Files

124 lines
4.3 KiB
Python

"""Common Crawl loader."""
import json
from pathlib import Path
from typing import Iterator
def download_commoncrawl(output_dir: Path, cc_month: str | None = None, limit: int | None = None) -> Path:
"""
Download Common Crawl data.
Args:
output_dir: Directory to save corpus
cc_month: Common Crawl month (e.g., 'CC-MAIN-2025-14')
limit: Optional limit on documents
Returns:
Path to corpus JSONL file
"""
output_dir.mkdir(parents=True, exist_ok=True)
corpus_file = output_dir / "web_pages.jsonl"
if corpus_file.exists():
print(f"Common Crawl corpus already exists at {corpus_file}")
return corpus_file
print("Common Crawl requires cc-downloader tool.")
print("Install: pip install common-crawl-download")
print("Usage: See https://github.com/commoncrawl/cc-downloader")
print("Be respectful of bandwidth when downloading.")
# Placeholder
print("Creating placeholder corpus...")
with open(corpus_file, "w", encoding="utf-8") as f:
size = limit or 10000
for i in range(size):
doc = {
"id": f"cc_{i}",
"text": f"Common Crawl web page {i} content. This is a placeholder.",
"meta": {"url": f"https://example.com/page{i}", "cc_month": cc_month or "CC-MAIN-2025-14"}
}
f.write(json.dumps(doc, ensure_ascii=False) + "\n")
print(f"Created placeholder corpus with {size} documents")
return corpus_file
def process_commoncrawl_warc(warc_file: Path, output_file: Path, limit: int | None = None) -> None:
"""
Process Common Crawl WARC file to JSONL.
Args:
warc_file: Path to WARC file
output_file: Output JSONL path
limit: Optional limit on documents
"""
output_file.parent.mkdir(parents=True, exist_ok=True)
try:
from warcio.archiveiterator import ArchiveIterator
HAS_WARC = True
except ImportError:
HAS_WARC = False
print("Warning: warcio not installed. Install with: pip install warcio")
if not HAS_WARC:
print("Creating placeholder corpus...")
with open(output_file, "w", encoding="utf-8") as f:
for i in range(limit or 10000):
doc = {
"id": f"cc_{i}",
"text": f"Web page {i} content.",
"meta": {"url": f"https://example.com/page{i}"}
}
f.write(json.dumps(doc, ensure_ascii=False) + "\n")
return
count = 0
with open(warc_file, "rb") as infile, \
open(output_file, "w", encoding="utf-8") as outfile:
for record in ArchiveIterator(infile):
if limit and count >= limit:
break
if record.rec_type == "response" and record.http_headers.get_header("Content-Type", "").startswith("text/html"):
# Extract text (simplified - in production use beautifulsoup)
text = record.read_stream().decode("utf-8", errors="ignore")
# Simple HTML stripping (in production use html2text or similar)
import re
text = re.sub(r"<[^>]+>", "", text)
text = " ".join(text.split())
if len(text) > 100: # Minimum length
doc = {
"id": record.rec_headers.get_header("WARC-Record-ID", f"cc_{count}"),
"text": text[:10000], # Limit text length
"meta": {"url": record.rec_headers.get_header("WARC-Target-URI", "")}
}
outfile.write(json.dumps(doc, ensure_ascii=False) + "\n")
count += 1
if count % 1000 == 0:
print(f"Processed {count} pages...")
print(f"Processed {count} Common Crawl pages to {output_file}")
def load_commoncrawl(corpus_file: Path) -> Iterator[dict]:
"""
Load Common Crawl corpus from JSONL file.
Args:
corpus_file: Path to corpus JSONL file
Yields:
Document dictionaries with 'id', 'text', 'meta'
"""
with open(corpus_file, "r", encoding="utf-8") as f:
for line in f:
if line.strip():
yield json.loads(line)