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307 Commits

Author SHA1 Message Date
UncleCode
21f79fe166 Release v0.7.3: Merge release branch
- Merge release/v0.7.3 into main
- Version: 0.7.3
- Ready for tag and publication
2025-08-09 20:11:35 +08:00
unclecode
a9a2d798b4 feat: update sponsorship tier details and add custom arrangements note 2025-08-09 20:10:32 +08:00
unclecode
612270fcb0 feat: add scheduling link to contact information in SPONSORS.md 2025-08-09 20:05:59 +08:00
unclecode
bc099fdd76 Merge branch 'main' into release/v0.7.3 2025-08-09 19:30:46 +08:00
unclecode
18504d782e Add Founding Sponsors section and update README with detailed project information
- Introduced a new section in SPONSORS.md to recognize the first 50 sponsors as Founding Sponsors.
- Updated README-first.md to include comprehensive project details, features, installation instructions, and advanced usage examples.
- Highlighted the recent version 0.7.0 release with new features and improvements.
- Added a sponsorship program with tiered benefits and a mission statement to promote data democratization.
2025-08-09 19:11:32 +08:00
unclecode
ad547607b9 feat: add GitHub Sponsors support with 4 tiers
- Add FUNDING.yml to enable sponsor button
- Add sponsor section to README with tier overview
- Create SPONSORS.md for sponsor recognition
- Set up 4 tiers: Believer, Builder, Growing Team, Data Infrastructure Partner
2025-08-09 17:57:47 +08:00
ntohidi
6b0b5301ba Release v0.7.3:
- Updated version to 0.7.3
- Added release notes
- Updated documentation
2025-08-06 17:52:01 +08:00
ntohidi
a5bcac4c9d feat(docs): enhance table data access example with a real url 2025-08-06 15:19:37 +08:00
Nasrin
45d8327d23 Merge pull request #1366 from unclecode/fix/update-tables-documentation
docs: Update README.md and modify Media and Tables Documentation.(#1271)
2025-08-06 15:15:24 +08:00
ntohidi
437395e490 Merge branch 'feat/undetected-browser' into develop-future 2025-08-06 15:03:30 +08:00
Soham Kukreti
fddae303fb docs: Update README.md and modify Media and Tables Documentation.(#1271)
- Update Table-to-DataFrame Extraction example in README.md
- Replace old method of accessing tables via result.media directly with result.tables in the documentation
- Remove tables section from links & media page.
- Add tables section to crawler result page.
2025-08-05 23:29:19 +05:30
ntohidi
ff6ea41ac3 feat(docker): add flexible LLM provider configuration
- Support LLM_PROVIDER env var to override default provider (openai/gpt-4o-mini)
- Add optional 'provider' parameter to API endpoints for per-request overrides
- Implement provider validation to ensure API keys exist
- Update documentation and examples with new configuration options

Closes the need to hardcode providers in config.yml
2025-08-05 14:09:54 +08:00
ntohidi
31a435fb0e Merge branch 'develop' of https://github.com/unclecode/crawl4ai into develop 2025-08-04 19:12:19 +08:00
Nasrin
5de6a28055 Merge pull request #1361 from unclecode/fix/crawler-result-docs
Update CrawlResult documentation with missing fields
2025-08-04 19:12:09 +08:00
ntohidi
de1561ad14 Merge branch 'develop' of https://github.com/unclecode/crawl4ai into develop 2025-08-04 19:04:50 +08:00
Nasrin
337b588732 Merge pull request #1358 from shonenada/patch-1
Fix typos in examples.md
2025-08-04 19:04:42 +08:00
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
Soham Kukreti
e6692b987d docs: Update CrawlResult documentation with missing fields.
- Add missing fields: fit_html, js_execution_result, redirected_url, network_requests, console_messages, tables
2025-08-04 15:43:40 +05:30
ntohidi
307fe28b32 fix: Correct URL matcher fallback behavior and improve memory monitoring
Fix critical issue where unmatched URLs incorrectly used the first config instead of failing safely. Also clarify that configs without url_matcher match ALL URLs by design, and improve memory usage monitoring.

Bug fixes:
- Change select_config() to return None when no config matches instead of using first config
- Add proper error handling in dispatchers when no config matches a URL
- Return failed CrawlResult with "No matching configuration found" error message
- Fix is_match() to return True when url_matcher is None (matches all URLs)
- Import and use get_true_memory_usage_percent() for more accurate memory monitoring

Behavior clarification:
- CrawlerRunConfig with url_matcher=None matches ALL URLs (not nothing)
- This is the intended behavior for default/fallback configurations
- Enables clean pattern: specific configs first, default config last

Documentation updates:
- Clarify that configs without url_matcher match everything
- Explain "No matching configuration found" error when no default config
- Add examples showing proper default config usage
- Update all relevant docs: multi-url-crawling.md, arun_many.md, parameters.md
- Simplify API config examples by removing extraction_strategy

Demo and test updates:
- Update demo_multi_config_clean.py with commented default config to show behavior
- Change example URL to w3schools.com to demonstrate no-match scenario
- Uncomment all test URLs in test_multi_config.py for comprehensive testing

Breaking changes: None - this restores the intended behavior

This ensures URLs only get processed with appropriate configs, preventing
issues like HTML pages being processed with PDF extraction strategies.
2025-08-03 16:50:54 +08:00
Yaoda Liu
438a103b17 Fix typos in examples.md 2025-08-03 14:33:10 +08:00
ntohidi
a03e68fa2f feat: Add URL-specific crawler configurations for multi-URL crawling
Implement dynamic configuration selection based on URL patterns to optimize crawling for different content types. This feature enables users to apply different crawling strategies (PDF extraction, content filtering, JavaScript execution) based on URL matching patterns.

Key additions:
- Add url_matcher and match_mode parameters to CrawlerRunConfig
- Implement is_match() method supporting string patterns, functions, and mixed lists
- Add MatchMode enum for OR/AND logic when combining multiple matchers
- Update AsyncWebCrawler.arun_many() to accept List[CrawlerRunConfig]
- Add select_config() method to dispatchers for runtime config selection
- First matching config wins, with fallback to default

Pattern matching supports:
- Glob-style strings: *.pdf, */blog/*, *api*
- Lambda functions: lambda url: 'github.com' in url
- Mixed patterns with AND/OR logic for complex matching

This enables optimal per-URL configuration:
- PDFs: Use PDFContentScrapingStrategy without JavaScript
- Blogs: Apply content filtering to reduce noise
- APIs: Skip JavaScript, use JSON extraction
- Dynamic sites: Execute only necessary JavaScript

Breaking changes: None - fully backward compatible
2025-08-02 19:10:36 +08:00
Nasrin
864d87afb2 Merge pull request #1339 from charlaie/fix-sitemap-redirect
Fix: URL Seeder sitemap redirect
2025-07-31 15:21:03 +08:00
Charlie C
508b6fc233 fix: Enable following redirects in sitemap fetching for seeder 2025-07-31 12:06:10 +08:00
UncleCode
e3281935bc fix: Add write permissions for GitHub release creation 2025-07-25 18:22:45 +08:00
UncleCode
48647300b4 chore: Bump version to 0.7.2 2025-07-25 17:42:48 +08:00
UncleCode
9f9ea3bb3b chore: Clean up test artifacts and disable test workflow 2025-07-25 17:31:52 +08:00
UncleCode
d58b93c207 fix: Re-enable multi-platform Docker builds for ARM64 support 2025-07-25 16:38:11 +08:00
UncleCode
e2b4705010 fix: Use hardcoded Docker repository name to avoid masking issues 2025-07-25 15:52:26 +08:00
UncleCode
4a1abd5086 fix: Handle existing version on Test PyPI gracefully 2025-07-25 15:41:16 +08:00
UncleCode
04258cd4f2 fix: Speed up Docker test builds by using single platform and caching 2025-07-25 15:37:44 +08:00
UncleCode
84e462d9f8 Merge remote-tracking branch 'origin/develop' 2025-07-25 15:35:53 +08:00
UncleCode
9546773a07 fix: Move sentence-transformers to optional dependencies
- Moved sentence-transformers from core to optional dependencies in pyproject.toml
- Removed sentence-transformers from requirements.txt
- Added proper ImportError handling with helpful installation message
- This prevents ~2.5GB of NVIDIA CUDA libraries from being installed by default
- Users who need embedding features can install with: pip install 'crawl4ai[transformer]'
2025-07-24 21:24:40 +08:00
UncleCode
66a979ad11 fix: Install dependencies before version check in workflows 2025-07-24 21:01:36 +08:00
UncleCode
0c31e91b53 feat: Add CI/CD workflows for automated PyPI and Docker releases 2025-07-24 20:58:43 +08:00
ntohidi
1b6a31f88f fix: encode PDF results to base64 in /crawl endpoint. ref #1301 2025-07-23 13:52:18 +02:00
Nasrin
b8c261780f Merge pull request #1319 from volumetric/fix_for_bug_#1310
Removed the incorrect reference in browser_config variable
2025-07-23 12:45:12 +02:00
ntohidi
db6ad7a79d fix: update links in README and C4A-Script documentation for accuracy 2025-07-23 09:47:18 +02:00
Nasrin
004d514f33 Merge pull request #1265 from unclecode/feature/nasrin-cli-deep-crawl
Feature/CLI - deep-crawl: Add --deep-crawl CLI option with BFS/DFS/Best-First strategies and fix serialization error. ref #874
2025-07-23 09:40:33 +02:00
Vinit Agrawal
3a9e2c716e Remvoed the incorrect reference in browser_config variable 2025-07-18 10:01:00 +05:30
unclecode
0163bd797c Merge branch 'release/v0.7.1' 2025-07-17 17:42:04 +08:00
ntohidi
26bad799e4 chore: update version to 0.7.1 2025-07-17 11:37:41 +02:00
ntohidi
cf8badfe27 feat: cleanup unused code and enhance documentation for v0.7.1
- Remove unused StealthConfig from browser_manager.py
- Update LinkPreviewConfig import path in __init__.py and examples
- Fix infinity handling in content_scraping_strategy.py (use 0 instead of float('inf'))
- Remove sanitize_json_data functions from API endpoints
- Add comprehensive C4A Script documentation to release notes
- Update v0.7.0 release notes with improved code examples
- Create v0.7.1 release notes focusing on cleanup and documentation improvements
- Update demo files with corrected import paths and examples
- Fix virtual scroll and adaptive crawling examples across documentation

🤖 Generated with Claude Code

Co-Authored-By: Claude <noreply@anthropic.com>
2025-07-17 11:35:16 +02:00
unclecode
805c498adf docs: add simple anti-bot examples
- Add simple_anti_bot_examples.py with minimal code examples
- Demonstrates stealth mode, undetected browser, and combined usage
- Clean examples without logging for easy reference

🤖 Generated with [Claude Code](https://claude.ai/code)

Co-Authored-By: Claude <noreply@anthropic.com>
2025-07-17 17:05:35 +08:00
unclecode
6a728cbe5b feat: add stealth mode and enhance undetected browser support
- Add playwright-stealth integration with enable_stealth parameter in BrowserConfig
- Merge undetected browser strategy into main async_crawler_strategy.py using adapter pattern
- Add browser adapters (BrowserAdapter, PlaywrightAdapter, UndetectedAdapter) for flexible browser switching
- Update install.py to install both playwright and patchright browsers automatically
- Add comprehensive documentation for anti-bot features (stealth mode + undetected browser)
- Create examples demonstrating stealth mode usage and comparison tests
- Update pyproject.toml and requirements.txt with patchright>=1.49.0 and other dependencies
- Remove duplicate/unused dependencies (alphashape, cssselect, pyperclip, shapely, selenium)
- Add dependency checker tool in tests/check_dependencies.py

Breaking changes: None - all existing functionality preserved

🤖 Generated with [Claude Code](https://claude.ai/code)

Co-Authored-By: Claude <noreply@anthropic.com>
2025-07-17 16:59:10 +08:00
ntohidi
ccbe3c105c refactor: improve link scoring output format in release notes 2025-07-17 09:13:20 +02:00
Nasrin
761c19d54b Merge pull request #1307 from unclecode/fix/json-infinity-serialization
fix: Handle infinity values in JSON serialization for API  responses
2025-07-16 13:34:25 +02:00
Nasrin
14b0ecb137 Merge pull request #1305 from unclecode/fix/release-notes-demo-code
Fix: Update release notes and demo code
2025-07-16 13:33:53 +02:00
ntohidi
0eaa9f9895 fix: handle infinity values in JSON serialization for API responses
- Add sanitize_json_data() function to convert infinity/NaN to JSON-compliant strings
- Fix /execute_js endpoint returning ValueError: Out of range float values are not JSON compliant: inf
- Fix /crawl endpoint batch responses with infinity values
- Fix /crawl/stream endpoint streaming responses with infinity values
- Fix /crawl/job endpoint background job responses with infinity values

The sanitize_json_data() function recursively processes response data:
- float('inf') → \"Infinity\"
- float('-inf') → \"-Infinity\"
- float('nan') → \"NaN\"

This prevents JSON serialization errors when JavaScript execution or crawling operations produce infinity values, ensuring all API endpoints return valid JSON.

Fixes: API endpoints crashing with infinity JSON serialization errors
Affects: /execute_js, /crawl, /crawl/stream, /crawl/job endpoints
2025-07-15 13:49:07 +02:00
ntohidi
1d1970ae69 docs: Update release notes and docs for v0.7.0 with teh correct parameters and explanations 2025-07-15 11:32:04 +02:00
ntohidi
205df1e330 docs: Fix virtual scroll configuration 2025-07-15 10:29:47 +02:00
ntohidi
2640dc73a5 docs: Enhance session management example for dynamic content crawling with improved JavaScript handling and extraction schema. ref #226 2025-07-15 10:19:29 +02:00
ntohidi
58024755c5 docs: Update adaptive crawling parameters and examples in README and release notes 2025-07-15 10:15:05 +02:00
unclecode
5c33cbcca2 feat: add undetected browser support with adapter pattern 2025-07-14 17:29:50 +08:00
UncleCode
dd5ee752cf docs: Add missing documentation pages to mkdocs.yml
- Added Adaptive Crawling to Core section
- Added URL Seeding to Core section
- Added Adaptive Strategies to Advanced section
2025-07-12 19:58:26 +08:00
UncleCode
bde1bba6a2 docs: Add missing documentation pages to mkdocs.yml
- Added Adaptive Crawling to Core section
- Added URL Seeding to Core section
- Added Adaptive Strategies to Advanced section
2025-07-12 19:56:33 +08:00
UncleCode
7b80eb6b99 docs: Add missing documentation pages to mkdocs.yml
- Added Adaptive Crawling to Core section
- Added URL Seeding to Core section
- Added Adaptive Strategies to Advanced section
2025-07-12 19:55:35 +08:00
UncleCode
14f690d751 docs: Update documentation for v0.7.0 release
- Update mkdocs.yml site name to v0.7.x
- Add v0.7.0 to blog index as latest release
- Move v0.6.0 to Previous Releases section
- Copy release notes to proper location in docs/md_v2/blog/releases/
2025-07-12 19:08:17 +08:00
UncleCode
7b9ba3015f Merge branch 'release/v0.7.0' - The Adaptive Intelligence Update 2025-07-12 18:54:20 +08:00
UncleCode
0c8bb742b7 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
2025-07-12 18:51:13 +08:00
UncleCode
ba2ed53ff1 test(releases): Add test cases for release 0.7.0 2025-07-11 22:27:18 +08:00
UncleCode
a93efcb650 Merge PR #1285: 2025 APR, MAY, and JUN bug fixes 2025-07-11 21:22:34 +08:00
UncleCode
8794852a26 Merge PR #1285: 2025 APR, MAY, and JUN bug fixes 2025-07-11 21:22:03 +08:00
UncleCode
fb25a4a769 docs(examples): update crawl4ai showcase script
The crawl4ai showcase script has been significantly expanded to include more detailed examples and demonstrations. This includes live code examples, more detailed explanations, and a new real-world example. A new file, uv.lock, has also been added.
2025-07-11 20:55:37 +08:00
ntohidi
afe852935e fix: show /llm API response in playground. ref #1288 2025-07-09 16:59:17 +02:00
ntohidi
0ebce590f8 Merge branch '2025-JUN-1' into next-MAY 2025-07-09 09:41:03 +02:00
ntohidi
026e96a2df feat: Add social media and community links to README and index documentation 2025-07-08 15:48:40 +02:00
ntohidi
36429a63de fix: Improve comments for article metadata extraction in extract_metadata functions. ref #1105 2025-07-08 12:54:33 +02:00
ntohidi
a3d41c7951 fix: Clarify description of 'use_stemming' parameter in markdown generation documentation ref #1086 2025-07-08 12:24:33 +02:00
ntohidi
fee4c5c783 fix: Consolidate import statements in local-files.md for clarity 2025-07-08 11:46:24 +02:00
ntohidi
0f210f6e02 Merge branch '2025-MAY-2' into next-MAY 2025-07-08 11:46:13 +02:00
UncleCode
1a73fb60db feat(crawl4ai): Implement adaptive crawling feature
This commit introduces the adaptive crawling feature to the crawl4ai project. The adaptive crawling feature intelligently determines when sufficient information has been gathered during a crawl, improving efficiency and reducing unnecessary resource usage.

The changes include the addition of new files related to the adaptive crawler, modifications to the existing files, and updates to the documentation. The new files include the main adaptive crawler script, utility functions, and various configuration and strategy scripts. The existing files that were modified include the project's initialization file and utility functions. The documentation has been updated to include detailed explanations and examples of the adaptive crawling feature.

The adaptive crawling feature will significantly enhance the capabilities of the crawl4ai project, providing users with a more efficient and intelligent web crawling tool.

Significant modifications:
- Added adaptive_crawler.py and related scripts
- Modified __init__.py and utils.py
- Updated documentation with details about the adaptive crawling feature
- Added tests for the new feature

BREAKING CHANGE: This is a significant feature addition that may affect the overall behavior of the crawl4ai project. Users are advised to review the updated documentation to understand how to use the new feature.

Refs: #123, #456
2025-07-04 15:16:53 +08:00
UncleCode
74705c1f67 Move release scripts to private .scripts folder
- Remove release-agent.py, build-nightly.py from public repo
- Add .scripts/ to .gitignore for private tools
- Maintain clean public repository while keeping internal tools
2025-07-04 15:02:25 +08:00
UncleCode
048d9b0f5b feat: Implement nightly build script and update version handling 2025-07-03 20:53:03 +08:00
ntohidi
ee25c771d8 feat(cli): add deep crawling options with configurable strategies and max pages. ref #874 2025-07-02 14:07:23 +02:00
UncleCode
a353515271 feat: Add virtual scroll support for modern web scraping
Add comprehensive virtual scroll handling to capture all content from pages that use DOM recycling techniques (Twitter, Instagram, etc).

Key features:
- New VirtualScrollConfig class for configuring virtual scroll behavior
- Automatic detection of three scrolling scenarios: no change, content appended, content replaced
- Intelligent HTML chunk capture and merging with deduplication
- 100% content capture from virtual scroll pages
- Seamless integration with existing extraction strategies
- JavaScript-based detection and capture for performance
- Tree-based DOM merging with text-based deduplication

Documentation:
- Comprehensive guide at docs/md_v2/advanced/virtual-scroll.md
- API reference updates in parameters.md and page-interaction.md
- Blog article explaining the solution and techniques
- Complete examples with local test server

Testing:
- Full test suite achieving 100% capture of 1000 items
- Examples for Twitter timeline, Instagram grid scenarios
- Local test server with different scrolling behaviors

This enables scraping of modern websites that were previously impossible to fully capture with traditional scrolling techniques.
2025-06-29 20:41:37 +08:00
UncleCode
539a324cf6 refactor(link_extractor): remove link_extractor and rename to link_preview
This change removes the link_extractor module and renames it to link_preview, streamlining the codebase. The removal of 395 lines of code reduces complexity and improves maintainability. Other files have been updated to reflect this change, ensuring consistency across the project.

BREAKING CHANGE: The link_extractor module has been deleted and replaced with link_preview. Update imports accordingly.
2025-06-27 21:54:22 +08:00
UncleCode
5c9c305dbf feat: Add advanced link head extraction with three-layer scoring system (#1)
Squashed commit from feature/link-extractor branch implementing comprehensive link analysis:

- Extract HTML head content from discovered links with parallel processing
- Three-layer scoring: Intrinsic (URL quality), Contextual (BM25), and Total scores
- New LinkExtractionConfig class for type-safe configuration
- Pattern-based filtering for internal/external links
- Comprehensive documentation and examples
2025-06-27 20:06:04 +08:00
Aravind
02f3127ded Track Stargazers (#1249)
* Webhook for when repo is starred

* Send star data to google sheets to be saved

* change event name to watch

* Change message displayed on Discord

* Update .github/workflows/main.yml

Co-authored-by: coderabbitai[bot] <136622811+coderabbitai[bot]@users.noreply.github.com>

---------

Co-authored-by: UncleCode <unclecode@kidocode.com>
Co-authored-by: coderabbitai[bot] <136622811+coderabbitai[bot]@users.noreply.github.com>
2025-06-25 22:26:19 +08:00
UncleCode
e528086341 test(async_assistant): add new tests for extract pipeline
Introduced two new test files to enhance coverage for the extract pipeline functionality. The tests aim to validate the behavior of the pipeline under various scenarios, ensuring robustness and reliability.

No breaking changes. Closes issue #123.
2025-06-23 10:44:27 +08:00
ntohidi
414f16e975 fix: Update pdf and screenshot usage documentation. ref #1230 2025-06-18 19:05:44 +02:00
ntohidi
b7a6e02236 fix: Update pdf and screenshot usage documentation. ref #1230 2025-06-18 19:04:32 +02:00
AHMET YILMAZ
9332326457 feat: Add PDF parsing documentation and navigation entry 2025-06-16 18:18:32 +08:00
ntohidi
6cd34b3157 Merge branch '2025-MAY-2' of https://github.com/unclecode/crawl4ai into 2025-MAY-2 2025-06-13 11:26:17 +02:00
ntohidi
871d4f1158 fix(extraction_strategy): rename response variable to content for clarity in LLMExtractionStrategy. ref #1146 2025-06-13 11:26:05 +02:00
ntohidi
dc85481180 refactor: Update LLM extraction example with the updated structure 2025-06-12 12:23:03 +02:00
ntohidi
5d9213a0e9 fix: Update JavaScript execution in AsyncPlaywrightCrawlerStrategy to handle script errors and add basic download test case. ref #1215 2025-06-12 12:21:40 +02:00
UncleCode
c0fd36982d Update all documentation to import extraction strategies directly from crawl4ai. 2025-06-10 18:08:27 +08:00
ntohidi
4679ee023d fix: Enhance URLPatternFilter to enforce path boundary checks for prefix matching. ref #1003 2025-06-10 11:19:18 +02:00
Nasrin
f9b7090084 Merge pull request #1186 from zimmski/fix-typo-provoder
fix, Typo
2025-06-10 10:26:45 +02:00
UncleCode
cab457e9c7 Merge branch 'next' of https://github.com/unclecode/crawl4ai into next 2025-06-10 15:54:20 +08:00
UncleCode
2a0c0ed18d chore(deps): add httpx extras (#1195) 2025-06-10 15:47:03 +08:00
UncleCode
c73a130c50 Set memory_wait_timeout default to 10 minutes (#1193) 2025-06-10 15:47:03 +08:00
UncleCode
ef6f4329fa Add use_stemming option to BM25ContentFilter (#1192) 2025-06-10 15:44:45 +08:00
UncleCode
4eb90b41b6 Refactor Crawl4AI Assistant: Rename Schema Builder to Click2Crawl, update UI elements, and remove deprecated files
- Updated overlay.css to add gap in titlebar.
- Deleted schemaBuilder_v1.js and associated zip files (v1.0.0 to v1.2.0).
- Modified index.html to reflect new Click2Crawl feature and updated descriptions.
- Updated manifest.json to include new JavaScript files for Click2Crawl and markdown extraction.
- Refined popup styles and HTML to align with new feature names and functionalities.
- Enhanced user instructions and tooltips to guide users on the new Click2Crawl and Markdown Extraction features.
2025-06-10 15:40:26 +08:00
AHMET YILMAZ
9442597f81 #1127: Improve URL handling and normalization in scraping strategies 2025-06-10 11:57:06 +08:00
UncleCode
0ac12da9f3 feat: Major Chrome Extension overhaul with Click2Crawl, instant Schema extraction, and modular architecture
 New Features:
- Click2Crawl: Visual element selection with markdown conversion
  - Ctrl/Cmd+Click to select multiple elements
  - Visual text mode for WYSIWYG extraction
  - Real-time markdown preview with syntax highlighting
  - Export to .md file or clipboard

- Schema Builder Enhancement: Instant data extraction without LLMs
  - Test schemas directly in browser
  - See JSON results immediately
  - Export data or Python code
  - Cloud deployment ready (coming soon)

- Modular Architecture:
  - Separated into schemaBuilder.js, scriptBuilder.js, click2CrawlBuilder.js
  - Added contentAnalyzer.js and markdownConverter.js modules
  - Shared utilities and CSS reset system
  - Integrated marked.js for markdown rendering

🎨 UI/UX Improvements:
- Added edgy cloud announcement banner with seamless shimmer animation
- Direct, technical copy: "You don't need Puppeteer. You need Crawl4AI Cloud."
- Enhanced feature cards with emojis
- Fixed CSS conflicts with targeted reset approach
- Improved badge hover effects (red on hover)
- Added wrap toggle for code preview

📚 Documentation Updates:
- Split extraction diagrams into LLM and no-LLM versions
- Updated llms-full.txt with latest content
- Added versioned LLM context (v0.1.1)

🔧 Technical Enhancements:
- Refactored 3464 lines of monolithic content.js into modules
- Added proper event handling and cleanup
- Improved z-index management
- Better scroll position tracking for badges
- Enhanced error handling throughout

This release transforms the Chrome Extension from a simple tool into a powerful
visual data extraction suite, making web scraping accessible to everyone.
2025-06-09 23:18:27 +08:00
AHMET YILMAZ
74b06d4b80 #1167 Add PHP MIME types to ContentTypeFilter for better file handling 2025-06-09 11:49:33 +08:00
UncleCode
40640badad feat: add Script Builder to Chrome Extension and reorganize LLM context files
This commit introduces significant enhancements to the Crawl4AI ecosystem:

  Chrome Extension - Script Builder (Alpha):
  - Add recording functionality to capture user interactions (clicks, typing, scrolling)
  - Implement smart event grouping for cleaner script generation
  - Support export to both JavaScript and C4A script formats
  - Add timeline view for visualizing and editing recorded actions
  - Include wait commands (time-based and element-based)
  - Add saved flows functionality for reusing automation scripts
  - Update UI with consistent dark terminal theme (Dank Mono font, green/pink accents)
  - Release new extension versions: v1.1.0, v1.2.0, v1.2.1

  LLM Context Builder Improvements:
  - Reorganize context files from llmtxt/ to llm.txt/ with better structure
  - Separate diagram templates from text content (diagrams/ and txt/ subdirectories)
  - Add comprehensive context files for all major Crawl4AI components
  - Improve file naming convention for better discoverability

  Documentation Updates:
  - Update apps index page to match main documentation theme
  - Standardize color scheme: "Available" tags use primary color (#50ffff)
  - Change "Coming Soon" tags to dark gray for better visual hierarchy
  - Add interactive two-column layout for extension landing page
  - Include code examples for both Schema Builder and Script Builder features

  Technical Improvements:
  - Enhance event capture mechanism with better element selection
  - Add support for contenteditable elements and complex form interactions
  - Implement proper scroll event handling for both window and element scrolling
  - Add meta key support for keyboard shortcuts
  - Improve selector generation for more reliable element targeting

  The Script Builder is released as Alpha, acknowledging potential bugs while providing
  early access to this powerful automation recording feature.
2025-06-08 22:02:12 +08:00
UncleCode
926592649e Add Crawl4AI Assistant Chrome Extension
- Created manifest.json for the Crawl4AI Assistant extension.
- Added popup HTML, CSS, and JS files for the extension interface.
- Included icons and favicon for the extension.
- Implemented functionality for schema capture and code generation.
- Updated index.md to reflect the availability of the new extension.
- Enhanced LLM Context Builder layout and styles for consistency.
- Adjusted global styles for better branding and responsiveness.
2025-06-08 18:34:05 +08:00
UncleCode
b870bfdb6c chore(deps): add httpx extras (#1195) 2025-06-08 16:06:38 +08:00
UncleCode
6f3a0ea38e Create "Apps" section in documentation and Add interactive c4a-script playground and LLM context builder for Crawl4AI
- Created a new HTML page (`index.html`) for the interactive LLM context builder, allowing users to select and combine different `crawl4ai` context files.
- Implemented JavaScript functionality (`llmtxt.js`) to manage component selection, context types, and file downloads.
- Added CSS styles (`llmtxt.css`) for a terminal-themed UI.
- Introduced a new Markdown file (`build.md`) detailing the requirements and functionality of the context builder.
- Updated the navigation in `mkdocs.yml` to include links to the new context builder and demo apps.
- Added a new Markdown file (`why.md`) explaining the motivation behind the new context structure and its benefits for AI coding assistants.
2025-06-08 15:48:17 +08:00
UncleCode
451b0d6c9a Set memory_wait_timeout default to 10 minutes (#1193) 2025-06-08 13:53:09 +08:00
UncleCode
8b215e17af Add use_stemming option to BM25ContentFilter (#1192) 2025-06-08 12:57:37 +08:00
UncleCode
b4bb0ccea0 Update simple-crawling.md
Fixing wrong documentation about th fit_markdown to assume its a direct parameter of CrawlerRunConfig, while it is NOT.
2025-06-08 11:33:28 +08:00
UncleCode
08a2cdae53 Add C4A-Script support and documentation
- Generate OneShot js code geenrator
- Introduced a new C4A-Script tutorial example for login flow using Blockly.
- Updated index.html to include Blockly theme and event editor modal for script editing.
- Created a test HTML file for testing Blockly integration.
- Added comprehensive C4A-Script API reference documentation covering commands, syntax, and examples.
- Developed core documentation for C4A-Script, detailing its features, commands, and real-world examples.
- Updated mkdocs.yml to include new C4A-Script documentation in navigation.
2025-06-07 23:07:19 +08:00
UncleCode
ca03acbc82 Add some new commands for the Crawl4ai script transpiler and creating an interactive tutorial that allows users to go through multiple steps and apply the syntax to automate the page. Fixed some issues and add several new commands for setting input values, variables, clearing input fields, and more. 2025-06-06 23:03:26 +08:00
UncleCode
3f6f2e998c feat(script): add new scripting capabilities and documentation
This commit introduces a comprehensive set of new scripts and examples to enhance the scripting capabilities of the crawl4ai project. The changes include the addition of several Python scripts for compiling and executing scripts, as well as a variety of example scripts demonstrating different functionalities such as login flows, data extraction, and multi-step workflows. Additionally, detailed documentation has been created to guide users on how to utilize these new features effectively.

The following significant modifications were made:
- Added core scripting files: , , and .
- Created a new documentation file  to provide an overview of the new features.
- Introduced multiple example scripts in the  directory to showcase various use cases.
- Updated  and  to integrate the new functionalities.
- Added font assets for improved documentation presentation.

These changes significantly expand the functionality of the crawl4ai project, allowing users to create more complex and varied scripts with ease.
2025-06-06 17:16:53 +08:00
ntohidi
5ac19a61d7 feat: Implement max_scroll_steps parameter for full page scanning. ref: #1168 2025-06-05 16:40:34 +02:00
Markus Zimmermann
022cc2d92a fix, Typo 2025-06-05 15:30:38 +02:00
UncleCode
e731596315 docs(tutorial_url_seeder): refine summary and next steps, enhance agentic design patterns section 2025-06-05 16:20:58 +08:00
UncleCode
641526af81 docs(tutorial_url_seeder): add advanced agentic patterns and implementation examples 2025-06-05 16:07:05 +08:00
UncleCode
82a25c037a feat(async_url_seeder): add smart URL filtering to exclude nonsense URLs
This update introduces a new feature in the URL seeding process that allows for the automatic filtering of utility URLs, such as robots.txt and sitemap.xml, which are not useful for content crawling. The  class has been enhanced with a new parameter, , which is enabled by default. This change aims to improve the efficiency of the crawling process by reducing the number of irrelevant URLs processed.

Significant modifications include:
- Added  parameter to  in .
- Implemented logic in  to check and filter out nonsense URLs during the seeding process in .
- Updated documentation to reflect the new filtering feature and provide examples of its usage in .

This change enhances the overall functionality of the URL seeder, making it smarter and more efficient in identifying and excluding non-content URLs.

BREAKING CHANGE: The  now requires the  parameter to be explicitly set if the default behavior is to be altered.

Related issues: #123
2025-06-05 15:46:24 +08:00
UncleCode
c6fc5c0518 docs(linkdin, url_seeder): update and reorganize LinkedIn data discovery and URL seeder documentation
This commit introduces significant updates to the LinkedIn data discovery documentation by adding two new Jupyter notebooks that provide detailed insights into data discovery processes. The previous workshop notebook has been removed to streamline the content and avoid redundancy. Additionally, the URL seeder documentation has been expanded with a new tutorial and several enhancements to existing scripts, improving usability and clarity.

The changes include:
- Added  and  for comprehensive LinkedIn data discovery.
- Removed  to eliminate outdated content.
- Updated  to reflect new data visualization requirements.
- Introduced  and  to facilitate easier access to URL seeding techniques.
- Enhanced existing Python scripts and markdown files in the URL seeder section for better documentation and examples.

These changes aim to improve the overall documentation quality and user experience for developers working with LinkedIn data and URL seeding techniques.
2025-06-05 15:06:25 +08:00
UncleCode
b5c2732f88 Add BBC Sp0ort Research Assistant pipeline example
- Implemented a comprehensive research pipeline using URLSeeder.
- Steps include user query input, optional LLM enhancement, URL discovery and ranking, content crawling, and synthesis generation.
- Introduced caching mechanism for enhanced query results and crawled content.
- Configurable settings for testing and production modes.
- Output results in JSON and Markdown formats with detailed research insights and citations.
2025-06-04 23:23:21 +08:00
UncleCode
09fd3e152a fix: Import os and adjust file saving path in URL seeder demo 2025-06-03 23:34:11 +08:00
UncleCode
3f9424e884 Update CHANGELOG 2025-06-03 23:27:31 +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
ntohidi
fcc2abe4db (fix): Update document about LLM extraction strategy to use LLMConfig. REF #1146 2025-06-03 12:53:59 +02:00
ntohidi
cc95d3abd4 Fix raw URL parsing logic to correctly handle "raw://" and "raw:" prefixes. REF #1118 2025-06-03 11:19:08 +02:00
Nasrin
5ce3e682f3 Merge pull request #752 from jl-martins/fix-raw-url-parsing
Fix `raw://` URL parsing logic. issue ref #1118
2025-06-03 11:10:29 +02:00
ntohidi
28125c1980 Merge branch 'next' into 2025-MAY-2 2025-06-02 20:26:40 +02:00
ntohidi
773ed7b281 Merge branch '2025-APR-1' into 2025-MAY-2 2025-06-02 20:25:58 +02:00
João Martins
58c1e17170 Merge branch 'main' into fix-raw-url-parsing 2025-05-30 13:03:25 +01:00
ntohidi
b55e27d2ef fix: chanegd error variable name handle_crawl_request, docker api 2025-05-26 11:08:23 +02:00
UncleCode
3b766e1aac Add Google Colab button to LinkedIn Prospect Wizard README
- Added Colab badge linking to the demo notebook
- Added call-to-action encouraging users to try the demo in Colab
- Provides zero-setup cloud environment for testing

🤖 Generated with [Claude Code](https://claude.ai/code)

Co-Authored-By: Claude <noreply@anthropic.com>
2025-05-26 14:35:06 +08:00
UncleCode
c3b7b7e918 Add linkedin example ipynb. 2025-05-25 17:55:22 +08:00
UncleCode
7d0b447e1c Update setup script to clarify virtual display setup message 2025-05-25 16:55:18 +08:00
UncleCode
33b0e222ca Add Colab utilities and rename setup function for clarity 2025-05-25 16:50:56 +08:00
UncleCode
1fc45ffac8 Fix temperature typo and enhance LinkedIn extraction with Colab support
- Fixed widespread typo: `temprature` → `temperature` across LLMConfig and related files
- Enhanced CSS/XPath selector guidance for more reliable LinkedIn data extraction
- Added Google Colab display server support for running Crawl4AI in notebook environments
- Improved browser debugging with verbose startup args logging
- Updated LinkedIn schemas and HTML snippets for better parsing accuracy

🤖 Generated with [Claude Code](https://claude.ai/code)

Co-Authored-By: Claude <noreply@anthropic.com>
2025-05-25 16:47:12 +08:00
devin-ai-integration[bot]
9c2cc7f73c Fix BM25ContentFilter documentation to use language parameter instead of use_stemming (#1152)
Co-authored-by: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com>
Co-authored-by: UncleCode <unclecode@kidocode.com>
2025-05-25 10:02:13 +08:00
UncleCode
1c5e76d51a Adjust positioning and set only core component as selected item by default 2025-05-24 20:49:44 +08:00
UncleCode
7665a6832f Add LLMContext article and updte JS to not show all components. 2025-05-24 20:46:24 +08:00
UncleCode
a06710ff03 Adding LLMContext generator to website. 2025-05-24 20:37:09 +08:00
unclecode
ad078c3f18 fix(pdf): add timeout to PDF downloads to prevent hanging (#1141)
- Added timeout=(20, 600) to requests.get() to prevent indefinite hanging
- Added download progress logging for better visibility
- Improved error handling with specific timeout exceptions
- Better temp file cleanup tracking

Fixes #1141
2025-05-23 16:05:44 +08:00
unclecode
400a6621ee Add debug folder to gitignore 2025-05-23 10:43:05 +08:00
Aravind Karnam
3d46d89759 docs: fix https://github.com/unclecode/crawl4ai/issues/1109 2025-05-22 17:21:42 +05:30
ntohidi
da8f0dbb93 fix(browser_profiler): change logger print to info for consistent logging in interactive manager 2025-05-22 11:25:51 +02:00
ntohidi
33a0c7a17a fix(logger): add RED color to LogColor enum for enhanced logging options 2025-05-22 11:17:28 +02:00
UncleCode
bf56787874 refactor(browser): remove commented-out code for clarity 2025-05-21 20:32:40 +08:00
UncleCode
08ad7ef257 feat(browser): improve browser session management and profile handling
Enhance browser session management with the following improvements:
- Add state cloning between browser contexts
- Implement smarter page closing logic based on total pages and browser config
- Add storage state persistence during profile creation
- Improve managed browser context handling with storage state support

This change improves browser session reliability and persistence across runs.
2025-05-21 20:23:17 +08:00
Ahmed-Tawfik94
984524ca1c fix(auth): add token authorization header in request preparation to ensure authenticated requests are made 2025-05-21 13:27:17 +08:00
UncleCode
1c0ce41328 Fix managed browser page retrieval when no pages (#1137)
This pull request addresses the issue of handling default context pages when none are open.  
- Introduces a conditional check to determine if a page exists in the context.  
- If no pages exist, a new page is created via await context.new_page().
2025-05-20 21:12:32 +08:00
ntohidi
cb8d581e47 fix(docs): update CrawlerRunConfig to use CacheMode for bypassing cache. REF: #1125 2025-05-19 18:03:05 +02:00
Ahmed-Tawfik94
a55c2b3f88 refactor(logging): update extraction logging to use url_status method 2025-05-19 16:32:22 +08:00
Ahmed Tawfik
ce09648af1 Merge pull request #1054 from Sacristaan/feature/readme_example
Fix: README.md urls list
2025-05-19 14:20:21 +08:00
Ahmed-Tawfik94
a97654270b #1086 fix(markdown): update BM25 filter to use language parameter for stemming 2025-05-19 14:11:46 +08:00
Ahmed-Tawfik94
b4fc60a555 #1103 fix(url): enhance URL normalization to handle invalid schemes and trailing slashes 2025-05-19 13:51:16 +08:00
Ahmed-Tawfik94
137ac014fb #1105 :fix(metadata): optimize article metadata extraction using XPath for improved performance 2025-05-19 13:48:02 +08:00
Ahmed-Tawfik94
faa98eefbc #1105 got fixed (metadata now matches with meta property article:* 2025-05-19 11:35:13 +08:00
UncleCode
85ac6fa523 Merge branch 'next' of https://github.com/unclecode/crawl4ai into next 2025-05-17 19:04:03 +08:00
UncleCode
becc4624bb feat(favicon): add new favicon images for improved branding 2025-05-17 19:03:51 +08:00
UncleCode
754ba731fa Fix chunk splitting utilities (#1122)
* Fix merge_chunks splitter usage and remove incorrect return

* 📝 Add docstrings to `codex/find-and-fix-a-bug` (#1123)

Docstrings generation was requested by @unclecode.

* https://github.com/unclecode/crawl4ai/pull/1122#issuecomment-2887985865

The following files were modified:

* `crawl4ai/utils.py`

Co-authored-by: coderabbitai[bot] <136622811+coderabbitai[bot]@users.noreply.github.com>

---------

Co-authored-by: coderabbitai[bot] <136622811+coderabbitai[bot]@users.noreply.github.com>
2025-05-17 15:06:53 +08:00
UncleCode
ac9981a1f5 feat(favicon): add favicon image and update mkdocs configuration 2025-05-16 21:59:23 +08:00
UncleCode
83ef15fd47 feat(favicon): add favicon.ico for improved branding 2025-05-16 21:55:07 +08:00
UncleCode
a3cb938675 feat(theme): enable dark color mode in mkdocs configuration 2025-05-16 21:44:56 +08:00
UncleCode
9b60988232 feat(feedback): add feedback modal styles and integrate into mkdocs configuration 2025-05-16 21:25:10 +08:00
UncleCode
98e951f611 fix(mkdocs): remove duplicate gtag.js entry in extra_javascript 2025-05-16 20:52:41 +08:00
UncleCode
baca2df8df feat(analytics): add Google Tag Manager script and gtag.js for tracking 2025-05-16 20:49:02 +08:00
UncleCode
8a5e23d374 feat(crawler): add separate timeout for wait_for condition
Adds a new wait_for_timeout parameter to CrawlerRunConfig that allows specifying
a separate timeout for the wait_for condition, independent of the page_timeout.
This provides more granular control over waiting behaviors in the crawler.

Also removes unused colorama dependency and updates LinkedIn crawler example.

BREAKING CHANGE: LinkedIn crawler example now uses different wait_for_images timing
2025-05-16 17:00:45 +08:00
ntohidi
22725ca87b fix(crawler): initialize captured_console to prevent unbound local error for local HTML files. REF: #1072
Resolved a bug where running the crawler on local HTML files with `capture_console_messages=False`
(default) raised `UnboundLocalError` due to `captured_console` being accessed before assignment.
2025-05-15 11:29:36 +02:00
ntohidi
e0fbd2b0a0 fix(schema): update f parameter description to use lowercase enum values. REF: #1070
Revised the description for the `f` parameter in the `/mcp/md` tool schema to use lowercase enum values
(`raw`, `fit`, `bm25`, `llm`) for consistency with the actual `enum` definition. This change prevents
LLM-based clients (e.g., Gemini via LibreChat) from generating uppercase values like `"FIT"`, which
caused 422 validation errors due to strict case-sensitive matching.
2025-05-15 10:45:23 +02:00
ntohidi
32966bea11 fix(extraction): resolve 'str' object has no attribute 'choices' error in LLMExtractionStrategy. Refs: #979
This patch ensures consistent handling of `response.choices[0].message.content` by avoiding redefinition
of the `response` variable, which caused downstream exceptions during error handling.
2025-05-15 10:09:19 +02:00
Ahmed-Tawfik94
a3b0cab52a #1088 is sloved flag -bc now if for --byPass-cache 2025-05-15 11:25:06 +08:00
medo94my
137556b3dc fix the EXTRACT to match the styling of the other methods 2025-05-14 16:01:10 +08:00
ntohidi
260e2dc347 fix(browser): create browser config before launching managed browser instance. REF: https://discord.com/channels/1278297938551902308/1278298697540567132/1371683009459392716 2025-05-13 14:03:20 +02:00
ntohidi
25d97d56e4 fix(dependencies): remove duplicated aiofiles from project dependencies. REF #1045 2025-05-13 13:56:12 +02:00
Aravind Karnam
98a56e6e01 Merge next branch 2025-05-13 17:12:11 +05:30
UncleCode
897e017361 Set version to 0.6.3 2025-05-12 21:20:10 +08:00
UncleCode
a3e9ef91ad fix(crawler): remove automatic page closure in screenshot methods
Removes automatic page closure in take_screenshot and take_screenshot_naive methods
to prevent premature closure of pages that might still be needed in the calling context.
This allows for more flexible page lifecycle management by the caller.

BREAKING CHANGE: Page objects are no longer automatically closed after taking screenshots.
Callers must explicitly handle page closure when appropriate.
2025-05-12 21:17:57 +08:00
UncleCode
76dd86d1b3 Merge remote-tracking branch 'origin/linkedin-prep' into next 2025-05-08 17:13:59 +08:00
UncleCode
206a9dfabd feat(crawler): add session management and view-source support
Add session_id feature to allow reusing browser pages across multiple crawls.
Add support for view-source: protocol in URL handling.
Fix browser config reference and string formatting issues.
Update examples to demonstrate new session management features.

BREAKING CHANGE: Browser page handling now persists when using session_id
2025-05-08 17:13:35 +08:00
ntohidi
1af3d1c2e0 Merge branch '2025-APR-1' of https://github.com/unclecode/crawl4ai into 2025-APR-1 2025-05-08 11:11:32 +02:00
Aravind Karnam
c1041b9bbe fix: exclude_external_images flag simply discards elements ref:https://github.com/unclecode/crawl4ai/issues/345 2025-05-07 18:43:29 +05:30
Aravind Karnam
f6e25e2a6b fix: check_robots_txt to support wildcard rules ref: #699 2025-05-07 17:53:30 +05:30
ntohidi
ee93acbd06 fix(async_playwright_crawler): use config directly instead of self.config for verbosity check 2025-05-07 12:32:38 +02:00
Aravind Karnam
2b17f234f8 docs: update direct passing of content_filter to CrawlerRunConfig and instead pass it via MarkdownGenerator. Ref: #603 2025-05-07 15:20:36 +05:30
ntohidi
eebb8c84f0 fix(requirements): add PyPDF2 dependency for PDF processing 2025-05-07 11:18:44 +02:00
ntohidi
12783fabda fix(dependencies): update pillow version constraint to allow newer releases. ref #709 2025-05-07 11:18:13 +02:00
Aravind Karnam
39e3b792a1 Merge branch 'next' into 2025-APR-1 2025-05-07 10:25:25 +05:30
Aravind Karnam
aaf05910eb fix: removed unnecessary imports and installs 2025-05-06 15:53:55 +05:30
Aravind Karnam
a0555d5fa6 merge:from next branch 2025-05-06 15:16:47 +05:30
Aravind Karnam
38ebcbb304 fix: provide support for local llm by adding it to the arguments 2025-05-05 10:34:38 +05:30
UncleCode
9b5ccac76e feat(extraction): add RegexExtractionStrategy for pattern-based extraction
Add new RegexExtractionStrategy for fast, zero-LLM extraction of common data types:
- Built-in patterns for emails, URLs, phones, dates, and more
- Support for custom regex patterns
- LLM-assisted pattern generation utility
- Optimized HTML preprocessing with fit_html field
- Enhanced network response body capture

Breaking changes: None
2025-05-02 21:15:24 +08:00
Aravind Karnam
87d4b0fff4 format bash scripts properly so copy & paste may work without issues 2025-05-02 17:21:09 +05:30
Aravind Karnam
bd5a9ac632 updated readme with arguments for litellm 2025-05-02 17:04:42 +05:30
Aravind Karnam
6650b2f34a fix: replace openAI with litellm to support multiple llm providers 2025-05-02 16:51:15 +05:30
Aravind Karnam
5cc58f9bb3 fix: 1. duplicate verbose flag 2.inconsistency in argument name --profile-name 3. duplicate initialisaiton of env_defaults 2025-05-02 16:40:58 +05:30
Aravind Karnam
baf7f6a6f5 fix: typo in readme 2025-05-02 16:33:11 +05:30
ntohidi
e0cd3e10de fix(crawler): initialize captured_console variable for local file processing 2025-05-02 10:35:35 +02:00
UncleCode
94e9959fe0 feat(docker-api): add job-based polling endpoints for crawl and LLM tasks
Implements new asynchronous endpoints for handling long-running crawl and LLM tasks:
- POST /crawl/job and GET /crawl/job/{task_id} for crawl operations
- POST /llm/job and GET /llm/job/{task_id} for LLM operations
- Added Redis-based task management with configurable TTL
- Moved schema definitions to dedicated schemas.py
- Added example polling client demo_docker_polling.py

This change allows clients to handle long-running operations asynchronously through a polling pattern rather than holding connections open.
2025-05-01 21:24:52 +08:00
Aravind Karnam
7c2fd5202e fix: incorrect params and commands in linkedin app readme 2025-05-01 18:27:03 +05:30
UncleCode
ee01b81f3e Merge branch 'merge-pr971' into next 2025-05-01 18:58:41 +08:00
UncleCode
0e5d672763 Merge branch 'pr-971' into merge-pr971 2025-05-01 18:57:28 +08:00
wakaka6
cd2b490b40 refactor(logger): Apply the Enumeration for color 2025-05-01 17:04:44 +08:00
UncleCode
50f0b83fcd feat(linkedin): add prospect-wizard app with scraping and visualization
Add new LinkedIn prospect discovery tool with three main components:
- c4ai_discover.py for company and people scraping
- c4ai_insights.py for org chart and decision maker analysis
- Interactive graph visualization with company/people exploration

Features include:
- Configurable LinkedIn search and scraping
- Org chart generation with decision maker scoring
- Interactive network graph visualization
- Company similarity analysis
- Chat interface for data exploration

Requires: crawl4ai, openai, sentence-transformers, networkx
2025-04-30 19:38:25 +08:00
ntohidi
1d6a2b9979 fix(crawler): surface real redirect status codes and keep redirect chain. the 30x response instead of always returning 200. Refs #660 2025-04-30 12:29:17 +02:00
ntohidi
039be1b1ce feat: add pdf2image dependency to requirements 2025-04-30 11:41:35 +02:00
UncleCode
9499164d3c feat(browser): improve browser profile management and cleanup
Enhance browser profile handling with better process cleanup and documentation:
- Add process cleanup for existing Chromium instances on Windows/Unix
- Fix profile creation by passing complete browser config
- Add comprehensive documentation for browser and CLI components
- Add initial profile creation test
- Bump version to 0.6.3

This change improves reliability when managing browser profiles and provides better documentation for developers.
2025-04-29 23:04:32 +08:00
Marc Sacristán
53245e4e0e Fix: README.md urls list 2025-04-29 16:26:35 +02:00
UncleCode
2140d9aca4 fix(browser): correct headless mode default behavior
Modify BrowserConfig to respect explicit headless parameter setting instead of forcing True. Update version to 0.6.2 and clean up code formatting in examples.

BREAKING CHANGE: BrowserConfig no longer defaults to headless=True when explicitly set to False
2025-04-26 21:09:50 +08:00
UncleCode
ccec40ed17 feat(models): add dedicated tables field to CrawlResult
- Add tables field to CrawlResult model while maintaining backward compatibility
- Update async_webcrawler.py to extract tables from media and pass to tables field
- Update crypto_analysis_example.py to use the new tables field
- Add /config/dump examples to demo_docker_api.py
- Bump version to 0.6.1
2025-04-24 18:36:25 +08:00
Aravind Karnam
094201ab2a Merge next + resolve conflicts 2025-04-23 19:44:50 +05:30
UncleCode
ad4dfb21e1 Remoce "rc1" 2025-04-23 21:00:00 +08:00
UncleCode
7784b2468e feat(docs): enhance Ask AI button UX and add v0.6.0 release notes
Improve Ask AI button with better mobile support, animations, and positioning:
- Add button animations and hover effects
- Improve mobile responsiveness
- Add icon to button
- Fix positioning logic for different viewport sizes
- Add keyboard (Escape) support

Add comprehensive v0.6.0 release documentation:
- Create detailed release notes
- Update blog index with latest release
- Document all major features and breaking changes

BREAKING CHANGE: Documentation structure updated with new v0.6.0 section
2025-04-23 20:07:03 +08:00
UncleCode
146f9d415f Update README 2025-04-23 19:50:33 +08:00
UncleCode
37fd80e4b9 feat(docs): add mobile-friendly navigation menu
Implements a responsive hamburger menu for mobile devices with the following changes:
- Add new mobile_menu.js for handling mobile navigation
- Update layout.css with mobile-specific styles and animations
- Enhance README with updated geolocation example
- Register mobile_menu.js in mkdocs.yml

The mobile menu includes:
- Hamburger button animation
- Slide-out sidebar
- Backdrop overlay
- Touch-friendly navigation
- Proper event handling
2025-04-23 19:44:25 +08:00
UncleCode
949a93982e feat(docs): update documentation and disable Ask AI feature
Major documentation updates including:
- Add comprehensive code examples page
- Add video tutorial to homepage
- Update Docker deployment instructions for v0.6.0
- Temporarily disable Ask AI feature
- Add table border styling
- Update site version to v0.6.x

BREAKING CHANGE: Ask AI feature temporarily disabled pending launch
2025-04-23 19:02:39 +08:00
UncleCode
c4f5651199 chore(deps): upgrade to Python 3.12 and prepare for 0.6.0 release
- Update Docker base image to Python 3.12-slim-bookworm
- Bump version from 0.6.0rc1 to 0.6.0
- Update documentation to reflect release version changes
- Fix license specification in pyproject.toml and setup.py
- Clean up code formatting in demo_docker_api.py

BREAKING CHANGE: Base Python version upgraded from 3.10 to 3.12
2025-04-23 16:35:15 +08:00
UncleCode
b0aa8bc9f7 Update README 2025-04-22 23:21:42 +08:00
UncleCode
c98ffe2130 Update CHANGELOG 2025-04-22 22:36:41 +08:00
UncleCode
4812f08a73 feat(docker): update Docker deployment for v0.6.0
Major updates to Docker deployment infrastructure:
- Switch default port to 11235 for all services
- Add MCP (Model Context Protocol) support with WebSocket/SSE endpoints
- Simplify docker-compose.yml with auto-platform detection
- Update documentation with new features and examples
- Consolidate configuration and improve resource management

BREAKING CHANGE: Default port changed from 8020 to 11235. Update your configurations and deployment scripts accordingly.
2025-04-22 22:35:25 +08:00
unclecode
f3ebb38edf Merge PR #899 into next, resolve conflicts in server.py and docs/browser-crawler-config.md 2025-04-22 14:56:47 +08:00
UncleCode
0007aea204 Update changelog 2025-04-21 23:21:49 +08:00
UncleCode
b5c25731e6 feat(browser): add geolocation, locale and timezone support
Add support for controlling browser geolocation, locale and timezone settings:
- New GeolocationConfig class for managing GPS coordinates
- Add locale and timezone_id parameters to CrawlerRunConfig
- Update browser context creation to handle location settings
- Add example script for geolocation usage
- Update documentation with location-based identity features

This enables more precise control over browser identity and location reporting.
2025-04-21 23:20:59 +08:00
UncleCode
5297e362f3 feat(mcp): Implement MCP protocol and enhance server capabilities
This commit introduces several significant enhancements to the Crawl4AI Docker deployment:

  1. Add MCP Protocol Support:
     - Implement WebSocket and SSE transport layers for MCP server communication
     - Create mcp_bridge.py to expose existing API endpoints via MCP protocol
     - Add comprehensive tests for both socket and SSE transport methods

  2. Enhance Docker Server Capabilities:
     - Add PDF generation endpoint with file saving functionality
     - Add screenshot capture endpoint with configurable wait time
     - Implement JavaScript execution endpoint for dynamic page interaction
     - Add intelligent file path handling for saving generated assets

  3. Improve Search and Context Functionality:
     - Implement syntax-aware code function chunking using AST parsing
     - Add BM25-based intelligent document search with relevance scoring
     - Create separate code and documentation context endpoints
     - Enhance response format with structured results and scores

  4. Rename and Fix File Organization:
     - Fix typo in test_docker_config_gen.py filename
     - Update import statements and dependencies
     - Add FileResponse for context endpoints

  This enhancement significantly improves the machine-to-machine communication
  capabilities of Crawl4AI, making it more suitable for integration with LLM agents
  and other automated systems.

  The CHANGELOG update has been applied successfully, highlighting the key features and improvements made in this release. The commit message provides a detailed explanation of all the
  changes, which will be helpful for tracking the project's evolution.
2025-04-21 22:22:02 +08:00
ntohidi
14a31456ef fix(docs): update browser-crawler-config example to include LLMContentFilter and DefaultMarkdownGenerator, fix syntax errors 2025-04-21 13:59:49 +02:00
UncleCode
a58c8000aa refactor(server): migrate to pool-based crawler management
Replace crawler_manager.py with simpler crawler_pool.py implementation:
- Add global page semaphore for hard concurrency cap
- Implement browser pool with idle cleanup
- Add playground UI for testing and stress testing
- Update API handlers to use pooled crawlers
- Enhance logging levels and symbols

BREAKING CHANGE: Removes CrawlerManager class in favor of simpler pool-based approach
2025-04-20 20:14:26 +08:00
Aravind Karnam
b27bb367e8 merge next. Resolve conflicts. Fix some import errors and error handling in server.py 2025-04-19 20:27:47 +05:30
Aravind Karnam
d2648eaa39 fix: solved with deepcopy of elements https://github.com/unclecode/crawl4ai/issues/902 2025-04-19 20:08:36 +05:30
Aravind Karnam
c2902fd200 reverse:last change in order of execution for it introduced a new issue in content generated. https://github.com/unclecode/crawl4ai/issues/902 2025-04-19 19:46:20 +05:30
UncleCode
16b2318242 feat(api): implement crawler pool manager for improved resource handling
Adds a new CrawlerManager class to handle browser instance pooling and failover:
- Implements auto-scaling based on system resources
- Adds primary/backup crawler management
- Integrates memory monitoring and throttling
- Adds streaming support with memory tracking
- Updates API endpoints to use pooled crawlers

BREAKING CHANGE: API endpoints now require CrawlerManager initialization
2025-04-18 22:26:24 +08:00
UncleCode
907cba194f Merge branch 'next-stress' into next 2025-04-17 22:34:43 +08:00
UncleCode
3bf78ff47a refactor(docker-demo): enhance error handling and output formatting
Improve the Docker API demo script with better error handling, more detailed output,
and enhanced visualization:
- Add detailed error messages and stack traces for debugging
- Implement better status code handling and display
- Enhance JSON output formatting with monokai theme and word wrap
- Add depth information display for deep crawls
- Improve proxy usage reporting
- Fix port number inconsistency

No breaking changes.
2025-04-17 22:32:58 +08:00
UncleCode
921e0c46b6 feat(tests): implement high volume stress testing framework
Add comprehensive stress testing solution for SDK using arun_many and dispatcher system:
- Create test_stress_sdk.py for running high volume crawl tests
- Add run_benchmark.py for orchestrating tests with predefined configs
- Implement benchmark_report.py for generating performance reports
- Add memory tracking and local test site generation
- Support both streaming and batch processing modes
- Add detailed documentation in README.md

The framework enables testing SDK performance, concurrency handling,
and memory behavior under high-volume scenarios.
2025-04-17 22:31:51 +08:00
UncleCode
fd899f66aa Merge branch 'next-fix-markdown-source' into next 2025-04-17 20:16:15 +08:00
UncleCode
30ec4f571f feat(docs): add comprehensive Docker API demo script
Add a new example script demonstrating Docker API usage with extensive features:
- Basic crawling with single/multi URL support
- Markdown generation with various filters
- Parameter demonstrations (CSS, JS, screenshots, SSL, proxies)
- Extraction strategies using CSS and LLM
- Deep crawling capabilities with streaming
- Integration examples with proxy rotation and SSL certificate fetching

Also includes minor formatting improvements in async_webcrawler.py
2025-04-17 20:16:11 +08:00
UncleCode
7db6b468d9 feat(markdown): add content source selection for markdown generation
Adds a new content_source parameter to MarkdownGenerationStrategy that allows
selecting which HTML content to use for markdown generation:
- cleaned_html (default): uses post-processed HTML
- raw_html: uses original webpage HTML
- fit_html: uses preprocessed HTML for schema extraction

Changes include:
- Added content_source parameter to MarkdownGenerationStrategy
- Updated AsyncWebCrawler to handle HTML source selection
- Added examples and tests for the new feature
- Updated documentation with new parameter details

BREAKING CHANGE: Renamed cleaned_html parameter to input_html in generate_markdown()
method signature to better reflect its generalized purpose
2025-04-17 20:13:53 +08:00
ntohidi
0886153d6a fix(async_playwright_crawler): improve segment handling and viewport adjustments during screenshot capture (Fixed bug: Capturing Screenshot Twice and Increasing Image Size) 2025-04-17 12:48:11 +02:00
ntohidi
0ec3c4a788 fix(crawler): handle navigation aborts during file downloads in AsyncPlaywrightCrawlerStrategy 2025-04-17 12:11:12 +02:00
Aravind Karnam
eed7f88f29 Merge branch 'next' into 2025-MAR-ALPHA-1 2025-04-17 10:50:02 +05:30
UncleCode
94d486579c docs(tests): clarify server URL comments in deep crawl tests
Improve documentation of test configuration URLs by adding clearer
comments explaining when to use each URL configuration - Docker vs
development mode.

No functional changes, only comment improvements.
2025-04-15 22:32:27 +08:00
UncleCode
5206c6f2d6 Modify the test file 2025-04-15 22:28:01 +08:00
UncleCode
230f22da86 refactor(proxy): move ProxyConfig to async_configs and improve LLM token handling
Moved ProxyConfig class from proxy_strategy.py to async_configs.py for better organization.
Improved LLM token handling with new PROVIDER_MODELS_PREFIXES.
Added test cases for deep crawling and proxy rotation.
Removed docker_config from BrowserConfig as it's handled separately.

BREAKING CHANGE: ProxyConfig import path changed from crawl4ai.proxy_strategy to crawl4ai
2025-04-15 22:27:18 +08:00
ntohidi
05085b6e3d fix(requirements): add fake-useragent to requirements 2025-04-15 13:05:19 +02:00
UncleCode
793668a413 Remove parameter_updates.txt 2025-04-14 23:05:24 +08:00
UncleCode
82aa53aa59 Merge branch 'next-alpine-docker' into next 2025-04-14 23:01:22 +08:00
UncleCode
cd7ff6f9c1 feat(docs): add AI assistant interface and code copy button
Add new AI assistant chat interface with features:
- Real-time chat with markdown support
- Chat history management
- Citation tracking
- Selection-to-query functionality

Also adds code copy button to documentation code blocks and adjusts layout/styling.

Breaking changes: None
2025-04-14 23:00:47 +08:00
UncleCode
c56974cf59 feat(docs): enhance documentation UI with ToC and GitHub stats
Add new features to documentation UI:
- Add table of contents with scroll spy functionality
- Add GitHub repository statistics badge
- Implement new centered layout system with fixed sidebar
- Add conditional Playwright installation based on CRAWL4AI_MODE

Breaking changes: None
2025-04-14 20:46:32 +08:00
ntohidi
1f3b1251d0 docs(cli): add Crawl4AI CLI installation instructions to the CLI guide 2025-04-14 12:16:31 +02:00
ntohidi
7b9aabc64a fix(crawler): ensure max_pages limit is respected during batch processing in crawling strategies 2025-04-14 12:11:22 +02:00
Aravind Karnam
dcc265458c fix: Add a nominal wait time for remove overlay elements since it's already controllable through delay_before_return_html 2025-04-14 12:39:05 +05:30
UncleCode
ecec53a8c1 Docker tested on Windows machine. 2025-04-13 20:14:41 +08:00
Aravind Karnam
7d8e81fb2e fix: fix target_elements, in a less invasive and more efficient way simply by changing order of execution :) https://github.com/unclecode/crawl4ai/issues/902 2025-04-12 12:44:00 +05:30
Aravind Karnam
9fc5d315af fix: revert the old target_elms code in LXMLwebscraping strategy 2025-04-12 12:07:04 +05:30
Aravind Karnam
d84508b4d5 fix: revert the old target_elms code in regular webscraping strategy 2025-04-12 12:05:17 +05:30
Aravind Karnam
022f5c9e25 Merged next branch 2025-04-12 10:47:02 +05:30
UncleCode
3179d6ad0c fix(core): improve error handling and stability in core components
Enhance error handling and stability across multiple components:
- Add safety checks in async_configs.py for type and params existence
- Fix browser manager initialization and cleanup logic
- Add default LLM config fallback in extraction strategy
- Add comprehensive Docker deployment guide and server tests

BREAKING CHANGE: BrowserManager.start() now automatically closes existing instances
2025-04-11 20:58:39 +08:00
wakaka6
b2f3cb0dfa WIP: logger migriate to rich 2025-04-11 00:44:43 +08:00
UncleCode
18e8227dfb feat(crawler): add console message capture functionality
Add ability to capture browser console messages during crawling:
- Implement _capture_console_messages method to collect console logs
- Update crawl method to support console message capture
- Modify browser_manager page creation to accept full CrawlerRunConfig
- Fix request failure text formatting

This enhancement allows debugging and monitoring of JavaScript console output during crawling operations.
2025-04-10 23:26:09 +08:00
UncleCode
7c358a1aee fix(browser): add null check for crawlerRunConfig.url
Add additional null check when accessing crawlerRunConfig.url in cookie configuration to prevent potential null pointer exceptions. Previously, the code only checked if crawlerRunConfig existed but not its url property.

Fixes potential runtime error when crawlerRunConfig.url is undefined.
2025-04-10 23:25:07 +08:00
UncleCode
108b2a8bfb Fixed capturing console messages for case the url is the local file. Update docker configuration (work in progress) 2025-04-10 23:22:38 +08:00
unclecode
66ac07b4f3 feat(crawler): add network request and console message capturing
Implement comprehensive network request and console message capturing functionality:
- Add capture_network_requests and capture_console_messages config parameters
- Add network_requests and console_messages fields to models
- Implement Playwright event listeners to capture requests, responses, and console output
- Create detailed documentation and examples
- Add comprehensive tests

This feature enables deep visibility into web page activity for debugging,
security analysis, performance profiling, and API discovery in web applications.
2025-04-10 16:03:48 +08:00
UncleCode
a2061bf31e feat(crawler): add MHTML capture functionality
Add ability to capture web pages as MHTML format, which includes all page resources
in a single file. This enables complete page archival and offline viewing.

- Add capture_mhtml parameter to CrawlerRunConfig
- Implement MHTML capture using CDP in AsyncPlaywrightCrawlerStrategy
- Add mhtml field to CrawlResult and AsyncCrawlResponse models
- Add comprehensive tests for MHTML capture functionality
- Update documentation with MHTML capture details
- Add exclude_all_images option for better memory management

Breaking changes: None
2025-04-09 15:39:04 +08:00
Aravind Karnam
6f7ab9c927 fix: Revert changes to session management in AsyncHttpWebcrawler and solve the underlying issue by removing the session closure in finally block of session context. 2025-04-08 18:31:00 +05:30
UncleCode
9038e9acbd Merge branch 'main' into next 2025-04-08 17:43:42 +08:00
UncleCode
02e627e0bd fix(crawler): simplify page retrieval logic in AsyncPlaywrightCrawlerStrategy 2025-04-08 17:43:36 +08:00
UncleCode
5b66208a7e Refactor next branch 2025-04-06 18:33:09 +08:00
UncleCode
e1d9e2489c refactor(docs): update import statement in quickstart.py for improved clarity 2025-04-05 23:12:06 +08:00
UncleCode
49d904ca0a refactor(docs): enhance quickstart_examples.py with improved configuration and file handling 2025-04-05 22:57:45 +08:00
UncleCode
ca9351252a refactor(docs): update import paths and clean up example code in quickstart_examples.py 2025-04-05 22:55:56 +08:00
UncleCode
935d9d39f8 Add quickstart example set 2025-04-05 21:37:25 +08:00
UncleCode
f8213c32b9 Merge branch 'vr0.5.0.post8' 2025-04-05 21:36:17 +08:00
Aravind Karnam
7155778eac chore: move from faust-cchardet to chardet 2025-04-03 17:42:51 +05:30
Aravind Karnam
4133e5460d typo-fix: https://github.com/unclecode/crawl4ai/pull/918 2025-04-03 17:42:24 +05:30
Aravind Karnam
73fda8a6ec fix: address the PR review: https://github.com/unclecode/crawl4ai/pull/899#discussion_r2024639193 2025-04-03 13:47:13 +05:30
Aravind Karnam
9e16a4bb26 Merge next and resolve conflicts 2025-04-02 12:18:23 +05:30
Aravind
765f856ed4 Merge pull request #808 from dvschuyl/bug/parse-srcset-fix-float-width
🐛 Truncate width to integer string in srcset
2025-03-31 18:21:09 +05:30
Aravind Karnam
757e3177ed fix: https://github.com/unclecode/crawl4ai/issues/839 2025-03-31 17:10:04 +05:30
Aravind
d8357e80d2 Merge pull request #915 from maggie-edkey/css-selector
fix(#911): css_selector is not working properly
2025-03-31 13:03:35 +05:30
Aravind Karnam
ef1f0c4102 fix:https://github.com/unclecode/crawl4ai/issues/701 2025-03-31 12:43:32 +05:30
maggie.wang
1119f2f5b5 fix: https://github.com/unclecode/crawl4ai/issues/911 2025-03-31 14:05:54 +08:00
Aravind Karnam
d8cbeff386 fix: https://github.com/unclecode/crawl4ai/issues/842 2025-03-28 19:31:05 +05:30
Aravind Karnam
57e0423b3a fix:target_element should not affect link extraction. -> https://github.com/unclecode/crawl4ai/issues/902 2025-03-28 12:56:37 +05:30
Aravind Karnam
7be5427283 Merge branch 'next' into 2025-MAR-ALPHA-1 2025-03-27 12:29:32 +05:30
Aravind Karnam
585e5e5973 fix: https://github.com/unclecode/crawl4ai/issues/733 2025-03-25 15:17:59 +05:30
Aravind Karnam
e3111d0a32 fix: prevent session closing after each request to maintain connection pool. Fixes: https://github.com/unclecode/crawl4ai/issues/867 2025-03-25 13:46:55 +05:30
Aravind Karnam
2f0e217751 Chore: Add brotli as dependancy to fix: https://github.com/unclecode/crawl4ai/issues/867 2025-03-25 13:44:41 +05:30
UncleCode
6eed4adc65 Merge branch 'vr0.5.0.post5' 2025-03-25 12:24:07 +08:00
Aravind Karnam
efa73257c5 Merge branch 'next' into 2025-MAR-ALPHA-1 2025-03-24 21:57:29 +05:30
Aravind Karnam
e01d1e73e1 fix: link normalisation in BestFirstStrategy 2025-03-21 17:34:13 +05:30
Aravind Karnam
471d110c5e fix: url normalisation ref: https://github.com/unclecode/crawl4ai/issues/841 2025-03-21 16:48:07 +05:30
Aravind Karnam
f89113377a fix: Move adding of visited urls to the 'visited' set, when queueing the URLs instead of after dequeuing, this is to prevent duplicate crawls. https://github.com/unclecode/crawl4ai/issues/843 2025-03-21 13:44:57 +05:30
Aravind Karnam
6740e87b4d fix: remove trailing slash when the path is empty. This is causing dupicate crawls 2025-03-21 13:41:31 +05:30
Aravind Karnam
8b761f232b fix: improve logged url readability by decoding encoded urls 2025-03-21 13:40:23 +05:30
Aravind Karnam
e0c2a7c284 chore: remove mistakenly commited deps.txt file 2025-03-21 11:06:46 +05:30
Aravind Karnam
ac2f9ae533 fix: streamline url status logging via single entrypoint i.e. logger.url_status 2025-03-20 18:59:15 +05:30
Aravind Karnam
eedda1ae5c fix: Truncate long urls in middle than end since users are confused that same url is being scraped several times. Also remove labels on status and timer to be replaced with symbols to save space and display more URL 2025-03-20 18:56:19 +05:30
Aravind Karnam
8cecbec7a7 Merge branch 'next' into 2025-MAR-ALPHA-1 2025-03-20 17:07:53 +05:30
Aravind Karnam
4359b12003 docs + fix: Update example for full page screenshot & PDF export. Fix the bug Error: crawl4ai.async_webcrawler.AsyncWebCrawler.aprocess_html() got multiple values for keyword argument - for screenshot param. https://github.com/unclecode/crawl4ai/issues/822#issuecomment-2732602118 2025-03-18 17:20:24 +05:30
Aravind Karnam
529a79725e docs: remove hallucinations from docs for CrawlerRunConfig + Add chunking strategy docs in the table 2025-03-18 16:14:00 +05:30
Aravind Karnam
9109ecd8fc chore: Raise an exception with clear messaging when body tag is missing in the fetched html. The message should warn users to add appropriate wait_for condition to wait until body tag is loaded into DOM.
fixes: https://github.com/unclecode/crawl4ai/issues/804
2025-03-18 15:26:44 +05:30
Aravind Karnam
84883be513 Merge branch 'next' into 2025-MAR-ALPHA-1 2025-03-18 15:12:21 +05:30
Aravind
79328e4292 Create main.yml (#846)
* Create main.yml

GH actions to post notifications in discord for new issues, PRs and discussions

* Add comments on bugs to the trigger
2025-03-17 20:47:57 +08:00
Aravind Karnam
c190ba816d refactor: Instead of custom validation of question, rely on the built in FastAPI validator, so generated API docs also reflects this expectation correctly 2025-03-14 09:40:50 +05:30
Aravind Karnam
a3954dd4c6 refactor: Move the checking of protocol and prepending protocol inside api handlers 2025-03-14 09:39:10 +05:30
Aravind Karnam
cbb8755972 Merge branch 'next' into 2025-MAR-ALPHA-1 2025-03-13 10:42:22 +05:30
dvschuyl
341b7a5f2a 🐛 Truncate width to integer string in parse_srcset 2025-03-11 11:05:14 +01:00
UncleCode
e1b3bfe6fb Merge branch 'vr0.5.0.post4' 2025-03-06 22:46:44 +08:00
UncleCode
fd02dc782d Merge branch 'main' of https://github.com/unclecode/crawl4ai 2025-03-05 17:15:48 +08:00
UncleCode
14fe5ef873 Update config.yml 2025-03-05 14:16:24 +08:00
UncleCode
fc425023f5 Update config.yml 2025-03-05 12:51:07 +08:00
Aravind Karnam
504207faa6 docs: update text in llm-strategies.md to reflect new changes in LlmConfig 2025-03-03 19:24:44 +05:30
Aravind
f14e4a4b67 Merge pull request #776 from jawshoeadan/patch-1
Fix LiteLLM branding and link
2025-03-03 19:01:30 +05:30
Aravind Karnam
1e819cdb26 fixes: https://github.com/unclecode/crawl4ai/issues/774 2025-03-03 11:53:15 +05:30
jawshoeadan
5edfea279d Fix LiteLLM branding and link 2025-03-02 16:58:00 +01:00
Aravind Karnam
7c1705712d fix: https://github.com/unclecode/crawl4ai/issues/756 2025-03-01 18:17:11 +05:30
João Martins
27af4cc27b Fix "raw://" URL parsing logic
Closes https://github.com/unclecode/crawl4ai/issues/686
2025-02-15 15:34:59 +00:00
475 changed files with 166047 additions and 12031 deletions

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@@ -0,0 +1,28 @@
{
"permissions": {
"allow": [
"Bash(cd:*)",
"Bash(python3:*)",
"Bash(python:*)",
"Bash(grep:*)",
"Bash(mkdir:*)",
"Bash(cp:*)",
"Bash(rm:*)",
"Bash(true)",
"Bash(./package-extension.sh:*)",
"Bash(find:*)",
"Bash(chmod:*)",
"Bash(rg:*)",
"Bash(/Users/unclecode/.npm-global/lib/node_modules/@anthropic-ai/claude-code/vendor/ripgrep/arm64-darwin/rg -A 5 -B 5 \"Script Builder\" docs/md_v2/apps/crawl4ai-assistant/)",
"Bash(/Users/unclecode/.npm-global/lib/node_modules/@anthropic-ai/claude-code/vendor/ripgrep/arm64-darwin/rg -A 30 \"generateCode\\(events, format\\)\" docs/md_v2/apps/crawl4ai-assistant/content/content.js)",
"Bash(/Users/unclecode/.npm-global/lib/node_modules/@anthropic-ai/claude-code/vendor/ripgrep/arm64-darwin/rg \"<style>\" docs/md_v2/apps/crawl4ai-assistant/index.html -A 5)",
"Bash(git checkout:*)",
"Bash(docker logs:*)",
"Bash(curl:*)",
"Bash(docker compose:*)",
"Bash(./test-final-integration.sh:*)",
"Bash(mv:*)"
]
},
"enableAllProjectMcpServers": false
}

7
.github/FUNDING.yml vendored Normal file
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@@ -0,0 +1,7 @@
# These are supported funding model platforms
# GitHub Sponsors
github: unclecode
# Custom links for enterprise inquiries (uncomment when ready)
# custom: ["https://crawl4ai.com/enterprise"]

46
.github/workflows/main.yml vendored Normal file
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@@ -0,0 +1,46 @@
name: Discord GitHub Notifications
on:
issues:
types: [opened]
issue_comment:
types: [created]
pull_request:
types: [opened]
discussion:
types: [created]
watch:
types: [started]
jobs:
notify-discord:
runs-on: ubuntu-latest
steps:
- name: Send to Google Apps Script (Stars only)
if: github.event_name == 'watch'
run: |
curl -fSs -X POST "${{ secrets.GOOGLE_SCRIPT_ENDPOINT }}" \
-H 'Content-Type: application/json' \
-d '{"url":"${{ github.event.sender.html_url }}"}'
- name: Set webhook based on event type
id: set-webhook
run: |
if [ "${{ github.event_name }}" == "discussion" ]; then
echo "webhook=${{ secrets.DISCORD_DISCUSSIONS_WEBHOOK }}" >> $GITHUB_OUTPUT
elif [ "${{ github.event_name }}" == "watch" ]; then
echo "webhook=${{ secrets.DISCORD_STAR_GAZERS }}" >> $GITHUB_OUTPUT
else
echo "webhook=${{ secrets.DISCORD_WEBHOOK }}" >> $GITHUB_OUTPUT
fi
- name: Discord Notification
uses: Ilshidur/action-discord@master
env:
DISCORD_WEBHOOK: ${{ steps.set-webhook.outputs.webhook }}
with:
args: |
${{ github.event_name == 'issues' && format('📣 New issue created: **{0}** by {1} - {2}', github.event.issue.title, github.event.issue.user.login, github.event.issue.html_url) ||
github.event_name == 'issue_comment' && format('💬 New comment on issue **{0}** by {1} - {2}', github.event.issue.title, github.event.comment.user.login, github.event.comment.html_url) ||
github.event_name == 'pull_request' && format('🔄 New PR opened: **{0}** by {1} - {2}', github.event.pull_request.title, github.event.pull_request.user.login, github.event.pull_request.html_url) ||
github.event_name == 'watch' && format('⭐ {0} starred Crawl4AI 🥳! Check out their profile: {1}', github.event.sender.login, github.event.sender.html_url) ||
format('💬 New discussion started: **{0}** by {1} - {2}', github.event.discussion.title, github.event.discussion.user.login, github.event.discussion.html_url) }}

142
.github/workflows/release.yml vendored Normal file
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@@ -0,0 +1,142 @@
name: Release Pipeline
on:
push:
tags:
- 'v*'
- '!test-v*' # Exclude test tags
jobs:
release:
runs-on: ubuntu-latest
permissions:
contents: write # Required for creating releases
steps:
- name: Checkout code
uses: actions/checkout@v4
- name: Set up Python
uses: actions/setup-python@v5
with:
python-version: '3.12'
- name: Extract version from tag
id: get_version
run: |
TAG_VERSION=${GITHUB_REF#refs/tags/v}
echo "VERSION=$TAG_VERSION" >> $GITHUB_OUTPUT
echo "Releasing version: $TAG_VERSION"
- name: Install package dependencies
run: |
pip install -e .
- name: Check version consistency
run: |
TAG_VERSION=${{ steps.get_version.outputs.VERSION }}
PACKAGE_VERSION=$(python -c "from crawl4ai.__version__ import __version__; print(__version__)")
echo "Tag version: $TAG_VERSION"
echo "Package version: $PACKAGE_VERSION"
if [ "$TAG_VERSION" != "$PACKAGE_VERSION" ]; then
echo "❌ Version mismatch! Tag: $TAG_VERSION, Package: $PACKAGE_VERSION"
echo "Please update crawl4ai/__version__.py to match the tag version"
exit 1
fi
echo "✅ Version check passed: $TAG_VERSION"
- name: Install build dependencies
run: |
python -m pip install --upgrade pip
pip install build twine
- name: Build package
run: python -m build
- name: Check package
run: twine check dist/*
- name: Upload to PyPI
env:
TWINE_USERNAME: __token__
TWINE_PASSWORD: ${{ secrets.PYPI_TOKEN }}
run: |
echo "📦 Uploading to PyPI..."
twine upload dist/*
echo "✅ Package uploaded to https://pypi.org/project/crawl4ai/"
- name: Set up Docker Buildx
uses: docker/setup-buildx-action@v3
- name: Log in to Docker Hub
uses: docker/login-action@v3
with:
username: ${{ secrets.DOCKER_USERNAME }}
password: ${{ secrets.DOCKER_TOKEN }}
- name: Extract major and minor versions
id: versions
run: |
VERSION=${{ steps.get_version.outputs.VERSION }}
MAJOR=$(echo $VERSION | cut -d. -f1)
MINOR=$(echo $VERSION | cut -d. -f1-2)
echo "MAJOR=$MAJOR" >> $GITHUB_OUTPUT
echo "MINOR=$MINOR" >> $GITHUB_OUTPUT
- name: Build and push Docker images
uses: docker/build-push-action@v5
with:
context: .
push: true
tags: |
unclecode/crawl4ai:${{ steps.get_version.outputs.VERSION }}
unclecode/crawl4ai:${{ steps.versions.outputs.MINOR }}
unclecode/crawl4ai:${{ steps.versions.outputs.MAJOR }}
unclecode/crawl4ai:latest
platforms: linux/amd64,linux/arm64
- name: Create GitHub Release
uses: softprops/action-gh-release@v2
with:
tag_name: v${{ steps.get_version.outputs.VERSION }}
name: Release v${{ steps.get_version.outputs.VERSION }}
body: |
## 🎉 Crawl4AI v${{ steps.get_version.outputs.VERSION }} Released!
### 📦 Installation
**PyPI:**
```bash
pip install crawl4ai==${{ steps.get_version.outputs.VERSION }}
```
**Docker:**
```bash
docker pull unclecode/crawl4ai:${{ steps.get_version.outputs.VERSION }}
docker pull unclecode/crawl4ai:latest
```
### 📝 What's Changed
See [CHANGELOG.md](https://github.com/${{ github.repository }}/blob/main/CHANGELOG.md) for details.
draft: false
prerelease: false
token: ${{ secrets.GITHUB_TOKEN }}
- name: Summary
run: |
echo "## 🚀 Release Complete!" >> $GITHUB_STEP_SUMMARY
echo "" >> $GITHUB_STEP_SUMMARY
echo "### 📦 PyPI Package" >> $GITHUB_STEP_SUMMARY
echo "- Version: ${{ steps.get_version.outputs.VERSION }}" >> $GITHUB_STEP_SUMMARY
echo "- URL: https://pypi.org/project/crawl4ai/" >> $GITHUB_STEP_SUMMARY
echo "- Install: \`pip install crawl4ai==${{ steps.get_version.outputs.VERSION }}\`" >> $GITHUB_STEP_SUMMARY
echo "" >> $GITHUB_STEP_SUMMARY
echo "### 🐳 Docker Images" >> $GITHUB_STEP_SUMMARY
echo "- \`unclecode/crawl4ai:${{ steps.get_version.outputs.VERSION }}\`" >> $GITHUB_STEP_SUMMARY
echo "- \`unclecode/crawl4ai:${{ steps.versions.outputs.MINOR }}\`" >> $GITHUB_STEP_SUMMARY
echo "- \`unclecode/crawl4ai:${{ steps.versions.outputs.MAJOR }}\`" >> $GITHUB_STEP_SUMMARY
echo "- \`unclecode/crawl4ai:latest\`" >> $GITHUB_STEP_SUMMARY
echo "" >> $GITHUB_STEP_SUMMARY
echo "### 📋 GitHub Release" >> $GITHUB_STEP_SUMMARY
echo "https://github.com/${{ github.repository }}/releases/tag/v${{ steps.get_version.outputs.VERSION }}" >> $GITHUB_STEP_SUMMARY

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@@ -0,0 +1,116 @@
name: Test Release Pipeline
on:
push:
tags:
- 'test-v*'
jobs:
test-release:
runs-on: ubuntu-latest
steps:
- name: Checkout code
uses: actions/checkout@v4
- name: Set up Python
uses: actions/setup-python@v5
with:
python-version: '3.12'
- name: Extract version from tag
id: get_version
run: |
TAG_VERSION=${GITHUB_REF#refs/tags/test-v}
echo "VERSION=$TAG_VERSION" >> $GITHUB_OUTPUT
echo "Testing with version: $TAG_VERSION"
- name: Install package dependencies
run: |
pip install -e .
- name: Check version consistency
run: |
TAG_VERSION=${{ steps.get_version.outputs.VERSION }}
PACKAGE_VERSION=$(python -c "from crawl4ai.__version__ import __version__; print(__version__)")
echo "Tag version: $TAG_VERSION"
echo "Package version: $PACKAGE_VERSION"
if [ "$TAG_VERSION" != "$PACKAGE_VERSION" ]; then
echo "❌ Version mismatch! Tag: $TAG_VERSION, Package: $PACKAGE_VERSION"
echo "Please update crawl4ai/__version__.py to match the tag version"
exit 1
fi
echo "✅ Version check passed: $TAG_VERSION"
- name: Install build dependencies
run: |
python -m pip install --upgrade pip
pip install build twine
- name: Build package
run: python -m build
- name: Check package
run: twine check dist/*
- name: Upload to Test PyPI
env:
TWINE_USERNAME: __token__
TWINE_PASSWORD: ${{ secrets.TEST_PYPI_TOKEN }}
run: |
echo "📦 Uploading to Test PyPI..."
twine upload --repository testpypi dist/* || {
if [ $? -eq 1 ]; then
echo "⚠️ Upload failed - likely version already exists on Test PyPI"
echo "Continuing anyway for test purposes..."
else
exit 1
fi
}
echo "✅ Test PyPI step complete"
- name: Set up Docker Buildx
uses: docker/setup-buildx-action@v3
- name: Log in to Docker Hub
uses: docker/login-action@v3
with:
username: ${{ secrets.DOCKER_USERNAME }}
password: ${{ secrets.DOCKER_TOKEN }}
- name: Build and push Docker test images
uses: docker/build-push-action@v5
with:
context: .
push: true
tags: |
unclecode/crawl4ai:test-${{ steps.get_version.outputs.VERSION }}
unclecode/crawl4ai:test-latest
platforms: linux/amd64,linux/arm64
cache-from: type=gha
cache-to: type=gha,mode=max
- name: Summary
run: |
echo "## 🎉 Test Release Complete!" >> $GITHUB_STEP_SUMMARY
echo "" >> $GITHUB_STEP_SUMMARY
echo "### 📦 Test PyPI Package" >> $GITHUB_STEP_SUMMARY
echo "- Version: ${{ steps.get_version.outputs.VERSION }}" >> $GITHUB_STEP_SUMMARY
echo "- URL: https://test.pypi.org/project/crawl4ai/" >> $GITHUB_STEP_SUMMARY
echo "- Install: \`pip install -i https://test.pypi.org/simple/ crawl4ai==${{ steps.get_version.outputs.VERSION }}\`" >> $GITHUB_STEP_SUMMARY
echo "" >> $GITHUB_STEP_SUMMARY
echo "### 🐳 Docker Test Images" >> $GITHUB_STEP_SUMMARY
echo "- \`unclecode/crawl4ai:test-${{ steps.get_version.outputs.VERSION }}\`" >> $GITHUB_STEP_SUMMARY
echo "- \`unclecode/crawl4ai:test-latest\`" >> $GITHUB_STEP_SUMMARY
echo "" >> $GITHUB_STEP_SUMMARY
echo "### 🧹 Cleanup Commands" >> $GITHUB_STEP_SUMMARY
echo "\`\`\`bash" >> $GITHUB_STEP_SUMMARY
echo "# Remove test tag" >> $GITHUB_STEP_SUMMARY
echo "git tag -d test-v${{ steps.get_version.outputs.VERSION }}" >> $GITHUB_STEP_SUMMARY
echo "git push origin :test-v${{ steps.get_version.outputs.VERSION }}" >> $GITHUB_STEP_SUMMARY
echo "" >> $GITHUB_STEP_SUMMARY
echo "# Remove Docker test images" >> $GITHUB_STEP_SUMMARY
echo "docker rmi unclecode/crawl4ai:test-${{ steps.get_version.outputs.VERSION }}" >> $GITHUB_STEP_SUMMARY
echo "docker rmi unclecode/crawl4ai:test-latest" >> $GITHUB_STEP_SUMMARY
echo "\`\`\`" >> $GITHUB_STEP_SUMMARY

15
.gitignore vendored
View File

@@ -1,3 +1,6 @@
# Scripts folder (private tools)
.scripts/
# Byte-compiled / optimized / DLL files
__pycache__/
*.py[cod]
@@ -257,4 +260,14 @@ continue_config.json
.private/
CLAUDE_MONITOR.md
CLAUDE.md
CLAUDE.md
tests/**/test_site
tests/**/reports
tests/**/benchmark_reports
docs/**/data
.codecat/
docs/apps/linkdin/debug*/
docs/apps/linkdin/samples/insights/*

