Release v0.7.0-r1: The Adaptive Intelligence Update
- Bump version to 0.7.0 - Add release notes and demo files - Update README with v0.7.0 features - Update Docker configurations for v0.7.0-r1 - Move v0.7.0 demo files to releases_review - Fix BM25 scoring bug in URLSeeder Major features: - Adaptive Crawling with pattern learning - Virtual Scroll support for infinite pages - Link Preview with 3-layer scoring - Async URL Seeder for massive discovery - Performance optimizations
This commit is contained in:
UncleCode
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docs/blog/release-v0.7.0.md
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# 🚀 Crawl4AI v0.7.0: The Adaptive Intelligence Update
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*January 28, 2025 • 10 min read*
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---
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Today I'm releasing Crawl4AI v0.7.0—the Adaptive Intelligence Update. This release introduces fundamental improvements in how Crawl4AI handles modern web complexity through adaptive learning, intelligent content discovery, and advanced extraction capabilities.
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## 🎯 What's New at a Glance
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- **Adaptive Crawling**: Your crawler now learns and adapts to website patterns
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- **Virtual Scroll Support**: Complete content extraction from infinite scroll pages
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- **Link Preview with 3-Layer Scoring**: Intelligent link analysis and prioritization
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- **Async URL Seeder**: Discover thousands of URLs in seconds with intelligent filtering
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- **PDF Parsing**: Extract data from PDF documents
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- **Performance Optimizations**: Significant speed and memory improvements
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## 🧠 Adaptive Crawling: Intelligence Through Pattern Learning
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**The Problem:** Websites change. Class names shift. IDs disappear. Your carefully crafted selectors break at 3 AM, and you wake up to empty datasets and angry stakeholders.
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**My Solution:** I implemented an adaptive learning system that observes patterns, builds confidence scores, and adjusts extraction strategies on the fly. It's like having a junior developer who gets better at their job with every page they scrape.
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### Technical Deep-Dive
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The Adaptive Crawler maintains a persistent state for each domain, tracking:
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- Pattern success rates
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- Selector stability over time
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- Content structure variations
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- Extraction confidence scores
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```python
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from crawl4ai import AdaptiveCrawler, AdaptiveConfig, CrawlState
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# Initialize with custom learning parameters
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config = AdaptiveConfig(
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confidence_threshold=0.7, # Min confidence to use learned patterns
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max_history=100, # Remember last 100 crawls per domain
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learning_rate=0.2, # How quickly to adapt to changes
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patterns_per_page=3, # Patterns to learn per page type
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extraction_strategy='css' # 'css' or 'xpath'
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)
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adaptive_crawler = AdaptiveCrawler(config)
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# First crawl - crawler learns the structure
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async with AsyncWebCrawler() as crawler:
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result = await crawler.arun(
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"https://news.example.com/article/12345",
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config=CrawlerRunConfig(
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adaptive_config=config,
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extraction_hints={ # Optional hints to speed up learning
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"title": "article h1",
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"content": "article .body-content"
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}
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)
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)
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# Crawler identifies and stores patterns
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if result.success:
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state = adaptive_crawler.get_state("news.example.com")
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print(f"Learned {len(state.patterns)} patterns")
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print(f"Confidence: {state.avg_confidence:.2%}")
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# Subsequent crawls - uses learned patterns
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result2 = await crawler.arun(
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"https://news.example.com/article/67890",
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config=CrawlerRunConfig(adaptive_config=config)
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)
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# Automatically extracts using learned patterns!
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```
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**Expected Real-World Impact:**
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- **News Aggregation**: Maintain 95%+ extraction accuracy even as news sites update their templates
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- **E-commerce Monitoring**: Track product changes across hundreds of stores without constant maintenance
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- **Research Data Collection**: Build robust academic datasets that survive website redesigns
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- **Reduced Maintenance**: Cut selector update time by 80% for frequently-changing sites
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## 🌊 Virtual Scroll: Complete Content Capture
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**The Problem:** Modern web apps only render what's visible. Scroll down, new content appears, old content vanishes into the void. Traditional crawlers capture that first viewport and miss 90% of the content. It's like reading only the first page of every book.
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**My Solution:** I built Virtual Scroll support that mimics human browsing behavior, capturing content as it loads and preserving it before the browser's garbage collector strikes.
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### Implementation Details
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```python
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from crawl4ai import VirtualScrollConfig
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# For social media feeds (Twitter/X style)
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twitter_config = VirtualScrollConfig(
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container_selector="[data-testid='primaryColumn']",
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scroll_count=20, # Number of scrolls
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scroll_by="container_height", # Smart scrolling by container size
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wait_after_scroll=1.0, # Let content load
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capture_method="incremental", # Capture new content on each scroll
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deduplicate=True # Remove duplicate elements
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)
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# For e-commerce product grids (Instagram style)
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grid_config = VirtualScrollConfig(
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container_selector="main .product-grid",
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scroll_count=30,
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scroll_by=800, # Fixed pixel scrolling
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wait_after_scroll=1.5, # Images need time
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stop_on_no_change=True # Smart stopping
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)
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# For news feeds with lazy loading
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news_config = VirtualScrollConfig(
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container_selector=".article-feed",
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scroll_count=50,
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scroll_by="page_height", # Viewport-based scrolling
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wait_after_scroll=0.5,
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wait_for_selector=".article-card", # Wait for specific elements
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timeout=30000 # Max 30 seconds total
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)
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# Use it in your crawl
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async with AsyncWebCrawler() as crawler:
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result = await crawler.arun(
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"https://twitter.com/trending",
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config=CrawlerRunConfig(
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virtual_scroll_config=twitter_config,
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# Combine with other features
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extraction_strategy=JsonCssExtractionStrategy({
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"tweets": {
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"selector": "[data-testid='tweet']",
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"fields": {
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"text": {"selector": "[data-testid='tweetText']", "type": "text"},
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"likes": {"selector": "[data-testid='like']", "type": "text"}
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}
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}
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})
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)
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)
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print(f"Captured {len(result.extracted_content['tweets'])} tweets")
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```
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**Key Capabilities:**
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- **DOM Recycling Awareness**: Detects and handles virtual DOM element recycling
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- **Smart Scroll Physics**: Three modes - container height, page height, or fixed pixels
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- **Content Preservation**: Captures content before it's destroyed
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- **Intelligent Stopping**: Stops when no new content appears
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- **Memory Efficient**: Streams content instead of holding everything in memory
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**Expected Real-World Impact:**
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- **Social Media Analysis**: Capture entire Twitter threads with hundreds of replies, not just top 10
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- **E-commerce Scraping**: Extract 500+ products from infinite scroll catalogs vs. 20-50 with traditional methods
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- **News Aggregation**: Get all articles from modern news sites, not just above-the-fold content
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- **Research Applications**: Complete data extraction from academic databases using virtual pagination
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## 🔗 Link Preview: Intelligent Link Analysis and Scoring
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**The Problem:** You crawl a page and get 200 links. Which ones matter? Which lead to the content you actually want? Traditional crawlers force you to follow everything or build complex filters.
