chore: import upstream snapshot with attribution

2026-07-13 12:12:13 +08:00
commit 0446c45d8e
898 changed files with 328024 additions and 0 deletions
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+"""
+Basic Adaptive Crawling Example
+
+This example demonstrates the simplest use case of adaptive crawling:
+finding information about a specific topic and knowing when to stop.
+"""
+
+import asyncio
+from crawl4ai import AsyncWebCrawler, AdaptiveCrawler
+
+
+async def main():
+    """Basic adaptive crawling example"""
+    
+    # Initialize the crawler
+    async with AsyncWebCrawler(verbose=True) as crawler:
+        # Create an adaptive crawler with default settings (statistical strategy)
+        adaptive = AdaptiveCrawler(crawler)
+        
+        # Note: You can also use embedding strategy for semantic understanding:
+        # from crawl4ai import AdaptiveConfig
+        # config = AdaptiveConfig(strategy="embedding")
+        # adaptive = AdaptiveCrawler(crawler, config)
+        
+        # Start adaptive crawling
+        print("Starting adaptive crawl for Python async programming information...")
+        result = await adaptive.digest(
+            start_url="https://docs.python.org/3/library/asyncio.html",
+            query="async await context managers coroutines"
+        )
+        
+        # Display crawl statistics
+        print("\n" + "="*50)
+        print("CRAWL STATISTICS")
+        print("="*50)
+        adaptive.print_stats(detailed=False)
+        
+        # Get the most relevant content found
+        print("\n" + "="*50)
+        print("MOST RELEVANT PAGES")
+        print("="*50)
+        
+        relevant_pages = adaptive.get_relevant_content(top_k=5)
+        for i, page in enumerate(relevant_pages, 1):
+            print(f"\n{i}. {page['url']}")
+            print(f"   Relevance Score: {page['score']:.2%}")
+            
+            # Show a snippet of the content
+            content = page['content'] or ""
+            if content:
+                snippet = content[:200].replace('\n', ' ')
+                if len(content) > 200:
+                    snippet += "..."
+                print(f"   Preview: {snippet}")
+        
+        # Show final confidence
+        print(f"\n{'='*50}")
+        print(f"Final Confidence: {adaptive.confidence:.2%}")
+        print(f"Total Pages Crawled: {len(result.crawled_urls)}")
+        print(f"Knowledge Base Size: {len(adaptive.state.knowledge_base)} documents")
+        
+        # Example: Check if we can answer specific questions
+        print(f"\n{'='*50}")
+        print("INFORMATION SUFFICIENCY CHECK")
+        print(f"{'='*50}")
+        
+        if adaptive.confidence >= 0.8:
+            print("✓ High confidence - can answer detailed questions about async Python")
+        elif adaptive.confidence >= 0.6:
+            print("~ Moderate confidence - can answer basic questions") 
+        else:
+            print("✗ Low confidence - need more information")
+
+
+if __name__ == "__main__":
+    asyncio.run(main())