참고소스 수정본

참고/crawl4ai-main/crawl4ai/link_preview.py

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+"""
+Link Extractor for Crawl4AI
+
+Extracts head content from links discovered during crawling using URLSeeder's
+efficient parallel processing and caching infrastructure.
+"""
+
+import asyncio
+import fnmatch
+from typing import Dict, List, Optional, Any
+from .async_logger import AsyncLogger
+from .async_url_seeder import AsyncUrlSeeder
+from .async_configs import SeedingConfig, CrawlerRunConfig
+from .models import Links, Link
+from .utils import calculate_total_score
+
+
+class LinkPreview:
+    """
+    Extracts head content from links using URLSeeder's parallel processing infrastructure.
+    
+    This class provides intelligent link filtering and head content extraction with:
+    - Pattern-based inclusion/exclusion filtering
+    - Parallel processing with configurable concurrency
+    - Caching for performance
+    - BM25 relevance scoring
+    - Memory-safe processing for large link sets
+    """
+    
+    def __init__(self, logger: Optional[AsyncLogger] = None):
+        """
+        Initialize the LinkPreview.
+        
+        Args:
+            logger: Optional logger instance for recording events
+        """
+        self.logger = logger
+        self.seeder: Optional[AsyncUrlSeeder] = None
+        self._owns_seeder = False
+    
+    async def __aenter__(self):
+        """Async context manager entry."""
+        await self.start()
+        return self
+    
+    async def __aexit__(self, exc_type, exc_val, exc_tb):
+        """Async context manager exit."""
+        await self.close()
+    
+    async def start(self):
+        """Initialize the URLSeeder instance."""
+        if not self.seeder:
+            self.seeder = AsyncUrlSeeder(logger=self.logger)
+            await self.seeder.__aenter__()
+            self._owns_seeder = True
+    
+    async def close(self):
+        """Clean up resources."""
+        if self.seeder and self._owns_seeder:
+            await self.seeder.__aexit__(None, None, None)
+            self.seeder = None
+            self._owns_seeder = False
+    
+    def _log(self, level: str, message: str, tag: str = "LINK_EXTRACT", **kwargs):
+        """Helper method to safely log messages."""
+        if self.logger:
+            log_method = getattr(self.logger, level, None)
+            if log_method:
+                log_method(message=message, tag=tag, params=kwargs.get('params', {}))
+    
+    async def extract_link_heads(
+        self, 
+        links: Links, 
+        config: CrawlerRunConfig
+    ) -> Links:
+        """
+        Extract head content for filtered links and attach to Link objects.
+        
+        Args:
+            links: Links object containing internal and external links
+            config: CrawlerRunConfig with link_preview_config settings
+            
+        Returns:
+            Links object with head_data attached to filtered Link objects
+        """
+        link_config = config.link_preview_config
+        
+        # Ensure seeder is initialized
+        await self.start()
+        
+        # Filter links based on configuration
+        filtered_urls = self._filter_links(links, link_config)
+        
+        if not filtered_urls:
+            self._log("info", "No links matched filtering criteria")
+            return links
+        
+        self._log("info", "Extracting head content for {count} filtered links",
+                  params={"count": len(filtered_urls)})
+        
+        # Extract head content using URLSeeder
+        head_results = await self._extract_heads_parallel(filtered_urls, link_config)
+        
+        # Merge results back into Link objects
+        updated_links = self._merge_head_data(links, head_results, config)
+        
+        self._log("info", "Completed head extraction for links, {success} successful",
+                  params={"success": len([r for r in head_results if r.get("status") == "valid"])})
+        
+        return updated_links
+    
+    def _filter_links(self, links: Links, link_config: Dict[str, Any]) -> List[str]:
+        """
+        Filter links based on configuration parameters.
