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markdown_crawler - by @paulpierre
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A multithreaded 🕸️ web crawler that recursively crawls a website and creates a 🔽 markdown file for each page
https://github.com/paulpierre
https://x.com/paulpierre                                                        

📝 Overview

This is a multithreaded web crawler that crawls a website and creates markdown files for each page. It was primarily created for large language model document parsing to simplify chunking and processing of large documents for RAG use cases. Markdown by nature is human readable and maintains document structure while keeping a small footprint.

✨ Features include

• 🧵 Threading support for faster crawling
• ⏯️ Continue scraping where you left off
• ⏬ Set the max depth of children you wish to crawl
• 📄 Support for tables, images, etc.
• ✅ Validates URLs, HTML, filepaths
• ⚙️ Configure list of valid base paths or base domains
• 🚫 Exclude specific paths from crawling (--exclude-paths)
• 🎨 Configurable markdown heading style (--heading-style)
• 🌐 Browser-like User-Agent to bypass JS checks
• 🍲 Uses BeautifulSoup to parse HTML
• 🪵 Verbose logging option
• 👩‍💻 Ready-to-go CLI interface
• 🧪 Comprehensive test suite (95% coverage)
  

📦 What's New in v0.0.9

This release resolves 6 open issues and introduces several community-requested features:

Issue	Fix
#7	UnicodeEncodeError on non-ASCII content — UTF-8 encoding on all file writes
#8	Bypass JavaScript checks — browser-like User-Agent header on all requests
#9	Exclude paths from crawling — new --exclude-paths / -x CLI flag
#10	target_content CSS selector handling — null href links now skipped
#11	UnboundLocalError fix in get_target_content
#17	Dependencies added to pyproject.toml for uvx support
#20	Configurable heading_style for markdownify (ATX, UNDERLINE, etc.)

Also includes a comprehensive test suite — 60 tests, 95% coverage. See the CHANGELOG.md for full details.

🏗️ Use cases

• RAG (retrieval augmented generation) - my primary usecase, use this to normalize large documents and chunk by header, pargraph or sentence
• LLM fine-tuning - Create a large corpus of markdown files as a first step and leverage gpt-3.5-turbo or Mistral-7B to extract Q&A pairs
• Agent knowledge - Leverage this with autogen for expert agents, for example if you wish to reconstruct the knowledge corpus of a videogame or movie, use this to generate the given expert corpus
• Agent / LLM tools - Use this for online RAG learning so your chatbot continues to learn. Use SERP and scrape + index top N results w/ markdown-crawler
• many more ..

🚀 Get started

If you wish to simply use it in the CLI, you can run the following command:

Install the package

pip install markdown-crawler

Execute the CLI

markdown-crawler -t 5 -d 3 -b ./markdown https://en.wikipedia.org/wiki/Morty_Smith

To run from the github repo, once you have it checked out:

pip install .
markdown-crawler -t 5 -d 3 -b ./markdown https://en.wikipedia.org/wiki/Morty_Smith

Or use the library in your own code:

from markdown_crawler import md_crawl
url = 'https://en.wikipedia.org/wiki/Morty_Smith'
md_crawl(url, max_depth=3, num_threads=5, base_path='markdown')

⚠️ Requirements

• Python 3.x
• BeautifulSoup4
• requests
• markdownify

🔍 Usage

The following arguments are supported

usage: markdown-crawler [-h] [--max-depth MAX_DEPTH] [--num-threads NUM_THREADS] [--base-dir