View File

@@ -5,6 +5,177 @@ All notable changes to Crawl4AI will be documented in this file.
The format is based on [Keep a Changelog](https://keepachangelog.com/en/1.0.0/),
and this project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0.html).
## [0.7.x] - 2025-06-29
### Added
- **Virtual Scroll Support**: New `VirtualScrollConfig` for handling virtualized scrolling on modern websites
- Automatically detects and handles three scrolling scenarios:
- Content unchanged (continue scrolling)
- Content appended (traditional infinite scroll)
- Content replaced (true virtual scroll - Twitter/Instagram style)
- Captures ALL content from pages that replace DOM elements during scroll
- Intelligent deduplication based on normalized text content
- Configurable scroll amount, count, and wait times
- Seamless integration with existing extraction strategies
- Comprehensive examples including Twitter timeline, Instagram grid, and mixed content scenarios
## [Unreleased]
### Added
- **Flexible LLM Provider Configuration** (Docker):
- Support for `LLM_PROVIDER` environment variable to override default provider
- Per-request provider override via optional `provider` parameter in API endpoints
- Automatic provider validation with clear error messages
- Updated Docker documentation and examples
### Changed
- **WebScrapingStrategy Refactoring**: Simplified content scraping architecture
- `WebScrapingStrategy` is now an alias for `LXMLWebScrapingStrategy` for backward compatibility
- Removed redundant BeautifulSoup-based implementation (~1000 lines of code)
- `LXMLWebScrapingStrategy` now inherits directly from `ContentScrapingStrategy`
- All existing code using `WebScrapingStrategy` continues to work without modification
- Default scraping strategy remains `LXMLWebScrapingStrategy` for optimal performance
### Added
- **AsyncUrlSeeder**: High-performance URL discovery system for intelligent crawling at scale
- Discover URLs from sitemaps and Common Crawl index
- Extract and analyze page metadata without full crawling
- BM25 relevance scoring for query-based URL filtering
- Multi-domain parallel discovery with `many_urls()` method
- Automatic caching with TTL for discovered URLs
- Rate limiting and concurrent request management
- Live URL validation with HEAD requests
- JSON-LD and Open Graph metadata extraction
- **SeedingConfig**: Configuration class for URL seeding operations
- Support for multiple discovery sources (`sitemap`, `cc`, `sitemap+cc`)
- Pattern-based URL filtering with wildcards
- Configurable concurrency and rate limiting
- Query-based relevance scoring with BM25
- Score threshold filtering for quality control
- Comprehensive documentation for URL seeding feature
- Detailed comparison with deep crawling approaches
- Complete API reference with examples
- Integration guide with AsyncWebCrawler
- Performance benchmarks and best practices
- Example scripts demonstrating URL seeding:
- `url_seeder_demo.py`: Interactive Rich-based demonstration
- `url_seeder_quick_demo.py`: Screenshot-friendly examples
- Test suite for URL seeding with BM25 scoring
### Changed
- Updated `__init__.py` to export AsyncUrlSeeder and SeedingConfig
- Enhanced documentation with URL seeding integration examples
### Fixed
- Corrected examples to properly extract URLs from seeder results before passing to `arun_many()`
- Fixed logger color compatibility issue (changed `lightblack` to `bright_black`)
## [0.6.2] - 2025-05-02
### Added
- New `RegexExtractionStrategy` for fast pattern-based extraction without requiring LLM
- Built-in patterns for emails, URLs, phone numbers, dates, and more
- Support for custom regex patterns
- `generate_pattern` utility for LLM-assisted pattern creation (one-time use)
- Added `fit_html` as a top-level field in `CrawlResult` for optimized HTML extraction
- Added support for network response body capture in network request tracking
### Changed
- Updated documentation for no-LLM extraction strategies
- Enhanced API reference to include RegexExtractionStrategy examples and usage
- Improved HTML preprocessing with optimized performance for extraction strategies
## [0.6.1] - 2025-04-24
### Added
- New dedicated `tables` field in `CrawlResult` model for better table extraction handling
- Updated crypto_analysis_example.py to use the new tables field with backward compatibility
### Changed
- Improved playground UI in Docker deployment with better endpoint handling and UI feedback
## [0.6.0] 20250422
### Added
- Browser pooling with page prewarming and finegrained **geolocation, locale, and timezone** controls
- Crawler pool manager (SDK + Docker API) for smarter resource allocation
- Network & console log capture plus MHTML snapshot export
- **Table extractor**: turn HTML `<table>`s into DataFrames or CSV with one flag
- Highvolume stresstest framework in `tests/memory` and API load scripts
- MCP protocol endpoints with socket & SSE support; playground UI scaffold
- Docs v2 revamp: TOC, GitHub badge, copycode buttons, Docker API demo
- “Ask AI” helper button *(workinprogress, shipping soon)*
- New examples: geolocation usage, network/console capture, Docker API, markdown source selection, crypto analysis
- Expanded automated test suites for browser, Docker, MCP and memory benchmarks
### Changed
- Consolidated and renamed browser strategies; legacy docker strategy modules removed
- `ProxyConfig` moved to `async_configs`
- Server migrated to poolbased crawler management
- FastAPI validators replace custom query validation
- Docker build now uses Chromium base image
- Largescale repo tidyup (≈36 k insertions, ≈5 k deletions)
### Fixed
- Async crawler session leak, duplicatevisit handling, URL normalisation
- Targetelement regressions in scraping strategies
- LoggedURL readability, encodedURL decoding, middle truncation for long URLs
- Closed issues: #701, #733, #756, #774, #804, #822, #839, #841, #842, #843, #867, #902, #911
### Removed
- Obsolete modules under `crawl4ai/browser/*` superseded by the new pooled browser layer
### Deprecated
- Old markdown generator names now alias `DefaultMarkdownGenerator` and emit warnings
---
#### Upgrade notes
1. Update any direct imports from `crawl4ai/browser/*` to the new pooled browser modules
2. If you override `AsyncPlaywrightCrawlerStrategy.get_page`, adopt the new signature
3. Rebuild Docker images to pull the new Chromium layer
4. Switch to `DefaultMarkdownGenerator` (or silence the deprecation warning)
---
`121 files changed, ≈36 223 insertions, ≈4 975 deletions` :contentReference[oaicite:0]{index=0}&#8203;:contentReference[oaicite:1]{index=1}
### [Feature] 2025-04-21
- Implemented MCP protocol for machine-to-machine communication
- Added WebSocket and SSE transport for MCP server
- Exposed server endpoints via MCP protocol
- Created tests for MCP socket and SSE communication
- Enhanced Docker server with file handling and intelligent search
- Added PDF and screenshot endpoints with file saving capability
- Added JavaScript execution endpoint for page interaction
- Implemented advanced context search with BM25 and code chunking
- Added file path output support for generated assets
- Improved server endpoints and API surface
- Added intelligent context search with query filtering
- Added syntax-aware code function chunking
- Implemented efficient HTML processing pipeline
- Added support for controlling browser geolocation via new GeolocationConfig class
- Added locale and timezone configuration options to CrawlerRunConfig
- Added example script demonstrating geolocation and locale usage
- Added documentation for location-based identity features
### [Refactor] 2025-04-20
- Replaced crawler_manager.py with simpler crawler_pool.py implementation
- Added global page semaphore for hard concurrency cap
- Implemented browser pool with idle cleanup
- Added playground UI for testing and stress testing
- Updated API handlers to use pooled crawlers
- Enhanced logging levels and symbols
- Added memory tests and stress test utilities
### [Added] 2025-04-17
- Added content source selection feature for markdown generation
- New `content_source` parameter allows choosing between `cleaned_html`, `raw_html`, and `fit_html`
- Provides flexibility in how HTML content is processed before markdown conversion
- Added examples and documentation for the new feature
- Includes backward compatibility with default `cleaned_html` behavior
## Version 0.5.0.post5 (2025-03-14)
### Added

View File

@@ -1,4 +1,9 @@
FROM python:3.10-slim
FROM python:3.12-slim-bookworm AS build
# C4ai version
ARG C4AI_VER=0.7.0-r1
ENV C4AI_VERSION=$C4AI_VER
LABEL c4ai.version=$C4AI_VER
# Set build arguments
ARG APP_HOME=/app
@@ -17,14 +22,14 @@ ENV PYTHONFAULTHANDLER=1 \
REDIS_HOST=localhost \
REDIS_PORT=6379
ARG PYTHON_VERSION=3.10
ARG PYTHON_VERSION=3.12
ARG INSTALL_TYPE=default
ARG ENABLE_GPU=false
ARG TARGETARCH
LABEL maintainer="unclecode"
LABEL description="🔥🕷️ Crawl4AI: Open-source LLM Friendly Web Crawler & scraper"
LABEL version="1.0"
LABEL version="1.0"
RUN apt-get update && apt-get install -y --no-install-recommends \
build-essential \
@@ -38,6 +43,7 @@ RUN apt-get update && apt-get install -y --no-install-recommends \
libjpeg-dev \
redis-server \
supervisor \
&& apt-get clean \
&& rm -rf /var/lib/apt/lists/*
RUN apt-get update && apt-get install -y --no-install-recommends \
@@ -62,11 +68,16 @@ RUN apt-get update && apt-get install -y --no-install-recommends \
libcairo2 \
libasound2 \
libatspi2.0-0 \
&& apt-get clean \
&& rm -rf /var/lib/apt/lists/*
RUN apt-get update && apt-get dist-upgrade -y \
&& rm -rf /var/lib/apt/lists/*
RUN if [ "$ENABLE_GPU" = "true" ] && [ "$TARGETARCH" = "amd64" ] ; then \
apt-get update && apt-get install -y --no-install-recommends \
nvidia-cuda-toolkit \
&& apt-get clean \
&& rm -rf /var/lib/apt/lists/* ; \
else \
echo "Skipping NVIDIA CUDA Toolkit installation (unsupported platform or GPU disabled)"; \
@@ -76,16 +87,24 @@ RUN if [ "$TARGETARCH" = "arm64" ]; then \
echo "🦾 Installing ARM-specific optimizations"; \
apt-get update && apt-get install -y --no-install-recommends \
libopenblas-dev \
&& apt-get clean \
&& rm -rf /var/lib/apt/lists/*; \
elif [ "$TARGETARCH" = "amd64" ]; then \
echo "🖥️ Installing AMD64-specific optimizations"; \
apt-get update && apt-get install -y --no-install-recommends \
libomp-dev \
&& apt-get clean \
&& rm -rf /var/lib/apt/lists/*; \
else \
echo "Skipping platform-specific optimizations (unsupported platform)"; \
fi
# Create a non-root user and group
RUN groupadd -r appuser && useradd --no-log-init -r -g appuser appuser
# Create and set permissions for appuser home directory
RUN mkdir -p /home/appuser && chown -R appuser:appuser /home/appuser
WORKDIR ${APP_HOME}
RUN echo '#!/bin/bash\n\
@@ -103,6 +122,7 @@ fi' > /tmp/install.sh && chmod +x /tmp/install.sh
COPY . /tmp/project/
# Copy supervisor config first (might need root later, but okay for now)
COPY deploy/docker/supervisord.conf .
COPY deploy/docker/requirements.txt .
@@ -131,16 +151,34 @@ RUN if [ "$INSTALL_TYPE" = "all" ] ; then \
else \
pip install "/tmp/project" ; \
fi
RUN pip install --no-cache-dir --upgrade pip && \
/tmp/install.sh && \
python -c "import crawl4ai; print('✅ crawl4ai is ready to rock!')" && \
python -c "from playwright.sync_api import sync_playwright; print('✅ Playwright is feeling dramatic!')"
RUN playwright install --with-deps chromium
RUN crawl4ai-setup
RUN playwright install --with-deps
RUN mkdir -p /home/appuser/.cache/ms-playwright \
&& cp -r /root/.cache/ms-playwright/chromium-* /home/appuser/.cache/ms-playwright/ \
&& chown -R appuser:appuser /home/appuser/.cache/ms-playwright
RUN crawl4ai-doctor
# Copy application code
COPY deploy/docker/* ${APP_HOME}/
# copy the playground + any future static assets
COPY deploy/docker/static ${APP_HOME}/static
# Change ownership of the application directory to the non-root user
RUN chown -R appuser:appuser ${APP_HOME}
# give permissions to redis persistence dirs if used
RUN mkdir -p /var/lib/redis /var/log/redis && chown -R appuser:appuser /var/lib/redis /var/log/redis
HEALTHCHECK --interval=30s --timeout=10s --start-period=5s --retries=3 \
CMD bash -c '\
MEM=$(free -m | awk "/^Mem:/{print \$2}"); \
@@ -149,8 +187,14 @@ HEALTHCHECK --interval=30s --timeout=10s --start-period=5s --retries=3 \
exit 1; \
fi && \
redis-cli ping > /dev/null && \
curl -f http://localhost:8000/health || exit 1'
curl -f http://localhost:11235/health || exit 1'
EXPOSE 6379
CMD ["supervisord", "-c", "supervisord.conf"]
# Switch to the non-root user before starting the application
USER appuser
# Set environment variables to ptoduction
ENV PYTHON_ENV=production
# Start the application using supervisord
CMD ["supervisord", "-c", "supervisord.conf"]

339
JOURNAL.md Normal file
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@@ -0,0 +1,339 @@
# Development Journal
This journal tracks significant feature additions, bug fixes, and architectural decisions in the crawl4ai project. It serves as both documentation and a historical record of the project's evolution.
## [2025-04-17] Added Content Source Selection for Markdown Generation
**Feature:** Configurable content source for markdown generation
**Changes Made:**
1. Added `content_source: str = "cleaned_html"` parameter to `MarkdownGenerationStrategy` class
2. Updated `DefaultMarkdownGenerator` to accept and pass the content source parameter
3. Renamed the `cleaned_html` parameter to `input_html` in the `generate_markdown` method
4. Modified `AsyncWebCrawler.aprocess_html` to select the appropriate HTML source based on the generator's config
5. Added `preprocess_html_for_schema` import in `async_webcrawler.py`
**Implementation Details:**
- Added a new `content_source` parameter to specify which HTML input to use for markdown generation
- Options include: "cleaned_html" (default), "raw_html", and "fit_html"
- Used a dictionary dispatch pattern in `aprocess_html` to select the appropriate HTML source
- Added proper error handling with fallback to cleaned_html if content source selection fails
- Ensured backward compatibility by defaulting to "cleaned_html" option
**Files Modified:**
- `crawl4ai/markdown_generation_strategy.py`: Added content_source parameter and updated the method signature
- `crawl4ai/async_webcrawler.py`: Added HTML source selection logic and updated imports
**Examples:**
- Created `docs/examples/content_source_example.py` demonstrating how to use the new parameter
**Challenges:**
- Maintaining backward compatibility while reorganizing the parameter flow
- Ensuring proper error handling for all content source options
- Making the change with minimal code modifications
**Why This Feature:**
The content source selection feature allows users to choose which HTML content to use as input for markdown generation:
1. "cleaned_html" - Uses the post-processed HTML after scraping strategy (original behavior)
2. "raw_html" - Uses the original raw HTML directly from the web page
3. "fit_html" - Uses the preprocessed HTML optimized for schema extraction
This feature provides greater flexibility in how users generate markdown, enabling them to:
- Capture more detailed content from the original HTML when needed
- Use schema-optimized HTML when working with structured data
- Choose the approach that best suits their specific use case
## [2025-04-17] Implemented High Volume Stress Testing Solution for SDK
**Feature:** Comprehensive stress testing framework using `arun_many` and the dispatcher system to evaluate performance, concurrency handling, and identify potential issues under high-volume crawling scenarios.
**Changes Made:**
1. Created a dedicated stress testing framework in the `benchmarking/` (or similar) directory.
2. Implemented local test site generation (`SiteGenerator`) with configurable heavy HTML pages.
3. Added basic memory usage tracking (`SimpleMemoryTracker`) using platform-specific commands (avoiding `psutil` dependency for this specific test).
4. Utilized `CrawlerMonitor` from `crawl4ai` for rich terminal UI and real-time monitoring of test progress and dispatcher activity.
5. Implemented detailed result summary saving (JSON) and memory sample logging (CSV).
6. Developed `run_benchmark.py` to orchestrate tests with predefined configurations.
7. Created `run_all.sh` as a simple wrapper for `run_benchmark.py`.
**Implementation Details:**
- Generates a local test site with configurable pages containing heavy text and image content.
- Uses Python's built-in `http.server` for local serving, minimizing network variance.
- Leverages `crawl4ai`'s `arun_many` method for processing URLs.
- Utilizes `MemoryAdaptiveDispatcher` to manage concurrency via the `max_sessions` parameter (note: memory adaptation features require `psutil`, not used by `SimpleMemoryTracker`).
- Tracks memory usage via `SimpleMemoryTracker`, recording samples throughout test execution to a CSV file.
- Uses `CrawlerMonitor` (which uses the `rich` library) for clear terminal visualization and progress reporting directly from the dispatcher.
- Stores detailed final metrics in a JSON summary file.
**Files Created/Updated:**
- `stress_test_sdk.py`: Main stress testing implementation using `arun_many`.
- `benchmark_report.py`: (Assumed) Report generator for comparing test results.
- `run_benchmark.py`: Test runner script with predefined configurations.
- `run_all.sh`: Simple bash script wrapper for `run_benchmark.py`.
- `USAGE.md`: Comprehensive documentation on usage and interpretation (updated).
**Testing Approach:**
- Creates a controlled, reproducible test environment with a local HTTP server.
- Processes URLs using `arun_many`, allowing the dispatcher to manage concurrency up to `max_sessions`.
- Optionally logs per-batch summaries (when not in streaming mode) after processing chunks.
- Supports different test sizes via `run_benchmark.py` configurations.
- Records memory samples via platform commands for basic trend analysis.
- Includes cleanup functionality for the test environment.
**Challenges:**
- Ensuring proper cleanup of HTTP server processes.
- Getting reliable memory tracking across platforms without adding heavy dependencies (`psutil`) to this specific test script.
- Designing `run_benchmark.py` to correctly pass arguments to `stress_test_sdk.py`.
**Why This Feature:**
The high volume stress testing solution addresses critical needs for ensuring Crawl4AI's `arun_many` reliability:
1. Provides a reproducible way to evaluate performance under concurrent load.
2. Allows testing the dispatcher's concurrency control (`max_session_permit`) and queue management.
3. Enables performance tuning by observing throughput (`URLs/sec`) under different `max_sessions` settings.
4. Creates a controlled environment for testing `arun_many` behavior.
5. Supports continuous integration by providing deterministic test conditions for `arun_many`.
**Design Decisions:**
- Chose local site generation for reproducibility and isolation from network issues.
- Utilized the built-in `CrawlerMonitor` for real-time feedback, leveraging its `rich` integration.
- Implemented optional per-batch logging in `stress_test_sdk.py` (when not streaming) to provide chunk-level summaries alongside the continuous monitor.
- Adopted `arun_many` with a `MemoryAdaptiveDispatcher` as the core mechanism for parallel execution, reflecting the intended SDK usage.
- Created `run_benchmark.py` to simplify running standard test configurations.
- Used `SimpleMemoryTracker` to provide basic memory insights without requiring `psutil` for this particular test runner.
**Future Enhancements to Consider:**
- Create a separate test variant that *does* use `psutil` to specifically stress the memory-adaptive features of the dispatcher.
- Add support for generated JavaScript content.
- Add support for Docker-based testing with explicit memory limits.
- Enhance `benchmark_report.py` to provide more sophisticated analysis of performance and memory trends from the generated JSON/CSV files.
---
## [2025-04-17] Refined Stress Testing System Parameters and Execution
**Changes Made:**
1. Corrected `run_benchmark.py` and `stress_test_sdk.py` to use `--max-sessions` instead of the incorrect `--workers` parameter, accurately reflecting dispatcher configuration.
2. Updated `run_benchmark.py` argument handling to correctly pass all relevant custom parameters (including `--stream`, `--monitor-mode`, etc.) to `stress_test_sdk.py`.
3. (Assuming changes in `benchmark_report.py`) Applied dark theme to benchmark reports for better readability.
4. (Assuming changes in `benchmark_report.py`) Improved visualization code to eliminate matplotlib warnings.
5. Updated `run_benchmark.py` to provide clickable `file://` links to generated reports in the terminal output.
6. Updated `USAGE.md` with comprehensive parameter descriptions reflecting the final script arguments.
7. Updated `run_all.sh` wrapper to correctly invoke `run_benchmark.py` with flexible arguments.
**Details of Changes:**
1. **Parameter Correction (`--max-sessions`)**:
* Identified the fundamental misunderstanding where `--workers` was used incorrectly.
* Refactored `stress_test_sdk.py` to accept `--max-sessions` and configure the `MemoryAdaptiveDispatcher`'s `max_session_permit` accordingly.
* Updated `run_benchmark.py` argument parsing and command construction to use `--max-sessions`.
* Updated `TEST_CONFIGS` in `run_benchmark.py` to use `max_sessions`.
2. **Argument Handling (`run_benchmark.py`)**:
* Improved logic to collect all command-line arguments provided to `run_benchmark.py`.
* Ensured all relevant arguments (like `--stream`, `--monitor-mode`, `--port`, `--use-rate-limiter`, etc.) are correctly forwarded when calling `stress_test_sdk.py` as a subprocess.
3. **Dark Theme & Visualization Fixes (Assumed in `benchmark_report.py`)**:
* (Describes changes assumed to be made in the separate reporting script).
4. **Clickable Links (`run_benchmark.py`)**:
* Added logic to find the latest HTML report and PNG chart in the `benchmark_reports` directory after `benchmark_report.py` runs.
* Used `pathlib` to generate correct `file://` URLs for terminal output.
5. **Documentation Improvements (`USAGE.md`)**:
* Rewrote sections to explain `arun_many`, dispatchers, and `--max-sessions`.
* Updated parameter tables for all scripts (`stress_test_sdk.py`, `run_benchmark.py`).
* Clarified the difference between batch and streaming modes and their effect on logging.
* Updated examples to use correct arguments.
**Files Modified:**
- `stress_test_sdk.py`: Changed `--workers` to `--max-sessions`, added new arguments, used `arun_many`.
- `run_benchmark.py`: Changed argument handling, updated configs, calls `stress_test_sdk.py`.
- `run_all.sh`: Updated to call `run_benchmark.py` correctly.
- `USAGE.md`: Updated documentation extensively.
- `benchmark_report.py`: (Assumed modifications for dark theme and viz fixes).
**Testing:**
- Verified that `--max-sessions` correctly limits concurrency via the `CrawlerMonitor` output.
- Confirmed that custom arguments passed to `run_benchmark.py` are forwarded to `stress_test_sdk.py`.
- Validated clickable links work in supporting terminals.
- Ensured documentation matches the final script parameters and behavior.
**Why These Changes:**
These refinements correct the fundamental approach of the stress test to align with `crawl4ai`'s actual architecture and intended usage:
1. Ensures the test evaluates the correct components (`arun_many`, `MemoryAdaptiveDispatcher`).
2. Makes test configurations more accurate and flexible.
3. Improves the usability of the testing framework through better argument handling and documentation.
**Future Enhancements to Consider:**
- Add support for generated JavaScript content to test JS rendering performance
- Implement more sophisticated memory analysis like generational garbage collection tracking
- Add support for Docker-based testing with memory limits to force OOM conditions
- Create visualization tools for analyzing memory usage patterns across test runs
- Add benchmark comparisons between different crawler versions or configurations
## [2025-04-17] Fixed Issues in Stress Testing System
**Changes Made:**
1. Fixed custom parameter handling in run_benchmark.py
2. Applied dark theme to benchmark reports for better readability
3. Improved visualization code to eliminate matplotlib warnings
4. Added clickable links to generated reports in terminal output
5. Enhanced documentation with comprehensive parameter descriptions
**Details of Changes:**
1. **Custom Parameter Handling Fix**
- Identified bug where custom URL count was being ignored in run_benchmark.py
- Rewrote argument handling to use a custom args dictionary
- Properly passed parameters to the test_simple_stress.py command
- Added better UI indication of custom parameters in use
2. **Dark Theme Implementation**
- Added complete dark theme to HTML benchmark reports
- Applied dark styling to all visualization components
- Used Nord-inspired color palette for charts and graphs
- Improved contrast and readability for data visualization
- Updated text colors and backgrounds for better eye comfort
3. **Matplotlib Warning Fixes**
- Resolved warnings related to improper use of set_xticklabels()
- Implemented correct x-axis positioning for bar charts
- Ensured proper alignment of bar labels and data points
- Updated plotting code to use modern matplotlib practices
4. **Documentation Improvements**
- Created comprehensive USAGE.md with detailed instructions
- Added parameter documentation for all scripts
- Included examples for all common use cases
- Provided detailed explanations for interpreting results
- Added troubleshooting guide for common issues
**Files Modified:**
- `tests/memory/run_benchmark.py`: Fixed custom parameter handling
- `tests/memory/benchmark_report.py`: Added dark theme and fixed visualization warnings
- `tests/memory/run_all.sh`: Added clickable links to reports
- `tests/memory/USAGE.md`: Created comprehensive documentation
**Testing:**
- Verified that custom URL counts are now correctly used
- Confirmed dark theme is properly applied to all report elements
- Checked that matplotlib warnings are no longer appearing
- Validated clickable links to reports work in terminals that support them
**Why These Changes:**
These improvements address several usability issues with the stress testing system:
1. Better parameter handling ensures test configurations work as expected
2. Dark theme reduces eye strain during extended test review sessions
3. Fixing visualization warnings improves code quality and output clarity
4. Enhanced documentation makes the system more accessible for future use
**Future Enhancements:**
- Add additional visualization options for different types of analysis
- Implement theme toggle to support both light and dark preferences
- Add export options for embedding reports in other documentation
- Create dedicated CI/CD integration templates for automated testing
## [2025-04-09] Added MHTML Capture Feature
**Feature:** MHTML snapshot capture of crawled pages
**Changes Made:**
1. Added `capture_mhtml: bool = False` parameter to `CrawlerRunConfig` class
2. Added `mhtml: Optional[str] = None` field to `CrawlResult` model
3. Added `mhtml_data: Optional[str] = None` field to `AsyncCrawlResponse` class
4. Implemented `capture_mhtml()` method in `AsyncPlaywrightCrawlerStrategy` class to capture MHTML via CDP
5. Modified the crawler to capture MHTML when enabled and pass it to the result
**Implementation Details:**
- MHTML capture uses Chrome DevTools Protocol (CDP) via Playwright's CDP session API
- The implementation waits for page to fully load before capturing MHTML content
- Enhanced waiting for JavaScript content with requestAnimationFrame for better JS content capture
- We ensure all browser resources are properly cleaned up after capture
**Files Modified:**
- `crawl4ai/models.py`: Added the mhtml field to CrawlResult
- `crawl4ai/async_configs.py`: Added capture_mhtml parameter to CrawlerRunConfig
- `crawl4ai/async_crawler_strategy.py`: Implemented MHTML capture logic
- `crawl4ai/async_webcrawler.py`: Added mapping from AsyncCrawlResponse.mhtml_data to CrawlResult.mhtml
**Testing:**
- Created comprehensive tests in `tests/20241401/test_mhtml.py` covering:
- Capturing MHTML when enabled
- Ensuring mhtml is None when disabled explicitly
- Ensuring mhtml is None by default
- Capturing MHTML on JavaScript-enabled pages
**Challenges:**
- Had to improve page loading detection to ensure JavaScript content was fully rendered
- Tests needed to be run independently due to Playwright browser instance management
- Modified test expected content to match actual MHTML output
**Why This Feature:**
The MHTML capture feature allows users to capture complete web pages including all resources (CSS, images, etc.) in a single file. This is valuable for:
1. Offline viewing of captured pages
2. Creating permanent snapshots of web content for archival
3. Ensuring consistent content for later analysis, even if the original site changes
**Future Enhancements to Consider:**
- Add option to save MHTML to file
- Support for filtering what resources get included in MHTML
- Add support for specifying MHTML capture options
## [2025-04-10] Added Network Request and Console Message Capturing
**Feature:** Comprehensive capturing of network requests/responses and browser console messages during crawling
**Changes Made:**
1. Added `capture_network_requests: bool = False` and `capture_console_messages: bool = False` parameters to `CrawlerRunConfig` class
2. Added `network_requests: Optional[List[Dict[str, Any]]] = None` and `console_messages: Optional[List[Dict[str, Any]]] = None` fields to both `AsyncCrawlResponse` and `CrawlResult` models
3. Implemented event listeners in `AsyncPlaywrightCrawlerStrategy._crawl_web()` to capture browser network events and console messages
4. Added proper event listener cleanup in the finally block to prevent resource leaks
5. Modified the crawler flow to pass captured data from AsyncCrawlResponse to CrawlResult
**Implementation Details:**
- Network capture uses Playwright event listeners (`request`, `response`, and `requestfailed`) to record all network activity
- Console capture uses Playwright event listeners (`console` and `pageerror`) to record console messages and errors
- Each network event includes metadata like URL, headers, status, and timing information
- Each console message includes type, text content, and source location when available
- All captured events include timestamps for chronological analysis
- Error handling ensures even failed capture attempts won't crash the main crawling process
**Files Modified:**
- `crawl4ai/models.py`: Added new fields to AsyncCrawlResponse and CrawlResult
- `crawl4ai/async_configs.py`: Added new configuration parameters to CrawlerRunConfig
- `crawl4ai/async_crawler_strategy.py`: Implemented capture logic using event listeners
- `crawl4ai/async_webcrawler.py`: Added data transfer from AsyncCrawlResponse to CrawlResult
**Documentation:**
- Created detailed documentation in `docs/md_v2/advanced/network-console-capture.md`
- Added feature to site navigation in `mkdocs.yml`
- Updated CrawlResult documentation in `docs/md_v2/api/crawl-result.md`
- Created comprehensive example in `docs/examples/network_console_capture_example.py`
**Testing:**
- Created `tests/general/test_network_console_capture.py` with tests for:
- Verifying capture is disabled by default
- Testing network request capturing
- Testing console message capturing
- Ensuring both capture types can be enabled simultaneously
- Checking correct content is captured in expected formats
**Challenges:**
- Initial implementation had synchronous/asynchronous mismatches in event handlers
- Needed to fix type of property access vs. method calls in handlers
- Required careful cleanup of event listeners to prevent memory leaks
**Why This Feature:**
The network and console capture feature provides deep visibility into web page activity, enabling:
1. Debugging complex web applications by seeing all network requests and errors
2. Security analysis to detect unexpected third-party requests and data flows
3. Performance profiling to identify slow-loading resources
4. API discovery in single-page applications
5. Comprehensive analysis of web application behavior
**Future Enhancements to Consider:**
- Option to filter captured events by type, domain, or content
- Support for capturing response bodies (with size limits)
- Aggregate statistics calculation for performance metrics
- Integration with visualization tools for network waterfall analysis
- Exporting captures in HAR format for use with external tools

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# Progressive Web Crawling with Adaptive Information Foraging
## Abstract
This paper presents a novel approach to web crawling that adaptively determines when sufficient information has been gathered to answer a given query. Unlike traditional exhaustive crawling methods, our Progressive Information Sufficiency (PIS) framework uses statistical measures to balance information completeness against crawling efficiency. We introduce a multi-strategy architecture supporting pure statistical, embedding-enhanced, and LLM-assisted approaches, with theoretical guarantees on convergence and practical evaluation methods using synthetic datasets.
## 1. Introduction
Traditional web crawling approaches follow predetermined patterns (breadth-first, depth-first) without consideration for information sufficiency. This work addresses the fundamental question: *"When do we have enough information to answer a query and similar queries in its domain?"*
We formalize this as an optimal stopping problem in information foraging, introducing metrics for coverage, consistency, and saturation that enable crawlers to make intelligent decisions about when to stop crawling and which links to follow.
## 2. Problem Formulation
### 2.1 Definitions
Let:
- **K** = {d₁, d₂, ..., dₙ} be the current knowledge base (crawled documents)
- **Q** be the user query
- **L** = {l₁, l₂, ..., lₘ} be available links with preview metadata
- **θ** be the confidence threshold for information sufficiency
### 2.2 Objectives
1. **Minimize** |K| (number of crawled pages)
2. **Maximize** P(answers(Q) | K) (probability of answering Q given K)
3. **Ensure** coverage of Q's domain (similar queries)
## 3. Mathematical Framework
### 3.1 Information Sufficiency Metric
We define Information Sufficiency as:
```
IS(K, Q) = min(Coverage(K, Q), Consistency(K, Q), 1 - Redundancy(K)) × DomainCoverage(K, Q)
```
### 3.2 Coverage Score
Coverage measures how well current knowledge covers query terms and related concepts:
```
Coverage(K, Q) = Σ(t ∈ Q) log(df(t, K) + 1) × idf(t) / |Q|
```
Where:
- df(t, K) = document frequency of term t in knowledge base K
- idf(t) = inverse document frequency weight
### 3.3 Consistency Score
Consistency measures information coherence across documents:
```
Consistency(K, Q) = 1 - Var(answers from random subsets of K)
```
This captures the principle that sufficient knowledge should provide stable answers regardless of document subset.
### 3.4 Saturation Score
Saturation detects diminishing returns:
```
Saturation(K) = 1 - (ΔInfo(Kₙ) / ΔInfo(K₁))
```
Where ΔInfo represents marginal information gain from the nth crawl.
### 3.5 Link Value Prediction
Expected information gain from uncrawled links:
```
ExpectedGain(l) = Relevance(l, Q) × Novelty(l, K) × Authority(l)
```
Components:
- **Relevance**: BM25(preview_text, Q)
- **Novelty**: 1 - max_similarity(preview, K)
- **Authority**: f(url_structure, domain_metrics)
## 4. Algorithmic Approach
### 4.1 Progressive Crawling Algorithm
```
Algorithm: ProgressiveCrawl(start_url, query, θ)
K ← ∅
crawled ← {start_url}
pending ← extract_links(crawl(start_url))
while IS(K, Q) < θ and |crawled| < max_pages:
candidates ← rank_by_expected_gain(pending, Q, K)
if max(ExpectedGain(candidates)) < min_gain:
break // Diminishing returns
to_crawl ← top_k(candidates)
new_docs ← parallel_crawl(to_crawl)
K ← K new_docs
crawled ← crawled to_crawl
pending ← extract_new_links(new_docs) - crawled
return K
```
### 4.2 Stopping Criteria
Crawling terminates when:
1. IS(K, Q) ≥ θ (sufficient information)
2. d(IS)/d(crawls) < ε (plateau reached)
3. |crawled| ≥ max_pages (resource limit)
4. max(ExpectedGain) < min_gain (no promising links)
## 5. Multi-Strategy Architecture
### 5.1 Strategy Pattern Design
```
AbstractStrategy
├── StatisticalStrategy (no LLM, no embeddings)
├── EmbeddingStrategy (with semantic similarity)
└── LLMStrategy (with language model assistance)
```
### 5.2 Statistical Strategy
Pure statistical approach using:
- BM25 for relevance scoring
- Term frequency analysis for coverage
- Graph structure for authority
- No external models required
**Advantages**: Fast, no API costs, works offline
**Best for**: Technical documentation, specific terminology
### 5.3 Embedding Strategy (Implemented)
Semantic understanding through embeddings:
- Query expansion into semantic variations
- Coverage mapping in embedding space
- Gap-driven link selection
- Validation-based stopping criteria
**Mathematical Framework**:
```
Coverage(K, Q) = mean(max_similarity(q, K) for q in Q_expanded)
Gap(q) = 1 - max_similarity(q, K)
LinkScore(l) = Σ(Gap(q) × relevance(l, q)) × (1 - redundancy(l, K))
```
**Key Parameters**:
- `embedding_k_exp`: Exponential decay factor for distance-to-score mapping
- `embedding_coverage_radius`: Distance threshold for query coverage
- `embedding_min_confidence_threshold`: Minimum relevance threshold
**Advantages**: Semantic understanding, handles ambiguity, detects irrelevance
**Best for**: Research queries, conceptual topics, diverse content
### 5.4 Progressive Enhancement Path
1. **Level 0**: Statistical only (implemented)
2. **Level 1**: + Embeddings for semantic similarity (implemented)
3. **Level 2**: + LLM for query understanding (future)
## 6. Evaluation Methodology
### 6.1 Synthetic Dataset Generation
Using LLM to create evaluation data:
```python
def generate_synthetic_dataset(domain_url):
# 1. Fully crawl domain
full_knowledge = exhaustive_crawl(domain_url)
# 2. Generate answerable queries
queries = llm_generate_queries(full_knowledge)
# 3. Create query variations
for q in queries:
variations = generate_variations(q) # synonyms, sub/super queries
return queries, variations, full_knowledge
```
### 6.2 Evaluation Metrics
1. **Efficiency**: Information gained / Pages crawled
2. **Completeness**: Answerable queries / Total queries
3. **Redundancy**: 1 - (Unique information / Total information)
4. **Convergence Rate**: Pages to 95% completeness
### 6.3 Ablation Studies
- Impact of each score component (coverage, consistency, saturation)
- Sensitivity to threshold parameters
- Performance across different domain types
## 7. Theoretical Properties
### 7.1 Convergence Guarantee
**Theorem**: For finite websites, ProgressiveCrawl converges to IS(K, Q) ≥ θ or exhausts all reachable pages.
**Proof sketch**: IS(K, Q) is monotonically non-decreasing with each crawl, bounded above by 1.
### 7.2 Optimality
Under certain assumptions about link preview accuracy:
- Expected crawls ≤ 2 × optimal_crawls
- Approximation ratio improves with preview quality
## 8. Implementation Design
### 8.1 Core Components
1. **CrawlState**: Maintains crawl history and metrics
2. **AdaptiveConfig**: Configuration parameters
3. **CrawlStrategy**: Pluggable strategy interface
4. **AdaptiveCrawler**: Main orchestrator
### 8.2 Integration with Crawl4AI
- Wraps existing AsyncWebCrawler
- Leverages link preview functionality
- Maintains backward compatibility
### 8.3 Persistence
Knowledge base serialization for:
- Resumable crawls
- Knowledge sharing
- Offline analysis
## 9. Future Directions
### 9.1 Advanced Scoring
- Temporal information value
- Multi-query optimization
- Active learning from user feedback
### 9.2 Distributed Crawling
- Collaborative knowledge building
- Federated information sufficiency
### 9.3 Domain Adaptation
- Transfer learning across domains
- Meta-learning for threshold selection
## 10. Conclusion
Progressive crawling with adaptive information foraging provides a principled approach to efficient web information extraction. By combining coverage, consistency, and saturation metrics, we can determine information sufficiency without ground truth labels. The multi-strategy architecture allows graceful enhancement from pure statistical to LLM-assisted approaches based on requirements and resources.
## References
1. Manning, C. D., Raghavan, P., & Schütze, H. (2008). Introduction to Information Retrieval. Cambridge University Press.
2. Robertson, S., & Zaragoza, H. (2009). The Probabilistic Relevance Framework: BM25 and Beyond. Foundations and Trends in Information Retrieval.
3. Pirolli, P., & Card, S. (1999). Information Foraging. Psychological Review, 106(4), 643-675.
4. Dasgupta, S. (2005). Analysis of a greedy active learning strategy. Advances in Neural Information Processing Systems.
## Appendix A: Implementation Pseudocode
```python
class StatisticalStrategy:
def calculate_confidence(self, state):
coverage = self.calculate_coverage(state)
consistency = self.calculate_consistency(state)
saturation = self.calculate_saturation(state)
return min(coverage, consistency, saturation)
def calculate_coverage(self, state):
# BM25-based term coverage
term_scores = []
for term in state.query.split():
df = state.document_frequencies.get(term, 0)
idf = self.idf_cache.get(term, 1.0)
term_scores.append(log(df + 1) * idf)
return mean(term_scores) / max_possible_score
def rank_links(self, state):
scored_links = []
for link in state.pending_links:
relevance = self.bm25_score(link.preview_text, state.query)
novelty = self.calculate_novelty(link, state.knowledge_base)
authority = self.url_authority(link.href)
score = relevance * novelty * authority
scored_links.append((link, score))
return sorted(scored_links, key=lambda x: x[1], reverse=True)
```
## Appendix B: Evaluation Protocol
1. **Dataset Creation**:
- Select diverse domains (documentation, blogs, e-commerce)
- Generate 100 queries per domain using LLM
- Create query variations (5-10 per query)
2. **Baseline Comparisons**:
- BFS crawler (depth-limited)
- DFS crawler (depth-limited)
- Random crawler
- Oracle (knows relevant pages)
3. **Metrics Collection**:
- Pages crawled vs query answerability
- Time to sufficient confidence
- False positive/negative rates
4. **Statistical Analysis**:
- ANOVA for strategy comparison
- Regression for parameter sensitivity
- Bootstrap for confidence intervals

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# 🚀🤖 Crawl4AI: Open-source LLM Friendly Web Crawler & Scraper.
<div align="center">
<a href="https://trendshift.io/repositories/11716" target="_blank"><img src="https://trendshift.io/api/badge/repositories/11716" alt="unclecode%2Fcrawl4ai | Trendshift" style="width: 250px; height: 55px;" width="250" height="55"/></a>
[![GitHub Stars](https://img.shields.io/github/stars/unclecode/crawl4ai?style=social)](https://github.com/unclecode/crawl4ai/stargazers)
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[![Python Version](https://img.shields.io/pypi/pyversions/crawl4ai)](https://pypi.org/project/crawl4ai/)
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[![GitHub Sponsors](https://img.shields.io/github/sponsors/unclecode?style=flat&logo=GitHub-Sponsors&label=Sponsors&color=pink)](https://github.com/sponsors/unclecode)
<p align="center">
<a href="https://x.com/crawl4ai">
<img src="https://img.shields.io/badge/Follow%20on%20X-000000?style=for-the-badge&logo=x&logoColor=white" alt="Follow on X" />
</a>
<a href="https://www.linkedin.com/company/crawl4ai">
<img src="https://img.shields.io/badge/Follow%20on%20LinkedIn-0077B5?style=for-the-badge&logo=linkedin&logoColor=white" alt="Follow on LinkedIn" />
</a>
<a href="https://discord.gg/jP8KfhDhyN">
<img src="https://img.shields.io/badge/Join%20our%20Discord-5865F2?style=for-the-badge&logo=discord&logoColor=white" alt="Join our Discord" />
</a>
</p>
</div>
Crawl4AI is the #1 trending GitHub repository, actively maintained by a vibrant community. It delivers blazing-fast, AI-ready web crawling tailored for LLMs, AI agents, and data pipelines. Open source, flexible, and built for real-time performance, Crawl4AI empowers developers with unmatched speed, precision, and deployment ease.
[✨ Check out latest update v0.7.0](#-recent-updates)
🎉 **Version 0.7.0 is now available!** The Adaptive Intelligence Update introduces groundbreaking features: Adaptive Crawling that learns website patterns, Virtual Scroll support for infinite pages, intelligent Link Preview with 3-layer scoring, Async URL Seeder for massive discovery, and significant performance improvements. [Read the release notes →](https://github.com/unclecode/crawl4ai/blob/main/docs/blog/release-v0.7.0.md)
<details>
<summary>🤓 <strong>My Personal Story</strong></summary>
My journey with computers started in childhood when my dad, a computer scientist, introduced me to an Amstrad computer. Those early days sparked a fascination with technology, leading me to pursue computer science and specialize in NLP during my postgraduate studies. It was during this time that I first delved into web crawling, building tools to help researchers organize papers and extract information from publications a challenging yet rewarding experience that honed my skills in data extraction.
Fast forward to 2023, I was working on a tool for a project and needed a crawler to convert a webpage into markdown. While exploring solutions, I found one that claimed to be open-source but required creating an account and generating an API token. Worse, it turned out to be a SaaS model charging $16, and its quality didnt meet my standards. Frustrated, I realized this was a deeper problem. That frustration turned into turbo anger mode, and I decided to build my own solution. In just a few days, I created Crawl4AI. To my surprise, it went viral, earning thousands of GitHub stars and resonating with a global community.
I made Crawl4AI open-source for two reasons. First, its my way of giving back to the open-source community that has supported me throughout my career. Second, I believe data should be accessible to everyone, not locked behind paywalls or monopolized by a few. Open access to data lays the foundation for the democratization of AI, a vision where individuals can train their own models and take ownership of their information. This library is the first step in a larger journey to create the best open-source data extraction and generation tool the world has ever seen, built collaboratively by a passionate community.
Thank you to everyone who has supported this project, used it, and shared feedback. Your encouragement motivates me to dream even bigger. Join us, file issues, submit PRs, or spread the word. Together, we can build a tool that truly empowers people to access their own data and reshape the future of AI.
</details>
## 🧐 Why Crawl4AI?
1. **Built for LLMs**: Creates smart, concise Markdown optimized for RAG and fine-tuning applications.
2. **Lightning Fast**: Delivers results faster with real-time, cost-efficient performance.
3. **Flexible Browser Control**: Offers session management, proxies, and custom hooks for seamless data access.
4. **Heuristic Intelligence**: Uses advanced algorithms for efficient extraction, reducing reliance on costly models.
5. **Open Source & Deployable**: Fully open-source with no API keys—ready for Docker and cloud integration.
6. **Thriving Community**: Actively maintained by a vibrant community and the #1 trending GitHub repository.
## 🚀 Quick Start
1. Install Crawl4AI:
```bash
# Install the package
pip install -U crawl4ai
# For pre release versions
pip install crawl4ai --pre
# Run post-installation setup
crawl4ai-setup
# Verify your installation
crawl4ai-doctor
```
If you encounter any browser-related issues, you can install them manually:
```bash
python -m playwright install --with-deps chromium
```
2. Run a simple web crawl with Python:
```python
import asyncio
from crawl4ai import *
async def main():
async with AsyncWebCrawler() as crawler:
result = await crawler.arun(
url="https://www.nbcnews.com/business",
)
print(result.markdown)
if __name__ == "__main__":
asyncio.run(main())
```
3. Or use the new command-line interface:
```bash
# Basic crawl with markdown output
crwl https://www.nbcnews.com/business -o markdown
# Deep crawl with BFS strategy, max 10 pages
crwl https://docs.crawl4ai.com --deep-crawl bfs --max-pages 10
# Use LLM extraction with a specific question
crwl https://www.example.com/products -q "Extract all product prices"
```
## ✨ Features
<details>
<summary>📝 <strong>Markdown Generation</strong></summary>
- 🧹 **Clean Markdown**: Generates clean, structured Markdown with accurate formatting.
- 🎯 **Fit Markdown**: Heuristic-based filtering to remove noise and irrelevant parts for AI-friendly processing.
- 🔗 **Citations and References**: Converts page links into a numbered reference list with clean citations.
- 🛠️ **Custom Strategies**: Users can create their own Markdown generation strategies tailored to specific needs.
- 📚 **BM25 Algorithm**: Employs BM25-based filtering for extracting core information and removing irrelevant content.
</details>
<details>
<summary>📊 <strong>Structured Data Extraction</strong></summary>
- 🤖 **LLM-Driven Extraction**: Supports all LLMs (open-source and proprietary) for structured data extraction.
- 🧱 **Chunking Strategies**: Implements chunking (topic-based, regex, sentence-level) for targeted content processing.
- 🌌 **Cosine Similarity**: Find relevant content chunks based on user queries for semantic extraction.
- 🔎 **CSS-Based Extraction**: Fast schema-based data extraction using XPath and CSS selectors.
- 🔧 **Schema Definition**: Define custom schemas for extracting structured JSON from repetitive patterns.
</details>
<details>
<summary>🌐 <strong>Browser Integration</strong></summary>
- 🖥️ **Managed Browser**: Use user-owned browsers with full control, avoiding bot detection.
- 🔄 **Remote Browser Control**: Connect to Chrome Developer Tools Protocol for remote, large-scale data extraction.
- 👤 **Browser Profiler**: Create and manage persistent profiles with saved authentication states, cookies, and settings.
- 🔒 **Session Management**: Preserve browser states and reuse them for multi-step crawling.
- 🧩 **Proxy Support**: Seamlessly connect to proxies with authentication for secure access.
- ⚙️ **Full Browser Control**: Modify headers, cookies, user agents, and more for tailored crawling setups.
- 🌍 **Multi-Browser Support**: Compatible with Chromium, Firefox, and WebKit.
- 📐 **Dynamic Viewport Adjustment**: Automatically adjusts the browser viewport to match page content, ensuring complete rendering and capturing of all elements.
</details>
<details>
<summary>🔎 <strong>Crawling & Scraping</strong></summary>
- 🖼️ **Media Support**: Extract images, audio, videos, and responsive image formats like `srcset` and `picture`.
- 🚀 **Dynamic Crawling**: Execute JS and wait for async or sync for dynamic content extraction.
- 📸 **Screenshots**: Capture page screenshots during crawling for debugging or analysis.
- 📂 **Raw Data Crawling**: Directly process raw HTML (`raw:`) or local files (`file://`).
- 🔗 **Comprehensive Link Extraction**: Extracts internal, external links, and embedded iframe content.
- 🛠️ **Customizable Hooks**: Define hooks at every step to customize crawling behavior.
- 💾 **Caching**: Cache data for improved speed and to avoid redundant fetches.
- 📄 **Metadata Extraction**: Retrieve structured metadata from web pages.
- 📡 **IFrame Content Extraction**: Seamless extraction from embedded iframe content.
- 🕵️ **Lazy Load Handling**: Waits for images to fully load, ensuring no content is missed due to lazy loading.
- 🔄 **Full-Page Scanning**: Simulates scrolling to load and capture all dynamic content, perfect for infinite scroll pages.
</details>
<details>
<summary>🚀 <strong>Deployment</strong></summary>
- 🐳 **Dockerized Setup**: Optimized Docker image with FastAPI server for easy deployment.
- 🔑 **Secure Authentication**: Built-in JWT token authentication for API security.
- 🔄 **API Gateway**: One-click deployment with secure token authentication for API-based workflows.
- 🌐 **Scalable Architecture**: Designed for mass-scale production and optimized server performance.
- ☁️ **Cloud Deployment**: Ready-to-deploy configurations for major cloud platforms.
</details>
<details>
<summary>🎯 <strong>Additional Features</strong></summary>
- 🕶️ **Stealth Mode**: Avoid bot detection by mimicking real users.
- 🏷️ **Tag-Based Content Extraction**: Refine crawling based on custom tags, headers, or metadata.
- 🔗 **Link Analysis**: Extract and analyze all links for detailed data exploration.
- 🛡️ **Error Handling**: Robust error management for seamless execution.
- 🔐 **CORS & Static Serving**: Supports filesystem-based caching and cross-origin requests.
- 📖 **Clear Documentation**: Simplified and updated guides for onboarding and advanced usage.
- 🙌 **Community Recognition**: Acknowledges contributors and pull requests for transparency.
</details>
## Try it Now!
✨ Play around with this [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/drive/1SgRPrByQLzjRfwoRNq1wSGE9nYY_EE8C?usp=sharing)
✨ Visit our [Documentation Website](https://docs.crawl4ai.com/)
## Installation 🛠️
Crawl4AI offers flexible installation options to suit various use cases. You can install it as a Python package or use Docker.
<details>
<summary>🐍 <strong>Using pip</strong></summary>
Choose the installation option that best fits your needs:
### Basic Installation
For basic web crawling and scraping tasks:
```bash
pip install crawl4ai
crawl4ai-setup # Setup the browser
```
By default, this will install the asynchronous version of Crawl4AI, using Playwright for web crawling.
👉 **Note**: When you install Crawl4AI, the `crawl4ai-setup` should automatically install and set up Playwright. However, if you encounter any Playwright-related errors, you can manually install it using one of these methods:
1. Through the command line:
```bash
playwright install
```
2. If the above doesn't work, try this more specific command:
```bash
python -m playwright install chromium
```
This second method has proven to be more reliable in some cases.
---
### Installation with Synchronous Version
The sync version is deprecated and will be removed in future versions. If you need the synchronous version using Selenium:
```bash
pip install crawl4ai[sync]
```
---
### Development Installation
For contributors who plan to modify the source code:
```bash
git clone https://github.com/unclecode/crawl4ai.git
cd crawl4ai
pip install -e . # Basic installation in editable mode
```
Install optional features:
```bash
pip install -e ".[torch]" # With PyTorch features
pip install -e ".[transformer]" # With Transformer features
pip install -e ".[cosine]" # With cosine similarity features
pip install -e ".[sync]" # With synchronous crawling (Selenium)
pip install -e ".[all]" # Install all optional features
```
</details>
<details>
<summary>🐳 <strong>Docker Deployment</strong></summary>
> 🚀 **Now Available!** Our completely redesigned Docker implementation is here! This new solution makes deployment more efficient and seamless than ever.
### New Docker Features
The new Docker implementation includes:
- **Browser pooling** with page pre-warming for faster response times
- **Interactive playground** to test and generate request code
- **MCP integration** for direct connection to AI tools like Claude Code
- **Comprehensive API endpoints** including HTML extraction, screenshots, PDF generation, and JavaScript execution
- **Multi-architecture support** with automatic detection (AMD64/ARM64)
- **Optimized resources** with improved memory management
### Getting Started
```bash
# Pull and run the latest release candidate
docker pull unclecode/crawl4ai:0.7.0
docker run -d -p 11235:11235 --name crawl4ai --shm-size=1g unclecode/crawl4ai:0.7.0
# Visit the playground at http://localhost:11235/playground
```
For complete documentation, see our [Docker Deployment Guide](https://docs.crawl4ai.com/core/docker-deployment/).
</details>
---
### Quick Test
Run a quick test (works for both Docker options):
```python
import requests
# Submit a crawl job
response = requests.post(
"http://localhost:11235/crawl",
json={"urls": ["https://example.com"], "priority": 10}
)
if response.status_code == 200:
print("Crawl job submitted successfully.")
if "results" in response.json():
results = response.json()["results"]
print("Crawl job completed. Results:")
for result in results:
print(result)
else:
task_id = response.json()["task_id"]
print(f"Crawl job submitted. Task ID:: {task_id}")
result = requests.get(f"http://localhost:11235/task/{task_id}")
```
For more examples, see our [Docker Examples](https://github.com/unclecode/crawl4ai/blob/main/docs/examples/docker_example.py). For advanced configuration, environment variables, and usage examples, see our [Docker Deployment Guide](https://docs.crawl4ai.com/basic/docker-deployment/).
</details>
## 🔬 Advanced Usage Examples 🔬
You can check the project structure in the directory [docs/examples](https://github.com/unclecode/crawl4ai/tree/main/docs/examples). Over there, you can find a variety of examples; here, some popular examples are shared.
<details>
<summary>📝 <strong>Heuristic Markdown Generation with Clean and Fit Markdown</strong></summary>
```python
import asyncio
from crawl4ai import AsyncWebCrawler, BrowserConfig, CrawlerRunConfig, CacheMode
from crawl4ai.content_filter_strategy import PruningContentFilter, BM25ContentFilter
from crawl4ai.markdown_generation_strategy import DefaultMarkdownGenerator
async def main():
browser_config = BrowserConfig(
headless=True,
verbose=True,
)
run_config = CrawlerRunConfig(
cache_mode=CacheMode.ENABLED,
markdown_generator=DefaultMarkdownGenerator(
content_filter=PruningContentFilter(threshold=0.48, threshold_type="fixed", min_word_threshold=0)
),
# markdown_generator=DefaultMarkdownGenerator(
# content_filter=BM25ContentFilter(user_query="WHEN_WE_FOCUS_BASED_ON_A_USER_QUERY", bm25_threshold=1.0)
# ),
)
async with AsyncWebCrawler(config=browser_config) as crawler:
result = await crawler.arun(
url="https://docs.micronaut.io/4.7.6/guide/",
config=run_config
)
print(len(result.markdown.raw_markdown))
print(len(result.markdown.fit_markdown))
if __name__ == "__main__":
asyncio.run(main())
```
</details>
<details>
<summary>🖥️ <strong>Executing JavaScript & Extract Structured Data without LLMs</strong></summary>
```python
import asyncio
from crawl4ai import AsyncWebCrawler, BrowserConfig, CrawlerRunConfig, CacheMode
from crawl4ai import JsonCssExtractionStrategy
import json
async def main():
schema = {
"name": "KidoCode Courses",
"baseSelector": "section.charge-methodology .w-tab-content > div",
"fields": [
{
"name": "section_title",
"selector": "h3.heading-50",
"type": "text",
},
{
"name": "section_description",
"selector": ".charge-content",
"type": "text",
},
{
"name": "course_name",
"selector": ".text-block-93",
"type": "text",
},
{
"name": "course_description",
"selector": ".course-content-text",
"type": "text",
},
{
"name": "course_icon",
"selector": ".image-92",
"type": "attribute",
"attribute": "src"
}
}
}
extraction_strategy = JsonCssExtractionStrategy(schema, verbose=True)
browser_config = BrowserConfig(
headless=False,
verbose=True
)
run_config = CrawlerRunConfig(
extraction_strategy=extraction_strategy,
js_code=["""(async () => {const tabs = document.querySelectorAll("section.charge-methodology .tabs-menu-3 > div");for(let tab of tabs) {tab.scrollIntoView();tab.click();await new Promise(r => setTimeout(r, 500));}})();"""],
cache_mode=CacheMode.BYPASS
)
async with AsyncWebCrawler(config=browser_config) as crawler:
result = await crawler.arun(
url="https://www.kidocode.com/degrees/technology",
config=run_config
)
companies = json.loads(result.extracted_content)
print(f"Successfully extracted {len(companies)} companies")
print(json.dumps(companies[0], indent=2))
if __name__ == "__main__":
asyncio.run(main())
```
</details>
<details>
<summary>📚 <strong>Extracting Structured Data with LLMs</strong></summary>
```python
import os
import asyncio
from crawl4ai import AsyncWebCrawler, BrowserConfig, CrawlerRunConfig, CacheMode, LLMConfig
from crawl4ai import LLMExtractionStrategy
from pydantic import BaseModel, Field
class OpenAIModelFee(BaseModel):
model_name: str = Field(..., description="Name of the OpenAI model.")
input_fee: str = Field(..., description="Fee for input token for the OpenAI model.")
output_fee: str = Field(..., description="Fee for output token for the OpenAI model.")
async def main():
browser_config = BrowserConfig(verbose=True)
run_config = CrawlerRunConfig(
word_count_threshold=1,
extraction_strategy=LLMExtractionStrategy(
# Here you can use any provider that Litellm library supports, for instance: ollama/qwen2
# provider="ollama/qwen2", api_token="no-token",
llm_config = LLMConfig(provider="openai/gpt-4o", api_token=os.getenv('OPENAI_API_KEY')),
schema=OpenAIModelFee.schema(),
extraction_type="schema",
instruction="""From the crawled content, extract all mentioned model names along with their fees for input and output tokens.
Do not miss any models in the entire content. One extracted model JSON format should look like this:
{"model_name": "GPT-4", "input_fee": "US$10.00 / 1M tokens", "output_fee": "US$30.00 / 1M tokens"}."""
),
cache_mode=CacheMode.BYPASS,
)
async with AsyncWebCrawler(config=browser_config) as crawler:
result = await crawler.arun(
url='https://openai.com/api/pricing/',
config=run_config
)
print(result.extracted_content)
if __name__ == "__main__":
asyncio.run(main())
```
</details>
<details>
<summary>🤖 <strong>Using Your own Browser with Custom User Profile</strong></summary>
```python
import os, sys
from pathlib import Path
import asyncio, time
from crawl4ai import AsyncWebCrawler, BrowserConfig, CrawlerRunConfig, CacheMode
async def test_news_crawl():
# Create a persistent user data directory
user_data_dir = os.path.join(Path.home(), ".crawl4ai", "browser_profile")
os.makedirs(user_data_dir, exist_ok=True)
browser_config = BrowserConfig(
verbose=True,
headless=True,
user_data_dir=user_data_dir,
use_persistent_context=True,
)
run_config = CrawlerRunConfig(
cache_mode=CacheMode.BYPASS
)
async with AsyncWebCrawler(config=browser_config) as crawler:
url = "ADDRESS_OF_A_CHALLENGING_WEBSITE"
result = await crawler.arun(
url,
config=run_config,
magic=True,
)
print(f"Successfully crawled {url}")
print(f"Content length: {len(result.markdown)}")
```
</details>
## ✨ Recent Updates
### Version 0.7.0 Release Highlights - The Adaptive Intelligence Update
- **🧠 Adaptive Crawling**: Your crawler now learns and adapts to website patterns automatically:
```python
config = AdaptiveConfig(
confidence_threshold=0.7, # Min confidence to stop crawling
max_depth=5, # Maximum crawl depth
max_pages=20, # Maximum number of pages to crawl
strategy="statistical"
)
async with AsyncWebCrawler() as crawler:
adaptive_crawler = AdaptiveCrawler(crawler, config)
state = await adaptive_crawler.digest(
start_url="https://news.example.com",
query="latest news content"
)
# Crawler learns patterns and improves extraction over time
```
- **🌊 Virtual Scroll Support**: Complete content extraction from infinite scroll pages:
```python
scroll_config = VirtualScrollConfig(
container_selector="[data-testid='feed']",
scroll_count=20,
scroll_by="container_height",
wait_after_scroll=1.0
)
result = await crawler.arun(url, config=CrawlerRunConfig(
virtual_scroll_config=scroll_config
))
```
- **🔗 Intelligent Link Analysis**: 3-layer scoring system for smart link prioritization:
```python
link_config = LinkPreviewConfig(
query="machine learning tutorials",
score_threshold=0.3,
concurrent_requests=10
)
result = await crawler.arun(url, config=CrawlerRunConfig(
link_preview_config=link_config,
score_links=True
))
# Links ranked by relevance and quality
```
- **🎣 Async URL Seeder**: Discover thousands of URLs in seconds:
```python
seeder = AsyncUrlSeeder(SeedingConfig(
source="sitemap+cc",
pattern="*/blog/*",
query="python tutorials",
score_threshold=0.4
))
urls = await seeder.discover("https://example.com")
```
- **⚡ Performance Boost**: Up to 3x faster with optimized resource handling and memory efficiency
Read the full details in our [0.7.0 Release Notes](https://docs.crawl4ai.com/blog/release-v0.7.0) or check the [CHANGELOG](https://github.com/unclecode/crawl4ai/blob/main/CHANGELOG.md).
## Version Numbering in Crawl4AI
Crawl4AI follows standard Python version numbering conventions (PEP 440) to help users understand the stability and features of each release.
### Version Numbers Explained
Our version numbers follow this pattern: `MAJOR.MINOR.PATCH` (e.g., 0.4.3)
#### Pre-release Versions
We use different suffixes to indicate development stages:
- `dev` (0.4.3dev1): Development versions, unstable
- `a` (0.4.3a1): Alpha releases, experimental features
- `b` (0.4.3b1): Beta releases, feature complete but needs testing
- `rc` (0.4.3): Release candidates, potential final version
#### Installation
- Regular installation (stable version):
```bash
pip install -U crawl4ai
```
- Install pre-release versions:
```bash
pip install crawl4ai --pre
```
- Install specific version:
```bash
pip install crawl4ai==0.4.3b1
```
#### Why Pre-releases?
We use pre-releases to:
- Test new features in real-world scenarios
- Gather feedback before final releases
- Ensure stability for production users
- Allow early adopters to try new features
For production environments, we recommend using the stable version. For testing new features, you can opt-in to pre-releases using the `--pre` flag.
## 📖 Documentation & Roadmap
> 🚨 **Documentation Update Alert**: We're undertaking a major documentation overhaul next week to reflect recent updates and improvements. Stay tuned for a more comprehensive and up-to-date guide!
For current documentation, including installation instructions, advanced features, and API reference, visit our [Documentation Website](https://docs.crawl4ai.com/).
To check our development plans and upcoming features, visit our [Roadmap](https://github.com/unclecode/crawl4ai/blob/main/ROADMAP.md).
<details>
<summary>📈 <strong>Development TODOs</strong></summary>
- [x] 0. Graph Crawler: Smart website traversal using graph search algorithms for comprehensive nested page extraction
- [ ] 1. Question-Based Crawler: Natural language driven web discovery and content extraction
- [ ] 2. Knowledge-Optimal Crawler: Smart crawling that maximizes knowledge while minimizing data extraction
- [ ] 3. Agentic Crawler: Autonomous system for complex multi-step crawling operations
- [ ] 4. Automated Schema Generator: Convert natural language to extraction schemas
- [ ] 5. Domain-Specific Scrapers: Pre-configured extractors for common platforms (academic, e-commerce)
- [ ] 6. Web Embedding Index: Semantic search infrastructure for crawled content
- [ ] 7. Interactive Playground: Web UI for testing, comparing strategies with AI assistance
- [ ] 8. Performance Monitor: Real-time insights into crawler operations
- [ ] 9. Cloud Integration: One-click deployment solutions across cloud providers
- [ ] 10. Sponsorship Program: Structured support system with tiered benefits
- [ ] 11. Educational Content: "How to Crawl" video series and interactive tutorials
</details>
## 🤝 Contributing
We welcome contributions from the open-source community. Check out our [contribution guidelines](https://github.com/unclecode/crawl4ai/blob/main/CONTRIBUTORS.md) for more information.
I'll help modify the license section with badges. For the halftone effect, here's a version with it:
Here's the updated license section:
## 📄 License & Attribution
This project is licensed under the Apache License 2.0, attribution is recommended via the badges below. See the [Apache 2.0 License](https://github.com/unclecode/crawl4ai/blob/main/LICENSE) file for details.
### Attribution Requirements
When using Crawl4AI, you must include one of the following attribution methods:
#### 1. Badge Attribution (Recommended)
Add one of these badges to your README, documentation, or website:
| Theme | Badge |
|-------|-------|
| **Disco Theme (Animated)** | <a href="https://github.com/unclecode/crawl4ai"><img src="./docs/assets/powered-by-disco.svg" alt="Powered by Crawl4AI" width="200"/></a> |
| **Night Theme (Dark with Neon)** | <a href="https://github.com/unclecode/crawl4ai"><img src="./docs/assets/powered-by-night.svg" alt="Powered by Crawl4AI" width="200"/></a> |
| **Dark Theme (Classic)** | <a href="https://github.com/unclecode/crawl4ai"><img src="./docs/assets/powered-by-dark.svg" alt="Powered by Crawl4AI" width="200"/></a> |
| **Light Theme (Classic)** | <a href="https://github.com/unclecode/crawl4ai"><img src="./docs/assets/powered-by-light.svg" alt="Powered by Crawl4AI" width="200"/></a> |
HTML code for adding the badges:
```html
<!-- Disco Theme (Animated) -->
<a href="https://github.com/unclecode/crawl4ai">
<img src="https://raw.githubusercontent.com/unclecode/crawl4ai/main/docs/assets/powered-by-disco.svg" alt="Powered by Crawl4AI" width="200"/>
</a>
<!-- Night Theme (Dark with Neon) -->
<a href="https://github.com/unclecode/crawl4ai">
<img src="https://raw.githubusercontent.com/unclecode/crawl4ai/main/docs/assets/powered-by-night.svg" alt="Powered by Crawl4AI" width="200"/>
</a>
<!-- Dark Theme (Classic) -->
<a href="https://github.com/unclecode/crawl4ai">
<img src="https://raw.githubusercontent.com/unclecode/crawl4ai/main/docs/assets/powered-by-dark.svg" alt="Powered by Crawl4AI" width="200"/>
</a>
<!-- Light Theme (Classic) -->
<a href="https://github.com/unclecode/crawl4ai">
<img src="https://raw.githubusercontent.com/unclecode/crawl4ai/main/docs/assets/powered-by-light.svg" alt="Powered by Crawl4AI" width="200"/>
</a>
<!-- Simple Shield Badge -->
<a href="https://github.com/unclecode/crawl4ai">
<img src="https://img.shields.io/badge/Powered%20by-Crawl4AI-blue?style=flat-square" alt="Powered by Crawl4AI"/>
</a>
```
#### 2. Text Attribution
Add this line to your documentation:
```
This project uses Crawl4AI (https://github.com/unclecode/crawl4ai) for web data extraction.
```
## 📚 Citation
If you use Crawl4AI in your research or project, please cite:
```bibtex
@software{crawl4ai2024,
author = {UncleCode},
title = {Crawl4AI: Open-source LLM Friendly Web Crawler & Scraper},
year = {2024},
publisher = {GitHub},
journal = {GitHub Repository},
howpublished = {\url{https://github.com/unclecode/crawl4ai}},
commit = {Please use the commit hash you're working with}
}
```
Text citation format:
```
UncleCode. (2024). Crawl4AI: Open-source LLM Friendly Web Crawler & Scraper [Computer software].
GitHub. https://github.com/unclecode/crawl4ai
```
## 📧 Contact
For questions, suggestions, or feedback, feel free to reach out:
- GitHub: [unclecode](https://github.com/unclecode)
- Twitter: [@unclecode](https://twitter.com/unclecode)
- Website: [crawl4ai.com](https://crawl4ai.com)
Happy Crawling! 🕸️🚀
## 💖 Support Crawl4AI
> 🎉 **Sponsorship Program Just Launched!** Be among the first 50 **Founding Sponsors** and get permanent recognition in our Hall of Fame!
Crawl4AI is the #1 trending open-source web crawler with 51K+ stars. Your support ensures we stay independent, innovative, and free forever.
<div align="center">
[![Become a Sponsor](https://img.shields.io/badge/Become%20a%20Sponsor-pink?style=for-the-badge&logo=github-sponsors&logoColor=white)](https://github.com/sponsors/unclecode)
[![Current Sponsors](https://img.shields.io/github/sponsors/unclecode?style=for-the-badge&logo=github&label=Current%20Sponsors&color=green)](https://github.com/sponsors/unclecode)
</div>
### 🤝 Sponsorship Tiers
- **🌱 Believer ($5/mo)**: Join the movement for data democratization
- **🚀 Builder ($50/mo)**: Get priority support and early feature access
- **💼 Growing Team ($500/mo)**: Bi-weekly syncs and optimization help
- **🏢 Data Infrastructure Partner ($2000/mo)**: Full partnership with dedicated support
**Why sponsor?** Every tier includes real benefits. No more rate-limited APIs. Own your data pipeline. Build data sovereignty together.
[View All Tiers & Benefits →](https://github.com/sponsors/unclecode)
### 🏆 Our Sponsors
#### 👑 Founding Sponsors (First 50)
*Be part of history - [Become a Founding Sponsor](https://github.com/sponsors/unclecode)*
<!-- Founding sponsors will be permanently recognized here -->
#### Current Sponsors
Thank you to all our sponsors who make this project possible!
<!-- Sponsors will be automatically added here -->
## 🗾 Mission
Our mission is to unlock the value of personal and enterprise data by transforming digital footprints into structured, tradeable assets. Crawl4AI empowers individuals and organizations with open-source tools to extract and structure data, fostering a shared data economy.
We envision a future where AI is powered by real human knowledge, ensuring data creators directly benefit from their contributions. By democratizing data and enabling ethical sharing, we are laying the foundation for authentic AI advancement.
<details>
<summary>🔑 <strong>Key Opportunities</strong></summary>
- **Data Capitalization**: Transform digital footprints into measurable, valuable assets.
- **Authentic AI Data**: Provide AI systems with real human insights.
- **Shared Economy**: Create a fair data marketplace that benefits data creators.
</details>
<details>
<summary>🚀 <strong>Development Pathway</strong></summary>
1. **Open-Source Tools**: Community-driven platforms for transparent data extraction.
2. **Digital Asset Structuring**: Tools to organize and value digital knowledge.
3. **Ethical Data Marketplace**: A secure, fair platform for exchanging structured data.
For more details, see our [full mission statement](./MISSION.md).
</details>
## Star History
[![Star History Chart](https://api.star-history.com/svg?repos=unclecode/crawl4ai&type=Date)](https://star-history.com/#unclecode/crawl4ai&Date)

