smart-api-gateway-js

v1.0.0

Published

a month ago

A customizable and feature-rich API gateway with security features, rate limiting, and more. Built with Node.js.

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marjanzadeh

API Gateway AI Node.js Security Rate Limiting CORS Logging

Smart API Gateway

Smart API Gateway is a robust API Gateway for Node.js, offering essential middleware functionalities and advanced features like AI-powered request routing and real-time analytics.

Installation

Install the package via npm:

npm install smart-api-gateway-js

Basic Setup

Create an Express Application

Set up a new Express application and integrate the Smart API Gateway:

import express from 'express';
import dotenv from 'dotenv';
import { loadMiddlewares, loadRoutes } from 'smart-api-gateway-js/src/utils/loader.js';
import { aiRoutingMiddleware } from 'smart-api-gateway-js/src/middlewares/aiRoutingMiddleware.js'; 

dotenv.config();

const app = express();
const PORT = process.env.PORT || 4000;

app.use(express.json());

// Load AI routing middleware if enabled
if (process.env.ENABLE_AI_ROUTING === 'true') {
  app.use(aiRoutingMiddleware);
}

// Load other middlewares and routes
loadMiddlewares(app);
loadRoutes(app);

app.listen(PORT, () => {
  console.log(`API Gateway running on port ${PORT}`);
});

Configure Environment Variables

Create a .env file in your project root with the following configuration:

PORT=4000
ENABLE_SECURITY_MIDDLEWARE=true
ENABLE_RATE_LIMIT_MIDDLEWARE=true
ENABLE_CORS_MIDDLEWARE=true
ENABLE_LOGGING_MIDDLEWARE=true
ENABLE_AI_ROUTING=true
ENABLE_ANALYTICS=true
ENCRYPTION_KEY=your_encryption_key_here

Features

AI-Powered Request Routing

The AI-powered request routing middleware dynamically routes requests based on predictions from an AI model.

Add Your AI Model
Place your AI model file (e.g., model.json) in an accessible directory.

Set Up AI Middleware

Ensure your AI middleware is configured correctly to use your AI model.

middlewares/aiRoutingMiddleware.js

import * as tf from '@tensorflow/tfjs';

let model;
async function loadModel() {
  model = await tf.loadLayersModel('file://path/to/your/model.json');
}
loadModel();

export async function aiRoutingMiddleware(req, res, next) {
  try {
    const features = [req.headers['user-agent'].length, req.body.length];
    const prediction = model.predict(tf.tensor([features])).dataSync();
    req.route = prediction > 0.5 ? '/route1' : '/route2'; // Customize routing logic as needed
    next();
  } catch (error) {
    next(error);
  }
}

Real-Time Analytics Dashboard

Monitor your API's performance with a real-time analytics dashboard.

Set Up Analytics Server

analytics/analyticsServer.js

import http from 'http';
import socketIo from 'socket.io';

const server = http.createServer();
const io = socketIo(server);

io.on('connection', (socket) => {
  console.log('A user connected');
  setInterval(() => {
    socket.emit('analytics', { requestsPerMinute: Math.random() * 100, errorRate: Math.random() * 10 });
  }, 1000);

  socket.on('disconnect', () => {
    console.log('User disconnected');
  });
});

server.listen(3001, () => {
  console.log('Analytics server running on port 3001');
});

Create Analytics Dashboard

public/dashboard.html

<!DOCTYPE html>
<html>
<head>
  <title>Real-Time Analytics Dashboard</title>
  <script src="/socket.io/socket.io.js"></script>
  <script src="https://cdn.jsdelivr.net/npm/chart.js"></script>
</head>
<body>
  <h1>API Analytics Dashboard</h1>
  <canvas id="chart" width="400" height="200"></canvas>

  <script>
    const socket = io('http://localhost:3001');
    const ctx = document.getElementById('chart').getContext('2d');

    const chart = new Chart(ctx, {
      type: 'line',
      data: {
        labels: [],
        datasets: [{
          label: 'Requests Per Minute',
          data: [],
          borderColor: 'rgba(75, 192, 192, 1)',
          borderWidth: 1,
          fill: false
        }]
      },
      options: {
        scales: {
          x: { type: 'time', time: { unit: 'minute' } },
          y: { beginAtZero: true }
        }
      }
    });

    socket.on('analytics', (data) => {
      const now = new Date();
      chart.data.labels.push(now);
      chart.data.datasets[0].data.push(data.requestsPerMinute);
      chart.update();
    });
  </script>
</body>
</html>

Serve Dashboard

Ensure your Express server serves the static files:

server.js

import express from 'express';
import path from 'path';
import { loadMiddlewares, loadRoutes } from './utils/loader.js';

const app = express();
const PORT = process.env.PORT || 4000;

app.use(express.json());
app.use(express.static(path.join(__dirname, 'public')));

loadMiddlewares(app);
loadRoutes(app);

app.listen(PORT, () => {
  console.log(`API Gateway running on port ${PORT}`);
});

Access the dashboard at:

http://localhost:4000/dashboard.html

License

MIT License.

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