https://github.com/jawherkl/ai-models-integration
AI Models Integration repository! This project provides seamless integration with various state-of-the-art AI models
https://github.com/jawherkl/ai-models-integration
artificial-intelligence chatgpt claude deepseek express gemini llm meta-llama mistralai nodejs openai openrouter
Last synced: 27 days ago
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AI Models Integration repository! This project provides seamless integration with various state-of-the-art AI models
- Host: GitHub
- URL: https://github.com/jawherkl/ai-models-integration
- Owner: JawherKl
- Created: 2025-02-04T13:28:05.000Z (3 months ago)
- Default Branch: main
- Last Pushed: 2025-02-12T13:34:43.000Z (3 months ago)
- Last Synced: 2025-02-12T14:49:02.413Z (3 months ago)
- Topics: artificial-intelligence, chatgpt, claude, deepseek, express, gemini, llm, meta-llama, mistralai, nodejs, openai, openrouter
- Language: JavaScript
- Homepage:
- Size: 151 KB
- Stars: 11
- Watchers: 2
- Forks: 4
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
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README
# 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.
- **Caching**: Redis is used to cache frequent queries for better performance.
- **Validation**: All user input is validated with Zod for a secure and reliable API.
- **Streaming**: Real-time responses for long-running processes.
- **Monitoring**: Integrated Prometheus metrics to track API usage and errors.
## 🧠 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.
- *Zod*: Input validation for secure and structured API interactions.
- *Prometheus*: Monitoring of API usage, errors, and performance.
- *ioredis*: Redis client used for integrating caching and rate limiting features.
## 📦 Installation
### Step 1: Clone the Repository
```bash
git clone https://github.com/JawherKl/ai-models-integration.git
cd ai-models-integration
```
### Step 2: Install Dependencies
```bash
npm install
```
### Step 3: Set up Environment Variables
- Create a `.env` file in the root directory.
- Add your configuration variables (e.g., API keys, model endpoints).
```bash
API_KEY=api_key_from_openrouter.ai
BASE_URL=https://openrouter.ai/api/v1
REDIS_HOST=127.0.0.1
REDIS_PORT=6379
```
## 🚀 Usage
### Start the Server
```bash
npm start
```
### Access the API
- The server will run on `http://localhost:3000` by default.
- Use the provided endpoints to interact with the integrated AI models.
### Example Requests
#### Search request
```bash
curl -X POST http://localhost:3000/search \
-H "Content-Type: application/json" \
-d '{"model": "change_with_model_example", "query": "Latest advancements in renewable energy 2025"}'
```
#### Analysis request
```bash
curl -X POST http://localhost:3000/analyze \
-H "Content-Type: application/json" \
-d '{"model": "change_with_model_example", "text": "Tesla announced new solar roof technology with 25% improved efficiency in Q4 2023."}'
```
## 🔒 Best Practices Implemented
- **Rate Limiting**: To prevent abuse and ensure fair usage, rate limiting is implemented with **exponential backoff**. If the user exceeds the allowed number of requests, they will be temporarily blocked from making more requests.
- **Caching**: Redis is used to cache frequent queries, reducing the load on the AI models and improving the response times.
- **Input Validation**: We validate all incoming requests using **Zod** to ensure the data is structured and safe.
- **Streaming**: For long-running processes, such as AI completions, we stream the responses in real-time.
- **Monitoring**: The API tracks usage metrics and errors via **Prometheus**, allowing you to monitor the health and performance of your system in real-time.
## 🤝 Contributing
Contributions are welcome! If you have suggestions, improvements, or bug fixes, please open an issue or submit a pull request.
## 📄 License
This project is licensed under the **MIT License**. See the [LICENSE](./LICENSE) file for details.
## 🙏 Acknowledgments
- Special thanks to all contributors and the open-source community.
- Gratitude to the maintainers of the libraries used in this project.
---
### **Additional Notes:**
- The **Rate Limiting** feature uses Redis for storing request counts, ensuring that users cannot flood the system with requests.
- **Caching** stores frequently requested data in Redis, which minimizes the number of redundant calls to the AI models, improving efficiency and speed.
- **Zod** ensures that all user inputs are validated before they are processed, making the application more secure and reliable.
- **Streaming** allows for real-time responses, reducing the wait time for users interacting with models that take longer to process.
- **Prometheus** provides valuable insights into the health of the API, making it easier to monitor usage, errors, and response times.