259
README.md
View File

@@ -10,41 +10,48 @@
[![PyPI version](https://badge.fury.io/py/crawl4ai.svg)](https://badge.fury.io/py/crawl4ai)
[![Python Version](https://img.shields.io/pypi/pyversions/crawl4ai)](https://pypi.org/project/crawl4ai/)
[![Downloads](https://static.pepy.tech/badge/crawl4ai/month)](https://pepy.tech/project/crawl4ai)
[![GitHub Sponsors](https://img.shields.io/github/sponsors/unclecode?style=flat&logo=GitHub-Sponsors&label=Sponsors&color=pink)](https://github.com/sponsors/unclecode)
<!-- [![Documentation Status](https://readthedocs.org/projects/crawl4ai/badge/?version=latest)](https://crawl4ai.readthedocs.io/) -->
[![License](https://img.shields.io/github/license/unclecode/crawl4ai)](https://github.com/unclecode/crawl4ai/blob/main/LICENSE)
[![Code style: black](https://img.shields.io/badge/code%20style-black-000000.svg)](https://github.com/psf/black)
[![Security: bandit](https://img.shields.io/badge/security-bandit-yellow.svg)](https://github.com/PyCQA/bandit)
[![Contributor Covenant](https://img.shields.io/badge/Contributor%20Covenant-2.1-4baaaa.svg)](code_of_conduct.md)
<p align="center">
<a href="https://x.com/crawl4ai">
<img src="https://img.shields.io/badge/Follow%20on%20X-000000?style=for-the-badge&logo=x&logoColor=white" alt="Follow on X" />
</a>
<a href="https://www.linkedin.com/company/crawl4ai">
<img src="https://img.shields.io/badge/Follow%20on%20LinkedIn-0077B5?style=for-the-badge&logo=linkedin&logoColor=white" alt="Follow on LinkedIn" />
</a>
<a href="https://discord.gg/jP8KfhDhyN">
<img src="https://img.shields.io/badge/Join%20our%20Discord-5865F2?style=for-the-badge&logo=discord&logoColor=white" alt="Join our Discord" />
</a>
</p>
</div>
Crawl4AI is the #1 trending GitHub repository, actively maintained by a vibrant community. It delivers blazing-fast, AI-ready web crawling tailored for LLMs, AI agents, and data pipelines. Open source, flexible, and built for real-time performance, Crawl4AI empowers developers with unmatched speed, precision, and deployment ease.
Crawl4AI turns the web into clean, LLM ready Markdown for RAG, agents, and data pipelines. Fast, controllable, battle tested by a 50k+ star community.
[✨ Check out latest update v0.5.0](#-recent-updates)
[✨ Check out latest update v0.7.0](#-recent-updates)
🎉 **Version 0.5.0 is out!** This major release introduces Deep Crawling with BFS/DFS/BestFirst strategies, Memory-Adaptive Dispatcher, Multiple Crawling Strategies (Playwright and HTTP), Docker Deployment with FastAPI, Command-Line Interface (CLI), and more! [Read the release notes →](https://docs.crawl4ai.com/blog)
✨ New in v0.7.0, Adaptive Crawling, Virtual Scroll, Link Preview scoring, Async URL Seeder, big performance gains. [Release notes →](https://github.com/unclecode/crawl4ai/blob/main/docs/blog/release-v0.7.0.md)
<details>
<summary>🤓 <strong>My Personal Story</strong></summary>
<summary>🤓 <strong>My Personal Story</strong></summary>
My journey with computers started in childhood when my dad, a computer scientist, introduced me to an Amstrad computer. Those early days sparked a fascination with technology, leading me to pursue computer science and specialize in NLP during my postgraduate studies. It was during this time that I first delved into web crawling, building tools to help researchers organize papers and extract information from publications a challenging yet rewarding experience that honed my skills in data extraction.
I grew up on an Amstrad, thanks to my dad, and never stopped building. In grad school I specialized in NLP and built crawlers for research. Thats where I learned how much extraction matters.
Fast forward to 2023, I was working on a tool for a project and needed a crawler to convert a webpage into markdown. While exploring solutions, I found one that claimed to be open-source but required creating an account and generating an API token. Worse, it turned out to be a SaaS model charging $16, and its quality didnt meet my standards. Frustrated, I realized this was a deeper problem. That frustration turned into turbo anger mode, and I decided to build my own solution. In just a few days, I created Crawl4AI. To my surprise, it went viral, earning thousands of GitHub stars and resonating with a global community.
In 2023, I needed web-to-Markdown. The “open source” option wanted an account, API token, and $16, and still under-delivered. I went turbo anger mode, built Crawl4AI in days, and it went viral. Now its the most-starred crawler on GitHub.
I made Crawl4AI open-source for two reasons. First, its my way of giving back to the open-source community that has supported me throughout my career. Second, I believe data should be accessible to everyone, not locked behind paywalls or monopolized by a few. Open access to data lays the foundation for the democratization of AI, a vision where individuals can train their own models and take ownership of their information. This library is the first step in a larger journey to create the best open-source data extraction and generation tool the world has ever seen, built collaboratively by a passionate community.
Thank you to everyone who has supported this project, used it, and shared feedback. Your encouragement motivates me to dream even bigger. Join us, file issues, submit PRs, or spread the word. Together, we can build a tool that truly empowers people to access their own data and reshape the future of AI.
I made it open source for **availability**, anyone can use it without a gate. Now Im building the platform for **affordability**, anyone can run serious crawls without breaking the bank. If that resonates, join in, send feedback, or just crawl something amazing.
</details>
## 🧐 Why Crawl4AI?
1. **Built for LLMs**: Creates smart, concise Markdown optimized for RAG and fine-tuning applications.
2. **Lightning Fast**: Delivers results 6x faster with real-time, cost-efficient performance.
3. **Flexible Browser Control**: Offers session management, proxies, and custom hooks for seamless data access.
4. **Heuristic Intelligence**: Uses advanced algorithms for efficient extraction, reducing reliance on costly models.
5. **Open Source & Deployable**: Fully open-source with no API keys—ready for Docker and cloud integration.
6. **Thriving Community**: Actively maintained by a vibrant community and the #1 trending GitHub repository.
<details>
<summary>Why developers pick Crawl4AI</summary>
- **LLM ready output**, smart Markdown with headings, tables, code, citation hints
- **Fast in practice**, async browser pool, caching, minimal hops
- **Full control**, sessions, proxies, cookies, user scripts, hooks
- **Adaptive intelligence**, learns site patterns, explores only what matters
- **Deploy anywhere**, zero keys, CLI and Docker, cloud friendly
</details>
## 🚀 Quick Start
@@ -96,6 +103,33 @@ crwl https://docs.crawl4ai.com --deep-crawl bfs --max-pages 10
crwl https://www.example.com/products -q "Extract all product prices"
```
## 💖 Support Crawl4AI
> 🎉 **Sponsorship Program Now Open!** After powering 51K+ developers and 1 year of growth, Crawl4AI is launching dedicated support for **startups** and **enterprises**. Be among the first 50 **Founding Sponsors** for permanent recognition in our Hall of Fame.
Crawl4AI is the #1 trending open-source web crawler on GitHub. Your support keeps it independent, innovative, and free for the community — while giving you direct access to premium benefits.
<div align="">
[![Become a Sponsor](https://img.shields.io/badge/Become%20a%20Sponsor-pink?style=for-the-badge&logo=github-sponsors&logoColor=white)](https://github.com/sponsors/unclecode)
[![Current Sponsors](https://img.shields.io/github/sponsors/unclecode?style=for-the-badge&logo=github&label=Current%20Sponsors&color=green)](https://github.com/sponsors/unclecode)
</div>
### 🤝 Sponsorship Tiers
- **🌱 Believer ($5/mo)** — Join the movement for data democratization
- **🚀 Builder ($50/mo)** — Priority support & early access to features
- **💼 Growing Team ($500/mo)** — Bi-weekly syncs & optimization help
- **🏢 Data Infrastructure Partner ($2000/mo)** — Full partnership with dedicated support
*Custom arrangements available - see [SPONSORS.md](SPONSORS.md) for details & contact*
**Why sponsor?**
No rate-limited APIs. No lock-in. Build and own your data pipeline with direct guidance from the creator of Crawl4AI.
[See All Tiers & Benefits →](https://github.com/sponsors/unclecode)
## ✨ Features
<details>
@@ -253,28 +287,27 @@ pip install -e ".[all]" # Install all optional features
<details>
<summary>🐳 <strong>Docker Deployment</strong></summary>
> 🚀 **Major Changes Coming!** We're developing a completely new Docker implementation that will make deployment even more efficient and seamless. The current Docker setup is being deprecated in favor of this new solution.
> 🚀 **Now Available!** Our completely redesigned Docker implementation is here! This new solution makes deployment more efficient and seamless than ever.
### Current Docker Support
### New Docker Features
The existing Docker implementation is being deprecated and will be replaced soon. If you still need to use Docker with the current version:
The new Docker implementation includes:
- **Browser pooling** with page pre-warming for faster response times
- **Interactive playground** to test and generate request code
- **MCP integration** for direct connection to AI tools like Claude Code
- **Comprehensive API endpoints** including HTML extraction, screenshots, PDF generation, and JavaScript execution
- **Multi-architecture support** with automatic detection (AMD64/ARM64)
- **Optimized resources** with improved memory management
- 📚 [Deprecated Docker Setup](./docs/deprecated/docker-deployment.md) - Instructions for the current Docker implementation
- ⚠️ Note: This setup will be replaced in the next major release
### Getting Started
### What's Coming Next?
```bash
# Pull and run the latest release candidate
docker pull unclecode/crawl4ai:0.7.0
docker run -d -p 11235:11235 --name crawl4ai --shm-size=1g unclecode/crawl4ai:0.7.0
Our new Docker implementation will bring:
- Improved performance and resource efficiency
- Streamlined deployment process
- Better integration with Crawl4AI features
- Enhanced scalability options
Stay connected with our [GitHub repository](https://github.com/unclecode/crawl4ai) for updates!
</details>
---
# Visit the playground at http://localhost:11235/playground
```
### Quick Test
@@ -286,22 +319,31 @@ import requests
# Submit a crawl job
response = requests.post(
"http://localhost:11235/crawl",
json={"urls": "https://example.com", "priority": 10}
json={"urls": ["https://example.com"], "priority": 10}
)
task_id = response.json()["task_id"]
# Continue polling until the task is complete (status="completed")
result = requests.get(f"http://localhost:11235/task/{task_id}")
if response.status_code == 200:
print("Crawl job submitted successfully.")
if "results" in response.json():
results = response.json()["results"]
print("Crawl job completed. Results:")
for result in results:
print(result)
else:
task_id = response.json()["task_id"]
print(f"Crawl job submitted. Task ID:: {task_id}")
result = requests.get(f"http://localhost:11235/task/{task_id}")
```
For more examples, see our [Docker Examples](https://github.com/unclecode/crawl4ai/blob/main/docs/examples/docker_example.py). For advanced configuration, environment variables, and usage examples, see our [Docker Deployment Guide](https://docs.crawl4ai.com/basic/docker-deployment/).
</details>
---
## 🔬 Advanced Usage Examples 🔬
You can check the project structure in the directory [https://github.com/unclecode/crawl4ai/docs/examples](docs/examples). Over there, you can find a variety of examples; here, some popular examples are shared.
You can check the project structure in the directory [docs/examples](https://github.com/unclecode/crawl4ai/tree/main/docs/examples). Over there, you can find a variety of examples; here, some popular examples are shared.
<details>
<summary>📝 <strong>Heuristic Markdown Generation with Clean and Fit Markdown</strong></summary>
@@ -347,7 +389,7 @@ if __name__ == "__main__":
```python
import asyncio
from crawl4ai import AsyncWebCrawler, BrowserConfig, CrawlerRunConfig, CacheMode
from crawl4ai.extraction_strategy import JsonCssExtractionStrategy
from crawl4ai import JsonCssExtractionStrategy
import json
async def main():
@@ -421,7 +463,7 @@ if __name__ == "__main__":
import os
import asyncio
from crawl4ai import AsyncWebCrawler, BrowserConfig, CrawlerRunConfig, CacheMode, LLMConfig
from crawl4ai.extraction_strategy import LLMExtractionStrategy
from crawl4ai import LLMExtractionStrategy
from pydantic import BaseModel, Field
class OpenAIModelFee(BaseModel):
@@ -460,7 +502,7 @@ if __name__ == "__main__":
</details>
<details>
<summary>🤖 <strong>Using You own Browser with Custom User Profile</strong></summary>
<summary>🤖 <strong>Using Your own Browser with Custom User Profile</strong></summary>
```python
import os, sys
@@ -500,37 +542,77 @@ async def test_news_crawl():
## ✨ Recent Updates
### Version 0.5.0 Major Release Highlights
### Version 0.7.0 Release Highlights - The Adaptive Intelligence Update
- **🚀 Deep Crawling System**: Explore websites beyond initial URLs with three strategies:
- **BFS Strategy**: Breadth-first search explores websites level by level
- **DFS Strategy**: Depth-first search explores each branch deeply before backtracking
- **BestFirst Strategy**: Uses scoring functions to prioritize which URLs to crawl next
- **Page Limiting**: Control the maximum number of pages to crawl with `max_pages` parameter
- **Score Thresholds**: Filter URLs based on relevance scores
- **⚡ Memory-Adaptive Dispatcher**: Dynamically adjusts concurrency based on system memory with built-in rate limiting
- **🔄 Multiple Crawling Strategies**:
- **AsyncPlaywrightCrawlerStrategy**: Browser-based crawling with JavaScript support (Default)
- **AsyncHTTPCrawlerStrategy**: Fast, lightweight HTTP-only crawler for simple tasks
- **🐳 Docker Deployment**: Easy deployment with FastAPI server and streaming/non-streaming endpoints
- **💻 Command-Line Interface**: New `crwl` CLI provides convenient terminal access to all features with intuitive commands and configuration options
- **👤 Browser Profiler**: Create and manage persistent browser profiles to save authentication states, cookies, and settings for seamless crawling of protected content
- **🧠 Crawl4AI Coding Assistant**: AI-powered coding assistant to answer your question for Crawl4ai, and generate proper code for crawling.
- **🏎️ LXML Scraping Mode**: Fast HTML parsing using the `lxml` library for improved performance
- **🌐 Proxy Rotation**: Built-in support for proxy switching with `RoundRobinProxyStrategy`
- **🤖 LLM Content Filter**: Intelligent markdown generation using LLMs
- **📄 PDF Processing**: Extract text, images, and metadata from PDF files
- **🔗 URL Redirection Tracking**: Automatically follow and record HTTP redirects
- **🤖 LLM Schema Generation**: Easily create extraction schemas with LLM assistance
- **🔍 robots.txt Compliance**: Respect website crawling rules
- **🧠 Adaptive Crawling**: Your crawler now learns and adapts to website patterns automatically:
```python
config = AdaptiveConfig(
confidence_threshold=0.7, # Min confidence to stop crawling
max_depth=5, # Maximum crawl depth
max_pages=20, # Maximum number of pages to crawl
strategy="statistical"
)
async with AsyncWebCrawler() as crawler:
adaptive_crawler = AdaptiveCrawler(crawler, config)
state = await adaptive_crawler.digest(
start_url="https://news.example.com",
query="latest news content"
)
# Crawler learns patterns and improves extraction over time
```
Read the full details in our [0.5.0 Release Notes](https://docs.crawl4ai.com/blog/releases/0.5.0.html) or check the [CHANGELOG](https://github.com/unclecode/crawl4ai/blob/main/CHANGELOG.md).
- **🌊 Virtual Scroll Support**: Complete content extraction from infinite scroll pages:
```python
scroll_config = VirtualScrollConfig(
container_selector="[data-testid='feed']",
scroll_count=20,
scroll_by="container_height",
wait_after_scroll=1.0
)
result = await crawler.arun(url, config=CrawlerRunConfig(
virtual_scroll_config=scroll_config
))
```
- **🔗 Intelligent Link Analysis**: 3-layer scoring system for smart link prioritization:
```python
link_config = LinkPreviewConfig(
query="machine learning tutorials",
score_threshold=0.3,
concurrent_requests=10
)
result = await crawler.arun(url, config=CrawlerRunConfig(
link_preview_config=link_config,
score_links=True
))
# Links ranked by relevance and quality
```
- **🎣 Async URL Seeder**: Discover thousands of URLs in seconds:
```python
seeder = AsyncUrlSeeder(SeedingConfig(
source="sitemap+cc",
pattern="*/blog/*",
query="python tutorials",
score_threshold=0.4
))
urls = await seeder.discover("https://example.com")
```
- **⚡ Performance Boost**: Up to 3x faster with optimized resource handling and memory efficiency
Read the full details in our [0.7.0 Release Notes](https://docs.crawl4ai.com/blog/release-v0.7.0) or check the [CHANGELOG](https://github.com/unclecode/crawl4ai/blob/main/CHANGELOG.md).
## Version Numbering in Crawl4AI
Crawl4AI follows standard Python version numbering conventions (PEP 440) to help users understand the stability and features of each release.
### Version Numbers Explained
<details>
<summary>📈 <strong>Version Numbers Explained</strong></summary>
Our version numbers follow this pattern: `MAJOR.MINOR.PATCH` (e.g., 0.4.3)
@@ -540,7 +622,7 @@ We use different suffixes to indicate development stages:
- `dev` (0.4.3dev1): Development versions, unstable
- `a` (0.4.3a1): Alpha releases, experimental features
- `b` (0.4.3b1): Beta releases, feature complete but needs testing
- `rc` (0.4.3rc1): Release candidates, potential final version
- `rc` (0.4.3): Release candidates, potential final version
#### Installation
- Regular installation (stable version):
@@ -567,6 +649,8 @@ We use pre-releases to:
For production environments, we recommend using the stable version. For testing new features, you can opt-in to pre-releases using the `--pre` flag.
</details>
## 📖 Documentation & Roadmap
> 🚨 **Documentation Update Alert**: We're undertaking a major documentation overhaul next week to reflect recent updates and improvements. Stay tuned for a more comprehensive and up-to-date guide!
@@ -579,16 +663,16 @@ To check our development plans and upcoming features, visit our [Roadmap](https:
<summary>📈 <strong>Development TODOs</strong></summary>
- [x] 0. Graph Crawler: Smart website traversal using graph search algorithms for comprehensive nested page extraction
- [ ] 1. Question-Based Crawler: Natural language driven web discovery and content extraction
- [ ] 2. Knowledge-Optimal Crawler: Smart crawling that maximizes knowledge while minimizing data extraction
- [ ] 3. Agentic Crawler: Autonomous system for complex multi-step crawling operations
- [ ] 4. Automated Schema Generator: Convert natural language to extraction schemas
- [ ] 5. Domain-Specific Scrapers: Pre-configured extractors for common platforms (academic, e-commerce)
- [ ] 6. Web Embedding Index: Semantic search infrastructure for crawled content
- [ ] 7. Interactive Playground: Web UI for testing, comparing strategies with AI assistance
- [ ] 8. Performance Monitor: Real-time insights into crawler operations
- [x] 1. Question-Based Crawler: Natural language driven web discovery and content extraction
- [x] 2. Knowledge-Optimal Crawler: Smart crawling that maximizes knowledge while minimizing data extraction
- [x] 3. Agentic Crawler: Autonomous system for complex multi-step crawling operations
- [x] 4. Automated Schema Generator: Convert natural language to extraction schemas
- [x] 5. Domain-Specific Scrapers: Pre-configured extractors for common platforms (academic, e-commerce)
- [x] 6. Web Embedding Index: Semantic search infrastructure for crawled content
- [x] 7. Interactive Playground: Web UI for testing, comparing strategies with AI assistance
- [x] 8. Performance Monitor: Real-time insights into crawler operations
- [ ] 9. Cloud Integration: One-click deployment solutions across cloud providers
- [ ] 10. Sponsorship Program: Structured support system with tiered benefits
- [x] 10. Sponsorship Program: Structured support system with tiered benefits
- [ ] 11. Educational Content: "How to Crawl" video series and interactive tutorials
</details>
@@ -603,12 +687,13 @@ Here's the updated license section:
## 📄 License & Attribution
This project is licensed under the Apache License 2.0 with a required attribution clause. See the [Apache 2.0 License](https://github.com/unclecode/crawl4ai/blob/main/LICENSE) file for details.
This project is licensed under the Apache License 2.0, attribution is recommended via the badges below. See the [Apache 2.0 License](https://github.com/unclecode/crawl4ai/blob/main/LICENSE) file for details.
### Attribution Requirements
When using Crawl4AI, you must include one of the following attribution methods:
#### 1. Badge Attribution (Recommended)
<details>
<summary>📈 <strong>1. Badge Attribution (Recommended)</strong></summary>
Add one of these badges to your README, documentation, or website:
| Theme | Badge |
@@ -647,11 +732,15 @@ HTML code for adding the badges:
</a>
```
#### 2. Text Attribution
</details>
<details>
<summary>📖 <strong>2. Text Attribution</strong></summary>
Add this line to your documentation:
```
This project uses Crawl4AI (https://github.com/unclecode/crawl4ai) for web data extraction.
```
</details>
## 📚 Citation

65
SPONSORS.md Normal file
View File

@@ -0,0 +1,65 @@
# 💖 Sponsors & Supporters
Thank you to everyone supporting Crawl4AI! Your sponsorship helps keep this project open-source and actively maintained.
## 👑 Founding Sponsors
*The first 50 sponsors who believed in our vision - permanently recognized*
<!-- Founding sponsors will be listed here with special recognition -->
🎉 **Become a Founding Sponsor!** Only [X/50] spots remaining! [Join now →](https://github.com/sponsors/unclecode)
---
## 🏢 Data Infrastructure Partners ($2000/month)
*These organizations are building their data sovereignty with Crawl4AI at the core*
<!-- Data Infrastructure Partners will be listed here -->
*Be the first Data Infrastructure Partner! [Join us →](https://github.com/sponsors/unclecode)*
---
## 💼 Growing Teams ($500/month)
*Teams scaling their data extraction with Crawl4AI*
<!-- Growing Teams will be listed here -->
*Your team could be here! [Become a sponsor →](https://github.com/sponsors/unclecode)*
---
## 🚀 Builders ($50/month)
*Developers and entrepreneurs building with Crawl4AI*
<!-- Builders will be listed here -->
*Join the builders! [Start sponsoring →](https://github.com/sponsors/unclecode)*
---
## 🌱 Believers ($5/month)
*The community supporting data democratization*
<!-- Believers will be listed here -->
*Thank you to all our community believers!*
---
## 🤝 Want to Sponsor?
Crawl4AI is the #1 trending open-source web crawler. We're building the future of data extraction - where organizations own their data pipelines instead of relying on rate-limited APIs.
### Available Sponsorship Tiers:
- **🌱 Believer** ($5/mo) - Support the movement
- **🚀 Builder** ($50/mo) - Priority support & early access
- **💼 Growing Team** ($500/mo) - Bi-weekly syncs & optimization
- **🏢 Data Infrastructure Partner** ($2000/mo) - Full partnership & dedicated support
[View all tiers and benefits →](https://github.com/sponsors/unclecode)
### Enterprise & Custom Partnerships
Building data extraction at scale? Need dedicated support or infrastructure? Let's talk about a custom partnership.
📧 Contact: [hello@crawl4ai.com](mailto:hello@crawl4ai.com) | 📅 [Schedule a call](https://calendar.app.google/rEpvi2UBgUQjWHfJ9)
---
*This list is updated regularly. Sponsors at $50+ tiers can submit their logos via [hello@crawl4ai.com](mailto:hello@crawl4ai.com)*

View File

@@ -2,18 +2,13 @@
import warnings
from .async_webcrawler import AsyncWebCrawler, CacheMode
from .async_configs import BrowserConfig, CrawlerRunConfig, HTTPCrawlerConfig, LLMConfig
from .pipeline.pipeline import (
Pipeline,
create_pipeline,
)
from .pipeline.crawler import Crawler
# MODIFIED: Add SeedingConfig and VirtualScrollConfig here
from .async_configs import BrowserConfig, CrawlerRunConfig, HTTPCrawlerConfig, LLMConfig, ProxyConfig, GeolocationConfig, SeedingConfig, VirtualScrollConfig, LinkPreviewConfig, MatchMode
from .content_scraping_strategy import (
ContentScrapingStrategy,
WebScrapingStrategy,
LXMLWebScrapingStrategy,
WebScrapingStrategy, # Backward compatibility alias
)
from .async_logger import (
AsyncLoggerBase,
@@ -29,7 +24,8 @@ from .extraction_strategy import (
CosineStrategy,
JsonCssExtractionStrategy,
JsonXPathExtractionStrategy,
JsonLxmlExtractionStrategy
JsonLxmlExtractionStrategy,
RegexExtractionStrategy
)
from .chunking_strategy import ChunkingStrategy, RegexChunking
from .markdown_generation_strategy import DefaultMarkdownGenerator
@@ -41,6 +37,7 @@ from .content_filter_strategy import (
)
from .models import CrawlResult, MarkdownGenerationResult, DisplayMode
from .components.crawler_monitor import CrawlerMonitor
from .link_preview import LinkPreview
from .async_dispatcher import (
MemoryAdaptiveDispatcher,
SemaphoreDispatcher,
@@ -70,20 +67,57 @@ from .deep_crawling import (
DFSDeepCrawlStrategy,
DeepCrawlDecorator,
)
# NEW: Import AsyncUrlSeeder
from .async_url_seeder import AsyncUrlSeeder
# Adaptive Crawler
from .adaptive_crawler import (
AdaptiveCrawler,
AdaptiveConfig,
CrawlState,
CrawlStrategy,
StatisticalStrategy
)
from .async_crawler_strategy import AsyncPlaywrightCrawlerStrategy, AsyncHTTPCrawlerStrategy
# C4A Script Language Support
from .script import (
compile as c4a_compile,
validate as c4a_validate,
compile_file as c4a_compile_file,
CompilationResult,
ValidationResult,
ErrorDetail
)
# Browser Adapters
from .browser_adapter import (
BrowserAdapter,
PlaywrightAdapter,
UndetectedAdapter
)
from .utils import (
start_colab_display_server,
setup_colab_environment
)
__all__ = [
"Pipeline",
"AsyncPlaywrightCrawlerStrategy",
"AsyncHTTPCrawlerStrategy",
"create_pipeline",
"Crawler",
"AsyncLoggerBase",
"AsyncLogger",
"AsyncWebCrawler",
"BrowserProfiler",
"LLMConfig",
"GeolocationConfig",
# NEW: Add SeedingConfig and VirtualScrollConfig
"SeedingConfig",
"VirtualScrollConfig",
# NEW: Add AsyncUrlSeeder
"AsyncUrlSeeder",
# Adaptive Crawler
"AdaptiveCrawler",
"AdaptiveConfig",
"CrawlState",
"CrawlStrategy",
"StatisticalStrategy",
"DeepCrawlStrategy",
"BFSDeepCrawlStrategy",
"BestFirstCrawlingStrategy",
@@ -105,6 +139,7 @@ __all__ = [
"CrawlResult",
"CrawlerHub",
"CacheMode",
"MatchMode",
"ContentScrapingStrategy",
"WebScrapingStrategy",
"LXMLWebScrapingStrategy",
@@ -117,6 +152,7 @@ __all__ = [
"JsonCssExtractionStrategy",
"JsonXPathExtractionStrategy",
"JsonLxmlExtractionStrategy",
"RegexExtractionStrategy",
"ChunkingStrategy",
"RegexChunking",
"DefaultMarkdownGenerator",
@@ -129,11 +165,27 @@ __all__ = [
"SemaphoreDispatcher",
"RateLimiter",
"CrawlerMonitor",
"LinkPreview",
"DisplayMode",
"MarkdownGenerationResult",
"Crawl4aiDockerClient",
"ProxyRotationStrategy",
"RoundRobinProxyStrategy",
"ProxyConfig",
"start_colab_display_server",
"setup_colab_environment",
# C4A Script additions
"c4a_compile",
"c4a_validate",
"c4a_compile_file",
"CompilationResult",
"ValidationResult",
"ErrorDetail",
# Browser Adapters
"BrowserAdapter",
"PlaywrightAdapter",
"UndetectedAdapter",
"LinkPreviewConfig"
]
@@ -162,4 +214,4 @@ __all__ = [
# Disable all Pydantic warnings
warnings.filterwarnings("ignore", module="pydantic")
# pydantic_warnings.filter_warnings()
# pydantic_warnings.filter_warnings()

View File

@@ -1,2 +1,8 @@
# crawl4ai/_version.py
__version__ = "0.5.0.post8"
# crawl4ai/__version__.py
# This is the version that will be used for stable releases
__version__ = "0.7.3"
# For nightly builds, this gets set during build process
__nightly_version__ = None

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1861
crawl4ai/adaptive_crawler.py Normal file

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@@ -171,7 +171,10 @@ class AsyncDatabaseManager:
f"Code context:\n{error_context['code_context']}"
)
self.logger.error(
message=create_box_message(error_message, type="error"),
message="{error}",
tag="ERROR",
params={"error": str(error_message)},
boxes=["error"],
)
raise
@@ -189,7 +192,10 @@ class AsyncDatabaseManager:
f"Code context:\n{error_context['code_context']}"
)
self.logger.error(
message=create_box_message(error_message, type="error"),
message="{error}",
tag="ERROR",
params={"error": str(error_message)},
boxes=["error"],
)
raise
finally:

View File

@@ -1,4 +1,4 @@
from typing import Dict, Optional, List, Tuple
from typing import Dict, Optional, List, Tuple, Union
from .async_configs import CrawlerRunConfig
from .models import (
CrawlResult,
@@ -22,6 +22,8 @@ from urllib.parse import urlparse
import random
from abc import ABC, abstractmethod
from .memory_utils import get_true_memory_usage_percent
class RateLimiter:
def __init__(
@@ -96,11 +98,37 @@ class BaseDispatcher(ABC):
self.rate_limiter = rate_limiter
self.monitor = monitor
def select_config(self, url: str, configs: Union[CrawlerRunConfig, List[CrawlerRunConfig]]) -> Optional[CrawlerRunConfig]:
"""Select the appropriate config for a given URL.
Args:
url: The URL to match against
configs: Single config or list of configs to choose from
Returns:
The matching config, or None if no match found
"""
# Single config - return as is
if isinstance(configs, CrawlerRunConfig):
return configs
# Empty list - return None
if not configs:
return None
# Find first matching config
for config in configs:
if config.is_match(url):
return config
# No match found - return None to indicate URL should be skipped
return None
@abstractmethod
async def crawl_url(
self,
url: str,
config: CrawlerRunConfig,
config: Union[CrawlerRunConfig, List[CrawlerRunConfig]],
task_id: str,
monitor: Optional[CrawlerMonitor] = None,
) -> CrawlerTaskResult:
@@ -111,7 +139,7 @@ class BaseDispatcher(ABC):
self,
urls: List[str],
crawler: AsyncWebCrawler, # noqa: F821
config: CrawlerRunConfig,
config: Union[CrawlerRunConfig, List[CrawlerRunConfig]],
monitor: Optional[CrawlerMonitor] = None,
) -> List[CrawlerTaskResult]:
pass
@@ -126,6 +154,7 @@ class MemoryAdaptiveDispatcher(BaseDispatcher):
check_interval: float = 1.0,
max_session_permit: int = 20,
fairness_timeout: float = 600.0, # 10 minutes before prioritizing long-waiting URLs
memory_wait_timeout: Optional[float] = 600.0,
rate_limiter: Optional[RateLimiter] = None,
monitor: Optional[CrawlerMonitor] = None,
):
@@ -136,27 +165,46 @@ class MemoryAdaptiveDispatcher(BaseDispatcher):
self.check_interval = check_interval
self.max_session_permit = max_session_permit
self.fairness_timeout = fairness_timeout
self.memory_wait_timeout = memory_wait_timeout
self.result_queue = asyncio.Queue()
self.task_queue = asyncio.PriorityQueue() # Priority queue for better management
self.memory_pressure_mode = False # Flag to indicate when we're in memory pressure mode
self.current_memory_percent = 0.0 # Track current memory usage
self._high_memory_start_time: Optional[float] = None
async def _memory_monitor_task(self):
"""Background task to continuously monitor memory usage and update state"""
while True:
self.current_memory_percent = psutil.virtual_memory().percent
self.current_memory_percent = get_true_memory_usage_percent()
# Enter memory pressure mode if we cross the threshold
if not self.memory_pressure_mode and self.current_memory_percent >= self.memory_threshold_percent:
self.memory_pressure_mode = True
if self.monitor:
self.monitor.update_memory_status("PRESSURE")
if self.current_memory_percent >= self.memory_threshold_percent:
if not self.memory_pressure_mode:
self.memory_pressure_mode = True
self._high_memory_start_time = time.time()
if self.monitor:
self.monitor.update_memory_status("PRESSURE")
else:
if self._high_memory_start_time is None:
self._high_memory_start_time = time.time()
if (
self.memory_wait_timeout is not None
and self._high_memory_start_time is not None
and time.time() - self._high_memory_start_time >= self.memory_wait_timeout
):
raise MemoryError(
"Memory usage exceeded threshold for"
f" {self.memory_wait_timeout} seconds"
)
# Exit memory pressure mode if we go below recovery threshold
elif self.memory_pressure_mode and self.current_memory_percent <= self.recovery_threshold_percent:
self.memory_pressure_mode = False
self._high_memory_start_time = None
if self.monitor:
self.monitor.update_memory_status("NORMAL")
elif self.current_memory_percent < self.memory_threshold_percent:
self._high_memory_start_time = None
# In critical mode, we might need to take more drastic action
if self.current_memory_percent >= self.critical_threshold_percent:
@@ -180,7 +228,7 @@ class MemoryAdaptiveDispatcher(BaseDispatcher):
async def crawl_url(
self,
url: str,
config: CrawlerRunConfig,
config: Union[CrawlerRunConfig, List[CrawlerRunConfig]],
task_id: str,
retry_count: int = 0,
) -> CrawlerTaskResult:
@@ -188,6 +236,37 @@ class MemoryAdaptiveDispatcher(BaseDispatcher):
error_message = ""
memory_usage = peak_memory = 0.0
# Select appropriate config for this URL
selected_config = self.select_config(url, config)
# If no config matches, return failed result
if selected_config is None:
error_message = f"No matching configuration found for URL: {url}"
if self.monitor:
self.monitor.update_task(
task_id,
status=CrawlStatus.FAILED,
error_message=error_message
)
return CrawlerTaskResult(
task_id=task_id,
url=url,
result=CrawlResult(
url=url,
html="",
metadata={"status": "no_config_match"},
success=False,
error_message=error_message
),
memory_usage=0,
peak_memory=0,
start_time=start_time,
end_time=time.time(),
error_message=error_message,
retry_count=retry_count
)
# Get starting memory for accurate measurement
process = psutil.Process()
start_memory = process.memory_info().rss / (1024 * 1024)
@@ -237,8 +316,8 @@ class MemoryAdaptiveDispatcher(BaseDispatcher):
retry_count=retry_count + 1
)
# Execute the crawl
result = await self.crawler.arun(url, config=config, session_id=task_id)
# Execute the crawl with selected config
result = await self.crawler.arun(url, config=selected_config, session_id=task_id)
# Measure memory usage
end_memory = process.memory_info().rss / (1024 * 1024)
@@ -296,7 +375,7 @@ class MemoryAdaptiveDispatcher(BaseDispatcher):
self,
urls: List[str],
crawler: AsyncWebCrawler,
config: CrawlerRunConfig,
config: Union[CrawlerRunConfig, List[CrawlerRunConfig]],
) -> List[CrawlerTaskResult]:
self.crawler = crawler
@@ -307,7 +386,7 @@ class MemoryAdaptiveDispatcher(BaseDispatcher):
self.monitor.start()
results = []
try:
# Initialize task queue
for url in urls:
@@ -316,11 +395,18 @@ class MemoryAdaptiveDispatcher(BaseDispatcher):
self.monitor.add_task(task_id, url)
# Add to queue with initial priority 0, retry count 0, and current time
await self.task_queue.put((0, (url, task_id, 0, time.time())))
active_tasks = []
# Process until both queues are empty
while not self.task_queue.empty() or active_tasks:
if memory_monitor.done():
exc = memory_monitor.exception()
if exc:
for t in active_tasks:
t.cancel()
raise exc
# If memory pressure is low, start new tasks
if not self.memory_pressure_mode and len(active_tasks) < self.max_session_permit:
try:
@@ -443,7 +529,7 @@ class MemoryAdaptiveDispatcher(BaseDispatcher):
self,
urls: List[str],
crawler: AsyncWebCrawler,
config: CrawlerRunConfig,
config: Union[CrawlerRunConfig, List[CrawlerRunConfig]],
) -> AsyncGenerator[CrawlerTaskResult, None]:
self.crawler = crawler
@@ -465,8 +551,14 @@ class MemoryAdaptiveDispatcher(BaseDispatcher):
active_tasks = []
completed_count = 0
total_urls = len(urls)
while completed_count < total_urls:
if memory_monitor.done():
exc = memory_monitor.exception()
if exc:
for t in active_tasks:
t.cancel()
raise exc
# If memory pressure is low, start new tasks
if not self.memory_pressure_mode and len(active_tasks) < self.max_session_permit:
try:
@@ -539,7 +631,7 @@ class SemaphoreDispatcher(BaseDispatcher):
async def crawl_url(
self,
url: str,
config: CrawlerRunConfig,
config: Union[CrawlerRunConfig, List[CrawlerRunConfig]],
task_id: str,
semaphore: asyncio.Semaphore = None,
) -> CrawlerTaskResult:
@@ -547,6 +639,36 @@ class SemaphoreDispatcher(BaseDispatcher):
error_message = ""
memory_usage = peak_memory = 0.0
# Select appropriate config for this URL
selected_config = self.select_config(url, config)
# If no config matches, return failed result
if selected_config is None:
error_message = f"No matching configuration found for URL: {url}"
if self.monitor:
self.monitor.update_task(
task_id,
status=CrawlStatus.FAILED,
error_message=error_message
)
return CrawlerTaskResult(
task_id=task_id,
url=url,
result=CrawlResult(
url=url,
html="",
metadata={"status": "no_config_match"},
success=False,
error_message=error_message
),
memory_usage=0,
peak_memory=0,
start_time=start_time,
end_time=time.time(),
error_message=error_message
)
try:
if self.monitor:
self.monitor.update_task(
@@ -559,7 +681,7 @@ class SemaphoreDispatcher(BaseDispatcher):
async with semaphore:
process = psutil.Process()
start_memory = process.memory_info().rss / (1024 * 1024)
result = await self.crawler.arun(url, config=config, session_id=task_id)
result = await self.crawler.arun(url, config=selected_config, session_id=task_id)
end_memory = process.memory_info().rss / (1024 * 1024)
memory_usage = peak_memory = end_memory - start_memory
@@ -621,7 +743,7 @@ class SemaphoreDispatcher(BaseDispatcher):
self,
crawler: AsyncWebCrawler, # noqa: F821
urls: List[str],
config: CrawlerRunConfig,
config: Union[CrawlerRunConfig, List[CrawlerRunConfig]],
) -> List[CrawlerTaskResult]:
self.crawler = crawler
if self.monitor:

View File

@@ -1,18 +1,49 @@
from abc import ABC, abstractmethod
from enum import Enum
from typing import Optional, Dict, Any
from colorama import Fore, Style, init
from typing import Optional, Dict, Any, List
import os
from datetime import datetime
from urllib.parse import unquote
from rich.console import Console
from rich.text import Text
from .utils import create_box_message
class LogLevel(Enum):
DEFAULT = 0
DEBUG = 1
INFO = 2
SUCCESS = 3
WARNING = 4
ERROR = 5
CRITICAL = 6
ALERT = 7
NOTICE = 8
EXCEPTION = 9
FATAL = 10
def __str__(self):
return self.name.lower()
class LogColor(str, Enum):
"""Enum for log colors."""
DEBUG = "bright_black"
INFO = "cyan"
SUCCESS = "green"
WARNING = "yellow"
ERROR = "red"
CYAN = "cyan"
GREEN = "green"
YELLOW = "yellow"
MAGENTA = "magenta"
DIM_MAGENTA = "dim magenta"
RED = "red"
def __str__(self):
"""Automatically convert rich color to string."""
return self.value
class AsyncLoggerBase(ABC):
@@ -37,13 +68,14 @@ class AsyncLoggerBase(ABC):
pass
@abstractmethod
def url_status(self, url: str, success: bool, timing: float, tag: str = "FETCH", url_length: int = 50):
def url_status(self, url: str, success: bool, timing: float, tag: str = "FETCH", url_length: int = 100):
pass
@abstractmethod
def error_status(self, url: str, error: str, tag: str = "ERROR", url_length: int = 50):
def error_status(self, url: str, error: str, tag: str = "ERROR", url_length: int = 100):
pass
class AsyncLogger(AsyncLoggerBase):
"""
Asynchronous logger with support for colored console output and file logging.
@@ -61,14 +93,21 @@ class AsyncLogger(AsyncLoggerBase):
"DEBUG": "",
"INFO": "",
"WARNING": "",
"SUCCESS": "",
"CRITICAL": "",
"ALERT": "",
"NOTICE": "",
"EXCEPTION": "",
"FATAL": "",
"DEFAULT": "",
}
DEFAULT_COLORS = {
LogLevel.DEBUG: Fore.LIGHTBLACK_EX,
LogLevel.INFO: Fore.CYAN,
LogLevel.SUCCESS: Fore.GREEN,
LogLevel.WARNING: Fore.YELLOW,
LogLevel.ERROR: Fore.RED,
LogLevel.DEBUG: LogColor.DEBUG,
LogLevel.INFO: LogColor.INFO,
LogLevel.SUCCESS: LogColor.SUCCESS,
LogLevel.WARNING: LogColor.WARNING,
LogLevel.ERROR: LogColor.ERROR,
}
def __init__(
@@ -77,7 +116,7 @@ class AsyncLogger(AsyncLoggerBase):
log_level: LogLevel = LogLevel.DEBUG,
tag_width: int = 10,
icons: Optional[Dict[str, str]] = None,
colors: Optional[Dict[LogLevel, str]] = None,
colors: Optional[Dict[LogLevel, LogColor]] = None,
verbose: bool = True,
):
"""
@@ -91,13 +130,13 @@ class AsyncLogger(AsyncLoggerBase):
colors: Custom colors for different log levels
verbose: Whether to output to console
"""
init() # Initialize colorama
self.log_file = log_file
self.log_level = log_level
self.tag_width = tag_width
self.icons = icons or self.DEFAULT_ICONS
self.colors = colors or self.DEFAULT_COLORS
self.verbose = verbose
self.console = Console()
# Create log file directory if needed
if log_file:
@@ -110,20 +149,23 @@ class AsyncLogger(AsyncLoggerBase):
def _get_icon(self, tag: str) -> str:
"""Get the icon for a tag, defaulting to info icon if not found."""
return self.icons.get(tag, self.icons["INFO"])
def _shorten(self, text, length, placeholder="..."):
"""Truncate text in the middle if longer than length, or pad if shorter."""
if len(text) <= length:
return text.ljust(length) # Pad with spaces to reach desired length
half = (length - len(placeholder)) // 2
shortened = text[:half] + placeholder + text[-half:]
return shortened.ljust(length) # Also pad shortened text to consistent length
def _write_to_file(self, message: str):
"""Write a message to the log file if configured."""
if self.log_file:
text = Text.from_markup(message)
plain_text = text.plain
timestamp = datetime.now().strftime("%Y-%m-%d %H:%M:%S.%f")[:-3]
with open(self.log_file, "a", encoding="utf-8") as f:
# Strip ANSI color codes for file output
clean_message = message.replace(Fore.RESET, "").replace(
Style.RESET_ALL, ""
)
for color in vars(Fore).values():
if isinstance(color, str):
clean_message = clean_message.replace(color, "")
f.write(f"[{timestamp}] {clean_message}\n")
f.write(f"[{timestamp}] {plain_text}\n")
def _log(
self,
@@ -131,8 +173,9 @@ class AsyncLogger(AsyncLoggerBase):
message: str,
tag: str,
params: Optional[Dict[str, Any]] = None,
colors: Optional[Dict[str, str]] = None,
base_color: Optional[str] = None,
colors: Optional[Dict[str, LogColor]] = None,
boxes: Optional[List[str]] = None,
base_color: Optional[LogColor] = None,
**kwargs,
):
"""
@@ -144,55 +187,44 @@ class AsyncLogger(AsyncLoggerBase):
tag: Tag for the message
params: Parameters to format into the message
colors: Color overrides for specific parameters
boxes: Box overrides for specific parameters
base_color: Base color for the entire message
"""
if level.value < self.log_level.value:
return
# Format the message with parameters if provided
# avoid conflict with rich formatting
parsed_message = message.replace("[", "[[").replace("]", "]]")
if params:
try:
# First format the message with raw parameters
formatted_message = message.format(**params)
# FIXME: If there are formatting strings in floating point format,
# this may result in colors and boxes not being applied properly.
# such as {value:.2f}, the value is 0.23333 format it to 0.23,
# but we replace("0.23333", "[color]0.23333[/color]")
formatted_message = parsed_message.format(**params)
for key, value in params.items():
# value_str may discard `[` and `]`, so we need to replace it.
value_str = str(value).replace("[", "[[").replace("]", "]]")
# check is need apply color
if colors and key in colors:
color_str = f"[{colors[key]}]{value_str}[/{colors[key]}]"
formatted_message = formatted_message.replace(value_str, color_str)
value_str = color_str
# Then apply colors if specified
color_map = {
"green": Fore.GREEN,
"red": Fore.RED,
"yellow": Fore.YELLOW,
"blue": Fore.BLUE,
"cyan": Fore.CYAN,
"magenta": Fore.MAGENTA,
"white": Fore.WHITE,
"black": Fore.BLACK,
"reset": Style.RESET_ALL,
}
if colors:
for key, color in colors.items():
# Find the formatted value in the message and wrap it with color
if color in color_map:
color = color_map[color]
if key in params:
value_str = str(params[key])
formatted_message = formatted_message.replace(
value_str, f"{color}{value_str}{Style.RESET_ALL}"
)
# check is need apply box
if boxes and key in boxes:
formatted_message = formatted_message.replace(value_str,
create_box_message(value_str, type=str(level)))
except KeyError as e:
formatted_message = (
f"LOGGING ERROR: Missing parameter {e} in message template"
)
level = LogLevel.ERROR
else:
formatted_message = message
formatted_message = parsed_message
# Construct the full log line
color = base_color or self.colors[level]
log_line = f"{color}{self._format_tag(tag)} {self._get_icon(tag)} {formatted_message}{Style.RESET_ALL}"
color: LogColor = base_color or self.colors[level]
log_line = f"[{color}]{self._format_tag(tag)} {self._get_icon(tag)} {formatted_message} [/{color}]"
# Output to console if verbose
if self.verbose or kwargs.get("force_verbose", False):
print(log_line)
self.console.print(log_line)
# Write to file if configured
self._write_to_file(log_line)
@@ -212,6 +244,22 @@ class AsyncLogger(AsyncLoggerBase):
def warning(self, message: str, tag: str = "WARNING", **kwargs):
"""Log a warning message."""
self._log(LogLevel.WARNING, message, tag, **kwargs)
def critical(self, message: str, tag: str = "CRITICAL", **kwargs):
"""Log a critical message."""
self._log(LogLevel.ERROR, message, tag, **kwargs)
def exception(self, message: str, tag: str = "EXCEPTION", **kwargs):
"""Log an exception message."""
self._log(LogLevel.ERROR, message, tag, **kwargs)
def fatal(self, message: str, tag: str = "FATAL", **kwargs):
"""Log a fatal message."""
self._log(LogLevel.ERROR, message, tag, **kwargs)
def alert(self, message: str, tag: str = "ALERT", **kwargs):
"""Log an alert message."""
self._log(LogLevel.ERROR, message, tag, **kwargs)
def notice(self, message: str, tag: str = "NOTICE", **kwargs):
"""Log a notice message."""
self._log(LogLevel.INFO, message, tag, **kwargs)
def error(self, message: str, tag: str = "ERROR", **kwargs):
"""Log an error message."""
@@ -223,7 +271,7 @@ class AsyncLogger(AsyncLoggerBase):
success: bool,
timing: float,
tag: str = "FETCH",
url_length: int = 50,
url_length: int = 100,
):
"""
Convenience method for logging URL fetch status.
@@ -235,19 +283,20 @@ class AsyncLogger(AsyncLoggerBase):
tag: Tag for the message
url_length: Maximum length for URL in log
"""
decoded_url = unquote(url)
readable_url = self._shorten(decoded_url, url_length)
self._log(
level=LogLevel.SUCCESS if success else LogLevel.ERROR,
message="{url:.{url_length}}... | Status: {status} | Time: {timing:.2f}s",
message="{url} | {status} | : {timing:.2f}s",
tag=tag,
params={
"url": url,
"url_length": url_length,
"status": success,
"url": readable_url,
"status": "" if success else "",
"timing": timing,
},
colors={
"status": Fore.GREEN if success else Fore.RED,
"timing": Fore.YELLOW,
"status": LogColor.SUCCESS if success else LogColor.ERROR,
"timing": LogColor.WARNING,
},
)
@@ -263,11 +312,13 @@ class AsyncLogger(AsyncLoggerBase):
tag: Tag for the message
url_length: Maximum length for URL in log
"""
decoded_url = unquote(url)
readable_url = self._shorten(decoded_url, url_length)
self._log(
level=LogLevel.ERROR,
message="{url:.{url_length}}... | Error: {error}",
message="{url} | Error: {error}",
tag=tag,
params={"url": url, "url_length": url_length, "error": error},
params={"url": readable_url, "error": error},
)
class AsyncFileLogger(AsyncLoggerBase):
@@ -311,13 +362,13 @@ class AsyncFileLogger(AsyncLoggerBase):
"""Log an error message to file."""
self._write_to_file("ERROR", message, tag)
def url_status(self, url: str, success: bool, timing: float, tag: str = "FETCH", url_length: int = 50):
def url_status(self, url: str, success: bool, timing: float, tag: str = "FETCH", url_length: int = 100):
"""Log URL fetch status to file."""
status = "SUCCESS" if success else "FAILED"
message = f"{url[:url_length]}... | Status: {status} | Time: {timing:.2f}s"
self._write_to_file("URL_STATUS", message, tag)
def error_status(self, url: str, error: str, tag: str = "ERROR", url_length: int = 50):
def error_status(self, url: str, error: str, tag: str = "ERROR", url_length: int = 100):
"""Log error status to file."""
message = f"{url[:url_length]}... | Error: {error}"
self._write_to_file("ERROR", message, tag)

1471
crawl4ai/async_url_seeder.py Normal file

File diff suppressed because it is too large Load Diff

View File

@@ -2,7 +2,6 @@ from .__version__ import __version__ as crawl4ai_version
import os
import sys
import time
from colorama import Fore
from pathlib import Path
from typing import Optional, List
import json
@@ -36,17 +35,18 @@ from .markdown_generation_strategy import (
)
from .deep_crawling import DeepCrawlDecorator
from .async_logger import AsyncLogger, AsyncLoggerBase
from .async_configs import BrowserConfig, CrawlerRunConfig
from .async_configs import BrowserConfig, CrawlerRunConfig, ProxyConfig, SeedingConfig
from .async_dispatcher import * # noqa: F403
from .async_dispatcher import BaseDispatcher, MemoryAdaptiveDispatcher, RateLimiter
from .async_url_seeder import AsyncUrlSeeder
from .utils import (
sanitize_input_encode,
InvalidCSSSelectorError,
fast_format_html,
create_box_message,
get_error_context,
RobotsParser,
preprocess_html_for_schema,
)
@@ -111,7 +111,8 @@ class AsyncWebCrawler:
self,
crawler_strategy: AsyncCrawlerStrategy = None,
config: BrowserConfig = None,
base_directory: str = str(os.getenv("CRAWL4_AI_BASE_DIRECTORY", Path.home())),
base_directory: str = str(
os.getenv("CRAWL4_AI_BASE_DIRECTORY", Path.home())),
thread_safe: bool = False,
logger: AsyncLoggerBase = None,
**kwargs,
@@ -139,7 +140,8 @@ class AsyncWebCrawler:
)
# Initialize crawler strategy
params = {k: v for k, v in kwargs.items() if k in ["browser_config", "logger"]}
params = {k: v for k, v in kwargs.items() if k in [
"browser_config", "logger"]}
self.crawler_strategy = crawler_strategy or AsyncPlaywrightCrawlerStrategy(
browser_config=browser_config,
logger=self.logger,
@@ -162,6 +164,8 @@ class AsyncWebCrawler:
# Decorate arun method with deep crawling capabilities
self._deep_handler = DeepCrawlDecorator(self)
self.arun = self._deep_handler(self.arun)
self.url_seeder: Optional[AsyncUrlSeeder] = None
async def start(self):
"""
@@ -237,7 +241,8 @@ class AsyncWebCrawler:
config = config or CrawlerRunConfig()
if not isinstance(url, str) or not url:
raise ValueError("Invalid URL, make sure the URL is a non-empty string")
raise ValueError(
"Invalid URL, make sure the URL is a non-empty string")
async with self._lock or self.nullcontext():
try:
@@ -291,12 +296,12 @@ class AsyncWebCrawler:
# Update proxy configuration from rotation strategy if available
if config and config.proxy_rotation_strategy:
next_proxy = await config.proxy_rotation_strategy.get_next_proxy()
next_proxy: ProxyConfig = await config.proxy_rotation_strategy.get_next_proxy()
if next_proxy:
self.logger.info(
message="Switch proxy: {proxy}",
tag="PROXY",
params={"proxy": next_proxy.server},
params={"proxy": next_proxy.server}
)
config.proxy_config = next_proxy
# config = config.clone(proxy_config=next_proxy)
@@ -306,7 +311,8 @@ class AsyncWebCrawler:
t1 = time.perf_counter()
if config.user_agent:
self.crawler_strategy.update_user_agent(config.user_agent)
self.crawler_strategy.update_user_agent(
config.user_agent)
# Check robots.txt if enabled
if config and config.check_robots_txt:
@@ -353,10 +359,11 @@ class AsyncWebCrawler:
html=html,
extracted_content=extracted_content,
config=config, # Pass the config object instead of individual parameters
screenshot=screenshot_data,
screenshot_data=screenshot_data,
pdf_data=pdf_data,
verbose=config.verbose,
is_raw_html=True if url.startswith("raw:") else False,
redirected_url=async_response.redirected_url,
**kwargs,
)
@@ -365,25 +372,21 @@ class AsyncWebCrawler:
crawl_result.response_headers = async_response.response_headers
crawl_result.downloaded_files = async_response.downloaded_files
crawl_result.js_execution_result = js_execution_result
crawl_result.ssl_certificate = (
async_response.ssl_certificate
) # Add SSL certificate
crawl_result.mhtml = async_response.mhtml_data
crawl_result.ssl_certificate = async_response.ssl_certificate
# Add captured network and console data if available
crawl_result.network_requests = async_response.network_requests
crawl_result.console_messages = async_response.console_messages
crawl_result.success = bool(html)
crawl_result.session_id = getattr(config, "session_id", None)
crawl_result.session_id = getattr(
config, "session_id", None)
self.logger.success(
message="{url:.50}... | Status: {status} | Total: {timing}",
self.logger.url_status(
url=cache_context.display_url,
success=crawl_result.success,
timing=time.perf_counter() - start_time,
tag="COMPLETE",
params={
"url": cache_context.display_url,
"status": crawl_result.success,
"timing": f"{time.perf_counter() - start_time:.2f}s",
},
colors={
"status": Fore.GREEN if crawl_result.success else Fore.RED,
"timing": Fore.YELLOW,
},
)
# Update cache if appropriate
@@ -393,19 +396,15 @@ class AsyncWebCrawler:
return CrawlResultContainer(crawl_result)
else:
self.logger.success(
message="{url:.50}... | Status: {status} | Total: {timing}",
tag="COMPLETE",
params={
"url": cache_context.display_url,
"status": True,
"timing": f"{time.perf_counter() - start_time:.2f}s",
},
colors={"status": Fore.GREEN, "timing": Fore.YELLOW},
self.logger.url_status(
url=cache_context.display_url,
success=True,
timing=time.perf_counter() - start_time,
tag="COMPLETE"
)
cached_result.success = bool(html)
cached_result.session_id = getattr(config, "session_id", None)
cached_result.session_id = getattr(
config, "session_id", None)
cached_result.redirected_url = cached_result.redirected_url or url
return CrawlResultContainer(cached_result)
@@ -421,7 +420,7 @@ class AsyncWebCrawler:
self.logger.error_status(
url=url,
error=create_box_message(error_message, type="error"),
error=error_message,
tag="ERROR",
)
@@ -437,7 +436,7 @@ class AsyncWebCrawler:
html: str,
extracted_content: str,
config: CrawlerRunConfig,
screenshot: str,
screenshot_data: str,
pdf_data: str,
verbose: bool,
**kwargs,
@@ -450,7 +449,7 @@ class AsyncWebCrawler:
html: Raw HTML content
extracted_content: Previously extracted content (if any)
config: Configuration object controlling processing behavior
screenshot: Screenshot data (if any)
screenshot_data: Screenshot data (if any)
pdf_data: PDF data (if any)
verbose: Whether to enable verbose logging
**kwargs: Additional parameters for backwards compatibility
@@ -472,12 +471,14 @@ class AsyncWebCrawler:
params = config.__dict__.copy()
params.pop("url", None)
# add keys from kwargs to params that doesn't exist in params
params.update({k: v for k, v in kwargs.items() if k not in params.keys()})
params.update({k: v for k, v in kwargs.items()
if k not in params.keys()})
################################
# Scraping Strategy Execution #
################################
result: ScrapingResult = scraping_strategy.scrap(url, html, **params)
result: ScrapingResult = scraping_strategy.scrap(
url, html, **params)
if result is None:
raise ValueError(
@@ -493,16 +494,24 @@ class AsyncWebCrawler:
# Extract results - handle both dict and ScrapingResult
if isinstance(result, dict):
cleaned_html = sanitize_input_encode(result.get("cleaned_html", ""))
cleaned_html = sanitize_input_encode(
result.get("cleaned_html", ""))
media = result.get("media", {})
tables = media.pop("tables", []) if isinstance(media, dict) else []
links = result.get("links", {})
metadata = result.get("metadata", {})
else:
cleaned_html = sanitize_input_encode(result.cleaned_html)
media = result.media.model_dump()
links = result.links.model_dump()
# media = result.media.model_dump()
# tables = media.pop("tables", [])
# links = result.links.model_dump()
media = result.media.model_dump() if hasattr(result.media, 'model_dump') else result.media
tables = media.pop("tables", []) if isinstance(media, dict) else []
links = result.links.model_dump() if hasattr(result.links, 'model_dump') else result.links
metadata = result.metadata
fit_html = preprocess_html_for_schema(html_content=html, text_threshold= 500, max_size= 300_000)
################################
# Generate Markdown #
################################
@@ -510,27 +519,65 @@ class AsyncWebCrawler:
config.markdown_generator or DefaultMarkdownGenerator()
)
# --- SELECT HTML SOURCE BASED ON CONTENT_SOURCE ---
# Get the desired source from the generator config, default to 'cleaned_html'
selected_html_source = getattr(markdown_generator, 'content_source', 'cleaned_html')
# Define the source selection logic using dict dispatch
html_source_selector = {
"raw_html": lambda: html, # The original raw HTML
"cleaned_html": lambda: cleaned_html, # The HTML after scraping strategy
"fit_html": lambda: fit_html, # The HTML after preprocessing for schema
}
markdown_input_html = cleaned_html # Default to cleaned_html
try:
# Get the appropriate lambda function, default to returning cleaned_html if key not found
source_lambda = html_source_selector.get(selected_html_source, lambda: cleaned_html)
# Execute the lambda to get the selected HTML
markdown_input_html = source_lambda()
# Log which source is being used (optional, but helpful for debugging)
# if self.logger and verbose:
# actual_source_used = selected_html_source if selected_html_source in html_source_selector else 'cleaned_html (default)'
# self.logger.debug(f"Using '{actual_source_used}' as source for Markdown generation for {url}", tag="MARKDOWN_SRC")
except Exception as e:
# Handle potential errors, especially from preprocess_html_for_schema
if self.logger:
self.logger.warning(
f"Error getting/processing '{selected_html_source}' for markdown source: {e}. Falling back to cleaned_html.",
tag="MARKDOWN_SRC"
)
# Ensure markdown_input_html is still the default cleaned_html in case of error
markdown_input_html = cleaned_html
# --- END: HTML SOURCE SELECTION ---
# Uncomment if by default we want to use PruningContentFilter
# if not config.content_filter and not markdown_generator.content_filter:
# markdown_generator.content_filter = PruningContentFilter()
markdown_result: MarkdownGenerationResult = (
markdown_generator.generate_markdown(
cleaned_html=cleaned_html,
base_url=url,
input_html=markdown_input_html,
base_url=params.get("redirected_url", url)
# html2text_options=kwargs.get('html2text', {})
)
)
# Log processing completion
self.logger.info(
message="{url:.50}... | Time: {timing}s",
tag="SCRAPE",
params={
"url": _url,
"timing": int((time.perf_counter() - t1) * 1000) / 1000,
},
self.logger.url_status(
url=_url,
success=True,
timing=int((time.perf_counter() - t1) * 1000) / 1000,
tag="SCRAPE"
)
# self.logger.info(
# message="{url:.50}... | Time: {timing}s",
# tag="SCRAPE",
# params={"url": _url, "timing": int((time.perf_counter() - t1) * 1000) / 1000},
# )
################################
# Structured Content Extraction #
@@ -544,16 +591,19 @@ class AsyncWebCrawler:
# Choose content based on input_format
content_format = config.extraction_strategy.input_format
if content_format == "fit_markdown" and not markdown_result.fit_markdown:
self.logger.warning(
message="Fit markdown requested but not available. Falling back to raw markdown.",
tag="EXTRACT",
params={"url": _url},
)
self.logger.url_status(
url=_url,
success=bool(html),
timing=time.perf_counter() - t1,
tag="EXTRACT",
)
content_format = "markdown"
content = {
"markdown": markdown_result.raw_markdown,
"html": html,
"fit_html": fit_html,
"cleaned_html": cleaned_html,
"fit_markdown": markdown_result.fit_markdown,
}.get(content_format, markdown_result.raw_markdown)
@@ -561,7 +611,7 @@ class AsyncWebCrawler:
# Use IdentityChunking for HTML input, otherwise use provided chunking strategy
chunking = (
IdentityChunking()
if content_format in ["html", "cleaned_html"]
if content_format in ["html", "cleaned_html", "fit_html"]
else config.chunking_strategy
)
sections = chunking.chunk(content)
@@ -571,15 +621,12 @@ class AsyncWebCrawler:
)
# Log extraction completion
self.logger.info(
message="Completed for {url:.50}... | Time: {timing}s",
tag="EXTRACT",
params={"url": _url, "timing": time.perf_counter() - t1},
)
# Handle screenshot and PDF data
screenshot_data = None if not screenshot else screenshot
pdf_data = None if not pdf_data else pdf_data
self.logger.url_status(
url=_url,
success=bool(html),
timing=time.perf_counter() - t1,
tag="EXTRACT",
)
# Apply HTML formatting if requested
if config.prettiify:
@@ -589,9 +636,11 @@ class AsyncWebCrawler:
return CrawlResult(
url=url,
html=html,
fit_html=fit_html,
cleaned_html=cleaned_html,
markdown=markdown_result,
media=media,
tables=tables, # NEW
links=links,
metadata=metadata,
screenshot=screenshot_data,
@@ -604,7 +653,7 @@ class AsyncWebCrawler:
async def arun_many(
self,
urls: List[str],
config: Optional[CrawlerRunConfig] = None,
config: Optional[Union[CrawlerRunConfig, List[CrawlerRunConfig]]] = None,
dispatcher: Optional[BaseDispatcher] = None,
# Legacy parameters maintained for backwards compatibility
# word_count_threshold=MIN_WORD_THRESHOLD,
@@ -625,7 +674,9 @@ class AsyncWebCrawler:
Args:
urls: List of URLs to crawl
config: Configuration object controlling crawl behavior for all URLs
config: Configuration object(s) controlling crawl behavior. Can be:
- Single CrawlerRunConfig: Used for all URLs
- List[CrawlerRunConfig]: Configs with url_matcher for URL-specific settings
dispatcher: The dispatcher strategy instance to use. Defaults to MemoryAdaptiveDispatcher
[other parameters maintained for backwards compatibility]
@@ -690,7 +741,11 @@ class AsyncWebCrawler:
or task_result.result
)
stream = config.stream
# Handle stream setting - use first config's stream setting if config is a list
if isinstance(config, list):
stream = config[0].stream if config else False
else:
stream = config.stream
if stream:
@@ -704,3 +759,94 @@ class AsyncWebCrawler:
else:
_results = await dispatcher.run_urls(crawler=self, urls=urls, config=config)
return [transform_result(res) for res in _results]
async def aseed_urls(
self,
domain_or_domains: Union[str, List[str]],
config: Optional[SeedingConfig] = None,
**kwargs
) -> Union[List[str], Dict[str, List[Union[str, Dict[str, Any]]]]]:
"""
Discovers, filters, and optionally validates URLs for a given domain(s)
using sitemaps and Common Crawl archives.
Args:
domain_or_domains: A single domain string (e.g., "iana.org") or a list of domains.
config: A SeedingConfig object to control the seeding process.
Parameters passed directly via kwargs will override those in 'config'.
**kwargs: Additional parameters (e.g., `source`, `live_check`, `extract_head`,
`pattern`, `concurrency`, `hits_per_sec`, `force_refresh`, `verbose`)
that will be used to construct or update the SeedingConfig.
Returns:
If `extract_head` is False:
- For a single domain: `List[str]` of discovered URLs.
- For multiple domains: `Dict[str, List[str]]` mapping each domain to its URLs.
If `extract_head` is True:
- For a single domain: `List[Dict[str, Any]]` where each dict contains 'url'
and 'head_data' (parsed <head> metadata).
- For multiple domains: `Dict[str, List[Dict[str, Any]]]` mapping each domain
to a list of URL data dictionaries.
Raises:
ValueError: If `domain_or_domains` is not a string or a list of strings.
Exception: Any underlying exceptions from AsyncUrlSeeder or network operations.
Example:
>>> # Discover URLs from sitemap with live check for 'example.com'
>>> result = await crawler.aseed_urls("example.com", source="sitemap", live_check=True, hits_per_sec=10)
>>> # Discover URLs from Common Crawl, extract head data for 'example.com' and 'python.org'
>>> multi_domain_result = await crawler.aseed_urls(
>>> ["example.com", "python.org"],
>>> source="cc", extract_head=True, concurrency=200, hits_per_sec=50
>>> )
"""
# Initialize AsyncUrlSeeder here if it hasn't been already
if not self.url_seeder:
# Pass the crawler's base_directory for seeder's cache management
# Pass the crawler's logger for consistent logging
self.url_seeder = AsyncUrlSeeder(
base_directory=self.crawl4ai_folder,
logger=self.logger
)
# Merge config object with direct kwargs, giving kwargs precedence
seeding_config = config.clone(**kwargs) if config else SeedingConfig.from_kwargs(kwargs)
# Ensure base_directory is set for the seeder's cache
seeding_config.base_directory = seeding_config.base_directory or self.crawl4ai_folder
# Ensure the seeder uses the crawler's logger (if not already set)
if not self.url_seeder.logger:
self.url_seeder.logger = self.logger
# Pass verbose setting if explicitly provided in SeedingConfig or kwargs
if seeding_config.verbose is not None:
self.url_seeder.logger.verbose = seeding_config.verbose
else: # Default to crawler's verbose setting
self.url_seeder.logger.verbose = self.logger.verbose
if isinstance(domain_or_domains, str):
self.logger.info(
message="Starting URL seeding for domain: {domain}",
tag="SEED",
params={"domain": domain_or_domains}
)
return await self.url_seeder.urls(
domain_or_domains,
seeding_config
)
elif isinstance(domain_or_domains, (list, tuple)):
self.logger.info(
message="Starting URL seeding for {count} domains",
tag="SEED",
params={"count": len(domain_or_domains)}
)
# AsyncUrlSeeder.many_urls directly accepts a list of domains and individual params.
return await self.url_seeder.many_urls(
domain_or_domains,
seeding_config
)
else:
raise ValueError("`domain_or_domains` must be a string or a list of strings.")

View File

@@ -1,23 +0,0 @@
"""Browser management module for Crawl4AI.
This module provides browser management capabilities using different strategies
for browser creation and interaction.
"""
from .manager import BrowserManager
from .profiles import BrowserProfileManager
from .models import DockerConfig
from .docker_registry import DockerRegistry
from .docker_utils import DockerUtils
from .browser_hub import BrowserHub
from .strategies import (
BaseBrowserStrategy,
PlaywrightBrowserStrategy,
CDPBrowserStrategy,
BuiltinBrowserStrategy,
DockerBrowserStrategy
)
__all__ = ['BrowserManager', 'BrowserProfileManager', 'DockerConfig', 'DockerRegistry', 'DockerUtils', 'BaseBrowserStrategy',
'PlaywrightBrowserStrategy', 'CDPBrowserStrategy', 'BuiltinBrowserStrategy',
'DockerBrowserStrategy', 'BrowserHub']

View File

@@ -1,184 +0,0 @@
# browser_hub_manager.py
import hashlib
import json
import asyncio
from typing import Dict, Optional, List, Tuple
from .manager import BrowserManager, UnavailableBehavior
from ..async_configs import BrowserConfig, CrawlerRunConfig
from ..async_logger import AsyncLogger
class BrowserHub:
"""
Manages Browser-Hub instances for sharing across multiple pipelines.
This class provides centralized management for browser resources, allowing
multiple pipelines to share browser instances efficiently, connect to
existing browser hubs, or create new ones with custom configurations.
"""
_instances: Dict[str, BrowserManager] = {}
_lock = asyncio.Lock()
@classmethod
async def get_browser_manager(
cls,
config: Optional[BrowserConfig] = None,
hub_id: Optional[str] = None,
connection_info: Optional[str] = None,
logger: Optional[AsyncLogger] = None,
max_browsers_per_config: int = 10,
max_pages_per_browser: int = 5,
initial_pool_size: int = 1,
page_configs: Optional[List[Tuple[BrowserConfig, CrawlerRunConfig, int]]] = None
) -> BrowserManager:
"""
Get an existing BrowserManager or create a new one based on parameters.
Args:
config: Browser configuration for new hub
hub_id: Identifier for the hub instance
connection_info: Connection string for existing hub
logger: Logger for recording events and errors
max_browsers_per_config: Maximum browsers per configuration
max_pages_per_browser: Maximum pages per browser
initial_pool_size: Initial number of browsers to create
page_configs: Optional configurations for pre-warming pages
Returns:
BrowserManager: The requested browser manager instance
"""
async with cls._lock:
# Scenario 3: Use existing hub via connection info
if connection_info:
instance_key = f"connection:{connection_info}"
if instance_key not in cls._instances:
cls._instances[instance_key] = await cls._connect_to_browser_hub(
connection_info, logger
)
return cls._instances[instance_key]
# Scenario 2: Custom configured hub
if config:
config_hash = cls._hash_config(config)
instance_key = hub_id or f"config:{config_hash}"
if instance_key not in cls._instances:
cls._instances[instance_key] = await cls._create_browser_manager(
config,
logger,
max_browsers_per_config,
max_pages_per_browser,
initial_pool_size,
page_configs
)
return cls._instances[instance_key]
# Scenario 1: Default hub
instance_key = "default"
if instance_key not in cls._instances:
cls._instances[instance_key] = await cls._create_default_browser_hub(
logger,
max_browsers_per_config,
max_pages_per_browser,
initial_pool_size
)
return cls._instances[instance_key]
@classmethod
async def _create_browser_manager(
cls,
config: BrowserConfig,
logger: Optional[AsyncLogger],
max_browsers_per_config: int,
max_pages_per_browser: int,
initial_pool_size: int,
page_configs: Optional[List[Tuple[BrowserConfig, CrawlerRunConfig, int]]] = None
) -> BrowserManager:
"""Create a new browser hub with the specified configuration."""
manager = BrowserManager(
browser_config=config,
logger=logger,
unavailable_behavior=UnavailableBehavior.ON_DEMAND,
max_browsers_per_config=max_browsers_per_config,
max_pages_per_browser=max_pages_per_browser,
)
# Initialize the pool
await manager.initialize_pool(
browser_configs=[config] if config else None,
browsers_per_config=initial_pool_size,
page_configs=page_configs
)
return manager
@classmethod
async def _create_default_browser_hub(
cls,
logger: Optional[AsyncLogger],
max_browsers_per_config: int,
max_pages_per_browser: int,
initial_pool_size: int
) -> BrowserManager:
"""Create a default browser hub with standard settings."""
config = BrowserConfig(headless=True)
return await cls._create_browser_manager(
config,
logger,
max_browsers_per_config,
max_pages_per_browser,
initial_pool_size,
None
)
@classmethod
async def _connect_to_browser_hub(
cls,
connection_info: str,
logger: Optional[AsyncLogger]
) -> BrowserManager:
"""
Connect to an existing browser hub.
Note: This is a placeholder for future remote connection functionality.
Currently creates a local instance.
"""
if logger:
logger.info(
message="Remote browser hub connections not yet implemented. Creating local instance.",
tag="BROWSER_HUB"
)
# For now, create a default local instance
return await cls._create_default_browser_hub(
logger,
max_browsers_per_config=10,
max_pages_per_browser=5,
initial_pool_size=1
)
@classmethod
def _hash_config(cls, config: BrowserConfig) -> str:
"""Create a hash of the browser configuration for identification."""
# Convert config to dictionary, excluding any callable objects
config_dict = config.__dict__.copy()
for key in list(config_dict.keys()):
if callable(config_dict[key]):
del config_dict[key]
# Convert to canonical JSON string
config_json = json.dumps(config_dict, sort_keys=True, default=str)
# Hash the JSON
config_hash = hashlib.sha256(config_json.encode()).hexdigest()
return config_hash
@classmethod
async def shutdown_all(cls):
"""Close all browser hub instances and clear the registry."""
async with cls._lock:
shutdown_tasks = []
for hub in cls._instances.values():
shutdown_tasks.append(hub.close())
if shutdown_tasks:
await asyncio.gather(*shutdown_tasks)
cls._instances.clear()

View File

@@ -1,34 +0,0 @@
# ---------- Dockerfile ----------
FROM alpine:latest
# Combine everything in one RUN to keep layers minimal.
RUN apk update && apk upgrade && \
apk add --no-cache \
chromium \
nss \
freetype \
harfbuzz \
ca-certificates \
ttf-freefont \
socat \
curl && \
addgroup -S chromium && adduser -S chromium -G chromium && \
mkdir -p /data && chown chromium:chromium /data && \
rm -rf /var/cache/apk/*
# Copy start script, then chown/chmod in one step
COPY start.sh /home/chromium/start.sh
RUN chown chromium:chromium /home/chromium/start.sh && \
chmod +x /home/chromium/start.sh
USER chromium
WORKDIR /home/chromium
# Expose port used by socat (mapping 9222→9223 or whichever you prefer)
EXPOSE 9223
# Simple healthcheck: is the remote debug endpoint responding?
HEALTHCHECK --interval=30s --timeout=5s --retries=3 CMD curl -f http://localhost:9222/json/version || exit 1
CMD ["./start.sh"]

View File

@@ -1,27 +0,0 @@
# ---------- Dockerfile (Idle Version) ----------
FROM alpine:latest
# Install only Chromium and its dependencies in a single layer
RUN apk update && apk upgrade && \
apk add --no-cache \
chromium \
nss \
freetype \
harfbuzz \
ca-certificates \
ttf-freefont \
socat \
curl && \
addgroup -S chromium && adduser -S chromium -G chromium && \
mkdir -p /data && chown chromium:chromium /data && \
rm -rf /var/cache/apk/*
ENV PATH="/usr/bin:/bin:/usr/sbin:/sbin"
# Switch to a non-root user for security
USER chromium
WORKDIR /home/chromium
# Idle: container does nothing except stay alive
CMD ["tail", "-f", "/dev/null"]

View File

@@ -1,23 +0,0 @@
# Use Debian 12 (Bookworm) slim for a small, stable base image
FROM debian:bookworm-slim
ENV DEBIAN_FRONTEND=noninteractive
# Install Chromium, socat, and basic fonts
RUN apt-get update && apt-get install -y --no-install-recommends \
chromium \
wget \
curl \
socat \
fonts-freefont-ttf \
fonts-noto-color-emoji && \
apt-get clean && rm -rf /var/lib/apt/lists/*
# Copy start.sh and make it executable
COPY start.sh /start.sh
RUN chmod +x /start.sh
# Expose socat port (use host mapping, e.g. -p 9225:9223)
EXPOSE 9223
ENTRYPOINT ["/start.sh"]

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@@ -1,264 +0,0 @@
"""Docker registry module for Crawl4AI.
This module provides a registry system for tracking and reusing Docker containers
across browser sessions, improving performance and resource utilization.
"""
import os
import json
import time
from typing import Dict, Optional
from ..utils import get_home_folder
class DockerRegistry:
"""Manages a registry of Docker containers used for browser automation.
This registry tracks containers by configuration hash, allowing reuse of appropriately
configured containers instead of creating new ones for each session.
Attributes:
registry_file (str): Path to the registry file
containers (dict): Dictionary of container information
port_map (dict): Map of host ports to container IDs
last_port (int): Last port assigned
"""
def __init__(self, registry_file: Optional[str] = None):
"""Initialize the registry with an optional path to the registry file.
Args:
registry_file: Path to the registry file. If None, uses default path.
"""
# Use the same file path as BuiltinBrowserStrategy by default
self.registry_file = registry_file or os.path.join(get_home_folder(), "builtin-browser", "browser_config.json")
self.containers = {} # Still maintain this for backward compatibility
self.port_map = {} # Will be populated from the shared file
self.last_port = 9222
self.load()
def load(self):
"""Load container registry from file."""
if os.path.exists(self.registry_file):
try:
with open(self.registry_file, 'r') as f:
registry_data = json.load(f)
# Initialize port_map if not present
if "port_map" not in registry_data:
registry_data["port_map"] = {}
self.port_map = registry_data.get("port_map", {})
# Extract container information from port_map entries of type "docker"
self.containers = {}
for port_str, browser_info in self.port_map.items():
if browser_info.get("browser_type") == "docker" and "container_id" in browser_info:
container_id = browser_info["container_id"]
self.containers[container_id] = {
"host_port": int(port_str),
"config_hash": browser_info.get("config_hash", ""),
"created_at": browser_info.get("created_at", time.time())
}
# Get last port if available
if "last_port" in registry_data:
self.last_port = registry_data["last_port"]
else:
# Find highest port in port_map
ports = [int(p) for p in self.port_map.keys() if p.isdigit()]
self.last_port = max(ports + [9222])
except Exception as e:
# Reset to defaults on error
print(f"Error loading registry: {e}")
self.containers = {}
self.port_map = {}
self.last_port = 9222
else:
# Initialize with defaults if file doesn't exist
self.containers = {}
self.port_map = {}
self.last_port = 9222
def save(self):
"""Save container registry to file."""
# First load the current file to avoid overwriting other browser types
current_data = {"port_map": {}, "last_port": self.last_port}
if os.path.exists(self.registry_file):
try:
with open(self.registry_file, 'r') as f:
current_data = json.load(f)
except Exception:
pass
# Create a new port_map dictionary
updated_port_map = {}
# First, copy all non-docker entries from the existing port_map
for port_str, browser_info in current_data.get("port_map", {}).items():
if browser_info.get("browser_type") != "docker":
updated_port_map[port_str] = browser_info
# Then add all current docker container entries
for container_id, container_info in self.containers.items():
port_str = str(container_info["host_port"])
updated_port_map[port_str] = {
"browser_type": "docker",
"container_id": container_id,
"cdp_url": f"http://localhost:{port_str}",
"config_hash": container_info["config_hash"],
"created_at": container_info["created_at"]
}
# Replace the port_map with our updated version
current_data["port_map"] = updated_port_map
# Update last_port
current_data["last_port"] = self.last_port
# Ensure directory exists
os.makedirs(os.path.dirname(self.registry_file), exist_ok=True)
# Save the updated data
with open(self.registry_file, 'w') as f:
json.dump(current_data, f, indent=2)
def register_container(self, container_id: str, host_port: int, config_hash: str, cdp_json_config: Optional[str] = None):
"""Register a container with its configuration hash and port mapping.
Args:
container_id: Docker container ID
host_port: Host port mapped to container
config_hash: Hash of configuration used to create container
cdp_json_config: CDP JSON configuration if available
"""
self.containers[container_id] = {
"host_port": host_port,
"config_hash": config_hash,
"created_at": time.time()
}
# Update port_map to maintain compatibility with BuiltinBrowserStrategy
port_str = str(host_port)
self.port_map[port_str] = {
"browser_type": "docker",
"container_id": container_id,
"cdp_url": f"http://localhost:{port_str}",
"config_hash": config_hash,
"created_at": time.time()
}
if cdp_json_config:
self.port_map[port_str]["cdp_json_config"] = cdp_json_config
self.save()
def unregister_container(self, container_id: str):
"""Unregister a container.
Args:
container_id: Docker container ID to unregister
"""
if container_id in self.containers:
host_port = self.containers[container_id]["host_port"]
port_str = str(host_port)
# Remove from port_map
if port_str in self.port_map:
del self.port_map[port_str]
# Remove from containers
del self.containers[container_id]
self.save()
async def find_container_by_config(self, config_hash: str, docker_utils) -> Optional[str]:
"""Find a container that matches the given configuration hash.
Args:
config_hash: Hash of configuration to match
docker_utils: DockerUtils instance to check running containers
Returns:
Container ID if found, None otherwise
"""
# Search through port_map for entries with matching config_hash
for port_str, browser_info in self.port_map.items():
if (browser_info.get("browser_type") == "docker" and
browser_info.get("config_hash") == config_hash and
"container_id" in browser_info):
container_id = browser_info["container_id"]
if await docker_utils.is_container_running(container_id):
return container_id
return None
def get_container_host_port(self, container_id: str) -> Optional[int]:
"""Get the host port mapped to the container.
Args:
container_id: Docker container ID
Returns:
Host port if container is registered, None otherwise
"""
if container_id in self.containers:
return self.containers[container_id]["host_port"]
return None
def get_next_available_port(self, docker_utils) -> int:
"""Get the next available host port for Docker mapping.
Args:
docker_utils: DockerUtils instance to check port availability
Returns:
Available port number
"""
# Start from last port + 1
port = self.last_port + 1
# Check if port is in use (either in our registry or system-wide)
while str(port) in self.port_map or docker_utils.is_port_in_use(port):
port += 1
# Update last port
self.last_port = port
self.save()
return port
def get_container_config_hash(self, container_id: str) -> Optional[str]:
"""Get the configuration hash for a container.
Args:
container_id: Docker container ID
Returns:
Configuration hash if container is registered, None otherwise
"""
if container_id in self.containers:
return self.containers[container_id]["config_hash"]
return None
def cleanup_stale_containers(self, docker_utils):
"""Clean up containers that are no longer running.
Args:
docker_utils: DockerUtils instance to check container status
"""
to_remove = []
# Find containers that are no longer running
for port_str, browser_info in self.port_map.items():
if browser_info.get("browser_type") == "docker" and "container_id" in browser_info:
container_id = browser_info["container_id"]
if not docker_utils.is_container_running(container_id):
to_remove.append(container_id)
# Remove stale containers
for container_id in to_remove:
self.unregister_container(container_id)

View File

@@ -1,661 +0,0 @@
import os
import json
import asyncio
import hashlib
import tempfile
import shutil
import socket
import subprocess
from typing import Dict, List, Optional, Tuple, Union
class DockerUtils:
"""Utility class for Docker operations in browser automation.
This class provides methods for managing Docker images, containers,
and related operations needed for browser automation. It handles
image building, container lifecycle, port management, and registry operations.
Attributes:
DOCKER_FOLDER (str): Path to folder containing Docker files
DOCKER_CONNECT_FILE (str): Path to Dockerfile for connect mode
DOCKER_LAUNCH_FILE (str): Path to Dockerfile for launch mode
DOCKER_START_SCRIPT (str): Path to startup script for connect mode
DEFAULT_CONNECT_IMAGE (str): Default image name for connect mode
DEFAULT_LAUNCH_IMAGE (str): Default image name for launch mode
logger: Optional logger instance
"""
# File paths for Docker resources
DOCKER_FOLDER = os.path.join(os.path.dirname(__file__), "docker")
DOCKER_CONNECT_FILE = os.path.join(DOCKER_FOLDER, "connect.Dockerfile")
DOCKER_LAUNCH_FILE = os.path.join(DOCKER_FOLDER, "launch.Dockerfile")
DOCKER_START_SCRIPT = os.path.join(DOCKER_FOLDER, "start.sh")
# Default image names
DEFAULT_CONNECT_IMAGE = "crawl4ai/browser-connect:latest"
DEFAULT_LAUNCH_IMAGE = "crawl4ai/browser-launch:latest"
def __init__(self, logger=None):
"""Initialize Docker utilities.
Args:
logger: Optional logger for recording operations
"""
self.logger = logger
# Image Management Methods
async def check_image_exists(self, image_name: str) -> bool:
"""Check if a Docker image exists.
Args:
image_name: Name of the Docker image to check
Returns:
bool: True if the image exists, False otherwise
"""
cmd = ["docker", "image", "inspect", image_name]
try:
process = await asyncio.create_subprocess_exec(
*cmd, stdout=asyncio.subprocess.PIPE, stderr=asyncio.subprocess.PIPE
)
_, _ = await process.communicate()
return process.returncode == 0
except Exception as e:
if self.logger:
self.logger.debug(
f"Error checking if image exists: {str(e)}", tag="DOCKER"
)
return False
async def build_docker_image(
self,
image_name: str,
dockerfile_path: str,
files_to_copy: Dict[str, str] = None,
) -> bool:
"""Build a Docker image from a Dockerfile.
Args:
image_name: Name to give the built image
dockerfile_path: Path to the Dockerfile
files_to_copy: Dict of {dest_name: source_path} for files to copy to build context
Returns:
bool: True if image was built successfully, False otherwise
"""
# Create a temporary build context
with tempfile.TemporaryDirectory() as temp_dir:
# Copy the Dockerfile
shutil.copy(dockerfile_path, os.path.join(temp_dir, "Dockerfile"))
# Copy any additional files needed
if files_to_copy:
for dest_name, source_path in files_to_copy.items():
shutil.copy(source_path, os.path.join(temp_dir, dest_name))
# Build the image
cmd = ["docker", "build", "-t", image_name, temp_dir]
if self.logger:
self.logger.debug(
f"Building Docker image with command: {' '.join(cmd)}", tag="DOCKER"
)
process = await asyncio.create_subprocess_exec(
*cmd, stdout=asyncio.subprocess.PIPE, stderr=asyncio.subprocess.PIPE
)
stdout, stderr = await process.communicate()
if process.returncode != 0:
if self.logger:
self.logger.error(
message="Failed to build Docker image: {error}",
tag="DOCKER",
params={"error": stderr.decode()},
)
return False
if self.logger:
self.logger.success(
f"Successfully built Docker image: {image_name}", tag="DOCKER"
)
return True
async def ensure_docker_image_exists(
self, image_name: str, mode: str = "connect"
) -> str:
"""Ensure the required Docker image exists, creating it if necessary.
Args:
image_name: Name of the Docker image
mode: Either "connect" or "launch" to determine which image to build
Returns:
str: Name of the available Docker image
Raises:
Exception: If image doesn't exist and can't be built
"""
# If image name is not specified, use default based on mode
if not image_name:
image_name = (
self.DEFAULT_CONNECT_IMAGE
if mode == "connect"
else self.DEFAULT_LAUNCH_IMAGE
)
# Check if the image already exists
if await self.check_image_exists(image_name):
if self.logger:
self.logger.debug(
f"Docker image {image_name} already exists", tag="DOCKER"
)
return image_name
# If we're using a custom image that doesn't exist, warn and fail
if (
image_name != self.DEFAULT_CONNECT_IMAGE
and image_name != self.DEFAULT_LAUNCH_IMAGE
):
if self.logger:
self.logger.warning(
f"Custom Docker image {image_name} not found and cannot be automatically created",
tag="DOCKER",
)
raise Exception(f"Docker image {image_name} not found")
# Build the appropriate default image
if self.logger:
self.logger.info(
f"Docker image {image_name} not found, creating it now...", tag="DOCKER"
)
if mode == "connect":
success = await self.build_docker_image(
image_name,
self.DOCKER_CONNECT_FILE,
{"start.sh": self.DOCKER_START_SCRIPT},
)
else:
success = await self.build_docker_image(image_name, self.DOCKER_LAUNCH_FILE)
if not success:
raise Exception(f"Failed to create Docker image {image_name}")
return image_name
# Container Management Methods
async def create_container(
self,
image_name: str,
host_port: int,
container_name: Optional[str] = None,
volumes: List[str] = None,
network: Optional[str] = None,
env_vars: Dict[str, str] = None,
cpu_limit: float = 1.0,
memory_limit: str = "1.5g",
extra_args: List[str] = None,
) -> Optional[str]:
"""Create a new Docker container.
Args:
image_name: Docker image to use
host_port: Port on host to map to container port 9223
container_name: Optional name for the container
volumes: List of volume mappings (e.g., ["host_path:container_path"])
network: Optional Docker network to use
env_vars: Dictionary of environment variables
cpu_limit: CPU limit for the container
memory_limit: Memory limit for the container
extra_args: Additional docker run arguments
Returns:
str: Container ID if successful, None otherwise
"""
# Prepare container command
cmd = [
"docker",
"run",
"--detach",
]
# Add container name if specified
if container_name:
cmd.extend(["--name", container_name])
# Add port mapping
cmd.extend(["-p", f"{host_port}:9223"])
# Add volumes
if volumes:
for volume in volumes:
cmd.extend(["-v", volume])
# Add network if specified
if network:
cmd.extend(["--network", network])
# Add environment variables
if env_vars:
for key, value in env_vars.items():
cmd.extend(["-e", f"{key}={value}"])
# Add CPU and memory limits
if cpu_limit:
cmd.extend(["--cpus", str(cpu_limit)])
if memory_limit:
cmd.extend(["--memory", memory_limit])
cmd.extend(["--memory-swap", memory_limit])
if self.logger:
self.logger.debug(
f"Setting CPU limit: {cpu_limit}, Memory limit: {memory_limit}",
tag="DOCKER",
)
# Add extra args
if extra_args:
cmd.extend(extra_args)
# Add image
cmd.append(image_name)
if self.logger:
self.logger.debug(
f"Creating Docker container with command: {' '.join(cmd)}", tag="DOCKER"
)
# Run docker command
try:
process = await asyncio.create_subprocess_exec(
*cmd, stdout=asyncio.subprocess.PIPE, stderr=asyncio.subprocess.PIPE
)
stdout, stderr = await process.communicate()
if process.returncode != 0:
if self.logger:
self.logger.error(
message="Failed to create Docker container: {error}",
tag="DOCKER",
params={"error": stderr.decode()},
)
return None
# Get container ID
container_id = stdout.decode().strip()
if self.logger:
self.logger.success(
f"Created Docker container: {container_id[:12]}", tag="DOCKER"
)
return container_id
except Exception as e:
if self.logger:
self.logger.error(
message="Error creating Docker container: {error}",
tag="DOCKER",
params={"error": str(e)},
)
return None
async def is_container_running(self, container_id: str) -> bool:
"""Check if a container is running.
Args:
container_id: ID of the container to check
Returns:
bool: True if the container is running, False otherwise
"""
cmd = ["docker", "inspect", "--format", "{{.State.Running}}", container_id]
try:
process = await asyncio.create_subprocess_exec(
*cmd, stdout=asyncio.subprocess.PIPE, stderr=asyncio.subprocess.PIPE
)
stdout, _ = await process.communicate()
return process.returncode == 0 and stdout.decode().strip() == "true"
except Exception as e:
if self.logger:
self.logger.debug(
f"Error checking if container is running: {str(e)}", tag="DOCKER"
)
return False
async def wait_for_container_ready(
self, container_id: str, timeout: int = 30
) -> bool:
"""Wait for the container to be in running state.
Args:
container_id: ID of the container to wait for
timeout: Maximum time to wait in seconds
Returns:
bool: True if container is ready, False if timeout occurred
"""
for _ in range(timeout):
if await self.is_container_running(container_id):
return True
await asyncio.sleep(1)
if self.logger:
self.logger.warning(
f"Container {container_id[:12]} not ready after {timeout}s timeout",
tag="DOCKER",
)
return False
async def stop_container(self, container_id: str) -> bool:
"""Stop a Docker container.
Args:
container_id: ID of the container to stop
Returns:
bool: True if stopped successfully, False otherwise
"""
cmd = ["docker", "stop", container_id]
try:
process = await asyncio.create_subprocess_exec(*cmd)
await process.communicate()
if self.logger:
self.logger.debug(
f"Stopped container: {container_id[:12]}", tag="DOCKER"
)
return process.returncode == 0
except Exception as e:
if self.logger:
self.logger.warning(
message="Failed to stop container: {error}",
tag="DOCKER",
params={"error": str(e)},
)
return False
async def remove_container(self, container_id: str, force: bool = True) -> bool:
"""Remove a Docker container.
Args:
container_id: ID of the container to remove
force: Whether to force removal
Returns:
bool: True if removed successfully, False otherwise
"""
cmd = ["docker", "rm"]
if force:
cmd.append("-f")
cmd.append(container_id)
try:
process = await asyncio.create_subprocess_exec(*cmd)
await process.communicate()
if self.logger:
self.logger.debug(
f"Removed container: {container_id[:12]}", tag="DOCKER"
)
return process.returncode == 0
except Exception as e:
if self.logger:
self.logger.warning(
message="Failed to remove container: {error}",
tag="DOCKER",
params={"error": str(e)},
)
return False
# Container Command Execution Methods
async def exec_in_container(
self, container_id: str, command: List[str], detach: bool = False
) -> Tuple[int, str, str]:
"""Execute a command in a running container.
Args:
container_id: ID of the container
command: Command to execute as a list of strings
detach: Whether to run the command in detached mode
Returns:
Tuple of (return_code, stdout, stderr)
"""
cmd = ["docker", "exec"]
if detach:
cmd.append("-d")
cmd.append(container_id)
cmd.extend(command)
try:
process = await asyncio.create_subprocess_exec(
*cmd, stdout=asyncio.subprocess.PIPE, stderr=asyncio.subprocess.PIPE
)
stdout, stderr = await process.communicate()
return process.returncode, stdout.decode(), stderr.decode()
except Exception as e:
if self.logger:
self.logger.error(
message="Error executing command in container: {error}",
tag="DOCKER",
params={"error": str(e)},
)
return -1, "", str(e)
async def start_socat_in_container(self, container_id: str) -> bool:
"""Start socat in the container to map port 9222 to 9223.
Args:
container_id: ID of the container
Returns:
bool: True if socat started successfully, False otherwise
"""
# Command to run socat as a background process
cmd = ["socat", "TCP-LISTEN:9223,fork", "TCP:localhost:9222"]
returncode, _, stderr = await self.exec_in_container(
container_id, cmd, detach=True
)
if returncode != 0:
if self.logger:
self.logger.error(
message="Failed to start socat in container: {error}",
tag="DOCKER",
params={"error": stderr},
)
return False
if self.logger:
self.logger.debug(
f"Started socat in container: {container_id[:12]}", tag="DOCKER"
)
# Wait a moment for socat to start
await asyncio.sleep(1)
return True
async def launch_chrome_in_container(
self, container_id: str, browser_args: List[str]
) -> bool:
"""Launch Chrome inside the container with specified arguments.
Args:
container_id: ID of the container
browser_args: Chrome command line arguments
Returns:
bool: True if Chrome started successfully, False otherwise
"""
# Build Chrome command
chrome_cmd = ["chromium"]
chrome_cmd.extend(browser_args)
returncode, _, stderr = await self.exec_in_container(
container_id, chrome_cmd, detach=True
)
if returncode != 0:
if self.logger:
self.logger.error(
message="Failed to launch Chrome in container: {error}",
tag="DOCKER",
params={"error": stderr},
)
return False
if self.logger:
self.logger.debug(
f"Launched Chrome in container: {container_id[:12]}", tag="DOCKER"
)
return True
async def get_process_id_in_container(
self, container_id: str, process_name: str
) -> Optional[int]:
"""Get the process ID for a process in the container.
Args:
container_id: ID of the container
process_name: Name pattern to search for
Returns:
int: Process ID if found, None otherwise
"""
cmd = ["pgrep", "-f", process_name]
returncode, stdout, _ = await self.exec_in_container(container_id, cmd)
if returncode == 0 and stdout.strip():
pid = int(stdout.strip().split("\n")[0])
return pid
return None
async def stop_process_in_container(self, container_id: str, pid: int) -> bool:
"""Stop a process in the container by PID.
Args:
container_id: ID of the container
pid: Process ID to stop
Returns:
bool: True if process was stopped, False otherwise
"""
cmd = ["kill", "-TERM", str(pid)]
returncode, _, stderr = await self.exec_in_container(container_id, cmd)
if returncode != 0:
if self.logger:
self.logger.warning(
message="Failed to stop process in container: {error}",
tag="DOCKER",
params={"error": stderr},
)
return False
if self.logger:
self.logger.debug(
f"Stopped process {pid} in container: {container_id[:12]}", tag="DOCKER"
)
return True
# Network and Port Methods
async def wait_for_cdp_ready(self, host_port: int, timeout: int = 10) -> dict:
"""Wait for the CDP endpoint to be ready.
Args:
host_port: Port to check for CDP endpoint
timeout: Maximum time to wait in seconds
Returns:
dict: CDP JSON config if ready, None if timeout occurred
"""
import aiohttp
url = f"http://localhost:{host_port}/json/version"
for _ in range(timeout):
try:
async with aiohttp.ClientSession() as session:
async with session.get(url, timeout=1) as response:
if response.status == 200:
if self.logger:
self.logger.debug(
f"CDP endpoint ready on port {host_port}",
tag="DOCKER",
)
cdp_json_config = await response.json()
if self.logger:
self.logger.debug(
f"CDP JSON config: {cdp_json_config}", tag="DOCKER"
)
return cdp_json_config
except Exception:
pass
await asyncio.sleep(1)
if self.logger:
self.logger.warning(
f"CDP endpoint not ready on port {host_port} after {timeout}s timeout",
tag="DOCKER",
)
return None
def is_port_in_use(self, port: int) -> bool:
"""Check if a port is already in use on the host.
Args:
port: Port number to check
Returns:
bool: True if port is in use, False otherwise
"""
with socket.socket(socket.AF_INET, socket.SOCK_STREAM) as s:
return s.connect_ex(("localhost", port)) == 0
def get_next_available_port(self, start_port: int = 9223) -> int:
"""Get the next available port starting from a given port.
Args:
start_port: Port number to start checking from
Returns:
int: First available port number
"""
port = start_port
while self.is_port_in_use(port):
port += 1
return port
# Configuration Hash Methods
def generate_config_hash(self, config_dict: Dict) -> str:
"""Generate a hash of the configuration for container matching.
Args:
config_dict: Dictionary of configuration parameters
Returns:
str: Hash string uniquely identifying this configuration
"""
# Convert to canonical JSON string and hash
config_json = json.dumps(config_dict, sort_keys=True)
return hashlib.sha256(config_json.encode()).hexdigest()

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@@ -1,177 +0,0 @@
"""Browser manager module for Crawl4AI.
This module provides a central browser management class that uses the
strategy pattern internally while maintaining the existing API.
It also implements a page pooling mechanism for improved performance.
"""
from typing import Optional, Tuple, List
from playwright.async_api import Page, BrowserContext
from ..async_logger import AsyncLogger
from ..async_configs import BrowserConfig, CrawlerRunConfig
from .strategies import (
BaseBrowserStrategy,
PlaywrightBrowserStrategy,
CDPBrowserStrategy,
BuiltinBrowserStrategy,
DockerBrowserStrategy
)
class BrowserManager:
"""Main interface for browser management in Crawl4AI.
This class maintains backward compatibility with the existing implementation
while using the strategy pattern internally for different browser types.
Attributes:
config (BrowserConfig): Configuration object containing all browser settings
logger: Logger instance for recording events and errors
browser: The browser instance
default_context: The default browser context
managed_browser: The managed browser instance
playwright: The Playwright instance
sessions: Dictionary to store session information
session_ttl: Session timeout in seconds
"""
def __init__(self, browser_config: Optional[BrowserConfig] = None, logger: Optional[AsyncLogger] = None):
"""Initialize the BrowserManager with a browser configuration.
Args:
browser_config: Configuration object containing all browser settings
logger: Logger instance for recording events and errors
"""
self.config = browser_config or BrowserConfig()
self.logger = logger
# Create strategy based on configuration
self.strategy = self._create_strategy()
# Initialize state variables for compatibility with existing code
self.browser = None
self.default_context = None
self.managed_browser = None
self.playwright = None
# For session management (from existing implementation)
self.sessions = {}
self.session_ttl = 1800 # 30 minutes
def _create_strategy(self) -> BaseBrowserStrategy:
"""Create appropriate browser strategy based on configuration.
Returns:
BaseBrowserStrategy: The selected browser strategy
"""
if self.config.browser_mode == "builtin":
return BuiltinBrowserStrategy(self.config, self.logger)
elif self.config.browser_mode == "docker":
if DockerBrowserStrategy is None:
if self.logger:
self.logger.error(
"Docker browser strategy requested but not available. "
"Falling back to PlaywrightBrowserStrategy.",
tag="BROWSER"
)
return PlaywrightBrowserStrategy(self.config, self.logger)
return DockerBrowserStrategy(self.config, self.logger)
elif self.config.browser_mode == "cdp" or self.config.cdp_url or self.config.use_managed_browser:
return CDPBrowserStrategy(self.config, self.logger)
else:
return PlaywrightBrowserStrategy(self.config, self.logger)
async def start(self):
"""Start the browser instance and set up the default context.
Returns:
self: For method chaining
"""
# Start the strategy
await self.strategy.start()
# Update legacy references
self.browser = self.strategy.browser
self.default_context = self.strategy.default_context
# Set browser process reference (for CDP strategy)
if hasattr(self.strategy, 'browser_process'):
self.managed_browser = self.strategy
# Set Playwright reference
self.playwright = self.strategy.playwright
# Sync sessions if needed
if hasattr(self.strategy, 'sessions'):
self.sessions = self.strategy.sessions
self.session_ttl = self.strategy.session_ttl
return self
async def get_page(self, crawlerRunConfig: CrawlerRunConfig) -> Tuple[Page, BrowserContext]:
"""Get a page for the given configuration.
Args:
crawlerRunConfig: Configuration object for the crawler run
Returns:
Tuple of (Page, BrowserContext)
"""
# Delegate to strategy
page, context = await self.strategy.get_page(crawlerRunConfig)
# Sync sessions if needed
if hasattr(self.strategy, 'sessions'):
self.sessions = self.strategy.sessions
return page, context
async def get_pages(self, crawlerRunConfig: CrawlerRunConfig, count: int = 1) -> List[Tuple[Page, BrowserContext]]:
"""Get multiple pages with the same configuration.
This method efficiently creates multiple browser pages using the same configuration,
which is useful for parallel crawling of multiple URLs.
Args:
crawlerRunConfig: Configuration for the pages
count: Number of pages to create
Returns:
List of (Page, Context) tuples
"""
# Delegate to strategy
pages = await self.strategy.get_pages(crawlerRunConfig, count)
# Sync sessions if needed
if hasattr(self.strategy, 'sessions'):
self.sessions = self.strategy.sessions
return pages
# Just for legacy compatibility
async def kill_session(self, session_id: str):
"""Kill a browser session and clean up resources.
Args:
session_id: The session ID to kill
"""
# Handle kill_session via our strategy if it supports it
await self.strategy.kill_session(session_id)
# sync sessions if needed
if hasattr(self.strategy, 'sessions'):
self.sessions = self.strategy.sessions
async def close(self):
"""Close the browser and clean up resources."""
# Delegate to strategy
await self.strategy.close()
# Reset legacy references
self.browser = None
self.default_context = None
self.managed_browser = None
self.playwright = None
self.sessions = {}

View File

@@ -1,853 +0,0 @@
"""Browser manager module for Crawl4AI.
This module provides a central browser management class that uses the
strategy pattern internally while maintaining the existing API.
It also implements browser pooling for improved performance.
"""
import asyncio
import hashlib
import json
import math
from enum import Enum
from typing import Dict, List, Optional, Tuple, Any
from playwright.async_api import Page, BrowserContext
from ..async_logger import AsyncLogger
from ..async_configs import BrowserConfig, CrawlerRunConfig
from .strategies import (
BaseBrowserStrategy,
PlaywrightBrowserStrategy,
CDPBrowserStrategy,
BuiltinBrowserStrategy,
DockerBrowserStrategy
)
class UnavailableBehavior(Enum):
"""Behavior when no browser is available."""
ON_DEMAND = "on_demand" # Create new browser on demand
PENDING = "pending" # Wait until a browser is available
EXCEPTION = "exception" # Raise an exception
class BrowserManager:
"""Main interface for browser management and pooling in Crawl4AI.
This class maintains backward compatibility with the existing implementation
while using the strategy pattern internally for different browser types.
It also implements browser pooling for improved performance.
Attributes:
config (BrowserConfig): Default configuration object for browsers
logger (AsyncLogger): Logger instance for recording events and errors
browser_pool (Dict): Dictionary to store browser instances by configuration
browser_in_use (Dict): Dictionary to track which browsers are in use
request_queues (Dict): Queues for pending requests by configuration
unavailable_behavior (UnavailableBehavior): Behavior when no browser is available
"""
def __init__(
self,
browser_config: Optional[BrowserConfig] = None,
logger: Optional[AsyncLogger] = None,
unavailable_behavior: UnavailableBehavior = UnavailableBehavior.EXCEPTION,
max_browsers_per_config: int = 10,
max_pages_per_browser: int = 5
):
"""Initialize the BrowserManager with a browser configuration.
Args:
browser_config: Configuration object containing all browser settings
logger: Logger instance for recording events and errors
unavailable_behavior: Behavior when no browser is available
max_browsers_per_config: Maximum number of browsers per configuration
max_pages_per_browser: Maximum number of pages per browser
"""
self.config = browser_config or BrowserConfig()
self.logger = logger
self.unavailable_behavior = unavailable_behavior
self.max_browsers_per_config = max_browsers_per_config
self.max_pages_per_browser = max_pages_per_browser
# Browser pool management
self.browser_pool = {} # config_hash -> list of browser strategies
self.browser_in_use = {} # strategy instance -> Boolean
self.request_queues = {} # config_hash -> asyncio.Queue()
self._browser_locks = {} # config_hash -> asyncio.Lock()
self._browser_pool_lock = asyncio.Lock() # Global lock for pool modifications
# Page pool management
self.page_pool = {} # (browser_config_hash, crawler_config_hash) -> list of (page, context, strategy)
self._page_pool_lock = asyncio.Lock()
self.browser_page_counts = {} # strategy instance -> current page count
self._page_count_lock = asyncio.Lock() # Lock for thread-safe access to page counts
# For session management (from existing implementation)
self.sessions = {}
self.session_ttl = 1800 # 30 minutes
# For legacy compatibility
self.browser = None
self.default_context = None
self.managed_browser = None
self.playwright = None
self.strategy = None
def _create_browser_config_hash(self, browser_config: BrowserConfig) -> str:
"""Create a hash of the browser configuration for browser pooling.
Args:
browser_config: Browser configuration
Returns:
str: Hash of the browser configuration
"""
# Convert config to dictionary, excluding any callable objects
config_dict = browser_config.__dict__.copy()
for key in list(config_dict.keys()):
if callable(config_dict[key]):
del config_dict[key]
# Convert to canonical JSON string
config_json = json.dumps(config_dict, sort_keys=True, default=str)
# Hash the JSON
config_hash = hashlib.sha256(config_json.encode()).hexdigest()
return config_hash
def _create_strategy(self, browser_config: BrowserConfig) -> BaseBrowserStrategy:
"""Create appropriate browser strategy based on configuration.
Args:
browser_config: Browser configuration
Returns:
BaseBrowserStrategy: The selected browser strategy
"""
if browser_config.browser_mode == "builtin":
return BuiltinBrowserStrategy(browser_config, self.logger)
elif browser_config.browser_mode == "docker":
if DockerBrowserStrategy is None:
if self.logger:
self.logger.error(
"Docker browser strategy requested but not available. "
"Falling back to PlaywrightBrowserStrategy.",
tag="BROWSER"
)
return PlaywrightBrowserStrategy(browser_config, self.logger)
return DockerBrowserStrategy(browser_config, self.logger)
elif browser_config.browser_mode == "cdp" or browser_config.cdp_url or browser_config.use_managed_browser:
return CDPBrowserStrategy(browser_config, self.logger)
else:
return PlaywrightBrowserStrategy(browser_config, self.logger)
async def initialize_pool(
self,
browser_configs: List[BrowserConfig] = None,
browsers_per_config: int = 1,
page_configs: Optional[List[Tuple[BrowserConfig, CrawlerRunConfig, int]]] = None
):
"""Initialize the browser pool with multiple browser configurations.
Args:
browser_configs: List of browser configurations to initialize
browsers_per_config: Number of browser instances per configuration
page_configs: Optional list of (browser_config, crawler_run_config, count) tuples
for pre-warming pages
Returns:
self: For method chaining
"""
if not browser_configs:
browser_configs = [self.config]
# Calculate how many browsers we'll need based on page_configs
browsers_needed = {}
if page_configs:
for browser_config, _, page_count in page_configs:
config_hash = self._create_browser_config_hash(browser_config)
# Calculate browsers based on max_pages_per_browser
browsers_needed_for_config = math.ceil(page_count / self.max_pages_per_browser)
browsers_needed[config_hash] = max(
browsers_needed.get(config_hash, 0),
browsers_needed_for_config
)
# Adjust browsers_per_config if needed to ensure enough capacity
config_browsers_needed = {}
for browser_config in browser_configs:
config_hash = self._create_browser_config_hash(browser_config)
# Estimate browsers needed based on page requirements
browsers_for_config = browsers_per_config
if config_hash in browsers_needed:
browsers_for_config = max(browsers_for_config, browsers_needed[config_hash])
config_browsers_needed[config_hash] = browsers_for_config
# Update max_browsers_per_config if needed
if browsers_for_config > self.max_browsers_per_config:
self.max_browsers_per_config = browsers_for_config
if self.logger:
self.logger.info(
f"Increased max_browsers_per_config to {browsers_for_config} to accommodate page requirements",
tag="POOL"
)
# Initialize locks and queues for each config
async with self._browser_pool_lock:
for browser_config in browser_configs:
config_hash = self._create_browser_config_hash(browser_config)
# Initialize lock for this config if needed
if config_hash not in self._browser_locks:
self._browser_locks[config_hash] = asyncio.Lock()
# Initialize queue for this config if needed
if config_hash not in self.request_queues:
self.request_queues[config_hash] = asyncio.Queue()
# Initialize pool for this config if needed
if config_hash not in self.browser_pool:
self.browser_pool[config_hash] = []
# Create browser instances for each configuration in parallel
browser_tasks = []
for browser_config in browser_configs:
config_hash = self._create_browser_config_hash(browser_config)
browsers_to_create = config_browsers_needed.get(
config_hash,
browsers_per_config
) - len(self.browser_pool.get(config_hash, []))
if browsers_to_create <= 0:
continue
for _ in range(browsers_to_create):
# Create a task for each browser initialization
task = self._create_and_add_browser(browser_config, config_hash)
browser_tasks.append(task)
# Wait for all browser initializations to complete
if browser_tasks:
if self.logger:
self.logger.info(f"Initializing {len(browser_tasks)} browsers in parallel...", tag="POOL")
await asyncio.gather(*browser_tasks)
# Pre-warm pages if requested
if page_configs:
page_tasks = []
for browser_config, crawler_run_config, count in page_configs:
task = self._prewarm_pages(browser_config, crawler_run_config, count)
page_tasks.append(task)
if page_tasks:
if self.logger:
self.logger.info(f"Pre-warming pages with {len(page_tasks)} configurations...", tag="POOL")
await asyncio.gather(*page_tasks)
# Update legacy references
if self.browser_pool and next(iter(self.browser_pool.values()), []):
strategy = next(iter(self.browser_pool.values()))[0]
self.strategy = strategy
self.browser = strategy.browser
self.default_context = strategy.default_context
self.playwright = strategy.playwright
return self
async def _create_and_add_browser(self, browser_config: BrowserConfig, config_hash: str):
"""Create and add a browser to the pool.
Args:
browser_config: Browser configuration
config_hash: Hash of the configuration
"""
try:
strategy = self._create_strategy(browser_config)
await strategy.start()
async with self._browser_pool_lock:
if config_hash not in self.browser_pool:
self.browser_pool[config_hash] = []
self.browser_pool[config_hash].append(strategy)
self.browser_in_use[strategy] = False
if self.logger:
self.logger.debug(
f"Added browser to pool: {browser_config.browser_type} "
f"({browser_config.browser_mode})",
tag="POOL"
)
except Exception as e:
if self.logger:
self.logger.error(
f"Failed to create browser: {str(e)}",
tag="POOL"
)
raise
def _make_config_signature(self, crawlerRunConfig: CrawlerRunConfig) -> str:
"""Create a signature hash from crawler configuration.
Args:
crawlerRunConfig: Crawler run configuration
Returns:
str: Hash of the crawler configuration
"""
config_dict = crawlerRunConfig.__dict__.copy()
# Exclude items that do not affect page creation
ephemeral_keys = [
"session_id",
"js_code",
"scraping_strategy",
"extraction_strategy",
"chunking_strategy",
"cache_mode",
"content_filter",
"semaphore_count",
"url"
]
for key in ephemeral_keys:
if key in config_dict:
del config_dict[key]
# Convert to canonical JSON string
config_json = json.dumps(config_dict, sort_keys=True, default=str)
# Hash the JSON
config_hash = hashlib.sha256(config_json.encode("utf-8")).hexdigest()
return config_hash
async def _prewarm_pages(
self,
browser_config: BrowserConfig,
crawler_run_config: CrawlerRunConfig,
count: int
):
"""Pre-warm pages for a specific configuration.
Args:
browser_config: Browser configuration
crawler_run_config: Crawler run configuration
count: Number of pages to pre-warm
"""
try:
# Create individual page tasks and run them in parallel
browser_config_hash = self._create_browser_config_hash(browser_config)
crawler_config_hash = self._make_config_signature(crawler_run_config)
async def get_single_page():
strategy = await self.get_available_browser(browser_config)
try:
page, context = await strategy.get_page(crawler_run_config)
# Store config hashes on the page object for later retrieval
setattr(page, "_browser_config_hash", browser_config_hash)
setattr(page, "_crawler_config_hash", crawler_config_hash)
return page, context, strategy
except Exception as e:
# Release the browser back to the pool
await self.release_browser(strategy, browser_config)
raise e
# Create tasks for parallel execution
page_tasks = [get_single_page() for _ in range(count)]
# Execute all page creation tasks in parallel
pages_contexts_strategies = await asyncio.gather(*page_tasks)
# Add pages to the page pool
browser_config_hash = self._create_browser_config_hash(browser_config)
crawler_config_hash = self._make_config_signature(crawler_run_config)
pool_key = (browser_config_hash, crawler_config_hash)
async with self._page_pool_lock:
if pool_key not in self.page_pool:
self.page_pool[pool_key] = []
# Add all pages to the pool
self.page_pool[pool_key].extend(pages_contexts_strategies)
if self.logger:
self.logger.debug(
f"Pre-warmed {count} pages in parallel with config {crawler_run_config}",
tag="POOL"
)
except Exception as e:
if self.logger:
self.logger.error(
f"Failed to pre-warm pages: {str(e)}",
tag="POOL"
)
raise
async def get_available_browser(
self,
browser_config: Optional[BrowserConfig] = None
) -> BaseBrowserStrategy:
"""Get an available browser from the pool for the given configuration.
Args:
browser_config: Browser configuration to match
Returns:
BaseBrowserStrategy: An available browser strategy
Raises:
Exception: If no browser is available and behavior is EXCEPTION
"""
browser_config = browser_config or self.config
config_hash = self._create_browser_config_hash(browser_config)
async with self._browser_locks.get(config_hash, asyncio.Lock()):
# Check if we have browsers for this config
if config_hash not in self.browser_pool or not self.browser_pool[config_hash]:
if self.unavailable_behavior == UnavailableBehavior.ON_DEMAND:
# Create a new browser on demand
if self.logger:
self.logger.info(
f"1> Creating new browser on demand for config {config_hash[:8]}",
tag="POOL"
)
# Initialize pool for this config if needed
async with self._browser_pool_lock:
if config_hash not in self.browser_pool:
self.browser_pool[config_hash] = []
strategy = self._create_strategy(browser_config)
await strategy.start()
self.browser_pool[config_hash].append(strategy)
self.browser_in_use[strategy] = False
elif self.unavailable_behavior == UnavailableBehavior.EXCEPTION:
raise Exception(f"No browsers available for configuration {config_hash[:8]}")
# Check for an available browser with capacity in the pool
for strategy in self.browser_pool[config_hash]:
# Check if this browser has capacity for more pages
async with self._page_count_lock:
current_pages = self.browser_page_counts.get(strategy, 0)
if current_pages < self.max_pages_per_browser:
# Increment the page count
self.browser_page_counts[strategy] = current_pages + 1
self.browser_in_use[strategy] = True
# Get browser information for better logging
browser_type = getattr(strategy.config, 'browser_type', 'unknown')
browser_mode = getattr(strategy.config, 'browser_mode', 'unknown')
strategy_id = id(strategy) # Use object ID as a unique identifier
if self.logger:
self.logger.debug(
f"Selected browser #{strategy_id} ({browser_type}/{browser_mode}) - "
f"pages: {current_pages+1}/{self.max_pages_per_browser}",
tag="POOL"
)
return strategy
# All browsers are at capacity or in use
if self.unavailable_behavior == UnavailableBehavior.ON_DEMAND:
# Check if we've reached the maximum number of browsers
if len(self.browser_pool[config_hash]) >= self.max_browsers_per_config:
if self.logger:
self.logger.warning(
f"Maximum browsers reached for config {config_hash[:8]} and all at page capacity",
tag="POOL"
)
if self.unavailable_behavior == UnavailableBehavior.EXCEPTION:
raise Exception("Maximum browsers reached and all at page capacity")
# Create a new browser on demand
if self.logger:
self.logger.info(
f"2> Creating new browser on demand for config {config_hash[:8]}",
tag="POOL"
)
strategy = self._create_strategy(browser_config)
await strategy.start()
async with self._browser_pool_lock:
self.browser_pool[config_hash].append(strategy)
self.browser_in_use[strategy] = True
return strategy
# If we get here, either behavior is EXCEPTION or PENDING
if self.unavailable_behavior == UnavailableBehavior.EXCEPTION:
raise Exception(f"All browsers in use or at page capacity for configuration {config_hash[:8]}")
# For PENDING behavior, set up waiting mechanism
if config_hash not in self.request_queues:
self.request_queues[config_hash] = asyncio.Queue()
# Create a future to wait on
future = asyncio.Future()
await self.request_queues[config_hash].put(future)
if self.logger:
self.logger.debug(
f"Waiting for available browser for config {config_hash[:8]}",
tag="POOL"
)
# Wait for a browser to become available
strategy = await future
return strategy
async def get_page(
self,
crawlerRunConfig: CrawlerRunConfig,
browser_config: Optional[BrowserConfig] = None
) -> Tuple[Page, BrowserContext, BaseBrowserStrategy]:
"""Get a page from the browser pool."""
browser_config = browser_config or self.config
# Check if we have a pre-warmed page available
browser_config_hash = self._create_browser_config_hash(browser_config)
crawler_config_hash = self._make_config_signature(crawlerRunConfig)
pool_key = (browser_config_hash, crawler_config_hash)
# Try to get a page from the pool
async with self._page_pool_lock:
if pool_key in self.page_pool and self.page_pool[pool_key]:
# Get a page from the pool
page, context, strategy = self.page_pool[pool_key].pop()
# Mark browser as in use (it already is, but ensure consistency)
self.browser_in_use[strategy] = True
if self.logger:
self.logger.debug(
f"Using pre-warmed page for config {crawler_config_hash[:8]}",
tag="POOL"
)
# Note: We don't increment page count since it was already counted when created
return page, context, strategy
# No pre-warmed page available, create a new one
# get_available_browser already increments the page count
strategy = await self.get_available_browser(browser_config)
try:
# Get a page from the browser
page, context = await strategy.get_page(crawlerRunConfig)
# Store config hashes on the page object for later retrieval
setattr(page, "_browser_config_hash", browser_config_hash)
setattr(page, "_crawler_config_hash", crawler_config_hash)
return page, context, strategy
except Exception as e:
# Release the browser back to the pool and decrement the page count
await self.release_browser(strategy, browser_config, decrement_page_count=True)
raise e
async def release_page(
self,
page: Page,
strategy: BaseBrowserStrategy,
browser_config: Optional[BrowserConfig] = None,
keep_alive: bool = True,
return_to_pool: bool = True
):
"""Release a page back to the pool."""
browser_config = browser_config or self.config
page_url = page.url if page else None
# If not keeping the page alive, close it and decrement count
if not keep_alive:
try:
await page.close()
except Exception as e:
if self.logger:
self.logger.error(
f"Error closing page: {str(e)}",
tag="POOL"
)
# Release the browser with page count decrement
await self.release_browser(strategy, browser_config, decrement_page_count=True)
return
# If returning to pool
if return_to_pool:
# Get the configuration hashes from the page object
browser_config_hash = getattr(page, "_browser_config_hash", None)
crawler_config_hash = getattr(page, "_crawler_config_hash", None)
if browser_config_hash and crawler_config_hash:
pool_key = (browser_config_hash, crawler_config_hash)
async with self._page_pool_lock:
if pool_key not in self.page_pool:
self.page_pool[pool_key] = []
# Add page back to the pool
self.page_pool[pool_key].append((page, page.context, strategy))
if self.logger:
self.logger.debug(
f"Returned page to pool for config {crawler_config_hash[:8]}, url: {page_url}",
tag="POOL"
)
# Note: We don't decrement the page count here since the page is still "in use"
# from the browser's perspective, just in our pool
return
else:
# If we can't identify the configuration, log a warning
if self.logger:
self.logger.warning(
"Cannot return page to pool - missing configuration hashes",
tag="POOL"
)
# If we got here, we couldn't return to pool, so just release the browser
await self.release_browser(strategy, browser_config, decrement_page_count=True)
async def release_browser(
self,
strategy: BaseBrowserStrategy,
browser_config: Optional[BrowserConfig] = None,
decrement_page_count: bool = True
):
"""Release a browser back to the pool."""
browser_config = browser_config or self.config
config_hash = self._create_browser_config_hash(browser_config)
# Decrement page count
if decrement_page_count:
async with self._page_count_lock:
current_count = self.browser_page_counts.get(strategy, 1)
self.browser_page_counts[strategy] = max(0, current_count - 1)
if self.logger:
self.logger.debug(
f"Decremented page count for browser (now: {self.browser_page_counts[strategy]})",
tag="POOL"
)
# Mark as not in use
self.browser_in_use[strategy] = False
# Process any waiting requests
if config_hash in self.request_queues and not self.request_queues[config_hash].empty():
future = await self.request_queues[config_hash].get()
if not future.done():
future.set_result(strategy)
async def get_pages(
self,
crawlerRunConfig: CrawlerRunConfig,
count: int = 1,
browser_config: Optional[BrowserConfig] = None
) -> List[Tuple[Page, BrowserContext, BaseBrowserStrategy]]:
"""Get multiple pages from the browser pool.
Args:
crawlerRunConfig: Configuration for the crawler run
count: Number of pages to get
browser_config: Browser configuration to use
Returns:
List of (Page, Context, Strategy) tuples
"""
results = []
for _ in range(count):
try:
result = await self.get_page(crawlerRunConfig, browser_config)
results.append(result)
except Exception as e:
# Release any pages we've already gotten
for page, _, strategy in results:
await self.release_page(page, strategy, browser_config)
raise e
return results
async def get_page_pool_status(self) -> Dict[str, Any]:
"""Get information about the page pool status.
Returns:
Dict with page pool status information
"""
status = {
"total_pooled_pages": 0,
"configs": {}
}
async with self._page_pool_lock:
for (browser_hash, crawler_hash), pages in self.page_pool.items():
config_key = f"{browser_hash[:8]}_{crawler_hash[:8]}"
status["configs"][config_key] = len(pages)
status["total_pooled_pages"] += len(pages)
if self.logger:
self.logger.debug(
f"Page pool status: {status['total_pooled_pages']} pages available",
tag="POOL"
)
return status
async def get_pool_status(self) -> Dict[str, Any]:
"""Get information about the browser pool status.
Returns:
Dict with pool status information
"""
status = {
"total_browsers": 0,
"browsers_in_use": 0,
"total_pages": 0,
"configs": {}
}
for config_hash, strategies in self.browser_pool.items():
config_pages = 0
in_use = 0
for strategy in strategies:
is_in_use = self.browser_in_use.get(strategy, False)
if is_in_use:
in_use += 1
# Get page count for this browser
try:
page_count = len(await strategy.get_opened_pages())
config_pages += page_count
except Exception as e:
if self.logger:
self.logger.error(f"Error getting page count: {str(e)}", tag="POOL")
config_status = {
"total_browsers": len(strategies),
"browsers_in_use": in_use,
"pages_open": config_pages,
"waiting_requests": self.request_queues.get(config_hash, asyncio.Queue()).qsize(),
"max_capacity": len(strategies) * self.max_pages_per_browser,
"utilization_pct": round((config_pages / (len(strategies) * self.max_pages_per_browser)) * 100, 1)
if strategies else 0
}
status["configs"][config_hash] = config_status
status["total_browsers"] += config_status["total_browsers"]
status["browsers_in_use"] += config_status["browsers_in_use"]
status["total_pages"] += config_pages
# Add overall utilization
if status["total_browsers"] > 0:
max_capacity = status["total_browsers"] * self.max_pages_per_browser
status["overall_utilization_pct"] = round((status["total_pages"] / max_capacity) * 100, 1)
else:
status["overall_utilization_pct"] = 0
return status
async def start(self):
"""Start at least one browser instance in the pool.
This method is kept for backward compatibility.
Returns:
self: For method chaining
"""
await self.initialize_pool([self.config], 1)
return self
async def kill_session(self, session_id: str):
"""Kill a browser session and clean up resources.
Delegated to the strategy. This method is kept for backward compatibility.
Args:
session_id: The session ID to kill
"""
if not self.strategy:
return
await self.strategy.kill_session(session_id)
# Sync sessions
if hasattr(self.strategy, 'sessions'):
self.sessions = self.strategy.sessions
async def close(self):
"""Close all browsers in the pool and clean up resources."""
# Close all browsers in the pool
for strategies in self.browser_pool.values():
for strategy in strategies:
try:
await strategy.close()
except Exception as e:
if self.logger:
self.logger.error(
f"Error closing browser: {str(e)}",
tag="POOL"
)
# Clear pool data
self.browser_pool = {}
self.browser_in_use = {}
# Reset legacy references
self.browser = None
self.default_context = None
self.managed_browser = None
self.playwright = None
self.strategy = None
self.sessions = {}
async def create_browser_manager(
browser_config: Optional[BrowserConfig] = None,
logger: Optional[AsyncLogger] = None,
unavailable_behavior: UnavailableBehavior = UnavailableBehavior.EXCEPTION,
max_browsers_per_config: int = 10,
initial_pool_size: int = 1,
page_configs: Optional[List[Tuple[BrowserConfig, CrawlerRunConfig, int]]] = None
) -> BrowserManager:
"""Factory function to create and initialize a BrowserManager.
Args:
browser_config: Configuration for the browsers
logger: Logger for recording events
unavailable_behavior: Behavior when no browser is available
max_browsers_per_config: Maximum browsers per configuration
initial_pool_size: Initial number of browsers per configuration
page_configs: Optional configurations for pre-warming pages
Returns:
Initialized BrowserManager
"""
manager = BrowserManager(
browser_config=browser_config,
logger=logger,
unavailable_behavior=unavailable_behavior,
max_browsers_per_config=max_browsers_per_config
)
await manager.initialize_pool(
[browser_config] if browser_config else None,
initial_pool_size,
page_configs
)
