websense 0.1.0

A modular AI-powered web scraper for data pipelines.

WebSense

"Making sense of the web."

WebSense is a Python library that transforms raw websites into structured, meaningful data. It leverages AI through the ask2api library to semantically understand page content, allowing you to extract complex data structures without writing brittle CSS selectors or XPath expressions.

Features

• Semantic Understanding: Uses LLMs to interpret content meaning, not just match patterns
• Resilient: Adapts to layout changes—if the meaning is there, WebSense finds it
• Minimalist API: Extract data in 3 lines of code
• Auto-Cleaning: Intelligent noise removal filters focus on meaningful content
• Flexible Schemas: Use JSON schemas or provide examples for schema inference
• Modular Design: Fetch, clean, and parse stages can be customized independently

Installation

pip install websense

For development:

git clone https://github.com/atasoglu/websense.git
cd websense
pip install -e ".[dev]"

Quick Start

Extract data with just an example:

from websense import Scraper

scraper = Scraper()

data = scraper.scrape(
    "https://github.com/atasoglu/ask2api",
    example={
        "project_name": "string",
        "description": "string",
        "stars": 0,
        "is_active": True
    }
)

print(data)

You can provide a strict JSON schema for validation:

schema = {
    "type": "object",
    "properties": {
        "title": {"type": "string"},
        "price": {"type": "number"},
        "in_stock": {"type": "boolean"}
    },
    "required": ["title", "price"]
}

data = scraper.scrape("https://example.com/product", schema=schema)

Specify a different language model for extraction:

scraper = Scraper(model="gpt-4")

See the environment variables in the ask2api repository to configure your LLM provider.

The examples/ directory contains real-world use cases:

How It Works

WebSense follows a three-stage pipeline:

1. Fetch (fetcher.py): Downloads and retrieves the webpage
2. Clean (cleaner.py): Removes noise and extracts meaningful text
3. Parse (parser.py): Uses AI to extract structured data based on your schema/example

Contributing

Contributions are welcome! Please:

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

License

MIT