+        
+        Args:
+            links: Links object containing internal and external links
+            link_config: Configuration dictionary for link extraction
+            
+        Returns:
+            List of filtered URL strings
+        """
+        filtered_urls = []
+        
+        # Include internal links if configured
+        if link_config.include_internal:
+            filtered_urls.extend([link.href for link in links.internal if link.href])
+            self._log("debug", "Added {count} internal links",
+                      params={"count": len(links.internal)})
+        
+        # Include external links if configured
+        if link_config.include_external:
+            filtered_urls.extend([link.href for link in links.external if link.href])
+            self._log("debug", "Added {count} external links",
+                      params={"count": len(links.external)})
+        
+        # Apply include patterns
+        include_patterns = link_config.include_patterns
+        if include_patterns:
+            filtered_urls = [
+                url for url in filtered_urls
+                if any(fnmatch.fnmatch(url, pattern) for pattern in include_patterns)
+            ]
+            self._log("debug", "After include patterns: {count} links remain",
+                      params={"count": len(filtered_urls)})
+        
+        # Apply exclude patterns
+        exclude_patterns = link_config.exclude_patterns
+        if exclude_patterns:
+            filtered_urls = [
+                url for url in filtered_urls
+                if not any(fnmatch.fnmatch(url, pattern) for pattern in exclude_patterns)
+            ]
+            self._log("debug", "After exclude patterns: {count} links remain",
+                      params={"count": len(filtered_urls)})
+        
+        # Limit number of links
+        max_links = link_config.max_links
+        if max_links > 0 and len(filtered_urls) > max_links:
+            filtered_urls = filtered_urls[:max_links]
+            self._log("debug", "Limited to {max_links} links",
+                      params={"max_links": max_links})
+        
+        # Remove duplicates while preserving order
+        seen = set()
+        unique_urls = []
+        for url in filtered_urls:
+            if url not in seen:
+                seen.add(url)
+                unique_urls.append(url)
+        
+        self._log("debug", "Final filtered URLs: {count} unique links",
+                  params={"count": len(unique_urls)})
+        
+        return unique_urls
+    
+    async def _extract_heads_parallel(
+        self, 
+        urls: List[str], 
+        link_config: Dict[str, Any]
+    ) -> List[Dict[str, Any]]:
+        """
+        Extract head content for URLs using URLSeeder's parallel processing.
+        
+        Args:
+            urls: List of URLs to process
+            link_config: Configuration dictionary for link extraction
+            
+        Returns:
+            List of dictionaries with url, status, head_data, and optional relevance_score
+        """
+        verbose = link_config.verbose
+        concurrency = link_config.concurrency
+        
+        if verbose:
+            self._log("info", "Starting batch processing: {total} links with {concurrency} concurrent workers",
+                      params={"total": len(urls), "concurrency": concurrency})
+        
+        # Create SeedingConfig for URLSeeder
+        seeding_config = SeedingConfig(
+            extract_head=True,
+            concurrency=concurrency,
+            hits_per_sec=getattr(link_config, 'hits_per_sec', None),
+            query=link_config.query,
+            score_threshold=link_config.score_threshold,
+            scoring_method="bm25" if link_config.query else None,
+            verbose=verbose
+        )
+        
+        # Use URLSeeder's extract_head_for_urls method with progress tracking
+        if verbose:
+            # Create a wrapper to track progress
+            results = await self._extract_with_progress(urls, seeding_config, link_config)
+        else:
+            results = await self.seeder.extract_head_for_urls(
+                urls=urls,
+                config=seeding_config,
+                concurrency=concurrency,
+                timeout=link_config.timeout
+            )
+        
+        return results
+    
+    async def _extract_with_progress(
+        self, 
+        urls: List[str], 
+        seeding_config: SeedingConfig, 
+        link_config: Dict[str, Any]
+    ) -> List[Dict[str, Any]]:
+        """Extract head content with progress reporting."""
+        
+        total_urls = len(urls)
+        concurrency = link_config.concurrency
+        batch_size = max(1, total_urls // 10)  # Report progress every 10%
+        
+        # Process URLs and track progress
+        completed = 0
+        successful = 0
+        failed = 0
+        
+        # Create a custom progress tracking version
+        # We'll modify URLSeeder's method to include progress callbacks
+        
+        # For now, let's use the existing method and report at the end
+        # In a production version, we would modify URLSeeder to accept progress callbacks
+        
+        self._log("info", "Processing links in batches...")
+        
+        # Use existing method
+        results = await self.seeder.extract_head_for_urls(
+            urls=urls,
+            config=seeding_config,
+            concurrency=concurrency,
+            timeout=link_config.timeout
+        )
+        
+        # Count results
+        for result in results:
+            completed += 1
+            if result.get("status") == "valid":
+                successful += 1
+            else:
+                failed += 1
+        
+        # Final progress report
+        self._log("info", "Batch processing completed: {completed}/{total} processed, {successful} successful, {failed} failed",
+                  params={
+                      "completed": completed,
+                      "total": total_urls,
+                      "successful": successful,
+                      "failed": failed
+                  })
+        
+        return results
+    
+    def _merge_head_data(
+        self, 
+        original_links: Links, 
+        head_results: List[Dict[str, Any]],
+        config: CrawlerRunConfig
+    ) -> Links:
+        """
+        Merge head extraction results back into Link objects.
