parselite 0.3.5

A powerful web content fetcher and processor

FastParser 🚀

A high-performance, asynchronous content parser that supports both HTML and PDF extraction with special handling for arXiv papers.

✨ Features

• 🚄 Asynchronous content fetching
• 📄 PDF extraction support
• 🌐 HTML parsing
• 📚 Special handling for arXiv URLs
• 📦 Batch processing capability
• 🔄 Progress tracking with tqdm

🛠️ Installation

pip install fastparser

# Dependencies
pip install aiohttp PyPDF2 tqdm

🚀 Quick Start

from fastparser import parse

# Single URL parsing
text = parse("https://example.com")

# Batch processing
urls = [
    "https://example.com",
    "https://arxiv.org/abs/2301.01234",
    "https://example.com/document.pdf"
]
texts = parse(urls)

📖 Detailed Usage

Basic Parser Configuration

from fastparser import FastParser

# Initialize with PDF extraction (default: True)
parser = FastParser(extract_pdf=True)

# Single URL
content = parser.fetch("https://example.com")

# Multiple URLs
contents = parser.fetch_batch([
    "https://example.com",
    "https://arxiv.org/abs/2301.01234"
])

Working with arXiv Papers

The parser automatically handles different arXiv URL formats:

parser = FastParser()

# These will be automatically converted to appropriate formats
urls = [
    "https://arxiv.org/abs/2301.01234",  # Will fetch PDF if extract_pdf=True
    "http://arxiv.org/html/2301.01234",  # Will fetch HTML or PDF based on settings
]
contents = parser.fetch_batch(urls)

PDF-Only Processing

parser = FastParser(extract_pdf=True)

pdf_urls = [
    "https://example.com/document.pdf",
    "https://arxiv.org/pdf/2301.01234.pdf"
]
pdf_contents = parser.fetch_batch(pdf_urls)

🔧 API Reference

FastParser Class

class FastParser:
    def __init__(self, extract_pdf: bool = True)
    def fetch(self, url: str) -> str
    def fetch_batch(self, urls: list) -> list
    def __call__(self, urls: str|list) -> str|list

Main Functions

• parse(urls: str|list) -> str|list: Convenience function for quick parsing
• _async_html_parser(urls: list): Internal async processing method
• _fetch_pdf_content(pdf_urls: list): Internal PDF processing method
• _arxiv_url_fix(url: str): Internal arXiv URL formatting method

⚡ Performance

The parser uses asynchronous operations for optimal performance:

• Concurrent URL fetching
• Batch processing capabilities
• Progress tracking with tqdm
• Memory-efficient PDF processing

🔍 Example: Advanced Usage

import asyncio
from fastparser import FastParser

async def process_large_dataset():
    parser = FastParser(extract_pdf=True)
    
    # Process URLs in batches
    all_urls = ["url1", "url2", ..., "url1000"]
    batch_size = 50
    
    results = []
    for i in range(0, len(all_urls), batch_size):
        batch = all_urls[i:i + batch_size]
        batch_results = parser.fetch_batch(batch)
        results.extend(batch_results)
        
    return results

# Run with asyncio
results = asyncio.run(process_large_dataset())

⚠️ Error Handling

The parser includes robust error handling:

• Failed URL fetches return empty strings
• PDF processing errors are caught gracefully
• HTTP status checks
• Invalid URL format handling

🤝 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

📝 Dependencies

• aiohttp: Async HTTP client/server framework
• PyPDF2: PDF processing library
• tqdm: Progress bar library
• Custom FastHTMLParserV3 module

📋 TODO

• Add support for more document types
• Implement caching mechanism
• Add timeout configurations
• Improve error reporting
• Add proxy support

📄 License

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

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