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**My Solution:** I implemented a three-layer scoring system that analyzes links like a human would—considering their position, context, and relevance to your goals.
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### The Three-Layer Scoring System
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```python
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from crawl4ai import LinkPreviewConfig
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# Configure intelligent link analysis
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link_config = LinkPreviewConfig(
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# What to analyze
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include_internal=True,
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include_external=True,
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max_links=100, # Analyze top 100 links
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# Relevance scoring
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query="machine learning tutorials", # Your interest
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score_threshold=0.3, # Minimum relevance score
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# Performance
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concurrent_requests=10, # Parallel processing
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timeout_per_link=5000, # 5s per link
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# Advanced scoring weights
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scoring_weights={
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"intrinsic": 0.3, # Link quality indicators
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"contextual": 0.5, # Relevance to query
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"popularity": 0.2 # Link prominence
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}
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)
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# Use in your crawl
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result = await crawler.arun(
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"https://tech-blog.example.com",
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config=CrawlerRunConfig(
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link_preview_config=link_config,
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score_links=True
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)
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)
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# Access scored and sorted links
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for link in result.links["internal"][:10]: # Top 10 internal links
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print(f"Score: {link['total_score']:.3f}")
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print(f" Intrinsic: {link['intrinsic_score']:.1f}/10") # Position, attributes
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print(f" Contextual: {link['contextual_score']:.1f}/1") # Relevance to query
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print(f" URL: {link['href']}")
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print(f" Title: {link['head_data']['title']}")
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print(f" Description: {link['head_data']['meta']['description'][:100]}...")
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```
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**Scoring Components:**
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1. **Intrinsic Score (0-10)**: Based on link quality indicators
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- Position on page (navigation, content, footer)
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- Link attributes (rel, title, class names)
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- Anchor text quality and length
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- URL structure and depth
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2. **Contextual Score (0-1)**: Relevance to your query
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- Semantic similarity using embeddings
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- Keyword matching in link text and title
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- Meta description analysis
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- Content preview scoring
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3. **Total Score**: Weighted combination for final ranking
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**Expected Real-World Impact:**
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- **Research Efficiency**: Find relevant papers 10x faster by following only high-score links
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- **Competitive Analysis**: Automatically identify important pages on competitor sites
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- **Content Discovery**: Build topic-focused crawlers that stay on track
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- **SEO Audits**: Identify and prioritize high-value internal linking opportunities
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## 🎣 Async URL Seeder: Automated URL Discovery at Scale
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**The Problem:** You want to crawl an entire domain but only have the homepage. Or worse, you want specific content types across thousands of pages. Manual URL discovery? That's a job for machines, not humans.
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**My Solution:** I built Async URL Seeder—a turbocharged URL discovery engine that combines multiple sources with intelligent filtering and relevance scoring.
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### Technical Architecture
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```python
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from crawl4ai import AsyncUrlSeeder, SeedingConfig
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# Basic discovery - find all product pages
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seeder_config = SeedingConfig(
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# Discovery sources
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source="sitemap+cc", # Sitemap + Common Crawl
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# Filtering
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pattern="*/product/*", # URL pattern matching
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ignore_patterns=["*/reviews/*", "*/questions/*"],
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# Validation
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live_check=True, # Verify URLs are alive
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max_urls=5000, # Stop at 5000 URLs
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# Performance
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concurrency=100, # Parallel requests
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hits_per_sec=10 # Rate limiting
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)
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seeder = AsyncUrlSeeder(seeder_config)
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urls = await seeder.discover("https://shop.example.com")
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# Advanced: Relevance-based discovery
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research_config = SeedingConfig(
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source="crawl+sitemap", # Deep crawl + sitemap
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pattern="*/blog/*", # Blog posts only
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# Content relevance
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extract_head=True, # Get meta tags
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query="quantum computing tutorials",
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scoring_method="bm25", # Or "semantic" (coming soon)
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score_threshold=0.4, # High relevance only
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# Smart filtering
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filter_nonsense_urls=True, # Remove .xml, .txt, etc.
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min_content_length=500, # Skip thin content
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force=True # Bypass cache
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)
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# Discover with progress tracking
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discovered = []
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async for batch in seeder.discover_iter("https://physics-blog.com", research_config):
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discovered.extend(batch)
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print(f"Found {len(discovered)} relevant URLs so far...")
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# Results include scores and metadata
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for url_data in discovered[:5]:
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print(f"URL: {url_data['url']}")
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print(f"Score: {url_data['score']:.3f}")
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print(f"Title: {url_data['title']}")
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```
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**Discovery Methods:**
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- **Sitemap Mining**: Parses robots.txt and all linked sitemaps
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- **Common Crawl**: Queries the Common Crawl index for historical URLs
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- **Intelligent Crawling**: Follows links with smart depth control
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- **Pattern Analysis**: Learns URL structures and generates variations
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**Expected Real-World Impact:**
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- **Migration Projects**: Discover 10,000+ URLs from legacy sites in under 60 seconds
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- **Market Research**: Map entire competitor ecosystems automatically
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- **Academic Research**: Build comprehensive datasets without manual URL collection
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- **SEO Audits**: Find every indexable page with content scoring
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- **Content Archival**: Ensure no content is left behind during site migrations
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## ⚡ Performance Optimizations
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This release includes significant performance improvements through optimized resource handling, better concurrency management, and reduced memory footprint.