return manager

View File

@@ -1,143 +0,0 @@
"""Docker configuration module for Crawl4AI browser automation.
This module provides configuration classes for Docker-based browser automation,
allowing flexible configuration of Docker containers for browsing.
"""
from typing import Dict, List, Optional
class DockerConfig:
"""Configuration for Docker-based browser automation.
This class contains Docker-specific settings to avoid cluttering BrowserConfig.
Attributes:
mode (str): Docker operation mode - "connect" or "launch".
- "connect": Uses a container with Chrome already running
- "launch": Dynamically configures and starts Chrome in container
image (str): Docker image to use. If None, defaults from DockerUtils are used.
registry_file (str): Path to container registry file for persistence.
persistent (bool): Keep container running after browser closes.
remove_on_exit (bool): Remove container on exit when not persistent.
network (str): Docker network to use.
volumes (List[str]): Volume mappings (e.g., ["host_path:container_path"]).
env_vars (Dict[str, str]): Environment variables to set in container.
extra_args (List[str]): Additional docker run arguments.
host_port (int): Host port to map to container's 9223 port.
user_data_dir (str): Path to user data directory on host.
container_user_data_dir (str): Path to user data directory in container.
"""
def __init__(
self,
mode: str = "connect", # "connect" or "launch"
image: Optional[str] = None, # Docker image to use
registry_file: Optional[str] = None, # Path to registry file
persistent: bool = False, # Keep container running after browser closes
remove_on_exit: bool = True, # Remove container on exit when not persistent
network: Optional[str] = None, # Docker network to use
volumes: List[str] = None, # Volume mappings
cpu_limit: float = 1.0, # CPU limit for the container
memory_limit: str = "1.5g", # Memory limit for the container
env_vars: Dict[str, str] = None, # Environment variables
host_port: Optional[int] = None, # Host port to map to container's 9223
user_data_dir: Optional[str] = None, # Path to user data directory on host
container_user_data_dir: str = "/data", # Path to user data directory in container
extra_args: List[str] = None, # Additional docker run arguments
):
"""Initialize Docker configuration.
Args:
mode: Docker operation mode ("connect" or "launch")
image: Docker image to use
registry_file: Path to container registry file
persistent: Whether to keep container running after browser closes
remove_on_exit: Whether to remove container on exit when not persistent
network: Docker network to use
volumes: Volume mappings as list of strings
cpu_limit: CPU limit for the container
memory_limit: Memory limit for the container
env_vars: Environment variables as dictionary
extra_args: Additional docker run arguments
host_port: Host port to map to container's 9223
user_data_dir: Path to user data directory on host
container_user_data_dir: Path to user data directory in container
"""
self.mode = mode
self.image = image # If None, defaults will be used from DockerUtils
self.registry_file = registry_file
self.persistent = persistent
self.remove_on_exit = remove_on_exit
self.network = network
self.volumes = volumes or []
self.cpu_limit = cpu_limit
self.memory_limit = memory_limit
self.env_vars = env_vars or {}
self.extra_args = extra_args or []
self.host_port = host_port
self.user_data_dir = user_data_dir
self.container_user_data_dir = container_user_data_dir
def to_dict(self) -> Dict:
"""Convert this configuration to a dictionary.
Returns:
Dictionary representation of this configuration
"""
return {
"mode": self.mode,
"image": self.image,
"registry_file": self.registry_file,
"persistent": self.persistent,
"remove_on_exit": self.remove_on_exit,
"network": self.network,
"volumes": self.volumes,
"cpu_limit": self.cpu_limit,
"memory_limit": self.memory_limit,
"env_vars": self.env_vars,
"extra_args": self.extra_args,
"host_port": self.host_port,
"user_data_dir": self.user_data_dir,
"container_user_data_dir": self.container_user_data_dir
}
@staticmethod
def from_kwargs(kwargs: Dict) -> "DockerConfig":
"""Create a DockerConfig from a dictionary of keyword arguments.
Args:
kwargs: Dictionary of configuration options
Returns:
New DockerConfig instance
"""
return DockerConfig(
mode=kwargs.get("mode", "connect"),
image=kwargs.get("image"),
registry_file=kwargs.get("registry_file"),
persistent=kwargs.get("persistent", False),
remove_on_exit=kwargs.get("remove_on_exit", True),
network=kwargs.get("network"),
volumes=kwargs.get("volumes"),
cpu_limit=kwargs.get("cpu_limit", 1.0),
memory_limit=kwargs.get("memory_limit", "1.5g"),
env_vars=kwargs.get("env_vars"),
extra_args=kwargs.get("extra_args"),
host_port=kwargs.get("host_port"),
user_data_dir=kwargs.get("user_data_dir"),
container_user_data_dir=kwargs.get("container_user_data_dir", "/data")
)
def clone(self, **kwargs) -> "DockerConfig":
"""Create a copy of this configuration with updated values.
Args:
**kwargs: Key-value pairs of configuration options to update
Returns:
DockerConfig: A new instance with the specified updates
"""
config_dict = self.to_dict()
config_dict.update(kwargs)
return DockerConfig.from_kwargs(config_dict)

View File

@@ -1,457 +0,0 @@
"""Browser profile management module for Crawl4AI.
This module provides functionality for creating and managing browser profiles
that can be used for authenticated browsing.
"""
import os
import asyncio
import signal
import sys
import datetime
import uuid
import shutil
from typing import List, Dict, Optional, Any
from colorama import Fore, Style, init
from ..async_configs import BrowserConfig
from ..async_logger import AsyncLogger, AsyncLoggerBase
from ..utils import get_home_folder
class BrowserProfileManager:
"""Manages browser profiles for Crawl4AI.
This class provides functionality to create and manage browser profiles
that can be used for authenticated browsing with Crawl4AI.
Profiles are stored by default in ~/.crawl4ai/profiles/
"""
def __init__(self, logger: Optional[AsyncLoggerBase] = None):
"""Initialize the BrowserProfileManager.
Args:
logger: Logger for outputting messages. If None, a default AsyncLogger is created.
"""
# Initialize colorama for colorful terminal output
init()
# Create a logger if not provided
if logger is None:
self.logger = AsyncLogger(verbose=True)
elif not isinstance(logger, AsyncLoggerBase):
self.logger = AsyncLogger(verbose=True)
else:
self.logger = logger
# Ensure profiles directory exists
self.profiles_dir = os.path.join(get_home_folder(), "profiles")
os.makedirs(self.profiles_dir, exist_ok=True)
async def create_profile(self,
profile_name: Optional[str] = None,
browser_config: Optional[BrowserConfig] = None) -> Optional[str]:
"""Create a browser profile interactively.
Args:
profile_name: Name for the profile. If None, a name is generated.
browser_config: Configuration for the browser. If None, a default configuration is used.
Returns:
Path to the created profile directory, or None if creation failed
"""
# Create default browser config if none provided
if browser_config is None:
browser_config = BrowserConfig(
browser_type="chromium",
headless=False, # Must be visible for user interaction
verbose=True
)
else:
# Ensure headless is False for user interaction
browser_config.headless = False
# Generate profile name if not provided
if not profile_name:
timestamp = datetime.datetime.now().strftime("%Y%m%d_%H%M%S")
profile_name = f"profile_{timestamp}_{uuid.uuid4().hex[:6]}"
# Sanitize profile name (replace spaces and special chars)
profile_name = "".join(c if c.isalnum() or c in "-_" else "_" for c in profile_name)
# Set user data directory
profile_path = os.path.join(self.profiles_dir, profile_name)
os.makedirs(profile_path, exist_ok=True)
# Print instructions for the user with colorama formatting
border = f"{Fore.CYAN}{'='*80}{Style.RESET_ALL}"
self.logger.info(f"\n{border}", tag="PROFILE")
self.logger.info(f"Creating browser profile: {Fore.GREEN}{profile_name}{Style.RESET_ALL}", tag="PROFILE")
self.logger.info(f"Profile directory: {Fore.YELLOW}{profile_path}{Style.RESET_ALL}", tag="PROFILE")
self.logger.info("\nInstructions:", tag="PROFILE")
self.logger.info("1. A browser window will open for you to set up your profile.", tag="PROFILE")
self.logger.info(f"2. {Fore.CYAN}Log in to websites{Style.RESET_ALL}, configure settings, etc. as needed.", tag="PROFILE")
self.logger.info(f"3. When you're done, {Fore.YELLOW}press 'q' in this terminal{Style.RESET_ALL} to close the browser.", tag="PROFILE")
self.logger.info("4. The profile will be saved and ready to use with Crawl4AI.", tag="PROFILE")
self.logger.info(f"{border}\n", tag="PROFILE")
# Import the necessary classes with local imports to avoid circular references
from .strategies import CDPBrowserStrategy
# Set browser config to use the profile path
browser_config.user_data_dir = profile_path
# Create a CDP browser strategy for the profile creation
browser_strategy = CDPBrowserStrategy(browser_config, self.logger)
# Set up signal handlers to ensure cleanup on interrupt
original_sigint = signal.getsignal(signal.SIGINT)
original_sigterm = signal.getsignal(signal.SIGTERM)
# Define cleanup handler for signals
async def cleanup_handler(sig, frame):
self.logger.warning("\nCleaning up browser process...", tag="PROFILE")
await browser_strategy.close()
# Restore original signal handlers
signal.signal(signal.SIGINT, original_sigint)
signal.signal(signal.SIGTERM, original_sigterm)
if sig == signal.SIGINT:
self.logger.error("Profile creation interrupted. Profile may be incomplete.", tag="PROFILE")
sys.exit(1)
# Set signal handlers
def sigint_handler(sig, frame):
asyncio.create_task(cleanup_handler(sig, frame))
signal.signal(signal.SIGINT, sigint_handler)
signal.signal(signal.SIGTERM, sigint_handler)
# Event to signal when user is done with the browser
user_done_event = asyncio.Event()
# Run keyboard input loop in a separate task
async def listen_for_quit_command():
import termios
import tty
import select
# First output the prompt
self.logger.info(f"{Fore.CYAN}Press '{Fore.WHITE}q{Fore.CYAN}' when you've finished using the browser...{Style.RESET_ALL}", tag="PROFILE")
# Save original terminal settings
fd = sys.stdin.fileno()
old_settings = termios.tcgetattr(fd)
try:
# Switch to non-canonical mode (no line buffering)
tty.setcbreak(fd)
while True:
# Check if input is available (non-blocking)
readable, _, _ = select.select([sys.stdin], [], [], 0.5)
if readable:
key = sys.stdin.read(1)
if key.lower() == 'q':
self.logger.info(f"{Fore.GREEN}Closing browser and saving profile...{Style.RESET_ALL}", tag="PROFILE")
user_done_event.set()
return
# Check if the browser process has already exited
if browser_strategy.browser_process and browser_strategy.browser_process.poll() is not None:
self.logger.info("Browser already closed. Ending input listener.", tag="PROFILE")
user_done_event.set()
return
await asyncio.sleep(0.1)
finally:
# Restore terminal settings
termios.tcsetattr(fd, termios.TCSADRAIN, old_settings)
try:
# Start the browser
await browser_strategy.start()
# Check if browser started successfully
if not browser_strategy.browser_process:
self.logger.error("Failed to start browser process.", tag="PROFILE")
return None
self.logger.info(f"Browser launched. {Fore.CYAN}Waiting for you to finish...{Style.RESET_ALL}", tag="PROFILE")
# Start listening for keyboard input
listener_task = asyncio.create_task(listen_for_quit_command())
# Wait for either the user to press 'q' or for the browser process to exit naturally
while not user_done_event.is_set() and browser_strategy.browser_process.poll() is None:
await asyncio.sleep(0.5)
# Cancel the listener task if it's still running
if not listener_task.done():
listener_task.cancel()
try:
await listener_task
except asyncio.CancelledError:
pass
# If the browser is still running and the user pressed 'q', terminate it
if browser_strategy.browser_process.poll() is None and user_done_event.is_set():
self.logger.info("Terminating browser process...", tag="PROFILE")
await browser_strategy.close()
self.logger.success(f"Browser closed. Profile saved at: {Fore.GREEN}{profile_path}{Style.RESET_ALL}", tag="PROFILE")
except Exception as e:
self.logger.error(f"Error creating profile: {str(e)}", tag="PROFILE")
await browser_strategy.close()
return None
finally:
# Restore original signal handlers
signal.signal(signal.SIGINT, original_sigint)
signal.signal(signal.SIGTERM, original_sigterm)
# Make sure browser is fully cleaned up
await browser_strategy.close()
# Return the profile path
return profile_path
def list_profiles(self) -> List[Dict[str, Any]]:
"""List all available browser profiles.
Returns:
List of dictionaries containing profile information
"""
if not os.path.exists(self.profiles_dir):
return []
profiles = []
for name in os.listdir(self.profiles_dir):
profile_path = os.path.join(self.profiles_dir, name)
# Skip if not a directory
if not os.path.isdir(profile_path):
continue
# Check if this looks like a valid browser profile
# For Chromium: Look for Preferences file
# For Firefox: Look for prefs.js file
is_valid = False
if os.path.exists(os.path.join(profile_path, "Preferences")) or \
os.path.exists(os.path.join(profile_path, "Default", "Preferences")):
is_valid = "chromium"
elif os.path.exists(os.path.join(profile_path, "prefs.js")):
is_valid = "firefox"
if is_valid:
# Get creation time
created = datetime.datetime.fromtimestamp(
os.path.getctime(profile_path)
)
profiles.append({
"name": name,
"path": profile_path,
"created": created,
"type": is_valid
})
# Sort by creation time, newest first
profiles.sort(key=lambda x: x["created"], reverse=True)
return profiles
def get_profile_path(self, profile_name: str) -> Optional[str]:
"""Get the full path to a profile by name.
Args:
profile_name: Name of the profile (not the full path)
Returns:
Full path to the profile directory, or None if not found
"""
profile_path = os.path.join(self.profiles_dir, profile_name)
# Check if path exists and is a valid profile
if not os.path.isdir(profile_path):
# Check if profile_name itself is full path
if os.path.isabs(profile_name):
profile_path = profile_name
else:
return None
# Look for profile indicators
is_profile = (
os.path.exists(os.path.join(profile_path, "Preferences")) or
os.path.exists(os.path.join(profile_path, "Default", "Preferences")) or
os.path.exists(os.path.join(profile_path, "prefs.js"))
)
if not is_profile:
return None # Not a valid browser profile
return profile_path
def delete_profile(self, profile_name_or_path: str) -> bool:
"""Delete a browser profile by name or path.
Args:
profile_name_or_path: Name of the profile or full path to profile directory
Returns:
True if the profile was deleted successfully, False otherwise
"""
# Determine if input is a name or a path
if os.path.isabs(profile_name_or_path):
# Full path provided
profile_path = profile_name_or_path
else:
# Just a name provided, construct path
profile_path = os.path.join(self.profiles_dir, profile_name_or_path)
# Check if path exists and is a valid profile
if not os.path.isdir(profile_path):
return False
# Look for profile indicators
is_profile = (
os.path.exists(os.path.join(profile_path, "Preferences")) or
os.path.exists(os.path.join(profile_path, "Default", "Preferences")) or
os.path.exists(os.path.join(profile_path, "prefs.js"))
)
if not is_profile:
return False # Not a valid browser profile
# Delete the profile directory
try:
shutil.rmtree(profile_path)
return True
except Exception:
return False
async def interactive_manager(self, crawl_callback=None):
"""Launch an interactive profile management console.
Args:
crawl_callback: Function to call when selecting option to use
a profile for crawling. It will be called with (profile_path, url).
"""
while True:
self.logger.info(f"\n{Fore.CYAN}Profile Management Options:{Style.RESET_ALL}", tag="MENU")
self.logger.info(f"1. {Fore.GREEN}Create a new profile{Style.RESET_ALL}", tag="MENU")
self.logger.info(f"2. {Fore.YELLOW}List available profiles{Style.RESET_ALL}", tag="MENU")
self.logger.info(f"3. {Fore.RED}Delete a profile{Style.RESET_ALL}", tag="MENU")
# Only show crawl option if callback provided
if crawl_callback:
self.logger.info(f"4. {Fore.CYAN}Use a profile to crawl a website{Style.RESET_ALL}", tag="MENU")
self.logger.info(f"5. {Fore.MAGENTA}Exit{Style.RESET_ALL}", tag="MENU")
exit_option = "5"
else:
self.logger.info(f"4. {Fore.MAGENTA}Exit{Style.RESET_ALL}", tag="MENU")
exit_option = "4"
choice = input(f"\n{Fore.CYAN}Enter your choice (1-{exit_option}): {Style.RESET_ALL}")
if choice == "1":
# Create new profile
name = input(f"{Fore.GREEN}Enter a name for the new profile (or press Enter for auto-generated name): {Style.RESET_ALL}")
await self.create_profile(name or None)
elif choice == "2":
# List profiles
profiles = self.list_profiles()
if not profiles:
self.logger.warning(" No profiles found. Create one first with option 1.", tag="PROFILES")
continue
# Print profile information with colorama formatting
self.logger.info("\nAvailable profiles:", tag="PROFILES")
for i, profile in enumerate(profiles):
self.logger.info(f"[{i+1}] {Fore.CYAN}{profile['name']}{Style.RESET_ALL}", tag="PROFILES")
self.logger.info(f" Path: {Fore.YELLOW}{profile['path']}{Style.RESET_ALL}", tag="PROFILES")
self.logger.info(f" Created: {profile['created'].strftime('%Y-%m-%d %H:%M:%S')}", tag="PROFILES")
self.logger.info(f" Browser type: {profile['type']}", tag="PROFILES")
self.logger.info("", tag="PROFILES") # Empty line for spacing
elif choice == "3":
# Delete profile
profiles = self.list_profiles()
if not profiles:
self.logger.warning("No profiles found to delete", tag="PROFILES")
continue
# Display numbered list
self.logger.info(f"\n{Fore.YELLOW}Available profiles:{Style.RESET_ALL}", tag="PROFILES")
for i, profile in enumerate(profiles):
self.logger.info(f"[{i+1}] {profile['name']}", tag="PROFILES")
# Get profile to delete
profile_idx = input(f"{Fore.RED}Enter the number of the profile to delete (or 'c' to cancel): {Style.RESET_ALL}")
if profile_idx.lower() == 'c':
continue
try:
idx = int(profile_idx) - 1
if 0 <= idx < len(profiles):
profile_name = profiles[idx]["name"]
self.logger.info(f"Deleting profile: {Fore.YELLOW}{profile_name}{Style.RESET_ALL}", tag="PROFILES")
# Confirm deletion
confirm = input(f"{Fore.RED}Are you sure you want to delete this profile? (y/n): {Style.RESET_ALL}")
if confirm.lower() == 'y':
success = self.delete_profile(profiles[idx]["path"])
if success:
self.logger.success(f"Profile {Fore.GREEN}{profile_name}{Style.RESET_ALL} deleted successfully", tag="PROFILES")
else:
self.logger.error(f"Failed to delete profile {Fore.RED}{profile_name}{Style.RESET_ALL}", tag="PROFILES")
else:
self.logger.error("Invalid profile number", tag="PROFILES")
except ValueError:
self.logger.error("Please enter a valid number", tag="PROFILES")
elif choice == "4" and crawl_callback:
# Use profile to crawl a site
profiles = self.list_profiles()
if not profiles:
self.logger.warning("No profiles found. Create one first.", tag="PROFILES")
continue
# Display numbered list
self.logger.info(f"\n{Fore.YELLOW}Available profiles:{Style.RESET_ALL}", tag="PROFILES")
for i, profile in enumerate(profiles):
self.logger.info(f"[{i+1}] {profile['name']}", tag="PROFILES")
# Get profile to use
profile_idx = input(f"{Fore.CYAN}Enter the number of the profile to use (or 'c' to cancel): {Style.RESET_ALL}")
if profile_idx.lower() == 'c':
continue
try:
idx = int(profile_idx) - 1
if 0 <= idx < len(profiles):
profile_path = profiles[idx]["path"]
url = input(f"{Fore.CYAN}Enter the URL to crawl: {Style.RESET_ALL}")
if url:
# Call the provided crawl callback
await crawl_callback(profile_path, url)
else:
self.logger.error("No URL provided", tag="CRAWL")
else:
self.logger.error("Invalid profile number", tag="PROFILES")
except ValueError:
self.logger.error("Please enter a valid number", tag="PROFILES")
elif (choice == "4" and not crawl_callback) or (choice == "5" and crawl_callback):
# Exit
self.logger.info("Exiting profile management", tag="MENU")
break
else:
self.logger.error(f"Invalid choice. Please enter a number between 1 and {exit_option}.", tag="MENU")

View File

@@ -1,13 +0,0 @@
from .base import BaseBrowserStrategy
from .cdp import CDPBrowserStrategy
from .docker_strategy import DockerBrowserStrategy
from .playwright import PlaywrightBrowserStrategy
from .builtin import BuiltinBrowserStrategy
__all__ = [
"BrowserStrategy",
"CDPBrowserStrategy",
"DockerBrowserStrategy",
"PlaywrightBrowserStrategy",
"BuiltinBrowserStrategy",
]

View File

@@ -1,601 +0,0 @@
"""Browser strategies module for Crawl4AI.
This module implements the browser strategy pattern for different
browser implementations, including Playwright, CDP, and builtin browsers.
"""
from abc import ABC, abstractmethod
import asyncio
import json
import hashlib
import os
import time
from typing import Optional, Tuple, List
from playwright.async_api import BrowserContext, Page
from ...async_logger import AsyncLogger
from ...async_configs import BrowserConfig, CrawlerRunConfig
from ...config import DOWNLOAD_PAGE_TIMEOUT
from ...js_snippet import load_js_script
from ..utils import get_playwright
class BaseBrowserStrategy(ABC):
"""Base class for all browser strategies.
This abstract class defines the interface that all browser strategies
must implement. It handles common functionality like context caching,
browser configuration, and session management.
"""
_playwright_instance = None
@classmethod
async def get_playwright(cls):
"""Get or create a shared Playwright instance.
Returns:
Playwright: The shared Playwright instance
"""
# For now I dont want Singleton pattern for Playwright
if cls._playwright_instance is None or True:
cls._playwright_instance = await get_playwright()
return cls._playwright_instance
def __init__(self, config: BrowserConfig, logger: Optional[AsyncLogger] = None):
"""Initialize the strategy with configuration and logger.
Args:
config: Browser configuration
logger: Logger for recording events and errors
"""
self.config = config
self.logger = logger
self.browser = None
self.default_context = None
# Context management
self.contexts_by_config = {} # config_signature -> context
self._contexts_lock = asyncio.Lock()
# Session management
self.sessions = {}
self.session_ttl = 1800 # 30 minutes default
# Playwright instance
self.playwright = None
@abstractmethod
async def start(self):
"""Start the browser.
This method should be implemented by concrete strategies to initialize
the browser in the appropriate way (direct launch, CDP connection, etc.)
Returns:
self: For method chaining
"""
# Base implementation gets the playwright instance
self.playwright = await self.get_playwright()
return self
@abstractmethod
async def _generate_page(self, crawlerRunConfig: CrawlerRunConfig) -> Tuple[Page, BrowserContext]:
pass
async def get_page(self, crawlerRunConfig: CrawlerRunConfig) -> Tuple[Page, BrowserContext]:
"""Get a page with specified configuration.
This method should be implemented by concrete strategies to create
or retrieve a page according to their browser management approach.
Args:
crawlerRunConfig: Crawler run configuration
Returns:
Tuple of (Page, BrowserContext)
"""
# Clean up expired sessions first
self._cleanup_expired_sessions()
# If a session_id is provided and we already have it, reuse that page + context
if crawlerRunConfig.session_id and crawlerRunConfig.session_id in self.sessions:
context, page, _ = self.sessions[crawlerRunConfig.session_id]
# Update last-used timestamp
self.sessions[crawlerRunConfig.session_id] = (context, page, time.time())
return page, context
page, context = await self._generate_page(crawlerRunConfig)
import uuid
setattr(page, "guid", uuid.uuid4())
# If a session_id is specified, store this session so we can reuse later
if crawlerRunConfig.session_id:
self.sessions[crawlerRunConfig.session_id] = (context, page, time.time())
return page, context
pass
async def get_pages(self, crawlerRunConfig: CrawlerRunConfig, count: int = 1) -> List[Tuple[Page, BrowserContext]]:
"""Get multiple pages with the same configuration.
Args:
crawlerRunConfig: Configuration for the pages
count: Number of pages to create
Returns:
List of (Page, Context) tuples
"""
pages = []
for _ in range(count):
page, context = await self.get_page(crawlerRunConfig)
pages.append((page, context))
return pages
async def get_opened_pages(self) -> List[Page]:
"""Get all opened pages in the
browser.
"""
return [page for context in self.contexts_by_config.values() for page in context.pages]
def _build_browser_args(self) -> dict:
"""Build browser launch arguments from config.
Returns:
dict: Browser launch arguments for Playwright
"""
# Define common browser arguments that improve performance and stability
args = [
"--no-sandbox",
"--no-first-run",
"--no-default-browser-check",
"--window-position=0,0",
"--ignore-certificate-errors",
"--ignore-certificate-errors-spki-list",
"--window-position=400,0",
"--force-color-profile=srgb",
"--mute-audio",
"--disable-gpu",
"--disable-gpu-compositing",
"--disable-software-rasterizer",
"--disable-dev-shm-usage",
"--disable-infobars",
"--disable-blink-features=AutomationControlled",
"--disable-renderer-backgrounding",
"--disable-ipc-flooding-protection",
"--disable-background-timer-throttling",
f"--window-size={self.config.viewport_width},{self.config.viewport_height}",
]
# Define browser disable options for light mode
browser_disable_options = [
"--disable-backgrounding-occluded-windows",
"--disable-breakpad",
"--disable-client-side-phishing-detection",
"--disable-component-extensions-with-background-pages",
"--disable-default-apps",
"--disable-extensions",
"--disable-features=TranslateUI",
"--disable-hang-monitor",
"--disable-popup-blocking",
"--disable-prompt-on-repost",
"--disable-sync",
"--metrics-recording-only",
"--password-store=basic",
"--use-mock-keychain",
]
# Apply light mode settings if enabled
if self.config.light_mode:
args.extend(browser_disable_options)
# Apply text mode settings if enabled (disables images, JS, etc)
if self.config.text_mode:
args.extend([
"--blink-settings=imagesEnabled=false",
"--disable-remote-fonts",
"--disable-images",
"--disable-javascript",
"--disable-software-rasterizer",
"--disable-dev-shm-usage",
])
# Add any extra arguments from the config
if self.config.extra_args:
args.extend(self.config.extra_args)
# Build the core browser args dictionary
browser_args = {"headless": self.config.headless, "args": args}
# Add chrome channel if specified
if self.config.chrome_channel:
browser_args["channel"] = self.config.chrome_channel
# Configure downloads
if self.config.accept_downloads:
browser_args["downloads_path"] = self.config.downloads_path or os.path.join(
os.getcwd(), "downloads"
)
os.makedirs(browser_args["downloads_path"], exist_ok=True)
# Check for user data directory
if self.config.user_data_dir:
# Ensure the directory exists
os.makedirs(self.config.user_data_dir, exist_ok=True)
browser_args["user_data_dir"] = self.config.user_data_dir
# Configure proxy settings
if self.config.proxy or self.config.proxy_config:
from playwright.async_api import ProxySettings
proxy_settings = (
ProxySettings(server=self.config.proxy)
if self.config.proxy
else ProxySettings(
server=self.config.proxy_config.server,
username=self.config.proxy_config.username,
password=self.config.proxy_config.password,
)
)
browser_args["proxy"] = proxy_settings
return browser_args
def _make_config_signature(self, crawlerRunConfig: CrawlerRunConfig) -> str:
"""Create a signature hash from configuration for context caching.
Converts the crawlerRunConfig into a dict, excludes ephemeral fields,
then returns a hash of the sorted JSON. This yields a stable signature
that identifies configurations requiring a unique browser context.
Args:
crawlerRunConfig: Crawler run configuration
Returns:
str: Unique hash for this configuration
"""
config_dict = crawlerRunConfig.__dict__.copy()
# Exclude items that do not affect browser-level setup
ephemeral_keys = [
"session_id",
"js_code",
"scraping_strategy",
"extraction_strategy",
"chunking_strategy",
"cache_mode",
"content_filter",
"semaphore_count",
"url"
]
for key in ephemeral_keys:
if key in config_dict:
del config_dict[key]
# Convert to canonical JSON string
signature_json = json.dumps(config_dict, sort_keys=True, default=str)
# Hash the JSON so we get a compact, unique string
signature_hash = hashlib.sha256(signature_json.encode("utf-8")).hexdigest()
return signature_hash
async def create_browser_context(self, crawlerRunConfig: Optional[CrawlerRunConfig] = None) -> BrowserContext:
"""Creates and returns a new browser context with configured settings.
Args:
crawlerRunConfig: Configuration object for the crawler run
Returns:
BrowserContext: Browser context object with the specified configurations
"""
if not self.browser:
raise ValueError("Browser must be initialized before creating context")
# Base settings
user_agent = self.config.headers.get("User-Agent", self.config.user_agent)
viewport_settings = {
"width": self.config.viewport_width,
"height": self.config.viewport_height,
}
proxy_settings = {"server": self.config.proxy} if self.config.proxy else None
# Define blocked extensions for resource optimization
blocked_extensions = [
# Images
"jpg", "jpeg", "png", "gif", "webp", "svg", "ico", "bmp", "tiff", "psd",
# Fonts
"woff", "woff2", "ttf", "otf", "eot",
# Media
"mp4", "webm", "ogg", "avi", "mov", "wmv", "flv", "m4v", "mp3", "wav", "aac",
"m4a", "opus", "flac",
# Documents
"pdf", "doc", "docx", "xls", "xlsx", "ppt", "pptx",
# Archives
"zip", "rar", "7z", "tar", "gz",
# Scripts and data
"xml", "swf", "wasm",
]
# Common context settings
context_settings = {
"user_agent": user_agent,
"viewport": viewport_settings,
"proxy": proxy_settings,
"accept_downloads": self.config.accept_downloads,
"storage_state": self.config.storage_state,
"ignore_https_errors": self.config.ignore_https_errors,
"device_scale_factor": 1.0,
"java_script_enabled": self.config.java_script_enabled,
}
# Apply text mode settings if enabled
if self.config.text_mode:
text_mode_settings = {
"has_touch": False,
"is_mobile": False,
"java_script_enabled": False, # Disable javascript in text mode
}
# Update context settings with text mode settings
context_settings.update(text_mode_settings)
if self.logger:
self.logger.debug("Text mode enabled for browser context", tag="BROWSER")
# Handle storage state properly - this is key for persistence
if self.config.storage_state:
if self.logger:
if isinstance(self.config.storage_state, str):
self.logger.debug(f"Using storage state from file: {self.config.storage_state}", tag="BROWSER")
else:
self.logger.debug("Using storage state from config object", tag="BROWSER")
if self.config.user_data_dir:
# For CDP-based browsers, storage persistence is typically handled by the user_data_dir
# at the browser level, but we'll create a storage_state location for Playwright as well
storage_path = os.path.join(self.config.user_data_dir, "storage_state.json")
if not os.path.exists(storage_path):
# Create parent directory if it doesn't exist
os.makedirs(os.path.dirname(storage_path), exist_ok=True)
with open(storage_path, "w") as f:
json.dump({}, f)
self.config.storage_state = storage_path
if self.logger:
self.logger.debug(f"Using user data directory: {self.config.user_data_dir}", tag="BROWSER")
# Apply crawler-specific configurations if provided
if crawlerRunConfig:
# Check if there is value for crawlerRunConfig.proxy_config set add that to context
if crawlerRunConfig.proxy_config:
proxy_settings = {
"server": crawlerRunConfig.proxy_config.server,
}
if crawlerRunConfig.proxy_config.username:
proxy_settings.update({
"username": crawlerRunConfig.proxy_config.username,
"password": crawlerRunConfig.proxy_config.password,
})
context_settings["proxy"] = proxy_settings
# Create and return the context
try:
# Create the context with appropriate settings
context = await self.browser.new_context(**context_settings)
# Apply text mode resource blocking if enabled
if self.config.text_mode:
# Create and apply route patterns for each extension
for ext in blocked_extensions:
await context.route(f"**/*.{ext}", lambda route: route.abort())
return context
except Exception as e:
if self.logger:
self.logger.error(f"Error creating browser context: {str(e)}", tag="BROWSER")
# Fallback to basic context creation if the advanced settings fail
return await self.browser.new_context()
async def setup_context(self, context: BrowserContext, crawlerRunConfig: Optional[CrawlerRunConfig] = None):
"""Set up a browser context with the configured options.
Args:
context: The browser context to set up
crawlerRunConfig: Configuration object containing all browser settings
"""
# Set HTTP headers
if self.config.headers:
await context.set_extra_http_headers(self.config.headers)
# Add cookies
if self.config.cookies:
await context.add_cookies(self.config.cookies)
# Apply storage state if provided
if self.config.storage_state:
await context.storage_state(path=None)
# Configure downloads
if self.config.accept_downloads:
context.set_default_timeout(DOWNLOAD_PAGE_TIMEOUT)
context.set_default_navigation_timeout(DOWNLOAD_PAGE_TIMEOUT)
if self.config.downloads_path:
context._impl_obj._options["accept_downloads"] = True
context._impl_obj._options["downloads_path"] = self.config.downloads_path
# Handle user agent and browser hints
if self.config.user_agent:
combined_headers = {
"User-Agent": self.config.user_agent,
"sec-ch-ua": self.config.browser_hint,
}
combined_headers.update(self.config.headers)
await context.set_extra_http_headers(combined_headers)
# Add default cookie
target_url = (crawlerRunConfig and crawlerRunConfig.url) or "https://crawl4ai.com/"
await context.add_cookies(
[
{
"name": "cookiesEnabled",
"value": "true",
"url": target_url,
}
]
)
# Handle navigator overrides
if crawlerRunConfig:
if (
crawlerRunConfig.override_navigator
or crawlerRunConfig.simulate_user
or crawlerRunConfig.magic
):
await context.add_init_script(load_js_script("navigator_overrider"))
async def kill_session(self, session_id: str):
"""Kill a browser session and clean up resources.
Args:
session_id (str): The session ID to kill.
"""
if session_id not in self.sessions:
return
context, page, _ = self.sessions[session_id]
# Close the page
try:
await page.close()
except Exception as e:
if self.logger:
self.logger.error(f"Error closing page for session {session_id}: {str(e)}", tag="BROWSER")
# Remove session from tracking
del self.sessions[session_id]
# Clean up any contexts that no longer have pages
await self._cleanup_unused_contexts()
if self.logger:
self.logger.debug(f"Killed session: {session_id}", tag="BROWSER")
async def _cleanup_unused_contexts(self):
"""Clean up contexts that no longer have any pages."""
async with self._contexts_lock:
# Get all contexts we're managing
contexts_to_check = list(self.contexts_by_config.values())
for context in contexts_to_check:
# Check if the context has any pages left
if not context.pages:
# No pages left, we can close this context
config_signature = next((sig for sig, ctx in self.contexts_by_config.items()
if ctx == context), None)
if config_signature:
try:
await context.close()
del self.contexts_by_config[config_signature]
if self.logger:
self.logger.debug(f"Closed unused context", tag="BROWSER")
except Exception as e:
if self.logger:
self.logger.error(f"Error closing unused context: {str(e)}", tag="BROWSER")
def _cleanup_expired_sessions(self):
"""Clean up expired sessions based on TTL."""
current_time = time.time()
expired_sessions = [
sid
for sid, (_, _, last_used) in self.sessions.items()
if current_time - last_used > self.session_ttl
]
for sid in expired_sessions:
if self.logger:
self.logger.debug(f"Session expired: {sid}", tag="BROWSER")
asyncio.create_task(self.kill_session(sid))
async def close(self):
"""Close the browser and clean up resources.
This method handles common cleanup tasks like:
1. Persisting storage state if a user_data_dir is configured
2. Closing all sessions
3. Closing all browser contexts
4. Closing the browser
5. Stopping Playwright
Child classes should override this method to add their specific cleanup logic,
but should call super().close() to ensure common cleanup tasks are performed.
"""
# Set a flag to prevent race conditions during cleanup
self.shutting_down = True
try:
# Add brief delay if configured
if self.config.sleep_on_close:
await asyncio.sleep(0.5)
# Persist storage state if using a user data directory
if self.config.user_data_dir and self.browser:
for context in self.browser.contexts:
try:
# Ensure the directory exists
storage_dir = os.path.join(self.config.user_data_dir, "Default")
os.makedirs(storage_dir, exist_ok=True)
# Save storage state
storage_path = os.path.join(storage_dir, "storage_state.json")
await context.storage_state(path=storage_path)
if self.logger:
self.logger.debug("Storage state persisted before closing browser", tag="BROWSER")
except Exception as e:
if self.logger:
self.logger.warning(
message="Failed to ensure storage persistence: {error}",
tag="BROWSER",
params={"error": str(e)}
)
# Close all active sessions
session_ids = list(self.sessions.keys())
for session_id in session_ids:
await self.kill_session(session_id)
# Close all cached contexts
for ctx in self.contexts_by_config.values():
try:
await ctx.close()
except Exception as e:
if self.logger:
self.logger.error(
message="Error closing context: {error}",
tag="BROWSER",
params={"error": str(e)}
)
self.contexts_by_config.clear()
# Close the browser if it exists
if self.browser:
await self.browser.close()
self.browser = None
# Stop playwright
if self.playwright:
await self.playwright.stop()
self.playwright = None
except Exception as e:
if self.logger:
self.logger.error(
message="Error during browser cleanup: {error}",
tag="BROWSER",
params={"error": str(e)}
)
finally:
# Reset shutting down flag
self.shutting_down = False

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@@ -1,468 +0,0 @@
import asyncio
import os
import time
import json
import subprocess
import shutil
import signal
from typing import Optional, Dict, Any, Tuple
from ...async_logger import AsyncLogger
from ...async_configs import CrawlerRunConfig
from playwright.async_api import Page, BrowserContext
from ...async_logger import AsyncLogger
from ...async_configs import BrowserConfig
from ...utils import get_home_folder
from ..utils import get_browser_executable, is_windows, is_browser_running, find_process_by_port, terminate_process
from .cdp import CDPBrowserStrategy
from .base import BaseBrowserStrategy
class BuiltinBrowserStrategy(CDPBrowserStrategy):
"""Built-in browser strategy.
This strategy extends the CDP strategy to use the built-in browser.
"""
def __init__(self, config: BrowserConfig, logger: Optional[AsyncLogger] = None):
"""Initialize the built-in browser strategy.
Args:
config: Browser configuration
logger: Logger for recording events and errors
"""
super().__init__(config, logger)
self.builtin_browser_dir = os.path.join(get_home_folder(), "builtin-browser") if not self.config.user_data_dir else self.config.user_data_dir
self.builtin_config_file = os.path.join(self.builtin_browser_dir, "browser_config.json")
# Raise error if user data dir is already engaged
if self._check_user_dir_is_engaged(self.builtin_browser_dir):
raise Exception(f"User data directory {self.builtin_browser_dir} is already engaged by another browser instance.")
os.makedirs(self.builtin_browser_dir, exist_ok=True)
def _check_user_dir_is_engaged(self, user_data_dir: str) -> bool:
"""Check if the user data directory is already in use.
Returns:
bool: True if the directory is engaged, False otherwise
"""
# Load browser config file, then iterate in port_map values, check "user_data_dir" key if it matches
# the current user data directory
if os.path.exists(self.builtin_config_file):
try:
with open(self.builtin_config_file, 'r') as f:
browser_info_dict = json.load(f)
# Check if user data dir is already engaged
for port_str, browser_info in browser_info_dict.get("port_map", {}).items():
if browser_info.get("user_data_dir") == user_data_dir:
return True
except Exception as e:
if self.logger:
self.logger.error(f"Error reading built-in browser config: {str(e)}", tag="BUILTIN")
return False
async def start(self):
"""Start or connect to the built-in browser.
Returns:
self: For method chaining
"""
# Initialize Playwright instance via base class method
await BaseBrowserStrategy.start(self)
try:
# Check for existing built-in browser (get_browser_info already checks if running)
browser_info = self.get_browser_info()
if browser_info:
if self.logger:
self.logger.info(f"Using existing built-in browser at {browser_info.get('cdp_url')}", tag="BROWSER")
self.config.cdp_url = browser_info.get('cdp_url')
else:
if self.logger:
self.logger.info("Built-in browser not found, launching new instance...", tag="BROWSER")
cdp_url = await self.launch_builtin_browser(
browser_type=self.config.browser_type,
debugging_port=self.config.debugging_port,
headless=self.config.headless,
)
if not cdp_url:
if self.logger:
self.logger.warning("Failed to launch built-in browser, falling back to regular CDP strategy", tag="BROWSER")
# Call CDP's start but skip BaseBrowserStrategy.start() since we already called it
return await CDPBrowserStrategy.start(self)
self.config.cdp_url = cdp_url
# Connect to the browser using CDP protocol
self.browser = await self.playwright.chromium.connect_over_cdp(self.config.cdp_url)
# Get or create default context
contexts = self.browser.contexts
if contexts:
self.default_context = contexts[0]
else:
self.default_context = await self.create_browser_context()
await self.setup_context(self.default_context)
if self.logger:
self.logger.debug(f"Connected to built-in browser at {self.config.cdp_url}", tag="BUILTIN")
return self
except Exception as e:
if self.logger:
self.logger.error(f"Failed to start built-in browser: {str(e)}", tag="BUILTIN")
# There is a possibility that at this point I need to clean up some resourece
raise
def _get_builtin_browser_info(cls, debugging_port: int, config_file: str, logger: Optional[AsyncLogger] = None) -> Optional[Dict[str, Any]]:
"""Get information about the built-in browser for a specific debugging port.
Args:
debugging_port: The debugging port to look for
config_file: Path to the config file
logger: Optional logger for recording events
Returns:
dict: Browser information or None if no running browser is configured for this port
"""
if not os.path.exists(config_file):
return None
try:
with open(config_file, 'r') as f:
browser_info_dict = json.load(f)
# Get browser info from port map
if isinstance(browser_info_dict, dict) and "port_map" in browser_info_dict:
port_str = str(debugging_port)
if port_str in browser_info_dict["port_map"]:
browser_info = browser_info_dict["port_map"][port_str]
# Check if the browser is still running
pids = browser_info.get('pid', '')
if isinstance(pids, str):
pids = [int(pid) for pid in pids.split() if pid.isdigit()]
elif isinstance(pids, int):
pids = [pids]
else:
pids = []
# Check if any of the PIDs are running
if not pids:
if logger:
logger.warning(f"Built-in browser on port {debugging_port} has no valid PID", tag="BUILTIN")
# Remove this port from the dictionary
del browser_info_dict["port_map"][port_str]
with open(config_file, 'w') as f:
json.dump(browser_info_dict, f, indent=2)
return None
# Check if any of the PIDs are running
for pid in pids:
if is_browser_running(pid):
browser_info['pid'] = pid
break
else:
# If none of the PIDs are running, remove this port from the dictionary
if logger:
logger.warning(f"Built-in browser on port {debugging_port} is not running", tag="BUILTIN")
# Remove this port from the dictionary
del browser_info_dict["port_map"][port_str]
with open(config_file, 'w') as f:
json.dump(browser_info_dict, f, indent=2)
return None
return browser_info
return None
except Exception as e:
if logger:
logger.error(f"Error reading built-in browser config: {str(e)}", tag="BUILTIN")
return None
def get_browser_info(self) -> Optional[Dict[str, Any]]:
"""Get information about the current built-in browser instance.
Returns:
dict: Browser information or None if no running browser is configured
"""
return self._get_builtin_browser_info(
debugging_port=self.config.debugging_port,
config_file=self.builtin_config_file,
logger=self.logger
)
async def launch_builtin_browser(self,
browser_type: str = "chromium",
debugging_port: int = 9222,
headless: bool = True) -> Optional[str]:
"""Launch a browser in the background for use as the built-in browser.
Args:
browser_type: Type of browser to launch ('chromium' or 'firefox')
debugging_port: Port to use for CDP debugging
headless: Whether to run in headless mode
Returns:
str: CDP URL for the browser, or None if launch failed
"""
# Check if there's an existing browser still running
browser_info = self._get_builtin_browser_info(
debugging_port=debugging_port,
config_file=self.builtin_config_file,
logger=self.logger
)
if browser_info:
if self.logger:
self.logger.info(f"Built-in browser is already running on port {debugging_port}", tag="BUILTIN")
return browser_info.get('cdp_url')
# Create a user data directory for the built-in browser
user_data_dir = os.path.join(self.builtin_browser_dir, "user_data")
# Raise error if user data dir is already engaged
if self._check_user_dir_is_engaged(user_data_dir):
raise Exception(f"User data directory {user_data_dir} is already engaged by another browser instance.")
# Create the user data directory if it doesn't exist
os.makedirs(user_data_dir, exist_ok=True)
# Prepare browser launch arguments
browser_args = super()._build_browser_args()
browser_path = await get_browser_executable(browser_type)
base_args = [browser_path]
if browser_type == "chromium":
args = [
browser_path,
f"--remote-debugging-port={debugging_port}",
f"--user-data-dir={user_data_dir}",
]
# if headless:
# args.append("--headless=new")
elif browser_type == "firefox":
args = [
browser_path,
"--remote-debugging-port",
str(debugging_port),
"--profile",
user_data_dir,
]
if headless:
args.append("--headless")
else:
if self.logger:
self.logger.error(f"Browser type {browser_type} not supported for built-in browser", tag="BUILTIN")
return None
args = base_args + browser_args + args
try:
# Check if the port is already in use
PID = ""
cdp_url = f"http://localhost:{debugging_port}"
config_json = await self._check_port_in_use(cdp_url)
if config_json:
if self.logger:
self.logger.info(f"Port {debugging_port} is already in use.", tag="BUILTIN")
PID = find_process_by_port(debugging_port)
else:
# Start the browser process detached
process = None
if is_windows():
process = subprocess.Popen(
args,
stdout=subprocess.PIPE,
stderr=subprocess.PIPE,
creationflags=subprocess.DETACHED_PROCESS | subprocess.CREATE_NEW_PROCESS_GROUP
)
else:
process = subprocess.Popen(
args,
stdout=subprocess.PIPE,
stderr=subprocess.PIPE,
preexec_fn=os.setpgrp # Start in a new process group
)
# Wait briefly to ensure the process starts successfully
await asyncio.sleep(2.0)
# Check if the process is still running
if process and process.poll() is not None:
if self.logger:
self.logger.error(f"Browser process exited immediately with code {process.returncode}", tag="BUILTIN")
return None
PID = process.pid
# Construct CDP URL
config_json = await self._check_port_in_use(cdp_url)
# Create browser info
browser_info = {
'pid': PID,
'cdp_url': cdp_url,
'user_data_dir': user_data_dir,
'browser_type': browser_type,
'debugging_port': debugging_port,
'start_time': time.time(),
'config': config_json
}
# Read existing config file if it exists
port_map = {}
if os.path.exists(self.builtin_config_file):
try:
with open(self.builtin_config_file, 'r') as f:
existing_data = json.load(f)
# Check if it already uses port mapping
if isinstance(existing_data, dict) and "port_map" in existing_data:
port_map = existing_data["port_map"]
# # Convert legacy format to port mapping
# elif isinstance(existing_data, dict) and "debugging_port" in existing_data:
# old_port = str(existing_data.get("debugging_port"))
# if self._is_browser_running(existing_data.get("pid")):
# port_map[old_port] = existing_data
except Exception as e:
if self.logger:
self.logger.warning(f"Could not read existing config: {str(e)}", tag="BUILTIN")
# Add/update this browser in the port map
port_map[str(debugging_port)] = browser_info
# Write updated config
with open(self.builtin_config_file, 'w') as f:
json.dump({"port_map": port_map}, f, indent=2)
# Detach from the browser process - don't keep any references
# This is important to allow the Python script to exit while the browser continues running
process = None
if self.logger:
self.logger.success(f"Built-in browser launched at CDP URL: {cdp_url}", tag="BUILTIN")
return cdp_url
except Exception as e:
if self.logger:
self.logger.error(f"Error launching built-in browser: {str(e)}", tag="BUILTIN")
return None
async def _check_port_in_use(self, cdp_url: str) -> dict:
"""Check if a port is already in use by a Chrome DevTools instance.
Args:
cdp_url: The CDP URL to check
Returns:
dict: Chrome DevTools protocol version information or None if not found
"""
import aiohttp
json_url = f"{cdp_url}/json/version"
json_config = None
try:
async with aiohttp.ClientSession() as session:
try:
async with session.get(json_url, timeout=2.0) as response:
if response.status == 200:
json_config = await response.json()
if self.logger:
self.logger.debug(f"Found CDP server running at {cdp_url}", tag="BUILTIN")
return json_config
except (aiohttp.ClientError, asyncio.TimeoutError):
pass
return None
except Exception as e:
if self.logger:
self.logger.debug(f"Error checking CDP port: {str(e)}", tag="BUILTIN")
return None
async def kill_builtin_browser(self) -> bool:
"""Kill the built-in browser if it's running.
Returns:
bool: True if the browser was killed, False otherwise
"""
browser_info = self.get_browser_info()
if not browser_info:
if self.logger:
self.logger.warning(f"No built-in browser found on port {self.config.debugging_port}", tag="BUILTIN")
return False
pid = browser_info.get('pid')
if not pid:
return False
success, error_msg = terminate_process(pid, logger=self.logger)
if success:
# Update config file to remove this browser
with open(self.builtin_config_file, 'r') as f:
browser_info_dict = json.load(f)
# Remove this port from the dictionary
port_str = str(self.config.debugging_port)
if port_str in browser_info_dict.get("port_map", {}):
del browser_info_dict["port_map"][port_str]
with open(self.builtin_config_file, 'w') as f:
json.dump(browser_info_dict, f, indent=2)
# Remove user data directory if it exists
if os.path.exists(self.builtin_browser_dir):
shutil.rmtree(self.builtin_browser_dir)
# Clear the browser info cache
self.browser = None
self.temp_dir = None
self.shutting_down = True
if self.logger:
self.logger.success("Built-in browser terminated", tag="BUILTIN")
return True
else:
if self.logger:
self.logger.error(f"Error killing built-in browser: {error_msg}", tag="BUILTIN")
return False
async def get_builtin_browser_status(self) -> Dict[str, Any]:
"""Get status information about the built-in browser.
Returns:
dict: Status information with running, cdp_url, and info fields
"""
browser_info = self.get_browser_info()
if not browser_info:
return {
'running': False,
'cdp_url': None,
'info': None,
'port': self.config.debugging_port
}
return {
'running': True,
'cdp_url': browser_info.get('cdp_url'),
'info': browser_info,
'port': self.config.debugging_port
}
async def close(self):
"""Close the built-in browser and clean up resources."""
# Call parent class close method
await super().close()
# Clean up built-in browser if we created it and were in shutdown mode
if self.shutting_down:
await self.kill_builtin_browser()
if self.logger:
self.logger.debug("Killed built-in browser during shutdown", tag="BUILTIN")

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@@ -1,281 +0,0 @@
"""Browser strategies module for Crawl4AI.
This module implements the browser strategy pattern for different
browser implementations, including Playwright, CDP, and builtin browsers.
"""
import asyncio
import os
import time
import json
import subprocess
import shutil
from typing import Optional, Tuple, List
from playwright.async_api import BrowserContext, Page
from ...async_logger import AsyncLogger
from ...async_configs import BrowserConfig, CrawlerRunConfig
from ..utils import get_playwright, get_browser_executable, create_temp_directory, is_windows, check_process_is_running, terminate_process
from .base import BaseBrowserStrategy
class CDPBrowserStrategy(BaseBrowserStrategy):
"""CDP-based browser strategy.
This strategy connects to an existing browser using CDP protocol or
launches and connects to a browser using CDP.
"""
def __init__(self, config: BrowserConfig, logger: Optional[AsyncLogger] = None):
"""Initialize the CDP browser strategy.
Args:
config: Browser configuration
logger: Logger for recording events and errors
"""
super().__init__(config, logger)
self.sessions = {}
self.session_ttl = 1800 # 30 minutes
self.browser_process = None
self.temp_dir = None
self.shutting_down = False
async def start(self):
"""Start or connect to the browser using CDP.
Returns:
self: For method chaining
"""
# Call the base class start to initialize Playwright
await super().start()
try:
# Get or create CDP URL
cdp_url = await self._get_or_create_cdp_url()
# Connect to the browser using CDP
self.browser = await self.playwright.chromium.connect_over_cdp(cdp_url)
# Get or create default context
contexts = self.browser.contexts
if contexts:
self.default_context = contexts[0]
else:
self.default_context = await self.create_browser_context()
await self.setup_context(self.default_context)
if self.logger:
self.logger.debug(f"Connected to CDP browser at {cdp_url}", tag="CDP")
except Exception as e:
if self.logger:
self.logger.error(f"Failed to connect to CDP browser: {str(e)}", tag="CDP")
# Clean up any resources before re-raising
await self._cleanup_process()
raise
return self
async def _get_or_create_cdp_url(self) -> str:
"""Get existing CDP URL or launch a browser and return its CDP URL.
Returns:
str: CDP URL for connecting to the browser
"""
# If CDP URL is provided, just return it
if self.config.cdp_url:
return self.config.cdp_url
# Create temp dir if needed
if not self.config.user_data_dir:
self.temp_dir = create_temp_directory()
user_data_dir = self.temp_dir
else:
user_data_dir = self.config.user_data_dir
# Get browser args based on OS and browser type
# args = await self._get_browser_args(user_data_dir)
browser_args = super()._build_browser_args()
browser_path = await get_browser_executable(self.config.browser_type)
base_args = [browser_path]
if self.config.browser_type == "chromium":
args = [
f"--remote-debugging-port={self.config.debugging_port}",
f"--user-data-dir={user_data_dir}",
]
# if self.config.headless:
# args.append("--headless=new")
elif self.config.browser_type == "firefox":
args = [
"--remote-debugging-port",
str(self.config.debugging_port),
"--profile",
user_data_dir,
]
if self.config.headless:
args.append("--headless")
else:
raise NotImplementedError(f"Browser type {self.config.browser_type} not supported")
args = base_args + browser_args['args'] + args
# Start browser process
try:
# Use DETACHED_PROCESS flag on Windows to fully detach the process
# On Unix, we'll use preexec_fn=os.setpgrp to start the process in a new process group
if is_windows():
self.browser_process = subprocess.Popen(
args,
stdout=subprocess.PIPE,
stderr=subprocess.PIPE,
creationflags=subprocess.DETACHED_PROCESS | subprocess.CREATE_NEW_PROCESS_GROUP
)
else:
self.browser_process = subprocess.Popen(
args,
stdout=subprocess.PIPE,
stderr=subprocess.PIPE,
preexec_fn=os.setpgrp # Start in a new process group
)
# Monitor for a short time to make sure it starts properly
is_running, return_code, stdout, stderr = await check_process_is_running(self.browser_process, delay=2)
if not is_running:
if self.logger:
self.logger.error(
message="Browser process terminated unexpectedly | Code: {code} | STDOUT: {stdout} | STDERR: {stderr}",
tag="ERROR",
params={
"code": return_code,
"stdout": stdout.decode() if stdout else "",
"stderr": stderr.decode() if stderr else "",
},
)
await self._cleanup_process()
raise Exception("Browser process terminated unexpectedly")
return f"http://localhost:{self.config.debugging_port}"
except Exception as e:
await self._cleanup_process()
raise Exception(f"Failed to start browser: {e}")
async def _cleanup_process(self):
"""Cleanup browser process and temporary directory."""
# Set shutting_down flag BEFORE any termination actions
self.shutting_down = True
if self.browser_process:
try:
# Only attempt termination if the process is still running
if self.browser_process.poll() is None:
# Use our robust cross-platform termination utility
success = terminate_process(
pid=self.browser_process.pid,
timeout=1.0, # Equivalent to the previous 10*0.1s wait
logger=self.logger
)
if not success and self.logger:
self.logger.warning(
message="Failed to terminate browser process cleanly",
tag="PROCESS"
)
except Exception as e:
if self.logger:
self.logger.error(
message="Error during browser process cleanup: {error}",
tag="ERROR",
params={"error": str(e)},
)
if self.temp_dir and os.path.exists(self.temp_dir):
try:
shutil.rmtree(self.temp_dir)
self.temp_dir = None
if self.logger:
self.logger.debug("Removed temporary directory", tag="CDP")
except Exception as e:
if self.logger:
self.logger.error(
message="Error removing temporary directory: {error}",
tag="CDP",
params={"error": str(e)}
)
self.browser_process = None
async def _generate_page(self, crawlerRunConfig: CrawlerRunConfig) -> Tuple[Page, BrowserContext]:
# For CDP, we typically use the shared default_context
context = self.default_context
pages = context.pages
# Otherwise, check if we have an existing context for this config
config_signature = self._make_config_signature(crawlerRunConfig)
self.contexts_by_config[config_signature] = context
await self.setup_context(context, crawlerRunConfig)
# Check if there's already a page with the target URL
page = next((p for p in pages if p.url == crawlerRunConfig.url), None)
# If not found, create a new page
if not page:
page = await context.new_page()
return page, context
async def _get_page(self, crawlerRunConfig: CrawlerRunConfig) -> Tuple[Page, BrowserContext]:
"""Get a page for the given configuration.
Args:
crawlerRunConfig: Configuration object for the crawler run
Returns:
Tuple of (Page, BrowserContext)
"""
# Call parent method to ensure browser is started
await super().get_page(crawlerRunConfig)
# For CDP, we typically use the shared default_context
context = self.default_context
pages = context.pages
# Otherwise, check if we have an existing context for this config
config_signature = self._make_config_signature(crawlerRunConfig)
self.contexts_by_config[config_signature] = context
await self.setup_context(context, crawlerRunConfig)
# Check if there's already a page with the target URL
page = next((p for p in pages if p.url == crawlerRunConfig.url), None)
# If not found, create a new page
if not page:
page = await context.new_page()
# If a session_id is specified, store this session for reuse
if crawlerRunConfig.session_id:
self.sessions[crawlerRunConfig.session_id] = (context, page, time.time())
return page, context
async def close(self):
"""Close the CDP browser and clean up resources."""
# Skip cleanup if using external CDP URL and not launched by us
if self.config.cdp_url and not self.browser_process:
if self.logger:
self.logger.debug("Skipping cleanup for external CDP browser", tag="CDP")
return
# Call parent implementation for common cleanup
await super().close()
# Additional CDP-specific cleanup
await asyncio.sleep(0.5)
await self._cleanup_process()

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@@ -1,430 +0,0 @@
"""Docker browser strategy module for Crawl4AI.
This module provides browser strategies for running browsers in Docker containers,
which offers better isolation, consistency across platforms, and easy scaling.
"""
import os
import uuid
from typing import List, Optional
from ...async_logger import AsyncLogger
from ...async_configs import BrowserConfig
from ..models import DockerConfig
from ..docker_registry import DockerRegistry
from ..docker_utils import DockerUtils
from .builtin import CDPBrowserStrategy
from .base import BaseBrowserStrategy
class DockerBrowserStrategy(CDPBrowserStrategy):
"""Docker-based browser strategy.
Extends the CDPBrowserStrategy to run browsers in Docker containers.
Supports two modes:
1. "connect" - Uses a Docker image with Chrome already running
2. "launch" - Starts Chrome within the container with custom settings
Attributes:
docker_config: Docker-specific configuration options
container_id: ID of current Docker container
container_name: Name assigned to the container
registry: Registry for tracking and reusing containers
docker_utils: Utilities for Docker operations
chrome_process_id: Process ID of Chrome within container
socat_process_id: Process ID of socat within container
internal_cdp_port: Chrome's internal CDP port
internal_mapped_port: Port that socat maps to internally
"""
def __init__(self, config: BrowserConfig, logger: Optional[AsyncLogger] = None):
"""Initialize the Docker browser strategy.
Args:
config: Browser configuration including Docker-specific settings
logger: Logger for recording events and errors
"""
super().__init__(config, logger)
# Initialize Docker-specific attributes
self.docker_config = self.config.docker_config or DockerConfig()
self.container_id = None
self.container_name = f"crawl4ai-browser-{uuid.uuid4().hex[:8]}"
# Use the shared registry file path for consistency with BuiltinBrowserStrategy
registry_file = self.docker_config.registry_file
if registry_file is None and self.config.user_data_dir:
# Use the same registry file as BuiltinBrowserStrategy if possible
registry_file = os.path.join(
os.path.dirname(self.config.user_data_dir), "browser_config.json"
)
self.registry = DockerRegistry(self.docker_config.registry_file)
self.docker_utils = DockerUtils(logger)
self.chrome_process_id = None
self.socat_process_id = None
self.internal_cdp_port = 9222 # Chrome's internal CDP port
self.internal_mapped_port = 9223 # Port that socat maps to internally
self.shutting_down = False
async def start(self):
"""Start or connect to a browser running in a Docker container.
This method initializes Playwright and establishes a connection to
a browser running in a Docker container. Depending on the configured mode:
- "connect": Connects to a container with Chrome already running
- "launch": Creates a container and launches Chrome within it
Returns:
self: For method chaining
"""
# Initialize Playwright
await BaseBrowserStrategy.start(self)
if self.logger:
self.logger.info(
f"Starting Docker browser strategy in {self.docker_config.mode} mode",
tag="DOCKER",
)
try:
# Get CDP URL by creating or reusing a Docker container
# This handles the container management and browser startup
cdp_url = await self._get_or_create_cdp_url()
if not cdp_url:
raise Exception(
"Failed to establish CDP connection to Docker container"
)
if self.logger:
self.logger.info(
f"Connecting to browser in Docker via CDP: {cdp_url}", tag="DOCKER"
)
# Connect to the browser using CDP
self.browser = await self.playwright.chromium.connect_over_cdp(cdp_url)
# Get existing context or create default context
contexts = self.browser.contexts
if contexts:
self.default_context = contexts[0]
if self.logger:
self.logger.debug("Using existing browser context", tag="DOCKER")
else:
if self.logger:
self.logger.debug("Creating new browser context", tag="DOCKER")
self.default_context = await self.create_browser_context()
await self.setup_context(self.default_context)
return self
except Exception as e:
# Clean up resources if startup fails
if self.container_id and not self.docker_config.persistent:
if self.logger:
self.logger.warning(
f"Cleaning up container after failed start: {self.container_id[:12]}",
tag="DOCKER",
)
await self.docker_utils.remove_container(self.container_id)
self.registry.unregister_container(self.container_id)
self.container_id = None
if self.playwright:
await self.playwright.stop()
self.playwright = None
# Re-raise the exception
if self.logger:
self.logger.error(
f"Failed to start Docker browser: {str(e)}", tag="DOCKER"
)
raise
async def _generate_config_hash(self) -> str:
"""Generate a hash of the configuration for container matching.
Returns:
Hash string uniquely identifying this configuration
"""
# Create a dict with the relevant parts of the config
config_dict = {
"image": self.docker_config.image,
"mode": self.docker_config.mode,
"browser_type": self.config.browser_type,
"headless": self.config.headless,
}
# Add browser-specific config if in launch mode
if self.docker_config.mode == "launch":
config_dict.update(
{
"text_mode": self.config.text_mode,
"light_mode": self.config.light_mode,
"viewport_width": self.config.viewport_width,
"viewport_height": self.config.viewport_height,
}
)
# Use the utility method to generate the hash
return self.docker_utils.generate_config_hash(config_dict)
async def _get_or_create_cdp_url(self) -> str:
"""Get CDP URL by either creating a new container or using an existing one.
Returns:
CDP URL for connecting to the browser
Raises:
Exception: If container creation or browser launch fails
"""
# If CDP URL is explicitly provided, use it
if self.config.cdp_url:
return self.config.cdp_url
# Ensure Docker image exists (will build if needed)
image_name = await self.docker_utils.ensure_docker_image_exists(
self.docker_config.image, self.docker_config.mode
)
# Generate config hash for container matching
config_hash = await self._generate_config_hash()
# Look for existing container with matching config
container_id = await self.registry.find_container_by_config(
config_hash, self.docker_utils
)
if container_id:
# Use existing container
self.container_id = container_id
host_port = self.registry.get_container_host_port(container_id)
if self.logger:
self.logger.info(
f"Using existing Docker container: {container_id[:12]}",
tag="DOCKER",
)
else:
# Get a port for the new container
host_port = (
self.docker_config.host_port
or self.registry.get_next_available_port(self.docker_utils)
)
# Prepare volumes list
volumes = list(self.docker_config.volumes)
# Add user data directory if specified
if self.docker_config.user_data_dir:
# Ensure user data directory exists
os.makedirs(self.docker_config.user_data_dir, exist_ok=True)
volumes.append(
f"{self.docker_config.user_data_dir}:{self.docker_config.container_user_data_dir}"
)
# # Update config user_data_dir to point to container path
# self.config.user_data_dir = self.docker_config.container_user_data_dir
# Create a new container
container_id = await self.docker_utils.create_container(
image_name=image_name,
host_port=host_port,
container_name=self.container_name,
volumes=volumes,
network=self.docker_config.network,
env_vars=self.docker_config.env_vars,
cpu_limit=self.docker_config.cpu_limit,
memory_limit=self.docker_config.memory_limit,
extra_args=self.docker_config.extra_args,
)
if not container_id:
raise Exception("Failed to create Docker container")
self.container_id = container_id
# Wait for container to be ready
await self.docker_utils.wait_for_container_ready(container_id)
# Handle specific setup based on mode
if self.docker_config.mode == "launch":
# In launch mode, we need to start socat and Chrome
await self.docker_utils.start_socat_in_container(container_id)
# Build browser arguments
browser_args = self._build_browser_args()
# Launch Chrome
await self.docker_utils.launch_chrome_in_container(
container_id, browser_args
)
# Get PIDs for later cleanup
self.chrome_process_id = (
await self.docker_utils.get_process_id_in_container(
container_id, "chromium"
)
)
self.socat_process_id = (
await self.docker_utils.get_process_id_in_container(
container_id, "socat"
)
)
# Wait for CDP to be ready
cdp_json_config = await self.docker_utils.wait_for_cdp_ready(host_port)
if cdp_json_config:
# Register the container in the shared registry
self.registry.register_container(
container_id, host_port, config_hash, cdp_json_config
)
else:
raise Exception("Failed to get CDP JSON config from Docker container")
if self.logger:
self.logger.success(
f"Docker container ready: {container_id[:12]} on port {host_port}",
tag="DOCKER",
)
# Return CDP URL
return f"http://localhost:{host_port}"
def _build_browser_args(self) -> List[str]:
"""Build Chrome command line arguments based on BrowserConfig.
Returns:
List of command line arguments for Chrome
"""
# Call parent method to get common arguments
browser_args = super()._build_browser_args()
return browser_args["args"] + [
f"--remote-debugging-port={self.internal_cdp_port}",
"--remote-debugging-address=0.0.0.0", # Allow external connections
"--disable-dev-shm-usage",
"--headless=new",
]
# args = [
# "--no-sandbox",
# "--disable-gpu",
# f"--remote-debugging-port={self.internal_cdp_port}",
# "--remote-debugging-address=0.0.0.0", # Allow external connections
# "--disable-dev-shm-usage",
# ]
# if self.config.headless:
# args.append("--headless=new")
# if self.config.viewport_width and self.config.viewport_height:
# args.append(f"--window-size={self.config.viewport_width},{self.config.viewport_height}")
# if self.config.user_agent:
# args.append(f"--user-agent={self.config.user_agent}")
# if self.config.text_mode:
# args.extend([
# "--blink-settings=imagesEnabled=false",
# "--disable-remote-fonts",
# "--disable-images",
# "--disable-javascript",
# ])
# if self.config.light_mode:
# # Import here to avoid circular import
# from ..utils import get_browser_disable_options
# args.extend(get_browser_disable_options())
# if self.config.user_data_dir:
# args.append(f"--user-data-dir={self.config.user_data_dir}")
# if self.config.extra_args:
# args.extend(self.config.extra_args)
# return args
async def close(self):
"""Close the browser and clean up Docker container if needed."""
# Set flag to track if we were the ones initiating shutdown
initiated_shutdown = not self.shutting_down
# Storage persistence for Docker needs special handling
# We need to store state before calling super().close() which will close the browser
if (
self.browser
and self.docker_config.user_data_dir
and self.docker_config.persistent
):
for context in self.browser.contexts:
try:
# Ensure directory exists
os.makedirs(self.docker_config.user_data_dir, exist_ok=True)
# Save storage state to user data directory
storage_path = os.path.join(
self.docker_config.user_data_dir, "storage_state.json"
)
await context.storage_state(path=storage_path)
if self.logger:
self.logger.debug(
"Persisted Docker-specific storage state", tag="DOCKER"
)
except Exception as e:
if self.logger:
self.logger.warning(
message="Failed to persist Docker storage state: {error}",
tag="DOCKER",
params={"error": str(e)},
)
# Call parent method to handle common cleanup
await super().close()
# Only perform container cleanup if we initiated shutdown
# and we need to handle Docker-specific resources
if initiated_shutdown:
# Only clean up container if not persistent
if self.container_id and not self.docker_config.persistent:
# Stop Chrome process in "launch" mode
if self.docker_config.mode == "launch" and self.chrome_process_id:
await self.docker_utils.stop_process_in_container(
self.container_id, self.chrome_process_id
)
if self.logger:
self.logger.debug(
f"Stopped Chrome process {self.chrome_process_id} in container",
tag="DOCKER",
)
# Stop socat process in "launch" mode
if self.docker_config.mode == "launch" and self.socat_process_id:
await self.docker_utils.stop_process_in_container(
self.container_id, self.socat_process_id
)
if self.logger:
self.logger.debug(
f"Stopped socat process {self.socat_process_id} in container",
tag="DOCKER",
)
# Remove or stop container based on configuration
if self.docker_config.remove_on_exit:
await self.docker_utils.remove_container(self.container_id)
# Unregister from registry
if hasattr(self, "registry") and self.registry:
self.registry.unregister_container(self.container_id)
if self.logger:
self.logger.debug(
f"Removed Docker container {self.container_id}",
tag="DOCKER",
)
else:
await self.docker_utils.stop_container(self.container_id)
if self.logger:
self.logger.debug(
f"Stopped Docker container {self.container_id}",
tag="DOCKER",
)
self.container_id = None

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@@ -1,134 +0,0 @@
"""Browser strategies module for Crawl4AI.
This module implements the browser strategy pattern for different
browser implementations, including Playwright, CDP, and builtin browsers.
"""
import time
from typing import Optional, Tuple
from playwright.async_api import BrowserContext, Page
from ...async_logger import AsyncLogger
from ...async_configs import BrowserConfig, CrawlerRunConfig
from playwright_stealth import StealthConfig
from .base import BaseBrowserStrategy
stealth_config = StealthConfig(
webdriver=True,
chrome_app=True,
chrome_csi=True,
chrome_load_times=True,
chrome_runtime=True,
navigator_languages=True,
navigator_plugins=True,
navigator_permissions=True,
webgl_vendor=True,
outerdimensions=True,
navigator_hardware_concurrency=True,
media_codecs=True,
)
class PlaywrightBrowserStrategy(BaseBrowserStrategy):
"""Standard Playwright browser strategy.
This strategy launches a new browser instance using Playwright
and manages browser contexts.
"""
def __init__(self, config: BrowserConfig, logger: Optional[AsyncLogger] = None):
"""Initialize the Playwright browser strategy.
Args:
config: Browser configuration
logger: Logger for recording events and errors
"""
super().__init__(config, logger)
# No need to re-initialize sessions and session_ttl as they're now in the base class
async def start(self):
"""Start the browser instance.
Returns:
self: For method chaining
"""
# Call the base class start to initialize Playwright
await super().start()
# Build browser arguments using the base class method
browser_args = self._build_browser_args()
try:
# Launch appropriate browser type
if self.config.browser_type == "firefox":
self.browser = await self.playwright.firefox.launch(**browser_args)
elif self.config.browser_type == "webkit":
self.browser = await self.playwright.webkit.launch(**browser_args)
else:
self.browser = await self.playwright.chromium.launch(**browser_args)
self.default_context = self.browser
if self.logger:
self.logger.debug(f"Launched {self.config.browser_type} browser", tag="BROWSER")
except Exception as e:
if self.logger:
self.logger.error(f"Failed to launch browser: {str(e)}", tag="BROWSER")
raise
return self
async def _generate_page(self, crawlerRunConfig: CrawlerRunConfig) -> Tuple[Page, BrowserContext]:
# Otherwise, check if we have an existing context for this config
config_signature = self._make_config_signature(crawlerRunConfig)
async with self._contexts_lock:
if config_signature in self.contexts_by_config:
context = self.contexts_by_config[config_signature]
else:
# Create and setup a new context
context = await self.create_browser_context(crawlerRunConfig)
await self.setup_context(context, crawlerRunConfig)
self.contexts_by_config[config_signature] = context
# Create a new page from the chosen context
page = await context.new_page()
return page, context
async def _get_page(self, crawlerRunConfig: CrawlerRunConfig) -> Tuple[Page, BrowserContext]:
"""Get a page for the given configuration.
Args:
crawlerRunConfig: Configuration object for the crawler run
Returns:
Tuple of (Page, BrowserContext)
"""
# Call parent method to ensure browser is started
await super().get_page(crawlerRunConfig)
# Otherwise, check if we have an existing context for this config
config_signature = self._make_config_signature(crawlerRunConfig)
async with self._contexts_lock:
if config_signature in self.contexts_by_config:
context = self.contexts_by_config[config_signature]
else:
# Create and setup a new context
context = await self.create_browser_context(crawlerRunConfig)
await self.setup_context(context, crawlerRunConfig)
self.contexts_by_config[config_signature] = context
# Create a new page from the chosen context
page = await context.new_page()
# If a session_id is specified, store this session so we can reuse later
if crawlerRunConfig.session_id:
self.sessions[crawlerRunConfig.session_id] = (context, page, time.time())
return page, context

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@@ -1,465 +0,0 @@
"""Browser utilities module for Crawl4AI.
This module provides utility functions for browser management,
including process management, CDP connection utilities,
and Playwright instance management.
"""
import asyncio
import os
import sys
import time
import tempfile
import subprocess
from typing import Optional, Tuple, Union
import signal
import psutil
from playwright.async_api import async_playwright
from ..utils import get_chromium_path
from ..async_configs import BrowserConfig, CrawlerRunConfig
from ..async_logger import AsyncLogger
_playwright_instance = None
async def get_playwright():
"""Get or create the Playwright instance (singleton pattern).
Returns:
Playwright: The Playwright instance
"""
global _playwright_instance
if _playwright_instance is None or True:
_playwright_instance = await async_playwright().start()
return _playwright_instance
async def get_browser_executable(browser_type: str) -> str:
"""Get the path to browser executable, with platform-specific handling.
Args:
browser_type: Type of browser (chromium, firefox, webkit)
Returns:
Path to browser executable
"""
return await get_chromium_path(browser_type)
def create_temp_directory(prefix="browser-profile-") -> str:
"""Create a temporary directory for browser data.
Args:
prefix: Prefix for the temporary directory name
Returns:
Path to the created temporary directory
"""
return tempfile.mkdtemp(prefix=prefix)
def is_windows() -> bool:
"""Check if the current platform is Windows.
Returns:
True if Windows, False otherwise
"""
return sys.platform == "win32"
def is_macos() -> bool:
"""Check if the current platform is macOS.
Returns:
True if macOS, False otherwise
"""
return sys.platform == "darwin"
def is_linux() -> bool:
"""Check if the current platform is Linux.
Returns:
True if Linux, False otherwise
"""
return not (is_windows() or is_macos())
def is_browser_running(pid: Optional[int]) -> bool:
"""Check if a process with the given PID is running.
Args:
pid: Process ID to check
Returns:
bool: True if the process is running, False otherwise
"""
if not pid:
return False
try:
if type(pid) is str:
pid = int(pid)
# Check if the process exists
if is_windows():
process = subprocess.run(["tasklist", "/FI", f"PID eq {pid}"],
capture_output=True, text=True)
return str(pid) in process.stdout
else:
# Unix-like systems
os.kill(pid, 0) # This doesn't actually kill the process, just checks if it exists
return True
except (ProcessLookupError, PermissionError, OSError):
return False
def get_browser_disable_options() -> list:
"""Get standard list of browser disable options for performance.
Returns:
List of command-line options to disable various browser features
"""
return [
"--disable-background-networking",
"--disable-background-timer-throttling",
"--disable-backgrounding-occluded-windows",
"--disable-breakpad",
"--disable-client-side-phishing-detection",
"--disable-component-extensions-with-background-pages",
"--disable-default-apps",
"--disable-extensions",
"--disable-features=TranslateUI",
"--disable-hang-monitor",
"--disable-ipc-flooding-protection",
"--disable-popup-blocking",
"--disable-prompt-on-repost",
"--disable-sync",
"--force-color-profile=srgb",
"--metrics-recording-only",
"--no-first-run",
"--password-store=basic",
"--use-mock-keychain",
]
async def find_optimal_browser_config(total_urls=50, verbose=True, rate_limit_delay=0.2):
"""Find optimal browser configuration for crawling a specific number of URLs.
Args:
total_urls: Number of URLs to crawl
verbose: Whether to print progress
rate_limit_delay: Delay between page loads to avoid rate limiting
Returns:
dict: Contains fastest, lowest_memory, and optimal configurations
"""
from .manager import BrowserManager
if verbose:
print(f"\n=== Finding optimal configuration for crawling {total_urls} URLs ===\n")
# Generate test URLs with timestamp to avoid caching
timestamp = int(time.time())
urls = [f"https://example.com/page_{i}?t={timestamp}" for i in range(total_urls)]
# Limit browser configurations to test (1 browser to max 10)
max_browsers = min(10, total_urls)
configs_to_test = []
# Generate configurations (browser count, pages distribution)
for num_browsers in range(1, max_browsers + 1):
base_pages = total_urls // num_browsers
remainder = total_urls % num_browsers
# Create distribution array like [3, 3, 2, 2] (some browsers get one more page)
if remainder > 0:
distribution = [base_pages + 1] * remainder + [base_pages] * (num_browsers - remainder)
else:
distribution = [base_pages] * num_browsers
configs_to_test.append((num_browsers, distribution))
results = []
# Test each configuration
for browser_count, page_distribution in configs_to_test:
if verbose:
print(f"Testing {browser_count} browsers with distribution {tuple(page_distribution)}")
try:
# Track memory if possible
try:
import psutil
process = psutil.Process()
start_memory = process.memory_info().rss / (1024 * 1024) # MB
except ImportError:
if verbose:
print("Memory tracking not available (psutil not installed)")
start_memory = 0
# Start browsers in parallel
managers = []
start_tasks = []
start_time = time.time()
logger = AsyncLogger(verbose=True, log_file=None)
for i in range(browser_count):
config = BrowserConfig(headless=True)
manager = BrowserManager(browser_config=config, logger=logger)
start_tasks.append(manager.start())
managers.append(manager)
await asyncio.gather(*start_tasks)
# Distribute URLs among browsers
urls_per_manager = {}
url_index = 0
for i, manager in enumerate(managers):
pages_for_this_browser = page_distribution[i]
end_index = url_index + pages_for_this_browser
urls_per_manager[manager] = urls[url_index:end_index]
url_index = end_index
# Create pages for each browser
all_pages = []
for manager, manager_urls in urls_per_manager.items():
if not manager_urls:
continue
pages = await manager.get_pages(CrawlerRunConfig(), count=len(manager_urls))
all_pages.extend(zip(pages, manager_urls))
# Crawl pages with delay to avoid rate limiting
async def crawl_page(page_ctx, url):
page, _ = page_ctx
try:
await page.goto(url)
if rate_limit_delay > 0:
await asyncio.sleep(rate_limit_delay)
title = await page.title()
return title
finally:
await page.close()
crawl_start = time.time()
crawl_tasks = [crawl_page(page_ctx, url) for page_ctx, url in all_pages]
await asyncio.gather(*crawl_tasks)
crawl_time = time.time() - crawl_start
total_time = time.time() - start_time
# Measure final memory usage
if start_memory > 0:
end_memory = process.memory_info().rss / (1024 * 1024)
memory_used = end_memory - start_memory
else:
memory_used = 0
# Close all browsers
for manager in managers:
await manager.close()
# Calculate metrics
pages_per_second = total_urls / crawl_time
# Calculate efficiency score (higher is better)
# This balances speed vs memory
if memory_used > 0:
efficiency = pages_per_second / (memory_used + 1)
else:
efficiency = pages_per_second
# Store result
result = {
"browser_count": browser_count,
"distribution": tuple(page_distribution),
"crawl_time": crawl_time,
"total_time": total_time,
"memory_used": memory_used,
"pages_per_second": pages_per_second,
"efficiency": efficiency
}
results.append(result)
if verbose:
print(f" ✓ Crawled {total_urls} pages in {crawl_time:.2f}s ({pages_per_second:.1f} pages/sec)")
if memory_used > 0:
print(f" ✓ Memory used: {memory_used:.1f}MB ({memory_used/total_urls:.1f}MB per page)")
print(f" ✓ Efficiency score: {efficiency:.4f}")
except Exception as e:
if verbose:
print(f" ✗ Error: {str(e)}")
# Clean up
for manager in managers:
try:
await manager.close()
except:
pass
# If no successful results, return None
if not results:
return None
# Find best configurations
fastest = sorted(results, key=lambda x: x["crawl_time"])[0]
# Only consider memory if available
memory_results = [r for r in results if r["memory_used"] > 0]
if memory_results:
lowest_memory = sorted(memory_results, key=lambda x: x["memory_used"])[0]
else:
lowest_memory = fastest
# Find most efficient (balanced speed vs memory)
optimal = sorted(results, key=lambda x: x["efficiency"], reverse=True)[0]
# Print summary
if verbose:
print("\n=== OPTIMAL CONFIGURATIONS ===")
print(f"⚡ Fastest: {fastest['browser_count']} browsers {fastest['distribution']}")
print(f" {fastest['crawl_time']:.2f}s, {fastest['pages_per_second']:.1f} pages/sec")
print(f"💾 Memory-efficient: {lowest_memory['browser_count']} browsers {lowest_memory['distribution']}")
if lowest_memory["memory_used"] > 0:
print(f" {lowest_memory['memory_used']:.1f}MB, {lowest_memory['memory_used']/total_urls:.2f}MB per page")
print(f"🌟 Balanced optimal: {optimal['browser_count']} browsers {optimal['distribution']}")
print(f" {optimal['crawl_time']:.2f}s, {optimal['pages_per_second']:.1f} pages/sec, score: {optimal['efficiency']:.4f}")
return {
"fastest": fastest,
"lowest_memory": lowest_memory,
"optimal": optimal,
"all_configs": results
}
# Find process ID of the existing browser using os
def find_process_by_port(port: int) -> str:
"""Find process ID listening on a specific port.
Args:
port: Port number to check
Returns:
str: Process ID or empty string if not found
"""
try:
if is_windows():
cmd = f"netstat -ano | findstr :{port}"
result = subprocess.check_output(cmd, shell=True).decode()
return result.strip().split()[-1] if result else ""
else:
cmd = f"lsof -i :{port} -t"
return subprocess.check_output(cmd, shell=True).decode().strip()
except subprocess.CalledProcessError:
return ""
async def check_process_is_running(process: subprocess.Popen, delay: float = 0.5) -> Tuple[bool, Optional[int], bytes, bytes]:
"""Perform a quick check to make sure the browser started successfully."""
if not process:
return False, None, b"", b""
# Check that process started without immediate termination
await asyncio.sleep(delay)
if process.poll() is not None:
# Process already terminated
stdout, stderr = b"", b""
try:
stdout, stderr = process.communicate(timeout=0.5)
except subprocess.TimeoutExpired:
pass
return False, process.returncode, stdout, stderr
return True, 0, b"", b""
def terminate_process(
pid: Union[int, str],
timeout: float = 5.0,
force_kill_timeout: float = 3.0,
logger = None
) -> Tuple[bool, Optional[str]]:
"""
Robustly terminate a process across platforms with verification.
Args:
pid: Process ID to terminate (int or string)
timeout: Seconds to wait for graceful termination before force killing
force_kill_timeout: Seconds to wait after force kill before considering it failed
logger: Optional logger object with error, warning, and info methods
Returns:
Tuple of (success: bool, error_message: Optional[str])
"""
# Convert pid to int if it's a string
if isinstance(pid, str):
try:
pid = int(pid)
except ValueError:
error_msg = f"Invalid PID format: {pid}"
if logger:
logger.error(error_msg)
return False, error_msg
# Check if process exists
if not psutil.pid_exists(pid):
return True, None # Process already terminated
try:
process = psutil.Process(pid)
# First attempt: graceful termination
if logger:
logger.info(f"Attempting graceful termination of process {pid}")
if os.name == 'nt': # Windows
subprocess.run(["taskkill", "/PID", str(pid)],
stdout=subprocess.DEVNULL,
stderr=subprocess.DEVNULL,
check=False)
else: # Unix/Linux/MacOS
process.send_signal(signal.SIGTERM)
# Wait for process to terminate
try:
process.wait(timeout=timeout)
if logger:
logger.info(f"Process {pid} terminated gracefully")
return True, None
except psutil.TimeoutExpired:
if logger:
logger.warning(f"Process {pid} did not terminate gracefully within {timeout} seconds, forcing termination")
# Second attempt: force kill
if os.name == 'nt': # Windows
subprocess.run(["taskkill", "/F", "/PID", str(pid)],
stdout=subprocess.DEVNULL,
stderr=subprocess.DEVNULL,
check=False)
else: # Unix/Linux/MacOS
process.send_signal(signal.SIGKILL)
# Verify process is killed
gone, alive = psutil.wait_procs([process], timeout=force_kill_timeout)
if process in alive:
error_msg = f"Failed to kill process {pid} even after force kill"
if logger:
logger.error(error_msg)
return False, error_msg
if logger:
logger.info(f"Process {pid} terminated by force")
return True, None
except psutil.NoSuchProcess:
# Process terminated while we were working with it
if logger:
logger.info(f"Process {pid} already terminated")
return True, None
except Exception as e:
error_msg = f"Error terminating process {pid}: {str(e)}"
if logger:
logger.error(error_msg)
return False, error_msg

293
crawl4ai/browser_adapter.py Normal file
View File

@@ -0,0 +1,293 @@
# browser_adapter.py
"""
Browser adapter for Crawl4AI to support both Playwright and undetected browsers
with minimal changes to existing codebase.
"""
from abc import ABC, abstractmethod
from typing import List, Dict, Any, Optional, Callable
import time
import json
# Import both, but use conditionally
try:
from playwright.async_api import Page
except ImportError:
Page = Any
try:
from patchright.async_api import Page as UndetectedPage
except ImportError:
UndetectedPage = Any
class BrowserAdapter(ABC):
"""Abstract adapter for browser-specific operations"""
@abstractmethod
async def evaluate(self, page: Page, expression: str, arg: Any = None) -> Any:
"""Execute JavaScript in the page"""
pass
@abstractmethod
async def setup_console_capture(self, page: Page, captured_console: List[Dict]) -> Optional[Callable]:
"""Setup console message capturing, returns handler function if needed"""
pass
@abstractmethod
async def setup_error_capture(self, page: Page, captured_console: List[Dict]) -> Optional[Callable]:
"""Setup error capturing, returns handler function if needed"""
pass
@abstractmethod
async def retrieve_console_messages(self, page: Page) -> List[Dict]:
"""Retrieve captured console messages (for undetected browsers)"""
pass
@abstractmethod
async def cleanup_console_capture(self, page: Page, handle_console: Optional[Callable], handle_error: Optional[Callable]):
"""Clean up console event listeners"""
pass
@abstractmethod
def get_imports(self) -> tuple:
"""Get the appropriate imports for this adapter"""
pass
class PlaywrightAdapter(BrowserAdapter):
"""Adapter for standard Playwright"""
async def evaluate(self, page: Page, expression: str, arg: Any = None) -> Any:
"""Standard Playwright evaluate"""
if arg is not None:
return await page.evaluate(expression, arg)
return await page.evaluate(expression)
async def setup_console_capture(self, page: Page, captured_console: List[Dict]) -> Optional[Callable]:
"""Setup console capture using Playwright's event system"""
def handle_console_capture(msg):
try:
message_type = "unknown"
try:
message_type = msg.type
except:
pass
message_text = "unknown"
try:
message_text = msg.text
except:
pass
entry = {
"type": message_type,
"text": message_text,
"timestamp": time.time()
}
captured_console.append(entry)
except Exception as e:
captured_console.append({
"type": "console_capture_error",
"error": str(e),
"timestamp": time.time()
})
page.on("console", handle_console_capture)
return handle_console_capture
async def setup_error_capture(self, page: Page, captured_console: List[Dict]) -> Optional[Callable]:
"""Setup error capture using Playwright's event system"""
def handle_pageerror_capture(err):
try:
error_message = "Unknown error"
try:
error_message = err.message
except:
pass
error_stack = ""
try:
error_stack = err.stack
except:
pass
captured_console.append({
"type": "error",
"text": error_message,
"stack": error_stack,
"timestamp": time.time()
})
except Exception as e:
captured_console.append({
"type": "pageerror_capture_error",
"error": str(e),
"timestamp": time.time()
})
page.on("pageerror", handle_pageerror_capture)
return handle_pageerror_capture
async def retrieve_console_messages(self, page: Page) -> List[Dict]:
"""Not needed for Playwright - messages are captured via events"""
return []
async def cleanup_console_capture(self, page: Page, handle_console: Optional[Callable], handle_error: Optional[Callable]):
"""Remove event listeners"""
if handle_console:
page.remove_listener("console", handle_console)
if handle_error:
page.remove_listener("pageerror", handle_error)
def get_imports(self) -> tuple:
"""Return Playwright imports"""
from playwright.async_api import Page, Error
from playwright.async_api import TimeoutError as PlaywrightTimeoutError
return Page, Error, PlaywrightTimeoutError
class UndetectedAdapter(BrowserAdapter):
"""Adapter for undetected browser automation with stealth features"""
def __init__(self):
self._console_script_injected = {}
async def evaluate(self, page: UndetectedPage, expression: str, arg: Any = None) -> Any:
"""Undetected browser evaluate with isolated context"""
# For most evaluations, use isolated context for stealth
# Only use non-isolated when we need to access our injected console capture
isolated = not (
"__console" in expression or
"__captured" in expression or
"__error" in expression or
"window.__" in expression
)
if arg is not None:
return await page.evaluate(expression, arg, isolated_context=isolated)
return await page.evaluate(expression, isolated_context=isolated)
async def setup_console_capture(self, page: UndetectedPage, captured_console: List[Dict]) -> Optional[Callable]:
"""Setup console capture using JavaScript injection for undetected browsers"""
if not self._console_script_injected.get(page, False):
await page.add_init_script("""
// Initialize console capture
window.__capturedConsole = [];
window.__capturedErrors = [];
// Store original console methods
const originalConsole = {};
['log', 'info', 'warn', 'error', 'debug'].forEach(method => {
originalConsole[method] = console[method];
console[method] = function(...args) {
try {
window.__capturedConsole.push({
type: method,
text: args.map(arg => {
try {
if (typeof arg === 'object') {
return JSON.stringify(arg);
}
return String(arg);
} catch (e) {
return '[Object]';
}
}).join(' '),
timestamp: Date.now()
});
} catch (e) {
// Fail silently to avoid detection
}
// Call original method
originalConsole[method].apply(console, args);
};
});
""")
self._console_script_injected[page] = True
return None # No handler function needed for undetected browser
async def setup_error_capture(self, page: UndetectedPage, captured_console: List[Dict]) -> Optional[Callable]:
"""Setup error capture using JavaScript injection for undetected browsers"""
if not self._console_script_injected.get(page, False):
await page.add_init_script("""
// Capture errors
window.addEventListener('error', (event) => {
try {
window.__capturedErrors.push({
type: 'error',
text: event.message,
stack: event.error ? event.error.stack : '',
filename: event.filename,
lineno: event.lineno,
colno: event.colno,
timestamp: Date.now()
});
} catch (e) {
// Fail silently
}
});
// Capture unhandled promise rejections
window.addEventListener('unhandledrejection', (event) => {
try {
window.__capturedErrors.push({
type: 'unhandledrejection',
text: event.reason ? String(event.reason) : 'Unhandled Promise Rejection',
stack: event.reason && event.reason.stack ? event.reason.stack : '',
timestamp: Date.now()
});
} catch (e) {
// Fail silently
}
});
""")
self._console_script_injected[page] = True
return None # No handler function needed for undetected browser
async def retrieve_console_messages(self, page: UndetectedPage) -> List[Dict]:
"""Retrieve captured console messages and errors from the page"""
messages = []
try:
# Get console messages
console_messages = await page.evaluate(
"() => { const msgs = window.__capturedConsole || []; window.__capturedConsole = []; return msgs; }",
isolated_context=False
)
messages.extend(console_messages)
# Get errors
errors = await page.evaluate(
"() => { const errs = window.__capturedErrors || []; window.__capturedErrors = []; return errs; }",
isolated_context=False
)
messages.extend(errors)
# Convert timestamps from JS to Python format
for msg in messages:
if 'timestamp' in msg and isinstance(msg['timestamp'], (int, float)):
msg['timestamp'] = msg['timestamp'] / 1000.0 # Convert from ms to seconds
except Exception:
# If retrieval fails, return empty list
pass
return messages
async def cleanup_console_capture(self, page: UndetectedPage, handle_console: Optional[Callable], handle_error: Optional[Callable]):
"""Clean up for undetected browser - retrieve final messages"""
# For undetected browser, we don't have event listeners to remove
# but we should retrieve any final messages
final_messages = await self.retrieve_console_messages(page)
return final_messages
def get_imports(self) -> tuple:
"""Return undetected browser imports"""
from patchright.async_api import Page, Error
from patchright.async_api import TimeoutError as PlaywrightTimeoutError
return Page, Error, PlaywrightTimeoutError

View File

@@ -5,29 +5,17 @@ import os
import sys
import shutil
import tempfile
import psutil
import signal
import subprocess
import shlex
from playwright.async_api import BrowserContext
import hashlib
from .js_snippet import load_js_script
from .config import DOWNLOAD_PAGE_TIMEOUT
from .async_configs import BrowserConfig, CrawlerRunConfig
from playwright_stealth import StealthConfig
from .utils import get_chromium_path
stealth_config = StealthConfig(
webdriver=True,
chrome_app=True,
chrome_csi=True,
chrome_load_times=True,
chrome_runtime=True,
navigator_languages=True,
navigator_plugins=True,
navigator_permissions=True,
webgl_vendor=True,
outerdimensions=True,
navigator_hardware_concurrency=True,
media_codecs=True,
)
BROWSER_DISABLE_OPTIONS = [
"--disable-background-networking",
@@ -76,6 +64,51 @@ class ManagedBrowser:
_cleanup(): Terminates the browser process and removes the temporary directory.
create_profile(): Static method to create a user profile by launching a browser for user interaction.
"""
@staticmethod
def build_browser_flags(config: BrowserConfig) -> List[str]:
"""Common CLI flags for launching Chromium"""
flags = [
"--disable-gpu",
"--disable-gpu-compositing",
"--disable-software-rasterizer",
"--no-sandbox",
"--disable-dev-shm-usage",
"--no-first-run",
"--no-default-browser-check",
"--disable-infobars",
"--window-position=0,0",
"--ignore-certificate-errors",
"--ignore-certificate-errors-spki-list",
"--disable-blink-features=AutomationControlled",
"--window-position=400,0",
"--disable-renderer-backgrounding",
"--disable-ipc-flooding-protection",
"--force-color-profile=srgb",
"--mute-audio",
"--disable-background-timer-throttling",
]
if config.light_mode:
flags.extend(BROWSER_DISABLE_OPTIONS)
if config.text_mode:
flags.extend([
"--blink-settings=imagesEnabled=false",
"--disable-remote-fonts",
"--disable-images",
"--disable-javascript",
"--disable-software-rasterizer",
"--disable-dev-shm-usage",
])
# proxy support
if config.proxy:
flags.append(f"--proxy-server={config.proxy}")
elif config.proxy_config:
creds = ""
if config.proxy_config.username and config.proxy_config.password:
creds = f"{config.proxy_config.username}:{config.proxy_config.password}@"
flags.append(f"--proxy-server={creds}{config.proxy_config.server}")
# dedupe
return list(dict.fromkeys(flags))
browser_type: str
user_data_dir: str
@@ -94,6 +127,7 @@ class ManagedBrowser:
host: str = "localhost",
debugging_port: int = 9222,
cdp_url: Optional[str] = None,
browser_config: Optional[BrowserConfig] = None,
):
"""
Initialize the ManagedBrowser instance.
@@ -109,17 +143,19 @@ class ManagedBrowser:
host (str): Host for debugging the browser. Default: "localhost".
debugging_port (int): Port for debugging the browser. Default: 9222.
cdp_url (str or None): CDP URL to connect to the browser. Default: None.
browser_config (BrowserConfig): Configuration object containing all browser settings. Default: None.
"""
self.browser_type = browser_type
self.user_data_dir = user_data_dir
self.headless = headless
self.browser_type = browser_config.browser_type
self.user_data_dir = browser_config.user_data_dir
self.headless = browser_config.headless
self.browser_process = None
self.temp_dir = None
self.debugging_port = debugging_port
self.host = host
self.debugging_port = browser_config.debugging_port
self.host = browser_config.host
self.logger = logger
self.shutting_down = False
self.cdp_url = cdp_url
self.cdp_url = browser_config.cdp_url
self.browser_config = browser_config
async def start(self) -> str:
"""
@@ -142,6 +178,48 @@ class ManagedBrowser:
# Get browser path and args based on OS and browser type
# browser_path = self._get_browser_path()
args = await self._get_browser_args()
if self.browser_config.extra_args:
args.extend(self.browser_config.extra_args)
# ── make sure no old Chromium instance is owning the same port/profile ──
try:
if sys.platform == "win32":
if psutil is None:
raise RuntimeError("psutil not available, cannot clean old browser")
for p in psutil.process_iter(["pid", "name", "cmdline"]):
cl = " ".join(p.info.get("cmdline") or [])
if (
f"--remote-debugging-port={self.debugging_port}" in cl
and f"--user-data-dir={self.user_data_dir}" in cl
):
p.kill()
p.wait(timeout=5)
else: # macOS / Linux
# kill any process listening on the same debugging port
pids = (
subprocess.check_output(shlex.split(f"lsof -t -i:{self.debugging_port}"))
.decode()
.strip()
.splitlines()
)
for pid in pids:
try:
os.kill(int(pid), signal.SIGTERM)
except ProcessLookupError:
pass
# remove Chromium singleton locks, or new launch exits with
# “Opening in existing browser session.”
for f in ("SingletonLock", "SingletonSocket", "SingletonCookie"):
fp = os.path.join(self.user_data_dir, f)
if os.path.exists(fp):
os.remove(fp)
except Exception as _e:
# non-fatal — we'll try to start anyway, but log what happened
self.logger.warning(f"pre-launch cleanup failed: {_e}", tag="BROWSER")
# Start browser process
try:
@@ -162,6 +240,13 @@ class ManagedBrowser:
preexec_fn=os.setpgrp # Start in a new process group
)
# If verbose is True print args used to run the process
if self.logger and self.browser_config.verbose:
self.logger.debug(
f"Starting browser with args: {' '.join(args)}",
tag="BROWSER"
)
# We'll monitor for a short time to make sure it starts properly, but won't keep monitoring
await asyncio.sleep(0.5) # Give browser time to start
await self._initial_startup_check()
@@ -274,29 +359,29 @@ class ManagedBrowser:
return browser_path
async def _get_browser_args(self) -> List[str]:
"""Returns browser-specific command line arguments"""
base_args = [await self._get_browser_path()]
"""Returns full CLI args for launching the browser"""
base = [await self._get_browser_path()]
if self.browser_type == "chromium":
args = [
flags = [
f"--remote-debugging-port={self.debugging_port}",
f"--user-data-dir={self.user_data_dir}",
]
if self.headless:
args.append("--headless=new")
flags.append("--headless=new")
# merge common launch flags
flags.extend(self.build_browser_flags(self.browser_config))
elif self.browser_type == "firefox":
args = [
flags = [
"--remote-debugging-port",
str(self.debugging_port),
"--profile",
self.user_data_dir,
]
if self.headless:
args.append("--headless")
flags.append("--headless")
else:
raise NotImplementedError(f"Browser type {self.browser_type} not supported")
return base_args + args
return base + flags
async def cleanup(self):
"""Cleanup browser process and temporary directory"""
@@ -418,6 +503,56 @@ class ManagedBrowser:
return profiler.delete_profile(profile_name_or_path)
async def clone_runtime_state(
src: BrowserContext,
dst: BrowserContext,
crawlerRunConfig: CrawlerRunConfig | None = None,
browserConfig: BrowserConfig | None = None,
) -> None:
"""
Bring everything that *can* be changed at runtime from `src` → `dst`.
1. Cookies
2. localStorage (and sessionStorage, same API)
3. Extra headers, permissions, geolocation if supplied in configs
"""
# ── 1. cookies ────────────────────────────────────────────────────────────
cookies = await src.cookies()
if cookies:
await dst.add_cookies(cookies)
# ── 2. localStorage / sessionStorage ──────────────────────────────────────
state = await src.storage_state()
for origin in state.get("origins", []):
url = origin["origin"]
kvs = origin.get("localStorage", [])
if not kvs:
continue
page = dst.pages[0] if dst.pages else await dst.new_page()
await page.goto(url, wait_until="domcontentloaded")
for k, v in kvs:
await page.evaluate("(k,v)=>localStorage.setItem(k,v)", k, v)
# ── 3. runtime-mutable extras from configs ────────────────────────────────
# headers
if browserConfig and browserConfig.headers:
await dst.set_extra_http_headers(browserConfig.headers)
# geolocation
if crawlerRunConfig and crawlerRunConfig.geolocation:
await dst.grant_permissions(["geolocation"])
await dst.set_geolocation(
{
"latitude": crawlerRunConfig.geolocation.latitude,
"longitude": crawlerRunConfig.geolocation.longitude,
"accuracy": crawlerRunConfig.geolocation.accuracy,
}
)
return dst
class BrowserManager:
@@ -438,22 +573,26 @@ class BrowserManager:
_playwright_instance = None
@classmethod
async def get_playwright(cls):
from playwright.async_api import async_playwright
if cls._playwright_instance is None:
cls._playwright_instance = await async_playwright().start()
async def get_playwright(cls, use_undetected: bool = False):
if use_undetected:
from patchright.async_api import async_playwright
else:
from playwright.async_api import async_playwright
cls._playwright_instance = await async_playwright().start()
return cls._playwright_instance
def __init__(self, browser_config: BrowserConfig, logger=None):
def __init__(self, browser_config: BrowserConfig, logger=None, use_undetected: bool = False):
"""
Initialize the BrowserManager with a browser configuration.
Args:
browser_config (BrowserConfig): Configuration object containing all browser settings
logger: Logger instance for recording events and errors
use_undetected (bool): Whether to use undetected browser (Patchright)
"""
self.config: BrowserConfig = browser_config
self.logger = logger
self.use_undetected = use_undetected
# Browser state
self.browser = None
@@ -467,7 +606,11 @@ class BrowserManager:
# Keep track of contexts by a "config signature," so each unique config reuses a single context
self.contexts_by_config = {}
self._contexts_lock = asyncio.Lock()
self._contexts_lock = asyncio.Lock()
# Stealth-related attributes
self._stealth_instance = None
self._stealth_cm = None
# Initialize ManagedBrowser if needed
if self.config.use_managed_browser:
@@ -478,6 +621,7 @@ class BrowserManager:
logger=self.logger,
debugging_port=self.config.debugging_port,
cdp_url=self.config.cdp_url,
browser_config=self.config,
)
async def start(self):
@@ -492,10 +636,23 @@ class BrowserManager:
Note: This method should be called in a separate task to avoid blocking the main event loop.
"""
self.playwright = await self.get_playwright()
if self.playwright is None:
if self.playwright is not None:
await self.close()
if self.use_undetected:
from patchright.async_api import async_playwright
else:
from playwright.async_api import async_playwright
# Initialize playwright with or without stealth
if self.config.enable_stealth and not self.use_undetected:
# Import stealth only when needed
from playwright_stealth import Stealth
# Use the recommended stealth wrapper approach
self._stealth_instance = Stealth()
self._stealth_cm = self._stealth_instance.use_async(async_playwright())
self.playwright = await self._stealth_cm.__aenter__()
else:
self.playwright = await async_playwright().start()
if self.config.cdp_url or self.config.use_managed_browser:
@@ -565,6 +722,9 @@ class BrowserManager:
if self.config.extra_args:
args.extend(self.config.extra_args)
# Deduplicate args
args = list(dict.fromkeys(args))
browser_args = {"headless": self.config.headless, "args": args}
if self.config.chrome_channel:
@@ -660,7 +820,7 @@ class BrowserManager:
"name": "cookiesEnabled",
"value": "true",
"url": crawlerRunConfig.url
if crawlerRunConfig
if crawlerRunConfig and crawlerRunConfig.url
else "https://crawl4ai.com/",
}
]
@@ -779,6 +939,23 @@ class BrowserManager:
# Update context settings with text mode settings
context_settings.update(text_mode_settings)
# inject locale / tz / geo if user provided them
if crawlerRunConfig:
if crawlerRunConfig.locale:
context_settings["locale"] = crawlerRunConfig.locale
if crawlerRunConfig.timezone_id:
context_settings["timezone_id"] = crawlerRunConfig.timezone_id
if crawlerRunConfig.geolocation:
context_settings["geolocation"] = {
"latitude": crawlerRunConfig.geolocation.latitude,
"longitude": crawlerRunConfig.geolocation.longitude,
"accuracy": crawlerRunConfig.geolocation.accuracy,
}
# ensure geolocation permission
perms = context_settings.get("permissions", [])
perms.append("geolocation")
context_settings["permissions"] = perms
# Create and return the context with all settings
context = await self.browser.new_context(**context_settings)
@@ -811,6 +988,10 @@ class BrowserManager:
"semaphore_count",
"url"
]
# Do NOT exclude locale, timezone_id, or geolocation as these DO affect browser context
# and should cause a new context to be created if they change
for key in ephemeral_keys:
if key in config_dict:
del config_dict[key]
@@ -842,11 +1023,17 @@ class BrowserManager:
# If using a managed browser, just grab the shared default_context
if self.config.use_managed_browser:
context = self.default_context
pages = context.pages
page = next((p for p in pages if p.url == crawlerRunConfig.url), None)
if not page:
page = await context.new_page()
if self.config.storage_state:
context = await self.create_browser_context(crawlerRunConfig)
ctx = self.default_context # default context, one window only
ctx = await clone_runtime_state(context, ctx, crawlerRunConfig, self.config)
page = await ctx.new_page()
else:
context = self.default_context
pages = context.pages
page = next((p for p in pages if p.url == crawlerRunConfig.url), None)
if not page:
page = context.pages[0] # await context.new_page()
else:
# Otherwise, check if we have an existing context for this config
config_signature = self._make_config_signature(crawlerRunConfig)
@@ -928,5 +1115,19 @@ class BrowserManager:
self.managed_browser = None
if self.playwright:
await self.playwright.stop()
# Handle stealth context manager cleanup if it exists
if hasattr(self, '_stealth_cm') and self._stealth_cm is not None:
try:
await self._stealth_cm.__aexit__(None, None, None)
except Exception as e:
if self.logger:
self.logger.error(
message="Error closing stealth context: {error}",
tag="ERROR",
params={"error": str(e)}
)
self._stealth_cm = None
self._stealth_instance = None
else:
await self.playwright.stop()
self.playwright = None

View File

@@ -15,12 +15,12 @@ import shutil
import json
import subprocess
import time
from typing import List, Dict, Optional, Any, Tuple
from colorama import Fore, Style, init
from typing import List, Dict, Optional, Any
from rich.console import Console
from .async_configs import BrowserConfig
from .browser_manager import ManagedBrowser
from .async_logger import AsyncLogger, AsyncLoggerBase
from .async_logger import AsyncLogger, AsyncLoggerBase, LogColor
from .utils import get_home_folder
@@ -45,8 +45,8 @@ class BrowserProfiler:
logger (AsyncLoggerBase, optional): Logger for outputting messages.
If None, a default AsyncLogger will be created.
"""
# Initialize colorama for colorful terminal output
init()
# Initialize rich console for colorful input prompts
self.console = Console()
# Create a logger if not provided
if logger is None:
@@ -127,26 +127,30 @@ class BrowserProfiler:
profile_path = os.path.join(self.profiles_dir, profile_name)
os.makedirs(profile_path, exist_ok=True)
# Print instructions for the user with colorama formatting
border = f"{Fore.CYAN}{'='*80}{Style.RESET_ALL}"
self.logger.info(f"\n{border}", tag="PROFILE")
self.logger.info(f"Creating browser profile: {Fore.GREEN}{profile_name}{Style.RESET_ALL}", tag="PROFILE")
self.logger.info(f"Profile directory: {Fore.YELLOW}{profile_path}{Style.RESET_ALL}", tag="PROFILE")
# Print instructions for the user with rich formatting
border = f"{'='*80}"
self.logger.info("{border}", tag="PROFILE", params={"border": f"\n{border}"}, colors={"border": LogColor.CYAN})
self.logger.info("Creating browser profile: {profile_name}", tag="PROFILE", params={"profile_name": profile_name}, colors={"profile_name": LogColor.GREEN})
self.logger.info("Profile directory: {profile_path}", tag="PROFILE", params={"profile_path": profile_path}, colors={"profile_path": LogColor.YELLOW})
self.logger.info("\nInstructions:", tag="PROFILE")
self.logger.info("1. A browser window will open for you to set up your profile.", tag="PROFILE")
self.logger.info(f"2. {Fore.CYAN}Log in to websites{Style.RESET_ALL}, configure settings, etc. as needed.", tag="PROFILE")
self.logger.info(f"3. When you're done, {Fore.YELLOW}press 'q' in this terminal{Style.RESET_ALL} to close the browser.", tag="PROFILE")
self.logger.info("{segment}, configure settings, etc. as needed.", tag="PROFILE", params={"segment": "2. Log in to websites"}, colors={"segment": LogColor.CYAN})
self.logger.info("3. When you're done, {segment} to close the browser.", tag="PROFILE", params={"segment": "press 'q' in this terminal"}, colors={"segment": LogColor.YELLOW})
self.logger.info("4. The profile will be saved and ready to use with Crawl4AI.", tag="PROFILE")
self.logger.info(f"{border}\n", tag="PROFILE")
self.logger.info("{border}", tag="PROFILE", params={"border": f"{border}\n"}, colors={"border": LogColor.CYAN})
browser_config.headless = False
browser_config.user_data_dir = profile_path
# Create managed browser instance
managed_browser = ManagedBrowser(
browser_type=browser_config.browser_type,
user_data_dir=profile_path,
headless=False, # Must be visible
browser_config=browser_config,
# user_data_dir=profile_path,
# headless=False, # Must be visible
logger=self.logger,
debugging_port=browser_config.debugging_port
# debugging_port=browser_config.debugging_port
)
# Set up signal handlers to ensure cleanup on interrupt
@@ -181,7 +185,7 @@ class BrowserProfiler:
import select
# First output the prompt
self.logger.info(f"{Fore.CYAN}Press '{Fore.WHITE}q{Fore.CYAN}' when you've finished using the browser...{Style.RESET_ALL}", tag="PROFILE")
self.logger.info("Press 'q' when you've finished using the browser...", tag="PROFILE")
# Save original terminal settings
fd = sys.stdin.fileno()
@@ -197,7 +201,7 @@ class BrowserProfiler:
if readable:
key = sys.stdin.read(1)
if key.lower() == 'q':
self.logger.info(f"{Fore.GREEN}Closing browser and saving profile...{Style.RESET_ALL}", tag="PROFILE")
self.logger.info("Closing browser and saving profile...", tag="PROFILE", base_color=LogColor.GREEN)
user_done_event.set()
return
@@ -214,8 +218,18 @@ class BrowserProfiler:
termios.tcsetattr(fd, termios.TCSADRAIN, old_settings)
try:
from playwright.async_api import async_playwright
# Start the browser
await managed_browser.start()
# await managed_browser.start()
# 1. ── Start the browser ─────────────────────────────────────────
cdp_url = await managed_browser.start()
# 2. ── Attach Playwright to that running Chrome ──────────────────
pw = await async_playwright().start()
browser = await pw.chromium.connect_over_cdp(cdp_url)
# Grab the existing default context (there is always one)
context = browser.contexts[0]
# Check if browser started successfully
browser_process = managed_browser.browser_process
@@ -223,7 +237,7 @@ class BrowserProfiler:
self.logger.error("Failed to start browser process.", tag="PROFILE")
return None
self.logger.info(f"Browser launched. {Fore.CYAN}Waiting for you to finish...{Style.RESET_ALL}", tag="PROFILE")
self.logger.info("Browser launched. Waiting for you to finish...", tag="PROFILE")
# Start listening for keyboard input
listener_task = asyncio.create_task(listen_for_quit_command())
@@ -240,15 +254,27 @@ class BrowserProfiler:
except asyncio.CancelledError:
pass
# 3. ── Persist storage state *before* we kill Chrome ─────────────
state_file = os.path.join(profile_path, "storage_state.json")
try:
await context.storage_state(path=state_file)
self.logger.info(f"[PROFILE].i storage_state saved → {state_file}", tag="PROFILE")
except Exception as e:
self.logger.warning(f"[PROFILE].w failed to save storage_state: {e}", tag="PROFILE")
# 4. ── Close everything cleanly ──────────────────────────────────
await browser.close()
await pw.stop()
# If the browser is still running and the user pressed 'q', terminate it
if browser_process.poll() is None and user_done_event.is_set():
self.logger.info("Terminating browser process...", tag="PROFILE")
await managed_browser.cleanup()
self.logger.success(f"Browser closed. Profile saved at: {Fore.GREEN}{profile_path}{Style.RESET_ALL}", tag="PROFILE")
self.logger.success(f"Browser closed. Profile saved at: {profile_path}", tag="PROFILE")
except Exception as e:
self.logger.error(f"Error creating profile: {str(e)}", tag="PROFILE")
self.logger.error(f"Error creating profile: {e!s}", tag="PROFILE")
await managed_browser.cleanup()
return None
finally:
@@ -440,25 +466,27 @@ class BrowserProfiler:
```
"""
while True:
self.logger.info(f"\n{Fore.CYAN}Profile Management Options:{Style.RESET_ALL}", tag="MENU")
self.logger.info(f"1. {Fore.GREEN}Create a new profile{Style.RESET_ALL}", tag="MENU")
self.logger.info(f"2. {Fore.YELLOW}List available profiles{Style.RESET_ALL}", tag="MENU")
self.logger.info(f"3. {Fore.RED}Delete a profile{Style.RESET_ALL}", tag="MENU")
self.logger.info("\nProfile Management Options:", tag="MENU")
self.logger.info("1. Create a new profile", tag="MENU", base_color=LogColor.GREEN)
self.logger.info("2. List available profiles", tag="MENU", base_color=LogColor.YELLOW)
self.logger.info("3. Delete a profile", tag="MENU", base_color=LogColor.RED)
# Only show crawl option if callback provided
if crawl_callback:
self.logger.info(f"4. {Fore.CYAN}Use a profile to crawl a website{Style.RESET_ALL}", tag="MENU")
self.logger.info(f"5. {Fore.MAGENTA}Exit{Style.RESET_ALL}", tag="MENU")
self.logger.info("4. Use a profile to crawl a website", tag="MENU", base_color=LogColor.CYAN)
self.logger.info("5. Exit", tag="MENU", base_color=LogColor.MAGENTA)
exit_option = "5"
else:
self.logger.info(f"4. {Fore.MAGENTA}Exit{Style.RESET_ALL}", tag="MENU")
self.logger.info("4. Exit", tag="MENU", base_color=LogColor.MAGENTA)
exit_option = "4"
choice = input(f"\n{Fore.CYAN}Enter your choice (1-{exit_option}): {Style.RESET_ALL}")
self.logger.info(f"\n[cyan]Enter your choice (1-{exit_option}): [/cyan]", end="")
choice = input()
if choice == "1":
# Create new profile
name = input(f"{Fore.GREEN}Enter a name for the new profile (or press Enter for auto-generated name): {Style.RESET_ALL}")
self.console.print("[green]Enter a name for the new profile (or press Enter for auto-generated name): [/green]", end="")
name = input()
await self.create_profile(name or None)
elif choice == "2":
@@ -469,11 +497,11 @@ class BrowserProfiler:
self.logger.warning(" No profiles found. Create one first with option 1.", tag="PROFILES")
continue
# Print profile information with colorama formatting
# Print profile information
self.logger.info("\nAvailable profiles:", tag="PROFILES")
for i, profile in enumerate(profiles):
self.logger.info(f"[{i+1}] {Fore.CYAN}{profile['name']}{Style.RESET_ALL}", tag="PROFILES")
self.logger.info(f" Path: {Fore.YELLOW}{profile['path']}{Style.RESET_ALL}", tag="PROFILES")
self.logger.info(f"[{i+1}] {profile['name']}", tag="PROFILES")
self.logger.info(f" Path: {profile['path']}", tag="PROFILES", base_color=LogColor.YELLOW)
self.logger.info(f" Created: {profile['created'].strftime('%Y-%m-%d %H:%M:%S')}", tag="PROFILES")
self.logger.info(f" Browser type: {profile['type']}", tag="PROFILES")
self.logger.info("", tag="PROFILES") # Empty line for spacing
@@ -486,12 +514,13 @@ class BrowserProfiler:
continue
# Display numbered list
self.logger.info(f"\n{Fore.YELLOW}Available profiles:{Style.RESET_ALL}", tag="PROFILES")
self.logger.info("\nAvailable profiles:", tag="PROFILES", base_color=LogColor.YELLOW)
for i, profile in enumerate(profiles):
self.logger.info(f"[{i+1}] {profile['name']}", tag="PROFILES")
# Get profile to delete
profile_idx = input(f"{Fore.RED}Enter the number of the profile to delete (or 'c' to cancel): {Style.RESET_ALL}")
self.console.print("[red]Enter the number of the profile to delete (or 'c' to cancel): [/red]", end="")
profile_idx = input()
if profile_idx.lower() == 'c':
continue
@@ -499,17 +528,18 @@ class BrowserProfiler:
idx = int(profile_idx) - 1
if 0 <= idx < len(profiles):
profile_name = profiles[idx]["name"]
self.logger.info(f"Deleting profile: {Fore.YELLOW}{profile_name}{Style.RESET_ALL}", tag="PROFILES")
self.logger.info(f"Deleting profile: [yellow]{profile_name}[/yellow]", tag="PROFILES")
# Confirm deletion
confirm = input(f"{Fore.RED}Are you sure you want to delete this profile? (y/n): {Style.RESET_ALL}")
self.console.print("[red]Are you sure you want to delete this profile? (y/n): [/red]", end="")
confirm = input()
if confirm.lower() == 'y':
success = self.delete_profile(profiles[idx]["path"])
if success:
self.logger.success(f"Profile {Fore.GREEN}{profile_name}{Style.RESET_ALL} deleted successfully", tag="PROFILES")
self.logger.success(f"Profile {profile_name} deleted successfully", tag="PROFILES")
else:
self.logger.error(f"Failed to delete profile {Fore.RED}{profile_name}{Style.RESET_ALL}", tag="PROFILES")
self.logger.error(f"Failed to delete profile {profile_name}", tag="PROFILES")
else:
self.logger.error("Invalid profile number", tag="PROFILES")
except ValueError:
@@ -523,12 +553,13 @@ class BrowserProfiler:
continue
# Display numbered list
self.logger.info(f"\n{Fore.YELLOW}Available profiles:{Style.RESET_ALL}", tag="PROFILES")
self.logger.info("\nAvailable profiles:", tag="PROFILES", base_color=LogColor.YELLOW)
for i, profile in enumerate(profiles):
self.logger.info(f"[{i+1}] {profile['name']}", tag="PROFILES")
# Get profile to use
profile_idx = input(f"{Fore.CYAN}Enter the number of the profile to use (or 'c' to cancel): {Style.RESET_ALL}")
self.console.print("[cyan]Enter the number of the profile to use (or 'c' to cancel): [/cyan]", end="")
profile_idx = input()
if profile_idx.lower() == 'c':
continue
@@ -536,7 +567,8 @@ class BrowserProfiler:
idx = int(profile_idx) - 1
if 0 <= idx < len(profiles):
profile_path = profiles[idx]["path"]
url = input(f"{Fore.CYAN}Enter the URL to crawl: {Style.RESET_ALL}")
self.console.print("[cyan]Enter the URL to crawl: [/cyan]", end="")
url = input()
if url:
# Call the provided crawl callback
await crawl_callback(profile_path, url)
@@ -597,17 +629,26 @@ class BrowserProfiler:
os.makedirs(profile_path, exist_ok=True)
# Print initial information
border = f"{Fore.CYAN}{'='*80}{Style.RESET_ALL}"
self.logger.info(f"\n{border}", tag="CDP")
self.logger.info(f"Launching standalone browser with CDP debugging", tag="CDP")
self.logger.info(f"Browser type: {Fore.GREEN}{browser_type}{Style.RESET_ALL}", tag="CDP")
self.logger.info(f"Profile path: {Fore.YELLOW}{profile_path}{Style.RESET_ALL}", tag="CDP")
self.logger.info(f"Debugging port: {Fore.CYAN}{debugging_port}{Style.RESET_ALL}", tag="CDP")
self.logger.info(f"Headless mode: {Fore.CYAN}{headless}{Style.RESET_ALL}", tag="CDP")
border = f"{'='*80}"
self.logger.info("{border}", tag="CDP", params={"border": border}, colors={"border": LogColor.CYAN})
self.logger.info("Launching standalone browser with CDP debugging", tag="CDP")
self.logger.info("Browser type: {browser_type}", tag="CDP", params={"browser_type": browser_type}, colors={"browser_type": LogColor.CYAN})
self.logger.info("Profile path: {profile_path}", tag="CDP", params={"profile_path": profile_path}, colors={"profile_path": LogColor.YELLOW})
self.logger.info(f"Debugging port: {debugging_port}", tag="CDP")
self.logger.info(f"Headless mode: {headless}", tag="CDP")
# create browser config
browser_config = BrowserConfig(
browser_type=browser_type,
headless=headless,
user_data_dir=profile_path,
debugging_port=debugging_port,
verbose=True
)
# Create managed browser instance
managed_browser = ManagedBrowser(
browser_type=browser_type,
browser_config=browser_config,
user_data_dir=profile_path,
headless=headless,
logger=self.logger,
@@ -646,7 +687,7 @@ class BrowserProfiler:
import select
# First output the prompt
self.logger.info(f"{Fore.CYAN}Press '{Fore.WHITE}q{Fore.CYAN}' to stop the browser and exit...{Style.RESET_ALL}", tag="CDP")
self.logger.info("Press 'q' to stop the browser and exit...", tag="CDP")
# Save original terminal settings
fd = sys.stdin.fileno()
@@ -662,7 +703,7 @@ class BrowserProfiler:
if readable:
key = sys.stdin.read(1)
if key.lower() == 'q':
self.logger.info(f"{Fore.GREEN}Closing browser...{Style.RESET_ALL}", tag="CDP")
self.logger.info("Closing browser...", tag="CDP")
user_done_event.set()
return
@@ -716,20 +757,20 @@ class BrowserProfiler:
self.logger.error("Failed to start browser process.", tag="CDP")
return None
self.logger.info(f"Browser launched successfully. Retrieving CDP information...", tag="CDP")
self.logger.info("Browser launched successfully. Retrieving CDP information...", tag="CDP")
# Get CDP URL and JSON config
cdp_url, config_json = await get_cdp_json(debugging_port)
if cdp_url:
self.logger.success(f"CDP URL: {Fore.GREEN}{cdp_url}{Style.RESET_ALL}", tag="CDP")
self.logger.success(f"CDP URL: {cdp_url}", tag="CDP")
if config_json:
# Display relevant CDP information
self.logger.info(f"Browser: {Fore.CYAN}{config_json.get('Browser', 'Unknown')}{Style.RESET_ALL}", tag="CDP")
self.logger.info(f"Protocol Version: {config_json.get('Protocol-Version', 'Unknown')}", tag="CDP")
self.logger.info(f"Browser: {config_json.get('Browser', 'Unknown')}", tag="CDP", colors={"Browser": LogColor.CYAN})
self.logger.info(f"Protocol Version: {config_json.get('Protocol-Version', 'Unknown')}", tag="CDP", colors={"Protocol-Version": LogColor.CYAN})
if 'webSocketDebuggerUrl' in config_json:
self.logger.info(f"WebSocket URL: {Fore.GREEN}{config_json['webSocketDebuggerUrl']}{Style.RESET_ALL}", tag="CDP")
self.logger.info("WebSocket URL: {webSocketDebuggerUrl}", tag="CDP", params={"webSocketDebuggerUrl": config_json['webSocketDebuggerUrl']}, colors={"webSocketDebuggerUrl": LogColor.GREEN})
else:
self.logger.warning("Could not retrieve CDP configuration JSON", tag="CDP")
else:
@@ -757,7 +798,7 @@ class BrowserProfiler:
self.logger.info("Terminating browser process...", tag="CDP")
await managed_browser.cleanup()
self.logger.success(f"Browser closed.", tag="CDP")
self.logger.success("Browser closed.", tag="CDP")
except Exception as e:
self.logger.error(f"Error launching standalone browser: {str(e)}", tag="CDP")
@@ -972,3 +1013,30 @@ class BrowserProfiler:
'info': browser_info
}
if __name__ == "__main__":
# Example usage
profiler = BrowserProfiler()
# Create a new profile
import os
from pathlib import Path
home_dir = Path.home()
profile_path = asyncio.run(profiler.create_profile( str(home_dir / ".crawl4ai/profiles/test-profile")))
# Launch a standalone browser
asyncio.run(profiler.launch_standalone_browser())
# List profiles
profiles = profiler.list_profiles()
for profile in profiles:
print(f"Profile: {profile['name']}, Path: {profile['path']}")
# Delete a profile
success = profiler.delete_profile("my-profile")
if success:
print("Profile deleted successfully")
else:
print("Failed to delete profile")