+        
+        Args:
+            original_links: Original Links object
+            head_results: Results from head extraction
+            
+        Returns:
+            Links object with head_data attached to matching links
+        """
+        # Create URL to head_data mapping (key by both final and original URL for redirects)
+        url_to_head_data = {}
+        for result in head_results:
+            url = result.get("url")
+            head_info = {
+                "head_data": result.get("head_data", {}),
+                "status": result.get("status", "unknown"),
+                "error": result.get("error"),
+                "relevance_score": result.get("relevance_score")
+            }
+            if url:
+                url_to_head_data[url] = head_info
+            original_url = result.get("original_url")
+            if original_url and original_url != url:
+                url_to_head_data[original_url] = head_info
+        
+        # Update internal links
+        updated_internal = []
+        for link in original_links.internal:
+            if link.href in url_to_head_data:
+                head_info = url_to_head_data[link.href]
+                # Create new Link object with head data and scoring
+                contextual_score = head_info.get("relevance_score")
+                
+                updated_link = Link(
+                    href=link.href,
+                    text=link.text,
+                    title=link.title,
+                    base_domain=link.base_domain,
+                    head_data=head_info["head_data"],
+                    head_extraction_status=head_info["status"],
+                    head_extraction_error=head_info.get("error"),
+                    intrinsic_score=getattr(link, 'intrinsic_score', None),
+                    contextual_score=contextual_score
+                )
+                
+                # Add relevance score to head_data for backward compatibility
+                if contextual_score is not None:
+                    updated_link.head_data = updated_link.head_data or {}
+                    updated_link.head_data["relevance_score"] = contextual_score
+                
+                # Calculate total score combining intrinsic and contextual scores
+                updated_link.total_score = calculate_total_score(
+                    intrinsic_score=updated_link.intrinsic_score,
+                    contextual_score=updated_link.contextual_score,
+                    score_links_enabled=getattr(config, 'score_links', False),
+                    query_provided=bool(config.link_preview_config.query)
+                )
+                
+                updated_internal.append(updated_link)
+            else:
+                # Calculate total_score even without head data, using intrinsic_score
+                link.total_score = calculate_total_score(
+                    intrinsic_score=getattr(link, 'intrinsic_score', None),
+                    contextual_score=None,
+                    score_links_enabled=getattr(config, 'score_links', False),
+                    query_provided=bool(config.link_preview_config.query)
+                )
+                updated_internal.append(link)
+
+        # Update external links
+        updated_external = []
+        for link in original_links.external:
+            if link.href in url_to_head_data:
+                head_info = url_to_head_data[link.href]
+                # Create new Link object with head data and scoring
+                contextual_score = head_info.get("relevance_score")
+                
+                updated_link = Link(
+                    href=link.href,
+                    text=link.text,
+                    title=link.title,
+                    base_domain=link.base_domain,
+                    head_data=head_info["head_data"],
+                    head_extraction_status=head_info["status"],
+                    head_extraction_error=head_info.get("error"),
+                    intrinsic_score=getattr(link, 'intrinsic_score', None),
+                    contextual_score=contextual_score
+                )
+                
+                # Add relevance score to head_data for backward compatibility
+                if contextual_score is not None:
+                    updated_link.head_data = updated_link.head_data or {}
+                    updated_link.head_data["relevance_score"] = contextual_score
+                
+                # Calculate total score combining intrinsic and contextual scores
+                updated_link.total_score = calculate_total_score(
+                    intrinsic_score=updated_link.intrinsic_score,
+                    contextual_score=updated_link.contextual_score,
+                    score_links_enabled=getattr(config, 'score_links', False),
+                    query_provided=bool(config.link_preview_config.query)
+                )
+                
+                updated_external.append(updated_link)
+            else:
+                # Calculate total_score even without head data, using intrinsic_score
+                link.total_score = calculate_total_score(
+                    intrinsic_score=getattr(link, 'intrinsic_score', None),
+                    contextual_score=None,
+                    score_links_enabled=getattr(config, 'score_links', False),
+                    query_provided=bool(config.link_preview_config.query)
+                )
+                updated_external.append(link)
+
+        # Sort links by relevance score if available
+        if any(hasattr(link, 'head_data') and link.head_data and 'relevance_score' in link.head_data 
+               for link in updated_internal + updated_external):
+            
+            def get_relevance_score(link):
+                if hasattr(link, 'head_data') and link.head_data and 'relevance_score' in link.head_data:
+                    return link.head_data['relevance_score']
+                return 0.0
+            
+            updated_internal.sort(key=get_relevance_score, reverse=True)
+            updated_external.sort(key=get_relevance_score, reverse=True)
+        
+        return Links(
+            internal=updated_internal,
+            external=updated_external
+        )