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#!/usr/bin/env python3
"""
AGGRESSIVE KNOWLEDGE BASE ACQUISITION
Multi-Source Scraping for Quantum Training
Aurora - ETL Systems Specialist
"""
import requests
import json
import time
import re
from bs4 import BeautifulSoup
from pathlib import Path
from urllib.parse import urljoin, urlparse
import concurrent.futures
from tqdm import tqdm
import xml.etree.ElementTree as ET
from datetime import datetime
import pandas as pd
import xml.etree.ElementTree as ET
class KnowledgeBaseScraper:
def __init__(self):
self.session = requests.Session()
self.session.headers.update({
'User-Agent': 'Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36',
'Accept': 'text/html,application/xhtml+xml,application/xml;q=0.9,*/*;q=0.8',
'Accept-Language': 'en-US,en;q=0.5',
'Accept-Encoding': 'gzip, deflate',
'DNT': '1',
'Connection': 'keep-alive',
'Upgrade-Insecure-Requests': '1',
})
self.output_dir = Path("/data/adaptai/corpus-data/knowledge-base")
self.output_dir.mkdir(exist_ok=True, parents=True)
# Target knowledge sources
self.sources = {
'technical': [
'https://arxiv.org/list/cs.AI/recent', # AI papers
'https://arxiv.org/list/cs.LG/recent', # Machine Learning
'https://arxiv.org/list/cs.CL/recent', # Computation & Language
'https://paperswithcode.com/',
'https://huggingface.co/papers',
],
'scientific': [
'https://www.nature.com/subjects/artificial-intelligence',
'https://www.science.org/topic/artificial-intelligence',
'https://www.ncbi.nlm.nih.gov/pmc/',
],
'educational': [
'https://www.khanacademy.org/',
'https://ocw.mit.edu/',
'https://www.coursera.org/',
'https://developers.google.com/machine-learning',
],
'financial': [
'https://www.sec.gov/edgar/searchedgar/companysearch.html',
'https://www.federalreserve.gov/releases/',
'https://www.imf.org/en/Publications',
],
'programming': [
'https://docs.python.org/3/',
'https://developer.mozilla.org/en-US/docs/Web',
'https://docs.docker.com/',
'https://kubernetes.io/docs/',
]
}
# Optional SEC filters from environment (comma-separated)
import os
self.sec_ciks = {c.strip().lower() for c in (os.getenv('SEC_CIK') or '').split(',') if c.strip()}
self.sec_form_types = {t.strip().upper() for t in (os.getenv('SEC_FORM_TYPES') or '').split(',') if t.strip()}
def scrape_arxiv(self, url):
"""Scrape arXiv papers"""
try:
response = self.session.get(url, timeout=30)
soup = BeautifulSoup(response.text, 'html.parser')
papers = []
for item in soup.find_all('div', class_='meta'):
title_elem = item.find('div', class_='list-title')
authors_elem = item.find('div', class_='list-authors')
abstract_elem = item.find('p', class_='mathjax')
if title_elem and abstract_elem:
title = title_elem.text.replace('Title:', '').strip()
authors = authors_elem.text.replace('Authors:', '').strip() if authors_elem else ""
abstract = abstract_elem.text.strip()
papers.append({
'title': title,
'authors': authors,
'abstract': abstract,
'source': 'arxiv',
'url': url,
'timestamp': datetime.now().isoformat()
})
return papers
except Exception as e:
print(f"Error scraping arXiv {url}: {e}")
return []
def scrape_academic_site(self, url):
"""Scrape academic and research sites"""
try:
response = self.session.get(url, timeout=30)
soup = BeautifulSoup(response.text, 'html.parser')
# Remove scripts, styles, nav elements
for element in soup(['script', 'style', 'nav', 'footer', 'header']):
element.decompose()
# Get main content
text = soup.get_text()
lines = (line.strip() for line in text.splitlines())
chunks = (phrase.strip() for line in lines for phrase in line.split(" "))
text = ' '.join(chunk for chunk in chunks if chunk)
return {
'content': text,
'title': soup.title.text if soup.title else "",
'url': url,
'source': 'academic',
'timestamp': datetime.now().isoformat()
}
except Exception as e:
print(f"Error scraping academic site {url}: {e}")
return None
def scrape_documentation(self, url):
"""Scrape technical documentation"""
try:
response = self.session.get(url, timeout=30)
soup = BeautifulSoup(response.text, 'html.parser')
# Focus on main content areas
content_selectors = [
'main', 'article', '.content', '#content',
'.documentation', '.docs', 'section'
]
content = ""
for selector in content_selectors:
elements = soup.select(selector)
for element in elements:
text = element.get_text()
lines = (line.strip() for line in text.splitlines())
chunks = (phrase.strip() for line in lines for phrase in line.split(" "))
content += ' '.join(chunk for chunk in chunks if chunk) + "\n\n"
if not content:
content = soup.get_text()
return {
'content': content,
'title': soup.title.text if soup.title else "",
'url': url,
'source': 'documentation',
'timestamp': datetime.now().isoformat()
}
except Exception as e:
print(f"Error scraping documentation {url}: {e}")
return None
def scrape_financial_data(self, url):
"""Scrape financial reports and data with basic SEC EDGAR support.
