Files
crawl4ai/docs/examples/url_seeder/url_seeder_quick_demo.py
UncleCode 3048cc1ff9 feat: Add AsyncUrlSeeder for intelligent URL discovery and filtering
This commit introduces AsyncUrlSeeder, a high-performance URL discovery system that enables intelligent crawling at scale by pre-discovering and filtering URLs before crawling.

## Core Features

### AsyncUrlSeeder Component
- Discovers URLs from multiple sources:
  - Sitemaps (including nested and gzipped)
  - Common Crawl index
  - Combined sources for maximum coverage
- Extracts page metadata without full crawling:
  - Title, description, keywords
  - Open Graph and Twitter Card tags
  - JSON-LD structured data
  - Language and charset information
- BM25 relevance scoring for intelligent filtering:
  - Query-based URL discovery
  - Configurable score thresholds
  - Automatic ranking by relevance
- Performance optimizations:
  - Async/concurrent processing with configurable workers
  - Rate limiting (hits per second)
  - Automatic caching with TTL
  - Streaming results for large datasets

### SeedingConfig
- Comprehensive configuration for URL seeding:
  - Source selection (sitemap, cc, or both)
  - URL pattern filtering with wildcards
  - Live URL validation options
  - Metadata extraction controls
  - BM25 scoring parameters
  - Concurrency and rate limiting

### Integration with AsyncWebCrawler
- Seamless pipeline: discover → filter → crawl
- Direct compatibility with arun_many()
- Significant resource savings by pre-filtering URLs

## Documentation
- Comprehensive guide comparing URL seeding vs deep crawling
- Complete API reference with parameter tables
- Practical examples showing all features
- Performance benchmarks and best practices
- Integration patterns with AsyncWebCrawler

## Examples
- url_seeder_demo.py: Interactive Rich-based demo with:
  - Basic discovery
  - Cache management
  - Live validation
  - BM25 scoring
  - Multi-domain discovery
  - Complete pipeline integration
- url_seeder_quick_demo.py: Screenshot-friendly examples:
  - Pattern-based filtering
  - Metadata exploration
  - Smart search with BM25

## Testing
- Comprehensive test suite (test_async_url_seeder_bm25.py)
- Coverage of all major features
- Edge cases and error handling
- Performance and consistency tests

## Implementation Details
- Built on httpx with HTTP/2 support
- Optional dependencies: lxml, brotli, rank_bm25
- Cache management in ~/.crawl4ai/seeder_cache/
- Logger integration with AsyncLoggerBase
- Proper error handling and retry logic

## Bug Fixes
- Fixed logger color compatibility (lightblack → bright_black)
- Corrected URL extraction from seeder results for arun_many()
- Updated all examples and documentation with proper usage

This feature enables users to crawl smarter, not harder, by discovering
and analyzing URLs before committing resources to crawling them.
2025-06-03 23:27:12 +08:00

128 lines
5.1 KiB
Python

"""
🚀 URL Seeder + AsyncWebCrawler = Magic!
Quick demo showing discovery → filter → crawl pipeline
"""
import asyncio
from crawl4ai import AsyncUrlSeeder, AsyncWebCrawler, SeedingConfig, CrawlerRunConfig, AsyncLogger, DefaultMarkdownGenerator
from crawl4ai.content_filter_strategy import PruningContentFilter
# 🔍 Example 1: Discover ALL → Filter → Crawl
async def discover_and_crawl():
"""Find Python module tutorials & extract them all!"""
seeder = AsyncUrlSeeder(
logger=AsyncLogger() # Log everything
)
# Step 1: See how many URLs exist (spoiler: A LOT!)
print("📊 Let's see what RealPython has...")
all_urls = await seeder.urls("realpython.com",
SeedingConfig(source="sitemap"))
print(f"😱 Found {len(all_urls)} total URLs!")
# Step 2: Filter for Python modules (perfect size ~13)
print("\n🎯 Filtering for 'python-modules' tutorials...")
module_urls = await seeder.urls("realpython.com",
SeedingConfig(
source="sitemap",
pattern="*python-modules*",
live_check=True # Make sure they're alive!
))
print(f"✨ Found {len(module_urls)} module tutorials")
for url in module_urls[:3]: # Show first 3
status = "" if url["status"] == "valid" else ""
print(f"{status} {url['url']}")
# Step 3: Crawl them all with pruning (keep it lean!)
print("\n🕷️ Crawling all module tutorials...")
async with AsyncWebCrawler() as crawler:
config = CrawlerRunConfig(
markdown_generator=DefaultMarkdownGenerator(
content_filter=PruningContentFilter( # Smart filtering!
threshold=0.48, # Remove fluff
threshold_type="fixed",
),
),
only_text=True,
stream=True,
)
# Extract just the URLs from the seeder results
urls_to_crawl = [u["url"] for u in module_urls[:5]]
results = await crawler.arun_many(urls_to_crawl, config=config)
# Process & save
saved = 0
async for result in results:
if result.success:
# Save each tutorial (name from URL)
name = result.url.split("/")[-2] + ".md"
with open(name, "w") as f:
f.write(result.markdown.fit_markdown)
saved += 1
print(f"💾 Saved: {name}")
print(f"\n🎉 Successfully saved {saved} tutorials!")
# 🔍 Example 2: Beautiful Soup articles with metadata peek
async def explore_beautifulsoup():
"""Discover BeautifulSoup content & peek at metadata"""
seeder = AsyncUrlSeeder(logger=AsyncLogger() )
print("🍲 Looking for Beautiful Soup articles...")
soup_urls = await seeder.urls("realpython.com",
SeedingConfig(
source="sitemap",
pattern="*beautiful-soup*",
extract_head=True # Get the metadata!
))
print(f"\n📚 Found {len(soup_urls)} Beautiful Soup articles:\n")
# Show what we discovered
for i, url in enumerate(soup_urls, 1):
meta = url["head_data"]["meta"]
print(f"{i}. {url['head_data']['title']}")
print(f" 📝 {meta.get('description', 'No description')[:60]}...")
print(f" 👤 By: {meta.get('author', 'Unknown')}")
print(f" 🔗 {url['url']}\n")
# 🔍 Example 3: Smart search with BM25 relevance scoring
async def smart_search_with_bm25():
"""Use AI-powered relevance scoring to find the best content"""
seeder = AsyncUrlSeeder(logger=AsyncLogger() )
print("🧠 Smart search: 'web scraping tutorial quiz'")
# Search with BM25 scoring - AI finds the best matches!
results = await seeder.urls("realpython.com",
SeedingConfig(
source="sitemap",
pattern="*beautiful-soup*",
extract_head=True,
query="web scraping tutorial quiz", # Our search
scoring_method="bm25",
score_threshold=0.2 # Quality filter
))
print(f"\n🎯 Top {len(results)} most relevant results:\n")
# Show ranked results with relevance scores
for i, result in enumerate(results[:3], 1):
print(f"{i}. [{result['relevance_score']:.2f}] {result['head_data']['title']}")
print(f" 🔗 {result['url'][:60]}...")
print("\n✨ BM25 automatically ranked by relevance!")
# 🎬 Run the show!
async def main():
# print("=" * 60)
# await discover_and_crawl()
# print("\n" + "=" * 60 + "\n")
# await explore_beautifulsoup()
# print("\n" + "=" * 60 + "\n")
await smart_search_with_bm25()
if __name__ == "__main__":
asyncio.run(main())