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Changelog

All notable changes to the Zero-Cost AI Scraper project will be documented in this file.

The format is based on Keep a Changelog, and this project adheres to Semantic Versioning.

[Unreleased]

[1.0.0] - 2024-12-16

🎉 Initial Release

The first release of Zero-Cost AI Scraper - a complete self-hosted AI web scraping stack with zero external API costs.

✨ Features

Core Functionality

  • Web Scraping: Extract content from any website using Firecrawl
  • AI Extraction: Structure data using local LLM processing (Ollama)
  • Vector Storage: Automatic embedding generation and storage (Qdrant)
  • Semantic Search: Find similar content across scraped data
  • Privacy-First: 100% local processing, no data leaves your machine

AI Processing

  • Local LLM: Qwen2.5-Coder 7B model for data extraction
  • Embeddings: Nomic Embed Text for vector generation
  • Zero Cost: No external API calls or subscription fees
  • Customizable: Support for different Ollama models

Web Search Integration

  • SearXNG: Privacy-focused meta search engine
  • Multiple Engines: Google, Bing, Brave, DuckDuckGo, Startpage
  • No Tracking: Search without data collection
  • Cached Results: Local caching for better performance

MCP Integration

  • 6 Specialized Tools for Claude Code integration:
    • firecrawl_scrape: Single page scraping with auto-embedding
    • firecrawl_extract: AI-powered structured data extraction
    • firecrawl_similarity_search: Vector-based content search
    • firecrawl_search: Web search with scraping
    • firecrawl_crawl: Multi-page crawling
    • firecrawl_map: Website structure discovery

Developer Experience

  • One-Command Setup: ./scripts/quick-start.sh
  • Health Monitoring: Comprehensive health check system
  • Auto-Installation: Automatic model downloading and setup
  • Rich Documentation: Complete guides, examples, and API docs

🏗️ Architecture

Services Stack

  • Firecrawl: Web scraping engine (with local AI patches)
  • Ollama: Local LLM runtime
  • Qdrant: Vector database
  • SearXNG: Privacy-focused search
  • PostgreSQL: Primary database
  • Redis: Caching (2 instances)
  • Playwright: Browser automation

Docker Configuration

  • Complete Stack: Self-contained docker-compose setup
  • Health Checks: All services monitored
  • Persistent Storage: Data survives container restarts
  • Network Isolation: Secure internal communication

📚 Documentation

Comprehensive Guides

  • README.md: Complete project overview and quick start
  • ARCHITECTURE.md: Detailed system architecture
  • CONTRIBUTING.md: Contribution guidelines
  • NOTICE.md: License compliance and attributions

Examples and Tutorials

  • Python SDK: Complete client library with examples
  • Node.js Examples: Advanced extraction workflows
  • cURL Examples: Direct API usage
  • MCP Examples: Claude Code integration guide

API Documentation

  • RESTful Endpoints: Complete API reference
  • MCP Tools: Detailed tool specifications
  • Error Handling: Comprehensive error codes and messages

🛠️ Setup and Automation

Installation Scripts

  • quick-start.sh: Complete one-command setup
  • install-models.sh: Automatic model downloading
  • health-check.sh: System verification and monitoring

Configuration

  • Environment Variables: Comprehensive .env.example
  • Service Configuration: Optimized default settings
  • Customizable Settings: Easy parameter adjustment

🔒 Security and Privacy

Privacy Features

  • No External APIs: All processing happens locally
  • No Data Collection: SearXNG configured for privacy
  • No Tracking: Complete anonymity
  • Local Storage: All data stays on your machine

Security Measures

  • Network Isolation: Docker network security
  • Authentication: Bearer token API access
  • Input Validation: Comprehensive request validation
  • Error Handling: Secure error responses

💰 Cost Savings

Zero API Costs

  • Web Scraping: $0.00 (vs $0.01-0.10 per page)
  • AI Extraction: $0.00 (vs $0.01-0.50 per request)
  • Vector Storage: $0.00 (vs $0.10-2.00 per GB/month)
  • Search Queries: $0.00 (vs $0.001-0.01 per query)

Performance Metrics

  • Scraping Speed: 10-50 pages/minute
  • Extraction Speed: 2-5 pages/minute
  • Search Latency: <500ms
  • Embedding Generation: 5-10 documents/minute

📦 Distribution

Repository Structure

  • Public Repository: MIT licensed components
  • Private Modifications: AGPL v3 compliance
  • Source Availability: Complete patch transparency

License Compliance

  • MIT License: Original components
  • AGPL v3: Firecrawl modifications
  • Attribution: Comprehensive NOTICE file
  • Open Source: All dependencies properly licensed

🎯 Use Cases

Supported Workflows

  • Research Automation: Academic paper collection and analysis
  • Content Aggregation: Knowledge base building
  • Competitive Intelligence: Market research and monitoring
  • Documentation Extraction: API docs and technical guides
  • News Monitoring: Industry news tracking and analysis

Integration Options

  • Claude Code: Native MCP integration
  • Python: Complete SDK with examples
  • Node.js: Advanced extraction workflows
  • Direct API: HTTP-based integration
  • Custom Tools: Extensible architecture

⚡ Performance

System Requirements

  • Minimum: 8GB RAM, 4 cores, 50GB disk
  • Recommended: 16GB RAM, 8+ cores, 100GB SSD
  • Optimal: GPU acceleration for faster inference

Optimization Features

  • Connection Pooling: Efficient database connections
  • Caching: Redis-based response caching
  • Batch Processing: Efficient bulk operations
  • Health Monitoring: Performance tracking

🔍 Monitoring

Health Checks

  • Service Availability: All components monitored
  • Model Status: AI model availability
  • Database Health: Connection and performance
  • API Responsiveness: Endpoint monitoring

Logging and Debugging

  • Structured Logs: Comprehensive logging
  • Error Tracking: Detailed error reporting
  • Performance Metrics: Resource usage monitoring
  • Debug Tools: Health check scripts

Future Roadmap

Planned Features

  • GPU Acceleration: CUDA/ROCm support for faster inference
  • Model Quantization: Reduced memory usage options
  • Horizontal Scaling: Multi-instance deployment
  • Web UI: Graphical interface for non-technical users
  • More Models: Support for additional LLM and embedding models

Community Features

  • Plugin System: Extensible tool architecture
  • Custom Models: Easy model integration
  • Template Library: Pre-built extraction templates
  • Community Hub: Shared configurations and workflows

Contributing

We welcome contributions! Please see CONTRIBUTING.md for guidelines.

License

  • Original Code: MIT License
  • Firecrawl Modifications: AGPL v3
  • Full Details: See NOTICE.md

Acknowledgments

Special thanks to the teams behind:

Making enterprise-level AI accessible to everyone, at zero cost.