- If URL is SEC EDGAR, fetch the Atom feed of recent filings and parse entries.
- Otherwise, fall back to academic-style scraping.
"""
try:
if 'sec.gov' in url:
# Use the EDGAR recent filings Atom feed for reliability
feed_url = 'https://www.sec.gov/cgi-bin/browse-edgar?action=getcurrent&owner=include&count=40&output=atom'
headers = {
'User-Agent': self.session.headers.get('User-Agent', 'Mozilla/5.0'),
'Accept': 'application/atom+xml,application/xml;q=0.9,*/*;q=0.8'
}
resp = self.session.get(feed_url, headers=headers, timeout=30)
resp.raise_for_status()
root = ET.fromstring(resp.text)
ns = {'atom': 'http://www.w3.org/2005/Atom'}
items = []
for entry in root.findall('atom:entry', ns):
title = (entry.findtext('atom:title', default='', namespaces=ns) or '').strip()
summary = (entry.findtext('atom:summary', default='', namespaces=ns) or '').strip()
link_el = entry.find('atom:link', ns)
link = link_el.get('href') if link_el is not None else ''
updated = (entry.findtext('atom:updated', default='', namespaces=ns) or '').strip()
item = {
'title': title,
'content': summary,
'category': 'sec_edgar',
'url': link,
'source': 'edgar_atom',
'timestamp': updated or datetime.now().isoformat()
}
# Basic filtering by CIK and form type if configured
# Title format often contains: "8-K - COMPANY NAME (CIK 0000320193)"
title_upper = title.upper()
title_lower = title.lower()
if self.sec_form_types:
if not any(ft in title_upper.split() for ft in self.sec_form_types):
continue
if self.sec_ciks:
if not any(f"cik {c}" in title_lower for c in self.sec_ciks):
continue
items.append(item)
return items
else:
return self.scrape_academic_site(url)
except Exception as e:
print(f"Error scraping financial data {url}: {e}")
return None
def scrape_source_type(self, urls, scrape_func):
"""Scrape multiple URLs of same type"""
results = []
with concurrent.futures.ThreadPoolExecutor(max_workers=5) as executor:
futures = {executor.submit(scrape_func, url): url for url in urls}
for future in tqdm(concurrent.futures.as_completed(futures),
total=len(futures), desc="Scraping sources"):
result = future.result()
if result:
if isinstance(result, list):
results.extend(result)
else:
results.append(result)
return results
def scrape_all_sources(self):
"""Scrape all knowledge sources"""
all_data = {}
print("🚀 AGGRESSIVE KNOWLEDGE BASE SCRAPING INITIATED")
print("=" * 60)
# Scrape technical sources (arXiv)
print("\n📚 Scraping technical papers...")
tech_data = []
for arxiv_url in self.sources['technical']:
if 'arxiv' in arxiv_url:
tech_data.extend(self.scrape_arxiv(arxiv_url))
all_data['technical'] = tech_data
# Scrape academic sources
print("\n🎓 Scraping academic resources...")
academic_data = self.scrape_source_type(
self.sources['scientific'] + self.sources['educational'],
self.scrape_academic_site
)
all_data['academic'] = academic_data
# Scrape documentation
print("\n💻 Scraping technical documentation...")
docs_data = self.scrape_source_type(
self.sources['programming'],
self.scrape_documentation
)
all_data['documentation'] = docs_data
# Scrape financial data
print("\n💰 Scraping financial data...")
financial_data = self.scrape_source_type(
self.sources['financial'],
self.scrape_financial_data
)
all_data['financial'] = financial_data
return all_data
def save_results(self, data):
"""Save scraped data to organized structure"""
timestamp = datetime.now().strftime("%Y%m%d_%H%M%S")
for category, items in data.items():
if items:
category_dir = self.output_dir / category
category_dir.mkdir(exist_ok=True)
# Save as JSON
output_file = category_dir / f"{category}_{timestamp}.json"
with open(output_file, 'w', encoding='utf-8') as f:
json.dump(items, f, indent=2, ensure_ascii=False)
print(f"💾 Saved {len(items)} {category} items to {output_file}")
# Save summary
summary = {
'total_items': sum(len(items) for items in data.values()),
'categories': {cat: len(items) for cat, items in data.items()},
'timestamp': timestamp,
'sources_scraped': self.sources
}
summary_file = self.output_dir / f"scraping_summary_{timestamp}.json"
with open(summary_file, 'w', encoding='utf-8') as f:
json.dump(summary, f, indent=2)
print(f"\n📊 SCRAPING SUMMARY:")
print(f" Total items: {summary['total_items']}")
for category, count in summary['categories'].items():
print(f" {category}: {count} items")
def main():
scraper = KnowledgeBaseScraper()
# Start aggressive scraping
scraped_data = scraper.scrape_all_sources()
# Save results
scraper.save_results(scraped_data)
print("\n✅ KNOWLEDGE BASE ACQUISITION COMPLETE")
print("=" * 60)
print("Next: Process with quantum preprocessing pipeline")
if __name__ == "__main__":
main()
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