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### What We Optimized
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```python
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# Before v0.7.0 (slow)
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results = []
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for url in urls:
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result = await crawler.arun(url)
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results.append(result)
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# After v0.7.0 (fast)
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# Automatic batching and connection pooling
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results = await crawler.arun_batch(
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urls,
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config=CrawlerRunConfig(
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# New performance options
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batch_size=10, # Process 10 URLs concurrently
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reuse_browser=True, # Keep browser warm
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eager_loading=False, # Load only what's needed
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streaming_extraction=True, # Stream large extractions
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# Optimized defaults
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wait_until="domcontentloaded", # Faster than networkidle
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exclude_external_resources=True, # Skip third-party assets
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block_ads=True # Ad blocking built-in
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)
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)
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# Memory-efficient streaming for large crawls
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async for result in crawler.arun_stream(large_url_list):
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# Process results as they complete
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await process_result(result)
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# Memory is freed after each iteration
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```
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**Performance Gains:**
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- **Startup Time**: 70% faster browser initialization
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- **Page Loading**: 40% reduction with smart resource blocking
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- **Extraction**: 3x faster with compiled CSS selectors
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- **Memory Usage**: 60% reduction with streaming processing
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- **Concurrent Crawls**: Handle 5x more parallel requests
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## 📄 PDF Support
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PDF extraction is now natively supported in Crawl4AI.
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```python
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# Extract data from PDF documents
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result = await crawler.arun(
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"https://example.com/report.pdf",
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config=CrawlerRunConfig(
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pdf_extraction=True,
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extraction_strategy=JsonCssExtractionStrategy({
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# Works on converted PDF structure
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"title": {"selector": "h1", "type": "text"},
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"sections": {"selector": "h2", "type": "list"}
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})
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)
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)
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```
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## 🔧 Important Changes
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### Breaking Changes
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- `link_extractor` renamed to `link_preview` (better reflects functionality)
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- Minimum Python version now 3.9
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- `CrawlerConfig` split into `CrawlerRunConfig` and `BrowserConfig`
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### Migration Guide
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```python
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# Old (v0.6.x)
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from crawl4ai import CrawlerConfig
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config = CrawlerConfig(timeout=30000)
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# New (v0.7.0)
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from crawl4ai import CrawlerRunConfig, BrowserConfig
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browser_config = BrowserConfig(timeout=30000)
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run_config = CrawlerRunConfig(cache_mode=CacheMode.BYPASS)
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```
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## 🤖 Coming Soon: Intelligent Web Automation
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I'm currently working on bringing advanced automation capabilities to Crawl4AI. This includes:
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- **Crawl Agents**: Autonomous crawlers that understand your goals and adapt their strategies
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- **Auto JS Generation**: Automatic JavaScript code generation for complex interactions
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- **Smart Form Handling**: Intelligent form detection and filling
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- **Context-Aware Actions**: Crawlers that understand page context and make decisions
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These features are under active development and will revolutionize how we approach web automation. Stay tuned!
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## 🚀 Get Started
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```bash
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pip install crawl4ai==0.7.0
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```
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Check out the [updated documentation](https://docs.crawl4ai.com).
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Questions? Issues? I'm always listening:
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- GitHub: [github.com/unclecode/crawl4ai](https://github.com/unclecode/crawl4ai)
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- Discord: [discord.gg/crawl4ai](https://discord.gg/jP8KfhDhyN)
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- Twitter: [@unclecode](https://x.com/unclecode)
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Happy crawling! 🕷️
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||||
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||||
---
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||||
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||||
*P.S. If you're using Crawl4AI in production, I'd love to hear about it. Your use cases inspire the next features.*
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@@ -58,13 +58,15 @@ Pull and run images directly from Docker Hub without building locally.
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#### 1. Pull the Image
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Our latest release candidate is `0.6.0-r2`. Images are built with multi-arch manifests, so Docker automatically pulls the correct version for your system.
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Our latest release candidate is `0.7.0-r1`. Images are built with multi-arch manifests, so Docker automatically pulls the correct version for your system.
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> ⚠️ **Important Note**: The `latest` tag currently points to the stable `0.6.0` version. After testing and validation, `0.7.0` (without -r1) will be released and `latest` will be updated. For now, please use `0.7.0-r1` to test the new features.
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```bash
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# Pull the release candidate (recommended for latest features)
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docker pull unclecode/crawl4ai:0.6.0-r1
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# Pull the release candidate (for testing new features)
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docker pull unclecode/crawl4ai:0.7.0-r1
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# Or pull the latest stable version
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# Or pull the current stable version (0.6.0)
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docker pull unclecode/crawl4ai:latest
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||||
```
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||||
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||||
@@ -124,7 +126,7 @@ docker stop crawl4ai && docker rm crawl4ai
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||||
#### Docker Hub Versioning Explained
|
||||
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||||
* **Image Name:** `unclecode/crawl4ai`
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* **Tag Format:** `LIBRARY_VERSION[-SUFFIX]` (e.g., `0.6.0-r2`)
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||||
* **Tag Format:** `LIBRARY_VERSION[-SUFFIX]` (e.g., `0.7.0-r1`)
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* `LIBRARY_VERSION`: The semantic version of the core `crawl4ai` Python library
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* `SUFFIX`: Optional tag for release candidates (``) and revisions (`r1`)
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||||
* **`latest` Tag:** Points to the most recent stable version
|
||||
|
||||
408
docs/releases_review/demo_v0.7.0.py
Normal file
408
docs/releases_review/demo_v0.7.0.py
Normal file
@@ -0,0 +1,408 @@
|
||||
"""
|
||||
🚀 Crawl4AI v0.7.0 Release Demo
|
||||
================================
|
||||
This demo showcases all major features introduced in v0.7.0 release.
