Commit Graph

5 Commits

Author SHA1 Message Date
ntohidi
7a6ad547f0 Squashed commit of the following:
commit 2def6524cdacb69c72760bf55a41089257c0bb07
Author: ntohidi <nasrin@kidocode.com>
Date:   Mon Aug 4 18:59:10 2025 +0800

    refactor: consolidate WebScrapingStrategy to use LXML implementation only

    BREAKING CHANGE: None - full backward compatibility maintained

    This commit simplifies the content scraping architecture by removing the
    redundant BeautifulSoup-based WebScrapingStrategy implementation and making
    it an alias for LXMLWebScrapingStrategy.

    Changes:
    - Remove ~1000 lines of BeautifulSoup-based WebScrapingStrategy code
    - Make WebScrapingStrategy an alias for LXMLWebScrapingStrategy
    - Update LXMLWebScrapingStrategy to inherit directly from ContentScrapingStrategy
    - Add required methods (scrap, ascrap, process_element, _log) to LXMLWebScrapingStrategy
    - Maintain 100% backward compatibility - existing code continues to work

    Code changes:
    - crawl4ai/content_scraping_strategy.py: Remove WebScrapingStrategy class, add alias
    - crawl4ai/async_configs.py: Remove WebScrapingStrategy from imports
    - crawl4ai/__init__.py: Update imports to show alias relationship
    - crawl4ai/types.py: Update type definitions
    - crawl4ai/legacy/web_crawler.py: Update import to use alias
    - tests/async/test_content_scraper_strategy.py: Update to use LXMLWebScrapingStrategy
    - docs/examples/scraping_strategies_performance.py: Update to use single strategy

    Documentation updates:
    - docs/md_v2/core/content-selection.md: Update scraping modes section
    - docs/md_v2/migration/webscraping-strategy-migration.md: Add migration guide
    - CHANGELOG.md: Document the refactoring under [Unreleased]

    Benefits:
    - 10-20x faster HTML parsing for large documents
    - Reduced memory usage and simplified codebase
    - Consistent parsing behavior
    - No migration required for existing users

    All existing code using WebScrapingStrategy continues to work without
    modification, while benefiting from LXML's superior performance.
2025-08-04 19:02:01 +08:00
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
UncleCode
dc36997a08 feat(schema): improve HTML preprocessing for schema generation
Add new preprocess_html_for_schema utility function to better handle HTML cleaning
for schema generation. This replaces the previous optimize_html function in the
GoogleSearchCrawler and includes smarter attribute handling and pattern detection.

Other changes:
- Update default provider to gpt-4o
- Add DEFAULT_PROVIDER_API_KEY constant
- Make LLMConfig creation more flexible with create_llm_config helper
- Add new dependencies: zstandard and msgpack

This change improves schema generation reliability while reducing noise in the
processed HTML.
2025-03-12 22:40:46 +08:00
UncleCode
baee4949d3 refactor(llm): rename LlmConfig to LLMConfig for consistency
Rename LlmConfig to LLMConfig across the codebase to follow consistent naming conventions.
Update all imports and usages to use the new name.
Update documentation and examples to reflect the change.

BREAKING CHANGE: LlmConfig has been renamed to LLMConfig. Users need to update their imports and usage.
2025-03-05 14:17:04 +08:00
UncleCode
a9415aaaf6 refactor(deep-crawling): reorganize deep crawling strategies and add new implementations
Split deep crawling code into separate strategy files for better organization and maintainability. Added new BFF (Best First) and DFS crawling strategies. Introduced base strategy class and common types.

BREAKING CHANGE: Deep crawling implementation has been split into multiple files. Import paths for deep crawling strategies have changed.
2025-02-05 22:50:39 +08:00