Webrip Lama | Validated

# Remove scripts & styles for tag in soup(["script", "style"]): tag.decompose()

You are Webrip Lama. From the HTML below, extract: - Main article text (no navigation, no ads) - All external links (href and link text) - Any visible publication date - Metadata: author, section, word count Return as JSON. webrip lama

If you meant something else (e.g., a misspelling of webrip + lama as a project name), this piece can still serve as a technical quick-start guide. Webrip Lama helps you systematically download, parse, and archive web content — with a large language model (LLM) acting as your extraction assistant. 1. Quick Setup # Install core tools pip install requests beautifulsoup4 lxml playwright playwright install Optional: LLM integration pip install openai anthropic 2. Basic Webrip with Fallback Parsing # webrip_lama.py import requests from bs4 import BeautifulSoup from urllib.parse import urljoin def simple_rip(url, output_file="rip.txt"): resp = requests.get(url, headers="User-Agent": "WebripLama/1.0") soup = BeautifulSoup(resp.text, "lxml") # Remove scripts & styles for tag in

text = soup.get_text(separator="\n", strip=True) with open(output_file, "w") as f: f.write(text) print(f"[✓] Saved: output_file") if == " main ": import sys simple_rip(sys.argv[1]) 3. LLM-Assisted Extraction Prompt Template Use an LLM to extract structured data from messy HTML: Webrip Lama helps you systematically download, parse, and

HTML: html_chunk # urls.txt — one URL per line cat urls.txt | xargs -P 4 -I {} python webrip_lama.py {} 5. Smart Retry & Rate Limiting (Good Citizen) import time from requests.adapters import HTTPAdapter from requests.packages.urllib3.util.retry import Retry session = requests.Session() retries = Retry(total=3, backoff_factor=1, status_forcelist=[429, 500, 502]) session.mount("http://", HTTPAdapter(max_retries=retries))