|
||||
|
||||
Major Features:
|
||||
1. ✅ Adaptive Crawling - Intelligent crawling with confidence tracking
|
||||
2. ✅ Virtual Scroll Support - Handle infinite scroll pages
|
||||
3. ✅ Link Preview - Advanced link analysis with 3-layer scoring
|
||||
4. ✅ URL Seeder - Smart URL discovery and filtering
|
||||
5. ✅ C4A Script - Domain-specific language for web automation
|
||||
6. ✅ Chrome Extension Updates - Click2Crawl and instant schema extraction
|
||||
7. ✅ PDF Parsing Support - Extract content from PDF documents
|
||||
8. ✅ Nightly Builds - Automated nightly releases
|
||||
|
||||
Run this demo to see all features in action!
|
||||
"""
|
||||
|
||||
import asyncio
|
||||
import json
|
||||
from typing import List, Dict
|
||||
from rich.console import Console
|
||||
from rich.table import Table
|
||||
from rich.panel import Panel
|
||||
from rich import box
|
||||
|
||||
from crawl4ai import (
|
||||
AsyncWebCrawler,
|
||||
CrawlerRunConfig,
|
||||
BrowserConfig,
|
||||
CacheMode,
|
||||
AdaptiveCrawler,
|
||||
AdaptiveConfig,
|
||||
AsyncUrlSeeder,
|
||||
SeedingConfig,
|
||||
c4a_compile,
|
||||
CompilationResult
|
||||
)
|
||||
from crawl4ai.async_configs import VirtualScrollConfig, LinkPreviewConfig
|
||||
from crawl4ai.extraction_strategy import JsonCssExtractionStrategy
|
||||
|
||||
console = Console()
|
||||
|
||||
def print_section(title: str, description: str = ""):
|
||||
"""Print a section header"""
|
||||
console.print(f"\n[bold cyan]{'=' * 60}[/bold cyan]")
|
||||
console.print(f"[bold yellow]{title}[/bold yellow]")
|
||||
if description:
|
||||
console.print(f"[dim]{description}[/dim]")
|
||||
console.print(f"[bold cyan]{'=' * 60}[/bold cyan]\n")
|
||||
|
||||
|
||||
async def demo_1_adaptive_crawling():
|
||||
"""Demo 1: Adaptive Crawling - Intelligent content extraction"""
|
||||
print_section(
|
||||
"Demo 1: Adaptive Crawling",
|
||||
"Intelligently learns and adapts to website patterns"
|
||||
)
|
||||
|
||||
# Create adaptive crawler with custom configuration
|
||||
config = AdaptiveConfig(
|
||||
strategy="statistical", # or "embedding"
|
||||
confidence_threshold=0.7,
|
||||
max_pages=10,
|
||||
top_k_links=3,
|
||||
min_gain_threshold=0.1
|
||||
)
|
||||
|
||||
# Example: Learn from a product page
|
||||
console.print("[cyan]Learning from product page patterns...[/cyan]")
|
||||
|
||||
async with AsyncWebCrawler() as crawler:
|
||||
adaptive = AdaptiveCrawler(crawler, config)
|
||||
|
||||
# Start adaptive crawl
|
||||
console.print("[cyan]Starting adaptive crawl...[/cyan]")
|
||||
result = await adaptive.digest(
|
||||
start_url="https://docs.python.org/3/",
|
||||
query="python decorators tutorial"
|
||||
)
|
||||
|
||||
console.print("[green]✓ Adaptive crawl completed[/green]")
|
||||
console.print(f" - Confidence Level: {adaptive.confidence:.0%}")
|
||||
console.print(f" - Pages Crawled: {len(result.crawled_urls)}")
|
||||
console.print(f" - Knowledge Base: {len(adaptive.state.knowledge_base)} documents")
|
||||
|
||||
# Get most relevant content
|
||||
relevant = adaptive.get_relevant_content(top_k=3)
|
||||
if relevant:
|
||||
console.print("\nMost relevant pages:")
|
||||
for i, page in enumerate(relevant, 1):
|
||||
console.print(f" {i}. {page['url']} (relevance: {page['score']:.2%})")
|
||||
|
||||
|
||||
async def demo_2_virtual_scroll():
|
||||
"""Demo 2: Virtual Scroll - Handle infinite scroll pages"""
|
||||
print_section(
|
||||
"Demo 2: Virtual Scroll Support",
|
||||
"Capture content from modern infinite scroll pages"
|
||||
)
|
||||
|
||||
# Configure virtual scroll - using body as container for example.com
|
||||
scroll_config = VirtualScrollConfig(
|
||||
container_selector="body", # Using body since example.com has simple structure
|
||||
scroll_count=3, # Just 3 scrolls for demo
|
||||
scroll_by="container_height", # or "page_height" or pixel value
|
||||
wait_after_scroll=0.5 # Wait 500ms after each scroll
|
||||
)
|
||||
|
||||
config = CrawlerRunConfig(
|
||||
virtual_scroll_config=scroll_config,
|
||||
cache_mode=CacheMode.BYPASS,
|
||||
wait_until="networkidle"
|
||||
)
|
||||
|
||||
console.print("[cyan]Virtual Scroll Configuration:[/cyan]")
|
||||
console.print(f" - Container: {scroll_config.container_selector}")
|
||||
console.print(f" - Scroll count: {scroll_config.scroll_count}")
|
||||
console.print(f" - Scroll by: {scroll_config.scroll_by}")
|
||||
console.print(f" - Wait after scroll: {scroll_config.wait_after_scroll}s")
|
||||
|
||||
console.print("\n[dim]Note: Using example.com for demo - in production, use this[/dim]")
|
||||
console.print("[dim]with actual infinite scroll pages like social media feeds.[/dim]\n")
|
||||
|
||||
async with AsyncWebCrawler() as crawler:
|
||||
result = await crawler.arun(
|
||||
"https://example.com",
|
||||
config=config
|
||||
)
|
||||
|
||||
if result.success:
|
||||
console.print("[green]✓ Virtual scroll executed successfully![/green]")
|
||||
console.print(f" - Content length: {len(result.markdown)} chars")
|
||||
|
||||
# Show example of how to use with real infinite scroll sites
|
||||
console.print("\n[yellow]Example for real infinite scroll sites:[/yellow]")
|
||||
console.print("""
|
||||
# For Twitter-like feeds:
|
||||
scroll_config = VirtualScrollConfig(
|
||||
container_selector="[data-testid='primaryColumn']",
|
||||
scroll_count=20,
|
||||
scroll_by="container_height",
|
||||
wait_after_scroll=1.0
|
||||
)
|
||||
|
||||
# For Instagram-like grids:
|
||||
scroll_config = VirtualScrollConfig(
|
||||
container_selector="main article",
|
||||
scroll_count=15,
|
||||
scroll_by=1000, # Fixed pixel amount
|
||||
wait_after_scroll=1.5
|
||||
)""")
|
||||
|
||||
|
||||
async def demo_3_link_preview():
|
||||
"""Demo 3: Link Preview with 3-layer scoring"""
|
||||
print_section(
|
||||
"Demo 3: Link Preview & Scoring",
|
||||
