AI Models Integration with Node.js

Welcome to the AI Models Integration repository! This project provides seamless integration with various state-of-the-art AI models, offering a unified API for efficient interaction.

🚀 Features

• Unified API: Interact with multiple AI models through a single, streamlined interface.
• Extensibility: Easily add and integrate new AI models as they become available.
• Scalability: Designed to handle multiple requests efficiently, ensuring optimal performance.
• Rate Limiting: Exponential backoff retries to prevent abuse and ensure fair usage.
• Caching: Redis is used to cache frequent queries for improved performance and reduced latency.
• Validation: All user inputs are validated with Zod to guarantee secure and structured API interactions.
• Streaming: Real-time streaming of responses for long-running AI processes.
• Monitoring: Integrated Prometheus metrics to track API usage, errors, and system performance.

🧠 Integrated AI Models

• Anthropic Claude 3.5 Sonnet
• Google Gemini Flash 1.5
• DeepSeek R1
• OpenAI GPT-4o Mini
• Meta LLaMA 3.2 3B Instruct
• Mistral Small

🛠️ Technologies Used

• Node.js: JavaScript runtime environment.
• Express: Fast, unopinionated, minimalist web framework for Node.js.
• OpenAI SDK: Integration with OpenAI's API for AI model interaction.
• Redis: In-memory data structure store used for caching and rate limiting.
• ioredis: Redis client library for Node.js.
• Zod: Input validation library for secure and reliable data handling.
• Prometheus: Monitoring system for tracking API metrics and health.

📦 Installation

Step 1: Clone the Repository

git clone https://github.com/JawherKl/ai-models-integration.git
cd ai-models-integration

Step 2: Install Dependencies

npm install

Step 3: Set up Redis

• Make sure Redis is installed and running locally or remotely.
• If you don't have Redis installed, follow the Redis installation guide.
• Alternatively, run Redis using Docker:

docker run -d --name redis -p 6379:6379 redis

• The app connects to Redis by default at redis://127.0.0.1:6379.

Step 4: Configure Environment Variables

• Create a .env file in the root directory.
• Add your API keys and service configurations:

API_KEY=your_api_key_here
BASE_URL=https://openrouter.ai/api/v1
REDIS_HOST=127.0.0.1
REDIS_PORT=6379

🚀 Usage

Start the Server

npm start

API Access

• Server runs by default at http://localhost:3000.
• Use the endpoints below to interact with integrated AI models.

Example Requests

Search Endpoint

curl -X POST http://localhost:3000/search \
  -H "Content-Type: application/json" \
  -d '{"model": "chatgpt", "query": "Latest advancements in renewable energy 2025"}'

Analyze Endpoint

curl -X POST http://localhost:3000/analyze \
  -H "Content-Type: application/json" \
  -d '{"model": "deepseek", "text": "Tesla announced new solar roof technology with 25% improved efficiency in Q4 2023."}'

List Supported Models

curl -X GET http://localhost:3000/models

🔒 Best Practices Implemented

• Rate Limiting: Implements exponential backoff retries using Redis to prevent abuse and control request rates.
• Caching: Frequently requested queries are cached in Redis to reduce redundant API calls and improve response times.
• Input Validation: User inputs are rigorously validated with Zod, ensuring API robustness and security.
• Streaming: Supports streaming AI responses in real-time for better user experience with long-running operations.
• Monitoring: Prometheus metrics integration tracks API usage, errors, and performance for observability and alerting.

🤝 Contributing

Contributions are welcome! Please open issues or submit pull requests for suggestions, improvements, or bug fixes.

📄 License

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

🙏 Acknowledgments

• Thanks to all contributors and the open-source community.
• Gratitude to maintainers of all utilized libraries and tools.

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Additional Notes

• Rate Limiting uses Redis to count requests and apply exponential backoff delays when limits are exceeded.
• Caching minimizes calls to external AI services by storing frequent query results in Redis with expiration.
• Zod Validation enforces strict input schemas preventing malformed or malicious data.
• Streaming capability enhances user experience by pushing partial AI completions as they are generated.
• Prometheus Monitoring enables tracking of detailed API metrics facilitating proactive maintenance and scaling.
• Error Handling is robust, ensuring that all exceptions are caught and logged for debugging and analysis.