# VisionLite: Lightweight Web Search With AI 🚀
[](https://opensource.org/licenses/MIT)
[](https://www.python.org/downloads/)
[](https://pypi.org/project/parselite/)
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
```bash
pip install parselite searchlite wordllama
```
## 🚀 Quick Start
### GoogleSearch+AI
```python
from visionlite import vision
results = vision("What is quantum computing?")
print(results)
```
### BingSearch+AI
```python
from visionlite import visionbing
results = visionbing("What is quantum computing?")
print(results)
```
## 📖 Usage Examples
### Basic Search with Google
```python
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
```python
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](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
[](https://star-history.com/#YourUsername/parselite&Date)
## 📊 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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"description": "# VisionLite: Lightweight Web Search With AI \ud83d\ude80\n\n[](https://opensource.org/licenses/MIT)\n[](https://www.python.org/downloads/)\n[](https://pypi.org/project/parselite/)\n\nA lightweight, efficient library for web search, text parsing, and semantic analysis using the WordLlama language model.\n\n## \ud83c\udf1f Features\n\n- \ud83d\udd0d Multiple search engine support (Google, Bing)\n- \ud83d\udcdd Efficient text parsing and cleaning\n- \ud83e\udde0 Integration with WordLlama for semantic analysis\n- \u26a1 Fast and lightweight implementation\n- \ud83c\udfa8 Optional search animation support\n- \ud83d\udcca Configurable result ranking\n\n## \ud83d\udce6 Installation\n\n```bash\npip install parselite searchlite wordllama\n```\n\n## \ud83d\ude80 Quick Start\n\n### GoogleSearch+AI\n```python\nfrom visionlite import vision\nresults = vision(\"What is quantum computing?\")\nprint(results)\n```\n### BingSearch+AI\n```python\nfrom visionlite import visionbing\nresults = visionbing(\"What is quantum computing?\")\nprint(results)\n```\n\n## \ud83d\udcd6 Usage Examples\n\n### Basic Search with Google\n\n```python\ndef vision(query, k=1, max_urls=5, animation=False):\n # Search, parse, and rank results\n results = llm.topk(\n query,\n llm.split(\"\".join(\n parse(google(query, max_urls=max_urls, animation=animation))\n )),\n k=k\n )\n return \"\\n\".join(results)\n\n# Example usage\nquantum_info = vision(\"quantum computing applications\", k=3, max_urls=10)\n```\n\n### Search with Bing\n\n```python\ndef visionbing(query, k=1, max_urls=5, animation=False):\n # Search using Bing, parse, and rank results\n results = llm.topk(\n query,\n llm.split(\"\".join(\n parse(bing(query, max_urls=max_urls, animation=animation))\n )),\n k=k\n )\n return \"\\n\".join(results)\n\n# Example usage\nai_results = visionbing(\"artificial intelligence trends\", k=5)\n```\n\n## \ud83d\udd27 Configuration\n\n### Search Parameters\n\n- `query`: Search query string\n- `k`: Number of top results to return (default: 1)\n- `max_urls`: Maximum number of URLs to process (default: 5)\n- `animation`: Enable/disable search animation (default: False)\n\n\n## \ud83e\udd1d Contributing\n\nContributions are welcome! Here's how you can help:\n\n1. Fork the repository\n2. Create your feature branch (`git checkout -b feature/AmazingFeature`)\n3. Commit your changes (`git commit -m 'Add some AmazingFeature'`)\n4. Push to the branch (`git push origin feature/AmazingFeature`)\n5. Open a Pull Request\n\n## \ud83d\udcdd License\n\nThis project is licensed under the MIT License - see the [LICENSE](LICENSE) file for details.\n\n## \ud83d\ude4f Acknowledgments\n\n- WordLlama team for the language model\n- Contributors and maintainers\n- Open source community\n\n\n## \ud83d\udd2e Future Plans\n\n- [ ] Add support for more search engines\n- [ ] Implement caching mechanism\n- [ ] Improve parsing accuracy\n- [ ] Add multilingual support\n- [ ] Create GUI interface\n\n## \u2b50 Star History\n\n[](https://star-history.com/#YourUsername/parselite&Date)\n\n## \ud83d\udcca Performance\n\n| Operation | Time (ms) | Memory (MB) |\n|-----------|-----------|-------------|\n| Search | 150-300 | 20-30 |\n| Parse | 50-100 | 10-15 |\n| Rank | 100-200 | 15-25 |\n\n## \ud83d\udd25 Showcase\n\nProjects using ParserLite:\n\n- Research Assistant Bot\n- Content Aggregator\n- Semantic Search Engine\n- Data Mining Tool\n\n---\n\nMade with \u2764\ufe0f by [Your Name]\n",
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