webshop-scraper

Webshop Scraper

A TypeScript-based web scraper for extracting information from webshops with persistent storage, scheduled scraping, and change detection.

Features

• Multi-webshop Support: ShopRenter, WooCommerce, and Shopify
• Persistent Storage: SQLite database with UUID tracking and analytics
• Scheduled Scraping: Automated scraping based on sitemap frequency rules
• Content Change Detection: SHA-256 hash-based change tracking for all content
• Vector Search Integration: Qdrant-powered semantic search with OpenRouter embeddings
• MCP Tools: Model Context Protocol integration for LLM applications
• REST API: Full CRUD operations for shops and scraping jobs
• Web Interface: Complete administrative dashboard with real-time monitoring
• Bearer Authentication: Secure API access
• Job Queue: Configurable concurrency control
• Webhook Support: Real-time notifications for scraping events
• Systemd Integration: Install as a system service

Quick Start

Installation

1. Clone and install:

   git clone <repository-url>
   cd webshop-scraper
   npm install
   

2. Build:

   npm run build
   

3. Configure environment:

   # Basic Configuration
   API_KEY=your-secret-key
   PORT=3000
   MAX_CONCURRENT_JOBS=3
   
   # Qdrant Vector Search (Optional)
   QDRANT_API_URL=http://localhost:6333
   QDRANT_API_KEY=your-qdrant-key
   
   # OpenRouter for Embeddings (Optional)
   OPENROUTER_API_KEY=your-openrouter-key
   OPENROUTER_MODEL=openai/text-embedding-3-large
   
   # MCP Integration (Optional)
   MCP_ENABLED=true
   MCP_TRANSPORT=stdio
   

4. Start:

   npm start
   

As a systemd service

sudo ./scripts/install.sh

Reset and Restore

Reset the system to a clean state (creates backup first):

sudo ./scripts/reset.sh

Restore from a previous backup:

sudo ./scripts/restore.sh

What gets backed up:

• SQLite database (data/shops.db)
• Environment configuration (.env)
• Service logs from journalctl

Backups are stored in /var/backups/webshop-scraper/ with timestamps.

Vector Search & MCP Integration

The system includes optional Qdrant vector search capabilities for semantic search of scraped content:

• Automatic Embedding: Content is automatically embedded using OpenRouter's text-embedding-3-large model
• Semantic Search: 4 MCP tools provide semantic search capabilities for LLM applications
• Real-time Management: Web interface for enabling/disabling and monitoring embeddings
• Error Tracking: Comprehensive error logging and debugging capabilities

MCP Tools Available

• search_shipping_info(shop_id, query, limit?) - Search shipping and delivery information
• search_contact_info(shop_id, query, limit?) - Search contact and support information
• search_terms_info(shop_id, query, limit?) - Search terms of service and policies
• search_faq_info(shop_id, query, limit?) - Search frequently asked questions

See the Qdrant Setup Guide for complete configuration instructions.

API Documentation

See API Documentation for complete endpoint reference. See Qdrant API Reference for vector search endpoints.

Quick Examples

Create a scraping job:

curl -X POST http://localhost:3000/api/jobs \
  -H "Authorization: Bearer your-secret-key" \
  -H "Content-Type: application/json" \
  -d '{"url": "https://example-shop.com"}'

Get shop information:

curl http://localhost:3000/api/shops/:id \
  -H "Authorization: Bearer your-secret-key"

Get shop results:

curl "http://localhost:3000/api/shops/:id/results?limit=10" \
  -H "Authorization: Bearer your-secret-key"

Requirements

• Node.js v16 or higher
• npm
• Linux with systemd (for service installation)

Development

# Development mode
npm run dev

# Watch mode
npm run watch

# Build
npm run build

Database

Data is stored in data/shops.db (SQLite). The database includes:

• Shop information with UUID tracking
• Scrape analytics and history
• Content with change detection
• Scheduled jobs queue

Logging

View logs using journalctl:

sudo journalctl -u webshop-scraper -f

License

ISC