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https://github.com/jawherkl/ai-models-integration

Node.js backend that unifies multiple AI models into one API featuring rate limiting, caching, input validation, streaming, and monitoring.
https://github.com/jawherkl/ai-models-integration

artificial-intelligence chatgpt claude deepseek express gemini llm meta-llama mistralai nodejs openai openrouter redis

Last synced: 5 months ago
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Node.js backend that unifies multiple AI models into one API featuring rate limiting, caching, input validation, streaming, and monitoring.

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README 

          
# AI Models Integration with Node.js



![openrouter-nodejs](https://github.com/JawherKl/ai-models-integration/blob/main/simple_code/open-node.png)



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

```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 Redis



* Make sure Redis is installed and running locally or remotely.

* If you don't have Redis installed, follow the [Redis installation guide](https://redis.io/docs/getting-started/installation/).

* Alternatively, run Redis using Docker:



```bash

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 created from openrouter and other service configurations:



```bash

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



```bash

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



```bash

curl -X POST http://localhost:3000/search \

  -H "Content-Type: application/json" \

  -d '{"model": "chatgpt", "query": "Latest advancements in renewable energy 2025"}'

```



#### Analyze Endpoint



```bash

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



```bash

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](./LICENSE) file for details.



## 🙏 Acknowledgments



* Thanks to all contributors and the open-source community.

* Gratitude to maintainers of all utilized libraries and tools.



---



### 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.



[![Stargazers over time](https://starchart.cc/JawherKl/ai-models-integration.svg?variant=adaptive)](https://starchart.cc/JawherKl/ai-models-integration)