Alert System

A real-time alert system that aggregates emergency alerts from various sources and provides location-based search capabilities.

New Features

• MongoDB integration for improved vector search capabilities
• Web scraping of emergency services websites
• Automated alert collection from multiple sources
• Local embeddings generation using Transformers.js

Prerequisites

• Node.js 18+
• MongoDB 6.0+
• npm or yarn

Setup

1. Install MongoDB:

   # macOS (using Homebrew)
   brew tap mongodb/brew
   brew install mongodb-community

2. Create data directory:

   mkdir -p data/db

3. Install dependencies:

   npm install

4. Set up environment variables: Create a .env.local file with the following:

   MONGODB_URI=mongodb://localhost:27017/alert-system
   NEXT_PUBLIC_MAPBOX_API_KEY=your_mapbox_key
   ENABLE_SCRAPER_SCHEDULER=true
   SCRAPER_SCHEDULE=0 * * * *
   SCRAPER_API_KEY=your_scraper_key
   SCHEDULER_API_KEY=your_scheduler_key
   

5. Initialize MongoDB:

   npm run init-db

6. If migrating from DataStax:

   npm run migrate

Running the Application

1. Start MongoDB:

   npm run init-db

2. In a new terminal, start the development server:

   npm run dev

3. Start the scraper service:

   npm run scrape

The application will be available at http://localhost:3000

Architecture

Database

• MongoDB for storing alerts and vector embeddings
• Local embedding generation using Transformers.js
• Geospatial indexing for location-based queries

Web Scraping

• Automated scraping of emergency services websites
• Configurable sources in src/lib/scraper.js
• Scheduled updates via API endpoint

API Routes

• /api/alerts: CRUD operations for alerts
• /api/query: Vector similarity search with location awareness
• /api/scheduler: Controls the web scraper service

APIs and AI Components

APIs Used

• Mapbox API: For map rendering and geocoding services
• MongoDB Atlas: Database and vector search capabilities
• Web Scraping APIs: For automated collection of emergency alerts

AI/ML Components

• Transformers.js: Local embedding generation for semantic search
• Vector Similarity Search: Powered by MongoDB's vector search capabilities
• Natural Language Processing: Used for processing and understanding alert descriptions
• Semantic Search: Implemented using vector embeddings for intelligent alert querying
• Claude Sonnet assistant when coding

Contributing

1. Fork the repository
2. Create your feature branch
3. Commit your changes
4. Push to the branch
5. Create a new Pull Request