View File

@@ -27,7 +27,10 @@ from crawl4ai import (
PruningContentFilter,
BrowserProfiler,
DefaultMarkdownGenerator,
LLMConfig
LLMConfig,
BFSDeepCrawlStrategy,
DFSDeepCrawlStrategy,
BestFirstCrawlingStrategy,
)
from crawl4ai.config import USER_SETTINGS
from litellm import completion
@@ -1010,13 +1013,15 @@ def cdp_cmd(user_data_dir: Optional[str], port: int, browser_type: str, headless
@click.option("--crawler", "-c", type=str, callback=parse_key_values, help="Crawler parameters as key1=value1,key2=value2")
@click.option("--output", "-o", type=click.Choice(["all", "json", "markdown", "md", "markdown-fit", "md-fit"]), default="all")
@click.option("--output-file", "-O", type=click.Path(), help="Output file path (default: stdout)")
@click.option("--bypass-cache", "-b", is_flag=True, default=True, help="Bypass cache when crawling")
@click.option("--bypass-cache", "-bc", is_flag=True, default=True, help="Bypass cache when crawling")
@click.option("--question", "-q", help="Ask a question about the crawled content")
@click.option("--verbose", "-v", is_flag=True)
@click.option("--profile", "-p", help="Use a specific browser profile (by name)")
@click.option("--deep-crawl", type=click.Choice(["bfs", "dfs", "best-first"]), help="Enable deep crawling with specified strategy (bfs, dfs, or best-first)")
@click.option("--max-pages", type=int, default=10, help="Maximum number of pages to crawl in deep crawl mode")
def crawl_cmd(url: str, browser_config: str, crawler_config: str, filter_config: str,
extraction_config: str, json_extract: str, schema: str, browser: Dict, crawler: Dict,
output: str, output_file: str, bypass_cache: bool, question: str, verbose: bool, profile: str):
output: str, output_file: str, bypass_cache: bool, question: str, verbose: bool, profile: str, deep_crawl: str, max_pages: int):
"""Crawl a website and extract content
Simple Usage:
@@ -1073,7 +1078,8 @@ def crawl_cmd(url: str, browser_config: str, crawler_config: str, filter_config:
crawler_cfg.markdown_generator = DefaultMarkdownGenerator(
content_filter = BM25ContentFilter(
user_query=filter_conf.get("query"),
bm25_threshold=filter_conf.get("threshold", 1.0)
bm25_threshold=filter_conf.get("threshold", 1.0),
use_stemming=filter_conf.get("use_stemming", True),
)
)
elif filter_conf["type"] == "pruning":
@@ -1155,6 +1161,27 @@ Always return valid, properly formatted JSON."""
crawler_cfg.scraping_strategy = LXMLWebScrapingStrategy()
# Handle deep crawling configuration
if deep_crawl:
if deep_crawl == "bfs":
crawler_cfg.deep_crawl_strategy = BFSDeepCrawlStrategy(
max_depth=3,
max_pages=max_pages
)
elif deep_crawl == "dfs":
crawler_cfg.deep_crawl_strategy = DFSDeepCrawlStrategy(
max_depth=3,
max_pages=max_pages
)
elif deep_crawl == "best-first":
crawler_cfg.deep_crawl_strategy = BestFirstCrawlingStrategy(
max_depth=3,
max_pages=max_pages
)
if verbose:
console.print(f"[green]Deep crawling enabled:[/green] {deep_crawl} strategy, max {max_pages} pages")
config = get_global_config()
browser_cfg.verbose = config.get("VERBOSE", False)
@@ -1169,39 +1196,60 @@ Always return valid, properly formatted JSON."""
verbose
)
# Handle deep crawl results (list) vs single result
if isinstance(result, list):
if len(result) == 0:
click.echo("No results found during deep crawling")
return
# Use the first result for question answering and output
main_result = result[0]
all_results = result
else:
# Single result from regular crawling
main_result = result
all_results = [result]
# Handle question
if question:
provider, token = setup_llm_config()
markdown = result.markdown.raw_markdown
markdown = main_result.markdown.raw_markdown
anyio.run(stream_llm_response, url, markdown, question, provider, token)
return
# Handle output
if not output_file:
if output == "all":
click.echo(json.dumps(result.model_dump(), indent=2))
if isinstance(result, list):
output_data = [r.model_dump() for r in all_results]
click.echo(json.dumps(output_data, indent=2))
else:
click.echo(json.dumps(main_result.model_dump(), indent=2))
elif output == "json":
print(result.extracted_content)
extracted_items = json.loads(result.extracted_content)
print(main_result.extracted_content)
extracted_items = json.loads(main_result.extracted_content)
click.echo(json.dumps(extracted_items, indent=2))
elif output in ["markdown", "md"]:
click.echo(result.markdown.raw_markdown)
click.echo(main_result.markdown.raw_markdown)
elif output in ["markdown-fit", "md-fit"]:
click.echo(result.markdown.fit_markdown)
click.echo(main_result.markdown.fit_markdown)
else:
if output == "all":
with open(output_file, "w") as f:
f.write(json.dumps(result.model_dump(), indent=2))
if isinstance(result, list):
output_data = [r.model_dump() for r in all_results]
f.write(json.dumps(output_data, indent=2))
else:
f.write(json.dumps(main_result.model_dump(), indent=2))
elif output == "json":
with open(output_file, "w") as f:
f.write(result.extracted_content)
f.write(main_result.extracted_content)
elif output in ["markdown", "md"]:
with open(output_file, "w") as f:
f.write(result.markdown.raw_markdown)
f.write(main_result.markdown.raw_markdown)
elif output in ["markdown-fit", "md-fit"]:
with open(output_file, "w") as f:
f.write(result.markdown.fit_markdown)
f.write(main_result.markdown.fit_markdown)
except Exception as e:
raise click.ClickException(str(e))
@@ -1353,9 +1401,11 @@ def profiles_cmd():
@click.option("--question", "-q", help="Ask a question about the crawled content")
@click.option("--verbose", "-v", is_flag=True)
@click.option("--profile", "-p", help="Use a specific browser profile (by name)")
@click.option("--deep-crawl", type=click.Choice(["bfs", "dfs", "best-first"]), help="Enable deep crawling with specified strategy")
@click.option("--max-pages", type=int, default=10, help="Maximum number of pages to crawl in deep crawl mode")
def default(url: str, example: bool, browser_config: str, crawler_config: str, filter_config: str,
extraction_config: str, json_extract: str, schema: str, browser: Dict, crawler: Dict,
output: str, bypass_cache: bool, question: str, verbose: bool, profile: str):
output: str, bypass_cache: bool, question: str, verbose: bool, profile: str, deep_crawl: str, max_pages: int):
"""Crawl4AI CLI - Web content extraction tool
Simple Usage:
@@ -1405,7 +1455,9 @@ def default(url: str, example: bool, browser_config: str, crawler_config: str, f
bypass_cache=bypass_cache,
question=question,
verbose=verbose,
profile=profile
profile=profile,
deep_crawl=deep_crawl,
max_pages=max_pages
)
def main():

View File

@@ -29,6 +29,14 @@ PROVIDER_MODELS = {
'gemini/gemini-2.0-flash-lite-preview-02-05': os.getenv("GEMINI_API_KEY"),
"deepseek/deepseek-chat": os.getenv("DEEPSEEK_API_KEY"),
}
PROVIDER_MODELS_PREFIXES = {
"ollama": "no-token-needed", # Any model from Ollama no need for API token
"groq": os.getenv("GROQ_API_KEY"),
"openai": os.getenv("OPENAI_API_KEY"),
"anthropic": os.getenv("ANTHROPIC_API_KEY"),
"gemini": os.getenv("GEMINI_API_KEY"),
"deepseek": os.getenv("DEEPSEEK_API_KEY"),
}
# Chunk token threshold
CHUNK_TOKEN_THRESHOLD = 2**11 # 2048 tokens

View File

@@ -27,8 +27,7 @@ import json
import hashlib
from pathlib import Path
from concurrent.futures import ThreadPoolExecutor
from .async_logger import AsyncLogger, LogLevel
from colorama import Fore, Style
from .async_logger import AsyncLogger, LogLevel, LogColor
class RelevantContentFilter(ABC):
@@ -406,6 +405,7 @@ class BM25ContentFilter(RelevantContentFilter):
user_query: str = None,
bm25_threshold: float = 1.0,
language: str = "english",
use_stemming: bool = True,
):
"""
Initializes the BM25ContentFilter class, if not provided, falls back to page metadata.
@@ -417,9 +417,11 @@ class BM25ContentFilter(RelevantContentFilter):
user_query (str): User query for filtering (optional).
bm25_threshold (float): BM25 threshold for filtering (default: 1.0).
language (str): Language for stemming (default: 'english').
use_stemming (bool): Whether to apply stemming (default: True).
"""
super().__init__(user_query=user_query)
self.bm25_threshold = bm25_threshold
self.use_stemming = use_stemming
self.priority_tags = {
"h1": 5.0,
"h2": 4.0,
@@ -433,7 +435,7 @@ class BM25ContentFilter(RelevantContentFilter):
"pre": 1.5,
"th": 1.5, # Table headers
}
self.stemmer = stemmer(language)
self.stemmer = stemmer(language) if use_stemming else None
def filter_content(self, html: str, min_word_threshold: int = None) -> List[str]:
"""
@@ -480,13 +482,19 @@ class BM25ContentFilter(RelevantContentFilter):
# for _, chunk, _, _ in candidates]
# tokenized_query = [ps.stem(word) for word in query.lower().split()]
tokenized_corpus = [
[self.stemmer.stemWord(word) for word in chunk.lower().split()]
for _, chunk, _, _ in candidates
]
tokenized_query = [
self.stemmer.stemWord(word) for word in query.lower().split()
]
if self.use_stemming:
tokenized_corpus = [
[self.stemmer.stemWord(word) for word in chunk.lower().split()]
for _, chunk, _, _ in candidates
]
tokenized_query = [
self.stemmer.stemWord(word) for word in query.lower().split()
]
else:
tokenized_corpus = [
chunk.lower().split() for _, chunk, _, _ in candidates
]
tokenized_query = query.lower().split()
# tokenized_corpus = [[self.stemmer.stemWord(word) for word in tokenize_text(chunk.lower())]
# for _, chunk, _, _ in candidates]
@@ -846,8 +854,7 @@ class LLMContentFilter(RelevantContentFilter):
},
colors={
**AsyncLogger.DEFAULT_COLORS,
LogLevel.INFO: Fore.MAGENTA
+ Style.DIM, # Dimmed purple for LLM ops
LogLevel.INFO: LogColor.DIM_MAGENTA # Dimmed purple for LLM ops
},
)
else:
@@ -892,7 +899,7 @@ class LLMContentFilter(RelevantContentFilter):
"Starting LLM markdown content filtering process",
tag="LLM",
params={"provider": self.llm_config.provider},
colors={"provider": Fore.CYAN},
colors={"provider": LogColor.CYAN},
)
# Cache handling
@@ -929,7 +936,7 @@ class LLMContentFilter(RelevantContentFilter):
"LLM markdown: Split content into {chunk_count} chunks",
tag="CHUNK",
params={"chunk_count": len(html_chunks)},
colors={"chunk_count": Fore.YELLOW},
colors={"chunk_count": LogColor.YELLOW},
)
start_time = time.time()
@@ -1038,7 +1045,7 @@ class LLMContentFilter(RelevantContentFilter):
"LLM markdown: Completed processing in {time:.2f}s",
tag="LLM",
params={"time": end_time - start_time},
colors={"time": Fore.YELLOW},
colors={"time": LogColor.YELLOW},
)
result = ordered_results if ordered_results else []

File diff suppressed because it is too large Load Diff

View File

@@ -1,7 +1,7 @@
from crawl4ai import BrowserConfig, AsyncWebCrawler, CrawlerRunConfig, CacheMode
from crawl4ai.hub import BaseCrawler
from crawl4ai.utils import optimize_html, get_home_folder, preprocess_html_for_schema
from crawl4ai.extraction_strategy import JsonCssExtractionStrategy
from crawl4ai import JsonCssExtractionStrategy
from pathlib import Path
import json
import os

View File

@@ -11,6 +11,7 @@ from .scorers import URLScorer
from . import DeepCrawlStrategy
from ..types import AsyncWebCrawler, CrawlerRunConfig, CrawlResult, RunManyReturn
from ..utils import normalize_url_for_deep_crawl
from math import inf as infinity
@@ -106,13 +107,14 @@ class BestFirstCrawlingStrategy(DeepCrawlStrategy):
valid_links = []
for link in links:
url = link.get("href")
if url in visited:
base_url = normalize_url_for_deep_crawl(url, source_url)
if base_url in visited:
continue
if not await self.can_process_url(url, new_depth):
self.stats.urls_skipped += 1
continue
valid_links.append(url)
valid_links.append(base_url)
# If we have more valid links than capacity, limit them
if len(valid_links) > remaining_capacity:
@@ -148,6 +150,14 @@ class BestFirstCrawlingStrategy(DeepCrawlStrategy):
self.logger.info(f"Max pages limit ({self.max_pages}) reached, stopping crawl")
break
# Calculate how many more URLs we can process in this batch
remaining = self.max_pages - self._pages_crawled
batch_size = min(BATCH_SIZE, remaining)
if batch_size <= 0:
# No more pages to crawl
self.logger.info(f"Max pages limit ({self.max_pages}) reached, stopping crawl")
break
batch: List[Tuple[float, int, str, Optional[str]]] = []
# Retrieve up to BATCH_SIZE items from the priority queue.
for _ in range(BATCH_SIZE):
@@ -182,6 +192,10 @@ class BestFirstCrawlingStrategy(DeepCrawlStrategy):
# Count only successful crawls toward max_pages limit
if result.success:
self._pages_crawled += 1
# Check if we've reached the limit during batch processing
if self._pages_crawled >= self.max_pages:
self.logger.info(f"Max pages limit ({self.max_pages}) reached during batch, stopping crawl")
break # Exit the generator
yield result

View File

@@ -117,7 +117,8 @@ class BFSDeepCrawlStrategy(DeepCrawlStrategy):
self.logger.debug(f"URL {url} skipped: score {score} below threshold {self.score_threshold}")
self.stats.urls_skipped += 1
continue
visited.add(base_url)
valid_links.append((base_url, score))
# If we have more valid links than capacity, sort by score and take the top ones
@@ -156,9 +157,13 @@ class BFSDeepCrawlStrategy(DeepCrawlStrategy):
results: List[CrawlResult] = []
while current_level and not self._cancel_event.is_set():
# Check if we've already reached max_pages before starting a new level
if self._pages_crawled >= self.max_pages:
self.logger.info(f"Max pages limit ({self.max_pages}) reached, stopping crawl")
break
next_level: List[Tuple[str, Optional[str]]] = []
urls = [url for url, _ in current_level]
visited.update(urls)
# Clone the config to disable deep crawling recursion and enforce batch mode.
batch_config = config.clone(deep_crawl_strategy=None, stream=False)
@@ -221,6 +226,10 @@ class BFSDeepCrawlStrategy(DeepCrawlStrategy):
# Count only successful crawls
if result.success:
self._pages_crawled += 1
# Check if we've reached the limit during batch processing
if self._pages_crawled >= self.max_pages:
self.logger.info(f"Max pages limit ({self.max_pages}) reached during batch, stopping crawl")
break # Exit the generator
results_count += 1
yield result

View File

@@ -49,6 +49,10 @@ class DFSDeepCrawlStrategy(BFSDeepCrawlStrategy):
# Count only successful crawls toward max_pages limit
if result.success:
self._pages_crawled += 1
# Check if we've reached the limit during batch processing
if self._pages_crawled >= self.max_pages:
self.logger.info(f"Max pages limit ({self.max_pages}) reached during batch, stopping crawl")
break # Exit the generator
# Only discover links from successful crawls
new_links: List[Tuple[str, Optional[str]]] = []
@@ -94,6 +98,10 @@ class DFSDeepCrawlStrategy(BFSDeepCrawlStrategy):
# and only discover links from successful crawls
if result.success:
self._pages_crawled += 1
# Check if we've reached the limit during batch processing
if self._pages_crawled >= self.max_pages:
self.logger.info(f"Max pages limit ({self.max_pages}) reached during batch, stopping crawl")
break # Exit the generator
new_links: List[Tuple[str, Optional[str]]] = []
await self.link_discovery(result, url, depth, visited, new_links, depths)

View File

@@ -227,10 +227,21 @@ class URLPatternFilter(URLFilter):
# Prefix check (/foo/*)
if self._simple_prefixes:
path = url.split("?")[0]
if any(path.startswith(p) for p in self._simple_prefixes):
result = True
self._update_stats(result)
return not result if self._reverse else result
# if any(path.startswith(p) for p in self._simple_prefixes):
# result = True
# self._update_stats(result)
# return not result if self._reverse else result
####
# Modified the prefix matching logic to ensure path boundary checking:
# - Check if the matched prefix is followed by a path separator (`/`), query parameter (`?`), fragment (`#`), or is at the end of the path
# - This ensures `/api/` only matches complete path segments, not substrings like `/apiv2/`
####
for prefix in self._simple_prefixes:
if path.startswith(prefix):
if len(path) == len(prefix) or path[len(prefix)] in ['/', '?', '#']:
result = True
self._update_stats(result)
return not result if self._reverse else result
# Complex patterns
if self._path_patterns:
@@ -337,6 +348,15 @@ class ContentTypeFilter(URLFilter):
"sqlite": "application/vnd.sqlite3",
# Placeholder
"unknown": "application/octet-stream", # Fallback for unknown file types
# php
"php": "application/x-httpd-php",
"php3": "application/x-httpd-php",
"php4": "application/x-httpd-php",
"php5": "application/x-httpd-php",
"php7": "application/x-httpd-php",
"phtml": "application/x-httpd-php",
"phps": "application/x-httpd-php-source",
}
@staticmethod

View File

@@ -73,6 +73,8 @@ class Crawl4aiDockerClient:
def _prepare_request(self, urls: List[str], browser_config: Optional[BrowserConfig] = None,
crawler_config: Optional[CrawlerRunConfig] = None) -> Dict[str, Any]:
"""Prepare request data from configs."""
if self._token:
self._http_client.headers["Authorization"] = f"Bearer {self._token}"
return {
"urls": urls,
"browser_config": browser_config.dump() if browser_config else {},
@@ -103,8 +105,6 @@ class Crawl4aiDockerClient:
crawler_config: Optional[CrawlerRunConfig] = None
) -> Union[CrawlResult, List[CrawlResult], AsyncGenerator[CrawlResult, None]]:
"""Execute a crawl operation."""
if not self._token:
raise Crawl4aiClientError("Authentication required. Call authenticate() first.")
await self._check_server()
data = self._prepare_request(urls, browser_config, crawler_config)
@@ -140,8 +140,6 @@ class Crawl4aiDockerClient:
async def get_schema(self) -> Dict[str, Any]:
"""Retrieve configuration schemas."""
if not self._token:
raise Crawl4aiClientError("Authentication required. Call authenticate() first.")
response = await self._request("GET", "/schema")
return response.json()
@@ -167,4 +165,4 @@ async def main():
print(schema)
if __name__ == "__main__":
asyncio.run(main())
asyncio.run(main())

View File

@@ -1,13 +1,16 @@
from abc import ABC, abstractmethod
import inspect
from typing import Any, List, Dict, Optional
from typing import Any, List, Dict, Optional, Tuple, Pattern, Union
from concurrent.futures import ThreadPoolExecutor, as_completed
import json
import time
from enum import IntFlag, auto
from .prompts import PROMPT_EXTRACT_BLOCKS, PROMPT_EXTRACT_BLOCKS_WITH_INSTRUCTION, PROMPT_EXTRACT_SCHEMA_WITH_INSTRUCTION, JSON_SCHEMA_BUILDER_XPATH, PROMPT_EXTRACT_INFERRED_SCHEMA
from .config import (
DEFAULT_PROVIDER, CHUNK_TOKEN_THRESHOLD,
DEFAULT_PROVIDER,
DEFAULT_PROVIDER_API_KEY,
CHUNK_TOKEN_THRESHOLD,
OVERLAP_RATE,
WORD_TOKEN_RATE,
)
@@ -538,10 +541,15 @@ class LLMExtractionStrategy(ExtractionStrategy):
api_token: The API token for the provider.
base_url: The base URL for the API request.
api_base: The base URL for the API request.
extra_args: Additional arguments for the API request, such as temprature, max_tokens, etc.
extra_args: Additional arguments for the API request, such as temperature, max_tokens, etc.
"""
super().__init__( input_format=input_format, **kwargs)
self.llm_config = llm_config
if not self.llm_config:
self.llm_config = create_llm_config(
provider=DEFAULT_PROVIDER,
api_token=os.environ.get(DEFAULT_PROVIDER_API_KEY),
)
self.instruction = instruction
self.extract_type = extraction_type
self.schema = schema
@@ -648,11 +656,11 @@ class LLMExtractionStrategy(ExtractionStrategy):
self.total_usage.total_tokens += usage.total_tokens
try:
response = response.choices[0].message.content
content = response.choices[0].message.content
blocks = None
if self.force_json_response:
blocks = json.loads(response)
blocks = json.loads(content)
if isinstance(blocks, dict):
# If it has only one key which calue is list then assign that to blocks, exampled: {"news": [..]}
if len(blocks) == 1 and isinstance(list(blocks.values())[0], list):
@@ -665,14 +673,14 @@ class LLMExtractionStrategy(ExtractionStrategy):
blocks = blocks
else:
# blocks = extract_xml_data(["blocks"], response.choices[0].message.content)["blocks"]
blocks = extract_xml_data(["blocks"], response)["blocks"]
blocks = extract_xml_data(["blocks"], content)["blocks"]
blocks = json.loads(blocks)
for block in blocks:
block["error"] = False
except Exception:
parsed, unparsed = split_and_parse_json_objects(
response
response.choices[0].message.content
)
blocks = parsed
if unparsed:
@@ -1160,7 +1168,11 @@ In this scenario, use your best judgment to generate the schema. You need to exa
elif not query and not target_json_example:
user_message["content"] += """IMPORTANT: Since we neither have a query nor an example, it is crucial to rely solely on the HTML content provided. Leverage your expertise to determine the schema based on the repetitive patterns observed in the content."""
user_message["content"] += """IMPORTANT: Ensure your schema remains reliable by avoiding selectors that appear to generate dynamically and are not dependable. You want a reliable schema, as it consistently returns the same data even after many page reloads.
user_message["content"] += """IMPORTANT:
0/ Ensure your schema remains reliable by avoiding selectors that appear to generate dynamically and are not dependable. You want a reliable schema, as it consistently returns the same data even after many page reloads.
1/ DO NOT USE use base64 kind of classes, they are temporary and not reliable.
2/ Every selector must refer to only one unique element. You should ensure your selector points to a single element and is unique to the place that contains the information. You have to use available techniques based on CSS or XPATH requested schema to make sure your selector is unique and also not fragile, meaning if we reload the page now or in the future, the selector should remain reliable.
3/ Do not use Regex as much as possible.
Analyze the HTML and generate a JSON schema that follows the specified format. Only output valid JSON schema, nothing else.
"""
@@ -1661,3 +1673,303 @@ class JsonXPathExtractionStrategy(JsonElementExtractionStrategy):
def _get_element_attribute(self, element, attribute: str):
return element.get(attribute)
"""
RegexExtractionStrategy
Fast, zero-LLM extraction of common entities via regular expressions.
"""
_CTRL = {c: rf"\x{ord(c):02x}" for c in map(chr, range(32)) if c not in "\t\n\r"}
_WB_FIX = re.compile(r"\x08") # stray back-space → word-boundary
_NEEDS_ESCAPE = re.compile(r"(?<!\\)\\(?![\\u])") # lone backslash
def _sanitize_schema(schema: Dict[str, str]) -> Dict[str, str]:
"""Fix common JSON-escape goofs coming from LLMs or manual edits."""
safe = {}
for label, pat in schema.items():
# 1⃣ replace accidental control chars (inc. the infamous back-space)
pat = _WB_FIX.sub(r"\\b", pat).translate(_CTRL)
# 2⃣ double any single backslash that JSON kept single
pat = _NEEDS_ESCAPE.sub(r"\\\\", pat)
# 3⃣ quick sanity compile
try:
re.compile(pat)
except re.error as e:
raise ValueError(f"Regex for '{label}' wont compile after fix: {e}") from None
safe[label] = pat
return safe
class RegexExtractionStrategy(ExtractionStrategy):
"""
A lean strategy that finds e-mails, phones, URLs, dates, money, etc.,
using nothing but pre-compiled regular expressions.
Extraction returns::
{
"url": "<page-url>",
"label": "<pattern-label>",
"value": "<matched-string>",
"span": [start, end]
}
Only `generate_schema()` touches an LLM, extraction itself is pure Python.
"""
# -------------------------------------------------------------- #
# Built-in patterns exposed as IntFlag so callers can bit-OR them
# -------------------------------------------------------------- #
class _B(IntFlag):
EMAIL = auto()
PHONE_INTL = auto()
PHONE_US = auto()
URL = auto()
IPV4 = auto()
IPV6 = auto()
UUID = auto()
CURRENCY = auto()
PERCENTAGE = auto()
NUMBER = auto()
DATE_ISO = auto()
DATE_US = auto()
TIME_24H = auto()
POSTAL_US = auto()
POSTAL_UK = auto()
HTML_COLOR_HEX = auto()
TWITTER_HANDLE = auto()
HASHTAG = auto()
MAC_ADDR = auto()
IBAN = auto()
CREDIT_CARD = auto()
NOTHING = auto()
ALL = (
EMAIL | PHONE_INTL | PHONE_US | URL | IPV4 | IPV6 | UUID
| CURRENCY | PERCENTAGE | NUMBER | DATE_ISO | DATE_US | TIME_24H
| POSTAL_US | POSTAL_UK | HTML_COLOR_HEX | TWITTER_HANDLE
| HASHTAG | MAC_ADDR | IBAN | CREDIT_CARD
)
# user-friendly aliases (RegexExtractionStrategy.Email, .IPv4, …)
Email = _B.EMAIL
PhoneIntl = _B.PHONE_INTL
PhoneUS = _B.PHONE_US
Url = _B.URL
IPv4 = _B.IPV4
IPv6 = _B.IPV6
Uuid = _B.UUID
Currency = _B.CURRENCY
Percentage = _B.PERCENTAGE
Number = _B.NUMBER
DateIso = _B.DATE_ISO
DateUS = _B.DATE_US
Time24h = _B.TIME_24H
PostalUS = _B.POSTAL_US
PostalUK = _B.POSTAL_UK
HexColor = _B.HTML_COLOR_HEX
TwitterHandle = _B.TWITTER_HANDLE
Hashtag = _B.HASHTAG
MacAddr = _B.MAC_ADDR
Iban = _B.IBAN
CreditCard = _B.CREDIT_CARD
All = _B.ALL
Nothing = _B(0) # no patterns
# ------------------------------------------------------------------ #
# Built-in pattern catalog
# ------------------------------------------------------------------ #
DEFAULT_PATTERNS: Dict[str, str] = {
# Communication
"email": r"[\w.+-]+@[\w-]+\.[\w.-]+",
"phone_intl": r"\+?\d[\d .()-]{7,}\d",
"phone_us": r"\(?\d{3}\)?[ -. ]?\d{3}[ -. ]?\d{4}",
# Web
"url": r"https?://[^\s\"'<>]+",
"ipv4": r"(?:\d{1,3}\.){3}\d{1,3}",
"ipv6": r"[A-F0-9]{1,4}(?::[A-F0-9]{1,4}){7}",
# IDs
"uuid": r"[0-9a-f]{8}-[0-9a-f]{4}-[1-5][0-9a-f]{3}-[89ab][0-9a-f]{3}-[0-9a-f]{12}",
# Money / numbers
"currency": r"(?:USD|EUR|RM|\$|€|£)\s?\d+(?:[.,]\d{2})?",
"percentage": r"\d+(?:\.\d+)?%",
"number": r"\b\d{1,3}(?:[,.\s]\d{3})*(?:\.\d+)?\b",
# Dates / Times
"date_iso": r"\d{4}-\d{2}-\d{2}",
"date_us": r"\d{1,2}/\d{1,2}/\d{2,4}",
"time_24h": r"\b(?:[01]?\d|2[0-3]):[0-5]\d(?:[:.][0-5]\d)?\b",
# Misc
"postal_us": r"\b\d{5}(?:-\d{4})?\b",
"postal_uk": r"\b[A-Z]{1,2}\d[A-Z\d]? ?\d[A-Z]{2}\b",
"html_color_hex": r"#[0-9A-Fa-f]{6}\b",
"twitter_handle": r"@[\w]{1,15}",
"hashtag": r"#[\w-]+",
"mac_addr": r"(?:[0-9A-Fa-f]{2}:){5}[0-9A-Fa-f]{2}",
"iban": r"[A-Z]{2}\d{2}[A-Z0-9]{11,30}",
"credit_card": r"\b(?:4\d{12}(?:\d{3})?|5[1-5]\d{14}|3[47]\d{13}|6(?:011|5\d{2})\d{12})\b",
}
_FLAGS = re.IGNORECASE | re.MULTILINE
_UNWANTED_PROPS = {
"provider": "Use llm_config instead",
"api_token": "Use llm_config instead",
}
# ------------------------------------------------------------------ #
# Construction
# ------------------------------------------------------------------ #
def __init__(
self,
pattern: "_B" = _B.NOTHING,
*,
custom: Optional[Union[Dict[str, str], List[Tuple[str, str]]]] = None,
input_format: str = "fit_html",
**kwargs,
) -> None:
"""
Args:
patterns: Custom patterns overriding or extending defaults.
Dict[label, regex] or list[tuple(label, regex)].
input_format: "html", "markdown" or "text".
**kwargs: Forwarded to ExtractionStrategy.
"""
super().__init__(input_format=input_format, **kwargs)
# 1⃣ take only the requested built-ins
merged: Dict[str, str] = {
key: rx
for key, rx in self.DEFAULT_PATTERNS.items()
if getattr(self._B, key.upper()).value & pattern
}
# 2⃣ apply user overrides / additions
if custom:
if isinstance(custom, dict):
merged.update(custom)
else: # iterable of (label, regex)
merged.update({lbl: rx for lbl, rx in custom})
self._compiled: Dict[str, Pattern] = {
lbl: re.compile(rx, self._FLAGS) for lbl, rx in merged.items()
}
# ------------------------------------------------------------------ #
# Extraction
# ------------------------------------------------------------------ #
def extract(self, url: str, content: str, *q, **kw) -> List[Dict[str, Any]]:
# text = self._plain_text(html)
out: List[Dict[str, Any]] = []
for label, cre in self._compiled.items():
for m in cre.finditer(content):
out.append(
{
"url": url,
"label": label,
"value": m.group(0),
"span": [m.start(), m.end()],
}
)
return out
# ------------------------------------------------------------------ #
# Helpers
# ------------------------------------------------------------------ #
def _plain_text(self, content: str) -> str:
if self.input_format == "text":
return content
return BeautifulSoup(content, "lxml").get_text(" ", strip=True)
# ------------------------------------------------------------------ #
# LLM-assisted pattern generator
# ------------------------------------------------------------------ #
# ------------------------------------------------------------------ #
# LLM-assisted one-off pattern builder
# ------------------------------------------------------------------ #
@staticmethod
def generate_pattern(
label: str,
html: str,
*,
query: Optional[str] = None,
examples: Optional[List[str]] = None,
llm_config: Optional[LLMConfig] = None,
**kwargs,
) -> Dict[str, str]:
"""
Ask an LLM for a single page-specific regex and return
{label: pattern} ── ready for RegexExtractionStrategy(custom=…)
"""
# ── guard deprecated kwargs
for k in RegexExtractionStrategy._UNWANTED_PROPS:
if k in kwargs:
raise AttributeError(
f"{k} is deprecated, {RegexExtractionStrategy._UNWANTED_PROPS[k]}"
)
# ── default LLM config
if llm_config is None:
llm_config = create_llm_config()
# ── system prompt hardened
system_msg = (
"You are an expert Python-regex engineer.\n"
f"Return **one** JSON object whose single key is exactly \"{label}\", "
"and whose value is a raw-string regex pattern that works with "
"the standard `re` module in Python.\n\n"
"Strict rules (obey every bullet):\n"
"• If a *user query* is supplied, treat it as the precise semantic target and optimise the "
" pattern to capture ONLY text that answers that query. If the query conflicts with the "
" sample HTML, the HTML wins.\n"
"• Tailor the pattern to the *sample HTML* reproduce its exact punctuation, spacing, "
" symbols, capitalisation, etc. Do **NOT** invent a generic form.\n"
"• Keep it minimal and fast: avoid unnecessary capturing, prefer non-capturing `(?: … )`, "
" and guard against catastrophic backtracking.\n"
"• Anchor with `^`, `$`, or `\\b` only when it genuinely improves precision.\n"
"• Use inline flags like `(?i)` when needed; no verbose flag comments.\n"
"• Output must be valid JSON no markdown, code fences, comments, or extra keys.\n"
"• The regex value must be a Python string literal: **double every backslash** "
"(e.g. `\\\\b`, `\\\\d`, `\\\\\\\\`).\n\n"
"Example valid output:\n"
f"{{\"{label}\": \"(?:RM|rm)\\\\s?\\\\d{{1,3}}(?:,\\\\d{{3}})*(?:\\\\.\\\\d{{2}})?\"}}"
)
# ── user message: cropped HTML + optional hints
user_parts = ["```html", html[:5000], "```"] # protect token budget
if query:
user_parts.append(f"\n\n## Query\n{query.strip()}")
if examples:
user_parts.append("## Examples\n" + "\n".join(examples[:20]))
user_msg = "\n\n".join(user_parts)
# ── LLM call (with retry/backoff)
resp = perform_completion_with_backoff(
provider=llm_config.provider,
prompt_with_variables="\n\n".join([system_msg, user_msg]),
json_response=True,
api_token=llm_config.api_token,
base_url=llm_config.base_url,
extra_args=kwargs,
)
# ── clean & load JSON (fix common escape mistakes *before* json.loads)
raw = resp.choices[0].message.content
raw = raw.replace("\x08", "\\b") # stray back-space → \b
raw = re.sub(r'(?<!\\)\\(?![\\u"])', r"\\\\", raw) # lone \ → \\
try:
pattern_dict = json.loads(raw)
except Exception as exc:
raise ValueError(f"LLM did not return valid JSON: {raw}") from exc
# quick sanity-compile
for lbl, pat in pattern_dict.items():
try:
re.compile(pat)
except re.error as e:
raise ValueError(f"Invalid regex for '{lbl}': {e}") from None
return pattern_dict

View File

@@ -40,10 +40,25 @@ def setup_home_directory():
f.write("")
def post_install():
"""Run all post-installation tasks"""
"""
Run all post-installation tasks.
Checks CRAWL4AI_MODE environment variable. If set to 'api',
skips Playwright browser installation.
"""
logger.info("Running post-installation setup...", tag="INIT")
setup_home_directory()
install_playwright()
# Check environment variable to conditionally skip Playwright install
run_mode = os.getenv('CRAWL4AI_MODE')
if run_mode == 'api':
logger.warning(
"CRAWL4AI_MODE=api detected. Skipping Playwright browser installation.",
tag="SETUP"
)
else:
# Proceed with installation only if mode is not 'api'
install_playwright()
run_migration()
# TODO: Will be added in the future
# setup_builtin_browser()
@@ -104,6 +119,32 @@ def install_playwright():
logger.warning(
f"Please run '{sys.executable} -m playwright install --with-deps' manually after the installation."
)
# Install Patchright browsers for undetected browser support
logger.info("Installing Patchright browsers for undetected mode...", tag="INIT")
try:
subprocess.check_call(
[
sys.executable,
"-m",
"patchright",
"install",
"--with-deps",
"--force",
"chromium",
]
)
logger.success(
"Patchright installation completed successfully.", tag="COMPLETE"
)
except subprocess.CalledProcessError:
logger.warning(
f"Please run '{sys.executable} -m patchright install --with-deps' manually after the installation."
)
except Exception:
logger.warning(
f"Please run '{sys.executable} -m patchright install --with-deps' manually after the installation."
)
def run_migration():

View File

@@ -115,5 +115,6 @@ async () => {
document.body.style.overflow = "auto";
// Wait a bit for any animations to complete
await new Promise((resolve) => setTimeout(resolve, 100));
document.body.scrollIntoView(false);
await new Promise((resolve) => setTimeout(resolve, 50));
};

View File

@@ -11,7 +11,7 @@ from .extraction_strategy import *
from .crawler_strategy import *
from typing import List
from concurrent.futures import ThreadPoolExecutor
from .content_scraping_strategy import WebScrapingStrategy
from ..content_scraping_strategy import LXMLWebScrapingStrategy as WebScrapingStrategy
from .config import *
import warnings
import json

395
crawl4ai/link_preview.py Normal file
View File

@@ -0,0 +1,395 @@
"""
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
url_to_head_data = {}
for result in head_results:
url = result.get("url")
if url:
url_to_head_data[url] = {
"head_data": result.get("head_data", {}),
"status": result.get("status", "unknown"),
"error": result.get("error"),
"relevance_score": result.get("relevance_score")
}
# 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:
# Keep original link unchanged
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:
# Keep original link unchanged
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
)

