RAGScraper is a Python library designed for efficient and intelligent scraping of web documentation and content. Tailored for Retrieval-Augmented Generation systems, RAGScraper extracts and preprocesses text into structured, machine-learning-ready formats. It emphasizes precision, context preservation, and ease of integration with RAG models, making it an ideal tool for developers looking to enhance AI-driven applications with rich, web-sourced knowledge.
RAGScraper is a simple Python package that scrapes webpages and converts them to markdown format for RAG usage.
To install RAGScraper, simply run:
pip install ragscraper
To use RAGScraper as a command-line tool:
rag-scraper <URL>
To use RAGScraper in a Python script:
from rag_scraper.scraper import Scraper
from rag_scraper.converter import Converter
# Fetch HTML content
url = "https://example.com"
html_content = Scraper.fetch_html(url)
# Convert to Markdown
markdown_content = Converter.html_to_markdown(
html=html_content,
base_url=base_url,
parser_features='html.parser',
ignore_links=True
)
print(markdown_content)
To run the tests for RAGScraper, navigate to the package directory and run:
python -m unittest discover tests
Contributions are welcome! Please feel free to submit a Pull Request.
FAQs
RAGScraper is a Python library designed for efficient and intelligent scraping of web documentation and content. Tailored for Retrieval-Augmented Generation systems, RAGScraper extracts and preprocesses text into structured, machine-learning-ready formats. It emphasizes precision, context preservation, and ease of integration with RAG models, making it an ideal tool for developers looking to enhance AI-driven applications with rich, web-sourced knowledge.
We found that RAGScraper demonstrated a healthy version release cadence and project activity because the last version was released less than a year ago. It has 1 open source maintainer collaborating on the project.
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