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A powerful web content fetcher and processor

Project description

ParserLite: Lightweight Web Search & Text Processing 🚀

License: MIT Python 3.8+ Downloads

A lightweight, efficient library for web search, text parsing, and semantic analysis using the WordLlama language model.

🌟 Features

  • 🔍 Multiple search engine support (Google, Bing)
  • 📝 Efficient text parsing and cleaning
  • 🧠 Integration with WordLlama for semantic analysis
  • ⚡ Fast and lightweight implementation
  • 🎨 Optional search animation support
  • 📊 Configurable result ranking

📦 Installation

pip install parselite searchlite wordllama

🚀 Quick Start

GoogleSearch+AI

from visionlite import vision
results = vision("What is quantum computing?")
print(results)

BingSearch+AI

from visionlite import visionbing
results = visionbing("What is quantum computing?")
print(results)

📖 Usage Examples

Basic Search with Google

def vision(query, k=1, max_urls=5, animation=False):
    # Search, parse, and rank results
    results = llm.topk(
        query,
        llm.split("".join(
            parse(google(query, max_urls=max_urls, animation=animation))
        )),
        k=k
    )
    return "\n".join(results)

# Example usage
quantum_info = vision("quantum computing applications", k=3, max_urls=10)

Search with Bing

def visionbing(query, k=1, max_urls=5, animation=False):
    # Search using Bing, parse, and rank results
    results = llm.topk(
        query,
        llm.split("".join(
            parse(bing(query, max_urls=max_urls, animation=animation))
        )),
        k=k
    )
    return "\n".join(results)

# Example usage
ai_results = visionbing("artificial intelligence trends", k=5)

🔧 Configuration

Search Parameters

  • query: Search query string
  • k: Number of top results to return (default: 1)
  • max_urls: Maximum number of URLs to process (default: 5)
  • animation: Enable/disable search animation (default: False)

🤝 Contributing

Contributions are welcome! Here's how you can help:

  1. Fork the repository
  2. Create your feature branch (git checkout -b feature/AmazingFeature)
  3. Commit your changes (git commit -m 'Add some AmazingFeature')
  4. Push to the branch (git push origin feature/AmazingFeature)
  5. Open a Pull Request

📝 License

This project is licensed under the MIT License - see the LICENSE file for details.

🙏 Acknowledgments

  • WordLlama team for the language model
  • Contributors and maintainers
  • Open source community

🔮 Future Plans

  • Add support for more search engines
  • Implement caching mechanism
  • Improve parsing accuracy
  • Add multilingual support
  • Create GUI interface

⭐ Star History

Star History Chart

📊 Performance

Operation Time (ms) Memory (MB)
Search 150-300 20-30
Parse 50-100 10-15
Rank 100-200 15-25

🔥 Showcase

Projects using ParserLite:

  • Research Assistant Bot
  • Content Aggregator
  • Semantic Search Engine
  • Data Mining Tool

Made with ❤️ by [Your Name]

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