scraipe 0.1.39

AI web scraping workflow.

Scraipe

Scraping and analysis framework.

Under development.

Features

• Versatile Scraping: Leverage custom scrapers that handle Telegram messages, news articles, and links that require multiple ingress rules.
• LLM Analysis: Process text using OpenAI models with built-in Pydantic validation.
• Workflow Management: Combine scraping and analysis in a single fault-tolerant workflow--ideal for Jupyter notebooks.
• High Performance: Asynchronous IO-bound tasks are seamlessly integrated in the synchronous API.
• Modular: Extend the framework with new scrapers or analyzers as your data sources evolve.
• Customizable Ingress: Easily define rules to dynamically route different links to their appropriate scrapers.
• Detailed Logging: Monitor scraping and analysis operations through robust errors for improved debugging and transparency.

Help

See documentation for more details.

Installation

Ensure you are using Python>=3.10. Install Scraipe and all built-in scrapers/analyzers:

pip install scraipe[extended]

Alternatively, install the core library with:

pip install scraipe

Example

 # Import components from scraipe
 from scraipe.defaults import TextScraper
 from scraipe.defaults import TextStatsAnalyzer
 from scraipe import Workflow

 # Initialize the scraper and analyzer
 scraper = TextScraper()
 analyzer = TextStatsAnalyzer()

 # Create the workflow instance
 workflow = Workflow(scraper, analyzer)

 # List urls to scrape
 urls = [
     "https://example.com",
     "https://rickandmortyapi.com/api/character/1",
     "https://ckaestne.github.io/seai/"
 ]

 # Run the workflow
 workflow.scrape(urls)
 workflow.analyze()

 # Print the results
 results = workflow.export()
 print(results)

Contributing

Contributions are welcome. Please open an issue or submit a pull request for improvements.

Maintainer

This project is maintained by nibs.