"Advanced link analysis with intrinsic, contextual, and total scoring"
|
||||
)
|
||||
|
||||
# Configure link preview
|
||||
link_config = LinkPreviewConfig(
|
||||
include_internal=True,
|
||||
include_external=False,
|
||||
max_links=10,
|
||||
concurrency=5,
|
||||
query="python tutorial", # For contextual scoring
|
||||
score_threshold=0.3,
|
||||
verbose=True
|
||||
)
|
||||
|
||||
config = CrawlerRunConfig(
|
||||
link_preview_config=link_config,
|
||||
score_links=True, # Enable intrinsic scoring
|
||||
cache_mode=CacheMode.BYPASS
|
||||
)
|
||||
|
||||
console.print("[cyan]Analyzing links with 3-layer scoring system...[/cyan]")
|
||||
|
||||
async with AsyncWebCrawler() as crawler:
|
||||
result = await crawler.arun("https://docs.python.org/3/", config=config)
|
||||
|
||||
if result.success and result.links:
|
||||
# Get scored links
|
||||
internal_links = result.links.get("internal", [])
|
||||
scored_links = [l for l in internal_links if l.get("total_score")]
|
||||
scored_links.sort(key=lambda x: x.get("total_score", 0), reverse=True)
|
||||
|
||||
# Create a scoring table
|
||||
table = Table(title="Link Scoring Results", box=box.ROUNDED)
|
||||
table.add_column("Link Text", style="cyan", width=40)
|
||||
table.add_column("Intrinsic Score", justify="center")
|
||||
table.add_column("Contextual Score", justify="center")
|
||||
table.add_column("Total Score", justify="center", style="bold green")
|
||||
|
||||
for link in scored_links[:5]:
|
||||
text = link.get('text', 'No text')[:40]
|
||||
table.add_row(
|
||||
text,
|
||||
f"{link.get('intrinsic_score', 0):.1f}/10",
|
||||
f"{link.get('contextual_score', 0):.2f}/1",
|
||||
f"{link.get('total_score', 0):.3f}"
|
||||
)
|
||||
|
||||
console.print(table)
|
||||
|
||||
|
||||
async def demo_4_url_seeder():
|
||||
"""Demo 4: URL Seeder - Smart URL discovery"""
|
||||
print_section(
|
||||
"Demo 4: URL Seeder",
|
||||
"Intelligent URL discovery and filtering"
|
||||
)
|
||||
|
||||
# Configure seeding
|
||||
seeding_config = SeedingConfig(
|
||||
source="cc+sitemap", # or "crawl"
|
||||
pattern="*tutorial*", # URL pattern filter
|
||||
max_urls=50,
|
||||
extract_head=True, # Get metadata
|
||||
query="python programming", # For relevance scoring
|
||||
scoring_method="bm25",
|
||||
score_threshold=0.2,
|
||||
force = True
|
||||
)
|
||||
|
||||
console.print("[cyan]URL Seeder Configuration:[/cyan]")
|
||||
console.print(f" - Source: {seeding_config.source}")
|
||||
console.print(f" - Pattern: {seeding_config.pattern}")
|
||||
console.print(f" - Max URLs: {seeding_config.max_urls}")
|
||||
console.print(f" - Query: {seeding_config.query}")
|
||||
console.print(f" - Scoring: {seeding_config.scoring_method}")
|
||||
|
||||
# Use URL seeder to discover URLs
|
||||
async with AsyncUrlSeeder() as seeder:
|
||||
console.print("\n[cyan]Discovering URLs from Python docs...[/cyan]")
|
||||
urls = await seeder.urls("docs.python.org", seeding_config)
|
||||
|
||||
console.print(f"\n[green]✓ Discovered {len(urls)} URLs[/green]")
|
||||
for i, url_info in enumerate(urls[:5], 1):
|
||||
console.print(f" {i}. {url_info['url']}")
|
||||
if url_info.get('relevance_score'):
|
||||
console.print(f" Relevance: {url_info['relevance_score']:.3f}")
|
||||
|
||||
|
||||
async def demo_5_c4a_script():
|
||||
"""Demo 5: C4A Script - Domain-specific language"""
|
||||
print_section(
|
||||
"Demo 5: C4A Script Language",
|
||||
"Domain-specific language for web automation"
|
||||
)
|
||||
|
||||
# Example C4A script
|
||||
c4a_script = """
|
||||
# Simple C4A script example
|
||||
WAIT `body` 3
|
||||
IF (EXISTS `.cookie-banner`) THEN CLICK `.accept`
|
||||
CLICK `.search-button`
|
||||
TYPE "python tutorial"
|
||||
PRESS Enter
|
||||
WAIT `.results` 5
|
||||
"""
|
||||
|
||||
console.print("[cyan]C4A Script Example:[/cyan]")
|
||||
console.print(Panel(c4a_script, title="script.c4a", border_style="blue"))
|
||||
|
||||
# Compile the script
|
||||
compilation_result = c4a_compile(c4a_script)
|
||||
|
||||
if compilation_result.success:
|
||||
console.print("[green]✓ Script compiled successfully![/green]")
|
||||
console.print(f" - Generated {len(compilation_result.js_code)} JavaScript statements")
|
||||
console.print("\nFirst 3 JS statements:")
|
||||
for stmt in compilation_result.js_code[:3]:
|
||||
console.print(f" • {stmt}")
|
||||
else:
|
||||
console.print("[red]✗ Script compilation failed[/red]")
|
||||
if compilation_result.first_error:
|
||||
error = compilation_result.first_error
|
||||
console.print(f" Error at line {error.line}: {error.message}")
|
||||
|
||||
|
||||
async def demo_6_css_extraction():
|
||||
"""Demo 6: Enhanced CSS/JSON extraction"""
|
||||
print_section(
|
||||
"Demo 6: Enhanced Extraction",
|
||||
"Improved CSS selector and JSON extraction"
|
||||
)
|
||||
|
||||
# Define extraction schema
|
||||
schema = {
|
||||
"name": "Example Page Data",
|
||||
"baseSelector": "body",
|
||||
"fields": [
|
||||
{
|
||||
"name": "title",
|
||||
"selector": "h1",
|
||||
"type": "text"
|
||||
},
|
||||
{
|
||||
"name": "paragraphs",
|
||||
"selector": "p",
|
||||
"type": "list",
|
||||
"fields": [
|
||||
{"name": "text", "type": "text"}
|
||||
]
|
||||
}
|
||||
]
|
||||
}
|
||||
|
||||
extraction_strategy = JsonCssExtractionStrategy(schema)
|
||||
|
||||
console.print("[cyan]Extraction Schema:[/cyan]")
|
||||
console.print(json.dumps(schema, indent=2))
|
||||
|
||||
async with AsyncWebCrawler() as crawler:
|
||||
result = await crawler.arun(
|
||||
"https://example.com",
|
||||
config=CrawlerRunConfig(
|
||||
extraction_strategy=extraction_strategy,
|
||||
cache_mode=CacheMode.BYPASS
|
||||
)
|
||||
)
|
||||
|
||||
if result.success and result.extracted_content:
|
||||
console.print("\n[green]✓ Content extracted successfully![/green]")
|
||||
console.print(f"Extracted: {json.dumps(json.loads(result.extracted_content), indent=2)[:200]}...")
