valid API keys for services
• Redis (for caching)
• Docker (optional)

📦 Installation

# Clone repository
git clone https://github.com/BjornMelin/ai-apis-integration.git
cd ai-apis-integration

# Create environment
python -m venv venv
source venv/bin/activate

# Install dependencies
pip install -r requirements.txt

# Set up environment variables
cp .env.example .env
# Edit .env with your API keys

🚀 Quick Start

from ai_integration import services, optimization

# Initialize service orchestrator
orchestrator = services.AIOrchestrator(
    providers=["openai", "anthropic"],
    cache_enabled=True
)

# Configure cost optimization
cost_manager = optimization.CostManager(
    budget_limit=100,
    optimization_level="aggressive"
)

# Make API request with automatic optimization
response = orchestrator.process_request(
    prompt="Generate a business analysis",
    cost_manager=cost_manager,
    fallback_enabled=True
)

📚 Documentation

Integrations

Service	Features	Latency	Cost/1K Tokens
OpenAI	GPT-4, Embeddings	500ms	$0.03
Anthropic	Claude, Analysis	600ms	$0.02
Google	PaLM, Vision	450ms	$0.01

Performance

• Intelligent request routing
• Response caching
• Rate limit management
• Error handling

Cost Optimization

Strategy	Savings	Impact
Smart Routing	30%	Minimal
Caching	45%	None
Batch Processing	25%	+100ms Latency

🤝 Contributing

• Contributing Guidelines
• Code of Conduct
• Development Guide

📌 Versioning

We use SemVer for versioning. For available versions, see the tags on this repository.

✍️ Authors

Bjorn Melin

• GitHub: @BjornMelin
• LinkedIn: Bjorn Melin

📝 Citation

@misc{melin2024aiapisintegration,
  author = {Melin, Bjorn},
  title = {AI APIs Integration: Production-Ready AI Service Orchestration},
  year = {2024},
  publisher = {GitHub},
  url = {https://github.com/BjornMelin/ai-apis-integration}
}

📄 License

This project is licensed under the MIT License - see the LICENSE file for details.

🙏 Acknowledgments

• OpenAI team
• Anthropic developers
• LangChain community
• FastAPI developers

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Made with 🔌 and ❤️ by Bjorn Melin