Implement initial MVP for Docker-based browser management in Crawl4ai, enabling
remote browser execution in containerized environments.
Key Changes:
- Add browser_farm module with Docker support components:
* BrowserFarmService: Manages browser endpoints
* DockerBrowser: Handles Docker browser communication
* Basic health check implementation
* Dockerfile with optimized Chrome/Playwright setup:
- Based on python:3.10-slim for minimal size
- Includes all required system dependencies
- Auto-installs crawl4ai and sets up Playwright
- Configures Chrome with remote debugging
- Uses socat for port forwarding (9223)
- Update core components:
* Rename use_managed_browser to use_remote_browser for clarity
* Modify BrowserManager to support Docker mode
* Add Docker configuration in BrowserConfig
* Update context handling for remote browsers
- Add example:
* hello_world_docker.py demonstrating Docker browser usage
Technical Details:
- Docker container exposes port 9223 (mapped to host:9333)
- Uses CDP (Chrome DevTools Protocol) for remote connection
- Maintains compatibility with existing managed browser features
- Simplified endpoint management for MVP phase
- Optimized Docker setup:
* Minimal dependencies installation
* Proper Chrome flags for containerized environment
* Headless mode with GPU disabled
* Security considerations (no-sandbox mode)
Testing:
- Extensive Docker configuration testing and optimization
- Verified with hello_world_docker.py example
- Confirmed remote browser connection and crawling functionality
- Tested basic health checks
This is the first step towards a scalable browser farm solution, setting up
the foundation for future enhancements like resource monitoring, multiple
browser instances, and container lifecycle management.
- Fix crawler text mode for improved performance; cover missing `srcset` and `data_srcset` attributes in image tags.
- Introduced Managed Browsers for enhanced crawling experience.
- Updated documentation for clearer navigation on configuration.
- Changed 'text_only' to 'text_mode' in configuration and methods.
- Improved performance and relevance in content filtering strategies.
- Introduced new configuration classes: BrowserConfig and CrawlerRunConfig.
- Refactored AsyncWebCrawler to leverage the new configuration system for cleaner parameter management.
- Updated AsyncPlaywrightCrawlerStrategy for better flexibility and reduced legacy parameters.
- Improved error handling with detailed context extraction during exceptions.
- Enhanced overall maintainability and usability of the web crawler.
- Introduced new async crawl strategy with session management.
- Added BrowserManager for improved browser management.
- Enhanced documentation, focusing on storage state and usage examples.
- Improved error handling and logging for sessions.
- Added JavaScript snippets for customizing navigator properties.
- Introduced the PruningContentFilter for better content relevance.
- Implemented comprehensive unit tests for verification of functionality.
- Enhanced existing BM25ContentFilter tests for edge case coverage.
- Updated documentation to include usage examples for new filter.
- Updated version to 0.3.743
- Improved ManagedBrowser configuration with dynamic host/port
- Implemented fast HTML formatting in web crawler
- Enhanced markdown generation with a new generator class
- Improved sanitization and utility functions
- Added contributor details and pull request acknowledgments
- Updated documentation for clearer usage scenarios
- Adjusted tests to reflect class name changes
- Another thing this commit introduces is the concept of the Relevance Content Filter. This is an improvement over Fit Markdown. This class of strategies aims to extract the main content from a given page - the part that really matters and is useful to be processed. One strategy has been created using the BM25 algorithm, which finds chunks of text from the web page relevant to its title, descriptions, and keywords, or supports a given user query and matches them. The result is then returned to the main engine to be converted to Markdown. Plans include adding approaches using language models as well.
- The cache database was updated to hold information about response headers and downloaded files.
- Implement smart_wait function in AsyncPlaywrightCrawlerStrategy
- Add screenshot support to AsyncCrawlResponse and AsyncWebCrawler
- Improve error handling and timeout management in crawling process
- Fix typo in CrawlResult model (responser_headers -> response_headers)
- Update .gitignore to exclude additional files
- Adjust import path in test_basic_crawling.py