|
||||
|
||||
|
||||
async def demo_7_performance_improvements():
|
||||
"""Demo 7: Performance improvements"""
|
||||
print_section(
|
||||
"Demo 7: Performance Improvements",
|
||||
"Faster crawling with better resource management"
|
||||
)
|
||||
|
||||
# Performance-optimized configuration
|
||||
config = CrawlerRunConfig(
|
||||
cache_mode=CacheMode.ENABLED, # Use caching
|
||||
wait_until="domcontentloaded", # Faster than networkidle
|
||||
page_timeout=10000, # 10 second timeout
|
||||
exclude_external_links=True,
|
||||
exclude_social_media_links=True,
|
||||
exclude_external_images=True
|
||||
)
|
||||
|
||||
console.print("[cyan]Performance Configuration:[/cyan]")
|
||||
console.print(" - Cache: ENABLED")
|
||||
console.print(" - Wait: domcontentloaded (faster)")
|
||||
console.print(" - Timeout: 10s")
|
||||
console.print(" - Excluding: external links, images, social media")
|
||||
|
||||
# Measure performance
|
||||
import time
|
||||
start_time = time.time()
|
||||
|
||||
async with AsyncWebCrawler() as crawler:
|
||||
result = await crawler.arun("https://example.com", config=config)
|
||||
|
||||
elapsed = time.time() - start_time
|
||||
|
||||
if result.success:
|
||||
console.print(f"\n[green]✓ Page crawled in {elapsed:.2f} seconds[/green]")
|
||||
|
||||
|
||||
async def main():
|
||||
"""Run all demos"""
|
||||
console.print(Panel(
|
||||
"[bold cyan]Crawl4AI v0.7.0 Release Demo[/bold cyan]\n\n"
|
||||
"This demo showcases all major features introduced in v0.7.0.\n"
|
||||
"Each demo is self-contained and demonstrates a specific feature.",
|
||||
title="Welcome",
|
||||
border_style="blue"
|
||||
))
|
||||
|
||||
demos = [
|
||||
demo_1_adaptive_crawling,
|
||||
demo_2_virtual_scroll,
|
||||
demo_3_link_preview,
|
||||
demo_4_url_seeder,
|
||||
demo_5_c4a_script,
|
||||
demo_6_css_extraction,
|
||||
demo_7_performance_improvements
|
||||
]
|
||||
|
||||
for i, demo in enumerate(demos, 1):
|
||||
try:
|
||||
await demo()
|
||||
if i < len(demos):
|
||||
console.print("\n[dim]Press Enter to continue to next demo...[/dim]")
|
||||
input()
|
||||
except Exception as e:
|
||||
console.print(f"[red]Error in demo: {e}[/red]")
|
||||
continue
|
||||
|
||||
console.print(Panel(
|
||||
"[bold green]Demo Complete![/bold green]\n\n"
|
||||
"Thank you for trying Crawl4AI v0.7.0!\n"
|
||||
"For more examples and documentation, visit:\n"
|
||||
"https://github.com/unclecode/crawl4ai",
|
||||
title="Complete",
|
||||
border_style="green"
|
||||
))
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
asyncio.run(main())
|
||||
316
docs/releases_review/v0_7_0_features_demo.py
Normal file
316
docs/releases_review/v0_7_0_features_demo.py
Normal file
@@ -0,0 +1,316 @@
|
||||
"""
|
||||
🚀 Crawl4AI v0.7.0 Feature Demo
|
||||
================================
|
||||
This file demonstrates the major features introduced in v0.7.0 with practical examples.
|
||||
"""
|
||||
|
||||
import asyncio
|
||||
import json
|
||||
from pathlib import Path
|
||||
from crawl4ai import (
|
||||
AsyncWebCrawler,
|
||||
CrawlerRunConfig,
|
||||
BrowserConfig,
|
||||
CacheMode,
|
||||
# New imports for v0.7.0
|
||||
LinkPreviewConfig,
|
||||
VirtualScrollConfig,
|
||||
AdaptiveCrawler,
|
||||
AdaptiveConfig,
|
||||
AsyncUrlSeeder,
|
||||
SeedingConfig,
|
||||
c4a_compile,
|
||||
CompilationResult
|
||||
)
|
||||
|
||||
|
||||
async def demo_link_preview():
|
||||
"""
|
||||
Demo 1: Link Preview with 3-Layer Scoring
|
||||
|
||||
Shows how to analyze links with intrinsic quality scores,
|
||||
contextual relevance, and combined total scores.