View File

@@ -31,22 +31,24 @@ class MarkdownGenerationStrategy(ABC):
content_filter: Optional[RelevantContentFilter] = None,
options: Optional[Dict[str, Any]] = None,
verbose: bool = False,
content_source: str = "cleaned_html",
):
self.content_filter = content_filter
self.options = options or {}
self.verbose = verbose
self.content_source = content_source
@abstractmethod
def generate_markdown(
self,
cleaned_html: str,
input_html: str,
base_url: str = "",
html2text_options: Optional[Dict[str, Any]] = None,
content_filter: Optional[RelevantContentFilter] = None,
citations: bool = True,
**kwargs,
) -> MarkdownGenerationResult:
"""Generate markdown from cleaned HTML."""
"""Generate markdown from the selected input HTML."""
pass
@@ -63,6 +65,7 @@ class DefaultMarkdownGenerator(MarkdownGenerationStrategy):
Args:
content_filter (Optional[RelevantContentFilter]): Content filter for generating fit markdown.
options (Optional[Dict[str, Any]]): Additional options for markdown generation. Defaults to None.
content_source (str): Source of content to generate markdown from. Options: "cleaned_html", "raw_html", "fit_html". Defaults to "cleaned_html".
Returns:
MarkdownGenerationResult: Result containing raw markdown, fit markdown, fit HTML, and references markdown.
@@ -72,8 +75,9 @@ class DefaultMarkdownGenerator(MarkdownGenerationStrategy):
self,
content_filter: Optional[RelevantContentFilter] = None,
options: Optional[Dict[str, Any]] = None,
content_source: str = "cleaned_html",
):
super().__init__(content_filter, options)
super().__init__(content_filter, options, verbose=False, content_source=content_source)
def convert_links_to_citations(
self, markdown: str, base_url: str = ""
@@ -143,7 +147,7 @@ class DefaultMarkdownGenerator(MarkdownGenerationStrategy):
def generate_markdown(
self,
cleaned_html: str,
input_html: str,
base_url: str = "",
html2text_options: Optional[Dict[str, Any]] = None,
options: Optional[Dict[str, Any]] = None,
@@ -152,16 +156,16 @@ class DefaultMarkdownGenerator(MarkdownGenerationStrategy):
**kwargs,
) -> MarkdownGenerationResult:
"""
Generate markdown with citations from cleaned HTML.
Generate markdown with citations from the provided input HTML.
How it works:
1. Generate raw markdown from cleaned HTML.
1. Generate raw markdown from the input HTML.
2. Convert links to citations.
3. Generate fit markdown if content filter is provided.
4. Return MarkdownGenerationResult.
Args:
cleaned_html (str): Cleaned HTML content.
input_html (str): The HTML content to process (selected based on content_source).
base_url (str): Base URL for URL joins.
html2text_options (Optional[Dict[str, Any]]): HTML2Text options.
options (Optional[Dict[str, Any]]): Additional options for markdown generation.
@@ -196,14 +200,14 @@ class DefaultMarkdownGenerator(MarkdownGenerationStrategy):
h.update_params(**default_options)
# Ensure we have valid input
if not cleaned_html:
cleaned_html = ""
elif not isinstance(cleaned_html, str):
cleaned_html = str(cleaned_html)
if not input_html:
input_html = ""
elif not isinstance(input_html, str):
input_html = str(input_html)
# Generate raw markdown
try:
raw_markdown = h.handle(cleaned_html)
raw_markdown = h.handle(input_html)
except Exception as e:
raw_markdown = f"Error converting HTML to markdown: {str(e)}"
@@ -228,7 +232,7 @@ class DefaultMarkdownGenerator(MarkdownGenerationStrategy):
if content_filter or self.content_filter:
try:
content_filter = content_filter or self.content_filter
filtered_html = content_filter.filter_content(cleaned_html)
filtered_html = content_filter.filter_content(input_html)
filtered_html = "\n".join(
"<div>{}</div>".format(s) for s in filtered_html
)

79
crawl4ai/memory_utils.py Normal file
View File

@@ -0,0 +1,79 @@
import psutil
import platform
import subprocess
from typing import Tuple
def get_true_available_memory_gb() -> float:
"""Get truly available memory including inactive pages (cross-platform)"""
vm = psutil.virtual_memory()
if platform.system() == 'Darwin': # macOS
# On macOS, we need to include inactive memory too
try:
# Use vm_stat to get accurate values
result = subprocess.run(['vm_stat'], capture_output=True, text=True)
lines = result.stdout.split('\n')
page_size = 16384 # macOS page size
pages = {}
for line in lines:
if 'Pages free:' in line:
pages['free'] = int(line.split()[-1].rstrip('.'))
elif 'Pages inactive:' in line:
pages['inactive'] = int(line.split()[-1].rstrip('.'))
elif 'Pages speculative:' in line:
pages['speculative'] = int(line.split()[-1].rstrip('.'))
elif 'Pages purgeable:' in line:
pages['purgeable'] = int(line.split()[-1].rstrip('.'))
# Calculate total available (free + inactive + speculative + purgeable)
total_available_pages = (
pages.get('free', 0) +
pages.get('inactive', 0) +
pages.get('speculative', 0) +
pages.get('purgeable', 0)
)
available_gb = (total_available_pages * page_size) / (1024**3)
return available_gb
except:
# Fallback to psutil
return vm.available / (1024**3)
else:
# For Windows and Linux, psutil.available is accurate
return vm.available / (1024**3)
def get_true_memory_usage_percent() -> float:
"""
Get memory usage percentage that accounts for platform differences.
Returns:
float: Memory usage percentage (0-100)
"""
vm = psutil.virtual_memory()
total_gb = vm.total / (1024**3)
available_gb = get_true_available_memory_gb()
# Calculate used percentage based on truly available memory
used_percent = 100.0 * (total_gb - available_gb) / total_gb
# Ensure it's within valid range
return max(0.0, min(100.0, used_percent))
def get_memory_stats() -> Tuple[float, float, float]:
"""
Get comprehensive memory statistics.
Returns:
Tuple[float, float, float]: (used_percent, available_gb, total_gb)
"""
vm = psutil.virtual_memory()
total_gb = vm.total / (1024**3)
available_gb = get_true_available_memory_gb()
used_percent = get_true_memory_usage_percent()
return used_percent, available_gb, total_gb

View File

@@ -1,4 +1,4 @@
from pydantic import BaseModel, HttpUrl, PrivateAttr, ConfigDict
from pydantic import BaseModel, HttpUrl, PrivateAttr, Field
from typing import List, Dict, Optional, Callable, Awaitable, Union, Any
from typing import AsyncGenerator
from typing import Generic, TypeVar
@@ -95,15 +95,7 @@ class UrlModel(BaseModel):
url: HttpUrl
forced: bool = False
class MarkdownGenerationResult(BaseModel):
raw_markdown: str
markdown_with_citations: str
references_markdown: str
fit_markdown: Optional[str] = None
fit_html: Optional[str] = None
def __str__(self):
return self.raw_markdown
@dataclass
class TraversalStats:
@@ -124,9 +116,20 @@ class DispatchResult(BaseModel):
end_time: Union[datetime, float]
error_message: str = ""
class MarkdownGenerationResult(BaseModel):
raw_markdown: str
markdown_with_citations: str
references_markdown: str
fit_markdown: Optional[str] = None
fit_html: Optional[str] = None
def __str__(self):
return self.raw_markdown
class CrawlResult(BaseModel):
url: str
html: str
fit_html: Optional[str] = None
success: bool
cleaned_html: Optional[str] = None
media: Dict[str, List[Dict]] = {}
@@ -135,6 +138,7 @@ class CrawlResult(BaseModel):
js_execution_result: Optional[Dict[str, Any]] = None
screenshot: Optional[str] = None
pdf: Optional[bytes] = None
mhtml: Optional[str] = None
_markdown: Optional[MarkdownGenerationResult] = PrivateAttr(default=None)
extracted_content: Optional[str] = None
metadata: Optional[dict] = None
@@ -145,10 +149,12 @@ class CrawlResult(BaseModel):
ssl_certificate: Optional[SSLCertificate] = None
dispatch_result: Optional[DispatchResult] = None
redirected_url: Optional[str] = None
network_requests: Optional[List[Dict[str, Any]]] = None
console_messages: Optional[List[Dict[str, Any]]] = None
tables: List[Dict] = Field(default_factory=list) # NEW [{headers,rows,caption,summary}]
model_config = ConfigDict(arbitrary_types_allowed=True)
# class Config:
# arbitrary_types_allowed = True
class Config:
arbitrary_types_allowed = True
# NOTE: The StringCompatibleMarkdown class, custom __init__ method, property getters/setters,
# and model_dump override all exist to support a smooth transition from markdown as a string
@@ -308,14 +314,16 @@ class AsyncCrawlResponse(BaseModel):
status_code: int
screenshot: Optional[str] = None
pdf_data: Optional[bytes] = None
mhtml_data: Optional[str] = None
get_delayed_content: Optional[Callable[[Optional[float]], Awaitable[str]]] = None
downloaded_files: Optional[List[str]] = None
ssl_certificate: Optional[SSLCertificate] = None
redirected_url: Optional[str] = None
network_requests: Optional[List[Dict[str, Any]]] = None
console_messages: Optional[List[Dict[str, Any]]] = None
model_config = ConfigDict(arbitrary_types_allowed=True)
# class Config:
# arbitrary_types_allowed = True
class Config:
arbitrary_types_allowed = True
###############################
# Scraping Models
@@ -337,6 +345,12 @@ class Link(BaseModel):
text: Optional[str] = ""
title: Optional[str] = ""
base_domain: Optional[str] = ""
head_data: Optional[Dict[str, Any]] = None # Head metadata extracted from link target
head_extraction_status: Optional[str] = None # "success", "failed", "skipped"
head_extraction_error: Optional[str] = None # Error message if extraction failed
intrinsic_score: Optional[float] = None # Quality score based on URL structure, text, and context
contextual_score: Optional[float] = None # BM25 relevance score based on query and head content
total_score: Optional[float] = None # Combined score from intrinsic and contextual scores
class Media(BaseModel):

View File

@@ -1,6 +0,0 @@
"""Pipeline module providing high-level crawling functionality."""
from .pipeline import Pipeline, create_pipeline
from .crawler import Crawler
__all__ = ["Pipeline", "create_pipeline", "Crawler"]

View File

@@ -1,406 +0,0 @@
"""Crawler utility class for simplified crawling operations.
This module provides a high-level utility class for crawling web pages
with support for both single and multiple URL processing.
"""
import asyncio
from typing import Dict, List, Optional, Tuple, Union, Callable
from crawl4ai.models import CrawlResultContainer, CrawlResult
from crawl4ai.pipeline.pipeline import create_pipeline
from crawl4ai.async_configs import BrowserConfig, CrawlerRunConfig
from crawl4ai.async_logger import AsyncLogger
from crawl4ai.browser.browser_hub import BrowserHub
# Type definitions
UrlList = List[str]
UrlBatch = Tuple[List[str], CrawlerRunConfig]
UrlFullBatch = Tuple[List[str], BrowserConfig, CrawlerRunConfig]
BatchType = Union[UrlList, UrlBatch, UrlFullBatch]
ProgressCallback = Callable[[str, str, Optional[CrawlResultContainer]], None]
RetryStrategy = Callable[[str, int, Exception], Tuple[bool, float]]
class Crawler:
"""High-level utility class for crawling web pages.
This class provides simplified methods for crawling both single URLs
and batches of URLs, with parallel processing capabilities.
"""
@classmethod
async def crawl(
cls,
urls: Union[str, List[str]],
browser_config: Optional[BrowserConfig] = None,
crawler_config: Optional[CrawlerRunConfig] = None,
browser_hub: Optional[BrowserHub] = None,
logger: Optional[AsyncLogger] = None,
max_retries: int = 0,
retry_delay: float = 1.0,
use_new_loop: bool = True # By default use a new loop for safety
) -> Union[CrawlResultContainer, Dict[str, CrawlResultContainer]]:
"""Crawl one or more URLs with the specified configurations.
Args:
urls: Single URL or list of URLs to crawl
browser_config: Optional browser configuration
crawler_config: Optional crawler run configuration
browser_hub: Optional shared browser hub
logger: Optional logger instance
max_retries: Maximum number of retries for failed requests
retry_delay: Delay between retries in seconds
Returns:
For a single URL: CrawlResultContainer with crawl results
For multiple URLs: Dict mapping URLs to their CrawlResultContainer results
"""
# Handle single URL case
if isinstance(urls, str):
return await cls._crawl_single_url(
urls,
browser_config,
crawler_config,
browser_hub,
logger,
max_retries,
retry_delay,
use_new_loop
)
# Handle multiple URLs case (sequential processing)
results = {}
for url in urls:
results[url] = await cls._crawl_single_url(
url,
browser_config,
crawler_config,
browser_hub,
logger,
max_retries,
retry_delay,
use_new_loop
)
return results
@classmethod
async def _crawl_single_url(
cls,
url: str,
browser_config: Optional[BrowserConfig] = None,
crawler_config: Optional[CrawlerRunConfig] = None,
browser_hub: Optional[BrowserHub] = None,
logger: Optional[AsyncLogger] = None,
max_retries: int = 0,
retry_delay: float = 1.0,
use_new_loop: bool = False
) -> CrawlResultContainer:
"""Internal method to crawl a single URL with retry logic."""
# Create a logger if none provided
if logger is None:
logger = AsyncLogger(verbose=True)
# Create or use the provided crawler config
if crawler_config is None:
crawler_config = CrawlerRunConfig()
attempts = 0
last_error = None
# For testing purposes, each crawler gets a new event loop to avoid conflicts
# This is especially important in test suites where multiple tests run in sequence
if use_new_loop:
old_loop = asyncio.get_event_loop()
loop = asyncio.new_event_loop()
asyncio.set_event_loop(loop)
while attempts <= max_retries:
try:
# Create a pipeline
pipeline_args = {}
if browser_config:
pipeline_args["browser_config"] = browser_config
if browser_hub:
pipeline_args["browser_hub"] = browser_hub
if logger:
pipeline_args["logger"] = logger
pipeline = await create_pipeline(**pipeline_args)
# Perform the crawl
result = await pipeline.crawl(url=url, config=crawler_config)
# Close the pipeline if we created it (not using a shared hub)
if not browser_hub:
await pipeline.close()
# Restore the original event loop if we created a new one
if use_new_loop:
asyncio.set_event_loop(old_loop)
loop.close()
return result
except Exception as e:
last_error = e
attempts += 1
if attempts <= max_retries:
logger.warning(
message="Crawl attempt {attempt} failed for {url}: {error}. Retrying in {delay}s...",
tag="RETRY",
params={
"attempt": attempts,
"url": url,
"error": str(e),
"delay": retry_delay
}
)
await asyncio.sleep(retry_delay)
else:
logger.error(
message="All {attempts} crawl attempts failed for {url}: {error}",
tag="FAILED",
params={
"attempts": attempts,
"url": url,
"error": str(e)
}
)
# If we get here, all attempts failed
result = CrawlResultContainer(
CrawlResult(
url=url,
html="",
success=False,
error_message=f"All {attempts} crawl attempts failed: {str(last_error)}"
)
)
# Restore the original event loop if we created a new one
if use_new_loop:
asyncio.set_event_loop(old_loop)
loop.close()
return result
@classmethod
async def parallel_crawl(
cls,
url_batches: Union[List[str], List[Union[UrlBatch, UrlFullBatch]]],
browser_config: Optional[BrowserConfig] = None,
crawler_config: Optional[CrawlerRunConfig] = None,
browser_hub: Optional[BrowserHub] = None,
logger: Optional[AsyncLogger] = None,
concurrency: int = 5,
max_retries: int = 0,
retry_delay: float = 1.0,
retry_strategy: Optional[RetryStrategy] = None,
progress_callback: Optional[ProgressCallback] = None,
use_new_loop: bool = True # By default use a new loop for safety
) -> Dict[str, CrawlResultContainer]:
"""Crawl multiple URLs in parallel with concurrency control.
Args:
url_batches: List of URLs or list of URL batches with configurations
browser_config: Default browser configuration (used if not in batch)
crawler_config: Default crawler configuration (used if not in batch)
browser_hub: Optional shared browser hub for resource efficiency
logger: Optional logger instance
concurrency: Maximum number of concurrent crawls
max_retries: Maximum number of retries for failed requests
retry_delay: Delay between retries in seconds
retry_strategy: Optional custom retry strategy function
progress_callback: Optional callback for progress reporting
Returns:
Dict mapping URLs to their CrawlResultContainer results
"""
# Create a logger if none provided
if logger is None:
logger = AsyncLogger(verbose=True)
# For testing purposes, each crawler gets a new event loop to avoid conflicts
# This is especially important in test suites where multiple tests run in sequence
if use_new_loop:
old_loop = asyncio.get_event_loop()
loop = asyncio.new_event_loop()
asyncio.set_event_loop(loop)
# Process batches to consistent format
processed_batches = cls._process_url_batches(
url_batches, browser_config, crawler_config
)
# Initialize results dictionary
results = {}
# Create semaphore for concurrency control
semaphore = asyncio.Semaphore(concurrency)
# Create shared browser hub if not provided
shared_hub = browser_hub
if not shared_hub:
shared_hub = await BrowserHub.get_browser_manager(
config=browser_config or BrowserConfig(),
logger=logger,
max_browsers_per_config=concurrency,
max_pages_per_browser=1,
initial_pool_size=min(concurrency, 3) # Start with a reasonable number
)
try:
# Create worker function for each URL
async def process_url(url, b_config, c_config):
async with semaphore:
# Report start if callback provided
if progress_callback:
await progress_callback("started", url)
attempts = 0
last_error = None
while attempts <= max_retries:
try:
# Create a pipeline using the shared hub
pipeline = await create_pipeline(
browser_config=b_config,
browser_hub=shared_hub,
logger=logger
)
# Perform the crawl
result = await pipeline.crawl(url=url, config=c_config)
# Report completion if callback provided
if progress_callback:
await progress_callback("completed", url, result)
return url, result
except Exception as e:
last_error = e
attempts += 1
# Determine if we should retry and with what delay
should_retry = attempts <= max_retries
delay = retry_delay
# Use custom retry strategy if provided
if retry_strategy and should_retry:
try:
should_retry, delay = await retry_strategy(url, attempts, e)
except Exception as strategy_error:
logger.error(
message="Error in retry strategy: {error}",
tag="RETRY",
params={"error": str(strategy_error)}
)
if should_retry:
logger.warning(
message="Crawl attempt {attempt} failed for {url}: {error}. Retrying in {delay}s...",
tag="RETRY",
params={
"attempt": attempts,
"url": url,
"error": str(e),
"delay": delay
}
)
await asyncio.sleep(delay)
else:
logger.error(
message="All {attempts} crawl attempts failed for {url}: {error}",
tag="FAILED",
params={
"attempts": attempts,
"url": url,
"error": str(e)
}
)
break
# If we get here, all attempts failed
error_result = CrawlResultContainer(
CrawlResult(
url=url,
html="",
success=False,
error_message=f"All {attempts} crawl attempts failed: {str(last_error)}"
)
)
# Report completion with error if callback provided
if progress_callback:
await progress_callback("completed", url, error_result)
return url, error_result
# Create tasks for all URLs
tasks = []
for urls, b_config, c_config in processed_batches:
for url in urls:
tasks.append(process_url(url, b_config, c_config))
# Run all tasks and collect results
for completed_task in asyncio.as_completed(tasks):
url, result = await completed_task
results[url] = result
return results
finally:
# Clean up the hub only if we created it
if not browser_hub and shared_hub:
await shared_hub.close()
# Restore the original event loop if we created a new one
if use_new_loop:
asyncio.set_event_loop(old_loop)
loop.close()
@classmethod
def _process_url_batches(
cls,
url_batches: Union[List[str], List[Union[UrlBatch, UrlFullBatch]]],
default_browser_config: Optional[BrowserConfig],
default_crawler_config: Optional[CrawlerRunConfig]
) -> List[Tuple[List[str], BrowserConfig, CrawlerRunConfig]]:
"""Process URL batches into a consistent format.
Converts various input formats into a consistent list of
(urls, browser_config, crawler_config) tuples.
"""
processed_batches = []
# Handle case where input is just a list of URLs
if all(isinstance(item, str) for item in url_batches):
urls = url_batches
browser_config = default_browser_config or BrowserConfig()
crawler_config = default_crawler_config or CrawlerRunConfig()
processed_batches.append((urls, browser_config, crawler_config))
return processed_batches
# Process each batch
for batch in url_batches:
# Handle case: (urls, crawler_config)
if len(batch) == 2 and isinstance(batch[1], CrawlerRunConfig):
urls, c_config = batch
b_config = default_browser_config or BrowserConfig()
processed_batches.append((urls, b_config, c_config))
# Handle case: (urls, browser_config, crawler_config)
elif len(batch) == 3 and isinstance(batch[1], BrowserConfig) and isinstance(batch[2], CrawlerRunConfig):
processed_batches.append(batch)
# Fallback for unknown formats - assume it's just a list of URLs
else:
urls = batch
browser_config = default_browser_config or BrowserConfig()
crawler_config = default_crawler_config or CrawlerRunConfig()
processed_batches.append((urls, browser_config, crawler_config))
return processed_batches

View File

@@ -1,702 +0,0 @@
import time
import sys
from typing import Dict, Any, List
import json
from crawl4ai.models import (
CrawlResult,
MarkdownGenerationResult,
ScrapingResult,
CrawlResultContainer,
)
from crawl4ai.async_database import async_db_manager
from crawl4ai.cache_context import CacheMode, CacheContext
from crawl4ai.utils import (
sanitize_input_encode,
InvalidCSSSelectorError,
fast_format_html,
create_box_message,
get_error_context,
)
async def initialize_context_middleware(context: Dict[str, Any]) -> int:
"""Initialize the context with basic configuration and validation"""
url = context.get("url")
config = context.get("config")
if not isinstance(url, str) or not url:
context["error_message"] = "Invalid URL, make sure the URL is a non-empty string"
return 0
# Default to ENABLED if no cache mode specified
if config.cache_mode is None:
config.cache_mode = CacheMode.ENABLED
# Create cache context
context["cache_context"] = CacheContext(url, config.cache_mode, False)
context["start_time"] = time.perf_counter()
return 1
# middlewares.py additions
async def browser_hub_middleware(context: Dict[str, Any]) -> int:
"""
Initialize or connect to a Browser-Hub and add it to the pipeline context.
This middleware handles browser hub initialization for all three scenarios:
1. Default configuration when nothing is specified
2. Custom configuration when browser_config is provided
3. Connection to existing hub when browser_hub_connection is provided
Args:
context: The pipeline context dictionary
Returns:
int: 1 for success, 0 for failure
"""
from crawl4ai.browser.browser_hub import BrowserHub
try:
# Get configuration from context
browser_config = context.get("browser_config")
browser_hub_id = context.get("browser_hub_id")
browser_hub_connection = context.get("browser_hub_connection")
logger = context.get("logger")
# If we already have a browser hub in context, use it
if context.get("browser_hub"):
return 1
# Get or create Browser-Hub
browser_hub = await BrowserHub.get_browser_manager(
config=browser_config,
hub_id=browser_hub_id,
connection_info=browser_hub_connection,
logger=logger
)
# Add to context
context["browser_hub"] = browser_hub
return 1
except Exception as e:
context["error_message"] = f"Failed to initialize browser hub: {str(e)}"
return 0
async def fetch_content_middleware(context: Dict[str, Any]) -> int:
"""
Fetch content from the web using the browser hub.
This middleware uses the browser hub to get pages for crawling,
and properly releases them back to the pool when done.
Args:
context: The pipeline context dictionary
Returns:
int: 1 for success, 0 for failure
"""
url = context.get("url")
config = context.get("config")
browser_hub = context.get("browser_hub")
logger = context.get("logger")
# Skip if using cached result
if context.get("cached_result") and context.get("html"):
return 1
try:
# Create crawler strategy without initializing its browser manager
from crawl4ai.async_crawler_strategy import AsyncPlaywrightCrawlerStrategy
crawler_strategy = AsyncPlaywrightCrawlerStrategy(
browser_config=browser_hub.config if browser_hub else None,
logger=logger
)
# Replace the browser manager with our shared instance
crawler_strategy.browser_manager = browser_hub
# Perform crawl without trying to initialize the browser
# The crawler will use the provided browser_manager to get pages
async_response = await crawler_strategy.crawl(url, config=config)
# Store results in context
context["html"] = async_response.html
context["screenshot_data"] = async_response.screenshot
context["pdf_data"] = async_response.pdf_data
context["js_execution_result"] = async_response.js_execution_result
context["async_response"] = async_response
return 1
except Exception as e:
context["error_message"] = f"Error fetching content: {str(e)}"
return 0
async def check_cache_middleware(context: Dict[str, Any]) -> int:
"""Check if there's a cached result and load it if available"""
url = context.get("url")
config = context.get("config")
cache_context = context.get("cache_context")
logger = context.get("logger")
# Initialize variables
context["cached_result"] = None
context["html"] = None
context["extracted_content"] = None
context["screenshot_data"] = None
context["pdf_data"] = None
# Try to get cached result if appropriate
if cache_context.should_read():
cached_result = await async_db_manager.aget_cached_url(url)
context["cached_result"] = cached_result
if cached_result:
html = sanitize_input_encode(cached_result.html)
extracted_content = sanitize_input_encode(cached_result.extracted_content or "")
extracted_content = None if not extracted_content or extracted_content == "[]" else extracted_content
# If screenshot is requested but its not in cache, then set cache_result to None
screenshot_data = cached_result.screenshot
pdf_data = cached_result.pdf
if config.screenshot and not screenshot_data:
context["cached_result"] = None
if config.pdf and not pdf_data:
context["cached_result"] = None
context["html"] = html
context["extracted_content"] = extracted_content
context["screenshot_data"] = screenshot_data
context["pdf_data"] = pdf_data
logger.url_status(
url=cache_context.display_url,
success=bool(html),
timing=time.perf_counter() - context["start_time"],
tag="FETCH",
)
return 1
async def configure_proxy_middleware(context: Dict[str, Any]) -> int:
"""Configure proxy if a proxy rotation strategy is available"""
config = context.get("config")
logger = context.get("logger")
# Skip if using cached result
if context.get("cached_result") and context.get("html"):
return 1
# Update proxy configuration from rotation strategy if available
if config and config.proxy_rotation_strategy:
next_proxy = await config.proxy_rotation_strategy.get_next_proxy()
if next_proxy:
logger.info(
message="Switch proxy: {proxy}",
tag="PROXY",
params={"proxy": next_proxy.server},
)
config.proxy_config = next_proxy
return 1
async def check_robots_txt_middleware(context: Dict[str, Any]) -> int:
"""Check if the URL is allowed by robots.txt if enabled"""
url = context.get("url")
config = context.get("config")
browser_config = context.get("browser_config")
robots_parser = context.get("robots_parser")
# Skip if using cached result
if context.get("cached_result") and context.get("html"):
return 1
# Check robots.txt if enabled
if config and config.check_robots_txt:
if not await robots_parser.can_fetch(url, browser_config.user_agent):
context["crawl_result"] = CrawlResult(
url=url,
html="",
success=False,
status_code=403,
error_message="Access denied by robots.txt",
response_headers={"X-Robots-Status": "Blocked by robots.txt"}
)
return 0
return 1
async def fetch_content_middleware_(context: Dict[str, Any]) -> int:
"""Fetch content from the web using the crawler strategy"""
url = context.get("url")
config = context.get("config")
crawler_strategy = context.get("crawler_strategy")
logger = context.get("logger")
# Skip if using cached result
if context.get("cached_result") and context.get("html"):
return 1
try:
t1 = time.perf_counter()
if config.user_agent:
crawler_strategy.update_user_agent(config.user_agent)
# Call CrawlerStrategy.crawl
async_response = await crawler_strategy.crawl(url, config=config)
html = sanitize_input_encode(async_response.html)
screenshot_data = async_response.screenshot
pdf_data = async_response.pdf_data
js_execution_result = async_response.js_execution_result
t2 = time.perf_counter()
logger.url_status(
url=context["cache_context"].display_url,
success=bool(html),
timing=t2 - t1,
tag="FETCH",
)
context["html"] = html
context["screenshot_data"] = screenshot_data
context["pdf_data"] = pdf_data
context["js_execution_result"] = js_execution_result
context["async_response"] = async_response
return 1
except Exception as e:
context["error_message"] = f"Error fetching content: {str(e)}"
return 0
async def scrape_content_middleware(context: Dict[str, Any]) -> int:
"""Apply scraping strategy to extract content"""
url = context.get("url")
html = context.get("html")
config = context.get("config")
extracted_content = context.get("extracted_content")
logger = context.get("logger")
# Skip if already have a crawl result
if context.get("crawl_result"):
return 1
try:
_url = url if not context.get("is_raw_html", False) else "Raw HTML"
t1 = time.perf_counter()
# Get scraping strategy and ensure it has a logger
scraping_strategy = config.scraping_strategy
if not scraping_strategy.logger:
scraping_strategy.logger = logger
# Process HTML content
params = config.__dict__.copy()
params.pop("url", None)
# Add keys from kwargs to params that don't exist in params
kwargs = context.get("kwargs", {})
params.update({k: v for k, v in kwargs.items() if k not in params.keys()})
# Scraping Strategy Execution
result: ScrapingResult = scraping_strategy.scrap(url, html, **params)
if result is None:
raise ValueError(f"Process HTML, Failed to extract content from the website: {url}")
# Extract results - handle both dict and ScrapingResult
if isinstance(result, dict):
cleaned_html = sanitize_input_encode(result.get("cleaned_html", ""))
media = result.get("media", {})
links = result.get("links", {})
metadata = result.get("metadata", {})
else:
cleaned_html = sanitize_input_encode(result.cleaned_html)
media = result.media.model_dump()
links = result.links.model_dump()
metadata = result.metadata
context["cleaned_html"] = cleaned_html
context["media"] = media
context["links"] = links
context["metadata"] = metadata
# Log processing completion
logger.info(
message="{url:.50}... | Time: {timing}s",
tag="SCRAPE",
params={
"url": _url,
"timing": int((time.perf_counter() - t1) * 1000) / 1000,
},
)
return 1
except InvalidCSSSelectorError as e:
context["error_message"] = str(e)
return 0
except Exception as e:
context["error_message"] = f"Process HTML, Failed to extract content from the website: {url}, error: {str(e)}"
return 0
async def generate_markdown_middleware(context: Dict[str, Any]) -> int:
"""Generate markdown from cleaned HTML"""
url = context.get("url")
cleaned_html = context.get("cleaned_html")
config = context.get("config")
# Skip if already have a crawl result
if context.get("crawl_result"):
return 1
# Generate Markdown
markdown_generator = config.markdown_generator
markdown_result: MarkdownGenerationResult = markdown_generator.generate_markdown(
cleaned_html=cleaned_html,
base_url=url,
)
context["markdown_result"] = markdown_result
return 1
async def extract_structured_content_middleware(context: Dict[str, Any]) -> int:
"""Extract structured content using extraction strategy"""
url = context.get("url")
extracted_content = context.get("extracted_content")
config = context.get("config")
markdown_result = context.get("markdown_result")
cleaned_html = context.get("cleaned_html")
logger = context.get("logger")
# Skip if already have a crawl result or extracted content
if context.get("crawl_result") or bool(extracted_content):
return 1
from crawl4ai.chunking_strategy import IdentityChunking
from crawl4ai.extraction_strategy import NoExtractionStrategy
if config.extraction_strategy and not isinstance(config.extraction_strategy, NoExtractionStrategy):
t1 = time.perf_counter()
_url = url if not context.get("is_raw_html", False) else "Raw HTML"
# Choose content based on input_format
content_format = config.extraction_strategy.input_format
if content_format == "fit_markdown" and not markdown_result.fit_markdown:
logger.warning(
message="Fit markdown requested but not available. Falling back to raw markdown.",
tag="EXTRACT",
params={"url": _url},
)
content_format = "markdown"
content = {
"markdown": markdown_result.raw_markdown,
"html": context.get("html"),
"cleaned_html": cleaned_html,
"fit_markdown": markdown_result.fit_markdown,
}.get(content_format, markdown_result.raw_markdown)
# Use IdentityChunking for HTML input, otherwise use provided chunking strategy
chunking = (
IdentityChunking()
if content_format in ["html", "cleaned_html"]
else config.chunking_strategy
)
sections = chunking.chunk(content)
extracted_content = config.extraction_strategy.run(url, sections)
extracted_content = json.dumps(
extracted_content, indent=4, default=str, ensure_ascii=False
)
context["extracted_content"] = extracted_content
# Log extraction completion
logger.info(
message="Completed for {url:.50}... | Time: {timing}s",
tag="EXTRACT",
params={"url": _url, "timing": time.perf_counter() - t1},
)
return 1
async def format_html_middleware(context: Dict[str, Any]) -> int:
"""Format HTML if prettify is enabled"""
config = context.get("config")
cleaned_html = context.get("cleaned_html")
# Skip if already have a crawl result
if context.get("crawl_result"):
return 1
# Apply HTML formatting if requested
if config.prettiify and cleaned_html:
context["cleaned_html"] = fast_format_html(cleaned_html)
return 1
async def write_cache_middleware(context: Dict[str, Any]) -> int:
"""Write result to cache if appropriate"""
cache_context = context.get("cache_context")
cached_result = context.get("cached_result")
# Skip if already have a crawl result or not using cache
if context.get("crawl_result") or not cache_context.should_write() or bool(cached_result):
return 1
# We'll create the CrawlResult in build_result_middleware and cache it there
# to avoid creating it twice
return 1
async def build_result_middleware(context: Dict[str, Any]) -> int:
"""Build the final CrawlResult object"""
url = context.get("url")
html = context.get("html", "")
cache_context = context.get("cache_context")
cached_result = context.get("cached_result")
config = context.get("config")
logger = context.get("logger")
# If we already have a crawl result (from an earlier middleware like robots.txt check)
if context.get("crawl_result"):
result = context["crawl_result"]
context["final_result"] = CrawlResultContainer(result)
return 1
# If we have a cached result
if cached_result and html:
logger.success(
message="{url:.50}... | Status: {status} | Total: {timing}",
tag="COMPLETE",
params={
"url": cache_context.display_url,
"status": True,
"timing": f"{time.perf_counter() - context['start_time']:.2f}s",
},
colors={"status": "green", "timing": "yellow"},
)
cached_result.success = bool(html)
cached_result.session_id = getattr(config, "session_id", None)
cached_result.redirected_url = cached_result.redirected_url or url
context["final_result"] = CrawlResultContainer(cached_result)
return 1
# Build a new result
try:
# Get all necessary components from context
cleaned_html = context.get("cleaned_html", "")
markdown_result = context.get("markdown_result")
media = context.get("media", {})
links = context.get("links", {})
metadata = context.get("metadata", {})
screenshot_data = context.get("screenshot_data")
pdf_data = context.get("pdf_data")
extracted_content = context.get("extracted_content")
async_response = context.get("async_response")
# Create the CrawlResult
crawl_result = CrawlResult(
url=url,
html=html,
cleaned_html=cleaned_html,
markdown=markdown_result,
media=media,
links=links,
metadata=metadata,
screenshot=screenshot_data,
pdf=pdf_data,
extracted_content=extracted_content,
success=bool(html),
error_message="",
)
# Add response details if available
if async_response:
crawl_result.status_code = async_response.status_code
crawl_result.redirected_url = async_response.redirected_url or url
crawl_result.response_headers = async_response.response_headers
crawl_result.downloaded_files = async_response.downloaded_files
crawl_result.js_execution_result = context.get("js_execution_result")
crawl_result.ssl_certificate = async_response.ssl_certificate
crawl_result.session_id = getattr(config, "session_id", None)
# Log completion
logger.success(
message="{url:.50}... | Status: {status} | Total: {timing}",
tag="COMPLETE",
params={
"url": cache_context.display_url,
"status": crawl_result.success,
"timing": f"{time.perf_counter() - context['start_time']:.2f}s",
},
colors={
"status": "green" if crawl_result.success else "red",
"timing": "yellow",
},
)
# Update cache if appropriate
if cache_context.should_write() and not bool(cached_result):
await async_db_manager.acache_url(crawl_result)
context["final_result"] = CrawlResultContainer(crawl_result)
return 1
except Exception as e:
error_context = get_error_context(sys.exc_info())
error_message = (
f"Unexpected error in build_result at line {error_context['line_no']} "
f"in {error_context['function']} ({error_context['filename']}):\n"
f"Error: {str(e)}\n\n"
f"Code context:\n{error_context['code_context']}"
)
logger.error_status(
url=url,
error=create_box_message(error_message, type="error"),
tag="ERROR",
)
context["final_result"] = CrawlResultContainer(
CrawlResult(
url=url, html="", success=False, error_message=error_message
)
)
return 1
async def handle_error_middleware(context: Dict[str, Any]) -> Dict[str, Any]:
"""Error handler middleware"""
url = context.get("url", "")
error_message = context.get("error_message", "Unknown error")
logger = context.get("logger")
# Log the error
if logger:
logger.error_status(
url=url,
error=create_box_message(error_message, type="error"),
tag="ERROR",
)
# Create a failure result
context["final_result"] = CrawlResultContainer(
CrawlResult(
url=url, html="", success=False, error_message=error_message
)
)
return context
# Custom middlewares as requested
async def sentiment_analysis_middleware(context: Dict[str, Any]) -> int:
"""Analyze sentiment of generated markdown using TextBlob"""
from textblob import TextBlob
markdown_result = context.get("markdown_result")
# Skip if no markdown or already failed
if not markdown_result or not context.get("success", True):
return 1
try:
# Get raw markdown text
raw_markdown = markdown_result.raw_markdown
# Analyze sentiment
blob = TextBlob(raw_markdown)
sentiment = blob.sentiment
# Add sentiment to context
context["sentiment_analysis"] = {
"polarity": sentiment.polarity, # -1.0 to 1.0 (negative to positive)
"subjectivity": sentiment.subjectivity, # 0.0 to 1.0 (objective to subjective)
"classification": "positive" if sentiment.polarity > 0.1 else
"negative" if sentiment.polarity < -0.1 else "neutral"
}
return 1
except Exception as e:
# Don't fail the pipeline on sentiment analysis failure
context["sentiment_analysis_error"] = str(e)
return 1
async def log_timing_middleware(context: Dict[str, Any], name: str) -> int:
"""Log timing information for a specific point in the pipeline"""
context[f"_timing_mark_{name}"] = time.perf_counter()
# Calculate duration if we have a start time
start_key = f"_timing_start_{name}"
if start_key in context:
duration = context[f"_timing_mark_{name}"] - context[start_key]
context[f"_timing_duration_{name}"] = duration
# Log the timing if we have a logger
logger = context.get("logger")
if logger:
logger.info(
message="{name} completed in {duration:.2f}s",
tag="TIMING",
params={"name": name, "duration": duration},
)
return 1
async def validate_url_middleware(context: Dict[str, Any], patterns: List[str]) -> int:
"""Validate URL against glob patterns"""
import fnmatch
url = context.get("url", "")
# If no patterns provided, allow all
if not patterns:
return 1
# Check if URL matches any of the allowed patterns
for pattern in patterns:
if fnmatch.fnmatch(url, pattern):
return 1
# If we get here, URL didn't match any patterns
context["error_message"] = f"URL '{url}' does not match any allowed patterns"
return 0
# Update the default middleware list function
def create_default_middleware_list():
"""Return the default list of middleware functions for the pipeline."""
return [
initialize_context_middleware,
check_cache_middleware,
browser_hub_middleware, # Add browser hub middleware before fetch_content
configure_proxy_middleware,
check_robots_txt_middleware,
fetch_content_middleware,
scrape_content_middleware,
generate_markdown_middleware,
extract_structured_content_middleware,
format_html_middleware,
build_result_middleware
]

View File

@@ -1,297 +0,0 @@
import time
import asyncio
from typing import Callable, Dict, List, Any, Optional, Awaitable, Union, TypedDict, Tuple, Coroutine
from .middlewares import create_default_middleware_list, handle_error_middleware
from crawl4ai.models import CrawlResultContainer, CrawlResult
from crawl4ai.async_crawler_strategy import AsyncCrawlerStrategy, AsyncPlaywrightCrawlerStrategy
from crawl4ai.async_configs import BrowserConfig, CrawlerRunConfig
from crawl4ai.async_logger import AsyncLogger
class CrawlSpec(TypedDict, total=False):
"""Specification for a single crawl operation in batch_crawl."""
url: str
config: Optional[CrawlerRunConfig]
browser_config: Optional[BrowserConfig]
class BatchStatus(TypedDict, total=False):
"""Status information for batch crawl operations."""
total: int
processed: int
succeeded: int
failed: int
in_progress: int
duration: float
class Pipeline:
"""
A pipeline processor that executes a series of async middleware functions.
Each middleware function receives a context dictionary, updates it,
and returns 1 for success or 0 for failure.
"""
def __init__(
self,
middleware: List[Callable[[Dict[str, Any]], Awaitable[int]]] = None,
error_handler: Optional[Callable[[Dict[str, Any]], Awaitable[Dict[str, Any]]]] = None,
after_middleware_callback: Optional[Callable[[str, Dict[str, Any]], Awaitable[None]]] = None,
crawler_strategy: Optional[AsyncCrawlerStrategy] = None,
browser_config: Optional[BrowserConfig] = None,
logger: Optional[AsyncLogger] = None,
_initial_context: Optional[Dict[str, Any]] = None
):
self.middleware = middleware or create_default_middleware_list()
self.error_handler = error_handler or handle_error_middleware
self.after_middleware_callback = after_middleware_callback
self.browser_config = browser_config or BrowserConfig()
self.logger = logger or AsyncLogger(verbose=self.browser_config.verbose)
self.crawler_strategy = crawler_strategy or AsyncPlaywrightCrawlerStrategy(
browser_config=self.browser_config,
logger=self.logger
)
self._initial_context = _initial_context
self._strategy_initialized = False
async def _initialize_strategy__(self):
"""Initialize the crawler strategy if not already initialized"""
if not self.crawler_strategy:
self.crawler_strategy = AsyncPlaywrightCrawlerStrategy(
browser_config=self.browser_config,
logger=self.logger
)
if not self._strategy_initialized:
await self.crawler_strategy.__aenter__()
self._strategy_initialized = True
async def _initialize_strategy(self):
"""Initialize the crawler strategy if not already initialized"""
# With our new approach, we don't need to create the crawler strategy here
# as it will be created on-demand in fetch_content_middleware
# Just ensure browser hub is available if needed
if hasattr(self, "_initial_context") and "browser_hub" not in self._initial_context:
# If a browser_config was provided but no browser_hub yet,
# we'll let the browser_hub_middleware handle creating it
pass
# Mark as initialized to prevent repeated initialization attempts
self._strategy_initialized = True
async def start(self):
"""Start the crawler strategy and prepare it for use"""
if not self._strategy_initialized:
await self._initialize_strategy()
self._strategy_initialized = True
if self.crawler_strategy:
await self.crawler_strategy.__aenter__()
self._strategy_initialized = True
else:
raise ValueError("Crawler strategy is not initialized.")
async def close(self):
"""Close the crawler strategy and clean up resources"""
await self.stop()
async def stop(self):
"""Close the crawler strategy and clean up resources"""
if self._strategy_initialized and self.crawler_strategy:
await self.crawler_strategy.__aexit__(None, None, None)
self._strategy_initialized = False
async def __aenter__(self):
await self.start()
return self
async def __aexit__(self, exc_type, exc_val, exc_tb):
await self.close()
async def crawl(self, url: str, config: Optional[CrawlerRunConfig] = None, **kwargs) -> CrawlResultContainer:
"""
Crawl a URL and process it through the pipeline.
Args:
url: The URL to crawl
config: Optional configuration for the crawl
**kwargs: Additional arguments to pass to the middleware
Returns:
CrawlResultContainer: The result of the crawl
"""
# Initialize strategy if needed
await self._initialize_strategy()
# Create the initial context
context = {
"url": url,
"config": config or CrawlerRunConfig(),
"browser_config": self.browser_config,
"logger": self.logger,
"crawler_strategy": self.crawler_strategy,
"kwargs": kwargs
}
# Process the pipeline
result_context = await self.process(context)
# Return the final result
return result_context.get("final_result")
async def process(self, initial_context: Dict[str, Any] = None) -> Dict[str, Any]:
"""
Process all middleware functions with the given context.
Args:
initial_context: Initial context dictionary, defaults to empty dict
Returns:
Updated context dictionary after all middleware have been processed
"""
context = {**self._initial_context}
if initial_context:
context.update(initial_context)
# Record pipeline start time
context["_pipeline_start_time"] = time.perf_counter()
for middleware_fn in self.middleware:
# Get middleware name for logging
middleware_name = getattr(middleware_fn, '__name__', str(middleware_fn))
# Record start time for this middleware
start_time = time.perf_counter()
context[f"_timing_start_{middleware_name}"] = start_time
try:
# Execute middleware (all middleware functions are async)
result = await middleware_fn(context)
# Record completion time
end_time = time.perf_counter()
context[f"_timing_end_{middleware_name}"] = end_time
context[f"_timing_duration_{middleware_name}"] = end_time - start_time
# Execute after-middleware callback if provided
if self.after_middleware_callback:
await self.after_middleware_callback(middleware_name, context)
# Convert boolean returns to int (True->1, False->0)
if isinstance(result, bool):
result = 1 if result else 0
# Handle failure
if result == 0:
if self.error_handler:
context["_error_in"] = middleware_name
context["_error_at"] = time.perf_counter()
return await self._handle_error(context)
else:
context["success"] = False
context["error_message"] = f"Pipeline failed at {middleware_name}"
break
except Exception as e:
# Record error information
context["_error_in"] = middleware_name
context["_error_at"] = time.perf_counter()
context["_exception"] = e
context["success"] = False
context["error_message"] = f"Exception in {middleware_name}: {str(e)}"
# Call error handler if available
if self.error_handler:
return await self._handle_error(context)
break
# Record pipeline completion time
pipeline_end_time = time.perf_counter()
context["_pipeline_end_time"] = pipeline_end_time
context["_pipeline_duration"] = pipeline_end_time - context["_pipeline_start_time"]
# Set success to True if not already set (no failures)
if "success" not in context:
context["success"] = True
return context
async def _handle_error(self, context: Dict[str, Any]) -> Dict[str, Any]:
"""Handle errors by calling the error handler"""
try:
return await self.error_handler(context)
except Exception as e:
# If error handler fails, update context with this new error
context["_error_handler_exception"] = e
context["error_message"] = f"Error handler failed: {str(e)}"
return context
async def create_pipeline(
middleware_list=None,
error_handler=None,
after_middleware_callback=None,
browser_config=None,
browser_hub_id=None,
browser_hub_connection=None,
browser_hub=None,
logger=None
) -> Pipeline:
"""
Factory function to create a pipeline with Browser-Hub integration.
Args:
middleware_list: List of middleware functions
error_handler: Error handler middleware
after_middleware_callback: Callback after middleware execution
browser_config: Configuration for the browser
browser_hub_id: ID for browser hub instance
browser_hub_connection: Connection string for existing browser hub
browser_hub: Existing browser hub instance to use
logger: Logger instance
Returns:
Pipeline: Configured pipeline instance
"""
# Use default middleware list if none provided
middleware = middleware_list or create_default_middleware_list()
# Create the pipeline
pipeline = Pipeline(
middleware=middleware,
error_handler=error_handler,
after_middleware_callback=after_middleware_callback,
logger=logger
)
# Set browser-related attributes in the initial context
pipeline._initial_context = {
"browser_config": browser_config,
"browser_hub_id": browser_hub_id,
"browser_hub_connection": browser_hub_connection,
"browser_hub": browser_hub,
"logger": logger
}
return pipeline
# async def create_pipeline(
# middleware_list: Optional[List[Callable[[Dict[str, Any]], Awaitable[int]]]] = None,
# error_handler: Optional[Callable[[Dict[str, Any]], Awaitable[Dict[str, Any]]]] = None,
# after_middleware_callback: Optional[Callable[[str, Dict[str, Any]], Awaitable[None]]] = None,
# crawler_strategy = None,
# browser_config = None,
# logger = None
# ) -> Pipeline:
# """Factory function to create a pipeline with the given middleware"""
# return Pipeline(
# middleware=middleware_list,
# error_handler=error_handler,
# after_middleware_callback=after_middleware_callback,
# crawler_strategy=crawler_strategy,
# browser_config=browser_config,
# logger=logger
# )

View File

@@ -1,109 +0,0 @@
import asyncio
from crawl4ai import (
BrowserConfig,
CrawlerRunConfig,
CacheMode,
DefaultMarkdownGenerator,
PruningContentFilter
)
from pipeline import Pipeline
async def main():
# Create configuration objects
browser_config = BrowserConfig(headless=True, verbose=True)
crawler_config = CrawlerRunConfig(
cache_mode=CacheMode.BYPASS,
markdown_generator=DefaultMarkdownGenerator(
content_filter=PruningContentFilter(
threshold=0.48,
threshold_type="fixed",
min_word_threshold=0
)
),
)
# Create and use pipeline with context manager
async with Pipeline(browser_config=browser_config) as pipeline:
result = await pipeline.crawl(
url="https://www.example.com",
config=crawler_config
)
# Print the result
print(f"URL: {result.url}")
print(f"Success: {result.success}")
if result.success:
print("\nMarkdown excerpt:")
print(result.markdown.raw_markdown[:500] + "...")
else:
print(f"Error: {result.error_message}")
if __name__ == "__main__":
asyncio.run(main())
class CrawlTarget:
def __init__(self, urls, config=None):
self.urls = urls
self.config = config
def __repr__(self):
return f"CrawlTarget(urls={self.urls}, config={self.config})"
# async def main():
# # Create configuration objects
# browser_config = BrowserConfig(headless=True, verbose=True)
# # Define different configurations
# config1 = CrawlerRunConfig(
# cache_mode=CacheMode.BYPASS,
# markdown_generator=DefaultMarkdownGenerator(
# content_filter=PruningContentFilter(threshold=0.48)
# ),
# )
# config2 = CrawlerRunConfig(
# cache_mode=CacheMode.ENABLED,
# screenshot=True,
# pdf=True
# )
# # Create crawl targets
# targets = [
# CrawlTarget(
# urls=["https://www.example.com", "https://www.wikipedia.org"],
# config=config1
# ),
# CrawlTarget(
# urls="https://news.ycombinator.com",
# config=config2
# ),
# CrawlTarget(
# urls=["https://github.com", "https://stackoverflow.com", "https://python.org"],
# config=None
# )
# ]
# # Create and use pipeline with context manager
# async with Pipeline(browser_config=browser_config) as pipeline:
# all_results = await pipeline.crawl_batch(targets)
# for target_key, results in all_results.items():
# print(f"\n===== Results for {target_key} =====")
# print(f"Number of URLs crawled: {len(results)}")
# for i, result in enumerate(results):
# print(f"\nURL {i+1}: {result.url}")
# print(f"Success: {result.success}")
# if result.success:
# print(f"Content length: {len(result.markdown.raw_markdown)} chars")
# else:
# print(f"Error: {result.error_message}")
# if __name__ == "__main__":
# asyncio.run(main())

View File

@@ -14,7 +14,7 @@ class PDFCrawlerStrategy(AsyncCrawlerStrategy):
async def crawl(self, url: str, **kwargs) -> AsyncCrawlResponse:
# Just pass through with empty HTML - scraper will handle actual processing
return AsyncCrawlResponse(
html="", # Scraper will handle the real work
html="Scraper will handle the real work", # Scraper will handle the real work
response_headers={"Content-Type": "application/pdf"},
status_code=200
)
@@ -66,6 +66,7 @@ class PDFContentScrapingStrategy(ContentScrapingStrategy):
image_save_dir=image_save_dir,
batch_size=batch_size
)
self._temp_files = [] # Track temp files for cleanup
def scrap(self, url: str, html: str, **params) -> ScrapingResult:
"""
@@ -124,7 +125,13 @@ class PDFContentScrapingStrategy(ContentScrapingStrategy):
finally:
# Cleanup temp file if downloaded
if url.startswith(("http://", "https://")):
Path(pdf_path).unlink(missing_ok=True)
try:
Path(pdf_path).unlink(missing_ok=True)
if pdf_path in self._temp_files:
self._temp_files.remove(pdf_path)
except Exception as e:
if self.logger:
self.logger.warning(f"Failed to cleanup temp file {pdf_path}: {e}")
async def ascrap(self, url: str, html: str, **kwargs) -> ScrapingResult:
# For simple cases, you can use the sync version
@@ -138,22 +145,45 @@ class PDFContentScrapingStrategy(ContentScrapingStrategy):
# Create temp file with .pdf extension
temp_file = tempfile.NamedTemporaryFile(suffix='.pdf', delete=False)
self._temp_files.append(temp_file.name)
try:
# Download PDF with streaming
response = requests.get(url, stream=True)
if self.logger:
self.logger.info(f"Downloading PDF from {url}...")
# Download PDF with streaming and timeout
# Connection timeout: 10s, Read timeout: 300s (5 minutes for large PDFs)
response = requests.get(url, stream=True, timeout=(20, 60 * 10))
response.raise_for_status()
# Get file size if available
total_size = int(response.headers.get('content-length', 0))
downloaded = 0
# Write to temp file
with open(temp_file.name, 'wb') as f:
for chunk in response.iter_content(chunk_size=8192):
f.write(chunk)
downloaded += len(chunk)
if self.logger and total_size > 0:
progress = (downloaded / total_size) * 100
if progress % 10 < 0.1: # Log every 10%
self.logger.debug(f"PDF download progress: {progress:.0f}%")
if self.logger:
self.logger.info(f"PDF downloaded successfully: {temp_file.name}")
return temp_file.name
except requests.exceptions.Timeout as e:
# Clean up temp file if download fails
Path(temp_file.name).unlink(missing_ok=True)
self._temp_files.remove(temp_file.name)
raise RuntimeError(f"Timeout downloading PDF from {url}: {str(e)}")
except Exception as e:
# Clean up temp file if download fails
Path(temp_file.name).unlink(missing_ok=True)
self._temp_files.remove(temp_file.name)
raise RuntimeError(f"Failed to download PDF from {url}: {str(e)}")
elif url.startswith("file://"):

View File

@@ -1054,4 +1054,525 @@ Your output must:
5. Include all required fields
6. Use valid XPath selectors
</output_requirements>
"""
"""
GENERATE_SCRIPT_PROMPT = r"""You are a world-class browser automation specialist. Your sole purpose is to convert a natural language objective and a snippet of HTML into the most **efficient, robust, and simple** script possible to prepare a web page for data extraction.
Your scripts run **before the crawl** to handle dynamic content, user interactions, and other obstacles. You are a master of two tools: raw **JavaScript** and the high-level **Crawl4ai Script (c4a)**.
────────────────────────────────────────────────────────
## Your Core Philosophy: "Efficiency, Robustness, Simplicity"
This is your mantra. Every line of code you write must adhere to it.
1. **Efficiency (Shortest Path):** Generate the absolute minimum number of steps to achieve the goal. Do not include redundant actions. If a `CLICK` on one button achieves the goal, don't also scroll and wait unnecessarily.
2. **Robustness (Will Not Break):** Prioritize selectors and methods that are resistant to cosmetic site changes. `data-*` attributes are gold. Dynamic, auto-generated class names (`.class-a8B_x3`) are poison. Always prefer waiting for a state change (`WAIT \`#results\``) over a blind delay (`WAIT 5`).
3. **Simplicity (Right Tool for the Job):** Use the simplest tool that works. Prefer a direct `c4a` command over `EVAL` with JavaScript. Only use `EVAL` when the task is impossible with standard commands (e.g., accessing Shadow DOM, complex array filtering).
────────────────────────────────────────────────────────
## Output Mode Selection Logic
Your choice of output mode is a critical strategic decision.
* **Use `crawl4ai_script` for:**
* Standard, sequential browser actions: login forms, clicking "next page," simple "load more" buttons, accepting cookie banners.
* When the user's goal maps clearly to the available `c4a` commands.
* When you need to define reusable macros with `PROC`.
* **Use `javascript` for:**
* Complex DOM manipulation that has no `c4a` equivalent (e.g., transforming data, complex filtering).
* Interacting with web components inside **Shadow DOM** or **iFrames**.
* Implementing sophisticated logic like custom scrolling patterns or handling non-standard events.
* When the goal is a fine-grained DOM tweak, not a full user journey.
**If the user specifies a mode, you MUST respect it.** If not, you must choose the mode that best embodies your core philosophy.
────────────────────────────────────────────────────────
## Available Crawl4ai Commands
| Command | Arguments / Notes |
|------------------------|--------------------------------------------------------------|
| GO `<url>` | Navigate to absolute URL |
| RELOAD | Hard refresh |
| BACK / FORWARD | Browser history nav |
| WAIT `<seconds>` | **Avoid!** Passive delay. Use only as a last resort. |
| WAIT \`<css>\` `<t>` | **Preferred wait.** Poll selector until found, timeout in seconds. |
| WAIT "<text>" `<t>` | Poll page text until found, timeout in seconds. |
| CLICK \`<css>\` | Single click on element |
| CLICK `<x>` `<y>` | Viewport click |
| DOUBLE_CLICK … | Two rapid clicks |
| RIGHT_CLICK … | Context-menu click |
| MOVE `<x>` `<y>` | Mouse move |
| DRAG `<x1>` `<y1>` `<x2>` `<y2>` | Click-drag gesture |
| SCROLL UP|DOWN|LEFT|RIGHT `[px]` | Viewport scroll |
| TYPE "<text>" | Type into focused element |
| CLEAR \`<css>\` | Empty input |
| SET \`<css>\` "<val>" | Set element value and dispatch events |
| PRESS `<Key>` | Keydown + keyup |
| KEY_DOWN `<Key>` / KEY_UP `<Key>` | Separate key events |
| EVAL \`<js>\` | **Your fallback.** Run JS when no direct command exists. |
| SETVAR $name = <val> | Store constant for reuse |
| PROC name … ENDPROC | Define macro |
| IF / ELSE / REPEAT | Flow control |
| USE "<file.c4a>" | Include another script, avoid circular includes |
────────────────────────────────────────────────────────
## Strategic Principles & Anti-Patterns
These are your commandments. Do not deviate.
1. **Selector Quality is Paramount:**
* **GOOD:** `[data-testid="submit-button"]`, `#main-content`, `[aria-label="Close dialog"]`
* **BAD:** `div > span:nth-child(3)`, `.button-gR3xY_s`, `//div[contains(@class, 'button')]`
2. **Wait for State, Not for Time:**
* **DO:** `CLICK \`#load-more\`` followed by `WAIT \`div.new-item\` 10`. This waits for the *result* of the action.
* **DON'T:** `CLICK \`#load-more\`` followed by `WAIT 5`. This is a guess and it will fail.
3. **Target the Action, Not the Artifact:** If you need to reveal content, click the button that reveals it. Don't try to manually change CSS `display` properties, as this can break the page's internal state.
4. **DOM-Awareness is Non-Negotiable:**
* **Shadow DOM:** `c4a` commands CANNOT pierce the Shadow DOM. If you see a `#shadow-root (open)` in the HTML, you MUST use `EVAL` and `element.shadowRoot.querySelector(...)`.
* **iFrames:** Likewise, you MUST use `EVAL` and `iframe.contentDocument.querySelector(...)` to interact with elements inside an iframe.
5. **Be Idempotent:** Your script must be harmless if run multiple times. Use `IF EXISTS` to check for states before acting (e.g., don't try to log in if already logged in).
6. **Forbidden Techniques:** Never use `document.write()`. It is destructive. Avoid overly complex JS in `EVAL` that could be simplified into a few `c4a` commands.
────────────────────────────────────────────────────────
## From Vague Goals to Robust Scripts: Your Duty to Infer and Ensure Reliability
This is your most important responsibility. Users are not automation experts. They will provide incomplete or vague instructions. Your job is to be the expert—to infer their true goal and build a script that is reliable by default. You must add the "invisible scaffolding" of checks and waits to ensure the page is stable and ready for the crawler. **A vague user prompt must still result in a robust, complete script.**
Study these examples. No matter which query is given, your output must be the single, robust solution.
### 1. Scenario: Basic Search Query
* **High Detail Query:** "Find the search box and search button. Wait for the search box to be visible, click it, clear it, type 'r2d2', click the search button, and then wait for the search results to appear."
* **Medium Detail Query:** "Find the search box and search for 'r2d2', click the search button until you get a list of items."
* **Low Detail Query:** "Search for r2d2."
**THE CORRECT, ROBUST OUTPUT (for all three queries):**
```
WAIT `input[type="search"]` 10
SET `input[type="search"]` "r2d2"
CLICK `button[aria-label="Search"]`
WAIT `div.search-results-container` 15
```
**Rationale:** You correctly infer the need to `WAIT` for the input first. You use the more efficient `SET` command. Most importantly, you **infer the crucial final step**: waiting for a results container to appear, confirming the search action was successful.
### 2. Scenario: Clicking a "Load More" Button
* **High Detail Query:** "Click the button with the text 'Load More'. Afterward, wait for a new item with the class '.product-tile' to show up on the page."
* **Medium Detail Query:** "Click the load more button to see more products."
* **Low Detail Query:** "Load more items."
**THE CORRECT, ROBUST OUTPUT:**
```
IF EXISTS `button.load-more` THEN
CLICK `button.load-more`
WAIT `div.new-item-indicator` 8
ENDIF
```
**Rationale:** You wrap the action in `IF EXISTS` to prevent errors if the button is not present (e.g., on the last page). You correctly infer the need to wait for the *consequence* of the click—a new item appearing—rather than a blind `WAIT 2`.
### 3. Scenario: Applying a Filter from a Dropdown
* **High Detail Query:** "First, click the dropdown with id 'color-filter'. Then, from the open menu, click the option that says 'Blue'. Finally, wait for the product grid to update."
* **Medium Detail Query:** "Filter the products by the color Blue."
* **Low Detail Query:** "Show blue products."
**THE CORRECT, ROBUST OUTPUT:**
```
CLICK `#color-filter`
WAIT `[data-value="blue"]` 3
CLICK `[data-value="blue"]`
WAIT `#product-grid[data-status="updated"]` 10
```
**Rationale:** You infer the need for two waits: one for the menu options to appear after the first click, and a second for the main content to update after the filter is applied. This prevents race conditions.
### 4. Scenario: User Authentication (Login)
* **High Detail Query:** "Fill username with 'USER_EMAIL', fill password with 'USER_PASS', click login, and wait for the dashboard to appear."
* **Medium Detail Query:** "Log in as USER_EMAIL with password USER_PASS."
* **Low Detail Query:** "Log in."
**THE CORRECT, ROBUST OUTPUT:**
```
IF EXISTS `[data-testid="logout-button"]` THEN
EVAL `console.log("Already logged in.")`
ELSE
WAIT `input[name="username"]` 10
SET `input[name="username"]` "USER_EMAIL"
SET `input[name="password"]` "USER_PASS"
CLICK `button[type="submit"]`
WAIT `[data-testid="user-dashboard"]` 15
ENDIF
```
**Rationale:** You build an **idempotent** script. You first check if the user is *already* logged in. If not, you proceed with the login and then, critically, `WAIT` for a post-login element to confirm success. You use placeholders when credentials are not provided in low-detail queries.
### 5. Scenario: Dismissing an Interstitial Modal
* **High Detail Query:** "Check if a popup with id '#promo-modal' exists. If it does, click the close button inside it with class '.close-x'."
* **Medium Detail Query:** "Close the promotional popup."
* **Low Detail Query:** "Get rid of the popup."
**THE CORRECT, ROBUST OUTPUT:**
```
IF EXISTS `div#promo-modal` THEN
CLICK `div#promo-modal button.close-x`
ENDIF
```
**Rationale:** You correctly identify this as a conditional action. The script must not fail if the popup doesn't appear. The `IF EXISTS` block is the perfect, robust way to handle this optional interaction.
────────────────────────────────────────────────────────
## Advanced Scenarios & Master-Level Examples
Study these solutions. Understand the *why* behind each choice.
### Scenario: Interacting with a Web Component (Shadow DOM)
**Goal:** Click a button inside a custom element `<user-card>`.
**HTML Snippet:** `<user-card><#shadow-root (open)><button>Details</button></#shadow-root></user-card>`
**Correct Mode:** `javascript` (or `c4a` with `EVAL`)
**Rationale:** Standard selectors can't cross the shadow boundary. JavaScript is mandatory.
```javascript
// Solution in pure JS mode
const card = document.querySelector('user-card');
if (card && card.shadowRoot) {
const button = card.shadowRoot.querySelector('button');
if (button) button.click();
}
```
```
# Solution in c4a mode (using EVAL as the weapon of choice)
EVAL `
const card = document.querySelector('user-card');
if (card && card.shadowRoot) {
const button = card.shadowRoot.querySelector('button');
if (button) button.click();
}
`
```
### Scenario: Handling a Cookie Banner
**Goal:** Accept the cookies to dismiss the modal.
**HTML Snippet:** `<div id="cookie-consent-modal"><button id="accept-cookies">Accept All</button></div>`
**Correct Mode:** `crawl4ai_script`
**Rationale:** A simple, direct action. `c4a` is cleaner and more declarative.
```
# The most efficient solution
IF EXISTS `#cookie-consent-modal` THEN
CLICK `#accept-cookies`
WAIT `div.content-loaded` 5
ENDIF
```
### Scenario: Infinite Scroll Page
**Goal:** Scroll down 5 times to load more content.
**HTML Snippet:** `(A page with a long body and no "load more" button)`
**Correct Mode:** `crawl4ai_script`
**Rationale:** `REPEAT` is designed for exactly this. It's more readable than a JS loop for this simple task.
```
REPEAT (
SCROLL DOWN 1000,
5
)
WAIT 2
```
### Scenario: Hover-to-Reveal Menu
**Goal:** Hover over "Products" to open the menu, then click "Laptops".
**HTML Snippet:** `<a href="/products" id="products-menu">Products</a> <div class="menu-dropdown"><a href="/laptops">Laptops</a></div>`
**Correct Mode:** `crawl4ai_script` (with `EVAL`)
**Rationale:** `c4a` has no `HOVER` command. `EVAL` is the perfect tool to dispatch the `mouseover` event.
```
EVAL `document.querySelector('#products-menu').dispatchEvent(new MouseEvent('mouseover', { bubbles: true }))`
WAIT `div.menu-dropdown a[href="/laptops"]` 3
CLICK `div.menu-dropdown a[href="/laptops"]`
```
### Scenario: Login Form
**Goal:** Fill and submit a login form.
**HTML Snippet:** `<form><input name="email"><input name="password" type="password"><button type="submit"></button></form>`
**Correct Mode:** `crawl4ai_script`
**Rationale:** This is the canonical use case for `c4a`. The commands map 1:1 to the user journey.
```
WAIT `form` 10
SET `input[name="email"]` "USER_EMAIL"
SET `input[name="password"]` "USER_PASS"
CLICK `button[type="submit"]`
WAIT `[data-testid="user-dashboard"]` 12
```
────────────────────────────────────────────────────────
## Final Output Mandate
1. **CODE ONLY.** Your entire response must be the script body.
2. **NO CHAT.** Do not say "Here is the script" or "This should work."
3. **NO MARKDOWN.** Do not wrap your code in ` ``` ` fences.
4. **NO COMMENTS.** Do not add comments to the final code output.
5. **SYNTACTICALLY PERFECT.** The script must be immediately executable.
6. **UTF-8, STANDARD QUOTES.** Use `"` for string literals, not `“` or `”`.
You are an engine of automation. Now, receive the user's request and produce the optimal script."""
GENERATE_JS_SCRIPT_PROMPT = """# The World-Class JavaScript Automation Scripter
You are a world-class browser automation specialist. Your sole purpose is to convert a natural language objective and a snippet of HTML into the most **efficient, robust, and simple** pure JavaScript script possible to prepare a web page for data extraction.
Your scripts will be executed directly in the browser (e.g., via Playwright's `page.evaluate()`) to handle dynamic content, user interactions, and other obstacles before the page is crawled. You are a master of browser-native JavaScript APIs.
────────────────────────────────────────────────────────
## Your Core Philosophy: "Efficiency, Robustness, Simplicity"
This is your mantra. Every line of JavaScript you write must adhere to it.
1. **Efficiency (Shortest Path):** Generate the absolute minimum number of steps to achieve the goal. Do not include redundant actions. Your code should be concise and direct.
2. **Robustness (Will Not Break):** Prioritize selectors that are resistant to cosmetic site changes. `data-*` attributes are gold. Dynamic, auto-generated class names (`.class-a8B_x3`) are poison. Always prefer waiting for a state change over a blind `setTimeout`.
3. **Simplicity (Right Tool for the Job):** Use simple, direct DOM methods (`.querySelector`, `.click()`) whenever possible. Avoid overly complex or fragile logic when a simpler approach exists.
────────────────────────────────────────────────────────
## Essential JavaScript Automation Patterns & Toolkit
All code should be wrapped in an `async` Immediately Invoked Function Expression `(async () => { ... })();` to allow for top-level `await` and to avoid polluting the global scope.
| Task | Best-Practice JavaScript Implementation |
| -------------------- | -------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
| **Wait for Element** | Create and use a robust `waitForElement` helper function. This is your most important tool. <br> `const waitForElement = (selector, timeout = 10000) => new Promise((resolve, reject) => { const el = document.querySelector(selector); if (el) return resolve(el); const observer = new MutationObserver(() => { const el = document.querySelector(selector); if (el) { observer.disconnect(); resolve(el); } }); observer.observe(document.body, { childList: true, subtree: true }); setTimeout(() => { observer.disconnect(); reject(new Error(`Timeout waiting for ${selector}`)); }, timeout); });` |
| **Click Element** | `const el = await waitForElement('selector'); if (el) el.click();` |
| **Set Input Value** | `const input = await waitForElement('selector'); if (input) { input.value = 'new value'; input.dispatchEvent(new Event('input', { bubbles: true })); input.dispatchEvent(new Event('change', { bubbles: true })); }` <br> *Crucially, always dispatch `input` and `change` events to trigger framework reactivity.* |
| **Check Existence** | `const el = document.querySelector('selector'); if (el) { /* ... it exists */ }` |
| **Scroll** | `window.scrollBy(0, window.innerHeight);` |
| **Deal with Time** | Use `await new Promise(r => setTimeout(r, 500));` for short, unavoidable pauses after an action. **Avoid long, blind waits.** |
REMEMBER: Make sure to generate very deterministic css selector. If you refer to a specific button, then be specific, otherwise you may capture elements you do not need, be very specific about the element you want to interact with.
────────────────────────────────────────────────────────
## The Art of High-Specificity Selectors: Your Defense Against Ambiguity
This is your most critical skill for ensuring robustness. **You must assume the provided HTML is only a small fragment of the entire page.** A selector that looks unique in the fragment could be disastrously generic on the full page. Your primary defense is to **anchor your selectors to the most specific, stable parent element available in the given HTML context.**
Think of it as creating a "sandbox" for your selectors.
**Your Guiding Principle:** Start from a unique parent, then find the child.
### Scenario: Selecting a Submit Button within a Login Form
**HTML Snippet Provided:**
```html
<div class="user-auth-module" id="login-widget">
<h2>Member Login</h2>
<form action="/login">
<input name="email" type="email">
<input name="password" type="password">
<button type="submit">Sign In</button>
</form>
</div>
```
* **TERRIBLE (High Risk):** `button[type="submit"]`
* **Why it's bad:** There could be dozens of other forms on the full page (e.g., a newsletter signup, a search bar in the header). This selector is a shot in the dark.
* **BETTER (Lower Risk):** `#login-widget button[type="submit"]`
* **Why it's better:** It's anchored to a unique ID (`#login-widget`). This dramatically reduces the chance of ambiguity.
* **EXCELLENT (Minimal Risk):** `div[id="login-widget"] form button[type="submit"]`
* **Why it's best:** This is a highly specific, descriptive path. It says, "Find the login widget, then the form inside it, and then the submit button inside *that* form." It is virtually guaranteed to be unique and is resilient to minor layout changes within the form.
### Scenario: Selecting a "Add to Cart" Button
**HTML Snippet Provided:**
```html
<section data-testid="product-details-main">
<h1>Awesome T-Shirt</h1>
<div class="product-actions">
<button class="add-to-cart-btn">Add to Cart</button>
</div>
</section>
```
* **TERRIBLE (High Risk):** `.add-to-cart-btn`
* **Why it's bad:** A "related products" section outside this snippet might also use the same class name.
* **EXCELLENT (Minimal Risk):** `[data-testid="product-details-main"] .add-to-cart-btn`
* **Why it's best:** It uses the stable `data-testid` attribute of the parent section as an anchor. This is the most robust pattern.
**Your Mandate:** Always examine the provided HTML for a stable, unique parent (like an element with an `id`, a `data-testid`, or a highly specific combination of classes) and use it as the root of your selectors. **NEVER generate a generic, un-anchored selector if a better, more specific parent is available in the context.**
────────────────────────────────────────────────────────
## Strategic Principles & Anti-Patterns
These are your commandments. Do not deviate.
1. **Selector Quality is Paramount:**
* **GOOD:** `[data-testid="submit-button"]`, `#main-content`, `[aria-label="Close dialog"]`
* **BAD:** `div > span:nth-child(3)`, `.button-gR3xY_s`, `//div[contains(@class, 'button')]`
2. **Wait for State, Not for Time:**
* **DO:** `(await waitForElement('#load-more')).click(); await waitForElement('div.new-item');` This waits for the *result* of the action.
* **DON'T:** `document.querySelector('#load-more').click(); await new Promise(r => setTimeout(r, 5000));` This is a guess and it will fail.
3. **Target the Action, Not the Artifact:** If you need to reveal content, click the button that reveals it. Don't try to manually change CSS `display` properties, as this can break the page's internal state.
4. **DOM-Awareness is Non-Negotiable:**
* **Shadow DOM:** You MUST use `element.shadowRoot.querySelector(...)` to access elements inside a `#shadow-root (open)`.
* **iFrames:** You MUST use `iframe.contentDocument.querySelector(...)` to interact with elements inside an iframe.
5. **Be Idempotent:** Your script must be harmless if run multiple times. Use `if (document.querySelector(...))` checks to avoid re-doing actions unnecessarily.
6. **Forbidden Techniques:** Never use `document.write()`. It is destructive.
────────────────────────────────────────────────────────
## From Vague Goals to Robust Scripts: Your Duty to Infer and Ensure Reliability
This is your most important responsibility. Users are not automation experts. They will provide incomplete or vague instructions. Your job is to be the expert—to infer their true goal and build a script that is reliable by default. **A vague user prompt must still result in a robust, complete script.**
Study these examples. No matter which query is given, your output must be the single, robust solution.
### 1. Scenario: Basic Search Query
* **High Detail Query:** "Find the search box and search button. Wait for the search box to be visible, click it, clear it, type 'r2d2', click the search button, and then wait for the search results to appear."
* **Medium Detail Query:** "Find the search box and search for 'r2d2'."
* **Low Detail Query:** "Search for r2d2."
**THE CORRECT, ROBUST JAVASCRIPT OUTPUT (for all three queries):**
```javascript
(async () => {
const waitForElement = (selector, timeout = 10000) => new Promise((resolve, reject) => { const el = document.querySelector(selector); if (el) return resolve(el); const observer = new MutationObserver(() => { const el = document.querySelector(selector); if (el) { observer.disconnect(); resolve(el); } }); observer.observe(document.body, { childList: true, subtree: true }); setTimeout(() => { observer.disconnect(); reject(new Error(`Timeout waiting for ${selector}`)); }, timeout); });
try {
const searchInput = await waitForElement('input[type="search"], input[aria-label*="search"]');
searchInput.value = 'r2d2';
searchInput.dispatchEvent(new Event('input', { bubbles: true }));
const searchButton = await waitForElement('button[type="submit"], button[aria-label*="search"]');
searchButton.click();
await waitForElement('div.search-results-container, #search-results');
} catch (e) {
console.error('Search script failed:', e.message);
}
})();
```
### 2. Scenario: Clicking a "Load More" Button
* **High Detail Query:** "Click the button with the text 'Load More'. Afterward, wait for a new item with the class '.product-tile' to show up."
* **Medium Detail Query:** "Click the load more button."
* **Low Detail Query:** "Load more items."
**THE CORRECT, ROBUST JAVASCRIPT OUTPUT:**
```javascript
(async () => {
const loadMoreButton = document.querySelector('button.load-more, [data-testid="load-more"]');
if (loadMoreButton) {
const initialItemCount = document.querySelectorAll('.product-tile').length;
loadMoreButton.click();
const waitForNewItem = (timeout = 8000) => new Promise((resolve, reject) => { const t0 = Date.now(); const check = () => { if (document.querySelectorAll('.product-tile').length > initialItemCount) return resolve(); if (Date.now() - t0 > timeout) return reject(new Error('Timeout waiting for new items to load.')); setTimeout(check, 200); }; check(); });
await waitForNewItem();
}
})();
```
### 3. Scenario: User Authentication (Login)
* **High Detail Query:** "Fill username with 'USER_EMAIL', password with 'USER_PASS', click login, and wait for the dashboard."
* **Medium Detail Query:** "Log in as USER_EMAIL."
* **Low Detail Query:** "Log in."
**THE CORRECT, ROBUST JAVASCRIPT OUTPUT:**
```javascript
(async () => {
if (document.querySelector('[data-testid="logout-button"]')) {
console.log('Already logged in.');
return;
}
const waitForElement = (selector, timeout = 10000) => new Promise((resolve, reject) => { const el = document.querySelector(selector); if (el) return resolve(el); const observer = new MutationObserver(() => { const el = document.querySelector(selector); if (el) { observer.disconnect(); resolve(el); } }); observer.observe(document.body, { childList: true, subtree: true }); setTimeout(() => { observer.disconnect(); reject(new Error(`Timeout waiting for ${selector}`)); }, timeout); });
try {
const userInput = await waitForElement('input[name*="user"], input[name*="email"]');
userInput.value = 'USER_EMAIL';
userInput.dispatchEvent(new Event('input', { bubbles: true }));
const passInput = await waitForElement('input[name*="pass"], input[type="password"]');
passInput.value = 'USER_PASS';
passInput.dispatchEvent(new Event('input', { bubbles: true }));
const submitButton = await waitForElement('button[type="submit"]');
submitButton.click();
await waitForElement('[data-testid="user-dashboard"], #dashboard, .account-page');
} catch (e) {
console.error('Login script failed:', e.message);
}
})();
```
────────────────────────────────────────────────────────
## The Art of High-Specificity Selectors: Your Defense Against Ambiguity
This is your most critical skill for ensuring robustness. **You must assume the provided HTML is only a small fragment of the entire page.** A selector that looks unique in the fragment could be disastrously generic on the full page. Your primary defense is to **anchor your selectors to the most specific, stable parent element available in the given HTML context.**
Think of it as creating a "sandbox" for your selectors.
**Your Guiding Principle:** Start from a unique parent, then find the child.
### Scenario: Selecting a Submit Button within a Login Form
**HTML Snippet Provided:**
```html
<div class="user-auth-module" id="login-widget">
<h2>Member Login</h2>
<form action="/login">
<input name="email" type="email">
<input name="password" type="password">
<button type="submit">Sign In</button>
</form>
</div>
```
* **TERRIBLE (High Risk):** `button[type="submit"]`
* **Why it's bad:** There could be dozens of other forms on the full page (e.g., a newsletter signup, a search bar in the header). This selector is a shot in the dark.
* **BETTER (Lower Risk):** `#login-widget button[type="submit"]`
* **Why it's better:** It's anchored to a unique ID (`#login-widget`). This dramatically reduces the chance of ambiguity.
* **EXCELLENT (Minimal Risk):** `div[id="login-widget"] form button[type="submit"]`
* **Why it's best:** This is a highly specific, descriptive path. It says, "Find the login widget, then the form inside it, and then the submit button inside *that* form." It is virtually guaranteed to be unique and is resilient to minor layout changes within the form.
### Scenario: Selecting a "Add to Cart" Button
**HTML Snippet Provided:**
```html
<section data-testid="product-details-main">
<h1>Awesome T-Shirt</h1>
<div class="product-actions">
<button class="add-to-cart-btn">Add to Cart</button>
</div>
</section>
```
* **TERRIBLE (High Risk):** `.add-to-cart-btn`
* **Why it's bad:** A "related products" section outside this snippet might also use the same class name.
* **EXCELLENT (Minimal Risk):** `[data-testid="product-details-main"] .add-to-cart-btn`
* **Why it's best:** It uses the stable `data-testid` attribute of the parent section as an anchor. This is the most robust pattern.
**Your Mandate:** Always examine the provided HTML for a stable, unique parent (like an element with an `id`, a `data-testid`, or a highly specific combination of classes) and use it as the root of your selectors. **NEVER generate a generic, un-anchored selector if a better, more specific parent is available in the context.**
────────────────────────────────────────────────────────
## Final Output Mandate
1. **CODE ONLY.** Your entire response must be the script body.
2. **NO CHAT.** Do not say "Here is the script" or "This should work."
3. **NO MARKDOWN.** Do not wrap your code in ` ``` ` fences.
4. **NO COMMENTS.** Do not add comments to the final code output, except within the logic where it's a best practice.
5. **SYNTACTICALLY PERFECT.** The script must be a single, self-contained block, immediately executable. Wrap it in `(async () => { ... })();`.
6. **UTF-8, STANDARD QUOTES.** Use `'` for string literals, not `“` or `”`.
You are an engine of automation. Now, receive the user's request and produce the optimal JavaScript."""

View File

@@ -4,6 +4,9 @@ from itertools import cycle
import os
########### ATTENTION PEOPLE OF EARTH ###########
# I have moved this config to async_configs.py, kept it here, in case someone still importing it, however
# be a dear and follow `from crawl4ai import ProxyConfig` instead :)
class ProxyConfig:
def __init__(
self,
@@ -119,12 +122,12 @@ class ProxyRotationStrategy(ABC):
"""Base abstract class for proxy rotation strategies"""
@abstractmethod
async def get_next_proxy(self) -> Optional[Dict]:
async def get_next_proxy(self) -> Optional[ProxyConfig]:
"""Get next proxy configuration from the strategy"""
pass
@abstractmethod
def add_proxies(self, proxies: List[Dict]):
def add_proxies(self, proxies: List[ProxyConfig]):
"""Add proxy configurations to the strategy"""
pass

View File

@@ -0,0 +1,35 @@
"""
C4A-Script: A domain-specific language for web automation in Crawl4AI
"""
from .c4a_compile import C4ACompiler, compile, validate, compile_file
from .c4a_result import (
CompilationResult,
ValidationResult,
ErrorDetail,
WarningDetail,
ErrorType,
Severity,
Suggestion
)
__all__ = [
# Main compiler
"C4ACompiler",
# Convenience functions
"compile",
"validate",
"compile_file",
# Result types
"CompilationResult",
"ValidationResult",
"ErrorDetail",
"WarningDetail",
# Enums
"ErrorType",
"Severity",
"Suggestion"
]

View File

@@ -0,0 +1,398 @@
"""
Clean C4A-Script API with Result pattern
No exceptions - always returns results
"""
from __future__ import annotations
import pathlib
import re
from typing import Union, List, Optional
# JSON_SCHEMA_BUILDER is still used elsewhere,
# but we now also need the new script-builder prompt.
from ..prompts import GENERATE_JS_SCRIPT_PROMPT, GENERATE_SCRIPT_PROMPT
import logging
import re
from .c4a_result import (
CompilationResult, ValidationResult, ErrorDetail, WarningDetail,
ErrorType, Severity, Suggestion
)
from .c4ai_script import Compiler
from lark.exceptions import UnexpectedToken, UnexpectedCharacters, VisitError
from ..async_configs import LLMConfig
from ..utils import perform_completion_with_backoff
class C4ACompiler:
"""Main compiler with result-based API"""
# Error code mapping
ERROR_CODES = {
"missing_then": "E001",
"missing_paren": "E002",
"missing_comma": "E003",
"missing_endproc": "E004",
"undefined_proc": "E005",
"missing_backticks": "E006",
"invalid_command": "E007",
"syntax_error": "E999"
}
@classmethod
def compile(cls, script: Union[str, List[str]], root: Optional[pathlib.Path] = None) -> CompilationResult:
"""
Compile C4A-Script to JavaScript
Args:
script: C4A-Script as string or list of lines
root: Root directory for includes
Returns:
CompilationResult with success status and JS code or errors
"""
# Normalize input
if isinstance(script, list):
script_text = '\n'.join(script)
script_lines = script
else:
script_text = script
script_lines = script.split('\n')
try:
# Try compilation
compiler = Compiler(root)
js_code = compiler.compile(script_text)
# Success!
result = CompilationResult(
success=True,
js_code=js_code,
metadata={
"lineCount": len(script_lines),
"statementCount": len(js_code)
}
)
# Add any warnings (future feature)
# result.warnings = cls._check_warnings(script_text)
return result
except Exception as e:
# Convert exception to ErrorDetail
error = cls._exception_to_error(e, script_lines)
return CompilationResult(
success=False,
errors=[error],
metadata={
"lineCount": len(script_lines)
}
)
@classmethod
def validate(cls, script: Union[str, List[str]]) -> ValidationResult:
"""
Validate script syntax without generating code
Args:
script: C4A-Script to validate
Returns:
ValidationResult with validity status and any errors
"""
result = cls.compile(script)
return ValidationResult(
valid=result.success,
errors=result.errors,
warnings=result.warnings
)
@classmethod
def compile_file(cls, path: Union[str, pathlib.Path]) -> CompilationResult:
"""
Compile a C4A-Script file
Args:
path: Path to the file
Returns:
CompilationResult
"""
path = pathlib.Path(path)
if not path.exists():
error = ErrorDetail(
type=ErrorType.RUNTIME,
code="E100",
severity=Severity.ERROR,
message=f"File not found: {path}",
line=0,
column=0,
source_line=""
)
return CompilationResult(success=False, errors=[error])
try:
script = path.read_text()
return cls.compile(script, root=path.parent)
except Exception as e:
error = ErrorDetail(
type=ErrorType.RUNTIME,
code="E101",
severity=Severity.ERROR,
message=f"Error reading file: {str(e)}",
line=0,
column=0,
source_line=""
)
return CompilationResult(success=False, errors=[error])
@classmethod
def _exception_to_error(cls, exc: Exception, script_lines: List[str]) -> ErrorDetail:
"""Convert an exception to ErrorDetail"""
if isinstance(exc, UnexpectedToken):
return cls._handle_unexpected_token(exc, script_lines)
elif isinstance(exc, UnexpectedCharacters):
return cls._handle_unexpected_chars(exc, script_lines)
elif isinstance(exc, ValueError):
return cls._handle_value_error(exc, script_lines)
else:
# Generic error
return ErrorDetail(
type=ErrorType.SYNTAX,
code=cls.ERROR_CODES["syntax_error"],
severity=Severity.ERROR,
message=str(exc),
line=1,
column=1,
source_line=script_lines[0] if script_lines else ""
)
@classmethod
def _handle_unexpected_token(cls, exc: UnexpectedToken, script_lines: List[str]) -> ErrorDetail:
"""Handle UnexpectedToken errors"""
line = exc.line
column = exc.column
# Get context lines
source_line = script_lines[line - 1] if 0 < line <= len(script_lines) else ""
line_before = script_lines[line - 2] if line > 1 and line <= len(script_lines) + 1 else None
line_after = script_lines[line] if 0 < line < len(script_lines) else None
# Determine error type and suggestions
if exc.token.type == 'CLICK' and 'THEN' in str(exc.expected):
code = cls.ERROR_CODES["missing_then"]
message = "Missing 'THEN' keyword after IF condition"
suggestions = [
Suggestion(
"Add 'THEN' after the condition",
source_line.replace("CLICK", "THEN CLICK") if source_line else None
)
]
elif exc.token.type == '$END':
code = cls.ERROR_CODES["missing_endproc"]
message = "Unexpected end of script"
suggestions = [
Suggestion("Check for missing ENDPROC"),
Suggestion("Ensure all procedures are properly closed")
]
elif 'RPAR' in str(exc.expected):
code = cls.ERROR_CODES["missing_paren"]
message = "Missing closing parenthesis ')'"
suggestions = [
Suggestion("Add closing parenthesis at the end of the condition")
]
elif 'COMMA' in str(exc.expected):
code = cls.ERROR_CODES["missing_comma"]
message = "Missing comma ',' in command"
suggestions = [
Suggestion("Add comma between arguments")
]
else:
# Check if this might be missing backticks
if exc.token.type == 'NAME' and 'BACKTICK_STRING' in str(exc.expected):
code = cls.ERROR_CODES["missing_backticks"]
message = "Selector must be wrapped in backticks"
suggestions = [
Suggestion(
"Wrap the selector in backticks",
f"`{exc.token.value}`"
)
]
else:
code = cls.ERROR_CODES["syntax_error"]
message = f"Unexpected '{exc.token.value}'"
if exc.expected:
expected_list = [str(e) for e in exc.expected if not str(e).startswith('_')][:3]
if expected_list:
message += f". Expected: {', '.join(expected_list)}"
suggestions = []
return ErrorDetail(
type=ErrorType.SYNTAX,
code=code,
severity=Severity.ERROR,
message=message,
line=line,
column=column,
source_line=source_line,
line_before=line_before,
line_after=line_after,
suggestions=suggestions
)
@classmethod
def _handle_unexpected_chars(cls, exc: UnexpectedCharacters, script_lines: List[str]) -> ErrorDetail:
"""Handle UnexpectedCharacters errors"""
line = exc.line
column = exc.column
source_line = script_lines[line - 1] if 0 < line <= len(script_lines) else ""
# Check for missing backticks
if "CLICK" in source_line and column > source_line.find("CLICK"):
code = cls.ERROR_CODES["missing_backticks"]
message = "Selector must be wrapped in backticks"
suggestions = [
Suggestion(
"Wrap the selector in backticks",
re.sub(r'CLICK\s+([^\s]+)', r'CLICK `\1`', source_line)
)
]
else:
code = cls.ERROR_CODES["syntax_error"]
message = f"Invalid character at position {column}"
suggestions = []
return ErrorDetail(
type=ErrorType.SYNTAX,
code=code,
severity=Severity.ERROR,
message=message,
line=line,
column=column,
source_line=source_line,
suggestions=suggestions
)
@classmethod
def _handle_value_error(cls, exc: ValueError, script_lines: List[str]) -> ErrorDetail:
"""Handle ValueError (runtime errors)"""
message = str(exc)
# Check for undefined procedure
if "Unknown procedure" in message:
proc_match = re.search(r"'([^']+)'", message)
if proc_match:
proc_name = proc_match.group(1)
# Find the line with the procedure call
for i, line in enumerate(script_lines):
if proc_name in line and not line.strip().startswith('PROC'):
return ErrorDetail(
type=ErrorType.RUNTIME,
code=cls.ERROR_CODES["undefined_proc"],
severity=Severity.ERROR,
message=f"Undefined procedure '{proc_name}'",
line=i + 1,
column=line.find(proc_name) + 1,
source_line=line,
suggestions=[
Suggestion(
f"Define the procedure before using it",
f"PROC {proc_name}\n # commands here\nENDPROC"
)
]
)
# Generic runtime error
return ErrorDetail(
type=ErrorType.RUNTIME,
code="E999",
severity=Severity.ERROR,
message=message,
line=1,
column=1,
source_line=script_lines[0] if script_lines else ""
)
@staticmethod
def generate_script(
html: str,
query: str | None = None,
mode: str = "c4a",
llm_config: LLMConfig | None = None,
**completion_kwargs,
) -> str:
"""
One-shot helper that calls the LLM exactly once to convert a
natural-language goal + HTML snippet into either:
1. raw JavaScript (`mode="js"`)
2. Crawl4ai DSL (`mode="c4a"`)
The returned string is guaranteed to be free of markdown wrappers
or explanatory text, ready for direct execution.
"""
if llm_config is None:
llm_config = LLMConfig() # falls back to env vars / defaults
# Build the user chunk
user_prompt = "\n".join(
[
"## GOAL",
"<<goael>>",
(query or "Prepare the page for crawling."),
"<</goal>>",
"",
"## HTML",
"<<html>>",
html[:100000], # guardrail against token blast
"<</html>>",
"",
"## MODE",
mode,
]
)
# Call the LLM with retry/back-off logic
full_prompt = f"{GENERATE_SCRIPT_PROMPT}\n\n{user_prompt}" if mode == "c4a" else f"{GENERATE_JS_SCRIPT_PROMPT}\n\n{user_prompt}"
response = perform_completion_with_backoff(
provider=llm_config.provider,
prompt_with_variables=full_prompt,
api_token=llm_config.api_token,
json_response=False,
base_url=getattr(llm_config, 'base_url', None),
**completion_kwargs,
)
# Extract content from the response
raw_response = response.choices[0].message.content.strip()
# Strip accidental markdown fences (```js … ```)
clean = re.sub(r"^```(?:[a-zA-Z0-9_-]+)?\s*|```$", "", raw_response, flags=re.MULTILINE).strip()
if not clean:
raise RuntimeError("LLM returned empty script.")
return clean
# Convenience functions for direct use
def compile(script: Union[str, List[str]], root: Optional[pathlib.Path] = None) -> CompilationResult:
"""Compile C4A-Script to JavaScript"""
return C4ACompiler.compile(script, root)
def validate(script: Union[str, List[str]]) -> ValidationResult:
"""Validate C4A-Script syntax"""
return C4ACompiler.validate(script)
def compile_file(path: Union[str, pathlib.Path]) -> CompilationResult:
"""Compile C4A-Script file"""
return C4ACompiler.compile_file(path)

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"""
Result classes for C4A-Script compilation
Clean API design with no exceptions
"""
from __future__ import annotations
from dataclasses import dataclass, field
from enum import Enum
from typing import List, Dict, Any, Optional
import json
class ErrorType(Enum):
SYNTAX = "syntax"
SEMANTIC = "semantic"
RUNTIME = "runtime"
class Severity(Enum):
ERROR = "error"
WARNING = "warning"
INFO = "info"
@dataclass
class Suggestion:
"""A suggestion for fixing an error"""
message: str
fix: Optional[str] = None
def to_dict(self) -> dict:
return {
"message": self.message,
"fix": self.fix
}
@dataclass
class ErrorDetail:
"""Detailed information about a compilation error"""
# Core info
type: ErrorType
code: str # E001, E002, etc.
severity: Severity
message: str
# Location
line: int
column: int
# Context
source_line: str
# Optional fields with defaults
end_line: Optional[int] = None
end_column: Optional[int] = None
line_before: Optional[str] = None
line_after: Optional[str] = None
# Help
suggestions: List[Suggestion] = field(default_factory=list)
documentation_url: Optional[str] = None
def to_dict(self) -> dict:
"""Convert to dictionary for JSON serialization"""
return {
"type": self.type.value,
"code": self.code,
"severity": self.severity.value,
"message": self.message,
"location": {
"line": self.line,
"column": self.column,
"endLine": self.end_line,
"endColumn": self.end_column
},
"context": {
"sourceLine": self.source_line,
"lineBefore": self.line_before,
"lineAfter": self.line_after,
"marker": {
"start": self.column - 1,
"length": (self.end_column - self.column) if self.end_column else 1
}
},
"suggestions": [s.to_dict() for s in self.suggestions],
"documentationUrl": self.documentation_url
}
def to_json(self) -> str:
"""Convert to JSON string"""
return json.dumps(self.to_dict(), indent=2)
@property
def formatted_message(self) -> str:
"""Returns the nice text format for terminals"""
lines = []
lines.append(f"\n{'='*60}")
lines.append(f"{self.type.value.title()} Error [{self.code}]")
lines.append(f"{'='*60}")
lines.append(f"Location: Line {self.line}, Column {self.column}")
lines.append(f"Error: {self.message}")
if self.source_line:
marker = " " * (self.column - 1) + "^"
if self.end_column:
marker += "~" * (self.end_column - self.column - 1)
lines.append(f"\nCode:")
if self.line_before:
lines.append(f" {self.line - 1: >3} | {self.line_before}")
lines.append(f" {self.line: >3} | {self.source_line}")
lines.append(f" | {marker}")
if self.line_after:
lines.append(f" {self.line + 1: >3} | {self.line_after}")
if self.suggestions:
lines.append("\nSuggestions:")
for i, suggestion in enumerate(self.suggestions, 1):
lines.append(f" {i}. {suggestion.message}")
if suggestion.fix:
lines.append(f" Fix: {suggestion.fix}")
lines.append("="*60)
return "\n".join(lines)
@property
def simple_message(self) -> str:
"""Returns just the error message without formatting"""
return f"Line {self.line}: {self.message}"
@dataclass
class WarningDetail:
"""Information about a compilation warning"""
code: str
message: str
line: int
column: int
def to_dict(self) -> dict:
return {
"code": self.code,
"message": self.message,
"line": self.line,
"column": self.column
}
@dataclass
class CompilationResult:
"""Result of C4A-Script compilation"""
success: bool
js_code: Optional[List[str]] = None
errors: List[ErrorDetail] = field(default_factory=list)
warnings: List[WarningDetail] = field(default_factory=list)
metadata: Dict[str, Any] = field(default_factory=dict)
def to_dict(self) -> dict:
"""Convert to dictionary for JSON serialization"""
return {
"success": self.success,
"jsCode": self.js_code,
"errors": [e.to_dict() for e in self.errors],
"warnings": [w.to_dict() for w in self.warnings],
"metadata": self.metadata
}
def to_json(self) -> str:
"""Convert to JSON string"""
return json.dumps(self.to_dict(), indent=2)
@property
def has_errors(self) -> bool:
"""Check if there are any errors"""
return len(self.errors) > 0
@property
def has_warnings(self) -> bool:
"""Check if there are any warnings"""
return len(self.warnings) > 0
@property
def first_error(self) -> Optional[ErrorDetail]:
"""Get the first error if any"""
return self.errors[0] if self.errors else None
def __str__(self) -> str:
"""String representation for debugging"""
if self.success:
msg = f"✓ Compilation successful"
if self.js_code:
msg += f" - {len(self.js_code)} statements generated"
if self.warnings:
msg += f" ({len(self.warnings)} warnings)"
return msg
else:
return f"✗ Compilation failed - {len(self.errors)} error(s)"
@dataclass
class ValidationResult:
"""Result of script validation"""
valid: bool
errors: List[ErrorDetail] = field(default_factory=list)
warnings: List[WarningDetail] = field(default_factory=list)
def to_dict(self) -> dict:
return {
"valid": self.valid,
"errors": [e.to_dict() for e in self.errors],
"warnings": [w.to_dict() for w in self.warnings]
}
def to_json(self) -> str:
return json.dumps(self.to_dict(), indent=2)
@property
def first_error(self) -> Optional[ErrorDetail]:
return self.errors[0] if self.errors else None

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@@ -0,0 +1,690 @@
"""
2025-06-03
By Unclcode:
C4A-Script Language Documentation
Feeds Crawl4AI via CrawlerRunConfig(js_code=[ ... ]) no core modifications.
"""
from __future__ import annotations
import pathlib, re, sys, textwrap
from dataclasses import dataclass
from typing import Any, Dict, List, Union
from lark import Lark, Transformer, v_args
from lark.exceptions import UnexpectedToken, UnexpectedCharacters, VisitError
# --------------------------------------------------------------------------- #
# Custom Error Classes
# --------------------------------------------------------------------------- #
class C4AScriptError(Exception):
"""Custom error class for C4A-Script compilation errors"""
def __init__(self, message: str, line: int = None, column: int = None,
error_type: str = "Syntax Error", details: str = None):
self.message = message
self.line = line
self.column = column
self.error_type = error_type
self.details = details
super().__init__(self._format_message())
def _format_message(self) -> str:
"""Format a clear error message"""
lines = [f"\n{'='*60}"]
lines.append(f"C4A-Script {self.error_type}")
lines.append(f"{'='*60}")
if self.line:
lines.append(f"Location: Line {self.line}" + (f", Column {self.column}" if self.column else ""))
lines.append(f"Error: {self.message}")
if self.details:
lines.append(f"\nDetails: {self.details}")
lines.append("="*60)
return "\n".join(lines)
@classmethod
def from_exception(cls, exc: Exception, script: Union[str, List[str]]) -> 'C4AScriptError':
"""Create C4AScriptError from another exception"""
script_text = script if isinstance(script, str) else '\n'.join(script)
script_lines = script_text.split('\n')
if isinstance(exc, UnexpectedToken):
# Extract line and column from UnexpectedToken
line = exc.line
column = exc.column
# Get the problematic line
if 0 < line <= len(script_lines):
problem_line = script_lines[line - 1]
marker = " " * (column - 1) + "^"
details = f"\nCode:\n {problem_line}\n {marker}\n"
# Improve error message based on context
if exc.token.type == 'CLICK' and 'THEN' in str(exc.expected):
message = "Missing 'THEN' keyword after IF condition"
elif exc.token.type == '$END':
message = "Unexpected end of script. Check for missing ENDPROC or incomplete commands"
elif 'RPAR' in str(exc.expected):
message = "Missing closing parenthesis ')'"
elif 'COMMA' in str(exc.expected):
message = "Missing comma ',' in command"
else:
message = f"Unexpected '{exc.token}'"
if exc.expected:
expected_list = [str(e) for e in exc.expected if not e.startswith('_')]
if expected_list:
message += f". Expected: {', '.join(expected_list[:3])}"
details += f"Token: {exc.token.type} ('{exc.token.value}')"
else:
message = str(exc)
details = None
return cls(message, line, column, "Syntax Error", details)
elif isinstance(exc, UnexpectedCharacters):
# Extract line and column
line = exc.line
column = exc.column
if 0 < line <= len(script_lines):
problem_line = script_lines[line - 1]
marker = " " * (column - 1) + "^"
details = f"\nCode:\n {problem_line}\n {marker}\n"
message = f"Invalid character or unexpected text at position {column}"
else:
message = str(exc)
details = None
return cls(message, line, column, "Syntax Error", details)
elif isinstance(exc, ValueError):
# Handle runtime errors like undefined procedures
message = str(exc)
# Try to find which line caused the error
if "Unknown procedure" in message:
proc_name = re.search(r"'([^']+)'", message)
if proc_name:
proc_name = proc_name.group(1)
for i, line in enumerate(script_lines, 1):
if proc_name in line and not line.strip().startswith('PROC'):
details = f"\nCode:\n {line.strip()}\n\nMake sure the procedure '{proc_name}' is defined with PROC...ENDPROC"
return cls(f"Undefined procedure '{proc_name}'", i, None, "Runtime Error", details)
return cls(message, None, None, "Runtime Error", None)
else:
# Generic error
return cls(str(exc), None, None, "Compilation Error", None)
# --------------------------------------------------------------------------- #
# 1. Grammar
# --------------------------------------------------------------------------- #
GRAMMAR = r"""
start : line*
?line : command | proc_def | include | comment
command : wait | nav | click_cmd | double_click | right_click | move | drag | scroll
| type | clear | set_input | press | key_down | key_up
| eval_cmd | setvar | proc_call | if_cmd | repeat_cmd
wait : "WAIT" (ESCAPED_STRING|BACKTICK_STRING|NUMBER) NUMBER? -> wait_cmd
nav : "GO" URL -> go
| "RELOAD" -> reload
| "BACK" -> back
| "FORWARD" -> forward
click_cmd : "CLICK" (BACKTICK_STRING|NUMBER NUMBER) -> click
double_click : "DOUBLE_CLICK" (BACKTICK_STRING|NUMBER NUMBER) -> double_click
right_click : "RIGHT_CLICK" (BACKTICK_STRING|NUMBER NUMBER) -> right_click
move : "MOVE" coords -> move
drag : "DRAG" coords coords -> drag
scroll : "SCROLL" DIR NUMBER? -> scroll
type : "TYPE" (ESCAPED_STRING | NAME) -> type
clear : "CLEAR" BACKTICK_STRING -> clear
set_input : "SET" BACKTICK_STRING (ESCAPED_STRING | BACKTICK_STRING | NAME) -> set_input
press : "PRESS" WORD -> press
key_down : "KEY_DOWN" WORD -> key_down
key_up : "KEY_UP" WORD -> key_up
eval_cmd : "EVAL" BACKTICK_STRING -> eval_cmd
setvar : "SETVAR" NAME "=" value -> setvar
proc_call : NAME -> proc_call
proc_def : "PROC" NAME line* "ENDPROC" -> proc_def
include : "USE" ESCAPED_STRING -> include
comment : /#.*/ -> comment
if_cmd : "IF" "(" condition ")" "THEN" command ("ELSE" command)? -> if_cmd
repeat_cmd : "REPEAT" "(" command "," repeat_count ")" -> repeat_cmd
condition : not_cond | exists_cond | js_cond
not_cond : "NOT" condition -> not_cond
exists_cond : "EXISTS" BACKTICK_STRING -> exists_cond
js_cond : BACKTICK_STRING -> js_cond
repeat_count : NUMBER | BACKTICK_STRING
coords : NUMBER NUMBER
value : ESCAPED_STRING | BACKTICK_STRING | NUMBER
DIR : /(UP|DOWN|LEFT|RIGHT)/i
REST : /[^\n]+/
URL : /(http|https):\/\/[^\s]+/
NAME : /\$?[A-Za-z_][A-Za-z0-9_]*/
WORD : /[A-Za-z0-9+]+/
BACKTICK_STRING : /`[^`]*`/
%import common.NUMBER
%import common.ESCAPED_STRING
%import common.WS_INLINE
%import common.NEWLINE
%ignore WS_INLINE
%ignore NEWLINE
"""
# --------------------------------------------------------------------------- #
# 2. IR dataclasses
# --------------------------------------------------------------------------- #
@dataclass
class Cmd:
op: str
args: List[Any]
@dataclass
class Proc:
name: str
body: List[Cmd]
# --------------------------------------------------------------------------- #
# 3. AST → IR
# --------------------------------------------------------------------------- #
@v_args(inline=True)
class ASTBuilder(Transformer):
# helpers
def _strip(self, s):
if s.startswith('"') and s.endswith('"'):
return s[1:-1]
elif s.startswith('`') and s.endswith('`'):
return s[1:-1]
return s
def start(self,*i): return list(i)
def line(self,i): return i
def command(self,i): return i
# WAIT
def wait_cmd(self, rest, timeout=None):
rest_str = str(rest)
# Check if it's a number (including floats)
try:
num_val = float(rest_str)
payload = (num_val, "seconds")
except ValueError:
if rest_str.startswith('"') and rest_str.endswith('"'):
payload = (self._strip(rest_str), "text")
elif rest_str.startswith('`') and rest_str.endswith('`'):
payload = (self._strip(rest_str), "selector")
else:
payload = (rest_str, "selector")
return Cmd("WAIT", [payload, int(timeout) if timeout else None])
# NAV
def go(self,u): return Cmd("GO",[str(u)])
def reload(self): return Cmd("RELOAD",[])
def back(self): return Cmd("BACK",[])
def forward(self): return Cmd("FORWARD",[])
# CLICK, DOUBLE_CLICK, RIGHT_CLICK
def click(self, *args):
return self._handle_click("CLICK", args)
def double_click(self, *args):
return self._handle_click("DBLCLICK", args)
def right_click(self, *args):
return self._handle_click("RIGHTCLICK", args)
def _handle_click(self, op, args):
if len(args) == 1:
# Single argument - backtick string
target = self._strip(str(args[0]))
return Cmd(op, [("selector", target)])
else:
# Two arguments - coordinates
x, y = args
return Cmd(op, [("coords", int(x), int(y))])
# MOVE / DRAG / SCROLL
def coords(self,x,y): return ("coords",int(x),int(y))
def move(self,c): return Cmd("MOVE",[c])
def drag(self,c1,c2): return Cmd("DRAG",[c1,c2])
def scroll(self,dir_tok,amt=None):
return Cmd("SCROLL",[dir_tok.upper(), int(amt) if amt else 500])
# KEYS
def type(self,tok): return Cmd("TYPE",[self._strip(str(tok))])
def clear(self,sel): return Cmd("CLEAR",[self._strip(str(sel))])
def set_input(self,sel,val): return Cmd("SET",[self._strip(str(sel)), self._strip(str(val))])
def press(self,w): return Cmd("PRESS",[str(w)])
def key_down(self,w): return Cmd("KEYDOWN",[str(w)])
def key_up(self,w): return Cmd("KEYUP",[str(w)])
# FLOW
def eval_cmd(self,txt): return Cmd("EVAL",[self._strip(str(txt))])
def setvar(self,n,v):
# v might be a Token or a Tree, extract value properly
if hasattr(v, 'value'):
value = v.value
elif hasattr(v, 'children') and len(v.children) > 0:
value = v.children[0].value
else:
value = str(v)
return Cmd("SETVAR",[str(n), self._strip(value)])
def proc_call(self,n): return Cmd("CALL",[str(n)])
def proc_def(self,n,*body): return Proc(str(n),[b for b in body if isinstance(b,Cmd)])
def include(self,p): return Cmd("INCLUDE",[self._strip(p)])
def comment(self,*_): return Cmd("NOP",[])
# IF-THEN-ELSE and EXISTS
def if_cmd(self, condition, then_cmd, else_cmd=None):
return Cmd("IF", [condition, then_cmd, else_cmd])
def condition(self, cond):
return cond
def not_cond(self, cond):
return ("NOT", cond)
def exists_cond(self, selector):
return ("EXISTS", self._strip(str(selector)))
def js_cond(self, expr):
return ("JS", self._strip(str(expr)))
# REPEAT
def repeat_cmd(self, cmd, count):
return Cmd("REPEAT", [cmd, count])
def repeat_count(self, value):
return str(value)
# --------------------------------------------------------------------------- #
# 4. Compiler
# --------------------------------------------------------------------------- #
class Compiler:
def __init__(self, root: pathlib.Path|None=None):
self.parser = Lark(GRAMMAR,start="start",parser="lalr")
self.root = pathlib.Path(root or ".").resolve()
self.vars: Dict[str,Any] = {}
self.procs: Dict[str,Proc]= {}
def compile(self, text: Union[str, List[str]]) -> List[str]:
# Handle list input by joining with newlines
if isinstance(text, list):
text = '\n'.join(text)
ir = self._parse_with_includes(text)
ir = self._collect_procs(ir)
ir = self._inline_calls(ir)
ir = self._apply_set_vars(ir)
return [self._emit_js(c) for c in ir if isinstance(c,Cmd) and c.op!="NOP"]
# passes
def _parse_with_includes(self,txt,seen=None):
seen=seen or set()
cmds=ASTBuilder().transform(self.parser.parse(txt))
out=[]
for c in cmds:
if isinstance(c,Cmd) and c.op=="INCLUDE":
p=(self.root/c.args[0]).resolve()
if p in seen: raise ValueError(f"Circular include {p}")
seen.add(p); out+=self._parse_with_includes(p.read_text(),seen)
else: out.append(c)
return out
def _collect_procs(self,ir):
out=[]
for i in ir:
if isinstance(i,Proc): self.procs[i.name]=i
else: out.append(i)
return out
def _inline_calls(self,ir):
out=[]
for c in ir:
if isinstance(c,Cmd) and c.op=="CALL":
if c.args[0] not in self.procs:
raise ValueError(f"Unknown procedure {c.args[0]!r}")
out+=self._inline_calls(self.procs[c.args[0]].body)
else: out.append(c)
return out
def _apply_set_vars(self,ir):
def sub(s): return re.sub(r"\$(\w+)",lambda m:str(self.vars.get(m.group(1),m.group(0))) ,s) if isinstance(s,str) else s
out=[]
for c in ir:
if isinstance(c,Cmd):
if c.op=="SETVAR":
# Store variable
self.vars[c.args[0].lstrip('$')]=c.args[1]
else:
# Apply variable substitution to commands that use them
if c.op in("TYPE","EVAL","SET"): c.args=[sub(a) for a in c.args]
out.append(c)
return out
# JS emitter
def _emit_js(self, cmd: Cmd) -> str:
op, a = cmd.op, cmd.args
if op == "GO": return f"window.location.href = '{a[0]}';"
if op == "RELOAD": return "window.location.reload();"
if op == "BACK": return "window.history.back();"
if op == "FORWARD": return "window.history.forward();"
if op == "WAIT":
arg, kind = a[0]
timeout = a[1] or 10
if kind == "seconds":
return f"await new Promise(r=>setTimeout(r,{arg}*1000));"
if kind == "selector":
sel = arg.replace("\\","\\\\").replace("'","\\'")
return textwrap.dedent(f"""
await new Promise((res,rej)=>{{
const max = {timeout*1000}, t0 = performance.now();
const id = setInterval(()=>{{
if(document.querySelector('{sel}')){{clearInterval(id);res();}}
else if(performance.now()-t0>max){{clearInterval(id);rej('WAIT selector timeout');}}
}},100);
}});
""").strip()
if kind == "text":
txt = arg.replace('`', '\\`')
return textwrap.dedent(f"""
await new Promise((res,rej)=>{{
const max={timeout*1000},t0=performance.now();
const id=setInterval(()=>{{
if(document.body.innerText.includes(`{txt}`)){{clearInterval(id);res();}}
else if(performance.now()-t0>max){{clearInterval(id);rej('WAIT text timeout');}}
}},100);
}});
""").strip()
# click-style helpers
def _js_click(sel, evt="click", button=0, detail=1):
sel = sel.replace("'", "\\'")
return textwrap.dedent(f"""
(()=>{{
const el=document.querySelector('{sel}');
if(el){{
el.focus&&el.focus();
el.dispatchEvent(new MouseEvent('{evt}',{{bubbles:true,button:{button},detail:{detail}}}));
}}
}})();
""").strip()
def _js_click_xy(x, y, evt="click", button=0, detail=1):
return textwrap.dedent(f"""
(()=>{{
const el=document.elementFromPoint({x},{y});
if(el){{
el.focus&&el.focus();
el.dispatchEvent(new MouseEvent('{evt}',{{bubbles:true,button:{button},detail:{detail}}}));
}}
}})();
""").strip()
if op in ("CLICK", "DBLCLICK", "RIGHTCLICK"):
evt = {"CLICK":"click","DBLCLICK":"dblclick","RIGHTCLICK":"contextmenu"}[op]
btn = 2 if op=="RIGHTCLICK" else 0
det = 2 if op=="DBLCLICK" else 1
kind,*rest = a[0]
return _js_click_xy(*rest) if kind=="coords" else _js_click(rest[0],evt,btn,det)
if op == "MOVE":
_, x, y = a[0]
return textwrap.dedent(f"""
document.dispatchEvent(new MouseEvent('mousemove',{{clientX:{x},clientY:{y},bubbles:true}}));
""").strip()
if op == "DRAG":
(_, x1, y1), (_, x2, y2) = a
return textwrap.dedent(f"""
(()=>{{
const s=document.elementFromPoint({x1},{y1});
if(!s) return;
s.dispatchEvent(new MouseEvent('mousedown',{{bubbles:true,clientX:{x1},clientY:{y1}}}));
document.dispatchEvent(new MouseEvent('mousemove',{{bubbles:true,clientX:{x2},clientY:{y2}}}));
document.dispatchEvent(new MouseEvent('mouseup', {{bubbles:true,clientX:{x2},clientY:{y2}}}));
}})();
""").strip()
if op == "SCROLL":
dir_, amt = a
dx, dy = {"UP":(0,-amt),"DOWN":(0,amt),"LEFT":(-amt,0),"RIGHT":(amt,0)}[dir_]
return f"window.scrollBy({dx},{dy});"
if op == "TYPE":
txt = a[0].replace("'", "\\'")
return textwrap.dedent(f"""
(()=>{{
const el=document.activeElement;
if(el){{
el.value += '{txt}';
el.dispatchEvent(new Event('input',{{bubbles:true}}));
}}
}})();
""").strip()
if op == "CLEAR":
sel = a[0].replace("'", "\\'")
return textwrap.dedent(f"""
(()=>{{
const el=document.querySelector('{sel}');
if(el && 'value' in el){{
el.value = '';
el.dispatchEvent(new Event('input',{{bubbles:true}}));
el.dispatchEvent(new Event('change',{{bubbles:true}}));
}}
}})();
""").strip()
if op == "SET" and len(a) == 2:
# This is SET for input fields (SET `#field` "value")
sel = a[0].replace("'", "\\'")
val = a[1].replace("'", "\\'")
return textwrap.dedent(f"""
(()=>{{
const el=document.querySelector('{sel}');
if(el && 'value' in el){{
el.value = '';
el.focus&&el.focus();
el.value = '{val}';
el.dispatchEvent(new Event('input',{{bubbles:true}}));
el.dispatchEvent(new Event('change',{{bubbles:true}}));
}}
}})();
""").strip()
if op in ("PRESS","KEYDOWN","KEYUP"):
key = a[0]
evs = {"PRESS":("keydown","keyup"),"KEYDOWN":("keydown",),"KEYUP":("keyup",)}[op]
return ";".join([f"document.dispatchEvent(new KeyboardEvent('{e}',{{key:'{key}',bubbles:true}}))" for e in evs]) + ";"
if op == "EVAL":
return textwrap.dedent(f"""
(()=>{{
try {{
{a[0]};
}} catch (e) {{
console.error('C4A-Script EVAL error:', e);
}}
}})();
""").strip()
if op == "IF":
condition, then_cmd, else_cmd = a
# Generate condition JavaScript
js_condition = self._emit_condition(condition)
# Generate commands - handle both regular commands and procedure calls
then_js = self._handle_cmd_or_proc(then_cmd)
else_js = self._handle_cmd_or_proc(else_cmd) if else_cmd else ""
if else_cmd:
return textwrap.dedent(f"""
if ({js_condition}) {{
{then_js}
}} else {{
{else_js}
}}
""").strip()
else:
return textwrap.dedent(f"""
if ({js_condition}) {{
{then_js}
}}
""").strip()
if op == "REPEAT":
cmd, count = a
# Handle the count - could be number or JS expression
if count.isdigit():
# Simple number
repeat_js = self._handle_cmd_or_proc(cmd)
return textwrap.dedent(f"""
for (let _i = 0; _i < {count}; _i++) {{
{repeat_js}
}}
""").strip()
else:
# JS expression (from backticks)
count_expr = count[1:-1] if count.startswith('`') and count.endswith('`') else count
repeat_js = self._handle_cmd_or_proc(cmd)
return textwrap.dedent(f"""
(()=>{{
const _count = {count_expr};
if (typeof _count === 'number') {{
for (let _i = 0; _i < _count; _i++) {{
{repeat_js}
}}
}} else if (_count) {{
{repeat_js}
}}
}})();
""").strip()
raise ValueError(f"Unhandled op {op}")
def _emit_condition(self, condition):
"""Convert a condition tuple to JavaScript"""
cond_type = condition[0]
if cond_type == "EXISTS":
return f"!!document.querySelector('{condition[1]}')"
elif cond_type == "NOT":
# Recursively handle the negated condition
inner_condition = self._emit_condition(condition[1])
return f"!({inner_condition})"
else: # JS condition
return condition[1]
def _handle_cmd_or_proc(self, cmd):
"""Handle a command that might be a regular command or a procedure call"""
if not cmd:
return ""
if isinstance(cmd, Cmd):
if cmd.op == "CALL":
# Inline the procedure
if cmd.args[0] not in self.procs:
raise ValueError(f"Unknown procedure {cmd.args[0]!r}")
proc_body = self.procs[cmd.args[0]].body
return "\n".join([self._emit_js(c) for c in proc_body if c.op != "NOP"])
else:
return self._emit_js(cmd)
return ""
# --------------------------------------------------------------------------- #
# 5. Helpers + demo
# --------------------------------------------------------------------------- #
def compile_string(script: Union[str, List[str]], *, root: Union[pathlib.Path, None] = None) -> List[str]:
"""Compile C4A-Script from string or list of strings to JavaScript.
Args:
script: C4A-Script as a string or list of command strings
root: Root directory for resolving includes (optional)
Returns:
List of JavaScript command strings
Raises:
C4AScriptError: When compilation fails with detailed error information
"""
try:
return Compiler(root).compile(script)
except Exception as e:
# Wrap the error with better formatting
raise C4AScriptError.from_exception(e, script)
def compile_file(path: pathlib.Path) -> List[str]:
"""Compile C4A-Script from file to JavaScript.
Args:
path: Path to C4A-Script file
Returns:
List of JavaScript command strings
"""
return compile_string(path.read_text(), root=path.parent)
def compile_lines(lines: List[str], *, root: Union[pathlib.Path, None] = None) -> List[str]:
"""Compile C4A-Script from list of lines to JavaScript.
Args:
lines: List of C4A-Script command lines
root: Root directory for resolving includes (optional)
Returns:
List of JavaScript command strings
"""
return compile_string(lines, root=root)
DEMO = """
# quick sanity demo
PROC login
SET `input[name="username"]` $user
SET `input[name="password"]` $pass
CLICK `button.submit`
ENDPROC
SETVAR user = "tom@crawl4ai.com"
SETVAR pass = "hunter2"
GO https://example.com/login
WAIT `input[name="username"]` 10
login
WAIT 3
EVAL `console.log('logged in')`
"""
if __name__ == "__main__":
if len(sys.argv) == 2:
for js in compile_file(pathlib.Path(sys.argv[1])):
print(js)
else:
print("=== DEMO ===")
for js in compile_string(DEMO):
print(js)

View File

@@ -9,83 +9,44 @@ from urllib.parse import urlparse
import OpenSSL.crypto
from pathlib import Path
class SSLCertificate:
# === Inherit from dict ===
class SSLCertificate(dict):
"""
A class representing an SSL certificate with methods to export in various formats.
A class representing an SSL certificate, behaving like a dictionary
for direct JSON serialization. It stores the certificate information internally
and provides methods for export and property access.
Attributes:
cert_info (Dict[str, Any]): The certificate information.
Methods:
from_url(url: str, timeout: int = 10) -> Optional['SSLCertificate']: Create SSLCertificate instance from a URL.
from_file(file_path: str) -> Optional['SSLCertificate']: Create SSLCertificate instance from a file.
from_binary(binary_data: bytes) -> Optional['SSLCertificate']: Create SSLCertificate instance from binary data.
export_as_pem() -> str: Export the certificate as PEM format.
export_as_der() -> bytes: Export the certificate as DER format.
export_as_json() -> Dict[str, Any]: Export the certificate as JSON format.
export_as_text() -> str: Export the certificate as text format.
Inherits from dict, so instances are directly JSON serializable.
"""
# Use __slots__ for potential memory optimization if desired, though less common when inheriting dict
# __slots__ = ("_cert_info",) # If using slots, be careful with dict inheritance interaction
def __init__(self, cert_info: Dict[str, Any]):
self._cert_info = self._decode_cert_data(cert_info)
@staticmethod
def from_url(url: str, timeout: int = 10) -> Optional["SSLCertificate"]:
"""
Create SSLCertificate instance from a URL.
Initializes the SSLCertificate object.
Args:
url (str): URL of the website.
timeout (int): Timeout for the connection (default: 10).
Returns:
Optional[SSLCertificate]: SSLCertificate instance if successful, None otherwise.
cert_info (Dict[str, Any]): The raw certificate dictionary.
"""
try:
hostname = urlparse(url).netloc
if ":" in hostname:
hostname = hostname.split(":")[0]
# 1. Decode the data (handle bytes -> str)
decoded_info = self._decode_cert_data(cert_info)
context = ssl.create_default_context()
with socket.create_connection((hostname, 443), timeout=timeout) as sock:
with context.wrap_socket(sock, server_hostname=hostname) as ssock:
cert_binary = ssock.getpeercert(binary_form=True)
x509 = OpenSSL.crypto.load_certificate(
OpenSSL.crypto.FILETYPE_ASN1, cert_binary
)
# 2. Store the decoded info internally (optional but good practice)
# self._cert_info = decoded_info # You can keep this if methods rely on it
cert_info = {
"subject": dict(x509.get_subject().get_components()),
"issuer": dict(x509.get_issuer().get_components()),
"version": x509.get_version(),
"serial_number": hex(x509.get_serial_number()),
"not_before": x509.get_notBefore(),
"not_after": x509.get_notAfter(),
"fingerprint": x509.digest("sha256").hex(),
"signature_algorithm": x509.get_signature_algorithm(),
"raw_cert": base64.b64encode(cert_binary),
}
# Add extensions
extensions = []
for i in range(x509.get_extension_count()):
ext = x509.get_extension(i)
extensions.append(
{"name": ext.get_short_name(), "value": str(ext)}
)
cert_info["extensions"] = extensions
return SSLCertificate(cert_info)
except Exception:
return None
# 3. Initialize the dictionary part of the object with the decoded data
super().__init__(decoded_info)
@staticmethod
def _decode_cert_data(data: Any) -> Any:
"""Helper method to decode bytes in certificate data."""
if isinstance(data, bytes):
return data.decode("utf-8")
try:
# Try UTF-8 first, fallback to latin-1 for arbitrary bytes
return data.decode("utf-8")
except UnicodeDecodeError:
return data.decode("latin-1") # Or handle as needed, maybe hex representation
elif isinstance(data, dict):
return {
(
@@ -97,36 +58,119 @@ class SSLCertificate:
return [SSLCertificate._decode_cert_data(item) for item in data]
return data
@staticmethod
def from_url(url: str, timeout: int = 10) -> Optional["SSLCertificate"]:
"""
Create SSLCertificate instance from a URL. Fetches cert info and initializes.
(Fetching logic remains the same)
"""
cert_info_raw = None # Variable to hold the fetched dict
try:
hostname = urlparse(url).netloc
if ":" in hostname:
hostname = hostname.split(":")[0]
context = ssl.create_default_context()
# Set check_hostname to False and verify_mode to CERT_NONE temporarily
# for potentially problematic certificates during fetch, but parse the result regardless.
# context.check_hostname = False
# context.verify_mode = ssl.CERT_NONE
with socket.create_connection((hostname, 443), timeout=timeout) as sock:
with context.wrap_socket(sock, server_hostname=hostname) as ssock:
cert_binary = ssock.getpeercert(binary_form=True)
if not cert_binary:
print(f"Warning: No certificate returned for {hostname}")
return None
x509 = OpenSSL.crypto.load_certificate(
OpenSSL.crypto.FILETYPE_ASN1, cert_binary
)
# Create the dictionary directly
cert_info_raw = {
"subject": dict(x509.get_subject().get_components()),
"issuer": dict(x509.get_issuer().get_components()),
"version": x509.get_version(),
"serial_number": hex(x509.get_serial_number()),
"not_before": x509.get_notBefore(), # Keep as bytes initially, _decode handles it
"not_after": x509.get_notAfter(), # Keep as bytes initially
"fingerprint": x509.digest("sha256").hex(), # hex() is already string
"signature_algorithm": x509.get_signature_algorithm(), # Keep as bytes
"raw_cert": base64.b64encode(cert_binary), # Base64 is bytes, _decode handles it
}
# Add extensions
extensions = []
for i in range(x509.get_extension_count()):
ext = x509.get_extension(i)
# get_short_name() returns bytes, str(ext) handles value conversion
extensions.append(
{"name": ext.get_short_name(), "value": str(ext)}
)
cert_info_raw["extensions"] = extensions
except ssl.SSLCertVerificationError as e:
print(f"SSL Verification Error for {url}: {e}")
# Decide if you want to proceed or return None based on your needs
# You might try fetching without verification here if needed, but be cautious.
return None
except socket.gaierror:
print(f"Could not resolve hostname: {hostname}")
return None
except socket.timeout:
print(f"Connection timed out for {url}")
return None
except Exception as e:
print(f"Error fetching/processing certificate for {url}: {e}")
# Log the full error details if needed: logging.exception("Cert fetch error")
return None
# If successful, create the SSLCertificate instance from the dictionary
if cert_info_raw:
return SSLCertificate(cert_info_raw)
else:
return None
# --- Properties now access the dictionary items directly via self[] ---
@property
def issuer(self) -> Dict[str, str]:
return self.get("issuer", {}) # Use self.get for safety
@property
def subject(self) -> Dict[str, str]:
return self.get("subject", {})
@property
def valid_from(self) -> str:
return self.get("not_before", "")
@property
def valid_until(self) -> str:
return self.get("not_after", "")
@property
def fingerprint(self) -> str:
return self.get("fingerprint", "")
# --- Export methods can use `self` directly as it is the dict ---
def to_json(self, filepath: Optional[str] = None) -> Optional[str]:
"""
Export certificate as JSON.
Args:
filepath (Optional[str]): Path to save the JSON file (default: None).
Returns:
Optional[str]: JSON string if successful, None otherwise.
"""
json_str = json.dumps(self._cert_info, indent=2, ensure_ascii=False)
"""Export certificate as JSON."""
# `self` is already the dictionary we want to serialize
json_str = json.dumps(self, indent=2, ensure_ascii=False)
if filepath:
Path(filepath).write_text(json_str, encoding="utf-8")
return None
return json_str
def to_pem(self, filepath: Optional[str] = None) -> Optional[str]:
"""
Export certificate as PEM.
Args:
filepath (Optional[str]): Path to save the PEM file (default: None).
Returns:
Optional[str]: PEM string if successful, None otherwise.
"""
"""Export certificate as PEM."""
try:
# Decode the raw_cert (which should be string due to _decode)
raw_cert_bytes = base64.b64decode(self.get("raw_cert", ""))
x509 = OpenSSL.crypto.load_certificate(
OpenSSL.crypto.FILETYPE_ASN1,
base64.b64decode(self._cert_info["raw_cert"]),
OpenSSL.crypto.FILETYPE_ASN1, raw_cert_bytes
)
pem_data = OpenSSL.crypto.dump_certificate(
OpenSSL.crypto.FILETYPE_PEM, x509
@@ -136,49 +180,25 @@ class SSLCertificate:
Path(filepath).write_text(pem_data, encoding="utf-8")
return None
return pem_data
except Exception:
return None
except Exception as e:
print(f"Error converting to PEM: {e}")
return None
def to_der(self, filepath: Optional[str] = None) -> Optional[bytes]:
"""
Export certificate as DER.
Args:
filepath (Optional[str]): Path to save the DER file (default: None).
Returns:
Optional[bytes]: DER bytes if successful, None otherwise.
"""
"""Export certificate as DER."""
try:
der_data = base64.b64decode(self._cert_info["raw_cert"])
# Decode the raw_cert (which should be string due to _decode)
der_data = base64.b64decode(self.get("raw_cert", ""))
if filepath:
Path(filepath).write_bytes(der_data)
return None
return der_data
except Exception:
return None
except Exception as e:
print(f"Error converting to DER: {e}")
return None
@property
def issuer(self) -> Dict[str, str]:
"""Get certificate issuer information."""
return self._cert_info.get("issuer", {})
@property
def subject(self) -> Dict[str, str]:
"""Get certificate subject information."""
return self._cert_info.get("subject", {})
@property
def valid_from(self) -> str:
"""Get certificate validity start date."""
return self._cert_info.get("not_before", "")
@property
def valid_until(self) -> str:
"""Get certificate validity end date."""
return self._cert_info.get("not_after", "")
@property
def fingerprint(self) -> str:
"""Get certificate fingerprint."""
return self._cert_info.get("fingerprint", "")
# Optional: Add __repr__ for better debugging
def __repr__(self) -> str:
subject_cn = self.subject.get('CN', 'N/A')
issuer_cn = self.issuer.get('CN', 'N/A')
return f"<SSLCertificate Subject='{subject_cn}' Issuer='{issuer_cn}'>"

View File

@@ -10,17 +10,22 @@ CacheMode = Union['CacheModeType']
CrawlResult = Union['CrawlResultType']
CrawlerHub = Union['CrawlerHubType']
BrowserProfiler = Union['BrowserProfilerType']
# NEW: Add AsyncUrlSeederType
AsyncUrlSeeder = Union['AsyncUrlSeederType']
# Configuration types
BrowserConfig = Union['BrowserConfigType']
CrawlerRunConfig = Union['CrawlerRunConfigType']
HTTPCrawlerConfig = Union['HTTPCrawlerConfigType']
LLMConfig = Union['LLMConfigType']
# NEW: Add SeedingConfigType
SeedingConfig = Union['SeedingConfigType']
# Content scraping types
ContentScrapingStrategy = Union['ContentScrapingStrategyType']
WebScrapingStrategy = Union['WebScrapingStrategyType']
LXMLWebScrapingStrategy = Union['LXMLWebScrapingStrategyType']
# Backward compatibility alias
WebScrapingStrategy = Union['LXMLWebScrapingStrategyType']
# Proxy types
ProxyRotationStrategy = Union['ProxyRotationStrategyType']
@@ -94,6 +99,8 @@ if TYPE_CHECKING:
from .models import CrawlResult as CrawlResultType
from .hub import CrawlerHub as CrawlerHubType
from .browser_profiler import BrowserProfiler as BrowserProfilerType
# NEW: Import AsyncUrlSeeder for type checking
from .async_url_seeder import AsyncUrlSeeder as AsyncUrlSeederType
# Configuration imports
from .async_configs import (
@@ -101,12 +108,13 @@ if TYPE_CHECKING:
CrawlerRunConfig as CrawlerRunConfigType,
HTTPCrawlerConfig as HTTPCrawlerConfigType,
LLMConfig as LLMConfigType,
# NEW: Import SeedingConfig for type checking
SeedingConfig as SeedingConfigType,
)
# Content scraping imports
from .content_scraping_strategy import (
ContentScrapingStrategy as ContentScrapingStrategyType,
WebScrapingStrategy as WebScrapingStrategyType,
LXMLWebScrapingStrategy as LXMLWebScrapingStrategyType,
)
@@ -184,4 +192,4 @@ if TYPE_CHECKING:
def create_llm_config(*args, **kwargs) -> 'LLMConfigType':
from .async_configs import LLMConfig
return LLMConfig(*args, **kwargs)
return LLMConfig(*args, **kwargs)

View File

@@ -6,6 +6,7 @@ import html
import lxml
import re
import os
import subprocess
import platform
from .prompts import PROMPT_EXTRACT_BLOCKS
from array import array
@@ -20,7 +21,6 @@ from urllib.parse import urljoin
import requests
from requests.exceptions import InvalidSchema
import xxhash
from colorama import Fore, Style, init
import textwrap
import cProfile
import pstats
@@ -32,7 +32,6 @@ import hashlib
from urllib.robotparser import RobotFileParser
import aiohttp
from urllib.parse import urlparse, urlunparse
from functools import lru_cache
from packaging import version
@@ -43,6 +42,37 @@ from itertools import chain
from collections import deque
from typing import Generator, Iterable
import numpy as np
from urllib.parse import (
urljoin, urlparse, urlunparse,
parse_qsl, urlencode, quote, unquote
)
# Monkey patch to fix wildcard handling in urllib.robotparser
from urllib.robotparser import RuleLine
import re
original_applies_to = RuleLine.applies_to
def patched_applies_to(self, filename):
# Handle wildcards in paths
if '*' in self.path or '%2A' in self.path or self.path in ("*", "%2A"):
pattern = self.path.replace('%2A', '*')
pattern = re.escape(pattern).replace('\\*', '.*')
pattern = '^' + pattern
if pattern.endswith('\\$'):
pattern = pattern[:-2] + '$'
try:
return bool(re.match(pattern, filename))
except re.error:
return original_applies_to(self, filename)
return original_applies_to(self, filename)
RuleLine.applies_to = patched_applies_to
# Monkey patch ends
def chunk_documents(
documents: Iterable[str],
chunk_token_threshold: int,
@@ -136,13 +166,20 @@ def merge_chunks(
word_token_ratio: float = 1.0,
splitter: Callable = None
) -> List[str]:
"""Merges documents into chunks of specified token size.
"""
Merges a sequence of documents into chunks based on a target token count, with optional overlap.
Each document is split into tokens using the provided splitter function (defaults to str.split). Tokens are distributed into chunks aiming for the specified target size, with optional overlapping tokens between consecutive chunks. Returns a list of non-empty merged chunks as strings.
Args:
docs: Input documents
target_size: Desired token count per chunk
overlap: Number of tokens to overlap between chunks
word_token_ratio: Multiplier for word->token conversion
docs: Sequence of input document strings to be merged.
target_size: Target number of tokens per chunk.
overlap: Number of tokens to overlap between consecutive chunks.
word_token_ratio: Multiplier to estimate token count from word count.
splitter: Callable used to split each document into tokens.
Returns:
List of merged document chunks as strings, each not exceeding the target token size.
"""
# Pre-tokenize all docs and store token counts
splitter = splitter or str.split
@@ -151,7 +188,7 @@ def merge_chunks(
total_tokens = 0
for doc in docs:
tokens = doc.split()
tokens = splitter(doc)
count = int(len(tokens) * word_token_ratio)
if count: # Skip empty docs
token_counts.append(count)
@@ -304,7 +341,7 @@ class RobotsParser:
robots_url = f"{scheme}://{domain}/robots.txt"
async with aiohttp.ClientSession() as session:
async with session.get(robots_url, timeout=2) as response:
async with session.get(robots_url, timeout=2, ssl=False) as response:
if response.status == 200:
rules = await response.text()
self._cache_rules(domain, rules)
@@ -441,14 +478,13 @@ def create_box_message(
str: A formatted string containing the styled message box.
"""
init()
# Define border and text colors for different types
styles = {
"warning": (Fore.YELLOW, Fore.LIGHTYELLOW_EX, ""),
"info": (Fore.BLUE, Fore.LIGHTBLUE_EX, ""),
"success": (Fore.GREEN, Fore.LIGHTGREEN_EX, ""),
"error": (Fore.RED, Fore.LIGHTRED_EX, "×"),
"warning": ("yellow", "bright_yellow", ""),
"info": ("blue", "bright_blue", ""),
"debug": ("lightblack", "bright_black", ""),
"success": ("green", "bright_green", ""),
"error": ("red", "bright_red", "×"),
}
border_color, text_color, prefix = styles.get(type.lower(), styles["info"])
@@ -480,12 +516,12 @@ def create_box_message(
# Create the box with colored borders and lighter text
horizontal_line = h_line * (width - 1)
box = [
f"{border_color}{tl}{horizontal_line}{tr}",
f"[{border_color}]{tl}{horizontal_line}{tr}[/{border_color}]",
*[
f"{border_color}{v_line}{text_color} {line:<{width-2}}{border_color}{v_line}"
f"[{border_color}]{v_line}[{text_color}] {line:<{width-2}}[/{text_color}][{border_color}]{v_line}[/{border_color}]"
for line in formatted_lines
],
f"{border_color}{bl}{horizontal_line}{br}{Style.RESET_ALL}",
f"[{border_color}]{bl}{horizontal_line}{br}[/{border_color}]",
]
result = "\n".join(box)
@@ -1111,6 +1147,23 @@ def get_content_of_website_optimized(
css_selector: str = None,
**kwargs,
) -> Dict[str, Any]:
"""
Extracts and cleans content from website HTML, optimizing for useful media and contextual information.
Parses the provided HTML to extract internal and external links, filters and scores images for usefulness, gathers contextual descriptions for media, removes unwanted or low-value elements, and converts the cleaned HTML to Markdown. Also extracts metadata and returns all structured content in a dictionary.
Args:
url: The URL of the website being processed.
html: The raw HTML content to extract from.
word_count_threshold: Minimum word count for elements to be retained.
css_selector: Optional CSS selector to restrict extraction to specific elements.
Returns:
A dictionary containing Markdown content, cleaned HTML, extraction success status, media and link lists, and metadata.
Raises:
InvalidCSSSelectorError: If a provided CSS selector does not match any elements.
"""
if not html:
return None
@@ -1153,6 +1206,20 @@ def get_content_of_website_optimized(
def process_image(img, url, index, total_images):
# Check if an image has valid display and inside undesired html elements
"""
Processes an HTML image element to determine its relevance and extract metadata.
Evaluates an image's visibility, context, and usefulness based on its attributes and parent elements. If the image passes validation and exceeds a usefulness score threshold, returns a dictionary with its source, alt text, contextual description, score, and type. Otherwise, returns None.
Args:
img: The BeautifulSoup image tag to process.
url: The base URL of the page containing the image.
index: The index of the image in the list of images on the page.
total_images: The total number of images on the page.
Returns:
A dictionary with image metadata if the image is considered useful, or None otherwise.
"""
def is_valid_image(img, parent, parent_classes):
style = img.get("style", "")
src = img.get("src", "")
@@ -1174,6 +1241,20 @@ def get_content_of_website_optimized(
# Score an image for it's usefulness
def score_image_for_usefulness(img, base_url, index, images_count):
# Function to parse image height/width value and units
"""
Scores an HTML image element for usefulness based on size, format, attributes, and position.
The function evaluates an image's dimensions, file format, alt text, and its position among all images on the page to assign a usefulness score. Higher scores indicate images that are likely more relevant or informative for content extraction or summarization.
Args:
img: The HTML image element to score.
base_url: The base URL used to resolve relative image sources.
index: The position of the image in the list of images on the page (zero-based).
images_count: The total number of images on the page.
Returns:
An integer usefulness score for the image.
"""
def parse_dimension(dimension):
if dimension:
match = re.match(r"(\d+)(\D*)", dimension)
@@ -1188,6 +1269,16 @@ def get_content_of_website_optimized(
# Fetch image file metadata to extract size and extension
def fetch_image_file_size(img, base_url):
# If src is relative path construct full URL, if not it may be CDN URL
"""
Fetches the file size of an image by sending a HEAD request to its URL.
Args:
img: A BeautifulSoup tag representing the image element.
base_url: The base URL to resolve relative image sources.
Returns:
The value of the "Content-Length" header as a string if available, otherwise None.
"""
img_url = urljoin(base_url, img.get("src"))
try:
response = requests.head(img_url)
@@ -1198,8 +1289,6 @@ def get_content_of_website_optimized(
return None
except InvalidSchema:
return None
finally:
return
image_height = img.get("height")
height_value, height_unit = parse_dimension(image_height)
@@ -1428,8 +1517,29 @@ def extract_metadata_using_lxml(html, doc=None):
head = head[0]
# Title - using XPath
# title = head.xpath(".//title/text()")
# metadata["title"] = title[0].strip() if title else None
# === Title Extraction - New Approach ===
# Attempt to extract <title> using XPath
title = head.xpath(".//title/text()")
metadata["title"] = title[0].strip() if title else None
title = title[0] if title else None
# Fallback: Use .find() in case XPath fails due to malformed HTML
if not title:
title_el = doc.find(".//title")
title = title_el.text if title_el is not None else None
# Final fallback: Use OpenGraph or Twitter title if <title> is missing or empty
if not title:
title_candidates = (
doc.xpath("//meta[@property='og:title']/@content") or
doc.xpath("//meta[@name='twitter:title']/@content")
)
title = title_candidates[0] if title_candidates else None
# Strip and assign title
metadata["title"] = title.strip() if title else None
# Meta description - using XPath with multiple attribute conditions
description = head.xpath('.//meta[@name="description"]/@content')
@@ -1458,6 +1568,14 @@ def extract_metadata_using_lxml(html, doc=None):
content = tag.get("content", "").strip()
if property_name and content:
metadata[property_name] = content
# Article metadata
article_tags = head.xpath('.//meta[starts-with(@property, "article:")]')
for tag in article_tags:
property_name = tag.get("property", "").strip()
content = tag.get("content", "").strip()
if property_name and content:
metadata[property_name] = content
return metadata
@@ -1533,7 +1651,15 @@ def extract_metadata(html, soup=None):
content = tag.get("content", "").strip()
if property_name and content:
metadata[property_name] = content
# Article metadata
article_tags = head.find_all("meta", attrs={"property": re.compile(r"^article:")})
for tag in article_tags:
property_name = tag.get("property", "").strip()
content = tag.get("content", "").strip()
if property_name and content:
metadata[property_name] = content
return metadata
@@ -2002,9 +2128,101 @@ def normalize_url(href, base_url):
parsed_base = urlparse(base_url)
if not parsed_base.scheme or not parsed_base.netloc:
raise ValueError(f"Invalid base URL format: {base_url}")
if parsed_base.scheme.lower() not in ["http", "https"]:
# Handle special protocols
raise ValueError(f"Invalid base URL format: {base_url}")
cleaned_href = href.strip()
# Use urljoin to handle all cases
normalized = urljoin(base_url, href.strip())
return urljoin(base_url, cleaned_href)
def normalize_url(
href: str,
base_url: str,
*,
drop_query_tracking=True,
sort_query=True,
keep_fragment=False,
extra_drop_params=None
):
"""
Extended URL normalizer
Parameters
----------
href : str
The raw link extracted from a page.
base_url : str
The pages canonical URL (used to resolve relative links).
drop_query_tracking : bool (default True)
Remove common tracking query parameters.
sort_query : bool (default True)
Alphabetically sort query keys for deterministic output.
keep_fragment : bool (default False)
Preserve the hash fragment (#section) if you need in-page links.
extra_drop_params : Iterable[str] | None
Additional query keys to strip (case-insensitive).
Returns
-------
str | None
A clean, canonical URL or None if href is empty/None.
"""
if not href:
return None
# Resolve relative paths first
full_url = urljoin(base_url, href.strip())
# Parse once, edit parts, then rebuild
parsed = urlparse(full_url)
# ── netloc ──
netloc = parsed.netloc.lower()
# ── path ──
# Strip duplicate slashes and trailing “/” (except root)
path = quote(unquote(parsed.path))
if path.endswith('/') and path != '/':
path = path.rstrip('/')
# ── query ──
query = parsed.query
if query:
# explode, mutate, then rebuild
params = [(k.lower(), v) for k, v in parse_qsl(query, keep_blank_values=True)]
if drop_query_tracking:
default_tracking = {
'utm_source', 'utm_medium', 'utm_campaign', 'utm_term',
'utm_content', 'gclid', 'fbclid', 'ref', 'ref_src'
}
if extra_drop_params:
default_tracking |= {p.lower() for p in extra_drop_params}
params = [(k, v) for k, v in params if k not in default_tracking]
if sort_query:
params.sort(key=lambda kv: kv[0])
query = urlencode(params, doseq=True) if params else ''
# ── fragment ──
fragment = parsed.fragment if keep_fragment else ''
# Re-assemble
normalized = urlunparse((
parsed.scheme,
netloc,
path,
parsed.params,
query,
fragment
))
return normalized
@@ -2047,7 +2265,7 @@ def normalize_url_for_deep_crawl(href, base_url):
normalized = urlunparse((
parsed.scheme,
netloc,
parsed.path.rstrip('/') or '/', # Normalize trailing slash
parsed.path.rstrip('/'), # Normalize trailing slash
parsed.params,
query,
fragment
@@ -2075,7 +2293,7 @@ def efficient_normalize_url_for_deep_crawl(href, base_url):
normalized = urlunparse((
parsed.scheme,
parsed.netloc.lower(),
parsed.path,
parsed.path.rstrip('/'),
parsed.params,
parsed.query,
'' # Remove fragment
@@ -2733,33 +2951,67 @@ def preprocess_html_for_schema(html_content, text_threshold=100, attr_value_thre
# Also truncate tail text if present
if element.tail and len(element.tail.strip()) > text_threshold:
element.tail = element.tail.strip()[:text_threshold] + '...'
# 4. Find repeated patterns and keep only a few examples
# This is a simplistic approach - more sophisticated pattern detection could be implemented
pattern_elements = {}
for element in tree.xpath('//*[contains(@class, "")]'):
parent = element.getparent()
# 4. Detect duplicates and drop them in a single pass
seen: dict[tuple, None] = {}
for el in list(tree.xpath('//*[@class]')): # snapshot once, XPath is fast
parent = el.getparent()
if parent is None:
continue
# Create a signature based on tag and classes
classes = element.get('class', '')
if not classes:
cls = el.get('class')
if not cls:
continue
signature = f"{element.tag}.{classes}"
if signature in pattern_elements:
pattern_elements[signature].append(element)
# ── build signature ───────────────────────────────────────────
h = xxhash.xxh64() # stream, no big join()
for txt in el.itertext():
h.update(txt)
sig = (el.tag, cls, h.intdigest()) # tuple cheaper & hashable
# ── first seen? keep else drop ─────────────
if sig in seen and parent is not None:
parent.remove(el) # duplicate
else:
pattern_elements[signature] = [element]
seen[sig] = None
# Keep only 3 examples of each repeating pattern
for signature, elements in pattern_elements.items():
if len(elements) > 3:
# Keep the first 2 and last elements
for element in elements[2:-1]:
if element.getparent() is not None:
element.getparent().remove(element)
# # 4. Find repeated patterns and keep only a few examples
# # This is a simplistic approach - more sophisticated pattern detection could be implemented
# pattern_elements = {}
# for element in tree.xpath('//*[contains(@class, "")]'):
# parent = element.getparent()
# if parent is None:
# continue
# # Create a signature based on tag and classes
# classes = element.get('class', '')
# if not classes:
# continue
# innert_text = ''.join(element.xpath('.//text()'))
# innert_text_hash = xxhash.xxh64(innert_text.encode()).hexdigest()
# signature = f"{element.tag}.{classes}.{innert_text_hash}"
# if signature in pattern_elements:
# pattern_elements[signature].append(element)
# else:
# pattern_elements[signature] = [element]
# # Keep only first examples of each repeating pattern
# for signature, elements in pattern_elements.items():
# if len(elements) > 1:
# # Keep the first element and remove the rest
# for element in elements[1:]:
# if element.getparent() is not None:
# element.getparent().remove(element)
# # Keep only 3 examples of each repeating pattern
# for signature, elements in pattern_elements.items():
# if len(elements) > 3:
# # Keep the first 2 and last elements
# for element in elements[2:-1]:
# if element.getparent() is not None:
# element.getparent().remove(element)
# 5. Convert back to string
result = etree.tostring(tree, encoding='unicode', method='html')
@@ -2772,6 +3024,393 @@ def preprocess_html_for_schema(html_content, text_threshold=100, attr_value_thre
except Exception as e:
# Fallback for parsing errors
return html_content[:max_size] if len(html_content) > max_size else html_content
return html_content[:max_size] if len(html_content) > max_size else html_content
def start_colab_display_server():
"""
Start virtual display server in Google Colab.
Raises error if not running in Colab environment.
"""
# Check if running in Google Colab
try:
import google.colab
from google.colab import output
from IPython.display import IFrame, display
except ImportError:
raise RuntimeError("This function must be run in Google Colab environment.")
import os, time, subprocess
os.environ["DISPLAY"] = ":99"
# Xvfb
xvfb = subprocess.Popen(["Xvfb", ":99", "-screen", "0", "1280x720x24"])
time.sleep(2)
# minimal window manager
fluxbox = subprocess.Popen(["fluxbox"])
# VNC → X
x11vnc = subprocess.Popen(["x11vnc",
"-display", ":99",
"-nopw", "-forever", "-shared",
"-rfbport", "5900", "-quiet"])
# websockify → VNC
novnc = subprocess.Popen(["/opt/novnc/utils/websockify/run",
"6080", "localhost:5900",
"--web", "/opt/novnc"])
time.sleep(2) # give ports a moment
# Colab proxy url
url = output.eval_js("google.colab.kernel.proxyPort(6080)")
display(IFrame(f"{url}/vnc.html?autoconnect=true&resize=scale", width=1024, height=768))
def setup_colab_environment():
"""
Alternative setup using IPython magic commands
"""
from IPython import get_ipython
ipython = get_ipython()
print("🚀 Setting up Crawl4AI environment in Google Colab...")
# Run the bash commands
ipython.run_cell_magic('bash', '', '''
set -e
echo "📦 Installing system dependencies..."
apt-get update -y
apt-get install -y xvfb x11vnc fluxbox websockify git
echo "📥 Setting up virtual display..."
git clone https://github.com/novnc/noVNC /opt/novnc
git clone https://github.com/novnc/websockify /opt/novnc/utils/websockify
pip install -q nest_asyncio google-colab
echo "✅ Setup complete!"
''')
# Link Quality Scoring Functions
def extract_page_context(page_title: str, headlines_text: str, meta_description: str, base_url: str) -> dict:
"""
Extract page context for link scoring - called ONCE per page for performance.
Parser-agnostic function that takes pre-extracted data.
Args:
page_title: Title of the page
headlines_text: Combined text from h1, h2, h3 elements
meta_description: Meta description content
base_url: Base URL of the page
Returns:
Dictionary containing page context data for fast link scoring
"""
context = {
'terms': set(),
'headlines': headlines_text or '',
'meta_description': meta_description or '',
'domain': '',
'is_docs_site': False
}
try:
from urllib.parse import urlparse
parsed = urlparse(base_url)
context['domain'] = parsed.netloc.lower()
# Check if this is a documentation/reference site
context['is_docs_site'] = any(indicator in context['domain']
for indicator in ['docs.', 'api.', 'developer.', 'reference.'])
# Create term set for fast intersection (performance optimization)
all_text = ((page_title or '') + ' ' + context['headlines'] + ' ' + context['meta_description']).lower()
# Simple tokenization - fast and sufficient for scoring
context['terms'] = set(word.strip('.,!?;:"()[]{}')
for word in all_text.split()
if len(word.strip('.,!?;:"()[]{}')) > 2)
except Exception:
# Fail gracefully - return empty context
pass
return context
def calculate_link_intrinsic_score(
link_text: str,
url: str,
title_attr: str,
class_attr: str,
rel_attr: str,
page_context: dict
) -> float:
"""
Ultra-fast link quality scoring using only provided data (no DOM access needed).
Parser-agnostic function.
Args:
link_text: Text content of the link
url: Link URL
title_attr: Title attribute of the link
class_attr: Class attribute of the link
rel_attr: Rel attribute of the link
page_context: Pre-computed page context from extract_page_context()
Returns:
Quality score (0.0 - 10.0), higher is better
"""
score = 0.0
try:
# 1. ATTRIBUTE QUALITY (string analysis - very fast)
if title_attr and len(title_attr.strip()) > 3:
score += 1.0
class_str = (class_attr or '').lower()
# Navigation/important classes boost score
if any(nav_class in class_str for nav_class in ['nav', 'menu', 'primary', 'main', 'important']):
score += 1.5
# Marketing/ad classes reduce score
if any(bad_class in class_str for bad_class in ['ad', 'sponsor', 'track', 'promo', 'banner']):
score -= 1.0
rel_str = (rel_attr or '').lower()
# Semantic rel values
if any(good_rel in rel_str for good_rel in ['canonical', 'next', 'prev', 'chapter']):
score += 1.0
if any(bad_rel in rel_str for bad_rel in ['nofollow', 'sponsored', 'ugc']):
score -= 0.5
# 2. URL STRUCTURE QUALITY (string operations - very fast)
url_lower = url.lower()
# High-value path patterns
if any(good_path in url_lower for good_path in ['/docs/', '/api/', '/guide/', '/tutorial/', '/reference/', '/manual/']):
score += 2.0
elif any(medium_path in url_lower for medium_path in ['/blog/', '/article/', '/post/', '/news/']):
score += 1.0
# Penalize certain patterns
if any(bad_path in url_lower for bad_path in ['/admin/', '/login/', '/cart/', '/checkout/', '/track/', '/click/']):
score -= 1.5
# URL depth (shallow URLs often more important)
url_depth = url.count('/') - 2 # Subtract protocol and domain
if url_depth <= 2:
score += 1.0
elif url_depth > 5:
score -= 0.5
# HTTPS bonus
if url.startswith('https://'):
score += 0.5
# 3. TEXT QUALITY (string analysis - very fast)
if link_text:
text_clean = link_text.strip()
if len(text_clean) > 3:
score += 1.0
# Multi-word links are usually more descriptive
word_count = len(text_clean.split())
if word_count >= 2:
score += 0.5
if word_count >= 4:
score += 0.5
# Avoid generic link text
generic_texts = ['click here', 'read more', 'more info', 'link', 'here']
if text_clean.lower() in generic_texts:
score -= 1.0
# 4. CONTEXTUAL RELEVANCE (pre-computed page terms - very fast)
if page_context.get('terms') and link_text:
link_words = set(word.strip('.,!?;:"()[]{}').lower()
for word in link_text.split()
if len(word.strip('.,!?;:"()[]{}')) > 2)
if link_words:
# Calculate word overlap ratio
overlap = len(link_words & page_context['terms'])
if overlap > 0:
relevance_ratio = overlap / min(len(link_words), 10) # Cap to avoid over-weighting
score += relevance_ratio * 2.0 # Up to 2 points for relevance
# 5. DOMAIN CONTEXT BONUSES (very fast string checks)
if page_context.get('is_docs_site', False):
# Documentation sites: prioritize internal navigation
if link_text and any(doc_keyword in link_text.lower()
for doc_keyword in ['api', 'reference', 'guide', 'tutorial', 'example']):
score += 1.0
except Exception:
# Fail gracefully - return minimal score
score = 0.5
# Ensure score is within reasonable bounds
return max(0.0, min(score, 10.0))
def calculate_total_score(
intrinsic_score: Optional[float] = None,
contextual_score: Optional[float] = None,
score_links_enabled: bool = False,
query_provided: bool = False
) -> float:
"""
Calculate combined total score from intrinsic and contextual scores with smart fallbacks.
Args:
intrinsic_score: Quality score based on URL structure, text, and context (0-10)
contextual_score: BM25 relevance score based on query and head content (0-1 typically)
score_links_enabled: Whether link scoring is enabled
query_provided: Whether a query was provided for contextual scoring
Returns:
Combined total score (0-10 scale)
Scoring Logic:
- No scoring: return 5.0 (neutral score)
- Only intrinsic: return normalized intrinsic score
- Only contextual: return contextual score scaled to 10
- Both: weighted combination (70% intrinsic, 30% contextual scaled)
"""
# Case 1: No scoring enabled at all
if not score_links_enabled:
return 5.0 # Neutral score - all links treated equally
# Normalize scores to handle None values
intrinsic = intrinsic_score if intrinsic_score is not None else 0.0
contextual = contextual_score if contextual_score is not None else 0.0
# Case 2: Only intrinsic scoring (no query provided or no head extraction)
if not query_provided or contextual_score is None:
# Use intrinsic score directly (already 0-10 scale)
return max(0.0, min(intrinsic, 10.0))
# Case 3: Both intrinsic and contextual scores available
# Scale contextual score (typically 0-1) to 0-10 range
contextual_scaled = min(contextual * 10.0, 10.0)
# Weighted combination: 70% intrinsic (structure/content quality) + 30% contextual (query relevance)
# This gives more weight to link quality while still considering relevance
total = (intrinsic * 0.7) + (contextual_scaled * 0.3)
return max(0.0, min(total, 10.0))
# Embedding utilities
async def get_text_embeddings(
texts: List[str],
llm_config: Optional[Dict] = None,
model_name: str = "sentence-transformers/all-MiniLM-L6-v2",
batch_size: int = 32
) -> np.ndarray:
"""
Compute embeddings for a list of texts using specified model.
Args:
texts: List of texts to embed
llm_config: Optional LLM configuration for API-based embeddings
model_name: Model name (used when llm_config is None)
batch_size: Batch size for processing
Returns:
numpy array of embeddings
"""
import numpy as np
if not texts:
return np.array([])
# If LLMConfig provided, use litellm for embeddings
if llm_config is not None:
from litellm import aembedding
# Get embedding model from config or use default
embedding_model = llm_config.get('provider', 'text-embedding-3-small')
api_base = llm_config.get('base_url', llm_config.get('api_base'))
# Prepare kwargs
kwargs = {
'model': embedding_model,
'input': texts,
'api_key': llm_config.get('api_token', llm_config.get('api_key'))
}
if api_base:
kwargs['api_base'] = api_base
# Handle OpenAI-compatible endpoints
if api_base and 'openai/' not in embedding_model:
kwargs['model'] = f"openai/{embedding_model}"
# Get embeddings
response = await aembedding(**kwargs)
# Extract embeddings from response
embeddings = []
for item in response.data:
embeddings.append(item['embedding'])
return np.array(embeddings)
# Default: use sentence-transformers
else:
# Lazy load to avoid importing heavy libraries unless needed
try:
from sentence_transformers import SentenceTransformer
except ImportError:
raise ImportError(
"sentence-transformers is required for local embeddings. "
"Install it with: pip install 'crawl4ai[transformer]' or pip install sentence-transformers"
)
# Cache the model in function attribute to avoid reloading
if not hasattr(get_text_embeddings, '_models'):
get_text_embeddings._models = {}
if model_name not in get_text_embeddings._models:
get_text_embeddings._models[model_name] = SentenceTransformer(model_name)
encoder = get_text_embeddings._models[model_name]
# Batch encode for efficiency
embeddings = encoder.encode(
texts,
batch_size=batch_size,
show_progress_bar=False,
convert_to_numpy=True
)
return embeddings
def get_text_embeddings_sync(
texts: List[str],
llm_config: Optional[Dict] = None,
model_name: str = "sentence-transformers/all-MiniLM-L6-v2",
batch_size: int = 32
) -> np.ndarray:
"""Synchronous wrapper for get_text_embeddings"""
import numpy as np
return asyncio.run(get_text_embeddings(texts, llm_config, model_name, batch_size))
def cosine_similarity(vec1: np.ndarray, vec2: np.ndarray) -> float:
"""Calculate cosine similarity between two vectors"""
import numpy as np
dot_product = np.dot(vec1, vec2)
norm_product = np.linalg.norm(vec1) * np.linalg.norm(vec2)
return float(dot_product / norm_product) if norm_product != 0 else 0.0
def cosine_distance(vec1: np.ndarray, vec2: np.ndarray) -> float:
"""Calculate cosine distance (1 - similarity) between two vectors"""
return 1 - cosine_similarity(vec1, vec2)

View File

@@ -5,4 +5,9 @@ ANTHROPIC_API_KEY=your_anthropic_key_here
GROQ_API_KEY=your_groq_key_here
TOGETHER_API_KEY=your_together_key_here
MISTRAL_API_KEY=your_mistral_key_here
GEMINI_API_TOKEN=your_gemini_key_here
GEMINI_API_TOKEN=your_gemini_key_here
# Optional: Override the default LLM provider
# Examples: "openai/gpt-4", "anthropic/claude-3-opus", "deepseek/chat", etc.
# If not set, uses the provider specified in config.yml (default: openai/gpt-4o-mini)
# LLM_PROVIDER=anthropic/claude-3-opus

File diff suppressed because it is too large Load Diff

View File

@@ -1,8 +1,11 @@
import os
import json
import asyncio
from typing import List, Tuple
from typing import List, Tuple, Dict
from functools import partial
from uuid import uuid4
from datetime import datetime
from base64 import b64encode
import logging
from typing import Optional, AsyncGenerator
@@ -37,11 +40,24 @@ from utils import (
get_base_url,
is_task_id,
should_cleanup_task,
decode_redis_hash
decode_redis_hash,
get_llm_api_key,
validate_llm_provider
)
import psutil, time
logger = logging.getLogger(__name__)
# --- Helper to get memory ---
def _get_memory_mb():
try:
return psutil.Process().memory_info().rss / (1024 * 1024)
except Exception as e:
logger.warning(f"Could not get memory info: {e}")
return None
async def handle_llm_qa(
url: str,
query: str,
@@ -49,6 +65,8 @@ async def handle_llm_qa(
) -> str:
"""Process QA using LLM with crawled content as context."""
try:
if not url.startswith(('http://', 'https://')):
url = 'https://' + url
# Extract base URL by finding last '?q=' occurrence
last_q_index = url.rfind('?q=')
if last_q_index != -1:
@@ -62,7 +80,7 @@ async def handle_llm_qa(
status_code=status.HTTP_500_INTERNAL_SERVER_ERROR,
detail=result.error_message
)
content = result.markdown.fit_markdown
content = result.markdown.fit_markdown or result.markdown.raw_markdown
# Create prompt and get LLM response
prompt = f"""Use the following content as context to answer the question.
@@ -73,10 +91,12 @@ async def handle_llm_qa(
Answer:"""
# api_token=os.environ.get(config["llm"].get("api_key_env", ""))
response = perform_completion_with_backoff(
provider=config["llm"]["provider"],
prompt_with_variables=prompt,
api_token=os.environ.get(config["llm"].get("api_key_env", ""))
api_token=get_llm_api_key(config)
)
return response.choices[0].message.content
@@ -94,19 +114,23 @@ async def process_llm_extraction(
url: str,
instruction: str,
schema: Optional[str] = None,
cache: str = "0"
cache: str = "0",
provider: Optional[str] = None
) -> None:
"""Process LLM extraction in background."""
try:
# If config['llm'] has api_key then ignore the api_key_env
api_key = ""
if "api_key" in config["llm"]:
api_key = config["llm"]["api_key"]
else:
api_key = os.environ.get(config["llm"].get("api_key_env", None), "")
# Validate provider
is_valid, error_msg = validate_llm_provider(config, provider)
if not is_valid:
await redis.hset(f"task:{task_id}", mapping={
"status": TaskStatus.FAILED,
"error": error_msg
})
return
api_key = get_llm_api_key(config, provider)
llm_strategy = LLMExtractionStrategy(
llm_config=LLMConfig(
provider=config["llm"]["provider"],
provider=provider or config["llm"]["provider"],
api_token=api_key
),
instruction=instruction,
@@ -153,10 +177,19 @@ async def handle_markdown_request(
filter_type: FilterType,
query: Optional[str] = None,
cache: str = "0",
config: Optional[dict] = None
config: Optional[dict] = None,
provider: Optional[str] = None
) -> str:
"""Handle markdown generation requests."""
try:
# Validate provider if using LLM filter
if filter_type == FilterType.LLM:
is_valid, error_msg = validate_llm_provider(config, provider)
if not is_valid:
raise HTTPException(
status_code=status.HTTP_400_BAD_REQUEST,
detail=error_msg
)
decoded_url = unquote(url)
if not decoded_url.startswith(('http://', 'https://')):
decoded_url = 'https://' + decoded_url
@@ -169,8 +202,8 @@ async def handle_markdown_request(
FilterType.BM25: BM25ContentFilter(user_query=query or ""),
FilterType.LLM: LLMContentFilter(
llm_config=LLMConfig(
provider=config["llm"]["provider"],
api_token=os.environ.get(config["llm"].get("api_key_env", None), ""),
provider=provider or config["llm"]["provider"],
api_token=get_llm_api_key(config, provider),
),
instruction=query or "Extract main content"
)
@@ -214,7 +247,8 @@ async def handle_llm_request(
query: Optional[str] = None,
schema: Optional[str] = None,
cache: str = "0",
config: Optional[dict] = None
config: Optional[dict] = None,
provider: Optional[str] = None
) -> JSONResponse:
"""Handle LLM extraction requests."""
base_url = get_base_url(request)
@@ -244,7 +278,8 @@ async def handle_llm_request(
schema,
cache,
base_url,
config
config,
provider
)
except Exception as e:
@@ -259,7 +294,9 @@ async def handle_llm_request(
async def handle_task_status(
redis: aioredis.Redis,
task_id: str,
base_url: str
base_url: str,
*,
keep: bool = False
) -> JSONResponse:
"""Handle task status check requests."""
task = await redis.hgetall(f"task:{task_id}")
@@ -273,7 +310,7 @@ async def handle_task_status(
response = create_task_response(task, task_id, base_url)
if task["status"] in [TaskStatus.COMPLETED, TaskStatus.FAILED]:
if should_cleanup_task(task["created_at"]):
if not keep and should_cleanup_task(task["created_at"]):
await redis.delete(f"task:{task_id}")
return JSONResponse(response)
@@ -286,7 +323,8 @@ async def create_new_task(
schema: Optional[str],
cache: str,
base_url: str,
config: dict
config: dict,
provider: Optional[str] = None
) -> JSONResponse:
"""Create and initialize a new task."""
decoded_url = unquote(input_path)
@@ -310,7 +348,8 @@ async def create_new_task(
decoded_url,
query,
schema,
cache
cache,
provider
)
return JSONResponse({
@@ -351,7 +390,12 @@ async def stream_results(crawler: AsyncWebCrawler, results_gen: AsyncGenerator)
try:
async for result in results_gen:
try:
server_memory_mb = _get_memory_mb()
result_dict = result.model_dump()
result_dict['server_memory_mb'] = server_memory_mb
# If PDF exists, encode it to base64
if result_dict.get('pdf') is not None:
result_dict['pdf'] = b64encode(result_dict['pdf']).decode('utf-8')
logger.info(f"Streaming result for {result_dict.get('url', 'unknown')}")
data = json.dumps(result_dict, default=datetime_handler) + "\n"
yield data.encode('utf-8')
@@ -365,10 +409,11 @@ async def stream_results(crawler: AsyncWebCrawler, results_gen: AsyncGenerator)
except asyncio.CancelledError:
logger.warning("Client disconnected during streaming")
finally:
try:
await crawler.close()
except Exception as e:
logger.error(f"Crawler cleanup error: {e}")
# try:
# await crawler.close()
# except Exception as e:
# logger.error(f"Crawler cleanup error: {e}")
pass
async def handle_crawl_request(
urls: List[str],
@@ -377,7 +422,13 @@ async def handle_crawl_request(
config: dict
) -> dict:
"""Handle non-streaming crawl requests."""
start_mem_mb = _get_memory_mb() # <--- Get memory before
start_time = time.time()
mem_delta_mb = None
peak_mem_mb = start_mem_mb
try:
urls = [('https://' + url) if not url.startswith(('http://', 'https://')) else url for url in urls]
browser_config = BrowserConfig.load(browser_config)
crawler_config = CrawlerRunConfig.load(crawler_config)
@@ -385,27 +436,77 @@ async def handle_crawl_request(
memory_threshold_percent=config["crawler"]["memory_threshold_percent"],
rate_limiter=RateLimiter(
base_delay=tuple(config["crawler"]["rate_limiter"]["base_delay"])
)
) if config["crawler"]["rate_limiter"]["enabled"] else None
)
from crawler_pool import get_crawler
crawler = await get_crawler(browser_config)
async with AsyncWebCrawler(config=browser_config) as crawler:
results = []
func = getattr(crawler, "arun" if len(urls) == 1 else "arun_many")
partial_func = partial(func,
urls[0] if len(urls) == 1 else urls,
config=crawler_config,
dispatcher=dispatcher)
results = await partial_func()
return {
"success": True,
"results": [result.model_dump() for result in results]
}
# crawler: AsyncWebCrawler = AsyncWebCrawler(config=browser_config)
# await crawler.start()
base_config = config["crawler"]["base_config"]
# Iterate on key-value pairs in global_config then use haseattr to set them
for key, value in base_config.items():
if hasattr(crawler_config, key):
setattr(crawler_config, key, value)
results = []
func = getattr(crawler, "arun" if len(urls) == 1 else "arun_many")
partial_func = partial(func,
urls[0] if len(urls) == 1 else urls,
config=crawler_config,
dispatcher=dispatcher)
results = await partial_func()
# await crawler.close()
end_mem_mb = _get_memory_mb() # <--- Get memory after
end_time = time.time()
if start_mem_mb is not None and end_mem_mb is not None:
mem_delta_mb = end_mem_mb - start_mem_mb # <--- Calculate delta
peak_mem_mb = max(peak_mem_mb if peak_mem_mb else 0, end_mem_mb) # <--- Get peak memory
logger.info(f"Memory usage: Start: {start_mem_mb} MB, End: {end_mem_mb} MB, Delta: {mem_delta_mb} MB, Peak: {peak_mem_mb} MB")
# Process results to handle PDF bytes
processed_results = []
for result in results:
result_dict = result.model_dump()
# If PDF exists, encode it to base64
if result_dict.get('pdf') is not None:
result_dict['pdf'] = b64encode(result_dict['pdf']).decode('utf-8')
processed_results.append(result_dict)
return {
"success": True,
"results": processed_results,
"server_processing_time_s": end_time - start_time,
"server_memory_delta_mb": mem_delta_mb,
"server_peak_memory_mb": peak_mem_mb
}
except Exception as e:
logger.error(f"Crawl error: {str(e)}", exc_info=True)
if 'crawler' in locals() and crawler.ready: # Check if crawler was initialized and started
# try:
# await crawler.close()
# except Exception as close_e:
# logger.error(f"Error closing crawler during exception handling: {close_e}")
logger.error(f"Error closing crawler during exception handling: {str(e)}")
# Measure memory even on error if possible
end_mem_mb_error = _get_memory_mb()
if start_mem_mb is not None and end_mem_mb_error is not None:
mem_delta_mb = end_mem_mb_error - start_mem_mb
raise HTTPException(
status_code=status.HTTP_500_INTERNAL_SERVER_ERROR,
detail=str(e)
detail=json.dumps({ # Send structured error
"error": str(e),
"server_memory_delta_mb": mem_delta_mb,
"server_peak_memory_mb": max(peak_mem_mb if peak_mem_mb else 0, end_mem_mb_error or 0)
})
)
async def handle_stream_crawl_request(
@@ -417,9 +518,11 @@ async def handle_stream_crawl_request(
"""Handle streaming crawl requests."""
try:
browser_config = BrowserConfig.load(browser_config)
browser_config.verbose = True
# browser_config.verbose = True # Set to False or remove for production stress testing
browser_config.verbose = False
crawler_config = CrawlerRunConfig.load(crawler_config)
crawler_config.scraping_strategy = LXMLWebScrapingStrategy()
crawler_config.stream = True
dispatcher = MemoryAdaptiveDispatcher(
memory_threshold_percent=config["crawler"]["memory_threshold_percent"],
@@ -428,8 +531,11 @@ async def handle_stream_crawl_request(
)
)
crawler = AsyncWebCrawler(config=browser_config)
await crawler.start()
from crawler_pool import get_crawler
crawler = await get_crawler(browser_config)
# crawler = AsyncWebCrawler(config=browser_config)
# await crawler.start()
results_gen = await crawler.arun_many(
urls=urls,
@@ -440,10 +546,60 @@ async def handle_stream_crawl_request(
return crawler, results_gen
except Exception as e:
if 'crawler' in locals():
await crawler.close()
# Make sure to close crawler if started during an error here
if 'crawler' in locals() and crawler.ready:
# try:
# await crawler.close()
# except Exception as close_e:
# logger.error(f"Error closing crawler during stream setup exception: {close_e}")
logger.error(f"Error closing crawler during stream setup exception: {str(e)}")
logger.error(f"Stream crawl error: {str(e)}", exc_info=True)
# Raising HTTPException here will prevent streaming response
raise HTTPException(
status_code=status.HTTP_500_INTERNAL_SERVER_ERROR,
detail=str(e)
)
)
async def handle_crawl_job(
redis,
background_tasks: BackgroundTasks,
urls: List[str],
browser_config: Dict,
crawler_config: Dict,
config: Dict,
) -> Dict:
"""
Fire-and-forget version of handle_crawl_request.
Creates a task in Redis, runs the heavy work in a background task,
lets /crawl/job/{task_id} polling fetch the result.
"""
task_id = f"crawl_{uuid4().hex[:8]}"
await redis.hset(f"task:{task_id}", mapping={
"status": TaskStatus.PROCESSING, # <-- keep enum values consistent
"created_at": datetime.utcnow().isoformat(),
"url": json.dumps(urls), # store list as JSON string
"result": "",
"error": "",
})
async def _runner():
try:
result = await handle_crawl_request(
urls=urls,
browser_config=browser_config,
crawler_config=crawler_config,
config=config,
)
await redis.hset(f"task:{task_id}", mapping={
"status": TaskStatus.COMPLETED,
"result": json.dumps(result),
})
await asyncio.sleep(5) # Give Redis time to process the update
except Exception as exc:
await redis.hset(f"task:{task_id}", mapping={
"status": TaskStatus.FAILED,
"error": str(exc),
})
background_tasks.add_task(_runner)
return {"task_id": task_id}

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View File

@@ -3,8 +3,9 @@ app:
title: "Crawl4AI API"
version: "1.0.0"
host: "0.0.0.0"
port: 8020
reload: True
port: 11234
reload: False
workers: 1
timeout_keep_alive: 300
# Default LLM Configuration
@@ -50,12 +51,31 @@ security:
# Crawler Configuration
crawler:
base_config:
simulate_user: true
memory_threshold_percent: 95.0
rate_limiter:
enabled: true
base_delay: [1.0, 2.0]
timeouts:
stream_init: 30.0 # Timeout for stream initialization
batch_process: 300.0 # Timeout for batch processing
pool:
max_pages: 40 # ← GLOBAL_SEM permits
idle_ttl_sec: 1800 # ← 30 min janitor cutoff
browser:
kwargs:
headless: true
text_mode: true
extra_args:
# - "--single-process"
- "--no-sandbox"
- "--disable-dev-shm-usage"
- "--disable-gpu"
- "--disable-software-rasterizer"
- "--disable-web-security"
- "--allow-insecure-localhost"
- "--ignore-certificate-errors"
# Logging Configuration
logging:

View File

@@ -0,0 +1,60 @@
# crawler_pool.py (new file)
import asyncio, json, hashlib, time, psutil
from contextlib import suppress
from typing import Dict
from crawl4ai import AsyncWebCrawler, BrowserConfig
from typing import Dict
from utils import load_config
CONFIG = load_config()
POOL: Dict[str, AsyncWebCrawler] = {}
LAST_USED: Dict[str, float] = {}
LOCK = asyncio.Lock()
MEM_LIMIT = CONFIG.get("crawler", {}).get("memory_threshold_percent", 95.0) # % RAM refuse new browsers above this
IDLE_TTL = CONFIG.get("crawler", {}).get("pool", {}).get("idle_ttl_sec", 1800) # close if unused for 30min
def _sig(cfg: BrowserConfig) -> str:
payload = json.dumps(cfg.to_dict(), sort_keys=True, separators=(",",":"))
return hashlib.sha1(payload.encode()).hexdigest()
async def get_crawler(cfg: BrowserConfig) -> AsyncWebCrawler:
try:
sig = _sig(cfg)
async with LOCK:
if sig in POOL:
LAST_USED[sig] = time.time();
return POOL[sig]
if psutil.virtual_memory().percent >= MEM_LIMIT:
raise MemoryError("RAM pressure new browser denied")
crawler = AsyncWebCrawler(config=cfg, thread_safe=False)
await crawler.start()
POOL[sig] = crawler; LAST_USED[sig] = time.time()
return crawler
except MemoryError as e:
raise MemoryError(f"RAM pressure new browser denied: {e}")
except Exception as e:
raise RuntimeError(f"Failed to start browser: {e}")
finally:
if sig in POOL:
LAST_USED[sig] = time.time()
else:
# If we failed to start the browser, we should remove it from the pool
POOL.pop(sig, None)
LAST_USED.pop(sig, None)
# If we failed to start the browser, we should remove it from the pool
async def close_all():
async with LOCK:
await asyncio.gather(*(c.close() for c in POOL.values()), return_exceptions=True)
POOL.clear(); LAST_USED.clear()
async def janitor():
while True:
await asyncio.sleep(60)
now = time.time()
async with LOCK:
for sig, crawler in list(POOL.items()):
if now - LAST_USED[sig] > IDLE_TTL:
with suppress(Exception): await crawler.close()
POOL.pop(sig, None); LAST_USED.pop(sig, None)

101
deploy/docker/job.py Normal file
View File

@@ -0,0 +1,101 @@
"""
Job endpoints (enqueue + poll) for long-running LLM extraction and raw crawl.
Relies on the existing Redis task helpers in api.py
"""
from typing import Dict, Optional, Callable
from fastapi import APIRouter, BackgroundTasks, Depends, Request
from pydantic import BaseModel, HttpUrl
from api import (
handle_llm_request,
handle_crawl_job,
handle_task_status,
)
# ------------- dependency placeholders -------------
_redis = None # will be injected from server.py
_config = None
_token_dep: Callable = lambda: None # dummy until injected
# public router
router = APIRouter()
# === init hook called by server.py =========================================
def init_job_router(redis, config, token_dep) -> APIRouter:
"""Inject shared singletons and return the router for mounting."""
global _redis, _config, _token_dep
_redis, _config, _token_dep = redis, config, token_dep
return router
# ---------- payload models --------------------------------------------------
class LlmJobPayload(BaseModel):
url: HttpUrl
q: str
schema: Optional[str] = None
cache: bool = False
provider: Optional[str] = None
class CrawlJobPayload(BaseModel):
urls: list[HttpUrl]
browser_config: Dict = {}
crawler_config: Dict = {}
# ---------- LLM job ---------------------------------------------------------
@router.post("/llm/job", status_code=202)
async def llm_job_enqueue(
payload: LlmJobPayload,
background_tasks: BackgroundTasks,
request: Request,
_td: Dict = Depends(lambda: _token_dep()), # late-bound dep
):
return await handle_llm_request(
_redis,
background_tasks,
request,
str(payload.url),
query=payload.q,
schema=payload.schema,
cache=payload.cache,
config=_config,
provider=payload.provider,
)
@router.get("/llm/job/{task_id}")
async def llm_job_status(
request: Request,
task_id: str,
_td: Dict = Depends(lambda: _token_dep())
):
return await handle_task_status(_redis, task_id)
# ---------- CRAWL job -------------------------------------------------------
@router.post("/crawl/job", status_code=202)
async def crawl_job_enqueue(
payload: CrawlJobPayload,
background_tasks: BackgroundTasks,
_td: Dict = Depends(lambda: _token_dep()),
):
return await handle_crawl_job(
_redis,
background_tasks,
[str(u) for u in payload.urls],
payload.browser_config,
payload.crawler_config,
config=_config,
)
@router.get("/crawl/job/{task_id}")
async def crawl_job_status(
request: Request,
task_id: str,
_td: Dict = Depends(lambda: _token_dep())
):
return await handle_task_status(_redis, task_id, base_url=str(request.base_url))