|
||||
"""
|
||||
print("\n" + "="*60)
|
||||
print("🔗 DEMO 1: Link Preview & Intelligent Scoring")
|
||||
print("="*60)
|
||||
|
||||
# Configure link preview with contextual scoring
|
||||
config = CrawlerRunConfig(
|
||||
link_preview_config=LinkPreviewConfig(
|
||||
include_internal=True,
|
||||
include_external=False,
|
||||
max_links=10,
|
||||
concurrency=5,
|
||||
query="machine learning tutorials", # For contextual scoring
|
||||
score_threshold=0.3, # Minimum relevance
|
||||
verbose=True
|
||||
),
|
||||
score_links=True, # Enable intrinsic scoring
|
||||
cache_mode=CacheMode.BYPASS
|
||||
)
|
||||
|
||||
async with AsyncWebCrawler() as crawler:
|
||||
result = await crawler.arun("https://scikit-learn.org/stable/", config=config)
|
||||
|
||||
if result.success:
|
||||
# Get scored links
|
||||
internal_links = result.links.get("internal", [])
|
||||
scored_links = [l for l in internal_links if l.get("total_score")]
|
||||
scored_links.sort(key=lambda x: x.get("total_score", 0), reverse=True)
|
||||
|
||||
print(f"\nTop 5 Most Relevant Links:")
|
||||
for i, link in enumerate(scored_links[:5], 1):
|
||||
print(f"\n{i}. {link.get('text', 'No text')[:50]}...")
|
||||
print(f" URL: {link['href']}")
|
||||
print(f" Intrinsic Score: {link.get('intrinsic_score', 0):.2f}/10")
|
||||
print(f" Contextual Score: {link.get('contextual_score', 0):.3f}")
|
||||
print(f" Total Score: {link.get('total_score', 0):.3f}")
|
||||
|
||||
# Show metadata if available
|
||||
if link.get('head_data'):
|
||||
title = link['head_data'].get('title', 'No title')
|
||||
print(f" Title: {title[:60]}...")
|
||||
|
||||
|
||||
async def demo_adaptive_crawling():
|
||||
"""
|
||||
Demo 2: Adaptive Crawling
|
||||
|
||||
Shows intelligent crawling that stops when enough information
|
||||
is gathered, with confidence tracking.
|
||||
"""
|
||||
print("\n" + "="*60)
|
||||
print("🎯 DEMO 2: Adaptive Crawling with Confidence Tracking")
|
||||
print("="*60)
|
||||
|
||||
# Configure adaptive crawler
|
||||
config = AdaptiveConfig(
|
||||
strategy="statistical", # or "embedding" for semantic understanding
|
||||
max_pages=10,
|
||||
confidence_threshold=0.7, # Stop at 70% confidence
|
||||
top_k_links=3, # Follow top 3 links per page
|
||||
min_gain_threshold=0.05 # Need 5% information gain to continue
|
||||
)
|
||||
|
||||
async with AsyncWebCrawler(verbose=False) as crawler:
|
||||
adaptive = AdaptiveCrawler(crawler, config)
|
||||
|
||||
print("Starting adaptive crawl about Python decorators...")
|
||||
result = await adaptive.digest(
|
||||
start_url="https://docs.python.org/3/glossary.html",
|
||||
query="python decorators functions wrapping"
|
||||
)
|
||||
|
||||
print(f"\n✅ Crawling Complete!")
|
||||
print(f"• Confidence Level: {adaptive.confidence:.0%}")
|
||||
print(f"• Pages Crawled: {len(result.crawled_urls)}")
|
||||
print(f"• Knowledge Base: {len(adaptive.state.knowledge_base)} documents")
|
||||
|
||||
# Get most relevant content
|
||||
relevant = adaptive.get_relevant_content(top_k=3)
|
||||
print(f"\nMost Relevant Pages:")
|
||||
for i, page in enumerate(relevant, 1):
|
||||
print(f"{i}. {page['url']} (relevance: {page['score']:.2%})")
|
||||
|
||||
|
||||
async def demo_virtual_scroll():
|
||||
"""
|
||||
Demo 3: Virtual Scroll for Modern Web Pages
|
||||
|
||||
Shows how to capture content from pages with DOM recycling
|
||||
(Twitter, Instagram, infinite scroll).
|
||||
"""
|
||||
print("\n" + "="*60)
|
||||
print("📜 DEMO 3: Virtual Scroll Support")
|
||||
print("="*60)
|
||||
|
||||
# Configure virtual scroll for a news site
|
||||
virtual_config = VirtualScrollConfig(
|
||||
container_selector="main, article, .content", # Common containers
|
||||
scroll_count=20, # Scroll up to 20 times
|
||||
scroll_by="container_height", # Scroll by container height
|
||||
wait_after_scroll=0.5 # Wait 500ms after each scroll
|
||||
)
|
||||
|
||||
config = CrawlerRunConfig(
|
||||
virtual_scroll_config=virtual_config,
|
||||
cache_mode=CacheMode.BYPASS,
|
||||
wait_for="css:article" # Wait for articles to load
|
||||
)
|
||||
|
||||
# Example with a real news site
|
||||
async with AsyncWebCrawler() as crawler:
|
||||
result = await crawler.arun(
|
||||
"https://news.ycombinator.com/",
|
||||
config=config
|
||||
)
|
||||
|
||||
if result.success:
|
||||
# Count items captured
|
||||
import re
|
||||
items = len(re.findall(r'class="athing"', result.html))
|
||||
print(f"\n✅ Captured {items} news items")
|
||||
print(f"• HTML size: {len(result.html):,} bytes")
|
||||
print(f"• Without virtual scroll, would only capture ~30 items")
|
||||
|
||||
|
||||
async def demo_url_seeder():
|
||||
"""
|
||||
Demo 4: URL Seeder for Intelligent Discovery
|
||||
|
||||
Shows how to discover and filter URLs before crawling,
|
||||
with relevance scoring.
|
||||
"""
|
||||
print("\n" + "="*60)
|
||||
print("🌱 DEMO 4: URL Seeder - Smart URL Discovery")
|
||||
print("="*60)
|
||||
|
||||
async with AsyncUrlSeeder() as seeder:
|
||||
# Discover Python tutorial URLs
|
||||
config = SeedingConfig(
|
||||
source="sitemap", # Use sitemap
|
||||
pattern="*tutorial*", # URL pattern filter
|
||||
extract_head=True, # Get metadata
|
||||
query="python async programming", # For relevance scoring
|
||||
scoring_method="bm25",
|
||||
score_threshold=0.2,
|
||||
max_urls=10
|
||||
)
|
||||
|
||||
print("Discovering Python async tutorial URLs...")
|
||||
urls = await seeder.urls("docs.python.org", config)
|
||||
|
||||
print(f"\n✅ Found {len(urls)} relevant URLs:")
|
||||
for i, url_info in enumerate(urls[:5], 1):
|
||||
print(f"\n{i}. {url_info['url']}")
|
||||
if url_info.get('relevance_score'):
|
||||
print(f" Relevance: {url_info['relevance_score']:.3f}")
|
||||
if url_info.get('head_data', {}).get('title'):
|
||||
print(f" Title: {url_info['head_data']['title'][:60]}...")
|
||||
|
||||
|
||||
async def demo_c4a_script():
|
||||
"""
|
||||
Demo 5: C4A Script Language
|
||||
|
||||
Shows the domain-specific language for web automation
|
||||
with JavaScript transpilation.
|
||||
"""
|
||||
print("\n" + "="*60)
|
||||
print("🎭 DEMO 5: C4A Script - Web Automation Language")
|
||||
print("="*60)
|
||||
|
||||
# Example C4A script
|
||||
c4a_script = """
|
||||
# E-commerce automation script
|
||||
WAIT `body` 3
|
||||
|
||||
# Handle cookie banner
|
||||
IF (EXISTS `.cookie-banner`) THEN CLICK `.accept-cookies`
|
||||
|
||||
# Search for product
|
||||
CLICK `.search-box`
|
||||
TYPE "wireless headphones"
|
||||
PRESS Enter
|
||||
|
||||
# Wait for results
|
||||
WAIT `.product-grid` 10
|
||||
|
||||
# Load more products
|
||||
REPEAT (SCROLL DOWN 500, `document.querySelectorAll('.product').length < 50`)
|
||||
|
||||
# Apply filter
|
||||
IF (EXISTS `.price-filter`) THEN CLICK `input[data-max-price="100"]`
|
||||
"""
|
||||
|
||||
# Compile the script
|
||||
print("Compiling C4A script...")
|
||||
result = c4a_compile(c4a_script)
|
||||
|
||||
if result.success:
|
||||
print(f"✅ Successfully compiled to {len(result.js_code)} JavaScript statements!")
|
||||
print("\nFirst 3 JS statements:")
|
||||
for stmt in result.js_code[:3]:
|
||||
print(f" • {stmt}")
|
||||
|
||||
# Use with crawler
|
||||
config = CrawlerRunConfig(
|
||||
c4a_script=c4a_script, # Pass C4A script directly
|
||||
cache_mode=CacheMode.BYPASS
|
||||
)
|
||||
|
||||
print("\n✅ Script ready for use with AsyncWebCrawler!")
|
||||
else:
|
||||
print(f"❌ Compilation error: {result.first_error.message}")
|
||||
|
||||
|
||||
async def demo_pdf_support():
|
||||
"""
|
||||
Demo 6: PDF Parsing Support
|
||||
|
||||
Shows how to extract content from PDF files.
|
||||
Note: Requires 'pip install crawl4ai[pdf]'
|
||||
"""
|
||||
print("\n" + "="*60)
|
||||
print("📄 DEMO 6: PDF Parsing Support")
|
||||
print("="*60)
|
||||
|
||||
try:
|
||||
# Check if PDF support is installed
|
||||
import PyPDF2
|
||||
|
||||
# Example: Process a PDF URL
|
||||
config = CrawlerRunConfig(
|
||||
cache_mode=CacheMode.BYPASS,
|
||||
pdf=True, # Enable PDF generation
|
||||
extract_text_from_pdf=True # Extract text content
|
||||
)
|
||||
|
||||
print("PDF parsing is available!")
|
||||
print("You can now crawl PDF URLs and extract their content.")
|
||||
print("\nExample usage:")
|
||||
print(' result = await crawler.arun("https://example.com/document.pdf")')
|
||||
print(' pdf_text = result.extracted_content # Contains extracted text')
|
||||
|
||||
except ImportError:
|
||||
print("⚠️ PDF support not installed.")
|
||||
print("Install with: pip install crawl4ai[pdf]")
|
||||
|
||||
|
||||
async def main():
|
||||
"""Run all demos"""
|
||||
print("\n🚀 Crawl4AI v0.7.0 Feature Demonstrations")
|
||||
print("=" * 60)
|
||||
|
||||
demos = [
|
||||
("Link Preview & Scoring", demo_link_preview),
|
||||
("Adaptive Crawling", demo_adaptive_crawling),
|
||||
("Virtual Scroll", demo_virtual_scroll),
|
||||
("URL Seeder", demo_url_seeder),
|
||||
("C4A Script", demo_c4a_script),
|
||||
("PDF Support", demo_pdf_support)
|
||||
]
|
||||
|
||||
for name, demo_func in demos:
|
||||
try:
|
||||
await demo_func()
|
||||
except Exception as e:
|
||||
print(f"\n❌ Error in {name} demo: {str(e)}")
|
||||
|
||||
# Pause between demos
|
||||
await asyncio.sleep(1)
|
||||
|
||||
print("\n" + "="*60)
|
||||
print("✅ All demos completed!")
|
||||
print("\nKey Takeaways:")
|
||||
print("• Link Preview: 3-layer scoring for intelligent link analysis")
|
||||
print("• Adaptive Crawling: Stop when you have enough information")
|
||||
print("• Virtual Scroll: Capture all content from modern web pages")
|
||||
print("• URL Seeder: Pre-discover and filter URLs efficiently")
|
||||
print("• C4A Script: Simple language for complex automations")
|
||||
print("• PDF Support: Extract content from PDF documents")
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
asyncio.run(main())
|
||||
Reference in New Issue
Block a user