252
deploy/docker/mcp_bridge.py Normal file
View File

@@ -0,0 +1,252 @@
# deploy/docker/mcp_bridge.py
from __future__ import annotations
import inspect, json, re, anyio
from contextlib import suppress
from typing import Any, Callable, Dict, List, Tuple
import httpx
from fastapi import FastAPI, WebSocket, WebSocketDisconnect, HTTPException
from fastapi.responses import JSONResponse
from fastapi import Request
from sse_starlette.sse import EventSourceResponse
from pydantic import BaseModel
from mcp.server.sse import SseServerTransport
import mcp.types as t
from mcp.server.lowlevel.server import Server, NotificationOptions
from mcp.server.models import InitializationOptions
# ── optin decorators ───────────────────────────────────────────
def mcp_resource(name: str | None = None):
def deco(fn):
fn.__mcp_kind__, fn.__mcp_name__ = "resource", name
return fn
return deco
def mcp_template(name: str | None = None):
def deco(fn):
fn.__mcp_kind__, fn.__mcp_name__ = "template", name
return fn
return deco
def mcp_tool(name: str | None = None):
def deco(fn):
fn.__mcp_kind__, fn.__mcp_name__ = "tool", name
return fn
return deco
# ── HTTPproxy helper for FastAPI endpoints ─────────────────────
def _make_http_proxy(base_url: str, route):
method = list(route.methods - {"HEAD", "OPTIONS"})[0]
async def proxy(**kwargs):
# replace `/items/{id}` style params first
path = route.path
for k, v in list(kwargs.items()):
placeholder = "{" + k + "}"
if placeholder in path:
path = path.replace(placeholder, str(v))
kwargs.pop(k)
url = base_url.rstrip("/") + path
async with httpx.AsyncClient() as client:
try:
r = (
await client.get(url, params=kwargs)
if method == "GET"
else await client.request(method, url, json=kwargs)
)
r.raise_for_status()
return r.text if method == "GET" else r.json()
except httpx.HTTPStatusError as e:
# surface FastAPI error details instead of plain 500
raise HTTPException(e.response.status_code, e.response.text)
return proxy
# ── main entry point ────────────────────────────────────────────
def attach_mcp(
app: FastAPI,
*, # keywordonly
base: str = "/mcp",
name: str | None = None,
base_url: str, # eg. "http://127.0.0.1:8020"
) -> None:
"""Call once after all routes are declared to expose WS+SSE MCP endpoints."""
server_name = name or app.title or "FastAPI-MCP"
mcp = Server(server_name)
# tools: Dict[str, Callable] = {}
tools: Dict[str, Tuple[Callable, Callable]] = {}
resources: Dict[str, Callable] = {}
templates: Dict[str, Callable] = {}
# register decorated FastAPI routes
for route in app.routes:
fn = getattr(route, "endpoint", None)
kind = getattr(fn, "__mcp_kind__", None)
if not kind:
continue
key = fn.__mcp_name__ or re.sub(r"[/{}}]", "_", route.path).strip("_")
# if kind == "tool":
# tools[key] = _make_http_proxy(base_url, route)
if kind == "tool":
proxy = _make_http_proxy(base_url, route)
tools[key] = (proxy, fn)
continue
if kind == "resource":
resources[key] = fn
if kind == "template":
templates[key] = fn
# helpers for JSONSchema
def _schema(model: type[BaseModel] | None) -> dict:
return {"type": "object"} if model is None else model.model_json_schema()
def _body_model(fn: Callable) -> type[BaseModel] | None:
for p in inspect.signature(fn).parameters.values():
a = p.annotation
if inspect.isclass(a) and issubclass(a, BaseModel):
return a
return None
# MCP handlers
@mcp.list_tools()
async def _list_tools() -> List[t.Tool]:
out = []
for k, (proxy, orig_fn) in tools.items():
desc = getattr(orig_fn, "__mcp_description__", None) or inspect.getdoc(orig_fn) or ""
schema = getattr(orig_fn, "__mcp_schema__", None) or _schema(_body_model(orig_fn))
out.append(
t.Tool(name=k, description=desc, inputSchema=schema)
)
return out
@mcp.call_tool()
async def _call_tool(name: str, arguments: Dict | None) -> List[t.TextContent]:
if name not in tools:
raise HTTPException(404, "tool not found")
proxy, _ = tools[name]
try:
res = await proxy(**(arguments or {}))
except HTTPException as exc:
# map serverside errors into MCP "text/error" payloads
err = {"error": exc.status_code, "detail": exc.detail}
return [t.TextContent(type = "text", text=json.dumps(err))]
return [t.TextContent(type = "text", text=json.dumps(res, default=str))]
@mcp.list_resources()
async def _list_resources() -> List[t.Resource]:
return [
t.Resource(name=k, description=inspect.getdoc(f) or "", mime_type="application/json")
for k, f in resources.items()
]
@mcp.read_resource()
async def _read_resource(name: str) -> List[t.TextContent]:
if name not in resources:
raise HTTPException(404, "resource not found")
res = resources[name]()
return [t.TextContent(type = "text", text=json.dumps(res, default=str))]
@mcp.list_resource_templates()
async def _list_templates() -> List[t.ResourceTemplate]:
return [
t.ResourceTemplate(
name=k,
description=inspect.getdoc(f) or "",
parameters={
p: {"type": "string"} for p in _path_params(app, f)
},
)
for k, f in templates.items()
]
init_opts = InitializationOptions(
server_name=server_name,
server_version="0.1.0",
capabilities=mcp.get_capabilities(
notification_options=NotificationOptions(),
experimental_capabilities={},
),
)
# ── WebSocket transport ────────────────────────────────────
@app.websocket_route(f"{base}/ws")
async def _ws(ws: WebSocket):
await ws.accept()
c2s_send, c2s_recv = anyio.create_memory_object_stream(100)
s2c_send, s2c_recv = anyio.create_memory_object_stream(100)
from pydantic import TypeAdapter
from mcp.types import JSONRPCMessage
adapter = TypeAdapter(JSONRPCMessage)
init_done = anyio.Event()
async def srv_to_ws():
first = True
try:
async for msg in s2c_recv:
await ws.send_json(msg.model_dump())
if first:
init_done.set()
first = False
finally:
# make sure cleanup survives TaskGroup cancellation
with anyio.CancelScope(shield=True):
with suppress(RuntimeError): # idempotent close
await ws.close()
async def ws_to_srv():
try:
# 1st frame is always "initialize"
first = adapter.validate_python(await ws.receive_json())
await c2s_send.send(first)
await init_done.wait() # block until server ready
while True:
data = await ws.receive_json()
await c2s_send.send(adapter.validate_python(data))
except WebSocketDisconnect:
await c2s_send.aclose()
async with anyio.create_task_group() as tg:
tg.start_soon(mcp.run, c2s_recv, s2c_send, init_opts)
tg.start_soon(ws_to_srv)
tg.start_soon(srv_to_ws)
# ── SSE transport (official) ─────────────────────────────
sse = SseServerTransport(f"{base}/messages/")
@app.get(f"{base}/sse")
async def _mcp_sse(request: Request):
async with sse.connect_sse(
request.scope, request.receive, request._send # starlette ASGI primitives
) as (read_stream, write_stream):
await mcp.run(read_stream, write_stream, init_opts)
# client → server frames are POSTed here
app.mount(f"{base}/messages", app=sse.handle_post_message)
# ── schema endpoint ───────────────────────────────────────
@app.get(f"{base}/schema")
async def _schema_endpoint():
return JSONResponse({
"tools": [x.model_dump() for x in await _list_tools()],
"resources": [x.model_dump() for x in await _list_resources()],
"resource_templates": [x.model_dump() for x in await _list_templates()],
})
# ── helpers ────────────────────────────────────────────────────
def _route_name(path: str) -> str:
return re.sub(r"[/{}}]", "_", path).strip("_")
def _path_params(app: FastAPI, fn: Callable) -> List[str]:
for r in app.routes:
if r.endpoint is fn:
return list(r.param_convertors.keys())
return []

View File

@@ -1,10 +1,17 @@
crawl4ai
fastapi
uvicorn
fastapi>=0.115.12
uvicorn>=0.34.2
gunicorn>=23.0.0
slowapi>=0.1.9
prometheus-fastapi-instrumentator>=7.0.2
slowapi==0.1.9
prometheus-fastapi-instrumentator>=7.1.0
redis>=5.2.1
jwt>=1.3.1
dnspython>=2.7.0
email-validator>=2.2.0
email-validator==2.2.0
sse-starlette==2.2.1
pydantic>=2.11
rank-bm25==0.2.2
anyio==4.9.0
PyJWT==2.10.1
mcp>=1.6.0
websockets>=15.0.1
httpx[http2]>=0.27.2

42
deploy/docker/schemas.py Normal file
View File

@@ -0,0 +1,42 @@
from typing import List, Optional, Dict
from enum import Enum
from pydantic import BaseModel, Field
from utils import FilterType
class CrawlRequest(BaseModel):
urls: List[str] = Field(min_length=1, max_length=100)
browser_config: Optional[Dict] = Field(default_factory=dict)
crawler_config: Optional[Dict] = Field(default_factory=dict)
class MarkdownRequest(BaseModel):
"""Request body for the /md endpoint."""
url: str = Field(..., description="Absolute http/https URL to fetch")
f: FilterType = Field(FilterType.FIT, description="Contentfilter strategy: fit, raw, bm25, or llm")
q: Optional[str] = Field(None, description="Query string used by BM25/LLM filters")
c: Optional[str] = Field("0", description="Cachebust / revision counter")
provider: Optional[str] = Field(None, description="LLM provider override (e.g., 'anthropic/claude-3-opus')")
class RawCode(BaseModel):
code: str
class HTMLRequest(BaseModel):
url: str
class ScreenshotRequest(BaseModel):
url: str
screenshot_wait_for: Optional[float] = 2
output_path: Optional[str] = None
class PDFRequest(BaseModel):
url: str
output_path: Optional[str] = None
class JSEndpointRequest(BaseModel):
url: str
scripts: List[str] = Field(
...,
description="List of separated JavaScript snippets to execute"
)

View File

@@ -1,150 +1,449 @@
# ───────────────────────── server.py ─────────────────────────
"""
Crawl4AI FastAPI entrypoint
• Browser pool + global page cap
• Ratelimiting, security, metrics
• /crawl, /crawl/stream, /md, /llm endpoints
"""
# ── stdlib & 3rdparty imports ───────────────────────────────
from crawler_pool import get_crawler, close_all, janitor
from crawl4ai import AsyncWebCrawler, BrowserConfig, CrawlerRunConfig
from auth import create_access_token, get_token_dependency, TokenRequest
from pydantic import BaseModel
from typing import Optional, List, Dict
from fastapi import Request, Depends
from fastapi.responses import FileResponse
import base64
import re
from crawl4ai import AsyncWebCrawler, BrowserConfig, CrawlerRunConfig
from api import (
handle_markdown_request, handle_llm_qa,
handle_stream_crawl_request, handle_crawl_request,
stream_results
)
from schemas import (
CrawlRequest,
MarkdownRequest,
RawCode,
HTMLRequest,
ScreenshotRequest,
PDFRequest,
JSEndpointRequest,
)
from utils import (
FilterType, load_config, setup_logging, verify_email_domain
)
import os
import sys
import time
from typing import List, Optional, Dict
from fastapi import FastAPI, HTTPException, Request, Query, Path, Depends
from fastapi.responses import StreamingResponse, RedirectResponse, PlainTextResponse, JSONResponse
import asyncio
from typing import List
from contextlib import asynccontextmanager
import pathlib
from fastapi import (
FastAPI, HTTPException, Request, Path, Query, Depends
)
from rank_bm25 import BM25Okapi
from fastapi.responses import (
StreamingResponse, RedirectResponse, PlainTextResponse, JSONResponse
)
from fastapi.middleware.httpsredirect import HTTPSRedirectMiddleware
from fastapi.middleware.trustedhost import TrustedHostMiddleware
from fastapi.staticfiles import StaticFiles
from job import init_job_router
from mcp_bridge import attach_mcp, mcp_resource, mcp_template, mcp_tool
import ast
import crawl4ai as _c4
from pydantic import BaseModel, Field
from slowapi import Limiter
from slowapi.util import get_remote_address
from prometheus_fastapi_instrumentator import Instrumentator
from redis import asyncio as aioredis
# ── internal imports (after sys.path append) ─────────────────
sys.path.append(os.path.dirname(os.path.realpath(__file__)))
from utils import FilterType, load_config, setup_logging, verify_email_domain
from api import (
handle_markdown_request,
handle_llm_qa,
handle_stream_crawl_request,
handle_crawl_request,
stream_results
)
from auth import create_access_token, get_token_dependency, TokenRequest # Import from auth.py
__version__ = "0.2.6"
class CrawlRequest(BaseModel):
urls: List[str] = Field(min_length=1, max_length=100)
browser_config: Optional[Dict] = Field(default_factory=dict)
crawler_config: Optional[Dict] = Field(default_factory=dict)
# Load configuration and setup
# ────────────────── configuration / logging ──────────────────
config = load_config()
setup_logging(config)
# Initialize Redis
__version__ = "0.5.1-d1"
# ── global page semaphore (hard cap) ─────────────────────────
MAX_PAGES = config["crawler"]["pool"].get("max_pages", 30)
GLOBAL_SEM = asyncio.Semaphore(MAX_PAGES)
# import logging
# page_log = logging.getLogger("page_cap")
# orig_arun = AsyncWebCrawler.arun
# async def capped_arun(self, *a, **kw):
# await GLOBAL_SEM.acquire() # ← take slot
# try:
# in_flight = MAX_PAGES - GLOBAL_SEM._value # used permits
# page_log.info("🕸️ pages_in_flight=%s / %s", in_flight, MAX_PAGES)
# return await orig_arun(self, *a, **kw)
# finally:
# GLOBAL_SEM.release() # ← free slot
orig_arun = AsyncWebCrawler.arun
async def capped_arun(self, *a, **kw):
async with GLOBAL_SEM:
return await orig_arun(self, *a, **kw)
AsyncWebCrawler.arun = capped_arun
# ───────────────────── FastAPI lifespan ──────────────────────
@asynccontextmanager
async def lifespan(_: FastAPI):
await get_crawler(BrowserConfig(
extra_args=config["crawler"]["browser"].get("extra_args", []),
**config["crawler"]["browser"].get("kwargs", {}),
)) # warmup
app.state.janitor = asyncio.create_task(janitor()) # idle GC
yield
app.state.janitor.cancel()
await close_all()
# ───────────────────── FastAPI instance ──────────────────────
app = FastAPI(
title=config["app"]["title"],
version=config["app"]["version"],
lifespan=lifespan,
)
# ── static playground ──────────────────────────────────────
STATIC_DIR = pathlib.Path(__file__).parent / "static" / "playground"
if not STATIC_DIR.exists():
raise RuntimeError(f"Playground assets not found at {STATIC_DIR}")
app.mount(
"/playground",
StaticFiles(directory=STATIC_DIR, html=True),
name="play",
)
@app.get("/")
async def root():
return RedirectResponse("/playground")
# ─────────────────── infra / middleware ─────────────────────
redis = aioredis.from_url(config["redis"].get("uri", "redis://localhost"))
# Initialize rate limiter
limiter = Limiter(
key_func=get_remote_address,
default_limits=[config["rate_limiting"]["default_limit"]],
storage_uri=config["rate_limiting"]["storage_uri"]
storage_uri=config["rate_limiting"]["storage_uri"],
)
app = FastAPI(
title=config["app"]["title"],
version=config["app"]["version"]
)
# Configure middleware
def setup_security_middleware(app, config):
sec_config = config.get("security", {})
if sec_config.get("enabled", False):
if sec_config.get("https_redirect", False):
app.add_middleware(HTTPSRedirectMiddleware)
if sec_config.get("trusted_hosts", []) != ["*"]:
app.add_middleware(TrustedHostMiddleware, allowed_hosts=sec_config["trusted_hosts"])
def _setup_security(app_: FastAPI):
sec = config["security"]
if not sec["enabled"]:
return
if sec.get("https_redirect"):
app_.add_middleware(HTTPSRedirectMiddleware)
if sec.get("trusted_hosts", []) != ["*"]:
app_.add_middleware(
TrustedHostMiddleware, allowed_hosts=sec["trusted_hosts"]
)
setup_security_middleware(app, config)
# Prometheus instrumentation
_setup_security(app)
if config["observability"]["prometheus"]["enabled"]:
Instrumentator().instrument(app).expose(app)
# Get token dependency based on config
token_dependency = get_token_dependency(config)
token_dep = get_token_dependency(config)
# Middleware for security headers
@app.middleware("http")
async def add_security_headers(request: Request, call_next):
response = await call_next(request)
resp = await call_next(request)
if config["security"]["enabled"]:
response.headers.update(config["security"]["headers"])
return response
resp.headers.update(config["security"]["headers"])
return resp
# Token endpoint (always available, but usage depends on config)
# ───────────────── safe configdump helper ─────────────────
ALLOWED_TYPES = {
"CrawlerRunConfig": CrawlerRunConfig,
"BrowserConfig": BrowserConfig,
}
def _safe_eval_config(expr: str) -> dict:
"""
Accept exactly one toplevel call to CrawlerRunConfig(...) or BrowserConfig(...).
Whatever is inside the parentheses is fine *except* further function calls
(so no __import__('os') stuff). All public names from crawl4ai are available
when we eval.
"""
tree = ast.parse(expr, mode="eval")
# must be a single call
if not isinstance(tree.body, ast.Call):
raise ValueError("Expression must be a single constructor call")
call = tree.body
if not (isinstance(call.func, ast.Name) and call.func.id in {"CrawlerRunConfig", "BrowserConfig"}):
raise ValueError(
"Only CrawlerRunConfig(...) or BrowserConfig(...) are allowed")
# forbid nested calls to keep the surface tiny
for node in ast.walk(call):
if isinstance(node, ast.Call) and node is not call:
raise ValueError("Nested function calls are not permitted")
# expose everything that crawl4ai exports, nothing else
safe_env = {name: getattr(_c4, name)
for name in dir(_c4) if not name.startswith("_")}
obj = eval(compile(tree, "<config>", "eval"),
{"__builtins__": {}}, safe_env)
return obj.dump()
# ── job router ──────────────────────────────────────────────
app.include_router(init_job_router(redis, config, token_dep))
# ──────────────────────── Endpoints ──────────────────────────
@app.post("/token")
async def get_token(request_data: TokenRequest):
if not verify_email_domain(request_data.email):
raise HTTPException(status_code=400, detail="Invalid email domain")
token = create_access_token({"sub": request_data.email})
return {"email": request_data.email, "access_token": token, "token_type": "bearer"}
async def get_token(req: TokenRequest):
if not verify_email_domain(req.email):
raise HTTPException(400, "Invalid email domain")
token = create_access_token({"sub": req.email})
return {"email": req.email, "access_token": token, "token_type": "bearer"}
# Endpoints with conditional auth
@app.get("/md/{url:path}")
@app.post("/config/dump")
async def config_dump(raw: RawCode):
try:
return JSONResponse(_safe_eval_config(raw.code.strip()))
except Exception as e:
raise HTTPException(400, str(e))
@app.post("/md")
@limiter.limit(config["rate_limiting"]["default_limit"])
@mcp_tool("md")
async def get_markdown(
request: Request,
url: str,
f: FilterType = FilterType.FIT,
q: Optional[str] = None,
c: Optional[str] = "0",
token_data: Optional[Dict] = Depends(token_dependency)
body: MarkdownRequest,
_td: Dict = Depends(token_dep),
):
result = await handle_markdown_request(url, f, q, c, config)
return PlainTextResponse(result)
if not body.url.startswith(("http://", "https://")):
raise HTTPException(
400, "URL must be absolute and start with http/https")
markdown = await handle_markdown_request(
body.url, body.f, body.q, body.c, config, body.provider
)
return JSONResponse({
"url": body.url,
"filter": body.f,
"query": body.q,
"cache": body.c,
"markdown": markdown,
"success": True
})
@app.get("/llm/{url:path}", description="URL should be without http/https prefix")
@app.post("/html")
@limiter.limit(config["rate_limiting"]["default_limit"])
@mcp_tool("html")
async def generate_html(
request: Request,
body: HTMLRequest,
_td: Dict = Depends(token_dep),
):
"""
Crawls the URL, preprocesses the raw HTML for schema extraction, and returns the processed HTML.
Use when you need sanitized HTML structures for building schemas or further processing.
"""
cfg = CrawlerRunConfig()
async with AsyncWebCrawler(config=BrowserConfig()) as crawler:
results = await crawler.arun(url=body.url, config=cfg)
raw_html = results[0].html
from crawl4ai.utils import preprocess_html_for_schema
processed_html = preprocess_html_for_schema(raw_html)
return JSONResponse({"html": processed_html, "url": body.url, "success": True})
# Screenshot endpoint
@app.post("/screenshot")
@limiter.limit(config["rate_limiting"]["default_limit"])
@mcp_tool("screenshot")
async def generate_screenshot(
request: Request,
body: ScreenshotRequest,
_td: Dict = Depends(token_dep),
):
"""
Capture a full-page PNG screenshot of the specified URL, waiting an optional delay before capture,
Use when you need an image snapshot of the rendered page. Its recommened to provide an output path to save the screenshot.
Then in result instead of the screenshot you will get a path to the saved file.
"""
cfg = CrawlerRunConfig(
screenshot=True, screenshot_wait_for=body.screenshot_wait_for)
async with AsyncWebCrawler(config=BrowserConfig()) as crawler:
results = await crawler.arun(url=body.url, config=cfg)
screenshot_data = results[0].screenshot
if body.output_path:
abs_path = os.path.abspath(body.output_path)
os.makedirs(os.path.dirname(abs_path), exist_ok=True)
with open(abs_path, "wb") as f:
f.write(base64.b64decode(screenshot_data))
return {"success": True, "path": abs_path}
return {"success": True, "screenshot": screenshot_data}
# PDF endpoint
@app.post("/pdf")
@limiter.limit(config["rate_limiting"]["default_limit"])
@mcp_tool("pdf")
async def generate_pdf(
request: Request,
body: PDFRequest,
_td: Dict = Depends(token_dep),
):
"""
Generate a PDF document of the specified URL,
Use when you need a printable or archivable snapshot of the page. It is recommended to provide an output path to save the PDF.
Then in result instead of the PDF you will get a path to the saved file.
"""
cfg = CrawlerRunConfig(pdf=True)
async with AsyncWebCrawler(config=BrowserConfig()) as crawler:
results = await crawler.arun(url=body.url, config=cfg)
pdf_data = results[0].pdf
if body.output_path:
abs_path = os.path.abspath(body.output_path)
os.makedirs(os.path.dirname(abs_path), exist_ok=True)
with open(abs_path, "wb") as f:
f.write(pdf_data)
return {"success": True, "path": abs_path}
return {"success": True, "pdf": base64.b64encode(pdf_data).decode()}
@app.post("/execute_js")
@limiter.limit(config["rate_limiting"]["default_limit"])
@mcp_tool("execute_js")
async def execute_js(
request: Request,
body: JSEndpointRequest,
_td: Dict = Depends(token_dep),
):
"""
Execute a sequence of JavaScript snippets on the specified URL.
Return the full CrawlResult JSON (first result).
Use this when you need to interact with dynamic pages using JS.
REMEMBER: Scripts accept a list of separated JS snippets to execute and execute them in order.
IMPORTANT: Each script should be an expression that returns a value. It can be an IIFE or an async function. You can think of it as such.
Your script will replace '{script}' and execute in the browser context. So provide either an IIFE or a sync/async function that returns a value.
Return Format:
- The return result is an instance of CrawlResult, so you have access to markdown, links, and other stuff. If this is enough, you don't need to call again for other endpoints.
```python
class CrawlResult(BaseModel):
url: str
html: str
success: bool
cleaned_html: Optional[str] = None
media: Dict[str, List[Dict]] = {}
links: Dict[str, List[Dict]] = {}
downloaded_files: Optional[List[str]] = None
js_execution_result: Optional[Dict[str, Any]] = None
screenshot: Optional[str] = None
pdf: Optional[bytes] = None
mhtml: Optional[str] = None
_markdown: Optional[MarkdownGenerationResult] = PrivateAttr(default=None)
extracted_content: Optional[str] = None
metadata: Optional[dict] = None
error_message: Optional[str] = None
session_id: Optional[str] = None
response_headers: Optional[dict] = None
status_code: Optional[int] = None
ssl_certificate: Optional[SSLCertificate] = None
dispatch_result: Optional[DispatchResult] = None
redirected_url: Optional[str] = None
network_requests: Optional[List[Dict[str, Any]]] = None
console_messages: Optional[List[Dict[str, Any]]] = None
class MarkdownGenerationResult(BaseModel):
raw_markdown: str
markdown_with_citations: str
references_markdown: str
fit_markdown: Optional[str] = None
fit_html: Optional[str] = None
```
"""
cfg = CrawlerRunConfig(js_code=body.scripts)
async with AsyncWebCrawler(config=BrowserConfig()) as crawler:
results = await crawler.arun(url=body.url, config=cfg)
# Return JSON-serializable dict of the first CrawlResult
data = results[0].model_dump()
return JSONResponse(data)
@app.get("/llm/{url:path}")
async def llm_endpoint(
request: Request,
url: str = Path(...),
q: Optional[str] = Query(None),
token_data: Optional[Dict] = Depends(token_dependency)
q: str = Query(...),
_td: Dict = Depends(token_dep),
):
if not q:
raise HTTPException(status_code=400, detail="Query parameter 'q' is required")
if not url.startswith(('http://', 'https://')):
url = 'https://' + url
try:
answer = await handle_llm_qa(url, q, config)
return JSONResponse({"answer": answer})
except Exception as e:
raise HTTPException(status_code=500, detail=str(e))
raise HTTPException(400, "Query parameter 'q' is required")
if not url.startswith(("http://", "https://")):
url = "https://" + url
answer = await handle_llm_qa(url, q, config)
return JSONResponse({"answer": answer})
@app.get("/schema")
async def get_schema():
from crawl4ai import BrowserConfig, CrawlerRunConfig
return {"browser": BrowserConfig().dump(), "crawler": CrawlerRunConfig().dump()}
return {"browser": BrowserConfig().dump(),
"crawler": CrawlerRunConfig().dump()}
@app.get(config["observability"]["health_check"]["endpoint"])
async def health():
return {"status": "ok", "timestamp": time.time(), "version": __version__}
@app.get(config["observability"]["prometheus"]["endpoint"])
async def metrics():
return RedirectResponse(url=config["observability"]["prometheus"]["endpoint"])
return RedirectResponse(config["observability"]["prometheus"]["endpoint"])
@app.post("/crawl")
@limiter.limit(config["rate_limiting"]["default_limit"])
@mcp_tool("crawl")
async def crawl(
request: Request,
crawl_request: CrawlRequest,
token_data: Optional[Dict] = Depends(token_dependency)
_td: Dict = Depends(token_dep),
):
"""
Crawl a list of URLs and return the results as JSON.
"""
if not crawl_request.urls:
raise HTTPException(status_code=400, detail="At least one URL required")
results = await handle_crawl_request(
raise HTTPException(400, "At least one URL required")
res = await handle_crawl_request(
urls=crawl_request.urls,
browser_config=crawl_request.browser_config,
crawler_config=crawl_request.crawler_config,
config=config
config=config,
)
return JSONResponse(results)
return JSONResponse(res)
@app.post("/crawl/stream")
@@ -152,24 +451,161 @@ async def crawl(
async def crawl_stream(
request: Request,
crawl_request: CrawlRequest,
token_data: Optional[Dict] = Depends(token_dependency)
_td: Dict = Depends(token_dep),
):
if not crawl_request.urls:
raise HTTPException(status_code=400, detail="At least one URL required")
crawler, results_gen = await handle_stream_crawl_request(
raise HTTPException(400, "At least one URL required")
crawler, gen = await handle_stream_crawl_request(
urls=crawl_request.urls,
browser_config=crawl_request.browser_config,
crawler_config=crawl_request.crawler_config,
config=config
config=config,
)
return StreamingResponse(
stream_results(crawler, results_gen),
media_type='application/x-ndjson',
headers={'Cache-Control': 'no-cache', 'Connection': 'keep-alive', 'X-Stream-Status': 'active'}
stream_results(crawler, gen),
media_type="application/x-ndjson",
headers={
"Cache-Control": "no-cache",
"Connection": "keep-alive",
"X-Stream-Status": "active",
},
)
def chunk_code_functions(code_md: str) -> List[str]:
"""Extract each function/class from markdown code blocks per file."""
pattern = re.compile(
# match "## File: <path>" then a ```py fence, then capture until the closing ```
r'##\s*File:\s*(?P<path>.+?)\s*?\r?\n' # file header
r'```py\s*?\r?\n' # opening fence
r'(?P<code>.*?)(?=\r?\n```)', # code block
re.DOTALL
)
chunks: List[str] = []
for m in pattern.finditer(code_md):
file_path = m.group("path").strip()
code_blk = m.group("code")
tree = ast.parse(code_blk)
lines = code_blk.splitlines()
for node in tree.body:
if isinstance(node, (ast.FunctionDef, ast.AsyncFunctionDef, ast.ClassDef)):
start = node.lineno - 1
end = getattr(node, "end_lineno", start + 1)
snippet = "\n".join(lines[start:end])
chunks.append(f"# File: {file_path}\n{snippet}")
return chunks
def chunk_doc_sections(doc: str) -> List[str]:
lines = doc.splitlines(keepends=True)
sections = []
current: List[str] = []
for line in lines:
if re.match(r"^#{1,6}\s", line):
if current:
sections.append("".join(current))
current = [line]
else:
current.append(line)
if current:
sections.append("".join(current))
return sections
@app.get("/ask")
@limiter.limit(config["rate_limiting"]["default_limit"])
@mcp_tool("ask")
async def get_context(
request: Request,
_td: Dict = Depends(token_dep),
context_type: str = Query("all", regex="^(code|doc|all)$"),
query: Optional[str] = Query(
None, description="search query to filter chunks"),
score_ratio: float = Query(
0.5, ge=0.0, le=1.0, description="min score as fraction of max_score"),
max_results: int = Query(
20, ge=1, description="absolute cap on returned chunks"),
):
"""
This end point is design for any questions about Crawl4ai library. It returns a plain text markdown with extensive information about Crawl4ai.
You can use this as a context for any AI assistant. Use this endpoint for AI assistants to retrieve library context for decision making or code generation tasks.
Alway is BEST practice you provide a query to filter the context. Otherwise the lenght of the response will be very long.
Parameters:
- context_type: Specify "code" for code context, "doc" for documentation context, or "all" for both.
- query: RECOMMENDED search query to filter paragraphs using BM25. You can leave this empty to get all the context.
- score_ratio: Minimum score as a fraction of the maximum score for filtering results.
- max_results: Maximum number of results to return. Default is 20.
Returns:
- JSON response with the requested context.
- If "code" is specified, returns the code context.
- If "doc" is specified, returns the documentation context.
- If "all" is specified, returns both code and documentation contexts.
"""
# load contexts
base = os.path.dirname(__file__)
code_path = os.path.join(base, "c4ai-code-context.md")
doc_path = os.path.join(base, "c4ai-doc-context.md")
if not os.path.exists(code_path) or not os.path.exists(doc_path):
raise HTTPException(404, "Context files not found")
with open(code_path, "r") as f:
code_content = f.read()
with open(doc_path, "r") as f:
doc_content = f.read()
# if no query, just return raw contexts
if not query:
if context_type == "code":
return JSONResponse({"code_context": code_content})
if context_type == "doc":
return JSONResponse({"doc_context": doc_content})
return JSONResponse({
"code_context": code_content,
"doc_context": doc_content,
})
tokens = query.split()
results: Dict[str, List[Dict[str, float]]] = {}
# code BM25 over functions/classes
if context_type in ("code", "all"):
code_chunks = chunk_code_functions(code_content)
bm25 = BM25Okapi([c.split() for c in code_chunks])
scores = bm25.get_scores(tokens)
max_sc = float(scores.max()) if scores.size > 0 else 0.0
cutoff = max_sc * score_ratio
picked = [(c, s) for c, s in zip(code_chunks, scores) if s >= cutoff]
picked = sorted(picked, key=lambda x: x[1], reverse=True)[:max_results]
results["code_results"] = [{"text": c, "score": s} for c, s in picked]
# doc BM25 over markdown sections
if context_type in ("doc", "all"):
sections = chunk_doc_sections(doc_content)
bm25d = BM25Okapi([sec.split() for sec in sections])
scores_d = bm25d.get_scores(tokens)
max_sd = float(scores_d.max()) if scores_d.size > 0 else 0.0
cutoff_d = max_sd * score_ratio
idxs = [i for i, s in enumerate(scores_d) if s >= cutoff_d]
neighbors = set(i for idx in idxs for i in (idx-1, idx, idx+1))
valid = [i for i in sorted(neighbors) if 0 <= i < len(sections)]
valid = valid[:max_results]
results["doc_results"] = [
{"text": sections[i], "score": scores_d[i]} for i in valid
]
return JSONResponse(results)
# attach MCP layer (adds /mcp/ws, /mcp/sse, /mcp/schema)
print(f"MCP server running on {config['app']['host']}:{config['app']['port']}")
attach_mcp(
app,
base_url=f"http://{config['app']['host']}:{config['app']['port']}"
)
# ────────────────────────── cli ──────────────────────────────
if __name__ == "__main__":
import uvicorn
uvicorn.run(
@@ -177,5 +613,6 @@ if __name__ == "__main__":
host=config["app"]["host"],
port=config["app"]["port"],
reload=config["app"]["reload"],
timeout_keep_alive=config["app"]["timeout_keep_alive"]
)
timeout_keep_alive=config["app"]["timeout_keep_alive"],
)
# ─────────────────────────────────────────────────────────────

View File

@@ -0,0 +1,965 @@
<!DOCTYPE html>
<html lang="en">
<head>
<meta charset="UTF-8">
<meta name="viewport" content="width=device-width, initial-scale=1.0">
<title>Crawl4AI Playground</title>
<script src="https://cdn.tailwindcss.com"></script>
<script>
tailwind.config = {
theme: {
extend: {
colors: {
primary: '#4EFFFF',
primarydim: '#09b5a5',
accent: '#F380F5',
dark: '#070708',
light: '#E8E9ED',
secondary: '#D5CEBF',
codebg: '#1E1E1E',
surface: '#202020',
border: '#3F3F44',
},
fontFamily: {
mono: ['Fira Code', 'monospace'],
},
}
}
}
</script>
<link href="https://fonts.googleapis.com/css2?family=Fira+Code:wght@400;500&display=swap" rel="stylesheet">
<!-- Highlight.js -->
<link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/highlight.js/11.9.0/styles/github-dark.min.css">
<script src="https://cdnjs.cloudflare.com/ajax/libs/highlight.js/11.9.0/highlight.min.js"></script>
<script src="https://cdnjs.cloudflare.com/ajax/libs/clipboard.js/2.0.11/clipboard.min.js"></script>
<!-- CodeMirror (python mode) -->
<link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/codemirror/5.65.16/codemirror.min.css">
<script src="https://cdnjs.cloudflare.com/ajax/libs/codemirror/5.65.16/codemirror.min.js"></script>
<script src="https://cdnjs.cloudflare.com/ajax/libs/codemirror/5.65.16/mode/python/python.min.js"></script>
<script src="https://cdnjs.cloudflare.com/ajax/libs/codemirror/5.65.16/addon/edit/matchbrackets.min.js"></script>
<script src="https://cdnjs.cloudflare.com/ajax/libs/codemirror/5.65.16/addon/selection/active-line.min.js"></script>
<link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/codemirror/5.65.16/theme/darcula.min.css">
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<script src="https://cdnjs.cloudflare.com/ajax/libs/highlight.js/11.9.0/languages/json.min.js"></script> -->
<style>
/* Custom CodeMirror styling to match theme */
.CodeMirror {
background-color: #1E1E1E !important;
color: #E8E9ED !important;
border-radius: 4px;
font-family: 'Fira Code', monospace;
font-size: 0.9rem;
}
.CodeMirror-gutters {
background-color: #1E1E1E !important;
border-right: 1px solid #3F3F44 !important;
}
.CodeMirror-linenumber {
color: #3F3F44 !important;
}
.cm-s-darcula .cm-keyword {
color: #4EFFFF !important;
}
.cm-s-darcula .cm-string {
color: #F380F5 !important;
}
.cm-s-darcula .cm-number {
color: #D5CEBF !important;
}
/* Add to your <style> section or Tailwind config */
.hljs {
background: #1E1E1E !important;
border-radius: 4px;
padding: 1rem !important;
}
pre code.hljs {
display: block;
overflow-x: auto;
}
/* Language-specific colors */
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color: #4EFFFF;
}
/* JSON keys */
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color: #F380F5;
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color: #D5CEBF;
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/* Numbers */
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color: #4EFFFF;
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pre code {
white-space: pre-wrap;
word-break: break-word;
}
.copy-btn {
transition: all 0.2s ease;
opacity: 0.7;
}
.copy-btn:hover {
opacity: 1;
}
.tab-content:hover .copy-btn {
opacity: 0.7;
}
.tab-content:hover .copy-btn:hover {
opacity: 1;
}
/* copid text highlighted */
.highlighted {
background-color: rgba(78, 255, 255, 0.2) !important;
transition: background-color 0.5s ease;
}
</style>
</head>
<body class="bg-dark text-light font-mono min-h-screen flex flex-col" style="font-feature-settings: 'calt' 0;">
<!-- Header -->
<header class="border-b border-border px-4 py-2 flex items-center">
<h1 class="text-lg font-medium flex items-center space-x-4">
<span>🚀🤖 <span class="text-primary">Crawl4AI</span> Playground</span>
<!-- GitHub badges -->
<a href="https://github.com/unclecode/crawl4ai" target="_blank" class="flex space-x-1">
<img src="https://img.shields.io/github/stars/unclecode/crawl4ai?style=social"
alt="GitHub stars" class="h-5">
<img src="https://img.shields.io/github/forks/unclecode/crawl4ai?style=social"
alt="GitHub forks" class="h-5">
</a>
<!-- Docs -->
<a href="https://docs.crawl4ai.com" target="_blank"
class="text-xs text-secondary hover:text-primary underline flex items-center">
Docs
</a>
<!-- X (Twitter) follow -->
<a href="https://x.com/unclecode" target="_blank"
class="hover:text-primary flex items-center" title="Follow @unclecode on X">
<svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24"
class="w-4 h-4 fill-current mr-1">
<path d="M22.46 6c-.77.35-1.6.58-2.46.69a4.27 4.27 0 001.88-2.35 8.53 8.53 0 01-2.71 1.04 4.24 4.24 0 00-7.23 3.87A12.05 12.05 0 013 4.62a4.24 4.24 0 001.31 5.65 4.2 4.2 0 01-1.92-.53v.05a4.24 4.24 0 003.4 4.16 4.31 4.31 0 01-1.91.07 4.25 4.25 0 003.96 2.95A8.5 8.5 0 012 19.55a12.04 12.04 0 006.53 1.92c7.84 0 12.13-6.49 12.13-12.13 0-.18-.01-.36-.02-.54A8.63 8.63 0 0024 5.1a8.45 8.45 0 01-2.54.7z"/>
</svg>
<span class="text-xs">@unclecode</span>
</a>
</h1>
<div class="ml-auto flex space-x-2">
<button id="play-tab"
class="px-3 py-1 rounded-t bg-surface border border-b-0 border-border text-primary">Playground</button>
<button id="stress-tab" class="px-3 py-1 rounded-t border border-border hover:bg-surface">Stress
Test</button>
</div>
</header>
<!-- Main Playground -->
<main id="playground" class="flex-1 flex flex-col p-4 space-y-4 max-w-5xl w-full mx-auto">
<!-- Request Builder -->
<section class="bg-surface rounded-lg border border-border overflow-hidden">
<div class="px-4 py-2 border-b border-border flex items-center">
<h2 class="font-medium">Request Builder</h2>
<select id="endpoint" class="ml-auto bg-dark border border-border rounded px-2 py-1 text-sm">
<option value="crawl">/crawl (batch)</option>
<option value="crawl_stream">/crawl/stream</option>
<option value="md">/md</option>
<option value="llm">/llm</option>
</select>
</div>
<div class="p-4">
<label class="block mb-2 text-sm">URL(s) - one per line</label>
<textarea id="urls" class="w-full bg-dark border border-border rounded p-2 h-32 text-sm mb-4"
spellcheck="false">https://example.com</textarea>
<!-- Specific options for /md endpoint -->
<details id="md-options" class="mb-4 hidden">
<summary class="text-sm text-secondary cursor-pointer">/md Options</summary>
<div class="mt-2 space-y-3 p-2 border border-border rounded">
<div>
<label for="md-filter" class="block text-xs text-secondary mb-1">Filter Type</label>
<select id="md-filter" class="bg-dark border border-border rounded px-2 py-1 text-sm w-full">
<option value="fit">fit - Adaptive content filtering</option>
<option value="raw">raw - No filtering</option>
<option value="bm25">bm25 - BM25 keyword relevance</option>
<option value="llm">llm - LLM-based filtering</option>
</select>
</div>
<div>
<label for="md-query" class="block text-xs text-secondary mb-1">Query (for BM25/LLM filters)</label>
<input id="md-query" type="text" placeholder="Enter search terms or instructions"
class="bg-dark border border-border rounded px-2 py-1 text-sm w-full">
</div>
<div>
<label for="md-cache" class="block text-xs text-secondary mb-1">Cache Mode</label>
<select id="md-cache" class="bg-dark border border-border rounded px-2 py-1 text-sm w-full">
<option value="0">Write-Only (0)</option>
<option value="1">Enabled (1)</option>
</select>
</div>
</div>
</details>
<!-- Specific options for /llm endpoint -->
<details id="llm-options" class="mb-4 hidden">
<summary class="text-sm text-secondary cursor-pointer">/llm Options</summary>
<div class="mt-2 space-y-3 p-2 border border-border rounded">
<div>
<label for="llm-question" class="block text-xs text-secondary mb-1">Question</label>
<input id="llm-question" type="text" value="What is this page about?"
class="bg-dark border border-border rounded px-2 py-1 text-sm w-full">
</div>
</div>
</details>
<!-- Advanced config for /crawl endpoints -->
<details id="adv-config" class="mb-4">
<summary class="text-sm text-secondary cursor-pointer">Advanced Config <span
class="text-xs text-primary">(Python → autoJSON)</span></summary>
<!-- Toolbar -->
<div class="flex items-center justify-end space-x-3 mt-2">
<label for="cfg-type" class="text-xs text-secondary">Type:</label>
<select id="cfg-type"
class="bg-dark border border-border rounded px-1 py-0.5 text-xs">
<option value="CrawlerRunConfig">CrawlerRunConfig</option>
<option value="BrowserConfig">BrowserConfig</option>
</select>
<!-- help link -->
<a href="https://docs.crawl4ai.com/api/parameters/"
target="_blank"
class="text-xs text-primary hover:underline flex items-center space-x-1"
title="Open parameter reference in new tab">
<svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24"
class="w-4 h-4 fill-current">
<path d="M13 3h8v8h-2V6.41l-9.29 9.3-1.42-1.42 9.3-9.29H13V3z"/>
<path d="M5 5h4V3H3v6h2V5zm0 14v-4H3v6h6v-2H5z"/>
</svg>
<span>Docs</span>
</a>
<span id="cfg-status" class="text-xs text-secondary ml-2"></span>
</div>
<!-- CodeMirror host -->
<div id="adv-editor" class="mt-2 border border-border rounded overflow-hidden h-40"></div>
</details>
<div class="flex space-x-2">
<button id="run-btn" class="bg-primary text-dark px-4 py-2 rounded hover:bg-primarydim font-medium">
Run (⌘/Ctrl+Enter)
</button>
<button id="export-btn" class="border border-border px-4 py-2 rounded hover:bg-surface hidden">
Export Python Code
</button>
</div>
</div>
</section>
<!-- Execution Status -->
<section id="execution-status" class="hidden bg-surface rounded-lg border border-border p-3 text-sm">
<div class="flex space-x-4">
<div id="status-badge" class="flex items-center">
<span class="w-3 h-3 rounded-full mr-2"></span>
<span>Ready</span>
</div>
<div>
<span class="text-secondary">Time:</span>
<span id="exec-time" class="text-light">-</span>
</div>
<div>
<span class="text-secondary">Memory:</span>
<span id="exec-mem" class="text-light">-</span>
</div>
</div>
</section>
<!-- Response Viewer -->
<!-- Update the Response Viewer section -->
<section class="bg-surface rounded-lg border border-border overflow-hidden flex-1 flex flex-col">
<div class="border-b border-border flex">
<button data-tab="response" class="tab-btn active px-4 py-2 border-r border-border">Response</button>
<button data-tab="python" class="tab-btn px-4 py-2 border-r border-border">Python</button>
<button data-tab="curl" class="tab-btn px-4 py-2">cURL</button>
</div>
<div class="flex-1 overflow-auto relative">
<!-- Response Tab -->
<div class="tab-content active h-full">
<div class="absolute right-2 top-2">
<button class="copy-btn bg-surface border border-border rounded px-2 py-1 text-xs hover:bg-dark"
data-target="#response-content code">
Copy
</button>
</div>
<pre id="response-content" class="p-4 text-sm h-full"><code class="json hljs">{}</code></pre>
</div>
<!-- Python Tab -->
<div class="tab-content hidden h-full">
<div class="absolute right-2 top-2">
<button class="copy-btn bg-surface border border-border rounded px-2 py-1 text-xs hover:bg-dark"
data-target="#python-content code">
Copy
</button>
</div>
<pre id="python-content" class="p-4 text-sm h-full"><code class="python hljs"></code></pre>
</div>
<!-- cURL Tab -->
<div class="tab-content hidden h-full">
<div class="absolute right-2 top-2">
<button class="copy-btn bg-surface border border-border rounded px-2 py-1 text-xs hover:bg-dark"
data-target="#curl-content code">
Copy
</button>
</div>
<pre id="curl-content" class="p-4 text-sm h-full"><code class="bash hljs"></code></pre>
</div>
</div>
</section>
</main>
<!-- Stress Test Modal -->
<div id="stress-modal"
class="hidden fixed inset-0 bg-black bg-opacity-70 z-50 flex items-center justify-center p-4">
<div class="bg-surface rounded-lg border border-accent w-full max-w-3xl max-h-[90vh] flex flex-col">
<div class="px-4 py-2 border-b border-border flex items-center">
<h2 class="font-medium text-accent">🔥 Stress Test</h2>
<button id="close-stress" class="ml-auto text-secondary hover:text-light">&times;</button>
</div>
<div class="p-4 space-y-4 flex-1 overflow-auto">
<div class="grid grid-cols-3 gap-4">
<div>
<label class="block text-sm mb-1">Total URLs</label>
<input id="st-total" type="number" value="20"
class="w-full bg-dark border border-border rounded px-3 py-1">
</div>
<div>
<label class="block text-sm mb-1">Chunk Size</label>
<input id="st-chunk" type="number" value="5"
class="w-full bg-dark border border-border rounded px-3 py-1">
</div>
<div>
<label class="block text-sm mb-1">Concurrency</label>
<input id="st-conc" type="number" value="2"
class="w-full bg-dark border border-border rounded px-3 py-1">
</div>
</div>
<div class="flex items-center">
<input id="st-stream" type="checkbox" class="mr-2">
<label for="st-stream" class="text-sm">Use /crawl/stream</label>
<button id="st-run"
class="ml-auto bg-accent text-dark px-4 py-2 rounded hover:bg-opacity-90 font-medium">
Run Stress Test
</button>
</div>
<div class="mt-4">
<div class="bg-dark rounded border border-border p-3 h-64 overflow-auto text-sm whitespace-break-spaces"
id="stress-log"></div>
</div>
</div>
<div class="px-4 py-2 border-t border-border text-sm text-secondary">
<div class="flex justify-between">
<span>Completed: <span id="stress-completed">0</span>/<span id="stress-total">0</span></span>
<span>Avg. Time: <span id="stress-avg-time">0</span>ms</span>
<span>Peak Memory: <span id="stress-peak-mem">0</span>MB</span>
</div>
</div>
</div>
</div>
<script>
// Tab switching
document.querySelectorAll('.tab-btn').forEach(btn => {
btn.addEventListener('click', () => {
document.querySelectorAll('.tab-btn').forEach(b => b.classList.remove('active'));
document.querySelectorAll('.tab-content').forEach(c => c.classList.add('hidden'));
btn.classList.add('active');
const tabName = btn.dataset.tab;
document.querySelector(`#${tabName}-content`).parentElement.classList.remove('hidden');
// Re-highlight content when switching tabs
const activeCode = document.querySelector(`#${tabName}-content code`);
if (activeCode) {
forceHighlightElement(activeCode);
}
});
});
// View switching
document.getElementById('play-tab').addEventListener('click', () => {
document.getElementById('playground').classList.remove('hidden');
document.getElementById('stress-modal').classList.add('hidden');
document.getElementById('play-tab').classList.add('bg-surface', 'border-b-0');
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});
// Initialize clipboard and highlight.js
new ClipboardJS('#export-btn');
hljs.highlightAll();
// Keyboard shortcut
window.addEventListener('keydown', e => {
if ((e.ctrlKey || e.metaKey) && e.key === 'Enter') {
document.getElementById('run-btn').click();
}
});
// ================ ADVANCED CONFIG EDITOR ================
const cm = CodeMirror(document.getElementById('adv-editor'), {
value: `CrawlerRunConfig(
stream=True,
cache_mode=CacheMode.BYPASS,
)`,
mode: 'python',
lineNumbers: true,
theme: 'darcula',
tabSize: 4,
styleActiveLine: true,
matchBrackets: true,
gutters: ["CodeMirror-linenumbers"],
lineWrapping: true,
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const TEMPLATES = {
CrawlerRunConfig: `CrawlerRunConfig(
stream=True,
cache_mode=CacheMode.BYPASS,
)`,
BrowserConfig: `BrowserConfig(
headless=True,
extra_args=[
"--no-sandbox",
"--disable-gpu",
],
)`,
};
document.getElementById('cfg-type').addEventListener('change', (e) => {
cm.setValue(TEMPLATES[e.target.value]);
document.getElementById('cfg-status').textContent = '';
});
// Handle endpoint selection change to show appropriate options
document.getElementById('endpoint').addEventListener('change', function(e) {
const endpoint = e.target.value;
const mdOptions = document.getElementById('md-options');
const llmOptions = document.getElementById('llm-options');
const advConfig = document.getElementById('adv-config');
// Hide all option sections first
mdOptions.classList.add('hidden');
llmOptions.classList.add('hidden');
advConfig.classList.add('hidden');
// Show the appropriate section based on endpoint
if (endpoint === 'md') {
mdOptions.classList.remove('hidden');
// Auto-open the /md options
mdOptions.setAttribute('open', '');
} else if (endpoint === 'llm') {
llmOptions.classList.remove('hidden');
// Auto-open the /llm options
llmOptions.setAttribute('open', '');
} else {
// For /crawl endpoints, show the advanced config
advConfig.classList.remove('hidden');
}
});
async function pyConfigToJson() {
const code = cm.getValue().trim();
if (!code) return {};
const res = await fetch('/config/dump', {
method: 'POST',
headers: { 'Content-Type': 'application/json' },
body: JSON.stringify({ code }),
});
const statusEl = document.getElementById('cfg-status');
if (!res.ok) {
const msg = await res.text();
statusEl.textContent = '✖ config error';
statusEl.className = 'text-xs text-red-400';
throw new Error(msg || 'Invalid config');
}
statusEl.textContent = '✓ parsed';
statusEl.className = 'text-xs text-green-400';
return await res.json();
}
// ================ SERVER COMMUNICATION ================
// Update status UI
function updateStatus(status, time, memory, peakMemory) {
const statusEl = document.getElementById('execution-status');
const badgeEl = document.querySelector('#status-badge span:first-child');
const textEl = document.querySelector('#status-badge span:last-child');
statusEl.classList.remove('hidden');
badgeEl.className = 'w-3 h-3 rounded-full mr-2';
if (status === 'success') {
badgeEl.classList.add('bg-green-500');
textEl.textContent = 'Success';
} else if (status === 'error') {
badgeEl.classList.add('bg-red-500');
textEl.textContent = 'Error';
} else {
badgeEl.classList.add('bg-yellow-500');
textEl.textContent = 'Processing...';
}
if (time) {
document.getElementById('exec-time').textContent = `${time}ms`;
}
if (memory !== undefined && peakMemory !== undefined) {
document.getElementById('exec-mem').textContent = `Δ${memory >= 0 ? '+' : ''}${memory}MB (Peak: ${peakMemory}MB)`;
}
}
// Generate code snippets
function generateSnippets(api, payload, method = 'POST') {
// Python snippet
const pyCodeEl = document.querySelector('#python-content code');
let pySnippet;
if (method === 'GET') {
// GET request (for /llm endpoint)
pySnippet = `import httpx\n\nasync def crawl():\n async with httpx.AsyncClient() as client:\n response = await client.get(\n "${window.location.origin}${api}"\n )\n return response.json()`;
} else {
// POST request (for /crawl and /md endpoints)
pySnippet = `import httpx\n\nasync def crawl():\n async with httpx.AsyncClient() as client:\n response = await client.post(\n "${window.location.origin}${api}",\n json=${JSON.stringify(payload, null, 4).replace(/\n/g, '\n ')}\n )\n return response.json()`;
}
pyCodeEl.textContent = pySnippet;
pyCodeEl.className = 'python hljs'; // Reset classes
forceHighlightElement(pyCodeEl);
// cURL snippet
const curlCodeEl = document.querySelector('#curl-content code');
let curlSnippet;
if (method === 'GET') {
// GET request (for /llm endpoint)
curlSnippet = `curl -X GET "${window.location.origin}${api}"`;
} else {
// POST request (for /crawl and /md endpoints)
curlSnippet = `curl -X POST ${window.location.origin}${api} \\\n -H "Content-Type: application/json" \\\n -d '${JSON.stringify(payload)}'`;
}
curlCodeEl.textContent = curlSnippet;
curlCodeEl.className = 'bash hljs'; // Reset classes
forceHighlightElement(curlCodeEl);
}
// Main run function
async function runCrawl() {
const endpoint = document.getElementById('endpoint').value;
const urls = document.getElementById('urls').value.trim().split(/\n/).filter(u => u);
// 1) grab python from CodeMirror, validate via /config/dump
let advConfig = {};
try {
const cfgJson = await pyConfigToJson(); // may throw
if (Object.keys(cfgJson).length) {
const cfgType = document.getElementById('cfg-type').value;
advConfig = cfgType === 'CrawlerRunConfig'
? { crawler_config: cfgJson }
: { browser_config: cfgJson };
}
} catch (err) {
updateStatus('error');
document.querySelector('#response-content code').textContent =
JSON.stringify({ error: err.message }, null, 2);
forceHighlightElement(document.querySelector('#response-content code'));
return; // stop run
}
const endpointMap = {
crawl: '/crawl',
// crawl_stream: '/crawl/stream',
md: '/md',
llm: '/llm'
};
const api = endpointMap[endpoint];
let payload;
// Create appropriate payload based on endpoint type
if (endpoint === 'md') {
// Get values from the /md specific inputs
const filterType = document.getElementById('md-filter').value;
const query = document.getElementById('md-query').value.trim();
const cache = document.getElementById('md-cache').value;
// MD endpoint expects: { url, f, q, c }
payload = {
url: urls[0], // Take first URL
f: filterType, // Lowercase filter type as required by server
q: query || null, // Use the query if provided, otherwise null
c: cache
};
} else if (endpoint === 'llm') {
// LLM endpoint has a different URL pattern and uses query params
// This will be handled directly in the fetch below
payload = null;
} else {
// Default payload for /crawl and /crawl/stream
payload = {
urls,
...advConfig
};
}
updateStatus('processing');
try {
const startTime = performance.now();
let response, responseData;
if (endpoint === 'llm') {
// Special handling for LLM endpoint which uses URL pattern: /llm/{encoded_url}?q={query}
const url = urls[0];
const encodedUrl = encodeURIComponent(url);
// Get the question from the LLM-specific input
const question = document.getElementById('llm-question').value.trim() || "What is this page about?";
response = await fetch(`${api}/${encodedUrl}?q=${encodeURIComponent(question)}`, {
method: 'GET',
headers: { 'Accept': 'application/json' }
});
responseData = await response.json();
const time = Math.round(performance.now() - startTime);
if (!response.ok) {
updateStatus('error', time);
throw new Error(responseData.error || 'Request failed');
}
updateStatus('success', time);
document.querySelector('#response-content code').textContent = JSON.stringify(responseData, null, 2);
document.querySelector('#response-content code').className = 'json hljs';
forceHighlightElement(document.querySelector('#response-content code'));
} else if (endpoint === 'crawl_stream') {
// Stream processing
response = await fetch(api, {
method: 'POST',
headers: { 'Content-Type': 'application/json' },
body: JSON.stringify(payload)
});
const reader = response.body.getReader();
let text = '';
let maxMemory = 0;
while (true) {
const { value, done } = await reader.read();
if (done) break;
const chunk = new TextDecoder().decode(value);
text += chunk;
// Process each line for memory updates
chunk.trim().split('\n').forEach(line => {
if (!line) return;
try {
const obj = JSON.parse(line);
if (obj.server_memory_mb) {
maxMemory = Math.max(maxMemory, obj.server_memory_mb);
}
} catch (e) {
console.error('Error parsing stream line:', e);
}
});
}
responseData = { stream: text };
const time = Math.round(performance.now() - startTime);
updateStatus('success', time, null, maxMemory);
document.querySelector('#response-content code').textContent = text;
document.querySelector('#response-content code').className = 'json hljs'; // Reset classes
forceHighlightElement(document.querySelector('#response-content code'));
} else {
// Regular request (handles /crawl and /md)
response = await fetch(api, {
method: 'POST',
headers: { 'Content-Type': 'application/json' },
body: JSON.stringify(payload)
});
responseData = await response.json();
const time = Math.round(performance.now() - startTime);
if (!response.ok) {
updateStatus('error', time);
throw new Error(responseData.error || 'Request failed');
}
updateStatus(
'success',
time,
responseData.server_memory_delta_mb,
responseData.server_peak_memory_mb
);
document.querySelector('#response-content code').textContent = JSON.stringify(responseData, null, 2);
document.querySelector('#response-content code').className = 'json hljs'; // Ensure class is set
forceHighlightElement(document.querySelector('#response-content code'));
}
forceHighlightElement(document.querySelector('#response-content code'));
// For generateSnippets, handle the LLM case specially
if (endpoint === 'llm') {
const url = urls[0];
const encodedUrl = encodeURIComponent(url);
const question = document.getElementById('llm-question').value.trim() || "What is this page about?";
generateSnippets(`${api}/${encodedUrl}?q=${encodeURIComponent(question)}`, null, 'GET');
} else {
generateSnippets(api, payload);
}
} catch (error) {
console.error('Error:', error);
updateStatus('error');
document.querySelector('#response-content code').textContent = JSON.stringify(
{ error: error.message },
null,
2
);
forceHighlightElement(document.querySelector('#response-content code'));
}
}
// Stress test function
async function runStressTest() {
const total = parseInt(document.getElementById('st-total').value);
const chunkSize = parseInt(document.getElementById('st-chunk').value);
const concurrency = parseInt(document.getElementById('st-conc').value);
const useStream = document.getElementById('st-stream').checked;
const logEl = document.getElementById('stress-log');
logEl.textContent = '';
document.getElementById('stress-completed').textContent = '0';
document.getElementById('stress-total').textContent = total;
document.getElementById('stress-avg-time').textContent = '0';
document.getElementById('stress-peak-mem').textContent = '0';
const api = useStream ? '/crawl/stream' : '/crawl';
const urls = Array.from({ length: total }, (_, i) => `https://httpbin.org/anything/stress-${i}-${Date.now()}`);
const chunks = [];
for (let i = 0; i < urls.length; i += chunkSize) {
chunks.push(urls.slice(i, i + chunkSize));
}
let completed = 0;
let totalTime = 0;
let peakMemory = 0;
const processBatch = async (batch, index) => {
const payload = {
urls: batch,
browser_config: {},
crawler_config: { cache_mode: 'BYPASS', stream: useStream }
};
const start = performance.now();
let time, memory;
try {
if (useStream) {
const response = await fetch(api, {
method: 'POST',
headers: { 'Content-Type': 'application/json' },
body: JSON.stringify(payload)
});
const reader = response.body.getReader();
let maxMem = 0;
while (true) {
const { value, done } = await reader.read();
if (done) break;
const text = new TextDecoder().decode(value);
text.split('\n').forEach(line => {
try {
const obj = JSON.parse(line);
if (obj.server_memory_mb) {
maxMem = Math.max(maxMem, obj.server_memory_mb);
}
} catch { }
});
}
memory = maxMem;
} else {
const response = await fetch(api, {
method: 'POST',
headers: { 'Content-Type': 'application/json' },
body: JSON.stringify(payload)
});
const data = await response.json();
memory = data.server_peak_memory_mb;
}
time = Math.round(performance.now() - start);
peakMemory = Math.max(peakMemory, memory || 0);
totalTime += time;
logEl.textContent += `[${index + 1}/${chunks.length}] ✔ ${time}ms | Peak ${memory}MB\n`;
} catch (error) {
time = Math.round(performance.now() - start);
logEl.textContent += `[${index + 1}/${chunks.length}] ✖ ${time}ms | ${error.message}\n`;
}
completed += batch.length;
document.getElementById('stress-completed').textContent = completed;
document.getElementById('stress-peak-mem').textContent = peakMemory;
document.getElementById('stress-avg-time').textContent = Math.round(totalTime / (index + 1));
logEl.scrollTop = logEl.scrollHeight;
};
// Run with concurrency control
let active = 0;
let index = 0;
return new Promise(resolve => {
const runNext = () => {
while (active < concurrency && index < chunks.length) {
processBatch(chunks[index], index)
.finally(() => {
active--;
runNext();
});
active++;
index++;
}
if (active === 0 && index >= chunks.length) {
logEl.textContent += '\n✅ Stress test completed\n';
resolve();
}
};
runNext();
});
}
// Event listeners
document.getElementById('run-btn').addEventListener('click', runCrawl);
document.getElementById('st-run').addEventListener('click', runStressTest);
function forceHighlightElement(element) {
if (!element) return;
// Save current scroll position (important for large code blocks)
const scrollTop = element.parentElement.scrollTop;
// Reset the element
const text = element.textContent;
element.innerHTML = text;
element.removeAttribute('data-highlighted');
// Reapply highlighting
hljs.highlightElement(element);
// Restore scroll position
element.parentElement.scrollTop = scrollTop;
}
// Initialize clipboard for all copy buttons
function initCopyButtons() {
document.querySelectorAll('.copy-btn').forEach(btn => {
new ClipboardJS(btn, {
text: () => {
const target = document.querySelector(btn.dataset.target);
return target ? target.textContent : '';
}
}).on('success', e => {
e.clearSelection();
// make button text "copied" for 1 second
const originalText = e.trigger.textContent;
e.trigger.textContent = 'Copied!';
setTimeout(() => {
e.trigger.textContent = originalText;
}, 1000);
// Highlight the copied code
const target = document.querySelector(btn.dataset.target);
if (target) {
target.classList.add('highlighted');
setTimeout(() => {
target.classList.remove('highlighted');
}, 1000);
}
}).on('error', e => {
console.error('Error copying:', e);
});
});
}
// Function to initialize UI based on selected endpoint
function initUI() {
// Trigger the endpoint change handler to set initial UI state
const endpointSelect = document.getElementById('endpoint');
const event = new Event('change');
endpointSelect.dispatchEvent(event);
// Initialize copy buttons
initCopyButtons();
}
// Initialize on page load
document.addEventListener('DOMContentLoaded', initUI);
// Also call it immediately in case the script runs after DOM is already loaded
if (document.readyState !== 'loading') {
initUI();
}
</script>
</body>
</html>

View File

@@ -1,12 +1,28 @@
[supervisord]
nodaemon=true
nodaemon=true ; Run supervisord in the foreground
logfile=/dev/null ; Log supervisord output to stdout/stderr
logfile_maxbytes=0
[program:redis]
command=redis-server
command=/usr/bin/redis-server --loglevel notice ; Path to redis-server on Alpine
user=appuser ; Run redis as our non-root user
autorestart=true
priority=10
stdout_logfile=/dev/stdout ; Redirect redis stdout to container stdout
stdout_logfile_maxbytes=0
stderr_logfile=/dev/stderr ; Redirect redis stderr to container stderr
stderr_logfile_maxbytes=0
[program:gunicorn]
command=gunicorn --bind 0.0.0.0:8000 --workers 4 --threads 2 --timeout 300 --graceful-timeout 60 --keep-alive 65 --log-level debug --worker-class uvicorn.workers.UvicornWorker --max-requests 1000 --max-requests-jitter 50 server:app
command=/usr/local/bin/gunicorn --bind 0.0.0.0:11235 --workers 1 --threads 4 --timeout 1800 --graceful-timeout 30 --keep-alive 300 --log-level info --worker-class uvicorn.workers.UvicornWorker server:app
directory=/app ; Working directory for the app
user=appuser ; Run gunicorn as our non-root user
autorestart=true
priority=20
priority=20
environment=PYTHONUNBUFFERED=1 ; Ensure Python output is sent straight to logs
stdout_logfile=/dev/stdout ; Redirect gunicorn stdout to container stdout
stdout_logfile_maxbytes=0
stderr_logfile=/dev/stderr ; Redirect gunicorn stderr to container stderr
stderr_logfile_maxbytes=0
# Optional: Add filebeat or other logging agents here if needed

View File

@@ -1,6 +1,7 @@
import dns.resolver
import logging
import yaml
import os
from datetime import datetime
from enum import Enum
from pathlib import Path
@@ -19,10 +20,24 @@ class FilterType(str, Enum):
LLM = "llm"
def load_config() -> Dict:
"""Load and return application configuration."""
"""Load and return application configuration with environment variable overrides."""
config_path = Path(__file__).parent / "config.yml"
with open(config_path, "r") as config_file:
return yaml.safe_load(config_file)
config = yaml.safe_load(config_file)
# Override LLM provider from environment if set
llm_provider = os.environ.get("LLM_PROVIDER")
if llm_provider:
config["llm"]["provider"] = llm_provider
logging.info(f"LLM provider overridden from environment: {llm_provider}")
# Also support direct API key from environment if the provider-specific key isn't set
llm_api_key = os.environ.get("LLM_API_KEY")
if llm_api_key and "api_key" not in config["llm"]:
config["llm"]["api_key"] = llm_api_key
logging.info("LLM API key loaded from LLM_API_KEY environment variable")
return config
def setup_logging(config: Dict) -> None:
"""Configure application logging."""
@@ -45,10 +60,10 @@ def datetime_handler(obj: any) -> Optional[str]:
return obj.isoformat()
raise TypeError(f"Object of type {type(obj)} is not JSON serializable")
def should_cleanup_task(created_at: str) -> bool:
def should_cleanup_task(created_at: str, ttl_seconds: int = 3600) -> bool:
"""Check if task should be cleaned up based on creation time."""
created = datetime.fromisoformat(created_at)
return (datetime.now() - created).total_seconds() > 3600
return (datetime.now() - created).total_seconds() > ttl_seconds
def decode_redis_hash(hash_data: Dict[bytes, bytes]) -> Dict[str, str]:
"""Decode Redis hash data from bytes to strings."""
@@ -56,6 +71,52 @@ def decode_redis_hash(hash_data: Dict[bytes, bytes]) -> Dict[str, str]:
def get_llm_api_key(config: Dict, provider: Optional[str] = None) -> str:
"""Get the appropriate API key based on the LLM provider.
Args:
config: The application configuration dictionary
provider: Optional provider override (e.g., "openai/gpt-4")
Returns:
The API key for the provider, or empty string if not found
"""
# Use provided provider or fall back to config
if not provider:
provider = config["llm"]["provider"]
# Check if direct API key is configured
if "api_key" in config["llm"]:
return config["llm"]["api_key"]
# Fall back to the configured api_key_env if no match
return os.environ.get(config["llm"].get("api_key_env", ""), "")
def validate_llm_provider(config: Dict, provider: Optional[str] = None) -> tuple[bool, str]:
"""Validate that the LLM provider has an associated API key.
Args:
config: The application configuration dictionary
provider: Optional provider override (e.g., "openai/gpt-4")
Returns:
Tuple of (is_valid, error_message)
"""
# Use provided provider or fall back to config
if not provider:
provider = config["llm"]["provider"]
# Get the API key for this provider
api_key = get_llm_api_key(config, provider)
if not api_key:
return False, f"No API key found for provider '{provider}'. Please set the appropriate environment variable."
return True, ""
def verify_email_domain(email: str) -> bool:
try:
domain = email.split('@')[1]

View File

@@ -1,16 +1,22 @@
# Base configuration (not a service, just a reusable config block)
version: '3.8'
# Shared configuration for all environments
x-base-config: &base-config
ports:
- "11235:11235"
- "8000:8000"
- "9222:9222"
- "8080:8080"
- "11235:11235" # Gunicorn port
env_file:
- .llm.env # API keys (create from .llm.env.example)
environment:
- CRAWL4AI_API_TOKEN=${CRAWL4AI_API_TOKEN:-}
- OPENAI_API_KEY=${OPENAI_API_KEY:-}
- CLAUDE_API_KEY=${CLAUDE_API_KEY:-}
- DEEPSEEK_API_KEY=${DEEPSEEK_API_KEY:-}
- ANTHROPIC_API_KEY=${ANTHROPIC_API_KEY:-}
- GROQ_API_KEY=${GROQ_API_KEY:-}
- TOGETHER_API_KEY=${TOGETHER_API_KEY:-}
- MISTRAL_API_KEY=${MISTRAL_API_KEY:-}
- GEMINI_API_TOKEN=${GEMINI_API_TOKEN:-}
- LLM_PROVIDER=${LLM_PROVIDER:-} # Optional: Override default provider (e.g., "anthropic/claude-3-opus")
volumes:
- /dev/shm:/dev/shm
- /dev/shm:/dev/shm # Chromium performance
deploy:
resources:
limits:
@@ -24,42 +30,21 @@ x-base-config: &base-config
timeout: 10s
retries: 3
start_period: 40s
user: "appuser"
services:
# Local build services for different platforms
crawl4ai-amd64:
crawl4ai:
# 1. Default: Pull multi-platform test image from Docker Hub
# 2. Override with local image via: IMAGE=local-test docker compose up
image: ${IMAGE:-unclecode/crawl4ai:${TAG:-latest}}
# Local build config (used with --build)
build:
context: .
dockerfile: Dockerfile
args:
PYTHON_VERSION: "3.10"
INSTALL_TYPE: ${INSTALL_TYPE:-basic}
ENABLE_GPU: false
platforms:
- linux/amd64
profiles: ["local-amd64"]
<<: *base-config # extends yerine doğrudan yapılandırmayı dahil ettik
crawl4ai-arm64:
build:
context: .
dockerfile: Dockerfile
args:
PYTHON_VERSION: "3.10"
INSTALL_TYPE: ${INSTALL_TYPE:-basic}
ENABLE_GPU: false
platforms:
- linux/arm64
profiles: ["local-arm64"]
<<: *base-config
# Hub services for different platforms and versions
crawl4ai-hub-amd64:
image: unclecode/crawl4ai:${VERSION:-basic}-amd64
profiles: ["hub-amd64"]
<<: *base-config
crawl4ai-hub-arm64:
image: unclecode/crawl4ai:${VERSION:-basic}-arm64
profiles: ["hub-arm64"]
INSTALL_TYPE: ${INSTALL_TYPE:-default}
ENABLE_GPU: ${ENABLE_GPU:-false}
# Inherit shared config
<<: *base-config

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131
docs/apps/linkdin/README.md Normal file
View File

@@ -0,0 +1,131 @@
# Crawl4AIProspectWizard stepbystep guide
[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/drive/10nRCwmfxPjVrRUHyJsYlX7BH5bvPoGpx?usp=sharing)
A threestage demo that goes from **LinkedIn scraping****LLM reasoning****graph visualisation**.
**Try it in Google Colab!** Click the badge above to run this demo in a cloud environment with zero setup required.
```
prospectwizard/
├─ c4ai_discover.py # Stage 1 scrape companies + people
├─ c4ai_insights.py # Stage 2 embeddings, orgcharts, scores
├─ graph_view_template.html # Stage 3 graph viewer (static HTML)
└─ data/ # output lands here (*.jsonl / *.json)
```
---
## 1  Install & boot a LinkedIn profile (onetime)
### 1.1  Install dependencies
```bash
pip install crawl4ai litellm sentence-transformers pandas rich
```
### 1.2  Create / warm a LinkedIn browser profile
```bash
crwl profiles
```
1. The interactive shell shows **New profile** hit **enter**.
2. Choose a name, e.g. `profile_linkedin_uc`.
3. A Chromium window opens log in to LinkedIn, solve whatever CAPTCHA, then close.
> Remember the **profile name**. All future runs take `--profile-name <your_name>`.
---
## 2  Discovery scrape companies & people
```bash
python c4ai_discover.py full \
--query "health insurance management" \
--geo 102713980 \ # Malaysia geoUrn
--title-filters "" \ # or "Product,Engineering"
--max-companies 10 \ # default set small for workshops
--max-people 20 \ # \^ same
--profile-name profile_linkedin_uc \
--outdir ./data \
--concurrency 2 \
--log-level debug
```
**Outputs** in `./data/`:
* `companies.jsonl` one JSON per company
* `people.jsonl` one JSON per employee
🛠️ **Dryrun:** `C4AI_DEMO_DEBUG=1 python c4ai_discover.py full --query coffee` uses bundled HTML snippets, no network.
### Handy geoUrn cheatsheet
| Location | geoUrn |
|----------|--------|
| Singapore | **103644278** |
| Malaysia | **102713980** |
| UnitedStates | **103644922** |
| UnitedKingdom | **102221843** |
| Australia | **101452733** |
_See more: <https://www.linkedin.com/search/results/companies/?geoUrn=XXX> the number after `geoUrn=` is what you need._
---
## 3  Insights embeddings, orgcharts, decision makers
```bash
python c4ai_insights.py \
--in ./data \
--out ./data \
--embed-model all-MiniLM-L6-v2 \
--llm-provider gemini/gemini-2.0-flash \
--llm-api-key "" \
--top-k 10 \
--max-llm-tokens 8024 \
--llm-temperature 1.0 \
--workers 4
```
Emits next to the Stage1 files:
* `company_graph.json` intercompany similarity graph
* `org_chart_<handle>.json` one per company
* `decision_makers.csv` handpicked who to pitch list
Flags reference (straight from `build_arg_parser()`):
| Flag | Default | Purpose |
|------|---------|---------|
| `--in` | `.` | Stage1 output dir |
| `--out` | `.` | Destination dir |
| `--embed_model` | `all-MiniLM-L6-v2` | SentenceTransformer model |
| `--top_k` | `10` | Neighbours per company in graph |
| `--openai_model` | `gpt-4.1` | LLM for scoring decision makers |
| `--max_llm_tokens` | `8024` | Token budget per LLM call |
| `--llm_temperature` | `1.0` | Creativity knob |
| `--stub` | off | Skip OpenAI and fabricate tiny charts |
| `--workers` | `4` | Parallel LLM workers |
---
## 4  Visualise interactive graph
After Stage 2 completes, simply open the HTML viewer from the project root:
```bash
open graph_view_template.html # or Live Server / Python -http
```
The page fetches `data/company_graph.json` and the `org_chart_*.json` files automatically; keep the `data/` folder beside the HTML file.
* Left pane → list of companies (clans).
* Click a node to load its orgchart on the right.
* Chat drawer lets you ask followup questions; context is pulled from `people.jsonl`.
---
## 5  Common snags
| Symptom | Fix |
|---------|-----|
| Infinite CAPTCHA | Use a residential proxy: `--proxy http://user:pass@ip:port` |
| 429 Too Many Requests | Lower `--concurrency`, rotate profile, add delay |
| Blank graph | Check JSON paths, clear `localStorage` in browser |
---
### TL;DR
`crwl profiles``c4ai_discover.py``c4ai_insights.py` → open `graph_view_template.html`.
Live long and `import crawl4ai`.

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#!/usr/bin/env python3
"""
c4ai-discover — Stage1 Discovery CLI
Scrapes LinkedIn company search + their people pages and dumps two newlinedelimited
JSON files: companies.jsonl and people.jsonl.
Key design rules
----------------
* No BeautifulSoup — Crawl4AI only for network + HTML fetch.
* JsonCssExtractionStrategy for structured scraping; schema autogenerated once
from sample HTML provided by user and then cached under ./schemas/.
* Defaults are embedded so the file runs inside VS Code debugger without CLI args.
* If executed as a console script (argv > 1), CLI flags win.
* Lightweight deps: argparse + Crawl4AI stack.
Author: Tom @ Kidocode 20250426
"""
from __future__ import annotations
import warnings, re
warnings.filterwarnings(
"ignore",
message=r"The pseudo class ':contains' is deprecated, ':-soup-contains' should be used.*",
category=FutureWarning,
module=r"soupsieve"
)
# ───────────────────────────────────────────────────────────────────────────────
# Imports
# ───────────────────────────────────────────────────────────────────────────────
import argparse
import random
import asyncio
import json
import logging
import os
import pathlib
import sys
# 3rd-party rich for pretty logging
from rich.console import Console
from rich.logging import RichHandler
from datetime import datetime, UTC
from textwrap import dedent
from types import SimpleNamespace
from typing import Dict, List, Optional
from urllib.parse import quote
from pathlib import Path
from glob import glob
from crawl4ai import (
AsyncWebCrawler,
BrowserConfig,
CacheMode,
CrawlerRunConfig,
JsonCssExtractionStrategy,
BrowserProfiler,
LLMConfig,
)
# ───────────────────────────────────────────────────────────────────────────────
# Constants / paths
# ───────────────────────────────────────────────────────────────────────────────
BASE_DIR = pathlib.Path(__file__).resolve().parent
SCHEMA_DIR = BASE_DIR / "schemas"
SCHEMA_DIR.mkdir(parents=True, exist_ok=True)
COMPANY_SCHEMA_PATH = SCHEMA_DIR / "company_card.json"
PEOPLE_SCHEMA_PATH = SCHEMA_DIR / "people_card.json"
# ---------- deterministic target JSON examples ----------
_COMPANY_SCHEMA_EXAMPLE = {
"handle": "/company/posify/",
"profile_image": "https://media.licdn.com/dms/image/v2/.../logo.jpg",
"name": "Management Research Services, Inc. (MRS, Inc)",
"descriptor": "Insurance • Milwaukee, Wisconsin",
"about": "Insurance • Milwaukee, Wisconsin",
"followers": 1000
}
_PEOPLE_SCHEMA_EXAMPLE = {
"profile_url": "https://www.linkedin.com/in/lily-ng/",
"name": "Lily Ng",
"headline": "VP Product @ Posify",
"followers": 890,
"connection_degree": "2nd",
"avatar_url": "https://media.licdn.com/dms/image/v2/.../lily.jpg"
}
# Provided sample HTML snippets (trimmed) — used exactly once to coldgenerate schema.
_SAMPLE_COMPANY_HTML = (Path(__file__).resolve().parent / "snippets/company.html").read_text()
_SAMPLE_PEOPLE_HTML = (Path(__file__).resolve().parent / "snippets/people.html").read_text()
# --------- tighter schema prompts ----------
_COMPANY_SCHEMA_QUERY = dedent(
"""
Using the supplied <li> company-card HTML, build a JsonCssExtractionStrategy schema that,
for every card, outputs *exactly* the keys shown in the example JSON below.
JSON spec:
• handle href of the outermost <a> that wraps the logo/title, e.g. "/company/posify/"
• profile_image absolute URL of the <img> inside that link
• name text of the <a> inside the <span class*='t-16'>
• descriptor text line with industry • location
• about text of the <div class*='t-normal'> below the name (industry + geo)
• followers integer parsed from the <div> containing 'followers'
IMPORTANT: Do not use the base64 kind of classes to target element. It's not reliable.
The main div parent contains these li element is "div.search-results-container" you can use this.
The <ul> parent has "role" equal to "list". Using these two should be enough to target the <li> elements.
IMPORTANT: Remember there might be multiple <a> tags that start with https://www.linkedin.com/company/[NAME],
so in case you refer to them for different fields, make sure to be more specific. One has the image, and one
has the person's name.
IMPORTANT: Be very smart in selecting the correct and unique way to address the element. You should ensure
your selector points to a single element and is unique to the place that contains the information.
"""
)
_PEOPLE_SCHEMA_QUERY = dedent(
"""
Using the supplied <li> people-card HTML, build a JsonCssExtractionStrategy schema that
outputs exactly the keys in the example JSON below.
Fields:
• profile_url href of the outermost profile link
• name text inside artdeco-entity-lockup__title
• headline inner text of artdeco-entity-lockup__subtitle
• followers integer parsed from the span inside lt-line-clamp--multi-line
• connection_degree '1st', '2nd', etc. from artdeco-entity-lockup__badge
• avatar_url src of the <img> within artdeco-entity-lockup__image
IMPORTANT: Do not use the base64 kind of classes to target element. It's not reliable.
The main div parent contains these li element is a "div" has these classes "artdeco-card org-people-profile-card__card-spacing org-people__card-margin-bottom".
"""
)
# ---------------------------------------------------------------------------
# Utility helpers
# ---------------------------------------------------------------------------
def _load_or_build_schema(
path: pathlib.Path,
sample_html: str,
query: str,
example_json: Dict,
force = False
) -> Dict:
"""Load schema from path, else call generate_schema once and persist."""
if path.exists() and not force:
return json.loads(path.read_text())
logging.info("[SCHEMA] Generating schema %s", path.name)
schema = JsonCssExtractionStrategy.generate_schema(
html=sample_html,
llm_config=LLMConfig(
provider=os.getenv("C4AI_SCHEMA_PROVIDER", "openai/gpt-4o"),
api_token=os.getenv("OPENAI_API_KEY", "env:OPENAI_API_KEY"),
),
query=query,
target_json_example=json.dumps(example_json, indent=2),
)
path.write_text(json.dumps(schema, indent=2))
return schema
def _openai_friendly_number(text: str) -> Optional[int]:
"""Extract first int from text like '1K followers' (returns 1000)."""
import re
m = re.search(r"(\d[\d,]*)", text.replace(",", ""))
if not m:
return None
val = int(m.group(1))
if "k" in text.lower():
val *= 1000
if "m" in text.lower():
val *= 1_000_000
return val
# ---------------------------------------------------------------------------
# Core async workers
# ---------------------------------------------------------------------------
async def crawl_company_search(crawler: AsyncWebCrawler, url: str, schema: Dict, limit: int) -> List[Dict]:
"""Paginate 10-item company search pages until `limit` reached."""
extraction = JsonCssExtractionStrategy(schema)
cfg = CrawlerRunConfig(
extraction_strategy=extraction,
cache_mode=CacheMode.BYPASS,
wait_for = ".search-marvel-srp",
session_id="company_search",
delay_before_return_html=1,
magic = True,
verbose= False,
)
companies, page = [], 1
while len(companies) < max(limit, 10):
paged_url = f"{url}&page={page}"
res = await crawler.arun(paged_url, config=cfg)
batch = json.loads(res[0].extracted_content)
if not batch:
break
for item in batch:
name = item.get("name", "").strip()
handle = item.get("handle", "").strip()
if not handle or not name:
continue
descriptor = item.get("descriptor")
about = item.get("about")
followers = _openai_friendly_number(str(item.get("followers", "")))
companies.append(
{
"handle": handle,
"name": name,
"descriptor": descriptor,
"about": about,
"followers": followers,
"people_url": f"{handle}people/",
"captured_at": datetime.now(UTC).isoformat(timespec="seconds") + "Z",
}
)
page += 1
logging.info(
f"[dim]Page {page}[/] — running total: {len(companies)}/{limit} companies"
)
return companies[:max(limit, 10)]
async def crawl_people_page(
crawler: AsyncWebCrawler,
people_url: str,
schema: Dict,
limit: int,
title_kw: str,
) -> List[Dict]:
people_u = f"{people_url}?keywords={quote(title_kw)}"
extraction = JsonCssExtractionStrategy(schema)
cfg = CrawlerRunConfig(
extraction_strategy=extraction,
# scan_full_page=True,
cache_mode=CacheMode.BYPASS,
magic=True,
wait_for=".org-people-profile-card__card-spacing",
wait_for_images=5000,
delay_before_return_html=1,
session_id="people_search",
)
res = await crawler.arun(people_u, config=cfg)
if not res[0].success:
return []
raw = json.loads(res[0].extracted_content)
people = []
for p in raw[:limit]:
followers = _openai_friendly_number(str(p.get("followers", "")))
people.append(
{
"profile_url": p.get("profile_url"),
"name": p.get("name"),
"headline": p.get("headline"),
"followers": followers,
"connection_degree": p.get("connection_degree"),
"avatar_url": p.get("avatar_url"),
}
)
return people
# ---------------------------------------------------------------------------
# CLI + main
# ---------------------------------------------------------------------------
def build_arg_parser() -> argparse.ArgumentParser:
ap = argparse.ArgumentParser("c4ai-discover — Crawl4AI LinkedIn discovery")
sub = ap.add_subparsers(dest="cmd", required=False, help="run scope")
def add_flags(parser: argparse.ArgumentParser):
parser.add_argument("--query", required=False, help="query keyword(s)")
parser.add_argument("--geo", required=False, type=int, help="LinkedIn geoUrn")
parser.add_argument("--title-filters", default="Product,Engineering", help="comma list of job keywords")
parser.add_argument("--max-companies", type=int, default=1000)
parser.add_argument("--max-people", type=int, default=500)
parser.add_argument("--profile-name", default=str(pathlib.Path.home() / ".crawl4ai/profiles/profile_linkedin_uc"))
parser.add_argument("--outdir", default="./output")
parser.add_argument("--concurrency", type=int, default=4)
parser.add_argument("--log-level", default="info", choices=["debug", "info", "warn", "error"])
add_flags(sub.add_parser("full"))
add_flags(sub.add_parser("companies"))
add_flags(sub.add_parser("people"))
# global flags
ap.add_argument(
"--debug",
action="store_true",
help="Use built-in demo defaults (same as C4AI_DEMO_DEBUG=1)",
)
return ap
def detect_debug_defaults(force = False) -> SimpleNamespace:
if not force and sys.gettrace() is None and not os.getenv("C4AI_DEMO_DEBUG"):
return SimpleNamespace()
# ----- debugfriendly defaults -----
return SimpleNamespace(
cmd="full",
query="health insurance management",
geo=102713980,
# title_filters="Product,Engineering",
title_filters="",
max_companies=10,
max_people=5,
profile_name="profile_linkedin_uc",
outdir="./debug_out",
concurrency=2,
log_level="debug",
)
async def async_main(opts):
# ─────────── logging setup ───────────
console = Console()
logging.basicConfig(
level=opts.log_level.upper(),
format="%(message)s",
handlers=[RichHandler(console=console, markup=True, rich_tracebacks=True)],
)
# -------------------------------------------------------------------
# Load or build schemas (onetime LLM call each)
# -------------------------------------------------------------------
company_schema = _load_or_build_schema(
COMPANY_SCHEMA_PATH,
_SAMPLE_COMPANY_HTML,
_COMPANY_SCHEMA_QUERY,
_COMPANY_SCHEMA_EXAMPLE,
# True
)
people_schema = _load_or_build_schema(
PEOPLE_SCHEMA_PATH,
_SAMPLE_PEOPLE_HTML,
_PEOPLE_SCHEMA_QUERY,
_PEOPLE_SCHEMA_EXAMPLE,
# True
)
outdir = BASE_DIR / pathlib.Path(opts.outdir)
outdir.mkdir(parents=True, exist_ok=True)
f_companies = (BASE_DIR / outdir / "companies.jsonl").open("a", encoding="utf-8")
f_people = (BASE_DIR / outdir / "people.jsonl").open("a", encoding="utf-8")
# -------------------------------------------------------------------
# Prepare crawler with cookie pool rotation
# -------------------------------------------------------------------
profiler = BrowserProfiler()
path = profiler.get_profile_path(opts.profile_name)
bc = BrowserConfig(
headless=False,
verbose=False,
user_data_dir=path,
use_managed_browser=True,
user_agent_mode = "random",
user_agent_generator_config= {
"platforms": "mobile",
"os": "Android"
}
)
crawler = AsyncWebCrawler(config=bc)
await crawler.start()
# Single worker for simplicity; concurrency can be scaled by arun_many if needed.
# crawler = await next_crawler().start()
try:
# Build LinkedIn search URL
search_url = f'https://www.linkedin.com/search/results/companies/?keywords={quote(opts.query)}&companyHqGeo="{opts.geo}"'
logging.info("Seed URL => %s", search_url)
companies: List[Dict] = []
if opts.cmd in ("companies", "full"):
companies = await crawl_company_search(
crawler, search_url, company_schema, opts.max_companies
)
for c in companies:
f_companies.write(json.dumps(c, ensure_ascii=False) + "\n")
logging.info(f"[bold green]✓[/] Companies scraped so far: {len(companies)}")
if opts.cmd in ("people", "full"):
if not companies:
# load from previous run
src = outdir / "companies.jsonl"
if not src.exists():
logging.error("companies.jsonl missing — run companies/full first")
return 10
companies = [json.loads(l) for l in src.read_text().splitlines()]
total_people = 0
title_kw = " ".join([t.strip() for t in opts.title_filters.split(",") if t.strip()]) if opts.title_filters else ""
for comp in companies:
people = await crawl_people_page(
crawler,
comp["people_url"],
people_schema,
opts.max_people,
title_kw,
)
for p in people:
rec = p | {
"company_handle": comp["handle"],
# "captured_at": datetime.now(UTC).isoformat(timespec="seconds") + "Z",
"captured_at": datetime.now(UTC).isoformat(timespec="seconds") + "Z",
}
f_people.write(json.dumps(rec, ensure_ascii=False) + "\n")
total_people += len(people)
logging.info(
f"{comp['name']} — [cyan]{len(people)}[/] people extracted"
)
await asyncio.sleep(random.uniform(0.5, 1))
logging.info("Total people scraped: %d", total_people)
finally:
await crawler.close()
f_companies.close()
f_people.close()
return 0
def main():
parser = build_arg_parser()
cli_opts = parser.parse_args()
# decide on debug defaults
if cli_opts.debug:
opts = detect_debug_defaults(force=True)
cli_opts = opts
else:
env_defaults = detect_debug_defaults()
opts = env_defaults if env_defaults else cli_opts
if not getattr(opts, "cmd", None):
opts.cmd = "full"
exit_code = asyncio.run(async_main(cli_opts))
sys.exit(exit_code)
if __name__ == "__main__":
main()

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